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Body Mass Index

Obesity, bmi, and health, a critical review.

Nuttall, Frank Q. MD, PhD

Frank Q. Nuttall, MD, PhD, is a full professor at the University of Minnesota, Minneapolis, and chief of the Endocrine, Metabolic and Nutrition Section at the Minneapolis VA Medical Center, Minnesota. His PhD degree is in biochemistry. He has more than 250 scientific publications in peer-reviewed journals, and he is the winner of numerous prestigious academic and scientific awards, including the 2014 Physician/Clinician Award of the American Diabetes Association.

The author has no conflicts of interest to disclose.

Correspondence: Frank Q. Nuttall, MD, PhD, Minneapolis VA Medical Center, One Veterans Dr 111G, Minneapolis, MN 55417 ( [email protected] ).

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License, where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially.

The body mass index (BMI) is the metric currently in use for defining anthropometric height/weight characteristics in adults and for classifying (categorizing) them into groups. The common interpretation is that it represents an index of an individual’s fatness. It also is widely used as a risk factor for the development of or the prevalence of several health issues. In addition, it is widely used in determining public health policies.The BMI has been useful in population-based studies by virtue of its wide acceptance in defining specific categories of body mass as a health issue. However, it is increasingly clear that BMI is a rather poor indicator of percent of body fat. Importantly, the BMI also does not capture information on the mass of fat in different body sites. The latter is related not only to untoward health issues but to social issues as well. Lastly, current evidence indicates there is a wide range of BMIs over which mortality risk is modest, and this is age related. All of these issues are discussed in this brief review.

Body fatness has been an important psychosocial issue among humans for millennia. It is clearly manifested by paleolithic statuettes of exceedingly plump women. This suggests being “full figured” was highly desirable at least for women. In contrast, images of obese people, males or females, are never exhibited in ancient Egyptian funerary wall paintings, stellae, or statues suggesting that fatness was not considered to be a desirable trait there. This also is the case in artifacts from other cultures in the Middle East in that era. Why the degree of fatness has varied in different cultures is not clear. However, it may have depended on the availability of a reliable food supply and the effort required in obtaining it.

More recently, the degree of rotundity considered ideal also has varied considerably in the general population, but particularly for young women. Before the 1920s, “full figured” women were considered to be desirable as long as the distribution was hourglass in type. However, the 1920s Flapper era introduced abbreviated and revealing dresses. The result was that thinness was not only desirable but also required. This concept has moderated but still influences women’s views of beauty and eating habits at present.

Fatness as a Personal or Society Issue

Traditionally, a person’s fatness has been defined at a personal level as well as at a societal level. However, this is difficult to quantify. That is, each individual has his/her own perception of how fat he/she should be. As indicated above, this often depends on a general concept of societal norms or is due to peer pressure. For example, currently in Western societies, young women are often concerned about their body image, and most consider themselves to be too fat, even though they are well within population-based references. This is not only due to societal concepts of an ideal degree of fatness, but also due to thinness being a goal promulgated by the fashion industry and reinforced by commercial advertising.

At a societal level, although poorly described or quantified, there also is a degree of fatness beyond which a person generally is considered to be unacceptably fat; that is, there is an ill-defined threshold at which a person is labeled as being “fat” or “obese.” However, it is based on the “I can’t define it but I know it when I see it” concept. In addition, implicit in this context is that the location of the excess fat plays a role, as does a person’s age. It is much more acceptable to be “overweight” when one is old than when one is young. Also particularly in women, the accumulation of fat in certain areas of the body is considered to be much more acceptable than in other areas. For example, truncal (belly fat) accumulation would be considered to be less acceptable than the accumulation of fat in the peripelvic and thigh areas as well as in the breast area 1 ; that is, one may be statistically “fat” but with an appropriate figure be merely referred to “as amply endowed” or “pleasingly plump.”

The social consequences of being “too fat” are severe. Discrimination begins in childhood and results in serious emotional scars. Societal discrimination limits career choices, and indeed many career paths are closed to those considered to be too fat. Also, societal stigmatization often impairs a person’s ability to express his/her intellectual and other talents; that is, they become underachievers. In addition, the potential pool of mates is limited because of their perceived unattractiveness. Thus, obese people tend to marry other obese people and, parenthetically, to produce obese children. 2–4

Fatness as a Medical Issue

Not only the societal but also the functional and indirectly the medical consequences of an excessive accumulation of fat also have been recognized for millennia. Nevertheless, the concept that “body build” (fatness) is a major population-based medical issue gained popularity in this country only shortly before 1900. Life insurance data accumulated at that time 5 and subsequently 6 indicated that body weight, adjusted for height (Wt/Ht), was an independent determinant of life expectancy, and in 1910, the effects of being overweight were noted to be greater for younger people than for the elderly. 6

Subsequently, the Metropolitan Life Insurance Company in 1959 published tables of average body weights for heights (Wt/Ht) by gender and at different ages. 7 This was based on data from 1935 to 1953 from more than 4 million adults, mostly men, insured by 26 different insurance companies. The risk for development of certain diseases as well as mortality data related to Wt/Ht differences also were analyzed and reported in the 1960 Statistical Bulletin of the Metropolitan Life Insurance Co. 8,9

The Wt/Ht tables were used for many years as a reference for population-based studies. If a person’s Wt/Ht was 20% above or below the mean for that height category, he/she was considered to be overweight or underweight, respectively. The insurance data also indicated the ratios of weights for heights (the term used was “body build”) at which mortality was lowest in adults. The latter was referred to as the “ideal” or later the “desirable” weight. All of these data were periodically updated. 10 Interestingly, from 1959 to 1983, the desirable weight, that is, the weight/height representing the lowest mortality had increased. 10–12 However, a “desirable body” weight for height was invariably lower than the average weight for height in the insured population. 7,10,13

Problems With the Wt/Ht (Body Build) Index

Early on it was recognized that tall people had a lower death rate than did short people 7,8,13 with the same Wt/Ht ratio. It also was recognized that a person’s height in general and leg length in particular could affect the calculated body mass adjusted for height. A person’s bony frame, that is, bone mass, also could affect the interpretation of this ratio. In general, it reflected whether one was narrowly or broadly built. Thus, efforts were made to eliminate lower limb length and frame size as variables. 7,10 The strategy was to develop representations of body build, that is, charts of weight/height that were independent of these variables. The overall goal was to have the same distribution of Wt/Ht at each level of height.

Although not stated, the implicit goal in developing these tables was to define a person’s fat mass as a proportion of their total mass, irrespective of their height or frame size. 14 Efforts were made to adjust for frame size (nonfat mass) by categorizing people as those with a small, medium, or large frame. Estimation of frame size was attempted using a number of measurements including shoulder width, elbow width, knee width, ankle width, and so on. 15 None of these were widely adopted. Nevertheless, frame size based on elbow width was included in the Metropolitan Life weight/height tables, 7,10 even though it was never validated.

Mathematical Adjustment of Body Build

Mathematically, the issue of adjusting body build for differences in height was approached with the concept that the body, particularly the trunk, could be considered as being a 3-dimensional volume or mass. Thus if a tall person were simply a scaled-up version of a short person, weight would increase approximately with the cube of height. 16 Indeed, several equations were developed and tested based on this concept; that is, the cube root of weight divided by height ( 3 √Wt/Ht) or weight/height, 3 and so on, but none were ideal. 17 This is because tall people are not just scaled-up versions of short people. As indicated previously, they tend to be more narrowly built resulting in a greater lean/fat proportion of body mass.

Later, it was shown that the body mass for height actually scaled best with weight for height when the height was raised to the 1.6 to 1.7 exponent (Wt/Ht, 1.6 etc). 18 Thus, with an increase in Ht, the effect of Ht on the ratio is exponential, whereas the change in Wt is linear. This has the effect of Ht on the ratio to be magnified as Ht increases. Overall, it results in a lower ratio in tall people than will be obtained with just a Wt/Ht ratio. Thus, it potentially compensates for a narrower build in tall compared with short people.

This exponent is not convenient for use in population-based studies, and it was determined that Wt/Ht 2 generally was satisfactory. 16,18 The latter represents the Quetelet Index. It was developed by Dr Quetelet in the 1800s.

Lambert Adolph Jacque Quetelet

I would like to briefly mention who Dr Quetelet was and how the “Quetelet Index” was derived. 19–21 Lambert Adolphe Jacque Quetelet (1796–1874) was a Flemish astronomer and statistician. Indeed, he is considered to be the patriarch of statisticians. He introduced the concept of “social averages.” In developing the “social average” concept, his goal was to determine the characteristics of an “average man” and the distribution of various human characteristics around the “average man.” Overall, it was his desire to obtain a distribution such that it formed a bell-shaped curve, that is, a Gaussian or normal distribution. He referred to his studies as “social physics.” Thus, this represents the first application of distribution mathematics to human characteristics. In 1835, Quetelet noted the body mass relationship to height in normal young adults was least affected by height when the ratio of weight to height squared was used rather than merely using the ratio of the weight to height or weight to height raised to the third power. 16

Adoption of the BMI as an Index of Obesity

In 1972, Keys et al 16 severely criticized the validity of Metropolitan Life Insurance published data per se, and the then-published tables of desirable weight for height, as well as the tables used to define people who were underweight or overweight. 7 (The pejorative term “obese” was rarely used in that era.) Instead, Keys et al, using better documented weight for height data, popularized the Quetelet Index in population-based studies. They referred to it as the body mass index (BMI). Thus, Quetelet Index = body weight (kilograms) divided by height squared (meters) = BMI.

As indicated above, by squaring the height, it reduces the contribution of leg length in the equation and tends to normalize the body mass distribution at each level of height; that is, it reduces the effect of a variance in height in the relationship of weight to height. This was considered to be important because most of body fat is in the trunk. Nevertheless, as also pointed out by Keys et al, 16 even the BMI rather poorly represents a person’s percent of body fat.

Despite all the criticisms, the Metropolitan Life Tables criteria for defining obesity were widely used in the United States until the early 1990s. 22–24 At about that time, the World Health Organization (WHO) classification of body weight for height, based on the BMI, was published, 25 and later it was widely adopted. 26

BMI Distribution in a Normal Population

Although a BMI determination reduces the effect of lower-extremity length on the Wt/Ht ratio, whether one uses the BMI or merely the ratio of weight to height, the population distribution is still not Gaussian. That is, it is not symmetrical but is always skewed to the right, that is, toward a higher ratio of weight (body mass) to height. For example, the distribution of BMIs in adult American men and women was determined in 1923 in 1026 individuals (Figure). 27 The median BMI was 24, but the mean BMI was 25. The distribution curve clearly indicated a skewing toward an increase in BMI, and this trend has continued. 26

This skewing is not surprising because a markedly reduced BMI, theoretically and actually, would be incompatible with life because of an excessive reduction in lean as well as fat mass as a result of under nutrition 28 or disease. In contrast, excessive accumulation of body fat with maintenance or usually an increase in lean mass 29,30 is at least compatible with life, even though it may eventually affect long-term survival.

WHO and the Categorization of BMIs Into Quartiles

In 1993, the WHO assembled an Expert Consultation Group with a charge of developing uniform categories of the BMI. The results were published as a technical report in 1995. 25 Four categories were established: underweight, normal, overweight, and obese. An individual would be considered to be underweight if his/her BMI was in the range of 15 to 19.9, normal weight if the BMI was 20 to 24.9, overweight if the BMI was 25 to 29.9, and obese if it was 30 to 35 or greater. Using linear regression, a BMI of 16.9 in men and 13.7 in women represents a complete absence of body fat stores. 31

The above 4 categories are similar to those suggested by John S. Garrow in 1981, 31,32 but the terminology was changed. The terminology he used was “desirable” for a BMI up to 25, “grade I obesity” between 25 and 29.9, “grade II obesity,” between 30 and 40, and “grade III obesity” for BMI greater than 40.

The latter classification was based on Rosenbaum and colleagues’ 33 own data obtained in a survey of an adult population, aged 16 to 64 years, in Great Britain and published in 1985.

The population-based data indicated the majority of people were in the “desirable” range of the BMI distribution as indicated in Table 1 . Unfortunately, this distribution is not and has not been similar to those found in other surveys. The BMIs have been higher.

At the time that the WHO classification was published, the National Institutes of Health (NIH) in the United States classified people with a BMI of 27.8 (men) and 27.3 (women) or greater as being overweight. If they were below this BMI, they were considered to be “normal.” This was based on an 85% cutoff point of people examined in the National Health and Nutrition Examination Study (NHANES) II. 12,22,34 Subsequently, in 1998, the cutoff point between normal and overweight was reduced to a BMI of 25 to bring it into line with the 4 categories in the WHO guidelines. 25,35 Parenthetically, this instantaneously converted millions of Americans from being “normal weight” to being “overweight.”

In 1997, the International Obesity Task Force expanded the number of BMI categories to include different degrees of obesity and changed the terminology modestly. 36 A BMI of 25 to 29.9 is referred as “preobesity,” a BMI of 30 to 34.9 is class I obesity, 34.9 to 39.9 is class II obesity, and a BMI of 40 or greater is class III obesity. 37,38

The new terminology appears to be a bit presumptuous and careless because the BMI is not a direct measure of percent of fat mass, and the dynamic concept that those in the former “overweight” category are now in the “preobesity” category invariably going on to “obesity” is not the case. Also those with a lower BMI initially, but with a dynamic weight gain over time, would have to transition through this category in order to become classified as “obese” regardless of the terminology. By analogy, should those classified as “underweight” now be referred to as being “prenormal”?

Distribution of BMI in the General Population

It should be understood that in Western population-based studies, generally the mean or median BMI is about 24 to 27. 22,27,39,40 Thus, the consequence of adopting the WHO classification is that ~50% or more of the general adult population will always be in the overweight (now preobese) and obese categories. Indeed, the term “overweight” or particularly “preobesity” is prejudicial since people in this category are a major part of the expected normal distribution of BMI in the general population, and this has been the case for decades. Unfortunately, in discussing the so-called “obesity epidemic,” the number of people in the overweight (preobese) category generally is lumped together with those in the obese category in order to advertise and dramatize the perceived seriousness of this issue.

Regardless of the terminology and population reference issues, at present the BMI is the currency by which we define the obesity issue throughout the Western world. It was developed for the convenience of the epidemiologists, and indeed it did provide a uniform codification of body weight for height reporting. The BMI categories are shown in ( Table 2 ).

BMI as a Determinant of Body Fat Mass

A particular problem with BMI as an index of obesity is that it does not differentiate between body lean mass and body fat mass; that is, a person can have a high BMI but still have a very low fat mass and vice versa. 39,41–46

From an anatomical and metabolic perspective, the term obesity should refer to an excessive accumulation of body fat (triacylglycerols), and upon these grounds, the accuracy of the BMI as a determinant of body fat mass has been repeatedly questioned, 16,39–41,46–48 because it clearly has limitations in this regard. Gender, age, ethnic group, and leg length are important variables. 45,49–55 It should be noted that in population-based studies women generally have a BMI that is lower than that in men, even though their fat mass relative to their body build or BMI is considerably greater (~20% to 45%+).

The relatively poor correlation between percent of body fat mass and BMI in males has been known for many years 16 and was clearly shown in a study in which percent of body fat was determined by a densitometric method. 56 For men with a BMI of 27 in that study, the 95% confidence intervals for percent of body fat were 10% to 32%; that is, in this group, the percent of body fat varied from very little to that considered to be in the obesity range. (NIH-suggested criterion for obesity based on percent of body fat for men is ≥25%, and that for women is ≥35%. 57 )

The relatively poor correlation between percent of body fat mass and BMI also clearly has been shown more recently in the NHANES III database in which bioelectrical impedance was used to estimate the fat component of body composition. 51 In subjects with a BMI of 25 kg/m 2 , the percent of body fat in men varied between 14% and 35%, and in women it varied between 26% and 43%. Thus, using the NIH-suggested criterion based on percent of body fat to define obesity, subjects with a BMI of 25, a group that would be considered to be essentially normal, were associated with a body fat mass that varied again between low normal to obese. Also it is of interest that in the entire NHANES cohort, the BMI correlated better with lean body mass than with fat mass in men. 51 More recent NHANES data also indicate a poor correlation of BMI with percent of body fat, particularly in men. 58

In addition, in a recent study in individuals with or without diabetes in which the loss of lean body mass with aging was reported, the mean BMI in those without diabetes was 26.8. In those with diabetes, the BMI was 29.1; that is, it was higher as generally expected. However, the percent of lean body mass was the same; that is, the increased BMI in those with diabetes was not due only to an excessive accumulation of fat. 59

Trends in Body Weight and Height

Over the past several decades, there has been an increase in BMI in the general population. This has resulted in predictions of a public health disaster. It should be recognized that in the United States during the period from 1960 to 2002 not only has the mean weight increased by 24 lb among men aged 20 to 74 years, but also the height has increased by about 1 in. We can then calculate that the weight increase per year has only been 0.57 lb, and as indicated above, this could be due to an increase in lean mass rather than fat mass, or it may be a combination of the two. In women, there was a similar increase in weight and height. 40

In an earlier report, life-insured men up to age 40 years were reported to be 0.5 to 1.5 inches taller and 2 to 9 lb heavier for the same height in 1959 than those studied 50 to 60 years prior to 1959. Also, in the earlier study, the mortality rate was lowest in those with higher weight-to-height ratios. This was attributed to the presence in the population of wasting diseases such as tuberculosis that resulted in an increased death rate. 13 Previously, a secular upward trend in height in adults in the United Kingdom also was reported. 60 In addition, in a twin study in the United Kingdom, children in 2005 were not only heavier but also taller than 1990 norms, whereas their BMIs were essentially the same. 3

Overall, the history of changes in height and weight in Western European men and probably women has been that of an increase in both weight and height. In the 17th century, the average height of men in Northern Europe was ~5 ft 3 in. It now approaches 6 ft. 61 These data suggest that the BMI categories should be adjusted upward periodically to accommodate population-based changes. Improvements in mortality rates also suggest an adjustment would be useful.

Body Fat Location

An additional limitation of the BMI is it does not capture body fat location information. This is an important variable in assessing the metabolic as well as mortality consequences of excessive fat accumulation. It was first recognized in France by Dr Jon Vague 62 in the 1940-1950s. He noted that accumulation of fat in the upper part of the body versus the lower part of the body was associated with an increased risk for coronary heart disease, diabetes, and also gallstones and gout. That is, individuals who accumulated excessive fat in the lower body segment were relatively spared from these complications. The body fat distribution was referred to as being “android” if it occurred in the upper body and “gynecoid” when it occurred in the lower segment of the body. This is because men tend to accumulate fat in the abdominal (upper body) area, whereas women tend to accumulate it in the peripelvic (gluteal) area and the thighs. A surrogate for this information has been to determine the abdominal circumference or an abdominal/hip circumference ratio. Subsequent data indicate that indeed the risk for development of diabetes and the so-called “metabolic syndrome,” as well as coronary heart disease, is more strongly related to the accumulation of upper body fat than lower body fat in both sexes. 63–67 That is, an android (male) distribution more closely predicts the development of the chronic diseases of aging than does the gynecoid (female) distribution.

More specifically, both visceral fat accumulation 68,69 and an expanded girth have been associated with development of insulin resistance, diabetes, and risk for coronary heart disease and hypertension. 63,64,70–74 Accumulation of fat in the abdominal area appears to correlate best with triacylglycerols accumulating in the liver and skeletal muscle. These may actually represent the pathogeneticially important metabolic consequences that result in insulin resistance and more directly correlate with development of the above adverse medical conditions. 68,75,76 Incidentally, the relatively small accumulation of fat in these organs would not be detectible by BMI determinations, and they do not correlate simply with total body fat mass. 75

The Life Cycle and Location of Accumulated Fat

Prior to puberty, boys and girls tend to be lean and not much different in this regard. Girls tend to accumulate relatively large amounts of fat during and after puberty, particularly in the peripelvic and thigh region; boys do not. During and after puberty, boys accumulate a relatively large amount of lean mass (bone and muscle) but not fat mass. In both sexes, these changes are reflected in an increased BMI. With aging, both sexes tend to develop fat in the upper part of the body (circumferentially), that is, the middle-age spread. 49,77–80 The reason for these changes in amount and distribution is not completely understood. Generally, it is considered to be due to hormonal changes.

It is of some interest that accumulation of fat in the lower body at puberty in females is unique to humans, is not present in any of the great apes, and most likely is estrogen mediated. 1

In a teleological sense, why this occurs, if due to estrogen, is uncertain. It could represent a means of maintaining body fat during pregnancy without an undue expansion in abdominal girth. It also may act as a counterbalance when women carry a child either during pregnancy or afterward. It also may be a space-filling fat site due to the relatively larger pelvis in postpubertal females. 81 Overall, it may represent an adaptation to the human upright bipedal posture. In any event, it results in a lower center of gravity among women compared with men. Indeed, the lower body segment in females becomes ~40% greater than in males (quoted in Singh, 1993), 1 and it has strong sex-related overtones.

Not only is thigh fat greater in women than in men, but also women generally have a preponderance of slow-twitch fibers, whereas men have a preponderance of fast-twitch fibers in their quadriceps muscles, as do upper-body-obese women, 82 suggesting either genetic or earlier developmental differentiation events. Could this be an adaptation for load-bearing versus speed as a group survival adaptation?

As indicated above, the accumulation of fat with aging in both sexes tends to occur in the truncal area and is associated with an increase in visceral fat. In women, this could be explained by a decrease in circulating estradiol, that is, a decreased estrogen/testosterone ratio associated with the menopause. (Again of some interest, it is only humans who have a defined menopause).

In men, with aging, there is a decrease in testosterone and a relative increase in estrogen, resulting in a decrease in the testosterone/estrogen ratio. 83 Thus, in men, a change in sex hormone concentrations could possibly explain the increased accumulation of fat in general. However, why there is a preferential accumulation in the truncal location, that is, why they too develop an increase in visceral fat, is unclear. Clearly, location of fat in this area would help to maintain mobility. The latter could be of great importance in hunter-gatherer societies and in defense of the tribe. Perhaps the distribution is programmed by gender earlier in life.

In this regard, it should be recognized that the accumulation of fat in certain body areas as well as the total amount of fat accumulated has a strong genetic or at least a familial component that diminishes with age. 3,27,84,85

Methods of Estimating Body Fat Mass and Location of the Fat

At present, simple, accurate methods for measuring percent of body fat and, in particular, body fat in different fat depots are not available. The indirect methods currently in use for estimating total percent of body fat include underwater weighing, an air displacement and density determination using a Bod Pod, a bioelectrical impedance analyzer, and a determination of the isotopically labeled water mass. In the past, determination of the total body radioactive potassium and thus metabolizing tissue mass have been used to estimate lean body mass, and by difference, the fat mass. 86

Anthropometric determination of fat mass directly has been done using skin-fold thickness measured at various sites. 87 A dual-energy x-ray absorptiometry (DEXA) scan, which provides a 3-dimensional picture of body organ densities, can be used for estimating total body fat. Its location also can be determined. Single computed tomography (CT) slices of the abdomen and thigh can be used to obtain 2 dimensions of those fat depots from which a 3-dimensional fat area can be reconstructed. This also can be done using magnetic resonance imaging, but magnetic resonance imaging is very expensive. One cannot do serial sections of the body using CT to determine fat mass because of the excess radiation associated with this procedure.

Because of their convenience, bioelectric impedance methods or DEXA scans are the most commonly used to estimate the amount and, with DEXA scans, the location of body fat depots. Estimates of abdominal and thigh fat depots also can be estimated using CT slices. 52,72,88

All of the previously mentioned methods use certain assumptions in the calculation of body fat mass, and all are subject to potential error. Nevertheless, there are more specific methods of determining body fat mass than is the BMI. Important information regarding the location of the stored fat also can be determined with some methods.

It now is generally accepted that a relationship between BMI and mortality risk should be applied only to large populations. It should not be applied to an individual in an unqualified fashion. As indicated previously, there is the issue of being “overweight” versus “over fat.” In addition, a segment of the population is now considered to be “fat” by any criteria but “fit” and not at risk for early mortality. 74,75,89–91

BMI and Morbidity and Mortality

The BMI classification system currently is being widely used in population-based studies to assess the risk for mortality in the different categories of BMI. It also is being used in regard to specific etiologies for mortality risk. However, as with its use to estimate percent of body fat, it is a rather crude approach. Even when some comorbidities are considered, the correlation of mortality rates with BMI often does not take into consideration such factors as family history of diabetes, hypertension, coronary heart disease, metabolic syndrome, dyslipidemias, familial longevity or the family prevalence of carcinomas, and so on. Recently it has been reported that more than 50% of susceptibility to coronary artery disease is accounted for by genetic variants. 92

Frequently, when correlations are made they also do not take into consideration a past as well as a current history of smoking, alcohol abuse, serious mental disorders or the duration of obesity, when in the life cycle it appeared, and whether the body weight is relatively stable or rapidly progressive, that is, type 1 or type 2 obesity. 93,94 In most population-based studies, only the initial weight and/or BMI are given, even though weight as well as fat stores are known to increase and height to decrease with aging. In addition, the rate of weight gain varies among individuals, 7,94,95 as does the loss of muscle mass. 95 Muscle mass has been correlated negatively with insulin resistance and prediabetes. 96 Lastly, population-based studies do not take into consideration the present and past history of a person’s occupation, medication-induced obesity, and how comorbidities are being treated. All of the above are significant issues.

The “Obesity Epidemic”

Recently, there has been concern that an epidemic of obesity is occurring, not only in the United States, but also worldwide based on BMI data. In the NHANES data, there has been a change in the mean but to a greater extent in the distribution of BMI for adults aged 20 to 74 years in the United States. 26 That is, the mean BMI has increased, but there has been a greater increase in skewing toward the right and very large BMI. This results in more individuals being categorized as “obese.” The reason for the recent increase in mean BMI, but particularly in those in the obese category, is unknown, although there are many speculations. The dramatic decrease in smoking is likely to have been a contributor. 91,103–106 Smoking contributes to population-based BMI by at least 2 mechanisms. Smoking impairs appetite per se. It also is pathogenetically important in the development of chronic obstructive pulmonary disease, which itself results in a lower body mass. Of some interest, NHANES data also indicate that the trend of an increase in BMI has not continued since 1999 in women and only modestly in men. 58 Smoking rates also have stabilized at a low level.

Is Being “Overweight” by BMI Criteria a Medical Issue?

Regardless of an observed increased skewing in the BMI distribution, it is important to note that several recent studies indicate that for most of us being a bit overweight (preobese?) as determined by BMI may not be so bad. 107–111

The EPIC observational study is a population-based study that includes 359 387 individuals aged 25 to 70 years living in Europe. 109 The mean age of this group at the initiation of the study was 51.5 years, and the mean follow-up has been 9.7 ± 2 years. In this study, both the crude and adjusted relative risk of death among men was actually the lowest in those with a BMI of 26.5 to 28, that is, those in the overweight (preobese) category. Also, a significant increase in risk of death was present only among those with a BMI of less than 21 or greater than 30. That is, there is a wide range of BMIs in the central part of this population in which there was relatively little impact of BMI on risk of death over a 9.7-year period.

Similar data were obtained in the NIH–American Association of Retired Persons study of 527 265 men and women between the ages of 50 and 71 years in the United States and followed for up to 10 years. 110 The lowest death rate in the entire cohort was among those in the “overweight” category, and this was particularly true among the men. There also was a broad range of BMIs over which there was little difference in mortality (BMI of 23.5 to 30).

The NHANES data going back to 1971 and up to 1994 also indicate that the relative mortality risk is lowest in men with a BMI of 25 to 30 in all age groups, that is, from the age of 25 years up to the age of 70 years. 107 In addition, the risk of mortality was little affected by a BMI from 18.5 up to a BMI of 30 in all age categories. Indeed, in those older than 70 years, there was little impact on the death rate even if they were in the obese category. Similar results have been reported for women in the NHANES reports. 112 The lowest mortality occurred with a BMI of 27.

In a Canadian study, the age-adjusted mortality rate over 13 years in men was essentially unchanged in those with a BMI of 18.5 up to 35, that is, from the Normal Weight category through the obesity class I category. In women, there was only a modest increase over the same range. 113

In summary, there is a large range of BMIs over which there is little association with the death rate. Generally, the range is from a BMI of 21 up to and often including 30. It is centered in the 24-to-28 BMI range. This information is not entirely new. Andres 114 in 1980 summarized 16 different population-based studies in which anthropometrically determined obesity was not associated with increased mortality rate. A detailed analysis in 1960 of the Metropolitan Life Insurance data also suggested little increase in mortality rates in people with a degree of overweight less than 20% or more above the average for a given height and age (quoted in Keys et al 97 ).

Interestingly, in the EPIC observational Study, 109 when the waist circumference–to–BMI ratio was calculated, that is, adjusting the waist circumference for BMI, it tended to linearize the association of BMI with risk for death, and the ratio was greatest for those with a low BMI. Thus, even if an individual had a low BMI but a relatively increased waist circumference, the risk was increased. Indeed, for any given BMI, a 5-cm increase in circumference increased the risk of death by a factor of 1.17 among men and 1.13 for women. Also in this study, the overall greatest mortality risk was in those individuals with the lowest BMI and not those with the highest BMI. Nevertheless, even in the category with the lowest BMI, adjusting for waist circumference affected the mortality rate negatively. This again indicates the importance of the location of body fat in addition to the total amount of fat accumulated.

A recent analysis of 50 prospective observational studies indicated the lowest mortality at a BMI of 23 to 25. However, these data were obtained in the 1970s and 1980s in an aggregate population with a mean BMI of 24.8, that is, lower than at present. The increased mortality at higher BMI’s was modest up to a BMI of 27.5, and the authors could account for the excess mortality largely on the risk factors known to be associated with obesity. The latter are currently being much better treated than in that era. 115

Issues to be Resolved When Relating BMI With Health Determinants

Overall, a major unresolved issue is which factor of the following is more important in the prediction of comorbidities such as cardiovascular disease, diabetes, hypertension, malignancies, or overall death rates. Is it BMI, total body fat mass, or the distribution of body fat, that is, visceral versus subcutaneous, or upper body fat accumulation (as determined by abdominal circumference, or a waist/hip ratio, or some combination of these, and so on)? The EPIC 109 data suggest that where fat is accumulated is much more important than merely the BMI, with the exception of those with an exceeding large total fat mass.

SUMMARY AND CONCLUSION

It is time to move beyond the BMI as a surrogate for determining body fat mass. Alternatively, if BMI continues to be used, the categories and definitions should be changed to reflect the current distribution of BMIs in the general population.

A better means than the BMI for estimating percent of body fat and its relationship to mortality and various morbidities clearly would be desirable.

The BMI was not originally developed for use specifically as an index of fatness in population-based studies. However, it has been coopted for this use because it is a readily obtained metric. It should be understood that the BMI has serious limitations when used as an indicator of percent of body fat mass. Indeed, it may be misleading in this regard, particularly in men. The terminology currently used also is prejudicial. By definition, one-half or more adults in the recent past and currently are overweight (preobese) or obese in Western, industrialized nations.

The current BMI classification system also is misleading in regard to effects of body fat mass on mortality rates. The role of fat distribution in the prediction of medically significant morbidities as well as for mortality risk is not captured by use of the BMI. Also, numerous comorbidities, lifestyle issues, gender, ethnicities, medically significant familial-determined mortality effectors, duration of time one spends in certain BMI categories, and the expected accumulation of fat with aging are likely to significantly affect interpretation of BMI data, particularly in regard to morbidity and mortality rates. Such confounders as well as the known clustering of obesity in families, the strong role of genetic factors in the development of obesity, the location in which excessive fat accumulates, its role in the development of type 2 diabetes and hypertension, and so on, need to be considered before promulgation of public health policies that are designed to apply to the general population and are based on BMI data alone.

Clearly, obesity, as determined by BMI, is not a monotypic, age-invariant condition requiring a general public health “preventative” approach. A BMI-determined categorization of an individual should not be used exclusively in counseling or in the design of a treatment regimen. In addition, when considering weight loss regimens, variations in body weight attributed to weight loss and dietary cycling may be hazardous. 116–120 They have been associated with an increased mortality rate. 116,117,119,121–124 The concept of starvation-associated obesity 125,126 also needs to be considered.

Prevention and/or Treatment of BMI-Determined Overweight or Obesity

Clearly episodic starvation or semistarvation regimens are not the answer, 127 nor are population-based efforts to increase fresh fruits and vegetables and tax soda pop, and so on. In my opinion, the major focus on prevention and treatment should be on those unfortunate individuals who are grossly obese, mechanically compromised, and who are at very high risk for death. 128 Surgical gastrointestinal intervention has proven to be at least partially successful in improving fuel regulation and storage. 129,130 Hopefully, medications will be developed that will reinstitute a metabolic fuel regulatory system that prevents the relentless accumulation of body fat, which is characteristic of those who are grossly obese. For others, an improvement in physical fitness may be salutary.

A Personal Perspective Regarding the Obesity Epidemic

Currently there are 4 truths regarding historical changes in body weights and the prevalence of obesity. People of Western European extraction are on average (1) heavier, (2) taller, and (3) more likely to be “overweight” or “obese” as defined by current BMI standards than those in other parts of the world. However, (4) it also should be pointed out they are healthier and are living longer than in any previous period in history. 131,132

Beginning in the 17th century, 61 the general underlying theme in all the studies done on weight gain in populations is an increase in height as well as weight. These changes are likely to be due to an increase in high-quality dietary protein (animal products), as well as an increased availability of total food energy in the diet. That is, there was not only an increase in food availability and variety, but also an increase in food quality. 133 The near elimination of chronic and serious acute infectious diseases also may have played a role, as has the dramatic decrease in cigarette smoking and its serious medical consequences.

The net effect of the above is that the chronic diseases of aging have become more of a public health problem, but better treatments are widely available. The prevalence of type 2 diabetes has increased, but overall the cardiovascular death rate has decreased dramatically. The death rate from malignancies is decreasing, and there has been a remarkable improvement in longevity, which is continuing. 131 The latter also is likely to continue into the future. 131,132

Some view the secular trend in the US population over the past 40 years as being one in which the population in general is “more obese, more diabetic, more arthritic, more disabled, and more medicated” but living longer. 134 A less sanguine view is indicated by others. 135 Many consider the overabundance of “calorie dense, processed foods,” the availability of soda pop, 136 and presence of fast-food restaurants and large food portion sizes to be strong, pathogenetic, obesity-inducing factors, 137 or more broadly, they consider obesity to be due to a “toxic” or “poisonous” food supply. 138 Some also are concerned that the increase in obesity (defined by BMI) will overwhelm any gains in health and life expectancy noted over the past several decades, that is, an Apocalypse awaits us. 139 I and others 140,141 do not share this pessimism.

Finally, I would like the political activists and doomsday prophets whose professional careers appear to depend on frightening the public and inducing politicians to pass restrictive laws without proven value, to be introduced to the prescient comments made by A. E. Harper 133 33 years ago. It is clear that currently we have a case of “déjà vu all over again.”

In regard to predicting the future, a wise person whose name I cannot recall stated presciently “Predicting the future is a fool’s playground”; the physicist Neils Bohr said, “Prediction is very difficult, especially about the future,” or as stated by that sage of the baseball world, Yogi Berra, “The future ain’t what it used to be.” Bertrand Russell said, “Fools and fanatics are always so sure of themselves, but wiser people are so full of doubt.” The true scientist should always be a skeptic.

Acknowledgments

The author thanks Rachel Anderson for help in preparing the manuscript for submission and Dr Mary C. Gannon for reading the manuscript and making numerous helpful comments.

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In BMI we trust: reframing the body mass index as a measure of health

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Published: 25 October 2017
Volume 16 , pages 256–271, ( 2018 )

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Iliya Gutin 1

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Recent work in medical sociology has provided critical insights into distinguishing between obesity as a condition with severe individual- and population-level health consequences, and obesity as a socially undesirable, stigmatizing construct opposing thinness as the healthy ideal. Less often considered is the role of body mass index (BMI) as the standard by which obesity and healthy weight are measured and defined. Addressing this issue, I begin by distinguishing between BMI as an empirical, objective measure of health, and BMI as an arbitrary, subjective label for categorizing the population. I further consider how BMI is empowered as a measurable quantity through the lens of medicalization and evidence-based medicine, and introduce the “performativity” of BMI as a superior framework for confronting the measure’s conceptual limitations. Emphasizing key parallels between BMI and self-rated health as measures with high predictive validity, yet unspecified mechanisms of action, I propose an epistemological shift away from classifying BMI as a biomarker and toward a more flexible view of the measure as a holistic appraisal of health. In closing, I argue that researchers may continue to leverage BMI’s ease of collection and interpretation, provided they are attuned to its definitional ambiguity across diverse research methods and contexts.

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Gutin, I. In BMI we trust: reframing the body mass index as a measure of health. Soc Theory Health 16 , 256–271 (2018). https://doi.org/10.1057/s41285-017-0055-0

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Body Mass Index: Obesity, BMI, and Health: A Critical Review

Affiliation.

1 is a full professor at the University of Minnesota, Minneapolis, and chief of the Endocrine, Metabolic and Nutrition Section at the Minneapolis VA Medical Center, Minnesota. His PhD degree is in biochemistry. He has more than 250 scientific publications in peer-reviewed journals, and he is the winner of numerous prestigious academic and scientific awards, including the 2014 Physician/Clinician Award of the American Diabetes Association.
PMID: 27340299
PMCID: PMC4890841
DOI: 10.1097/NT.0000000000000092

The body mass index (BMI) is the metric currently in use for defining anthropometric height/weight characteristics in adults and for classifying (categorizing) them into groups. The common interpretation is that it represents an index of an individual's fatness. It also is widely used as a risk factor for the development of or the prevalence of several health issues. In addition, it is widely used in determining public health policies.The BMI has been useful in population-based studies by virtue of its wide acceptance in defining specific categories of body mass as a health issue. However, it is increasingly clear that BMI is a rather poor indicator of percent of body fat. Importantly, the BMI also does not capture information on the mass of fat in different body sites. The latter is related not only to untoward health issues but to social issues as well. Lastly, current evidence indicates there is a wide range of BMIs over which mortality risk is modest, and this is age related. All of these issues are discussed in this brief review.

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Published: 22 February 2021

Body mass index changes: an assessment of the effects of age and gender using the e-norms method

Joe F. Jabre 1 &
Jeremy D.P. Bland 2

BMC Medical Research Methodology volume 21 , Article number: 40 ( 2021 ) Cite this article

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To validate e-norms methodology in establishing a reference range for body mass index measures. A new method, the extrapolated norms (e-norms) method of determining normal ranges for biological variables is easy to use and recently was validated for several biological measurements. We aimed to determine whether this new method provides BMI results in agreement with established traditionally collected BMI values.

We applied the e-norms method to BMI data from 34,384 individuals and compared the ranges derived from this method with those from a large actuarially based study and explored differences in the normal range by gender, and age.

The e-norms derived range of healthy BMI in adults is from 22.7 to 30.6, and showed that BMI is consistently higher in men than in women and increases with age, except in subjects aged 80–98 years in whom healthy BMI appears to be lower.

Conclusions

Our e-norms derived healthy BMI ranges agree with traditionally obtained actuarially based methods, supporting the validity and ease of use of our method.

Peer Review reports

The US CDC, [ 1 ] in common with the WHO, [ 2 ] defines a BMI range from 18.5 to 24.9 as “Normal or healthy weight” for adults over 20 years of age, and a BMI > 30 as ‘obese’.

These threshold values are derived from historical studies of actuarial insurance databases and more recent studies of large epidemiological databases collected in comprehensive healthcare systems. These demonstrate that BMI values outside this range are associated with an increased incidence of either all causes mortality, [ 3 , 4 ] or specific conditions such as Hodgkin’s lymphoma.

In this work, we propose to use a novel technique, the extrapolated norms or e-norms method to calculate BMI normative values from BMI data that contains both normal and abnormal values collected in our practice.

The method relies on a behavior we refer to as “e-norms clustering” [ 5 ] that reveals a narrow range between the minimum and maximum values of a variable obtained from normal individuals as compared to a variable obtained from individuals with pathology that displays a much larger minimum to maximum range. This e-norms clustering behavior is independent of the type of pathology being investigated and will be described in more detail in the e-norms method section below.

To date, the e-norms method has been validated by various authors for neurophysiological and non-neurophysiological data. This work represents our first attempt to use it in evaluating Body Mass Index (BMI) normative data.

This work was undertaken to show that normative data extracted from a mixed dataset using the e-norms method yields similar results to existing traditionally obtained BMI estimates derived from actuarial data that are costly to conduct and require long-term follow-up of many subjects.

Despite long-established WHO recommendations on healthy BMI, there remain uncertainties regarding the interpretation of BMI. A recent study of 3.6 million individuals concluded - “…further work is needed to establish whether increased weight is actually beneficial for older individuals.” [ 3 ] There is also evidence that a single range for healthy BMI is not appropriate for all ethnic groups, [ 6 , 7 ] but large, long term prospective studies are especially difficult to conduct in these subpopulations. Even the largest recent studies leave residual uncertainty in their conclusions as to whether healthy BMI has the same range in different age groups, notably the elderly [ 8 ]. These shortfalls notwithstanding, the age groups we chose to calculate BMI normal values for were selected for comparing our results to those derived from an actuarial study of 3.6 million individuals in the UK [ 3 ].

We applied the e-norms method to BMI data derived from 34,384 individuals we collected in our practice between 1994 and 2019 to compare the ranges we derive by this method to those obtained from a large actuarially based study of 3.6 million individuals identifying the incident disease, and the ascertainment of death.

The source data for our study are the records of patients attending the clinical neurophysiology department in Canterbury, UK for investigation of possible carpal tunnel syndrome (CTS). Presence of significant risk factors and chronic conditions known to be risk factors for CTS were also collected. These included age, gender, height and weight, as well as occupational status and the presence or absence of thyroid disease, diabetes, acromegaly, arthritis and wrist trauma, smoking status, and family history of CTS.

The records include BMI because of a previously well documented relationship between high BMI and an increased incidence of CTS [ 9 ]. Indeed 57 % of the patients in our dataset were positive for CTS and 43 % were negative. The advantage our material presented was that it was collected by the same investigator (JDPB), in the same hospital, from the same patient referral pool, using the same collection and analysis methods.

We have generic ethics permission to use anonymized data from this database for our research. The research ethics approval was obtained from South Central (Hampshire A) National Research Ethics Service committee in the UK.

We extracted records made at the patient’s first presentation to the department for diagnosis, thus excluding follow-up visits and including each subject only once. We excluded subjects under 17 years of age and 65 subjects with missing data for height or weight. The extracted data fields for analysis were BMI, age, sex, and the presence or absence of laboratory confirmed CTS, the last being included only so that we would be able to conduct exploratory analyses of whether the disease specific nature of the population might influence the results. Thirty-four thousand and three hundred and eighty-four (34,384) BMI measures of 22,661 females and 11,723 males aged 17–98 years old were analyzed. We derived e-norms based healthy BMI estimates for the entire cohort; for males and females separately; and for four age groups in each (17–49, 50–69, 70–79 and 80–98 years). We chose these age groups to match those used in the study we were using for comparison [ 3 ]. Paired two sample t-tests were used to compare e-norms BMI values between males and females of the same age groups, both of which were Gaussian distributed.

The e-norms method

The e-norms method [ 10 ] allows the use of data derived from a provider’s own cohort to produce normative values for any parameter in their database. The method has been validated by various authors for neurophysiological and non-neurophysiological studies ranging from electrodiagnostic studies, [ 11 , 12 , 13 , 14 , 15 ] to acetyl choline receptor antibodies (AchRAb) for diagnosing myasthenia gravis (Guan Y, unpublished data), and more recently to Ophthalmology, for deriving biometric normative data used for intraocular lens (IOL) power calculation prior to cataract surgery [ 16 ]. To date, normative data derived using the e-norms method in all these studies was found to closely match data obtained from traditional studies, producing much needed normal values in populations and cohorts for which none were available. The method has been proven particularly useful in pediatric cohorts where normal values change rapidly with age and works as follows:

A variable’s data is sorted in ascending order in an Excel spreadsheet and plotted against its rank order producing a cumulative distribution plot that reveals an inverted S curve consisting of a steep lower left; a flat or “plateau” middle; and a steep upper right.

First-order derivatives are then calculated for each successive data point by subtracting the second value from the first, the third from the second, and so on until all the differences between successive values have been calculated. The first-order derivatives are then plotted on the same graph as the sorted variables to help in identifying the plateau part of the curve, the one corresponding to the lowest first order differences, consistent with the e-norms clustering behavior.

The e-norms clustering behavior is data neutral and can be illustrated using blood glucose levels as follows:

Fasting blood glucose levels have a minimum to maximum range between 70 and 99 mg/dl, with a min to max difference a mere 29 mg/dl.

In diabetic patients, such differences are hundreds of times that range. The Guinness World Records lists “Michael Patrick Buonocore (as having) survived a blood sugar level of 2,656 mg/dl when admitted to the Pocono Emergency Room in East Stroudsburg, Pennsylvania on 23 March 2008” [ 17 ]. Since an abnormal fasting Blood Glucose can be as low 100 mg/dl, the min to max difference in patients with known diabetes can in theory be an astounding 2556 mg/dl, a much greater difference than in individuals with normal Blood Glucose. E-norms clustering leverages this behavior and can be used in identifying datasets derived from normal subjects from those derived from subjects with pathology.

To illustrate this concept, we will use a simulation study that displays 1,000 simulated values that have a mean of 20 and a SD of 1.5, with a mean ± 4 SD value of 14 and 26, respectively. We will determine if we can extract the mean ± 2 SD values of 17 and 23 from this graph from the data that lies within the plateau part of the curve. The plot of this simulated data can be seen in Fig. 1 .

Sample e-norms plot. Cumulative density (curve) first-order difference (dots) of a simulated variable with 1,000 data points and a mean of 20 and standard deviation of 1.5. When a straight-line fit is overlaid on the inverted S curve, the plot of the first-order difference reveals the range of data points with low first-order difference, helping to identify inflection points A and B. Reprinted by permission from Wolters Kluwer Health, the Journal of Clinical Neurophysiology: Jabre JF, Pitt MC, Deeb J, Chui KKH. E-norms: a method to extrapolate reference values from a laboratory population. 2015;32(3):265–270. Copyright 2015 by the American Clinical Neurophysiology Society.

Data points at the left and right extremes of the curve, display higher first order differences between them than those between points A and B that mark the curve’s points of inflection. Descriptive statistics of the data lying between points A and B within the plateau part of the curve reveals values that lie between 17 and 23 identical to the normal limits of the data as predicted by the mean ± 2 SD we set out to represent.

In a recently completed study, plateau identification and determination of the left and right inflection points of the e-norms plot has been proven reliable. Twenty different observers recruited from a diverse pool of hospital workers were asked to visually identify the e-norms plateau in 393 upper and 284 lower limb nerve conduction studies while blinded to the variable they were analyzing. An inter-rater ANOVA without replication testing showed no significant difference between their findings. [ 18 ].

A significant advantage of the e-norms method is that it can be performed in minutes using a Microsoft Excel spreadsheet that is uploaded anonymously and securely to an encrypted e-norms web application developed by one of the authors (JFJ) for this purpose [ 19 ].

The age group and sex distribution of the entire patient cohort is shown in Table 1 and the e-norms plot from our data is shown in Fig. 2 .

E-norms plot of BMI values. Cumulative density curve of 50-69 years old women BMI values. Points A and B on the cumulative density curve delineate the boundaries of the e-norms plateau. Note the plateau correspondance with the lowest first order differences.

The estimate of healthy BMI derived with the e-norms method for our entire dataset showed a mean of 26.5, and a standard deviation of 2.2 with a range from 22.7 to 30.6. The distribution is not normal (Skewness 0.07, Kurtosis − 1.09, Kolmogorov-Smirnov test d = 0.057 p < .01, Lilliefors p < .01).

E-norms derived estimates of healthy BMI in subgroups of the population displayed variations with age and sex as seen in Fig. 3 .

E-norms derived BMI normal values. Mean (bars) and minimum to maximum range (whiskers) of normal BMI values by sex and age derived by the e-norms method

In men it reaches a peak in the 50–69 age group but reveals a marked decrease in healthy BMI in the oldest age group. In women however, it reaches a peak in the 70–79 age group before showing the same marked decrease in the oldest members of the population. The ranges for men were consistently higher than those for women in all age groups, with the minimum and maximum differences being highest in the 17–49 and 50–69 age groups, and lowest in the 70–79 age group. All of the men to women differences were statistically significant at the p < .05 level using a paired t-test two sample analysis for means.

A simple web search for BMI normal values in sites ranging from the World Health Organization (WHO) [ 2 ], to the Center for Disease Control and Prevention (CDC) [ 1 , 20 ] the American Cancer Society [ 21 ], the American Heart Association [ 22 ], and the NHS [ 23 ], lists BMI normal values as indicative of underweight, for BMI less than 18.5; normal weight; for BMI between 18.5 and 24.9; overweight, for BMI between 25 and 29.9; and obese, for a BMI of 30 or more.

Numerous studies have investigated the relationship between obesity and life expectancy by studying the relationship between BMI measures and mortality. But the evidence of this remains inconclusive varying from none [ 24 ], to an inverse relation [ 25 ], or a direct one [ 26 ] due to differences in analyses methods using U versus J shaped relations. Wong et al [ 27 ] used a method they refer to as multivariable fractional polynomials (MFP) to “determine the best fitting functional form for BMI .. to capture the relationship between mortality and BMI in a compact, parsimonious model.”

Given these inconsistencies, an easy breakdown of BMI normal values by age and gender that does not require long and costly actuarial studies will add great value to the investigation of these relationships. Our work set out to investigate the use of this novel approach to derive normative BMI measures broken down by age and gender, and aimed to determine whether this method provides BMI results in agreement with established and traditionally collected BMI values. Our 30.6 BMI estimate of the upper limit of healthy BMI derived using the e-norms is remarkably close to the currently accepted threshold of obesity. This value also corresponds quite closely to the level at which the hazard ratio for all-causes mortality in the UK population of 3.6 million individuals rises above 1.0 with increasing BMI.

Where underweight is concerned however, the e-norms estimate of the lower boundary of healthy BMI of 22.7 is markedly higher than the WHO suggested figure of 18.5. When examining the data for all-causes mortality in Bhaskaran et al’s Fig. 1 however, it is clear that there is a sharp increase in mortality as BMI falls, long before the WHO threshold of 18.5 is reached. The hazard ratio for all-causes mortality in that study rises above 1 at a BMI of approximately 22. To that end, unlike with traditional methods, the e-norms analysis has estimated a lower boundary for healthy BMI that actually corresponds to the level at which all causes mortality becomes significantly elevated.

This leads us to believe that the current WHO definition of ‘underweight’ as BMI < 18.5 is too conservative and that the adverse health implications of low BMI have perhaps received insufficient attention.

Having established that our estimate of the range of healthy BMI corresponds closely to the range of lowest all-causes mortality we then set out to examine if our e-norms estimates support earlier suggestions regarding variation in healthy BMI by age and sex.

Data from Bhaskaran et al’s Fig. 3 suggests that the range of healthy BMI may be wider with an extended upper limit in women compared to men, although this data was limited to the subset of ‘never smokers’. [ 3 ] In smokers however, several workers [ 28 , 29 , 30 ] have shown a higher risk for pregnant smokers with obesity and adverse maternal and infants birth outcomes.

Earlier studies have suggested that the BMI associated with minimal mortality rises gradually with age, from about 22 at age < 50 to 25 at age > 80 years. [ 3 ] A systematic review concluded that the optimal BMI range for the elderly was between 25 and 35 and that the relationship between BMI and mortality is weaker in older age groups. [ 8 ].

Our finding that ‘normal’ BMI in the very elderly is noticeably lower than the figure suggested by actuarial studies could be accounted for by a high prevalence of chronic pathologies in this age group that, either by direct effects (diabetes) or indirect ones (relative inactivity as a result of arthritis for example), are associated with higher BMI, thus introducing an overall upward bias into BMI measures in this age group when they are studied by conventional actuarial methods.

One cannot exclude however that survivor bias may account for our finding in the > 80 years age group. In this scenario, individuals who reached this advanced age were probably healthier, and likely had a lower BMI earlier than those who didn’t. The e-norms method would therefore not be determining the healthy BMI range for a hypothetical aged 80 + population but for a healthy sub-population of survivors in this age group. It would be interesting to study mortality data in relation to BMI in demonstrably healthy elderly individuals who have been screened for the absence of common chronic disease.

Such a study would be handicapped however by the intrinsic difficulty of following up patients in the their 80 s and above for the long periods required for mortality studies. In the meantime, we would suggest that it is not safe to assume that a BMI of 31 in an 80-year-old is necessarily of less concern than a similar measurement in a 30-year-old.

We investigated whether the e-norms method can be applied to develop BMI, considered here as just another biological marker. The e-norms method is a recently developed technique that has been successfully applied in other disciplines to derive normative data from a laboratory population. The close agreement between our e-norms estimates of the upper limit of a normal BMI and those derived from the literature at which all-causes mortality begins to rise markedly, serves as validation of this technique. The strengths of our study include a relatively large source dataset and an entirely different analytical approach to existing actuarial studies of healthy BMI that are costly to perform and require long-term follow-ups. Although the source population in our cohort were all suspected of having CTS, we are mindful that by no means they represent a random sample of the population. But given the close correspondence between our estimates of healthy BMI and those derived from mortality studies, we believe that our assumptions are valid and would allow practitioners with limited time and resources to take advantage of existing datasets to determine the range of normal BMI in their own cohorts in a fast and efficient manner.

Availability of data and materials

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Abbreviations

Extrapolated norms

Body Mass Index

United States Center for Disease Control and Prevention

The World Health Organization.

Carpal tunnel syndrome

Acetyl choline receptor antibodies

Intra-ocular lens

Standard Deviation

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Joe F. Jabre

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JFJ analyzed the patients BMI data and was involved in the conceptualization of this project and the development of its methodology and validation. He was a major contributor in writing the manuscript, and in reviewing and editing it. JDPB collected the patient data and was involved in the investigation and verification of the patients results and data curation, and was a major contributor in writing the manuscript, and reviewing and editing it. All authors read and approved the final manuscript.

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Correspondence to Joe F. Jabre .

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Jabre, J.F., Bland, J.D. Body mass index changes: an assessment of the effects of age and gender using the e-norms method . BMC Med Res Methodol 21 , 40 (2021). https://doi.org/10.1186/s12874-021-01222-z

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Long-term body mass index changes in overweight and obese adults and the risk of heart failure, cardiovascular disease and mortality: a cohort study of over 260,000 adults in the UK

Barbara Iyen ORCID: orcid.org/0000-0001-9720-1180 1 ,
Stephen Weng 1 ,
Yana Vinogradova 1 ,
Ralph K. Akyea 1 ,
Nadeem Qureshi 1 &
Joe Kai 1

BMC Public Health volume 21 , Article number: 576 ( 2021 ) Cite this article

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Although obesity is a well-recognised risk factor for cardiovascular disease (CVD), the impact of long-term body mass index (BMI) changes in overweight or obese adults, on the risk of heart failure, CVD and mortality has not been quantified.

This population-based cohort study used routine UK primary care electronic health data linked to secondary care and death-registry records. We identified adults who were overweight or obese, free from CVD and who had repeated BMI measures. Using group-based trajectory modelling, we examined the BMI trajectories of these individuals and then determined incidence rates of CVD, heart failure and mortality associated with the different trajectories. Cox-proportional hazards regression determined hazards ratios for incident outcomes.

264,230 individuals (mean age 49.5 years (SD 12.7) and mean BMI 33.8 kg/m 2 (SD 6.1)) were followed-up for a median duration of 10.9 years. Four BMI trajectories were identified, corresponding at baseline, with World Health Organisation BMI classifications for overweight, class-1, class-2 and class-3 obesity respectively. In all four groups, there was a small, stable upwards trajectory in BMI (mean BMI increase of 1.06 kg/m 2 (± 3.8)). Compared with overweight individuals, class-3 obese individuals had hazards ratios (HR) of 3.26 (95% CI 2.98–3.57) for heart failure, HR of 2.72 (2.58–2.87) for all-cause mortality and HR of 3.31 (2.84–3.86) for CVD-related mortality, after adjusting for baseline demographic and cardiovascular risk factors.

The majority of adults who are overweight or obese retain their degree of overweight or obesity over the long term. Individuals with stable severe obesity experience the worst heart failure, CVD and mortality outcomes. These findings highlight the high cardiovascular toll exacted by continuing failure to tackle obesity.

Peer Review reports

Obesity and overweight increase the risk of cardiovascular disease [ 1 ] and heart failure [ 2 ], and are important causes of morbidity and mortality globally. In 2016, an estimated 1.9 billion adults globally were overweight, of which 650 million were obese [ 3 ]. In recent UK estimates from the Health Survey of England [ 4 ], 27% of adults were obese, and the proportion of overweight and obese adults increased from 53% in 1993 to 63% in 2017 [ 4 ].

A meta-analysis has shown that for every 5-unit increase in BMI, there is a 29% increase in the risk of coronary heart disease (CHD) which drops to 16% after adjustment for blood pressure and cholesterol levels [ 5 ]. Long-term weight loss in obese individuals through self-guided efforts or behavioural interventions is not always sustained. Most behavioural interventions in obese individuals achieve only modest weight loss ranging from only − 0.3 to − 3.1 kg at 12 months to − 0.3 to + 1.3 kg weight gain at 24 months [ 6 ], and many severely obese individuals remain severely obese for at least 5 years [ 7 ]. It is thought that a multi-disciplinary approach and intervention is required to prevent obesity, reduce progression to more severe forms and lead to substantive and sustainable public health outcomes [ 8 ]. While weight loss interventions may improve cardiovascular risk factors [ 9 ], metabolic function [ 10 ] or even reverse type 2 diabetes [ 11 ], it is unclear what the long term effect of weight changes are on cardiovascular endpoints.

Few studies have explored how BMI in overweight or obese individuals may change over time, and any cardiovascular impact this may have. No existing research on BMI trajectories over time [ 12 , 13 , 14 , 15 , 16 ] has focused entirely on obese and overweight adult populations, so the BMI course of these individuals is unknown. This aim of this study was firstly to examine BMI trajectories in the general population of adults who were overweight or obese adults; and secondly to explore the risk of heart failure, cardiovascular disease (CVD), CVD-related mortality and all-cause mortality, associated with different BMI trajectories.

Source of data

We conducted a cohort study using data from the UK Clinical Practice Research Datalink (CPRD), a nationally representative database of routinely recorded primary care electronic health records [ 17 ]. General practice is the first point of contact for non-emergency healthcare in the UK, and over 98% of the UK population are registered with a General Practitioner. The CPRD database contains anonymised longitudinal data entered during routine consultations from general practices who have agreed to provide patient data. The database has a coverage rate of approximately 15% of the UK population and patients are broadly representative of the UK general population in terms of age, sex and ethnicity [ 17 ]. Patient records were available from 790 general practices which contributed to the database during the study period from 1999 to 2018. Primary care records from CPRD were linked with secondary care records (Hospital Episode Statistics) and death registration records from the Office of National Statistics (ONS). Data access and ethical approval was granted by the CPRD Independent Scientific Advisory Committee (Protocol number 18_195) in August 2018.

Study participants and covariates

We identified individuals aged 18 years or older, with a recorded or computed BMI (weight divided by the square of height) of 25 kg/m 2 or greater, and subsequent records of BMI during the study period. Each individual could have up to five BMI data points – the first recorded overweight or obese BMI was defined as the baseline BMI, and then subsequent BMI measures at 2 years (±90 days), 5 years (±6 months), 8 years (±6 months) and 10 years (±12 months). To be included in the study, participants had to be registered with the general practice for at least one year before the date of their baseline BMI. Individuals with pre-existing records of CVD (defined as coronary heart disease, peripheral vascular disease, stroke or transient ischaemic attack) or heart failure, were excluded from the study. All participants were followed up until diagnosis of CVD, heart failure, death, transfer out of the practice or last date of data collection, whichever occurred first. To ensure that there were sufficient BMI records for the trajectory analyses, we included only individuals who had BMI records at baseline and at least one other defined time point, prior to the incidence of CVD, heart failure or death.

In all study participants, we collected data on covariates such as patient demographics (age, sex, ethnicity and socioeconomic status) as well as comorbidities that could alter their risk of developing CVD or heart failure [ 18 ]. Our measure of material socioeconomic deprivation was the 2015 English index of multiple deprivation (IMD) in quintiles [ 19 ], and this was available for individuals with linked secondary-care records. Records of comorbidities collected at baseline were type-2 diabetes, atrial fibrillation, chronic kidney disease, hypertension, rheumatoid arthritis and other inflammatory diseases. Smoking status and records of alcohol consumption were collected at baseline as well as at the 2-year, 5-year, 8-year and 10-year follow-up time points.

Outcome ascertainment

Incident CVD was defined as any new clinical diagnosis of coronary heart disease, stroke, transient ischaemic attack (TIA) or peripheral vascular disease. CVD, heart failure and death records were identified from individuals’ primary care, secondary care and ONS death registry records during the study period. Disease codes used for identification of CVD and heart failure are shown in the supplementary online file .

Analyses were conducted using Stata SE version 15. Baseline descriptive statistics were presented for the entire study population, including missing data. Although the inclusion criteria ensured that individuals had a minimum of 2 BMI records (BMI at baseline and a minimum of one other BMI record), all values of BMI that were missing at the study set time points and before the incidence of CVD, heart failure or death, were estimated using multiple imputation by chained equations procedure. This approach provides estimates for missing values when data are assumed to be missing at random [ 20 ]. It is also the recommended approach for handing missing weight data in epidemiological studies using primary care health records [ 21 ], where the practical analytical approach is to include in the imputation model, variables that are predictive of the missing data [ 22 ]. Previous research has shown that the use of multiple imputation for missing weight records in primary care databases provided results comparable with population surveys [ 23 ]. Baseline BMI, demographic variables such as age and sex, clinical comorbidities (cardiovascular disease, diabetes, hypertension, rheumatoid arthritis and other inflammatory conditions, chronic kidney disease and atrial fibrillation), smoking status and alcohol consumption were included in the imputation models to create 10 imputed datasets. Body mass index measures at baseline, 2 years, 5 years, 8 years and 10 years were used to assign individuals into trajectories of BMI using group based trajectory modelling (GBTM). GBTM provides a statistical method to identify distinctive clusters of individuals who follow a similar developmental trajectory and enables profiling of the characteristics of individuals within the clusters [ 24 ]. In the GBTM models, BMI was the dependent variable and time (baseline, 2, 5, 8 and 10 year time-points) was the independent variable. Models took account of the effect of time-varying covariates: age, smoking status and alcohol consumption on individuals’ probability of group membership, and assigned individuals to the group to which they had the highest probability. The Stata plug-in program ( Traj ) was used to estimate group-based trajectory models using the maximum likelihood estimation method [ 25 ]. Data was modelled using the censored normal distribution and BMI values that were considered clinically implausible (BMI less than 10 kg/m 2 or greater than 131 kg/m 2 ) were excluded from analyses. The Bayesian information criterion (BIC) was used as criterion for selection of the best-fitting trajectory model whereby models with the lower value of BIC was preferred. BIC captures generalised trends over time while also minimising the risk of over-fitting the models. While the Akaike’s information criterion (AIC) and BIC both aim at achieving a compromise between model goodness of fit and model complexity, with maximum likelihood estimates driven to penalise free parameters, BIC are more stringent than AIC [ 26 ] and is asymptotically consistent, in that it will select the true model if, among other assumptions, the true model is among the candidate models considered [ 27 ]. Similar to GBTM methods employed in a previous study [ 28 ], we identified the ideal number of trajectory groups for our study population, by estimating the BIC in 2- group models, 3-group models and 4-group models. This was followed by testing zero-order, linear, quadratic and cubic specifications for the different trajectory shapes until the best fitting shape was derived. In selecting the final model, we ensured each trajectory group had a minimum of 5% of the study population. The 4-group model with four cubic trajectories was selected as the model with the best-fit due to the low BIC value as well as having an adequate proportion of the study population per trajectory group (BIC data are shown in supplementary Table 1 ).

Socio-demographic characteristics, clinical profile, comorbidities as well as CVD, heart failure and mortality outcomes were assessed for individuals in the trajectory groups. Baseline characteristics of individuals between BMI trajectory groups were compared using the ANOVA test for continuous variables or the chi-test for categorical variables. We used survival analyses to estimate the incidence rates of outcomes for individuals in the different trajectory groups. The proportional hazards assumption was checked using statistical tests (Schoenfeld residuals). Multivariate Cox proportional hazards modelling was used to derive hazards ratios for CVD, heart failure and mortality in the groups, adjusting for demographic and clinical covariates that were significantly associated with the exposure and outcome. The lowest BMI trajectory group, the overweight trajectory group, was the reference for comparison because it was the largest and most normative group. We then estimated the mean change in BMI over the 10 year period, in the subgroup of individuals who had BMI data at 10 years. To assess the validity of our assumption that missing data were missing at random, a sensitivity analyses was done using complete case analyses. Owing to the possibility that not all CVD events are captured are recorded in primary care records, further sensitivity analyses was restricted to only individuals whose primary care records were linked with secondary care (hospital episode statistics) and the ONS (for mortality records).

Study population

A total of 264,230 overweight and obese individuals were included in the study. The flowchart in Fig. 1 demonstrates how the study population were derived from the overall CPRD population of obese and overweight subjects.

Flow chart showing how the study population of overweight and obese subjects were derived

Baseline characteristics of the study subjects are shown in Table 1 . Females comprised 62% of individuals in the study, the mean age at baseline was 42.5 (SD 12.7) years and the mean BMI was 33.8 (SD 6.1) kg/m 2 . Linkage with hospital and death registration data was available for 138,755 (52.5%) individuals in CPRD. There were complete BMI records at all 5 set time points for 17.3% of the study population. 20% of subjects had 4 BMI records and 30% had 3 BMI records. Multiple imputation was used to estimate missing BMI values to ensure BMI records were available at all set times prior to the incidence of CVD, heart failure or death. Ethnicity records were available for 69.2% of the study population, with the majority of individuals being white (63.7%). The most prevalent comorbidities at baseline were hypertension (19.9%) and type 2 diabetes (8.6%).

In the study population of 264,230 overweight or obese adults, we identified four distinct BMI trajectories over time, with a statistically significant difference in the BMI at baseline for individuals in the different trajectories ( p < 0.001). The estimates for posterior probability of group membership are shown in Supplementary Table 2 . Across the trajectory groups, the average posterior probability was greater than 0.93, which is above the recommended minimum average probability of 0.70, and the odds of correct classification for the BMI GBTM groups were above 30 for all groups, indicating good accuracy of model assignment (Supplementary Table 2 ).

The mean BMI at baseline, for individuals in trajectory group 1 ( n = 95,944, 36.3%) was 28.7 kg/m 2 , corresponding to the WHO overweight BMI category. Individuals in trajectory group 2 ( n = 104,616, 39.6%) had a mean baseline BMI of 33.7 kg/m 2 corresponding to WHO class 1 obesity category. The mean baseline BMI in trajectory group 3 ( n = 50,866, 19.3%) was 39.9 kg/m 2 corresponding to WHO class 2 obesity while those in trajectory group 4 ( n = 12,804, 4.9%) had a mean baseline BMI of 49.1 kg/m 2 corresponding to WHO class 3 obesity. Although BMI remained relatively stable across the 4 trajectory groups, individuals had a mean BMI increase of 1.06 kg/m 2 (± 3.8) over 10 years (Fig. 2 ).

Body mass index (BMI) trajectories using BMI measures at baseline and then follow-up at 2 years, 5 years, 8 years and 10 years. Percentages below plot represent percentage of study population within each trajectory group. Mean BMI change in trajectory group 1 (overweight-stable group): + 0.99 (SD 3.10) kg/m 2 . Mean BMI change in trajectory group 2 (obese class 1-stable group): + 1.19 (SD 1.67) kg/m 2 . Mean BMI change in trajectory group 3 (obese class 2-stable group): + 1.04 (SD 4.59) kg/m 2 Mean BMI change in trajectory group 4 (obese class 3-stable group): + 0.62 (SD 6.27) kg/m 2

The characteristics of individuals belonging to the different BMI trajectory groups are shown in Table 2 . Compared to other trajectory groups, the obese class 3-stable trajectory group comprised of the highest proportion of females. There were greater levels of deprivation among particularly the most severely obese trajectory groups. There was also an increasing trend in the prevalence of clinical morbidities such as hypertension, atrial fibrillation, chronic kidney disease and type 2 diabetes, with increasing severity of obesity, such that individuals in the overweight-stable trajectory group had the lowest prevalence whereas those in obese class 3-stable trajectory group had the highest prevalence of comorbidities at baseline.

Cardiovascular disease, heart failure and mortality outcomes

There were a total of 30,400 incident cases of cardiovascular disease over 2,829,075 person-years of follow-up (median follow-up of 10.9 years (IQR 7.0–14.1)). Table 3 shows the overall CVD incidence rates as well as incidence rates of coronary heart disease (CHD), stroke/transient ischemic attack (TIA), peripheral vascular disease, heart failure, and mortality in the trajectory groups. The incidence rate of CVD in the entire study population (per 1000 person-years) was 10.75 (95% CI 10.61–10.87). The CVD incidence rate (per 1000 person-years) among individuals in the overweight-stable trajectory group was 9.30 (9.12–9.49). Higher incidence rates of overall CVD and CVD subtypes were observed in obese class 1-stable trajectory group compared to overweight-stable individuals but no further increase in incidence rates of CHD, stroke/TIA or peripheral vascular disease, with more severe categories of obesity from obese class 1-stable to obese class 3-stable groups. There was however a substantial and significant gradient in heart failure incidence with increasing severity of obesity from overweight-stable to obese class 3-stable trajectory groups, such that heart failure incidence rate (per 1000 person years) in overweight-stable trajectory individuals was 1.70 (1.6–1.8), and 5.70 (5.3–6.1) in obese class 3-stable trajectory individuals (score test for trend across BMI groups p < 0.0001).

A total of 24,022 deaths occurred during the period of follow-up, of which 2827 (11.8%) were cardiovascular deaths. The overall mortality rate in the study population (per 1000 person-years) was 8.5 (8.4–8.6). All-cause mortality and CVD-related mortality rates increased with more severe categories of obesity (shown in Table 3 ). As well as having the earliest onset of incident CVD, obese class 3-stable trajectory individuals had the highest all-cause mortality rate, CVD-related mortality rate and the youngest age at death.

Table 4 shows the hazards ratios for CVD, heart failure and mortality outcomes for individuals in obese 1-stable, obese 2-stable and obese 3-stable trajectory groups, compared to individuals in the overweight-stable trajectory group. After adjusting for age, sex and comorbidities, individuals in obese class 1, 2 and 3 trajectory groups had higher risks of all CVD, heart failure and mortality outcomes, compared to individuals in the overweight-stable trajectory group. An increase in coronary heart disease (HR 1.14 (95% CI 1.10–1.18)) and stroke/TIA risk (HR 1.09 (1.03–1.15)) was observed in obese class 1-stable compared to overweight individuals but no further increase in risk of these conditions were found with more severe categories of obesity. In the most severely obese (obese 3-stable) group, there was no statistically significant difference in the risk of coronary heart disease (HR 1.06 (0.99–1.16)) and stroke (HR 1.04 (0.92–1.18)), compared to individuals in the overweight-stable group. The risk of peripheral vascular disease did not differ significantly between those in the overweight, obese 1-stable and obese 2-stable trajectory groups. There was however, a reduced risk of peripheral vascular disease risk in obese 3-stable adults compared to those in the overweight-stable group (HR 0.73 (0.60–0.89)).

The risk of heart failure, all-cause mortality and CVD-related mortality increased considerably with increasing severity of obesity such that after adjusting for age, sex and comorbidities, individuals in obese class 3-stable trajectory group had hazards ratios of 3.3 for heart failure, 3.3 for CVD-related deaths and 2.7-for all-cause mortality, compared to individuals in the overweight-stable trajectory group.

Figure 3 illustrates the cumulative outcome-free survival of individuals in the trajectory groups.

Kaplan Meier survival plots showing cumulative outcome-free survival of individuals in the 4 BMI trajectory groups. With increasing BMI trajectory groups, individuals had higher risks of non-fatal cardiovascular outcomes, heart failure and mortality

In sensitivity analyses done using only the BMI records available in patients’ electronic health records (without multiple imputation of missing BMI records), four BMI trajectory groups were identified and the BMI measures across the BMI trajectories were similar to measures in the main study which used multiple imputation to estimate missing BMI records. Also, similar to findings in the main study, there was a small stable upwards trajectory across all 4 groups, with an overall mean BMI increase of 1.26 kg/m2 (SD 4.47) over 10 years (supplementary Table 3 and supplementary Figure 1 ).

In further sensitivity analyses restricted to 138,755 individuals whose primary care records were linked with secondary care and ONS death registration records, the hazards ratios for overall CVD, coronary heart disease, stroke/TIA, heart failure, all-cause mortality and CVD-related death remained significantly higher in obese class 1-stable, class 2-stable and class 3-stable individuals, compared with the overweight-stable trajectory group of individuals. As in the main analyses, there was no observed increase in the risk of peripheral vascular disease in adults who were obese compared to overweight adults (supplementary Table 4 ).

Main findings of paper

In this large general population cohort study of adults who were overweight or obese, we observed a stable upwards BMI trajectory over time whereby most subjects retained their degree of obesity over the long term. The overall risk of CVD, heart failure and mortality increased with increasing severity of obesity. Whilst there was no significant increase in risk of coronary heart disease and stroke in the most severely obese group, the increase in CVD risk was most marked for heart failure and mortality. After adjusting for the effect of age, sex and comorbidities, individuals in the most severely obese group had a 3.3-fold higher risk of heart failure, 3.3 fold higher risk of CVD-related mortality and 2.7-fold higher risk of all-cause mortality compared with overweight individuals. There were greater levels of socioeconomic deprivation with increasing severity of obesity, confirming that this is disproportionately an issue in the materially deprived.

Strengths and limitations

To our knowledge, this is the first and largest study to analyse overweight and obese adults’ BMI trajectories and their impact on CVD endpoints, heart failure, and mortality. We had a large sample size of obese and overweight individuals who were studied prospectively with multiple BMI measures per individual over an extensive follow-up period. Linkage of individuals’ routine electronic primary care records to their secondary care and death registration records, enabled more robust extrapolation of CVD diagnosis and mortality data. The use of health professional-recorded rather than self-reported BMI measures minimised the risk of inaccuracies in the study. By using data from a large nationally representative database of UK electronic health records, the study findings can be generalised to the general population of overweight and obese adults.

Some limitations of this study are recognised. Body mass index is a surrogate measure of adiposity. Body composition of fat and skeletal muscle mass changes with age [ 29 ] and differs between sexes and ethnic groups [ 30 ]. While other indices such as waist-hip ratio and waist circumference are more suitable and accurate measures of adiposity than BMI, these are not used routinely in clinical practice and are not routinely available in electronic heath records. Over 60% of our study population were White, and so the CVD risk profile and CVD-related outcomes in the study population may not be directly generalizable to, or may underestimate obesity-related heart failure, CVD and mortality risk in other ethnic populations. There was no information on physical activity level or dietary intake so it remains unclear whether weight change observed in individuals’ was intentional or non-intentional and due to presence of disease. Lastly, a study inclusion criterion was a minimum of 2 BMI entries in subjects’ primary care records so there is a small risk of selection bias in the population studied. We had missing BMI data and acknowledge that this can constitute considerable challenges in the analyses and interpretation of results as well as potentially weaken the validity of results and conclusion [ 20 ]. However, we estimated these missing BMI records using multiple imputation based on the missing at random assumption. A sensitivity analysis examined the effect of this assumption and found that results of analyses using only available BMI records were similar to results of analyses using multiple imputation.

Comparison with existing literature

This is the first study to evaluate the long-term impact of overweight and obese individuals’ BMI trajectory on cardiovascular endpoints, heart failure and mortality outcomes. While the association between obesity and cardiovascular disease is established [ 1 , 2 ], our study sought to assess the effect of long-term BMI changes, rather than single BMI measures, on the risk of CVD and related outcomes. We particularly observed a strong significant gradient in heart failure risk which increased with more severe forms of obesity. This provides confirmatory evidence of the graded increase in heart failure risk with increasing obesity. The lack of a clear relationship between degree of obesity and the risk of peripheral vascular disease, as well as the reduced risk of peripheral vascular disease in the most severely obese group, is similar to findings in the Framingham heart study. As had been previously suggested [ 31 ], this unclear relationship may be either due to under-diagnosis of peripheral vascular disease, or a difference in the underlying disease mechanism compared to other types of CVD.

In relation solely to obesity, our findings in a large general adult population expand on those of a smaller study of 3070 Canadian adults which similarly found no significant change in individuals’ BMI over time [ 32 ]. Similarly, a retrospective cohort study of 11,735 adults with severe obesity (BMI 35 kg/m 2 or greater) in the US, found that severely obese individuals remained in that BMI category over at least 5 years [ 7 ]. The current study is the largest prospective investigation to assess long term changes in BMI over time. Our finding that the general population of adults who were overweight or obese followed one of four stable upwards BMI trajectories over a decade, elaborates on previous research.

Previous studies of the association between obesity and mortality have produced conflicting results. In the original Framingham heart study and the Framingham offspring study, maximum BMI over a 24 year period was strongly associated with subsequent all-cause mortality [ 33 ]. However a systematic review of the risk of all-cause mortality in overweight and obese relative to normal weight individuals in the general population, found lower risk of mortality in overweight compared to normal weight subjects, but the highest mortality risk in more severely obese subjects with class 2 and 3 obesity [ 34 ]. More recently, a population-based cohort study found a J-shaped association between BMI and overall mortality such that lower BMI was associated with increased mortality risk, but the absolute mortality burden was predominantly driven by obesity [ 35 ]. In the current study, we observed a stepwise increase in the risk of all-cause and CVD-related mortality with increasing severity of obesity. This persisted after adjusting for the effect of age, sex, hypertension, type 2 diabetes, atrial fibrillation and chronic kidney disease. This observed association may be due to several plausible mechanisms. Severe obesity is a risk factor for dyslipidaemia and is associated with devastating health consequences such as obesity hypoventilation syndrome, obstructive sleep apnoea, liver disease and certain types of cancers [ 36 ], which could independently or synergistically increase the risk of mortality.

Some studies have reported an ‘Obesity paradox’ with clinically better outcomes in overweight and obese patients compared to normal weight patients in the context of prevalent cardiovascular disease such as heart failure [ 37 ] or following an acute coronary event [ 38 ]. In contrast, in the current study population, free from CVD at the start of follow-up, individuals with more severe obesity had earlier onset of incident CVD and earlier age at death, than overweight individuals. Our study provides compelling evidence of poor health outcomes associated with obesity.

Conclusions

Despite widespread efforts to prevent and manage obesity, the majority of adults who are overweight or obese in the general population continue to remain so in the long term. This is associated with a three-fold increase in heart failure, cardiovascular disease and mortality risk in the stably severe obese population. This research highlights the high cardiovascular toll exacted by continuing failure to tackle obesity, particularly among more socio-economically deprived populations. More effective policies and weight-management interventions at societal, cultural and health service levels are needed to address this increasing burden. Further research is also needed to explore whether interventions to change BMI trajectories would have an impact on future CVD outcomes.

Availability of data and materials

The CPRD and linked Hospital Episode Statistics datasets analysed during this study are available from the Clinical Practice Research Datalink (CPRD) ( [email protected] ) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the CPRD Independent Scientific Advisory Committee (ISAC) ( [email protected] ).

Abbreviations

Body mass index

Coronary heart disease

Clinical Practice Research datalink

Cardiovascular disease

Group based trajectory modelling

Hospital Episode Statistics

Index of Multiple Deprivation

Office for National Statistics

Transient ischaemic attack

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BI’s clinical academic lectureship is fully funded by the NIHR. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health & Social Care.

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BI conceptualised the study idea, developed the study design, obtained ethical approvals to access data, analysed the data, and wrote the initial manuscript draft and subsequent revisions. SW contributed to conceptualising the study design and methods, interpreted and reviewed study findings and critically reviewed the manuscript. YV was responsible for data extraction and contributed to study design methodology. RA contributed to study design methodology and data management. NQ contributed to conceptualising the study design and interpretation of study findings. JK contributed to conceptualising the study design and methods, overall supervision of the research study, review and interpretation of study findings and critical revision of manuscript. All study authors have contributed to revising, writing and finalising the final draft of the manuscript prior to submission. The author(s) read and approved the final manuscript

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Additional file 1..

Supplementary online file showing cardiovascular disease diagnostic Read codes (list of diagnostic Read codes for coronary artery disease, peripheralvascular disease, cerebrovascular accident (Stroke) and transient ischaemic attack (TIA) and congestive cardiac failure (heart failure).

Additional file 2: Table S1.

BIC for body mass index GBTM according to number of groups and trajectory shapes. Table S2. Average posterior probability and odds of correct classification for body mass index GBTM groups. Table S3. Sensitivity analyses of body mass index measures at baseline, 2, 5,8 and 10 years, by trajectory group (Analyses done using only BMI records available in GP records* ( n = 260,962). Table S4. Risk of cardiovascular disease, heart failure and mortality in BMI trajectory groups 2, 3 and 4 compared to group 1. Sensitivity analyses restricted to individuals with CPRD data linked to hospital episode statistics and office of national statistics death records ( n = 138,755). Figure S1. Sensitivity analyses of body mass index (BMI) trajectories using BMI measures at baseline and then follow-up at 2 years, 5 years, 8 years and 10 years (Analyses done using only records available in GP records (n = 260,962)).

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Iyen, B., Weng, S., Vinogradova, Y. et al. Long-term body mass index changes in overweight and obese adults and the risk of heart failure, cardiovascular disease and mortality: a cohort study of over 260,000 adults in the UK. BMC Public Health 21 , 576 (2021). https://doi.org/10.1186/s12889-021-10606-1

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Is BMI the best measure of obesity?

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Is BMI the best measure of obesity? - May 23, 2018
Peymane Adab , professor of chronic disease epidemiology and public health 1 ,
Miranda Pallan , senior clinical lecturer in public health 1 ,
Peter H Whincup , director 2
1 Institute of Applied Health Research, University of Birmingham, Birmingham, UK
2 Population Health Research Institute, St George’s, University of London, London, UK
p.adab{at}bham.ac.uk

It works for most people most of the time

Obesity, defined as abnormal accumulation of fat such that health is impaired, 1 is most commonly assessed using the body mass index (BMI). But some people have questioned whether BMI is the best diagnostic measure.

To answer this, we need to consider the objectives of measurement (clinical assessment, surveillance, evaluating response to interventions), the definition of “abnormal” fat accumulation, and the characteristics of a good measurement tool (accuracy and acceptability). Accurate diagnosis of obesity is important, not only for the individual, when misdiagnosis could lead to undertreatment or potential stigma, but also at the population and policy levels. Inaccurate measurements could mislead our interpretation of the epidemiology of obesity or planning of services.

The most accurate direct measures of the amount and distribution of adipose tissue include dual energy x ray absorptiometry (DEXA) and imaging techniques. Increasing total body fat, measured by DEXA, is associated with higher mortality risk. 2 However, imaging techniques have shown that fat distribution (specifically visceral fat) is a more important predictor than total fat levels. 3 Despite their accuracy, these techniques are cumbersome …

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Research article
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Published: 23 June 2014

Body mass index and measures of body fat for defining obesity and underweight: a cross-sectional, population-based study

Julie A Pasco 1 , 2 ,
Kara L Holloway 1 ,
Amelia G Dobbins 1 ,
Mark A Kotowicz 1 , 2 ,
Lana J Williams 1 &
Sharon L Brennan 1 , 2

BMC Obesity volume 1 , Article number: 9 ( 2014 ) Cite this article

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The body mass index (BMI) is commonly used as a surrogate marker for adiposity. However, the BMI indicates weight-for-height without considering differences in body composition and the contribution of body fat to overall body weight.

The aim of this cross-sectional study was to identify sex-and-age-specific values for percentage body fat (%BF), measured using whole body dual energy x-ray absorptiometry (DXA), that correspond to BMI 18.5 kg/m 2 (threshold for underweight), 25.0 kg/m 2 (overweight) and 30.0 kg/m 2 (obesity) and compare the prevalence of underweight, overweight and obesity in the adult white Australian population using these BMI thresholds and equivalent values for %BF. These analyses utilise data from randomly-selected men (n = 1446) and women (n = 1045), age 20–96 years, who had concurrent anthropometry and DXA assessments as part of the Geelong Osteoporosis Study, 2001–2008.

Values for %BF cut-points for underweight, overweight and obesity were predicted from sex, age and BMI. Using these cut-points, the age-standardised prevalence among men for underweight was 3.1% (95% CI 2.1, 4.1), overweight 40.4% (95% CI 37.7, 43.1) and obesity 24.7% (95% CI 22.2, 27.1); among women, prevalence for underweight was 3.8% (95% CI 2.6, 5.0), overweight 32.3% (95% CI 29.5, 35.2) and obesity 29.5% (95% CI 26.7, 32.3). Prevalence estimates using BMI criteria for men were: underweight 0.6% (95% CI 0.2, 1.1), overweight 45.5% (95% CI 42.7, 48.2) and obesity 19.7% (95% CI 17.5, 21.9); and for women, underweight 1.4% (95% CI 0.7, 2.0), overweight 30.3% (95% CI 27.5, 33.1) and obesity 28.2% (95% CI 25.4, 31.0).

Conclusions

Utilising a single BMI threshold may underestimate the true extent of obesity in the white population, particularly among men. Similarly, the BMI underestimates the prevalence of underweight, suggesting that this body build is apparent in the population, albeit at a low prevalence. Optimal thresholds for defining underweight and obesity will ultimately depend on risk assessment for impaired health and early mortality.

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Globally, obesity has nearly doubled over the last three decades [ 1 ]. In Australia, the prevalence of obesity among adults has increased from 11.1% in 1995 to 16.4% in 2004–05 [ 2 ] and this upward trend is predicted to continue over coming decades [ 3 ]. Global and local prevalence estimates are based on the body mass index (BMI) which provide a guide to obesity levels as recognised by BMI values greater than or equal to 30. Yet the BMI is a ratio of body weight-for-height [ 4 ] thereby limiting its usefulness as an indicator for adiposity because no account is made of variations in body composition. Clear shortcomings are evident when the BMI overestimates adiposity in muscular body builds and underestimates adiposity in the elderly [ 5 , 6 ].

The simplicity and ease of measurement have entrenched the widespread use of the BMI as a marker of adiposity, not only for epidemiological purposes, but also in clinical practice. The aim of this study was to measure body fat mass using whole body dual energy x-ray absorptiometry (DXA) and BMI in a population-based sample of men and women in order to identify sex-and-age-specific values for percentage body fat (%BF) that correspond to internationally recognised BMI cut-points for defining underweight, overweight and obesity. This approach extends previous studies that utilised %BF thresholds of 25% for men and 35% for obesity [ 6 , 7 ]. We have previously reported a temporal shift in the distribution of BMI in the population such that the prevalence of underweight women diminished between 1993–1997 and 2004–2007 [ 8 ], but that study did not investigate changes in body composition. The aim of this study was to compare prevalence estimates for underweight, overweight and obesity in the adult white Australian population using BMI thresholds for each category and the equivalent sex-and-age-specific cut-points for %BF.

Ethics statement

The study was approved by the Barwon Health Human Research Ethics Committee. All participants provided informed, written consent.

This cross-sectional study was conducted as part of the Geelong Osteoporosis Study (GOS), a population-based cohort study, set in the Barwon Statistical Division in south-eastern Australia [ 9 ]. Age-stratified samples of men and women were selected at random from the Commonwealth electoral roll, which provides an ideal sampling frame for epidemiological research in Australia because registration with the Australian Electoral Commission is compulsory for residents aged 18 years and over. In total, 1467 men were recruited 2001–2006 (67% participation, age 20–96 years) and 1494 women were recruited 1993–1997 (77% participation, age 20–93 years). This set of analyses utilises data collected at the baseline visit for 1467 men, and the 10-year follow-up (2003–2008) for 882 women (82% retention of eligible women). A further 194 women aged 20–29 years were recruited 2005–2008 (82% participation), providing a total sample 1076 women for this analysis. The cohort was essentially white; no indigenous Australians participated in the study. Details of participation and non-participation have been described elsewhere [ 9 , 10 ].

Body composition measures

Body weight was measured to ± 0.1 kg using electronic scales, standing height was measured to ± 0.001 m using a wall mounted stadiometer and BMI was calculated as weight/height 2 (kg/m 2 ). Based on WHO criteria [ 11 ], underweight was identified as BMI < 18.5 kg/m 2 , overweight as BMI 25.0-29.9 kg/m 2 , and obese as BMI ≥ 30.0 kg/m 2 . Measures of body fat mass, lean mass and bone mineral content were provided by whole body DXA using a Lunar DPX-L densitometer (software version 1.31; Lunar, Madison, WI, USA); however, 923 of the men were scanned on a GE-Lunar Prodigy (Prodigy; GE Lunar, Madison, WI, USA) when the DPX-L was decommissioned. No significant differences were detected in lumbar spine or femoral neck bone mineral density measurements when the scanners were cross calibrated on 40 subjects aged 21 to 82 years. The percentage body fat (%BF) was calculated as body fat mass expressed as a percentage of the sum of body fat mass, lean mass and bone mineral content. Individuals without valid whole body scans (21 men and 31 women) were excluded. Anthropometry was performed by trained personnel and the densitometer operators had completed the accredited Australian and New Zealand Bone and Mineral Society (ANZBMS) Clinical Densitometry Training Course and were licenced through the Department of Health State Government of Victoria to use radiation sources for research.

Statistical analyses

The %BF values equivalent to the BMI cut-points 18.5, 25.0 and 30.0 kg/m 2 , which are used to identify underweight, overweight and obesity, respectively, were predicted using the following equation [ 5 ].

Variables include: sex (male = 1, female = 0), age (years) and BMI (kg/m 2 ) centred around the mean (26.4 kg/m 2 ) to reduce collinearity. The model includes interaction terms between sex and BMI, and sex and age. The equation had been derived previously using a subset of 1299 men and 855 women from the Geelong Osteoporosis Study for whom whole body DXA scans provided valid measures of body fat mass. Details of the development of this equation have been described elsewhere [ 5 ].

Individuals were classified as underweight, normal weight, overweight or obese according to published BMI cut-points and according to sex-and-age-specific %BF cut-points. A kappa (κ) statistic indicated the level of agreement between categories using the two sets of criteria. Sex-stratified prevalence estimates for underweight, overweight and obesity were determined according to BMI thresholds and the corresponding (calculated) %BF thresholds for age decades 20–79 years and 80 years and older, using mid-decade ages of 25, 35, 45, 55, 65, 75 and 85 years. Overall prevalence estimates were age-standardised to national age profiles using data from the Australian Bureau of Statistics (ABS cat. no. 2068.0 – 2006 Census Tables). A sensitivity analysis that compared prevalence estimates of obesity derived from BMI and %BF criteria was performed after excluding men scanned on the DPX-L densitometer. Statistical analyses were performed using Minitab (version 16, Minitab, State College, PA, USA).

%BF thresholds for underweight, overweight and obesity

Mean predicted sex-and-age-specific %BF values that are equivalent to the BMI values of 18.5, 25.0 and 30.0 kg/m 2 are shown in Table 1 , where the data are stratified by sex and age-group. The %BF thresholds increased with age and were consistently lower for men than for women across all age-groups. The sex-and-age-specific thresholds for %BF were subsequently used to identify underweight, normal weight, overweight and obese individuals.

Prevalence of underweight, overweight and obesity

Numbers of men and women in the categories of underweight, ideal weight, overweight and obese by age-group are shown in Table 2 . Mean BMI and %BF values for men and women by age-group are listed in Table 3 . Mean prevalence estimates for underweight, overweight and obesity according to BMI thresholds and sex-and-age-specific %BF thresholds, for men and women stratified by age-group, are shown in Table 4 and presented graphically for underweight and obesity in Figure 1 . In women, both methods indicated that the prevalence of obesity increased with age until 50–59 years, followed by an age-related decline. The age-related profile for men according to BMI criteria showed an age-related increase that peaked at age 60–69 years followed by an age-related decline; however, %BF values indicate that obesity was under-estimated in younger men and elderly men than BMI would suggest. For both sexes, the prevalence of overweight was similar for both BMI and %BF criteria. The BMI tended to under-estimate the prevalence of underweight in both sexes particularly for young adults.

Age-specific prevalence of obesity and underweight using body mass index and percentage body fat criteria. Age-specific prevalence of obesity (solid lines and symbols) and underweight (broken lines and hollow symbols) defined using body mass index (BMI) thresholds (grey lines and square symbols) and sex-and-age-specific percentage body fat (%BF) thresholds (black lines and circular symbols). Data are for (A) men and (B) women by age decades (20 represents 20–29 years, etc.). Data are shown as mean and 95% confidence intervals.

A sensitivity analysis that excluded men scanned on the DPX-L densitometer showed that the age-related patterns based on %BF criteria were sustained and, importantly, significant differences persisted between prevalence estimates based on BMI and %BF criteria for the two age groups 20–29 years and 80+ years obesity: prevalence for age 20–29 years BMI 8.2% (95% CI 4.5, 13.4) and %BF 25.1% (95% CI 18.8, 32.3), and for age 80+ BMI 13.4% (95% CI 8.7, 19.5) and %BF 29.8% (95% CI 23.1, 37.3).

According to age-specific %BF criteria for men, the overall mean age-standardised prevalence for underweight was 3.1% (95% CI 2.1, 4.1), overweight 40.4% (95% CI 37.7, 43.1) and obesity 24.7% (95% CI 22.2, 27.1). For women, the prevalence for underweight was 3.8% (95% CI 2.6, 5.0), overweight 32.3% (95% CI 29.5, 35.2) and obesity 29.5% (95% CI 26.7, 32.3).

According to BMI criteria for men, the overall mean age-standardised prevalence for underweight was 0.6% (95% CI 0.2, 1.1), overweight 45.5% (95% CI 42.7, 48.2) and obesity 19.7% (95% CI 17.5, 21.9). For women, the prevalence for underweight was 1.4% (95% CI 0.7, 2.0), overweight 30.3% (95% CI 27.5, 33.1) and obesity 28.2% (95% CI 25.4, 31.0). Thus, the mean age-standardised prevalence for underweight for both men and women was lower according to BMI. For men, the age-standardised prevalence for obesity was similarly lower according to BMI; for women the difference in estimates of age-standardised prevalence for obesity based on BMI and %BF was not significant. No differences were detected in age-standardised prevalence estimates for overweight in either sex.

Agreement between categories based on BMI and %BF criteria

There was exact agreement using sex-and-age-specific %BF and BMI criteria for categorising underweight, ideal weight, overweight and obese groups for 62.6% men (κ = 0.4) and 73.9% women (κ = 0.6); agreement to within one category was observed for 98.7% men and 99.8% women. Whereas 82.7% of women classed as obese according to BMI were also identified as obese according to sex-and-age-specific %BF criteria, only 68.4% of men classed as obese by BMI were similarly classified by sex-and-age-specific %BF. On the other hand, 80.1% of women and 53.9% of men who were identified as obese according to sex-and-age-specific %BF criteria, had BMI ≥ 30.0 k/m 2 .

Using sex-and-age-specific cut-points for %BF equivalent to BMI 30.0 kg/m 2 , we report that 24.7% (95% CI 22.2, 27.1) of men and 29.5% (95% CI 26.7, 32.3) of women were obese. The prevalence estimate for men was greater than the estimate of 19.7% (95% CI 17.5, 21.9), which was based on BMI criteria. The pattern was similar for women for whom the prevalence of obesity according to the BMI was 28.2% (95% CI 25.4, 31.0); however, the difference in the estimates was not significant. For both sexes, the prevalence of underweight was lower according to BMI. Whereas three-quarters of the women were similarly classified into groupings ranging from underweight to obese according to both %BF and BMI criteria, exact agreement was observed for less than two-thirds of the men.

Our approach was similar to that reported by Gallagher et al. [ 12 ] who derived %BF cut-points from several diagnostic techniques, including DXA, which corresponded to the published BMI thresholds for underweight, overweight and obesity. Prediction equations for %BF were evaluated for adults (from BMI, sex, age and ethnicity) in order to identify healthy %BF ranges. Similar techniques have been employed by others in order to evaluate the validity of the BMI threshold for obesity in different populations [ 6 , 7 , 13 , 14 ]. However, to our knowledge, no other study has utilised sex-and-age-specific %BF thresholds equivalent to published BMI thresholds to compare prevalence estimates of underweight, overweight and obesity.

Our results suggest that 17.3% of women and 31.6% of men who were identified as obese according to BMI were misclassified according to sex-and-age-specific %BF criteria. The inability to distinguish the different contributions to body weight, of fat and non-fat tissue (such as muscle and bone, which have greater densities than fat), explains why the BMI might overestimate adiposity in muscular and lean body builds. On the other hand, only 80.1% of women and 53.9% of men in our study who were classified as obese using sex-and-age-specific %BF thresholds had BMI in the obese range. As a corollary, 19.9% of women and 46.1% men with high %BF were overlooked as being obese according to BMI criteria. The BMI might underestimate adiposity as a consequence of age-related lean tissue loss, particularly skeletal muscle, and accumulation of fat; these are characteristics of sarcopenic obesity seen in the elderly [ 15 , 16 ]. Results from our study support the contention that BMI underestimates adiposity in elderly men (aged 70 years and older). Paradoxically, our study also suggests that the BMI markedly underestimated adiposity in young men (aged 20–29 years). It seems likely that for this group, body fat contributes more, and lean tissue less, to body weight than in other groups. While the reasons for this remain unclear, we might speculate that the fat-to-lean tissue mass ratio is disproportionately high as a result of unhealthy lifestyle choices including sedentary behaviour and poor nutrition. Differences in body composition might also be related to an increasing prevalence of growth hormone deficiency with increasing age, resulting in loss of lean tissue and increases in body fat [ 17 ]. These findings have public health implications, as the prevalence of adult obesity as described by the BMI, may be underestimated at a population level, particularly among men.

Both the BMI and %BF identify weight or fat mass relative to the whole body, but this has been conceptualized differently for the two indices. The BMI expresses body weight (kg) relative to stature (height, m 2 ) and it should be noted that adjustment for height in this index is suboptimal [ 18 ]. The second order polynomial relationship between BMI and %BF [ 5 ] is partly explained by the relative relationship of body fat mass to total body weight; increments in body fat mass result in diminishing increments in %BF. Furthermore, accumulation of body fat in healthy bodies is generally accompanied by a compensatory response from the musculoskeletal system, acting through mechanoreceptors in muscle and bone, as it adapts to better cope with the increasing mechanical load [ 19 ]. Adipokines also act as regulatory messengers between adipocytes in fat deposits, muscle [ 20 ] and bone [ 21 , 22 ]. However, with excessive accumulation of body fat, the increased loading could exceed compensatory musculoskeletal responses thereby altering the proportions of fat, lean and bone issue. As a consequence, increases in BMI could reflect increased weight-for-height yet mask changes in body composition. Considering the obesity epidemic, a more accurate indicator of body fatness is required to better assess obesity-related health risks.

Our study has several strengths and limitations. The major strength is that study participants were selected at random from a clearly-defined population and this is important when reporting prevalence estimates. Furthermore, body composition was measured using anthropometric values (weight and height) in addition to whole body densitometry which provided a more accurate assessment of body fat mass. In the absence of cross-calibration data between the two densitometers, a sensitivity analysis that restricted comparisons for men scanned on one densitometer alone showed similar patterns to the full dataset. However, we cannot exclude the possibility of differences between the two machines. We acknowledge, however, that DXA measurements may be obscured by increasing levels of body fat. Lastly, our data relate to an essentially white population and the findings may not be pertinent to other ethnicities.

We report that the prevalence of obesity using a BMI threshold may underestimate the true extent of obesity in the white population, particularly among young and elderly men. We also report that for both sexes, the prevalence of underweight using a BMI threshold may underestimate the true extent in the population. We suggest that optimal sex-and-age-specific thresholds be implemented for defining underweight and obesity in terms of body fat and recognise that such definitions will depend on risk assessment for disease, morbidity and mortality.

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Acknowledgments

The study was funded by the National Health and Medical Research Council (NHMRC) of Australia and the Geelong Regional Medical Foundation, but they played no part in the design or conduct of the study; collection, management, analysis, and interpretation of the data; or in preparation, review, or approval of the manuscript. LJW is supported by NHMRC Career Development Fellowship (1064272) and SLB is supported by NHMRC Early Career Fellowship (1012472).

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Julie A Pasco, Kara L Holloway, Amelia G Dobbins, Mark A Kotowicz, Lana J Williams & Sharon L Brennan

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Authors’ contributions

Conceived and designed the experiments: JAP, KLH, AGD, MAK, LJW, SLB. Drafted the article: JAP. Critically revised the article for important intellectual content: JAP, KLH, AGD, MAK, LJW, SLB. Approved the final version for submission: JAP, KLH, AGD, MAK, LJW, SLB.

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Pasco, J.A., Holloway, K.L., Dobbins, A.G. et al. Body mass index and measures of body fat for defining obesity and underweight: a cross-sectional, population-based study. BMC Obes 1 , 9 (2014). https://doi.org/10.1186/2052-9538-1-9

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Limited information exists linking food habits, diet quality, and lifestyle scores with anthropometric indices and happiness levels. Our aim was to examine the association between food habits, diet quality, and lifestyle scores with anthropometric indices and happiness levels in the Iranian population.

This cross-sectional study included 200 students randomly selected from a university in Iran. Dietary intakes, physical activity (PA), and happiness levels of study participants were assessed using validated questionnaires. The anthropometric indices examined in this study included the body shape index (ABSI), body roundness index (BRI), and abdominal volume index (AVI). Multiple logistic regression models were used to examine the association between food habits, diet quality, and lifestyle scores with anthropometric indices and happiness levels.

The mean age and body mass index (BMI) of study participants were 23.5 years ± 4.52 and 23.8 kg/m2 ± 3.17, respectively. In the study population, no significant association was seen between ABSI, BRI, AVI and happiness with food habits, diet quality, and lifestyle scores respectively. After adjusting for potential confounders (age, energy intake, marital status, education, smoking, physical activity, gender, and BMI), the association remained not significant for ABSI and food habits, diet quality, and lifestyle scores respectively (OR: 0.56, 95% CI (0.25–1.34), P = 0.193; OR: 0.59, 95% CI (0.22–1.57), P = 0.413; OR:1.19, 95%CI (0.54–2.63), P = 0.652), BRI and food habits, diet quality, and lifestyle scores respectively (OR:1.98, 95% CI (0.41–9.49), P = 0.381; OR: 0.57, 95%CI (0.12–2.74), P = 0.512; OR: 1.19, 95% CI (0.3–4.71), P = 0.811), AVI and food habits, diet quality, and lifestyle scores (OR:1.15, 95% CI (0.53–2.48), P = 0.743, OR:1.01, 95% CI (0.47–2.18), P = 0.965; OR: 1.3, 95% CI (0.64–2.65), P = 0.465) and happiness and food habits, diet quality, and lifestyle scores respectively (OR:0.3, 95%CI (0.07–1.25), P = 0.972; OR: 0.77, 95%CI (0.18–3.19), P = 0.724, OR: 0.3, 95% CI (0.07–1.25), P = 0.083).

Conclusions

No significant association was detected between food habits, diet quality, and lifestyle scores with anthropometric indices and happiness levels. However, longitudinal studies are required to confirm these findings.

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Introduction

Nutritional approaches, as one of the main components related to lifestyle factors, play a crucial role in the prevention and treatment of chronic diseases. Among these, interventions related to nutritional behaviors are of paramount importance [ 1 , 2 , 3 ]. Recent years have seen a growing interest in the relationship between dietary behavior, diet quality, lifestyle scores, and anthropometric indices such as body mass index (BMI) and waist circumference (WC) [ 4 , 5 , 6 , 7 , 8 ].

Lifestyle factors such as physical activity, smoking habits, and alcohol consumption, which are often included in lifestyle scores, along with the quality of diet, have been associated with various health outcomes, including anthropometric indices [ 5 , 9 ]. In addition to physical health, happiness levels, which are indicators of mental health and life satisfaction, have been associated with dietary behavior and diet quality [ 10 ] and have also been positively correlated with lifestyle scores [ 11 ].

In the pre-pandemic era, public health was already under siege from a wave of lifestyle-related diseases, including cardiovascular diseases, hypertension, dyslipidemia, obesity, diabetes, colon cancer, osteoporosis, depression, and anxiety, largely attributed to physical inactivity according to the World Health Organization (WHO). The advent of the pandemic has further underscored the importance of physical activity, not only for its well-established benefits in mitigating these conditions, but also for its potential role in enhancing mental health and equipping individuals with the resilience to navigate the challenges of confinement [ 12 ].

Adopting healthy eating habits can serve as a protective measure for overall health and aid in preventing weight gain. [ 13 ]. Conversely, neglecting to adhere to physical activity recommendations can lead to functional and structural deterioration of the body. This can manifest in various ways, including reduced physical fitness, deteriorated metabolic and cardiovascular parameters, altered body composition with a decrease in muscle mass and increase in fat mass, increased depressive symptoms, and a decrease in general well-being [ 14 ]. This global issue has the potential to impact public mental health and, consequently, quality of life.

In the context of home confinement, dietary behaviors may undergo changes; hence, maintaining proper nutrition becomes crucial to support the immune system and improve energy balance, thereby reducing the risk of chronic and infectious diseases [ 15 ]. This is particularly important for dormitory students. It’s worth noting that staying at home can present challenges such as difficulties in procuring fresh food and shortages of certain food products. On a positive note, closer contact with family members and increased home cooking due to the coronavirus disease of 2019 (COVID-19) pandemic can provide adolescents with opportunities to learn skills that can enhance their nutritional knowledge and behaviors, as reported in several studies [ 16 , 17 ]. However, some studies have reported that staying at home and working remotely can influence daily eating habits, leading to increased energy intake and a heightened desire for “comfort food” due to boredom and stress [ 18 ].

Research has shown that regular physical activity improves overall health and has the capacity to reduce the risk of chronic diseases such as cardiovascular diseases, cancer, and diabetes [ 19 ]. A systematic review found that healthy dietary patterns such as the Mediterranean diet are associated with better health-related quality of life in both physical and mental summaries, while unhealthy dietary patterns and Western dietary patterns are associated with lower scores of health-related quality of life [ 20 ]. Another study found that high levels of physical activity in combination with other positive lifestyle choices are associated with better health outcomes [ 21 ].

The association of dietary behavior, diet quality, lifestyle scores, anthropometric indices, and happiness levels among university students, a population group that undergoes significant lifestyle and dietary changes, is less explored. Given the conflicting results in the current literature and the need for better planning for future epidemics, there is a clear need for further research on these behaviors. This study aims to fill this gap by investigating these associations among students at Tehran University of Medical Sciences during the COVID-19 pandemic. By focusing on this specific population and time, we aim to provide valuable insights that can inform future research and public health planning.

Study population and sampling method

This cross-sectional study included 200 students who met the inclusion criteria. Inclusion criteria included personal willingness, students taking theoretical courses, apparently healthy individuals, students with no chronic illness or other infectious diseases (such as diabetes mellitus (DM), coronary heart disease, hypertension (HTN), multiple sclerosis (MS) or other nervous system disorders, irritable bowel syndrome (IBS), irritable bowel disease (IBD), rheumatoid arthritis (RA), pulmonary thromboendarterectomy (PTE), Crohn’s disease (CD), ischemic heart disease (IHD), chronic kidney disease (CKD) and other kidney diseases, liver diseases, anemia, thalassemia, cancer, thyroid problems, asthma, Crohn’s, colitis, Addison syndrome or Cushing’s disease), age ranging between 18 and 40 years, not being pregnant or breastfeeding (in the past year), not adhering to a special diet, not having problems such as stress, anxiety, depression or the occurrence of unfortunate events in the last six months (self-declaration), having Iranian citizenship, not having any active infectious or inflammatory diseases and not being on a special diet. Exclusion criteria included dissatisfaction, lack of cooperation, and under-reporting or over-reporting results. Students were sampled randomly in proportion to the number of students of each faculty of Tehran University of Medical Sciences (TUMS). We also evaluated demographic, socio-economic, and lifestyle variables through face-to-face questionnaires. General information included age, contact number, place of residence, marital status, history of chronic diseases (such as: diabetes, cardiovascular disease, cancer, liver disease, kidney disease, lung disease, thyroid disease, and central nervous system disorders), medication and dietary supplementation, and smoking.

The sampling method used in this study is a two-phase cross-sectional design.

In the first phase, demographic information was collected from 200 students via email using a questionnaire. This method allowed for a broad collection of data from a large group of individuals. In the second phase, a separate questionnaire was administered which focused on the students’ eating habits both currently and prior to the COVID-19 pandemic. This questionnaire, known as the Eating Habits Questionnaire (EHQ) [ 22 ] allowed for a more specific exploration of the students’ dietary behaviors. Additionally, the students were asked to self-report their height, weight, waist circumference, and other anthropometric indicators. While this method may not be as accurate as in-person measurements, it was deemed necessary due to the COVID-19 quarantine. The high education level of the target community and the provision of training on how to properly measure these indices helped to ensure the accuracy of this data [ 23 , 24 ].

Physical activity data was also collected using the International Physical Activity Questionnaire (IPAQ). The student’s food intake was collected by self-reported food frequency questionnaire (FFQ).

The Oxford Happiness Index (OHI) [ 25 , 26 ] was included in our study to assess the overall well-being and happiness levels of the students. The OHI is a widely recognized tool that measures subjective well-being, which is an important aspect of mental health. In the context of our study, understanding the students’ happiness levels can provide valuable insights into their overall quality of life and mental health status. This is particularly relevant given the potential impact of dietary habits and physical activity levels on mental health. By including the OHI in our study, we aimed to explore the potential correlations between dietary habits, physical activity, and happiness levels. This could help us understand whether and how lifestyle factors might influence mental well-being among students. The OHI has been previously validated and found to be reliable in Iran [ 27 ], making it a suitable tool for our study population. The questionnaire consists of several items that ask about different aspects of happiness and well-being, and respondents rate their agreement with each item on a scale. The scores are then summed to create an overall happiness score.

Previously, the validity and reliability of the IPAQ [ 28 ], FFQ [ 29 ] and OHI [ 26 , 27 ] in Iran have been confirmed. This method of sampling allowed for a comprehensive collection of data from a large group of individuals, providing a broad overview of the students’ demographic information, physical activity levels, dietary habits, and happiness levels.

Sample size calculation

In our study, we calculated the sample size using a two-phase cross-sectional design. We considered a type 1 error of 95% and a power of 80%. The prevalence of obesity in people who consume more white bread (P1) was 0.18 and in those who consume more whole wheat bread (P2) was 0.08. The observed ratio between healthy and unhealthy subjects was 30/70. The sample size was calculated based on a previous study [ 30 ], which found a significant difference in the prevalence of obesity between consumers of white bread and whole wheat bread. However, due to constraints, we were only able to randomly select 200 students for our research project. A detailed explanation for the sample size calculation is provided in the supplementary file .

Sampling method

In our study, we used a stratified random sampling method to ensure that our sample was representative of the student population at Tehran University of Medical Sciences (TUMS). Stratified random sampling is a method of sampling that involves dividing a population into smaller groups known as strata. In this case, the strata were the different faculties at TUMS. This method was chosen because it ensures that students from all faculties are adequately represented in the study. The process involved listing all the students in each faculty and then using a random number generator to select students from each list. The number of students selected from each faculty was proportional to the size of the faculty, ensuring a balanced representation across all faculties. We also evaluated demographic, socio-economic, and lifestyle variables through face-to-face questionnaires. General information included age, contact number, place of residence, marital status, history of chronic diseases (such as diabetes, cardiovascular disease, cancer, liver disease, kidney disease, lung disease, thyroid disease and central nervous system disorders), medication and dietary supplementation, and smoking. This method of sampling allowed us to obtain a sample that was representative of the student population at TUMS, while also ensuring that each student had an equal chance of being selected for the study. This helps to reduce selection bias and increase the external validity of our findings.

Assessment of dietary behavior

To assess healthy dietary behavior, we used the Food Habits section of a previously constructed dietary questionnaire, whose reliability has been previously reported [ 31 ]. This questionnaire assesses food habits which consists of 14 questions asking about daily consumption of main meals and especially regarding breakfast content, fruits and vegetables, cakes and desserts, soft and alcoholic beverages, etc. The responses ranges from “never” to “always” which reflected a score from zero to three points respectively with a maximum score of 42 points [ 31 ].

Assessment of dietary quality

Participants’ food consumption information was obtained through a 168-item Food Frequency Questionnaire (FFQ). This questionnaire also has validity and reliability confirmed in Iran [ 29 ]. To assess healthy dietary intake, we used Healthy Eating Index 2015 (HEI-2015). HEI-2015 is a scoring system used to evaluate the quality of diet to which degree it aligns to key dietary recommendations from the Dietary Guidelines of Americans. This indicator measures the quality of the diet and health outcomes such as the risk of death from cardiovascular disease. The score ranges from zero to 100 where a higher score reflects better adherence to HEI-2015 index [ 32 ].

To clarify the calculation of food frequency, each item in the FFQ represented a specific food or drink. Participants were asked to indicate their average frequency of consumption of each item over the past year. The frequency options ranged from ‘never or less than once per month’ to ‘6 + times per day’. Each frequency response was converted into a daily intake. For example, a response of ‘2–3 times per week’ was converted to 0.36 servings per day (2.5 times per week divided by 7 days). Portion sizes were specified for each food item in the FFQ, and participants were asked to indicate whether their usual portion size was smaller, larger, or about the same as the specified portion size. This information was used to adjust the daily intake calculations.

The HEI-2015 scores were then calculated based on the daily intake data from the FFQ. Each of the 13 components of the HEI-2015 (total fruits, whole fruits, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, fatty acids, refined grains, sodium, added sugars, and saturated fats) was scored proportionally based on the intake levels, with higher scores indicating greater adherence to the recommended intake levels. The component scores were then summed to give the total HEI-2015 score.

Assessment of healthy lifestyle score (HLS)

Data on diet, physical activity (PA), smoking status and other healthy habits were used to establish a healthy lifestyle score. HEI-2015 was used to assess healthy dietary intake. HLS score was calculated to assess adherence to a healthy lifestyle. Each participant was given one point for every one of the following ten habits: non-smoking, moderate to high physical activity (> 20 MET hours per week), following Mediterranean diet (more than or equal to 4 adherence points), body mass index (BMI) less than or equal to 22, moderate alcohol consumption, low TV exposure (less than 2 h/day), not being a heavy drinker (less than 5 alcoholic drinks/d for men and less than 4 alcoholic drinks/d for women), a short afternoon nap (10–20 min/d), meeting friends for more than 6 h/day or at least 40 h/week. On this HLS scale, the score obtained can be between 0 points (unhealthy lifestyle) to 10 points (healthiest lifestyle).

Assessment of physical activity

The International Physical Activity Questionnaire (IPAQ) was assessed and analyzed based on the metabolic equivalent of a task (MET).h/d. This questionnaire consists of physical activity related to work, transportation, housekeeping, recreation, sports, time spent sitting and leisure activities. Patients were asked to report all their intense and moderate activities last week along with the time taken to do them. Then the intensity of each activity (MET) was multiplied by the time taken to do it, and finally these values were added together to determine the amount of MET.h / d. The validity and reliability of this questionnaire has been confirmed in Iran [ 28 , 33 ].

Assessment of smoking

Smoking was assessed using a self-administered questionnaire which asked: “Are you a smoker or not a smoker or an ex-smoker?”

Assessment of anthropometric indices

Information related to weight, height, and abdomen circumference was obtained with accurate training to the target community, using a tape measure and self-reported information. A pilot study including 171 individuals reported the validity of the self-reported anthropometric values [ 34 ]. This study compared self-reported values of weight, height, and WC to the values measured by a trained nutritionist. The correlation coefficients for the self-reported height, weight and WC compared to measured values were 0.83 ( P < 0.001), 0.95 ( P < 0.001), and 0.60 ( P < 0.001), respectively. Moreover, the correlation between BMI values calculated from self-reported measures and the nutritionist values was 0.70 ( P < 0.001). These results indicate that self-reported measures can be a reasonable choice for anthropometric indices.

ABSI and BRI were calculated by previously mentioned formulas [ 35 , 36 ] using WC (m), BMI (kg/m2), and height (m) as below:

ABSI and BRI were not previously validated against gold-standard measurements since these assessment methods are expensive and hard to use in large populations.

Abdominal volume index is a new indices to estimate abdominal volume which we calculated according to previous studies [ 37 ] according to the formula:

In accordance with the existing literature, the following cutoff values have been established:

ABSI [ 38 ]: The optimal cutoff value is 0.083 m^11/6 kg^-2/3.

BRI [ 39 ]: The optimal cutoff values are 3.49 (for males < 60 years), 3.46 (for males ≥ 60 years), 3.47 (for females < 60 years), and 3.60 (for females ≥ 60 years).

AVI [ 40 ]: The optimal cutoff values is > 15.56 for men and > 18.49 for women.

However, it is important to note that these cutoff values are population-specific and may not be directly comparable across different populations.

Assessment of happiness levels

Students’ happiness levels were measured using the Oxford Happiness Questionnaire. This questionnaire consists of 29 questions with each question having a scale from zero to six ranging from strongly disagree to strongly agree respectively. Eventually the happiness score will be estimated in the range of 29 to 174, with a higher score indicating more happiness. The validity and reliability of this questionnaire in Iran have been mentioned previously [ 27 ].

Statistical analysis

General characteristics and dietary intakes of study participants across categories of food habits, diet quality and HLS scores were examined using one-way analysis of variance (ANOVA) for continuous variables and chi-square for categorical variables. The objective of this analysis was to identify any significant differences in these characteristics and intakes across the different categories. The associations of food habits, diet quality and HLS with anthropometric indices (ABSI, BRI, AVI) and happiness levels were assessed by using multiple logistic regression in different models. The aim of this analysis was to understand the relationship between these factors and to identify any potential predictors of anthropometric indices and happiness levels.

In our statistical analysis, we explored the relationships between food habits, diet quality, the Healthy Lifestyle Score (HLS), and various anthropometric indices (ABSI, BRI, AVI) as well as happiness levels. Our dependent variables included these anthropometric indices and happiness levels, while food habits, diet quality, and the HLS served as our primary independent variables. For logistic regression models, we categorized the dependent variables (e.g., high vs. low ABSI) and adjusted for several confounders. Age (continuous) and energy intake (continuous) were adjusted in the first model. Marital status (non/married/not married), education (educated/not educated), smoking (smoker/not smoker/ex-smoker), physical activity (continuous) and gender (male/female) were adjusted in the second model. BMI (continuous) was additionally adjusted in the third model. All confounders were selected based on previous publications. The statistical analyses were carried out by using IBM SPSS statistics 25. Significance level was considered at P < 0.05. The purpose of adjusting for these variables was to control for potential confounding factors that could influence the relationships we were investigating.

General characteristics of study participants

The general characteristics of study participants among diet quality, food behavior and lifestyle score tertiles are presented in Table 1 . Participants in the highest tertile of diet quality score were more likely to be employed (12% versus 2.5%, P < 0.001) and physically active (275.92 min/week ± 77.92 versus 233.28 min/week ± 79.02, P = 0.003) compared to participants in the lowest tertile. The participants did not differ significantly in terms of other general characteristics among the tertiles of the diet quality score. Also, participants in the highest tertile of food habits score had significantly higher weight (73.43 kg ± 13.65 versus 67.49 kg ± 9.53, P = 0.013) compared to participants in the lowest tertile. Other general characteristics of the participants among the tertiles of food habits score were not significantly different. Participants in the lowest tertile of the healthy lifestyle score were more likely to be higher in weight (69.54 kg ± 12.52 versus 69.70 kg ± 9.44, P = 0.012) and physically active (256.39 min/week ± 72.62 versus 257.23 min/week ± 87.05) compared to participants in the highest tertile and this result was statistically significant ( P < 0.05). Other general characteristics of study participants in the healthy lifestyle score tertiles were not significantly different.

Dietary intake of study participants

The dietary intake of study participants in the study among the tertiles of food habits score, diet quality score and healthy lifestyle score are show in Table 2 . No significant difference was observed between the dietary intake of participants among the tertiles of food habits score, diet quality score and healthy lifestyle score ( P > 0.05).

Multivariable adjusted odds ratio for ABSI, BRI, AVI indices and happiness levels among tertiles of food habits, diet quality and healthy lifestyle score tertiles are shown in Table 3 . No significant difference was shown in either of the models before or after adjustment for confounders between ABSI and food habits, diet quality, and lifestyle scores respectively (OR: 0.56, 95% CI (0.25–1.34), P = 0.193; OR: 0.59, 95% CI (0.22–1.57), P = 0.413; OR:1.19, 95%CI (0.54–2.63), P = 0.652), BRI and food habits, diet quality, and lifestyle scores respectively (OR:1.98, 95% CI (0.41–9.49), P = 0.381; OR: 0.57, 95%CI (0.12–2.74), P = 0.512; OR: 1.19, 95% CI (0.3–4.71), P = 0.811), AVI and food habits, diet quality, and lifestyle scores (OR:1.15, 95% CI (0.53–2.48), P = 0.743, OR:1.01, 95% CI (0.47–2.18), P = 0.965; OR: 1.3, 95% CI (0.64–2.65), P = 0.465) and happiness and food habits, diet quality, and lifestyle scores respectively (OR:0.3, 95%CI (0.07–1.25), P = 0.972; OR: 0.77, 95%CI (0.18–3.19), P = 0.724, OR: 0.3, 95% CI (0.07–1.25), P = 0.083).

This study aimed to assess the relationship between dietary behaviors, diet quality and lifestyle scores with novel anthropometric indices and happiness levels in Iranian university students. No significant association was found between any of the anthropometric indices and happiness levels with dietary behavior, diet quality and healthy lifestyle scores.

ABSI was not significantly associated with either food habits score, diet quality score or lifestyle score. ABSI is a metric which includes human body weight, height, and waist circumference. Waist circumference in ABSI made it a better indicator of mortality risk coming from weight excess than did the BMI [ 41 ]. A main drawback of BMI is that it doesn’t discriminate between fat and muscle mass unlike a high ABSI which reveals central obesity than the BMI [ 42 ]. Few studies to date have studied the association between ABSI and diet. In a recent study, Krakauer et al. found that an increased consumption of animal fat, protein and high energy intake was associated with higher ABSI, while higher intake of plant fat, protein and carbohydrates was associated with lower ABSI [ 43 ]. Several reasons could explain the differences between our findings and the previous study. The larger sample size (15000 adults) used in Krakauer et al. study increased the power to detect significant association. Also, the different study design used (cohort study) also plays an important role. An important contributor also might be the adjustment for more confounders in their study. Furthermore, psychological disorders could contribute to a low lifestyle score. In contrast to our study finding, Lotfi et al. found a direct association between ABSI and anxiety, depression, and psychological distress [ 44 ] while Hadi et al. found no significant association between ABSI and anxiety and depression [ 45 ]. Several factors could explain the differences between our findings and previous studies. Similar to our study low sample size (200 students), Hadi et al. study constituted 307 adults while Lotfi et al. study large sample size (3213 adults) has more power to reach a significant association. As stated, there is a lack of evidence regarding the association between dietary behavior/quality and lifestyle score and ABSI, so further prospective cohort studies are needed to investigate this relationship.

No significant association was found between BRI and either food habits score, diet quality score or lifestyle score. Limited studies have investigated the association between BRI and the mentioned scores. In contrary to our findings, Sanchez et al. in a large sample of middle-aged adults found that moderate to vigorous physical activity practice was associated with lower obesity indices, while Mediterranean diet revealed a minor impact on anthropometric indices [ 46 ]. Several factors have contributed to the differences between Sanchez et al. study and our results. First, they used a larger sample which increased the power of the study to detect these associations. Second, information about socio-demographic and lifestyle characteristics was not available in their study, while it was present in the current study. Third, our study included healthy young students below 50 years of age while the mentioned study included participants above 50 years of age with at least one cardiovascular risk factor. Fourth, we used different dietary tools to assess the diet of participants. Similarly, in a controlled trial, nutritional advice and yoga which contributes to healthy lifestyle, was associated with decreased BRI [ 47 ]. A major difference between our study and the study conducted by Telles et al. is the difference in study design (interventional versus observational). A significant direct association was detected in Kohansal et al. study where plant proteins consumption was associated with higher BRI [ 48 ]. However, our study has failed to detect this association. On the other hand and similar to our study, Lotfi et al. found no significant association between BRI and psychological factors after adjusting for potential confounders [ 44 ].

No significant association was found between AVI and either food habits score, diet quality score or lifestyle score. Similarly, Kohansal et al. found no significant association between plant proteins consumption and AVI [ 48 ]. In the study of Hadi et al., participants with depression and anxiety have higher AVI [ 45 ]. In the current study, lifestyle score was not significantly associated with AVI. A major discrepancy between both studies is the use of different questionnaires to assess the psychological health of participants. In Telles et al. study, participants having yoga which contributes to a healthy lifestyle have a decreased AVI [ 47 ]. In contrast, the current study failed to find this association. An important reason for this difference is that in Telles et al. study, participants were female vegetarians while our study included students of both genders having a general diet. Similarly, Cameron et al. found that moderate-to-vigorous physical activity was inversely associated with visceral adipose tissue and percent body fatness in adults [ 49 ].

In the current study, no association was seen between food habits score and any of the anthropometric indices or happiness. However, dietary behavior might have an important impact on anthropometric indices. In Cameron et al. study [ 49 ], the inverse association detected between physical activity and percentage of body fat was greater for non-Latinos compared to Latinos which introduces the possibility that differences in eating habits might have an important impact of physical activity on anthropometric indices. However, some discrepancies such as the study design (randomized controlled trial) and including overweight participants compared to normal weight participants in our study, might explain the difference in the study findings.

No significant association was found between happiness and either food habits score, diet quality score or lifestyle score. Cascales et al. found that adherence to Mediterranean diet was associated with greater subjective happiness among adolescents [ 50 ]. Several factors might have contributed to the differences in the study findings. Cascales et al. study used larger sample size and both studies used different scales to assess happiness for participants. Similarly, Mujcic et el found that increased fruits and vegetable consumption was associated with greater happiness [ 51 ]. A major discrepancy between both studies is that they used a much larger sample size and used food diaries to assess food intake while our study used FFQ.

The underlying mechanisms driving the positive impact of a healthy lifestyle on anthropometric indices should be carefully addressed. Physical activity is considered an important component of a healthy lifestyle. Skeletal muscles during intense exercise secrete interleukin (IL)-6 into the circulatory system which acts as a pro-inflammatory cytokine, an anti-inflammatory myokine and lipolytic agent [ 52 , 53 ]. Also, a healthy diet could have an important effect in increasing happiness levels in individuals. This is related to self-perception of healthy food where consumption of certain healthy foods might be related to an increase in self-awareness of developing a healthy lifestyle which in turn increase happiness levels and overall wellbeing [ 54 ]. Another mechanism is that healthy food which is rich in antioxidants such as vitamins C and E were found to have a beneficial effect in decreasing depressive symptoms [ 55 ] which in turn results in a better lifestyle.

This study has several strengths. First, few studies to date have been conducted investigating the association between healthy dietary scores, healthy dietary behavior, and lifestyle scores with the mentioned novel anthropometric indices and happiness. Second, we used a relatively representative sample size and utilized new anthropometric indices. It’s also worth noting that this is the first study to analyze the association between food habits score, dietary quality score and lifestyle score with novel body composition indices. Third, we adjusted energy intake in our study which is an important confounder. However, there are some limitations. The cross-sectional design of study, and the probability to miss some important confounders which prevent us to draw causal relationships of the associations between variables, resulting to insignificant findings. Also, one of the limitations of our study is related to the sample size. We were only able to randomly select 200 students for our research project due to constraints. While this number was determined based on the resources available and the feasibility of reaching the students, it’s important to note that this smaller sample size may affect the power of our study and the reliability of our results. Future studies may benefit from ensuring a larger sample size to increase the statistical power and the precision of the estimates. This would allow for a more robust analysis and potentially more generalizable results. Another limitation of the study is that diets have been self-reported which might have led to misinterpretation of questions resulting in incorrect responses. Moreover, happiness is subjective by nature, which might be related to self-reporting of the responder resulting in biased findings.

In conclusion, our study found no significant association between food habits score, diet quality score, and lifestyle score with anthropometric indices and happiness in healthy Iranian students. This suggests that these factors may not play a significant role in determining anthropometric indices and happiness levels in this population. However, it’s important to note that these findings are specific to the context of our study and may not be generalizable to other populations or settings. Despite these findings, the role of food habits, diet quality, and lifestyle in health and well-being should not be discounted. These factors have been shown to be important in other studies and contexts, and further research is needed to fully understand their impact. Our study also highlights the need for further prospective cohort studies to clarify these associations. Such studies could provide more robust evidence and help to identify potential causal relationships. They could also explore other potential confounding factors that were not considered in our study.

Finally, our findings have implications for public health and education. Even though we did not find a significant association in our study, promoting healthy food habits, a quality diet, and a healthy lifestyle remains important for overall health and well-being. Educational programs could focus on these areas to improve the health outcomes of students and other populations.

Data availability

The dataset generated and analyzed during the current study is available from the corresponding author on reasonable request.

Abbreviations

A Body Shape Index

Body roundness index

Abdominal volume index

Body Mass Index

Physical Activity

Coronavirus Disease of 2019

Healthy Eating Index 2015

Food Frequency Questionnaire

International Physical Activity Questionnaire

Metabolic Equivalent of a Task

Waist Circumference

One-Way Analysis of Variance

Healthy Lifestyle Score

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Acknowledgements

We thank all those involved and participants for taking part in the present study.

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Amir Hosein Shahroukh Ghahfarokhi and Batoul Ghosn contributed equally to this work.

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Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, P.O. Box 14155-6117, Tehran, Tehran, Iran

Amir Hosein Shahroukh Ghahfarokhi, Batoul Ghosn & Leila Azadbakht

Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Pamela J. Surkan

Psychiatric Research Center, Roozbeh Psychiatric Hospital, Tehran University of Medical Sciences, Tehran, Iran

Shahin Akhondzadeh

Obesity and Eating Habits Research Center, Endocrinology and Metabolism Molecular -Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

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AHSG contributed to data collection, data analysis, data interpretation and manuscript drafting. BG contributed to data analysis, data interpretation, manuscript drafting and manuscript revision. PS contributed to study design and revised the final manuscript. LA contributed to idea conception, design, data collection, supervised the whole study and revised the final manuscript. AS contributed to study design, supervised the psychiatric section in the study and supervised the whole study. All authors approved the final manuscript.

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Published: 26 August 2024

The consumption of ultra-processed foods was associated with adiposity, but not with metabolic indicators in a prospective cohort study of Chilean preschool children

Camila Zancheta 1 , 2 ,
Natalia Rebolledo 2 ,
Lindsey Smith Taillie 3 ,
Marcela Reyes 2 &
Camila Corvalán 2

BMC Medicine volume 22 , Article number: 340 ( 2024 ) Cite this article

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Increasing consumption of ultra-processed foods (UPF) has been identified as a risk factor for obesity and various diseases, primarily in adults. Nonetheless, research in children is limited, especially regarding longitudinal studies with metabolic outcomes. We aimed to evaluate the longitudinal association between consumption of UPF, adiposity, and metabolic indicators in Chilean preschool children.

We conducted a prospective analysis of 962 children enrolled in the Food and Environment Chilean Cohort (FECHIC). Dietary data were collected in 2016 at age 4 years with 24-h recalls. All reported foods and beverages were classified according to the NOVA food classification, and the usual consumption of UPF in calories and grams was estimated using the Multiple Source Method. Adiposity ( z -score of body mass index [BMI z -score], waist circumference [WC], and fat mass [in kg and percentage]) and metabolic indicators (fasting glucose, insulin, HOMA-IR, triglycerides, total cholesterol, and cholesterol fractions) were measured in 2018, at the age of 6 years. Linear regression models ((0) crude, (1) adjusted for covariables, and (2) adjusted for covariables plus total caloric intake) were used to evaluate the association between UPF and outcomes. All models included inverse probability weights to account for the loss to the follow-up.

At 4 years, usual consumption of UPF represented 48% of the total calories and 39% of the total food and beverages grams. In models adjusted for covariables plus caloric intake, we found a positive association between UPF and BMI z -score (for 100 kcal and 100 g, respectively: b = 0.24 [95%CI 0.16–0.33]; b = 0.21 [95%CI 0.10–0.31]), WC in cm ( b = 0.89 [95%CI 0.41–1.37]; b = 0.86 [95%CI 0.32–1.40]), log-fat mass in kg b = 0.06 [95%CI 0.03–0.09]; b = 0.04 [95%CI 0.01–0.07]), and log-percentage fat mass ( b = 0.03 [95%CI 0.01–0.04]; b = 0.02 [95%CI 0.003–0.04]), but no association with metabolic indicators.

Conclusions

In this sample of Chilean preschoolers, we observed that higher consumption of UPF was associated with adiposity indicators 2 years later, but not with metabolic outcomes. Longer follow-up might help clarify the natural history of UPF consumption and metabolic risks in children.

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Childhood obesity has become an escalating health concern worldwide. According to the 2019 projections by the World Obesity Federation, it is anticipated that by 2025, approximately 206 million children and adolescents aged 5–19 years will be affected by obesity, mainly due to increasing rates in emerging countries [ 1 ]. In Latin America, 7% of children under 5 years of age and 20–25% of children and adolescents up to 19 years are estimated to living with overweight or obesity [ 2 ]. In Chile, data from a survey including students in the public education system in 2019 revealed that 26.5 and 24.9% of preschool children (kinder) presented overweight and obesity, respectively [ 3 ]. Childhood obesity tends to persist over time and is associated with metabolic disturbances, which increasingly manifest at younger ages [ 4 ]. Several determinants are associated with childhood obesity, with changes in eating patterns being described as one of the main ones.

During the last decades, the food system has changed in different countries, and traditional diets have been increasingly replaced by ultra-processed foods (UPF) [ 5 ]. UPF are industrial formulations made mainly of substances extracted or derived from foods (e.g., sugar and fats), with little or no whole food in their composition and which typically contain added additives such as flavorings, colorings, and other additives used to modify the sensory attributes of the final product [ 6 ]. Children and adolescents have been described as the primary consumers of UPF in national surveys from Australia [ 7 ], Canada [ 8 ], the USA [ 9 ], Mexico [ 10 ], and Chile [ 11 ]. In developed countries such as the UK and the USA, UPF represents more than 60% of the calories consumed in children’s and adolescents’ diets [ 12 , 13 ]. In some Latin American countries such as Chile and Mexico, it is more than one-third of the total calories consumed by children 1–19 years old [ 10 , 11 ]. Non-representative studies in Brazil and Chile have reported that more than 40% of the total caloric intake comes from UPF in preschoolers at 4 years old [ 14 , 15 ].

Nationally representative data from food purchases and consumption from different countries showed that high amounts of UPF in diets are related to higher amounts of sugar and sodium, high energy density, and lower quantities of protein, micronutrients, and fiber [ 5 , 16 , 17 , 18 , 19 , 20 ]. In adults, systematic reviews and meta-analyses indicated a direct association between UPF consumption and overweight, obesity, metabolic syndrome, diabetes, and all-cause mortality [ 21 , 22 , 23 , 24 ]. However, evidence regarding health impacts in children is still scarce and inconsistent [ 25 ]. A recent systematic review of the effects of UPF, as defined by NOVA, on obesity and cardiometabolic comorbidities in children and adolescents showed that higher consumption of UPF was associated with greater adiposity in most studies. In the case of metabolic indicators, studies available are only a few and mostly from Brazil. Moreover, results have shown conflicting results [ 25 ]. For instance, prospective studies with children aged between 3 and 6 years found a direct relationship between the consumption of UPF and total cholesterol [ 26 , 27 ], LDL cholesterol [ 26 ], and triglycerides [ 27 ], but not with the glycemic profile [ 14 ].

Given the extent of the public health burden related to poor nutrition in children and the exponential increase in the consumption of UPF, a better understanding of the effects of UPF on indicators of metabolic risk in children is crucial. To our knowledge, no previous study on this topic has been conducted in Chile, so we aimed to prospectively evaluate the association between the consumption of ultra-processed foods, adiposity, and metabolic indicators in a sample of low-to-middle-income Chilean preschool children after 2 years of follow-up.

Study design and subjects

We used data from the Food and Environment Chilean Cohort (FECHIC), a cohort of 962 Chilean low-to-middle-income preschoolers from Southeast Santiago, Chile, started in 2016. Mothers were recruited in public schools to participate in the study with their 4- to 6-year-old children. Details on the recruitment and inclusion criteria are available elsewhere [ 28 ]. Briefly, the inclusion criteria were mothers as the primary caregivers for food purchases and childcare, absence of mental illness in the mother and child, and of other diseases with an impact on food consumption and child development, besides children of non-twin gestation, born at term and with normal birth weight. The present study included children with dietary data at baseline (year 2016, average age: 4.9 years) and anthropometric, body composition, or metabolic indicators measured after 2 years (year 2018, average age: 6.1 years).

Dietary intake

At baseline, trained dietitians collected 24-h dietary recalls (24HR) following the United States Department of Agriculture (USDA) Automated Multiple-Pass method [ 29 ]. They used a photographic atlas to help estimate portion sizes accurately [ 30 ], and recorded data on portion size, type of preparation, type of food, and product brand and flavor in the case of packaged foods, as well as the source of the food and eating location. This information was entered into SER-24, a software developed by the Center for Research in Food Environment and Prevention of Obesity and Non-Communicable Diseases (CIAPEC), INTA, that includes over 6000 foods and beverages and 1400 standard recipes of traditional Chilean dishes and estimates nutrient intake using the Food Composition Table of the USDA [ 28 , 31 ]. The mother was the primary respondent and reported 1 day of their child’s food consumption in a face-to-face interview. Children were present during the interview and complemented the information for the eating occasions when the respondent was absent (e.g., school time). In the case of receiving meals from the School Feeding Program, these preparations were also recorded to link them to the recipes and nutrient contents of the food providers. A second dietary recall was collected within 30 days in a random subsample of 20.1% of participants.

Food consumption according to the NOVA food classification system

Briefly, the NOVA classification considers the extent and purpose of industrial processing and classifies all foods and beverages into four groups: group 1—natural or minimally processed foods (MPF); group 2—processed culinary ingredients (PCI); group 3—processed foods (PF); and group 4—ultra-processed foods (UPF). Examples of UPF include industrialized sodas, toddler milk, confectionaries, chocolates, ice cream, hamburgers, reconstituted meat products, pizzas and other frozen dishes, instant soups, and packaged bakery products, among others [ 6 ]. We identified UPF based on food descriptions, food categories and type of food, whether packaged or unpackaged, brand, and flavor, when available. Simple preparations included in the software SER-24 (e.g., cooked rice) were classified based on their main component. Other homemade recipes were disaggregated into their components, and each of them was individually classified. Food classification was carried out by a postgraduate dietitian at CIAPEC and reviewed by a second dietitian. Disagreements (0.4%) were discussed and resolved by consensus. To verify the interrater agreement, a third dietitian independently classified a random subset of 5% of SER-24 records ( n = 306). We found an agreement of 97.4% and a kappa coefficient of 0.95, indicating almost perfect agreement between the raters. More details about the methodology applied were published elsewhere [ 32 ].

We calculated the consumption of UPF in calories and in grams for each participant. Most published studies used the caloric share of UPF; however, presenting UPF grams allowed us to consider the consumption of low or non-calorie UPF, such as artificially sweetened beverages commonly consumed by Chilean children at this age [ 33 ].

Exclusion of outliers in dietary data

We identified outliers using two techniques: comparing the total calories consumed and the energy requirements of each participant and considering the extremes in the distribution of UPF (both in calories and grams).

We estimated the energy requirements with the Dietary Reference Intake (DRI) equation according to age and sex [ 34 ], using sedentary and very active levels of physical activity to calculate the lower and the higher cutoff points, respectively [ 35 ]. We used the subsample with two dietary recalls to calculate the standard deviation (SD) for the ratio (in %) between reported energy intake (rEI) and predicted energy requirement (pER), using the formula provided by Huang [ 36 ]. The formula considers the pooled coefficient of variation (CV) of the rEI (CVrEI = 32.6%, calculated for our sample [ 37 ]), the number of days of dietary assessment ( d = 2), the CV of the pER (CVpER = 12.1%, calculated with the mean and SD for the total energy of 3- to 18-year-old boys and girls described in the DRI [ 34 ]), and the coefficient of variation in the measured total energy expenditure (CVmTEE = 8.2%, obtained from literature [ 36 , 37 ]). The value of SD for our sample was 27.3%, and we defined implausible diets as those in which reported energy was from < − 3 or + 3 SD away from predicted energy requirements (i.e., < 18.1% or > 181.9% of the pER).

Additionally, diets under the 1st and above the 99th percentile of UPF consumption in calories and grams were excluded (UPF consumption < 42 kcal or > 1478.5 kcal and < 27 g or > 1554.5 g).

Of the 1154 records collected at the beginning of the study, 15 were considered implausible, and 30 were considered extreme UPF consumption. Then, the estimates of usual consumption included 743 children with a unique and 183 with two measures of 24HR.

Usual consumption of UPF

We estimated the usual consumption of UPF using the Multiple Source Method (MSM). This method assumes that the 24HR is not biased for the usual consumption and models the probability of consumption—with logistic regression—and the amount consumed in a day of consumption—with linear regression—allowing the incorporation of covariates and is based on the premise that habitual consumption is equal to the probability of consumption times the usual amount consumed. Usual consumption can be estimated for dietary components that have frequent or daily consumption (e.g., nutrients), but also for those that have episodic consumption (e.g., food categories), as long as at least two measurements for a part of participants are available [ 38 ]. A minimum of 50 individuals with at least two 24HR is required to apply statistical methods to account for within- and between-person variation and estimate the usual consumption for food groups consumed almost every day [ 39 ].

The MSM was applied using free access online software developed by the Department of Epidemiology of the German Institute of Human Nutrition Potsdam-Rehbrücke, available at https://nugo.dife.de/msm/ . Covariables included for the estimates were sex, age, baseline body mass index (BMI) z -score for sex and age, and maternal variables (age, BMI, work outside the home, and education level).

All outcomes were measured after approximately 2 years of follow-up when the children were, on average, 6.1 years old.

Anthropometric indicators

We used data collected by trained dietitians following standard procedures. Height was measured using a portable stadiometer (Seca 217, to the nearest 0.1 cm), and weight was measured using a digital electronic scale (Seca 803 or 813, precision of 0.1 kg). Weight and height were taken in duplicate, and we used their average to calculate BMI. We compared the BMI of each child with the World Health Organization (WHO) growth references specific for age and sex [ 40 ] to obtain their z -score value (BMI z -score). Waist circumference (WC) was measured with a metal tape (Lufkin W 606 PM, USA, precision 0.1 cm) and taken in duplicate. A third measurement was required if the difference between both measurements was greater than 0.5 cm. We calculated the average WC for each child in cm.

Body composition

Body composition was estimated using the bioelectrical impedance (BIA) method using Tanita BC-418 (Tanita Corp.) and following the manufacturer’s recommendations. The child’s age, sex, and height were entered manually. Children stood barefoot on the appliance while holding the handles for approximately 30 s. We used predicted values of fat mass (kg) and percentage of fat mass calculated by the device using impedance, weight, height, and age with standard calibrated equations based on data from dual-energy X-ray absorptiometry [ 41 ].

Metabolic indicators

A nursing team collected the blood samples from the children after 8 to 12 h of fasting. We used the serum triglycerides, total cholesterol, high-density cholesterol (HDL-c), and low-density cholesterol (LDL-c) levels as lipid profile variables. For the glycemic profile, we used fasting glucose, insulin, and the HOMA-IR (acronym in English for homeostatic model to assess insulin resistance). Triglycerides, total cholesterol, and HDL-c were measured using enzymatic colorimetric assays. LDL-c was calculated using the Friedewald formula [ 42 ]. All lipid profile markers were expressed in mg/dl. Glycemia was measured by the enzymatic colorimetric method and expressed in mg/dL, and insulin by electrochemiluminescent immunoassay and expressed in μU/ml. HOMA-IR was calculated as insulin (μU/ml) × glucose (mmol/l) /22.5. All metabolic outcomes were considered continuous variables in the analysis.

Covariables in the association models

Directed acyclic graphs (DAGs) were used to represent the structures of the causal networks that link exposure (consumption of UPF) and the outcomes of interest (adiposity and metabolic profile) and support the identification of confounding variables in the associations studied [ 43 ]. Given that we have two primary groups of outcomes (adiposity and metabolic indicators), we constructed two separate DAGs using the online application DAGitty (Fig. 1 ) [ 44 ].

Conceptual framework for the relationship between UPF consumption at 4 years and adiposity ( A ) and metabolic indicators ( B ) at 6 years. Notes: UPF – ultra-processed foods, SES – socioeconomic status, BMI – body mass index, (i) – initial values at 4 years, (p) – other values during the study period, (f) – final values at 6 years

Considering the DAGs, to estimate the total effect of the consumption of ultra-processed foods at 4 years on adiposity and metabolic responses at 6 years of age, the minimally sufficient adjustment set of variables included socioeconomic status (SES), maternal BMI and age, sex, age, and children’s television time (displayed in white in Fig. 1 ).

To approximate SES, we considered in the models mother’s educational level, categorized as “low” (less than high school), “medium” (at least high school), or “high” (more than high school), and whether they worked outside the home (“yes” or “no”), considering that in Chile the unemployment rate is higher in poor than in non-poor [ 45 ] and women with higher educational levels more often work outside the home [ 46 ]. We also included other maternal variables such as maternal age (self-reported) and BMI (calculated using maternal weight and height measurements collected by trained dietitians).

Among the variables for the children, we considered sex (male or female), age (in months), and television time. To estimate the total hours children spent watching television on weekdays, we summed the time spent watching TV before and after school and in the evening based on information provided by the mothers.

Full completeness was obtained for all covariates except maternal BMI, for which data for 4.2% of the total sample were missing. All covariables included in the models were measured at baseline.

Statistical analysis

Descriptive analyses were presented using mean and SD for quantitative variables and absolute and relative frequency for qualitative variables.

All participants whose dietary reports did not fulfill the above exclusion criteria and who provided data for at least one or more health outcomes were included in the association models. The proportion of loss to follow-up was 23.7% for anthropometric indicators, 33.5% for body composition, and 39.9% for metabolic indicators. We compared the characteristics of participants included and lost in the analysis by presenting the percentual difference between them and applying a T -test for quantitative and chi-square for qualitative variables, and differential loss related to maternal educational level was identified. Given the loss to follow-up and to address the potential selection bias, we incorporated the stabilized inverse probability of censuring weights (SW) in all models. This method creates a pseudo-population with characteristics comparable to the initial population to simulate random censuring of covariates of interest [ 47 ]. We calculated different SW for anthropometric, body composition, and metabolic indicators since the number of participants in each analysis differed. The calculation of SW uses as a numerator the probability of censuring (i.e., proportion of participants lost in the follow-up) and as a denominator the probability of censuring based on the covariables included in the model [ 47 , 48 ]. The probability of censorship was obtained with logistic regression with loss of follow-up as the response variable (yes or no), and the covariates included were sex, age, and initial BMI z -score of the child, and maternal age, BMI, work outside the home, and educational level of the mother. Using SW results in the same estimate as unstabilized inverse probability weights, but typically in narrower 95% confidence intervals and increased statistical efficiency [ 47 , 48 ]. SW were included in all regression analysis using the option pweight.

We used linear regression models to investigate the associations between the consumption of UPF at 4 years (in 100 cal and grams), adiposity, and metabolic indicators at 6 years. We reported regression coefficients and 95% confidence intervals (95% CI) for crude and adjusted models.

The model 1 was adjusted for covariables presented in the DAG: socioeconomic status (represented by maternal education and work outside the home), maternal BMI and age, and sex, age, and television time of children. The model 2 was adjusted for the same covariables plus caloric intake. The coefficient is then interpreted as the effect of substituting 1 unit of UPF with 1 unit of non-UPF, maintaining a constant caloric intake [ 49 , 50 ].

Given the low prevalence of missing data in the covariates (less than 5% and in only one variable), we assumed that missing data were completely at random and performed regressions with complete case analysis [ 51 ]. The goodness-of-fit of the models was evaluated via graphical analysis of the residuals and inflation factors of variance. The distribution of residues was not random for insulin, HOMA-IR, triglycerides, fat mass, and fat mass percentage, so the final models included the log-transformed version of these variables. As sensibility analysis, we considered models without SW and models with quartiles of UPF as the exposure variable. All analyses were conducted using Stata v18.0 (College Station, TX).

The baseline characteristics of all FECHIC children and sub-samples with anthropometric, body composition, and metabolic indicators are presented in Table 1 . The characteristics of the children included in each evaluation were similar to those of the reference cohort. At the start of the FECHIC cohort study, the children had an average age of 4.9 ± 0.5 years old, were comparable by sex (51.9% girls), and had a mean BMI z -score of 1. The mothers were 31.4 ± 6.7 years old, and most had a medium education level (55.1%). Children lost in the follow-up presented differences primarily related to their mothers’ educational level; more children from mothers of low education level were lost for anthropometric indicators ( p = 0.003) and body composition ( p < 0.001), and more children from mothers of high education level were lost for metabolic outcomes ( p < 0.001).

Estimated usual consumption of UPF

Table 2 shows the estimated usual consumption of each NOVA food group at baseline (4 years of age). Children consumed approximately 48% of their diet by calories from UPF and 39% of their diet by grams from UPF. Among the NOVA groups, UPF contributed the highest percentage of children’s calories, while MPF contributed the highest percentage of grams to children’s diet (57.0%).

Adiposity and metabolic outcomes

A description of the outcomes included in the study is available in Table 3 . After 2 years of follow-up, the mean BMI z -score was 1.1 ± 1.3, and the mean fat mass percentage was 24.2 ± 5.3%. The mean fasting blood glucose was 81.8 mg/dL.

Associations between consumption of UPF and adiposity and metabolic indicators

Tables 4 and 5 present the associations between the usual consumption of UPF at 4 years and adiposity and metabolic indicators at 6 years, considering the three types of models (crude, adjusted for covariables, adjusted for covariables plus total caloric intake). We did not find an association between UPF and adiposity in crude and covariable adjustment models. However, when UPF was adjusted for covariables plus total caloric intake, we observed a positive association of small magnitude with BMI z -score (respectively for 100 kcal and 100 g of UPF: b = 0.24 [95% CI 0.16–0.33]; b = 0.21 [95% CI 0.10–0.31]), WC ( b = 0.89 [95% CI 0.41–1.37]; b = 0.86 [95% CI 0.32–1.40]), log-fat mass ( b = 0.06 [95% CI 0.03–0.09]; b = 0.04 [95% CI 0.01–0.07]), and log-percentage fat mass ( b = 0.03 [95% CI 0.01–0.04]; b = 0.02 [95% CI 0.003–0.04]). For metabolic outcomes, the coefficients of UPF and their 95% CI for both 100 cal and 100 g were close to null values for all models.

Sensitivity analysis

The results obtained in models without SW (Additional file 1 : Tables S1 and S2) and in models with the consumption of UPF in quartiles (Additional file 1 : Tables S3 and S4) were consistent with those obtained in main analysis.

In this study, we found a high consumption of UPF in terms of calories and grams in a sample of low- and middle-income preschoolers from Santiago, Chile. We also found a positive association between the consumption of UPF at the age of 4 years and several markers of adiposity measured at 6 years old. However, we did not find an association between UPF consumption and metabolic indicators after 2 years of follow-up.

We remark that we found associations only in models that included a total caloric intake adjustment. In nutritional epidemiology, an energy adjustment is used to study the consumption of nutrients or foods in terms of total energy. The underlying reason is that interventions at the individual or population level usually aim to modify the consumption of certain nutrients or foods, with changes in the composition of the diet, but not in the overall amount of food consumed. The energy adjustment also controls for the confounding effect resulting from the association between total energy intake with physical activity, differences in body size, and metabolic efficiency [ 49 , 50 ]. On this basis, we consider the estimates that include the energy adjustment as the more reliable in our study. Analysis that takes into account the total calories by using the caloric share of UPF is the most prevalent in studies focused on investigating UPF and health outcomes [ 52 ]. The fact our results showed associations between UPF and adiposity markers only when adjusting for total calories provides further support to suggest that the relative contribution of UPF in the diet is more important than their absolute amount, and the health effects observed are a consequence of a displacement of traditional dietary patterns [ 53 ].

In the present study, we found that almost half of the calories of preschool children were derived from UPF, in line with the findings of previous studies with similar populations [ 14 , 15 ]. We also found that consumption of UPF during preschool years was positively associated with increases in BMI z -score and WC after 2 years of follow-up. Similarly, a study with 307 children of low socioeconomic status from Brazil found that the consumption of UPF in 4 year-old children predicted a higher increase in WC at 8 years old [ 14 ]. On the other hand, our findings do not align with the results of a previous study conducted on 7-year-old children from Portugal. In Portuguese children, there was no association between UPF and BMI z -score and WC z -score after 3 years of follow-up [ 54 ]. One potential explanation for the discrepancy in results is the difference in the amount of UPF consumed between both populations. Chilean children consumed more UPF than did Portuguese children. The percentage of grams and calories from UPF in the diet of Chilean children was 39 and 48%, while in Portuguese children, UPF represented 25 and 31% of the total grams and calories consumed, respectively. Another potential explanation is the age difference of the participants between studies. Our study followed children from 4 to 6 years, when they were starting the adipose rebound [ 55 ], while the study from Portugal followed children between 7 and 10 years old. Age and duration of follow-up could be a relevant factor. For example, a prior study from the Avon Longitudinal Study of Parents and Children that assessed longitudinal associations between UPF and adiposity trajectories from 7 to 24 years old showed that differences in BMI and fat mass by UPF consumption become more accentuated starting adolescence, another critical period for development [ 56 ].

Regarding metabolic indicators, we did not find an association with any included indicator. A recent review assessing the effect of UPF on metabolic syndrome components in children and adolescents based on nine cohort studies found mixed results. Some longitudinal studies have reported a positive association between UPF and blood lipids, but not with blood glucose; it is important to highlight that only a few prospective studies on metabolic outcomes in children are available in the literature to date [ 57 ]. Our results suggest that adiposity indicators could be altered before we observe metabolic marker alterations. However, metabolic alterations associated with adiposity during childhood are an increasingly common problem. A study with more than 26,000 children with obesity (average: 12.6 ± 2.9 years) from European countries found metabolic alterations in more than half of the participants, the most prevalent being high blood pressure (34%), dyslipidemias (32%), and less common alterations in glucose metabolism (3.3%) [ 58 ]. Similar results were also described in Mexico [ 59 ]. The results of these studies could indicate that alterations in blood pressure and lipid metabolism occur early in children with obesity. It is proposed that, with respect to glucose parameters, alterations may occur in the late stage of the development of metabolic alterations [ 60 , 61 ]. There is also evidence of the role of diet in metabolic risk from an early age. In a population-based cohort analyzing diet trajectories from ages 2–3 to 11–12 years, individuals who consistently adhered to an “unhealthy” diet trajectory showed compromised cardiovascular function and poorer metabolic health when compared to children consistently following a “healthy” diet, again suggesting that adolescence would be a critical period for observing the emergence of metabolic traits [ 62 ].

Various UPF characteristics have been examined to explain their detrimental impact on health. The most explored explanation revolves around the inadequate nutritional profile of UPF, characterized by a higher density of added sugars and saturated fats, and a lower density of vitamins and minerals compared to non-ultra-processed foods [ 16 ]. However, the nutritional imbalance in UPF seems incapable of fully explaining the observed effects. Findings from different studies have shown that the association between consumption of UPF and health outcomes persists even after adjusting for the nutritional profile of the diet [ 63 , 64 ]. UPF manufacturing often involves processed and refined ingredients that lack the natural food matrix, leading to reduced satiety and heightened glycemic response [ 65 ]. Additionally, UPF tend to have a higher energy density due to their ingredients and low water content, making them easy to consume rapidly in terms of volume and calories, facilitating excessive intake [ 66 , 67 ]. Furthermore, UPF typically exhibit a lower protein density, and it has been hypothesized that this lower protein content could lead individuals to overconsume other foods and, consequently, excess energy [ 68 ]. Another hypothesis considered to explain these associations beyond the nutritional profile is that the widespread consumption of UPF may result in increased intake of substances that are rare or absent in nature, such as food additives [ 69 ].

The consumption of UPF by children is a matter of concern. We know that children are the main consumers of these products in several countries, with the percentage of consumption higher than that observed in adults [ 70 ]. In fact, media marketing that encourages increased consumption of UPF targets children, given their high vulnerability. Additionally, eating habits built during childhood tend to persist throughout life [ 71 ]; therefore, becoming accustomed to consuming high levels of sugars, sodium, and fats is worrisome. Moreover, children have a lower body size; thus, they have a higher risk of exposure to critical levels of substances found in UPF. Thus, several countries, mostly in the Latin-American region, have adopted food-based guidelines with messages advising against the consumption of UPF [ 72 , 73 , 74 , 75 ]. In Brazil, dietary guidelines for children under 2 years of age explicitly recommend offering MPF and avoiding UPF [ 76 ]. Additionally, in Brazil, the legislation of the school feeding program prohibits the provision of UPF for children under 3 years of age and mandates that at least 75% of resources be allocated to the acquisition of MPF [ 77 ]. While not explicitly incorporating the concept of UPF into its regulations, Chile has one of the most comprehensive frameworks to protect children from packaged foods and beverages high in nutrients of concern, such as sugar, salt, and saturated fats (mostly UPF). The Chilean Food-Labeling and Advertising Law implemented in 2016 (after our dietary data collection) mandates the inclusion of warning labels “high in” on the front of the package, restricts the marketing of regulated foods to children under 14 years of age, and prohibits selling or offering of these foods in schools [ 78 ]. These measures should be reinforced and globally promoted to create environments in which children have restricted or no access to UPF given the risks associated with their consumption.

Our study has several strengths, including its longitudinal design, detailed dietary information that includes specific brand names of packaged foods, objective measurements of adiposity and metabolic profiles, and the estimation of usual consumption of UPF employing statistical methods to account for within-person variability in food consumption. However, some limitations should also be considered for interpreting our results. In observational studies, there is an inherent measurement error in the dietary data, which refers to the difference between the reported dietary intake and the true usual dietary intake. However, we attempted to select only plausible reports by excluding diets very far from the estimated considered children’s sex and age, and we also excluded diets with extreme values of UPF (< p1 and > p99). We also gathered dietary information using the standardized 24-h dietary recall technique, deemed the method with the least misreporting in children [ 79 ], and included children in the interviews which could reduce errors due to lack of awareness of parents regarding children’s dietary consumption. Additionally, we applied a statistical method to estimate the usual consumption of UPF; however, our estimate could not represent the usual consumption over the entire follow-up period. Still, dietary recalls can be subjected to social desirability bias, which may lead to the underestimation of UPF and bias in the associations toward the null. The proportion of loss on the follow-up was significant, especially for metabolic indicators, and we found differential losses related to mothers’ education. However, maternal education was not associated with outcomes, except glycemia (data not shown), so the estimates should not be importantly modified with the observed differential loss to follow-up; besides, we applied inverse probability of censoring weights to adjust all analyses to make more correct inferences considering the characteristics of our initial sample. Although we controlled for potential confounders, we cannot rule out unmeasured or residual confounding as this was an observational study. Finally, the findings might lack broad generalizability because our sample consisted of preschoolers attending public schools in Santiago’s low- to middle-income region.

We observed that a higher consumption of UPF was associated with adiposity indicators in this sample of Chilean preschoolers. Our results suggest the need for a longer exposure time for metabolic effects to emerge, so strategies to prevent the consumption of UPF aimed at schoolchildren could still improve these trajectories. Therefore, policies promoting food environments that facilitate the consumption of minimally processed foods and make it difficult for children to access UPF should be encouraged.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

24-H Diet Recall

Body mass index

Center of Research in Food Environment and Prevention of Obesity and Non-Communicable Diseases

Coefficient of variation

Directed acyclic graph

Dietary Reference Intake

Food and Environment Chilean Cohort

High-density cholesterol

Homeostatic Model to Assess Insulin Resistance

Institute of Nutrition and Food Technology

Low-density cholesterol

Minimally processed foods

Multiple Source Method

Predicted energy requirement

Processed culinary ingredients

Processed foods

Reported energy intake

Socioeconomic status

Stabilized inverse probability of censuring weights

Ultra-processed food

United States Department of Agriculture

Waist circumference

World Health Organization

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Acknowledgements

We thank the participants of the Food and Environment Chilean Cohort. We also thank the research teams at CIAPEC (Center of Research in Food Environment and Prevention of Obesity and Non-Communicable Diseases) at INTA (Institute of Nutrition and Food Technology), University of Chile, and at the Global Food Research Program, University of North Carolina at Chapel Hill.

This work was supported by Bloomberg Philanthropies, and the ANID/Fondo Nacional de Desarrollo Científico y Tecnológico-FONDECYT Regular (#1201633 and #1181370). CZ is supported by the National Agency for Research and Development (ANID)/Scholarship Program/Doctorado Becas Chile #21200883. NR is supported by the ANID/Fondo Nacional de Desarrollo Científico y Tecnológico-FONDECYT Postdoctorado (#3230125). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Camila Zancheta

Center of Research in Food Environment and Prevention of Obesity and Non-Communicable Diseases (CIAPEC), Institute of Nutrition and Food Technology (INTA), University of Chile, Santiago, Chile

Camila Zancheta, Natalia Rebolledo, Marcela Reyes & Camila Corvalán

Carolina Population Center, Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

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Conceptualization: CZ and CC. Methodology: CZ and CC. Investigation: CZ. Funding acquisition: LST, MR and CC. Supervision: CC. Writing—original draft: CZ. Writing—review and editing: CZ, NR, LST, MR, and CC. All authors read and approved the final manuscript.

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Correspondence to Camila Corvalán .

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The original study was approved by the ethics committee of the Institute of Nutrition and Food Technology (INTA), University of Chile (Nº 7–2016, Nº 19–2017). All mothers signed an informed consent form on behalf of their children. The ethics committee of the Faculty of Medicine, University of Chile, also approved the current analyses (Nº 159–2021).

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12916_2024_3556_moesm1_esm.docx.

Additional file 1: Tables S1-S4. Table S1 – Associations between the consumption of UPF at 4 y, anthropometric indicators, and body composition at 6 y without considering SW. Table S2 – Associations between the consumption of UPF at 4 y and metabolic indicators at 6 y without considering SW. Table S3 – Associations between quartiles of consumption of UPF at 4 y, anthropometric indicators and body composition at 6 y. Table S4 – Associations between quartiles of consumption of UPF at 4 y and metabolic indicators at 6 y

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Zancheta, C., Rebolledo, N., Smith Taillie, L. et al. The consumption of ultra-processed foods was associated with adiposity, but not with metabolic indicators in a prospective cohort study of Chilean preschool children. BMC Med 22 , 340 (2024). https://doi.org/10.1186/s12916-024-03556-z

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Published : 26 August 2024

DOI : https://doi.org/10.1186/s12916-024-03556-z

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National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Food and Nutrition Board; Roundtable on Obesity Solutions; Callahan EA, editor. Translating Knowledge of Foundational Drivers of Obesity into Practice: Proceedings of a Workshop Series. Washington (DC): National Academies Press (US); 2023 Jul 31.

Cover of Translating Knowledge of Foundational Drivers of Obesity into Practice

Translating Knowledge of Foundational Drivers of Obesity into Practice: Proceedings of a Workshop Series.

Hardcopy Version at National Academies Press

10 The Science, Strengths, and Limitations of Body Mass Index

Highlights from the speaker presentation (dietz).

Body mass index (BMI) is associated with but is not a direct measure of body fat. Therefore, it is useful in screening for obesity, but is not a diagnostic measure of obesity and does not displace clinical judgment.
BMI's association with health risk is inconsistent and varies with age, sex, and ethnicity, and it does not assess risk related to body fat distribution.
BMI does not assess the concomitant presence of comorbid conditions, disease risks, or functionality. The Edmonton Obesity Staging System assesses co-occurring medical, mental, or functional clinical risk factors, but gives each complication equal weight despite differences in the risk of severe disease, the risk of mortality, and costs.
Much discussion is occurring about how to use the term “obesity” and whether the term itself is stigmatizing, but a large body of technical support exists for the diagnosis.

The third (October 2022) workshop began with an introductory, stage-setting session.

THE SCIENCE, STRENGTHS, AND LIMITATIONS OF BODY MASS INDEX AS A MEASURE OF OBESITY

William (Bill) Dietz, chair of the Sumner M. Redstone Global Center on Prevention and Wellness in the Milken Institute School of Public Health at The George Washington University, discussed the science, strengths, and limitations of body mass index (BMI) as a measure of obesity. The core of deliberations and disputes about BMI's utility, Dietz proposed, is the question of how to assess the health impact of body fat.

Dietz began by listing various methods used to determine body composition, which can be divided into measures of total body fat and measures of fat distribution. He stated that most measures of total body fat are not clinical measures. They include densitometry approaches, such as underwater weighing and body plethysmography (i.e., air displacement in a vessel called the Bod Pod); isotope dilution of deuterium oxide to determine the body water space and convert the figure to lean tissue by multiplying by 0.73; dual x-ray absorptiometry (DEXA); and bioimpedance, which in Dietz's view is a relatively crude measure that does not offer much value beyond that offered by BMI.

Body fat distribution is a key indicator of obesity-associated risk, Dietz explained, as he turned to listing measures of this indicator: waist circumference, waist:hip ratio, DEXA, and computed tomography (CT) scans. He noted that a waist circumference greater than 40 inches for men and 35 inches for women indicates abdominal obesity and increased risk for a variety of adverse health outcomes. Waist:hip ratio has been used in the past, Dietz observed, with measures greater than 1.0 for men and 0.8 for women being deemed high risk. Because waist:hip ratio is governed largely by abdominal circumference, he elaborated, it is unusual to observe a waist:hip ratio of less than 1.0 in people with severe obesity when body fat is present in both the abdominal and gynoid (below the hips) regions. He flagged gynoid obesity as a subset of obesity that is not well defined, wherein the hip measurement is markedly increased but the waist measurement is not, resulting in a ratio below 0.8 despite the presence of excess body fat. CT scans and magnetic resonance imaging provide the most accurate measure of fat distribution, Dietz continued, and can distinguish visceral from subcutaneous abdominal fat. DEXA cannot make this differentiation, he said, but is useful for measuring total body fat and abdominal fat. Those two fat deposits, he added, as well as the fat deposit around the buttocks, have multiple differences in physiologic function.

Dietz next shared four points to support his rationale for why BMI is a reasonable measure for assessment of obesity in children and adolescents. His first point was that BMI reflects the presence of increased body fat. At BMI ≥95th percentile, 75 percent of youth have increased body fat (defined as ≥85th percentile of total body fat as determined by DEXA); a smaller percentage of youth in the 85th–95th percentiles have increased body fat (using the same definition). Thus, Dietz concluded, BMI is a reasonable measure of body fat in children and adolescents.

To support this point, Dietz presented a figure showing the findings of one study with respect to age- and sex-specific BMI percentiles in relation to DEXA fat mass in children of various races/ethnicities aged 6.5–10.9 ( Figure 10-1 ) ( Boeke et al., 2013 ). Correlation is low at BMI percentiles 0 through 80, he explained, and begins to rise around the 85th percentile before increasing linearly. This pattern indicates that BMI is a suboptimal measure of body fat for the population overall but is a reasonable measure for individuals with BMI ≥85th percentile.

FIGURE 10-1

Age- and sex-specific body mass index (BMI) percentile in relation to dual x-ray absorptiometry (DXA) fat mass index at 6.5–10.9 years, by race/ethnicity. NOTE: Data from 875 participants in Project Viva. BMI percentiles based on Centers for Disease (more...)

Dietz remarked that BMI is a simple calculation of weight (kilograms) divided by height (meters) squared. Its measurement is easy, he pointed out, because it is not hindered by clothing or dependent on specialized equipment. In another assessment involving the same study cohort, Dietz reported, correlations of three anthropometric measures (sum of skinfolds, waist circumference, and bioimpedance) with body fat as measured by DEXA were comparable to those indicated by BMI ( Boeke et al., 2013 ).

A second point to support the use of BMI in children and adolescents, Dietz continued, is that BMI ﹥95th percentile increases disease risk. For instance, the long-running Bogalusa Heart Study found that among children in its cohort with BMI ≥95th percentile, 70 percent had one risk factor for cardiovascular disease, and 39 percent had two or more ( Freedman et al., 2007 ).

Dietz's third point was that increased BMI is associated with risk of persistent obesity. That risk increases with age and the severity of obesity ( Geserick et al., 2018 ).

Finally, Dietz remarked that BMI in children and adolescents corresponds to BMI in adults. For example, a BMI in the 95th percentile in late adolescence corresponds to a BMI of 30 in adults, and BMI in the 82nd or 83rd percentile corresponds to a BMI of 25 in adults.

Dietz next discussed the rationale for using BMI in adults. As with the data for children and adolescents, he observed, BMI does not correlate with body fat in the 0–30 BMI category, but a reasonable correlation (~0.70) exists for BMI and body fat above BMI of 30 ( Flegal et al., 2009 ). Adult weight increases proportionally to height squared (i.e., it is not a linear function of height). Dietz explained that this is why the BMI calculation is weight (kg)/height (m) 2 , which helps make BMI a stature-independent measure of weight. BMI is applicable across most ethnicities and throughout the life cycle, Dietz elaborated, although correlations between total body fat and BMI may differ by sex and ethnicity. Lastly, Dietz pointed out that the lowest mortality across the distribution of BMI has been used to define a “healthy” weight; the term “normal” weight is no longer used because there is no normal weight distribution.

Dietz turned to describing “what BMI is not.” BMI is associated with body fat, he reiterated, but is not a direct measure of body fat. It also does not assess the concomitant presence of comorbid conditions, disease risks, or functionality. BMI's association with health risk is inconsistent and varies with age, sex, and ethnicity, and BMI does not assess risk related to body fat distribution. Finally, Dietz emphasized that BMI is not a diagnostic measure of obesity.

As an example of a situation in which BMI is less useful, Dietz showed a bar graph comparing the prevalence of obesity among non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and Hispanic men and women ( Figure 10-2 ). The prevalence of obesity among Non-Hispanic Asian adults, he pointed out, is consistently low in such comparisons based on BMI.

FIGURE 10-2

Obesity prevalence among non-Hispanic White (NHW), non-Hispanic Black (NHB), non-Hispanic Asian (NHA), and Hispanic (HISP) men and women, 2017–2018. SOURCE: Presented by William H. Dietz, October 25, 2022. Hales et al. (2020). Reprinted with permission. (more...)

One explanation for this, he said, is that at a given BMI, Asian populations have a higher risk of diseases associated with obesity compared with non-Asian populations at the same BMI. A study critical to informing BMI cutpoints for Asian populations determined the BMI in seven different Asian populations that corresponded to a total body fat of 25 percent and 30 percent (based on DEXA) ( WHO Expert Consultation, 2004 ). For the overweight category, which by U.S. standards is a BMI of 25 corresponding to a total body fat of 25 percent, Asian populations with the same percentage of body fat had a BMI of 23. The same relationship held for obesity, Dietz reported, which is defined in the United States as BMI ≥30, but for Asian populations, as BMI ≥27.5. It would be helpful to see this adjustment made in National Health and Nutrition Examination Survey data, he remarked, which would show how the use of an Asian-specific standard would affect the prevalence of obesity in Asian populations.

Dietz moved on to report the results of a 2004 study comparing waist circumference with BMI with respect to the risk of metabolic syndrome among approximately 15,000 men and women with BMIs between 18.5 and 35 ( Janssen et al., 2004 ). Prior to adjustment for waist circumference in men and women (i.e., based on BMI alone), he explained, the odds ratio for metabolic syndrome increased significantly with higher BMI. The increased odds ratio held true when BMI was adjusted for waist circumference category (elevated or not elevated) but disappeared when BMI was adjusted for absolute waist circumference.

Dietz transitioned to discussing the Edmonton Obesity Staging System (EOSS) ( Figure 10-3 ), which represents a shift in how obesity's comorbidities are assessed. EOSS Stage 0 represents zero medical, mental, or functional clinical risk factors. Stage 1 represents subclinical obesity-related risk factors in those three areas, such as elevated blood pressure, impaired fasting glucose, elevated liver enzymes, and mild functional limitations. Stage 2 represents established diseases such as sleep apnea, hypertension, diabetes, nonalcoholic fatty liver disease, and polycystic ovary disease. Stage 3 represents severe disease, such as heart attack or diabetes complications. Finally, stage 4 represents end-stage disease, such as liver failure or the need for dialysis in renal failure.

FIGURE 10-3

Edmonton Obesity Staging System (EOSS). SOURCE: Presented by William H. Dietz, October 25, 2022. Swaleh et al. (2021). Reprinted with permission.

Dietz pointed out that the distribution of Class I (BMI 30.0–34.9), Class II (BMI 35.0–39.9), and Class III (BMI ≥40.0) obesity is relatively even throughout the EOSS ( Figure 10-4 ), although the EOSS and BMI measure different things. BMI can still be used for screening, he clarified, but EOSS staging points to the comorbidities of obesity. In his view, a challenge with the EOSS is that all complications have equal weight despite differences among them in risk of severe disease, risk of mortality, and costs. Another challenge, he said, is that assignment to Stage 2 requires only one abnormal finding, but the presence of two or more abnormal findings does not change the EOSS's assessed risk for that stage.

FIGURE 10-4

Distribution of obesity classes at various levels of the Edmonton Obesity Staging System (EOSS). NOTE: BMI = body mass index. SOURCE: Presented by William H. Dietz, October 25, 2022. Swaleh et al. (2021). Reprinted with permission.

Next, Dietz described the contribution of adipose tissue to the pathophysiology of obesity. He reiterated that visceral fat—the deposition of body fat intra-abdominally and distributed throughout organs—is a key risk factor for disease. He explained that as obesity develops, adipocytes (i.e., fat cells) multiply and increase in size within visceral adipose tissue, changes that are associated with increased inflammation. Visceral adipose tissue releases inflammatory mediators, which adversely affect both pancreatic and liver function, resulting in decreased insulin secretion and increased insulin resistance, respectively. Muscle is also negatively affected by the inflammatory mediators released from visceral adipose tissue, Dietz added, which impairs glucose uptake and contributes to insulin resistance. An easy measure of visceral fat does not yet exist, he observed, nor does an easy measure with which to examine the degree to which the intermediary factors released by adipocytes contribute to disease.

Finally, Dietz shared a consensus statement of six obesity groups—the Academy of Nutrition and Dietetics, the STOP Obesity Alliance, The Obesity Society, the Obesity Medicine Association, the American Society for Metabolic and Bariatric Surgery, and the Obesity Action Coalition—to ensure consistency of messaging about the disease of obesity. 1 Key points from this statement include the following:

Obesity is a highly prevalent chronic disease characterized by excessive fat accumulation or distribution that presents a risk to health and requires lifelong care. Virtually every system in the body is affected by obesity. Other major chronic diseases associated with obesity include diabetes, heart disease, and cancer.
BMI is used to screen for obesity, but it does not displace clinical judgment. BMI is not a measure of body fat. Social determinants, race, ethnicity, and age may modify the risk associated with a given BMI.
Bias and stigmatization directed at people with obesity contributes to poor health and impairs treatment.
Every person with obesity should have access to evidence-based treatment.

In closing, Dietz observed that much discussion is occurring, particularly among “fat acceptance groups” and people with larger bodies, about how to use the term “obesity” and whether the term itself is stigmatizing. He raised doubt that the field can avoid naming obesity from a medical perspective given the large body of technical support for the diagnosis, but if it is indeed a stigmatizing term, asked, “How do we talk about obesity if we can't talk about obesity?”

Following Dietz's presentation, Ihuoma Eneli, professor of clinical pediatrics at The Ohio State University College of Medicine and associate director of the American Academy of Pediatrics Institute for Healthy Childhood Weight, moderated the discussion. She posed two questions: one about the Centers for Disease Control and Prevention's (CDC's) maps that visualize the U.S. prevalence of obesity by state, and another about advice for new physicians counseling patients with obesity.

U.S. State Maps of Prevalence of Obesity

Eneli referenced the CDC's popular maps illustrating the prevalence of obesity in each U.S. state and asked Dietz for his prediction as to whether these maps will still be in use 5 years from now. Dietz responded that the maps have effectively illustrated the spread of obesity in the United States and have seen widespread use since their development in 1999. He shared his view that the maps are still compelling, and that using BMI to create the maps remains the best tool currently available for assessing the proliferation of obesity across the United States.

Guidance for New Physicians about Discussing BMI with Patients with Obesity

Dietz urged avoidance of the term “obesity” given evidence that patients regard it as pejorative and would rather talk about “elevated BMI” or “excess weight.” He also suggested that a provider might not need to share a patient's BMI with the patient, and instead could use the BMI as an indication for how to proceed. He encouraged new physicians to be sensitive to the location where weight is measured (i.e., a private space is best) and how weight is shared with both patient and provider so as not to add to the stigma experienced by people with obesity. He recounted his experience treating patients and shared that a helpful way to begin the conversation is to ask patients whether they are concerned about their weight. He urged the next generation of physicians and trainees to make shared decision making a cornerstone of care, which he noted requires that patients trust and be willing to engage with their providers.

The consensus statement is viewable at https://stop .publichealth .gwu.edu/obesity-statement#:~:text =Obesity %20is%20a%20highly %20prevalent,%2C %20heart%20disease%2C%20and%20cancer (accessed January 5, 2023).

Cite this Page National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Food and Nutrition Board; Roundtable on Obesity Solutions; Callahan EA, editor. Translating Knowledge of Foundational Drivers of Obesity into Practice: Proceedings of a Workshop Series. Washington (DC): National Academies Press (US); 2023 Jul 31. 10, The Science, Strengths, and Limitations of Body Mass Index.
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Published: 31 August 2024

Effects of inulin on intestinal flora and metabolism-related indicators in obese polycystic ovary syndrome patients

Xiaorong Li 1 , 2 , 5 na1 ,
Bo Jiang 2 , 3 , 5 na1 ,
Ting Gao 6 ,
Yan Nian 1 ,
Xing Bai 3 , 5 ,
Jiawen Zhong 2 , 3 , 5 ,
Ling Qin 5 ,
Zhengzheng Gao 4 ,
Hao Wang 4 &
Xiaohong Ma 1

European Journal of Medical Research volume 29 , Article number: 443 ( 2024 ) Cite this article

Metrics details

Polycystic ovary syndrome (PCOS), a common endocrine disorder in women of reproductive age, is closely associated with chronic low-grade inflammation and metabolic disturbances. In PCOS mice, dietary inulin has been demonstrated to regulate intestinal flora and inflammation. However, the efficacy of dietary inulin in clinical PCOS remains unclear.

The intestinal flora and related metabolic indexes of obese patients with polycystic ovary syndrome (PCOS) after 3 months of inulin treatment were analyzed.

Setting and design

To analyze the intestinal flora and related metabolic indexes in healthy controls and obese patients with polycystic ovary syndrome after 3 months of inulin treatment.

The results showed that dietary inulin improved sex hormone disorders, reduced BMI and WHR levels in obese women with PCOS. In addition, the inulin intervention reduced plasma TNF-α, IL-1β, IL-6, and MCP-1levels. Inulin intervention increased the abundance of Actinobacteria , Fusobacteria, Lachnospira, and Bifidobacterium , as well as decreased the ratio of F/B and the abundance of proteobacteria , Sutterella, and Enterobacter . Correlation analyses showed a strong relationship among plasma inflammatory factors, sex steroid hormones, and the intestinal flora of patients.

Conclusions

Dietary inulin may improve obese PCOS women disease through the gut flora–inflammation–steroid hormone pathway.

The clinical trial registration number: ChiCTR-IOR-17012281.

Introduction

Polycystic ovary syndrome (PCOS), a common endocrine disorder, is one of the most important causes of infertility in women of childbearing age [ 1 ], with a prevalence of approximately 18% (17.8 ± 2.8%) [ 2 ], seriously affecting female reproductive, metabolic, and psychological health. The exact pathogenesis of polycystic ovary syndrome is poorly understood, with the main pathological basis as an imbalance in hormone levels with elevated androgen and/or insulin levels, and a chronic low-grade inflammatory response.

Studies have continuously reported that intestinal flora plays a key role in the development of PCOS. Significant changes in intestinal flora diversity and flora fractions have been reported in mice with PCOS or rodent models [ 3 ]. Mice developed insulin resistance and ovarian polycystic changes after gavage of feces from PCOS patients [ 4 ]. Bifidobacterium lactis V9 can reduce androgen level in patients with PCOS by modulating the gut–brain axis [ 5 ]. Lipopolysaccharide (LPS) released by certain bacteria in the gut translocates into the circulation, leading to insulin resistance and the apoptosis of ovarian granulosa cells [ 6 ]. Intestinal flora can cause menstrual disorders and insulin resistance by altering intestinal permeability [ 7 ]. Overall, insulin resistance and hyperandrogenemia in PCOS are critically influenced by the gut microbiota.

As a chronic inflammatory disease, the occurrence and development of chronic inflammation of PCOS is closely related to intestinal dysbiosis [ 8 , 9 ]. It is reported that Bacteroides vulgatus was markedly elevated in the gut microbiota of individuals with PCOS, modifying the gut microbiota may be of value for the treatment of PCOS [ 4 ]. It has been reported that gut microbiota-mediated priming/activation of neutrophils has been shown to increase the number of activated/aged neutrophils in the circulation, which secrete pro-inflammatory cytokines and granule proteases that damage tissues and exacerbate disease [ 10 ]. It has been widely demonstrated by many researchers that microbiota composition changes and dysbiosis occurs in PCOS animal models and women with PCOS [ 11 ].Therefore, how to improve the dysbiosis of intestinal flora in PCOS patients has become the key to treating PCOS.

Probiotics have been strongly demonstrated to show pleiotropic benefits consisting of regulating intestinal flora and suppressing the inflammation, improving glycolipid metabolism [ 12 ], enhancing immunity [ 13 ], enhancing cognitive function [ 14 ], enhancing anti-cancer efficacy, and reducing side effects of chemotherapy drugs [ 15 ], and antioxidant damage [ 16 ]. As a kind of dietary fiber, inulin has been widely used in food supplementation with properties such as regulating intestinal microbiota, influencing lipid metabolism, and anti-inflammatory and antioxidant properties [ 17 , 18 ]. Our previous studies had also shown that inulin can improve inflammation and intestinal flora diversity in mice with letrozole-induced PCOS [ 19 ]. However, whether this phenomenon still remains valid for patients with PCOS has not been illustrated.

This study aims to investigate the potential value of inulin in the treatment of PCOS by altering the gut microbiota, which may be a new therapy for the control of clinical PCOS.

Materials and methods

Ethics statement.

This observational prospective study was carried out at the Reproductive Center of the General Hospital of Ningxia Medical University from August 2017 to August 2020. It was approved by the medical ethics committee of General Hospital of Ningxia Medical University (ethics number: KYLL-2016-017) and conducted according to the principles expressed in the Declaration of Helsinki. The written informed consent was obtained from all enrolled cases and all data for research analyses were anonymized.

Inclusion criteria

(1) Patients who meet the diagnostic criteria for PCOS in the 2003 Rotterdam Consensus Statement [ 20 ]: ① Ovulation is sparse or non-ovulation; ② Clinical or biochemical evidence of Hyperandrogenemia; ③ Polycystic changes of the ovary. Two of the above three items can be diagnosed.

(2) Someone who can understand the purpose of the study, and willing to cooperate with the experimenter.

Exclusion criteria

(1) Patients with a combination of endometriosis, premature ovarian failure, ovarian resistance, hyperprolactinemia, ovarian tumors, or other reproductive disorders that are not diagnostic criteria for PCOS;

(2) Patients with uterine malformations or severe organic endometrial lesions and a previous history of pelvic tuberculosis;

(3) Patients with severe combined cardiovascular, cerebrovascular, hepatic, renal and hematopoietic diseases;

(4) Suffers from hypertension, abnormal glycolipid regulation, and other endocrine diseases;

(5) Hyperandrogenemia caused by other possible causes;

(6) People who smoke, drink alcohol, and are allergic to dietary fiber inulin;

(7) Furthermore, patients with a recent history of impact on gut flora were excluded, including those undergoing a weight loss lifestyle, those who had undergone intensive exercise training within the previous four weeks, and those who had used antibiotics, microecological modulators, hormones, and insulin sensitizers within three months.

Human subjects

Fifty-Five overweight women were enrolled trough public announcement in the Reproductive Center of the General Hospital of Ningxia Medical University from August 2017 to August 2020. The selection criteria are described above. Subjects ( n = 55) were divided into 3 groups: obese PCOS patients (FDB group, n = 19), obese control group (NFD group, n = 16), and non-obese control group (NSD group, n = 20). After the intervention, the 13 patients in the FDB group who strictly adhered to the intervention criteria were renamed to the FDA group. According to the regulations of World Health Organization [ 21 ], obesity is defined as BMI ≥ 25 kg/m 2 , and non-obesity is defined as BMI < 25 kg/m 2 . This study was approved by the ethical committee of General Hospital of Ningxia Medical University (2016-017) and signed the informed consent form with the subjects after ensuring their rights, interests, and safety. The clinical trial was registered with the Chinese Clinical Trials Registry, registration account: chiCTR-TRC-17012281.

Research methods

Intervention.

A uniformly trained reproductive endocrinology professional promoted and disseminated health information to all subjects, took fasting blood, and fresh stool samples from all subjects for the first time, explained to the intervention subjects (obese PCOS group) the purpose of the experiment, the duration of the intervention, and precautions to be taken during the intervention, and started the inulin intervention for 3 months. The control group was given about 150 ml of warm water every morning on an empty stomach. The subjects were instructed to take one bar (15 g, inulin produced by Fengning Ping a Hi-Tech Industry Co., Ltd.) every morning and pour it into about 150 ml warm water and drink it on an empty stomach.

After 3 months of routine administration, fasting blood and stool specimens were retained from 13 patients who had taken inulin strictly in accordance with the requirements of this study.

Collection of basic indicators

The height (cm) and weight (kg) were measured after fasting defecation and urination in the morning. A circle around the upper border of the pubic bone to the midpoint of the lower rib cage on both sides is defined as waist circumference (cm) and a circle around the most prominent point of the hip is defined as hip circumference (cm). Body Mass Index (BMI = weight/height 2 (kg/m 2 )) and Waist-to-Hip Ratio (WHR = waist/hip circumference) was calculated.

Detection of plasma lipid and glucose metabolism indicators

All subjects were collected from superficial vein (median cubital vein) blood of the forearm after 10–12 h of overnight fasting and water ban. Before starting the dietary protocol participants came to the Reproductive Center of the General Hospital of Ningxia Medical University for the basal measurements. Samples for lipid metabolism and glucose metabolism tests were submitted to the hospital's blood analysis department for testing. The plasma fasting aspartate transaminase (AST), alanine aminotransferase (ALT), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), creatinine (CRE), uric acid (UA), and fasting plasma glucose (FPG) levels were separately measured using a fully automated rapid test biochemistry analyzer (SIEMENS Germany). All test kits are purchased from SIEMENS. The plasma C-reactive protein (CRP) and Fasting insulin (FINS) were measured with immunoassay. Glycosylated Hemoglobin (GHb) was measured using a D-10 high-resolution glycosylated hemoglobin meter (Bio-RAD, USA).

Oral glucose tolerance test (OGTT): After collecting the fasting forearm median cubital vein blood mentioned above, 75 g anhydrous glucose was mixed with 300 ml sugar-free pure water and asked the subjects to take it all within 5 min. The same blood collector 2 h later was asked to draw the patient’s forearm median cubital vein blood again and immediately sent to the laboratory for 2 h of glucose detection.

Insulin beta-cell function index (HMOA-β) and insulin resistance index (HOMA-IR) were calculated as HOMA-β = 20 × FINS (mU/L)/(FPG (mmol/L)-3.5) and HOMA-IR = FPG (mmol/L) × FINS (mU/L)/22.5) respectively.

Determination of plasma sex steroid hormones

All participants had 10 mL of blood collected from a superficial forearm vein (median cubital vein) at 8 a.m. on days 3–5 of the menstrual cycle, for further detection of luteotropic hormone (LH), follicle-stimulating hormone (FSH), testosterone (T), progesterone (P), prolactin (PRL), Anti-mullerian hormone (AMH), and Estradiol 2 (E 2 ). The complete set of hormones was detected by chemiluminescence immunoassay (Atellica IM 1600, SIEMENS, Germany) in the laboratory department, test kits were purchased from SIEMENS.

Determination of plasma inflammatory indicators

The preparation work is the same as the above glucose and lipid metabolism. Plasma inflammatory factors including tumor necrosis factor-α, interleukin (IL)-1β, IL-6, and IL-10 were measured by using enzyme linked immunosorbent assay (ELISA) kits according to the manufacturer’s instructions (Shanghai Jianglai Biotechnology Co., Ltd., China). The monocyte chemoattractant protein-1 (MCP-1) was measured by using ELISA kits (Fankewei Biology, Shanghai, China). The sensitivities of the assays were 1.0, 0.1, 1.0, 0.1, and 0.1 pg/mL for TNF-α, IL-1β, IL-6, IL-10, and MCP-1, respectively. Each sample was tested in triplicate.

Gut microbiota sequencing analysis

The subjects were instructed to discharge the fecal samples into a clean container, and immediately after defecation, fresh and clean feces (about 5–10 g) were collected in an aseptic spoon and put in 4 aseptic airtight containers, and immediately stored at – 80 ℃. All fecal samples were used to measure the sequence of gut microbiota by 16S rRNA in Beijing Novogene Co., Ltd., for detection.

Fecal genomic DNA was extracted by the SDS method, and the purity and concentration of DNA were detected by agarose gel electrophoresis. The sample DNA was diluted to 1 ng/μL, the high-quality DNA purification template was used as polymerase chain reaction template, and barcode with high specificity was selected as a primer. The PCR in the reaction program was amplified by Phusion ®High-FidelityPCR Master Mix with GC Buffer produced by New England Biology Co., Ltd. (NEB), which was the characteristics of high fidelity and high performance and minimizes the error of experimental data. The reaction procedure was carried out in accordance with Bio-rad Bole PCR instrument T100. Then the amplified products were detected using agarose gel electrophoresis. DNA fragments were purified with the GeneJET gel recovery kit of Thermo Scientific company. Libraries were constructed using the Ion Plus Fragment Library Kit 48 rxns (Thermofisher) and sequenced using Ion S5 TM XL (Thermofisher) after passing Qubit quantification and library testing.

Statistical analysis

Clinical data were analyzed using SPSS 25.0 (IBM Corp., NY, USA) and inflammation data were analyzed and plotted using GraphPad Prism software 8.0 (GraphPad Software Inc., CA, USA). All experimental data are expressed as mean ± standard deviation of at least three independent experiments. Data were determined by one-way analysis of variance to compare the mean values of variables among the groups. Tukey’s post hoc test was used to identify the significance of the pairwise comparison of mean values among the groups. Comparisons between the two groups were made using the rank sum test. Spearman correlation analysis was also performed to understand the relationship between gut microbes and inflammatory and metabolic markers. P < 0.05 was considered statistically significant.

Analysis of changes in clinical indicators with the inulin treatment

BMI, WHR, TG, UA, FIN, and HOMA-IR levels were significantly elevated in the NFD group compared to the NSD group ( P = 0, 0, 0.034, 0, 0.006,0.003), while sex hormone levels were decreased except FSH and TSTO, and only significant differences in LH/FSH and AMH ( P = 0.047, 0.003). In the FDB group compared with the NFD group, all indicators of glycolipid metabolism increased except for TG, TC, HDL-C, and CRE levels, with a significant difference in the increase in ALT and UA levels and the decrease in HDL-C levels ( P = 0.024,0. 002,0.011). In addition, sex hormones LH, LH/FSH, TSTO, PRL, AMH, and E2 levels were increased and FSH levels were decreased, with significant differences in the changes of LH, LH/FSH, TSTO, AMH, E2 and FSH levels ( P = 0.02,0,0.033,0.042). BMI was also significantly higher. After the inulin intervention, AST, TC, LDL-C, HDL-C, CRE, UA, FPG, FIN, HOMA-IR, LH, LH/FSH, TSTO, P, PRL, AMH, BMI, and WHR levels in plasma were all decreased in the FDA group compared to those in the FDB group, with a statistically significant difference in the decrease in BMI ( P = 0.046), while FSH and E2 levels increased. These two indicators showed no significant difference between the FDA and FDB groups (Table 1 ).

Differences in abundance and diversity of intestinal flora among groups

We found that when the number of sequences increased to 5479, the curve flattened out, indicating that the amount of sequencing data was reasonable (Fig. 1 A). The results of the β-diversity analysis suggested that the abundance and diversity of the gut microbiota were reduced in the FDA group compared to the FDB, NFD, and NSD groups ( P = 0.0222, 0.0021, 0.0012) (Fig. 1 B). In 68 fecal samples, 407 intestinal microorganisms were found in all groups, while 358, 77, 72, and 11 intestinal microorganisms were found in the NSD, NFD, FDB, and FDA groups alone, respectively. There was significantly more gut microbial species in the NSD group than in the NFD and FDB groups, while the intestinal microbial species decreased further after the inulin intervention compared to the FDB group (Fig. 1 C).

Sequencing data plausibility analysis and species diversity analysis. A Rarefaction curve, reflecting the plausibility of the sequencing data; B Box plot of β-diversity, reflecting species differences among groups; C Venn diagram, indicating the number of unique and common OTUs in the different groups

Diversity of the overall composition of the gut microbiota

PCoA analysis showed that the community composition structure was similar between groups with no significant differences, indicating that inulin did not significantly improve the gut microbial community in obese PCOS patients (Fig. 2 ).

PcoA analysis showing the difference in terms of species in fecal samples. Beta diversity was on Unweighted-Unifrac. A : NFD vs. NSD; B : FDB vs. NFD; C : FDA vs. FDB; D : FDA vs. NFD

Analysis of intestinal microflora abundance changes and differential microflora

At the phylum level, Firmicutes and Bacteroidetes constituted the two dominant phylum in the four populations, followed by Proteobacteria and Actinobacteria in higher abundance, and the rest accounted for a low abundance (Fig. 3 A, C). The predominant Firmicutes and Bacteroidetes showed no significant change among the diverse groups. The ratio of Firmicutes to Bacteroidetes ( F/B ) and the relative abundance of proteobacteria were significantly higher in the NFD and FDB groups than in the NSD group, and highest in the FDB group (Fig S1A, B). After inulin intervention, the ratio of F/B and the relative abundance of proteobacteria were significantly lower in the FDA group than in the FDB group. The relative abundances of Actinobacteria and Fusobacteria were significantly lower in the NFD and FDB groups than in the NSD group. While after inulin intervention, the relative abundance of Actinobacteria and Fusobacteria in the FDA group was increased significantly compared to the FDB group (Fig S1C, D). Collectively, inulin had important effects on the ratio of Firmicutes/Bacteroidetes, as well as the abundance of proteobacteria , Actinobacteria, and Fusobacteria in obese PCOS patients.

Relative abundance of microbial species at the phylum and genus level in intestinal feces of different people and the biomarker of significant differences between groups. A , B Analysis of the relative abundance of intestinal microorganisms at the phylum and genus level; C , D Heat map of the relative abundance of gut microorganisms at the level of the top 35 phylum and genus at P ≤ 0.05. E–H Analysis of Biomarkers with significant differences between groups based on LDA Effect Size. ( E : NFD vs. NSD; F : FDB vs. NFD; G : FDA vs. FDB; H : FDA vs. NFD.)

At the genus level, Bacteroidetes and Faecalibacterium were the most widely distributed genera in the intestinal tract of patients in each group. The overall relative abundance of intestinal genera in the NFD group was significantly lower than that in the NSD group, with a statistically significant decrease in the abundance of unidentified_Ruminococcaceae ( P = 0.02). In addition, the relative abundance of Roseburia , Dialister , Blautia , Agathobacter , unidentified_Lachnospiraceae , Parabacteroides , Lactobacillus , Streptococcus, Intestinibacter , Romboutsia , Fusicatenibacter , Dorea, and some other conditionally pathogenic bacteria had higher relative abundance in the NFD group than in the NSD group, with Megamonas, Allisonella, and Howardella having significantly higher relative abundance ( P = 0.041, 0.002, 0.046) (Fig. 3 D, E). The relative abundances of Bacteroidetes , Fusobacterium , unidentified_Ruminococcaceae , and Lachnospira were lower in the FDB group than in the NSD and NFD groups, while the relative abundances of these genera were increased in the FDA group compared to the FDB group after inulin intervention, with the relative abundance of Lachnospira being significantly higher in the FDA group than in the FDB group ( P = 0.04) (Fig. 3 G). Besides, the relative abundance of Sutterella , Lactobacillus, Lactococcus, and Enterobacter was significantly lower in the FDA group than in the FDB group ( P = 0.047, 0.021, 0.002, 0.022) (Fig. 3 E). Interestingly, the opposite trend was observed for the genera Megamonas , Enterococcus , Blautia , unidentified_Lachnospiraceae , Fusicatenibacter , and unidentified_Erysipelotrichaceae , of which the relative abundances were higher in the FDB group than in the NSD and NFD groups, and decreased in the FDA group after inulin intervention, but without statistically significantly difference (Fig. 3 D). In addition, we found that the relative abundance of Lactococcus was significantly higher in the FDB group than in the NFD group ( P = 0.022), while the relative abundance of Alloprevotella and Holdemanella was significantly lower in the FDB group than in the NFD group ( P = 0.023, 0.043) (Fig. 3 F). Surprisingly, the relative abundance of Methylobacterium was significantly higher in the FDB group than in the other four groups and was significantly different when compared to the NFD group ( P = 0.036) (Fig. 3 B, F). Overall, dietary inulin dramatically changed the abnormal proportions of genus components in obesity PCOS by increasing the abundance of Lachnospira , and Fusobacterium as well as decreasing Sutterella , Lactobacillus , Lactococcus , and Enterobacter .

Changes in plasma inflammatory levels

Plasma levels of the pro-inflammatory factors TNF-α, IL-1β, IL-6, MCP-1, and the anti-inflammatory factor IL-10 were significantly higher in the NFD group compared to the NSD group ( P = 0.0005, 0.0008, 0.0045, 0.0361, < 0.0001). Compared to the NFD group, plasma levels of the inflammatory factors TNF-α, IL-1β, IL-6 and MCP-1 in the FDB group were further increased ( P = 0.0131, 0.0222, 0.0182, 0.0348), while the level of IL-10 decreased. Excitingly, plasma TNF-α, IL-1β, IL-6, and MCP-1 levels were significantly lower in the FDA group after the inulin intervention than in the FDB group (P = 0.0034, 0.0215, 0.0024, 0.0266), but there was no significant change in IL-10 level. The above further confirms that both obesity and PCOS disease were accompanied by an inflammatory state, which could be improved by dietary inulin (Fig. 4 ).

Detection of plasma inflammatory factors levels in diverse groups. Data are expressed as mean ± SEM. * P < 0.05, ** P < 0.01, *** P < 0.001

Correlation analysis

Due to the low abundance of some differential genus, only the top 40 genus in terms of abundance were selected for analysis among all differential genus. We found that unidentified-Ruminococcaceae abundance was negatively correlated with TG, UA, BMI, WHR, IL-1β, IL-6, and IL-10 levels, respectively ( P = 0.043, 0.025, 0.014, 0.001, 0.001, 0.006, 0.002), and positively correlated with HDL-C ( P = 0.026). Megamonas abundance was positively correlated with HDL-C levels and negatively correlated with HOMA-β and OGTT levels ( P = 0.027, 0.024, 0.039). Lactococcus abundance was positively correlated with TNF-α and IL-6 levels ( P = 0.01, 0.031). Methylobacterium abundance was positively correlated with TNF-α, IL-1β, IL-6, AST, ALT, UA, LH/FSH, and HOMA-β, respectively ( P = 0.041, 0.013, 0.028, 0.025, 0.009, 0, 0.037, 0.023) and negatively correlated with TC, HDL levels ( P = 0.022, 0.016). Lactobacillus abundance was negatively correlated with IL-10, TG, FPG ( P = 0.033, 0.024, 0.04). Anaerostipes abundance was significantly positively correlated with UA, E2, FIN, HOMA-IR, BMI, WHR levels, respectively ( P = 0.031, 0.005, 0.011, 0.013, 0.001, 0.005) and significantly negatively correlated with HDL-C ( P = 0). Fusobaterium abundance was significantly positively correlated with IL-β, CRP, AST, and WHR levels, respectively ( P = 0.025, 0.003, 0.014, 0.02).

In addition, we found that plasma TNF-α expression level was significantly and positively correlated with UA, T, E2, FIN, and HOMA-IR levels ( P = 0, 0, 0.01, 0.04,0.032) and negatively correlated with HDL ( P = 0.006). Plasma IL-1β expression levels were significantly and positively correlated with plasma AST, ALT, FIN, and HOMA- IR. WHR levels were significantly positively correlated ( P = 0.001, 0.007, 0.002, 0.001, 0.001) and negatively correlated with HDL-C levels ( P = 0.032). Plasma IL-6, UA, T, and WHR levels were significantly positively correlated ( P = 0.001, 0.038, 0.037), negatively correlated with HDL ( P = 0.04). Plasma IL-10 expression level was significantly positively correlated with TG, TC, LDL-C, and BMI levels ( P = 0.023, 0.013, 0.047, 0.014). Plasma MCP-1 expression level was significantly positively correlated with ALT, UA, OGTT, GHb, and BMI levels ( P = 0.012, 0, 0.018, 0.011, 0.001) and negatively correlated with FSH ( P = 0.018). Plasma CRP expression levels were significantly positively correlated with AST, ALT, WBC, LH/FSH, P, FPG, FIN, HOMA-IR, OGTT, and BMI levels ( P = 0.009, 0.032, 0.001, 0.033, 0.032, 0.012, 0.001, 0, 0, 0.001). UA and BMI were positively associated with all inflammatory factors, while HDL levels were significantly negatively associated with all inflammatory factors(Fig. 5 ). Taken together, there were close correlations among gut bacteria, inflammation, sex steroid hormones, and clinical metabolic indicators.

Correlation analysis between the relative abundance of gut microbiota with plasma inflammatory factors and clinical indicators. A Heat map of correlation analysis of clinical indicators and inflammatory factors with differential gut microbial abundance; B Heat map of correlation analysis between inflammatory factors and clinical indicators. (* P < 0.05, * * P < 0.01, * * * P < 0.001)

In the present study, we observed and analyzed the abnormal changes and correlation of clinical metabolic indexes, intestinal flora, and inflammatory factor levels in obese women with PCOS before and after inulin intervention to investigate the therapeutic effects and possible mechanisms of inulin in obese women with PCOS. We demonstrated that dietary inulin modulated steroid hormone homeostasis, and gut microbiota components and suppressed inflammation in obese women with PCOS. This provided a theoretical basis for the use of inulin as an inexpensive intervention for obese PCOS.

The development of PCOS as a chronic endocrine metabolic disorder is mainly characterized by the disruption of sex steroid hormones [ 22 ]. The main pathological feature of PCOS is hyperandrogenemia due to elevated serum testosterone (T) and luteinizing hormone (LH) levels [ 23 ]. Elevated LH levels drive the synthesis of sex steroid hormones (androgens and estrogens) by ovarian theca cells, further exacerbating hyperandrogenemia [ 24 ]. Elevations in T levels will also lead to impaired progesterone sensitivity in the inferior colliculus, with an increased GnRH pulse frequency, and a decrease in progesterone (P) levels [ 25 ]. Secondly, the ovaries of patients with PCOS mainly exhibit impaired follicular development, leading to an excessive accumulation of antral follicles and small sinus follicles, further manifested by decreased levels of folliculopoietin (FSH) expression and increased levels of anti-Müllerian hormone (AMH) [ 26 ]. Therefore, the LH/FSH ratio is considered to be a major biomarker for the diagnosis of PCOS disease [ 27 , 28 ]. AMH levels are more sensitive than ultrasound sinus follicle count (AFC), which reflects antral and small sinus follicles (< 2 mm) that are barely visible on ultrasound, and AMH levels may replace the more expensive and less accessible ultrasound in the diagnosis of PCOS [ 29 ]. In the present study, we found that plasma T, LH, AMH, E 2 levels, and LH/FSH ratio were significantly higher in obese women with PCOS compared to non-PCOS obese women, while FSH levels were significantly lower, and all indicators were significantly corrected after inulin intervention. Although there was no statistically significant difference, this does not negate the fact that dietary inulin improved steroid hormone homeostasis in obese PCOS patients, and the results may be more significant by increasing our sample size and the duration of the inulin intervention.

In addition, a large body of data suggests that patients with PCOS also often have dyslipidemia and insulin resistance, which may be caused by hyperandrogenemia [ 30 , 31 ]. Studies have reported that higher levels of endogenous testosterone can raise LDL-C levels and lower HDL-C levels [ 32 ]. At the same time, high levels of androgens can cause increased insulin resistance, which leads to a decrease in insulin-mediated intramuscular glucose utilization and reduced insulin sensitivity, further exacerbating insulin resistance levels. These are consistent with our current findings. The alterations in plasma AST, TC, and LDL-C levels before and after the intervention suggested that dietary inulin mitigated lipid metabolism in obese women with PCOS, despite the increases in ALT, TG, and HDL-C levels. Besides, we found that dietary inulin could reduce plasma CRE and UA levels. Studies have demonstrated that UA can form NLRP3 inflammatory vesicles and release various pro-inflammatory factors that further impair insulin signaling, thereby mediating the development of insulin resistance (IR) and hyperandrogenemia and triggering ovarian ovulation disorders [ 33 ]. This indicates that dietary inulin is a safe probiotic supplement that does not cause toxic damage to liver and kidney function and has some protective effects. For clinical diagnosis and treatment in patients with polycystic ovary syndrome, we should pay attention to uric acid level change. FPG, FIN, and HOMA-IR levels were significantly higher in the obese population and obese PCOS population, which was consistent with previous studies [ 34 , 35 ]. Although dietary inulin did not reduce plasma glucose and insulin resistance levels in obese women with PCOS, their fasting blood glucose did not fluctuate beyond normal values after the intervention. Excitingly, dietary inulin effectively reduced plasma OGTT levels in patients, suggesting that inulin improved insulin sensitivity.

Growing evidences have demonstrated that intestinal microbes and their metabolites are closely related to the occurrence and development of PCOS [ 36 , 37 , 38 ]. Studies have reported that compared with healthy people, patients with PCOS gut microbes beta diversity decreased, and beta diversity and high negative correlation between androgen hematic disease [ 39 ]. If we are to validate the existence of such disparities, we need to further broaden the geographical scope and increase the sample size of the included population. Consistent with previous findings that dietary supplementation with a single fermentable substrate can reduce indicators of fecal bacterial diversity in humans [ 40 ] and improve metabolic responses [ 41 ]. Our findings suggest that inulin does not increase overall gut microbial species richness in obese women with PCOS, but can significantly alter the composition of the gut microbial community.

The gut microbiota in healthy populations consists of two major phylum, Firmicutes and Bacteroidetes , while obese humans exhibit a higher Firmicutes/Bacteroidetes ( F/B ) ratio [ 42 , 43 , 44 ]; elevated F/B ratios are associated with a variety of diseases [ 45 , 46 , 47 ], and vary with human aging [ 48 ]. Our results also showed a consistent trend, but the increased ratio of F/B in obese PCOS patient was rectified by dietary inulin administration, including Bacteroides and Megamonas genus. Proteobacteria is a Gram-negative bacterium whose outer membrane is composed mainly of lipopolysaccharides (LPS), and a phylum that contains a variety of pathogenic bacteria including Enterobacter , Salmonella , Vibrio cholera , and Helicobacter pylori , with the elevated abundance of Proteobacteria in a variety of diseases [ 49 , 50 , 51 ]. However, Actinobacteria is often used in the research and development of antibiotics and has a crucial role in maintaining intestinal homeostasis. Bifidobacterium within the phylum Actinobacteria is widely used in the development of various pharmaceuticals and foods, showing beneficial effects in many pathological conditions [ 52 ]. Dietary fiber supplementation has been reported to significantly increase its abundance and reduce obesity [ 53 , 54 , 55 ]. It has also been reported that a water extract of Ganoderma lucidum mycelium (WEGL) can downregulate the levels of proteobacteria in mice fed a high-fat diet, thereby achieving a reduction in body weight, inflammation, and insulin resistance [ 56 ]; an inulin intervention in an obese people was found to increase the abundance of Actinobacteria [ 57 ]. In the present study, we maintained consistent results that inulin intervention downregulates intestinal proteobacteria abundance and upregulated Actinobacteria abundance in obese PCOS women. Furthermore, at the genus level, we used the LEfSe method to compare the gut flora composition after the inulin intervention with that before the intervention and we found that inulin restored the gut ecological dysbiosis in PCOS by significantly upregulating the abundance of intestinal Lachnospira flora and downregulating the abundance of Sutterella , Lactobacillus , Lactococcus , and Enterobacter in the obese PCOS population. Surprisingly, the inulin intervention also significantly downregulated the abundance of Lactobacillus and Lactococcus in the intestine of obese PCOS patients. When the groups were analyzed together, the abundance of Lactobacillus was highest in the FDB group, while Lactococcus was the most abundant in the intestine of the healthy population. Lactobacillus is usually added to dairy products as a safe beneficial bacterium and its pathogenicity has rarely been reported. In combination with the lifestyle habits of the study subjects, contamination from dietary sources cannot be excluded. In contrast, the abundance of these two genera decreased further after the inulin intervention, perhaps as a result of the effects of prolonged supplementation with a single dietary fiber. Besides, we found that supplementation with dietary inulin upregulated the abundance of Bifidobacterium . The results imply that obese women with PCOS have varying degrees of gut flora disorders and that dietary inulin may have anti-obesity and improve PCOS by altering the ratio of F/B in the gut of obese women with PCOS and by altering the relative abundance of other specific bacterial species. Excitingly, in this study, we found that Methylobacterium was significantly enriched in the gut of obese women with PCOS, with a clear reduction in abundance after the inulin intervention. Regrettably, the post-intervention changes were not statistically significant compared to the pre-intervention, which may be related to the size of our sample. Methylobacterium is present in all corners of our living environment as conditionally pathogenic bacteria and are often contracted by immunocompromised individuals [ 58 ]. The abundance of this genus has been found to be significantly higher in patients with ulcerative colitis and constipating irritable bowel syndrome, but there is no clear indication that the abundance of this genus interacts with inflammation [ 59 , 60 ]. It is not known whether upregulated Methylobacterium abundance in this study was associated with external infection or by endogenous infection of the intestine. Once it is clear that it is endogenously upregulated, Methylobacterium abundance may be a biomarker for the diagnosis of obese PCOS patients. However, we need to involve larger samples for validation and further studies to understand the role of individual components of the gut microbiota in its pathogenesis.

Numerous studies have reported a key role of chronic low-grade inflammation in the development of PCOS disease [ 35 , 61 , 62 ]. In our study, we found that obese people as well as those with PCOS had higher levels of inflammatory factor expression, especially in obese PCOS patients, further confirming the notion that obesity is a chronic inflammatory state [ 39 , 63 ]. At the same time, we demonstrated that dietary inulin alleviated systemic inflammation by inhibiting pro-inflammatory cytokines (TNF-α, IL-1β, IL-6, MCP-1), suggesting an anti-inflammatory effect of dietary inulin in PCOS. Lipopolysaccharide (LPS), a metabolite of the gut flora, can induce a chronic subclinical inflammatory process and obesity, leading to insulin resistance through activation of TLR4. A reduction in circulating SCFA may also play an important role in reducing insulin sensitivity and promoting the development of inflammation and obesity [ 36 , 64 ]. Evidence from several studies suggests that probiotic supplementation reduces the level of LPS produced by intestinal pathogenic microorganisms and increases the level of short-chain fatty acids (SCFAs), decreasing intestinal permeability and reducing LPS translocation, further reducing the systemic inflammatory cascade [ 65 , 66 ]. In addition, some studies have reported that impairment of intestinal tight junction proteins (e.g., ZO-1 and occludins) enhances intestinal permeability and is critical for LPS translocation [ 67 , 68 ]. Probiotics have been shown to improve intestinal permeability and integrity by upregulating tight junction proteins (ZO-1 and occludins) to inhibit LPS translocation [ 69 ]. LPS and SCFA levels, as well as changes in the expression of the TJs or the possible role of dietary inulin as important targets, deserve further research.

This study highlighted that dietary inulin may ameliorated obesity PCOS via the gut microbiota–inflammation–sex steroid hormones axis in human, which may potentially serve as an inexpensive intervention for the control of obesity PCOS patients.

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession numbers can be found in NCBI, accession number PRJNA903127.

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Acknowledgements

The authors greatly appreciate all the patients for their cooperation.

This work was supported by National Natural Science Foundation of China (recipient: XL, grant number: 81660806; 82260947). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Xiaorong Li and Bo Jiang contributed equally to this work and are listed as the first authors.

Authors and Affiliations

Center for Reproductive Medicine, General Hospital of Ningxia Medical University, 164, Zhiping Road, Yinchuan, 750004, Ningxia, China

Xiaorong Li, Yan Nian & Xiaohong Ma

Key Laboratory of Fertility Maintenance, Ningxia Medical University, 1160, Shengli Street, Yinchuan, 750004, Ningxia, China

Xiaorong Li, Bo Jiang & Jiawen Zhong

Key Laboratory of Modernization of Hui Medicine, Ministry of Education, School of Traditional Chinese Medicine, Ningxia Medical University, 1160, Shengli Street, Yinchuan, 750004, Ningxia, China

Bo Jiang, Xing Bai & Jiawen Zhong

Department of Pathogenic Biology and Medical Immunology, School of Basic Medical Sciences, Ningxia Medical University, 1160, Shengli Street, Yinchuan, 750004, Ningxia, China

Zhengzheng Gao & Hao Wang

College of Traditional Chinese Medicine, Ningxia Medical University, 1160, Shengli Street, Yinchuan, 750004, Ningxia, China

Xiaorong Li, Bo Jiang, Xing Bai, Jiawen Zhong & Ling Qin

Chengdu Integrated, TCM&Western Medicine Hospital, 18, Wanxiang North Road, Chengdu, 610095, Sichuan, China

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XL, TG, BJ and HW designed and wrote the paper. YN, XB, JZ, LQ, ZG, and XM performed the research. All authors have read and approved the fifinal manuscript.

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The clinical study was approved by the Ethics Committee of General Hospital of Ningxia Medical University (No. 2016-017).

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40001_2024_2034_moesm1_esm.tif.

Supplementary Material 1. Fig S1. Relative abundance of gut microbial species at the phylum levels in the feces of human.Firmicutes/Bacteroidetes.Proteobacteria.Actinobacteria.Fusobacteria

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Li, X., Jiang, B., Gao, T. et al. Effects of inulin on intestinal flora and metabolism-related indicators in obese polycystic ovary syndrome patients. Eur J Med Res 29 , 443 (2024). https://doi.org/10.1186/s40001-024-02034-9

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Body Mass Index

Fatness as a Personal or Society Issue

Fatness as a Medical Issue

Problems With the Wt/Ht (Body Build) Index

Mathematical Adjustment of Body Build

Lambert Adolph Jacque Quetelet

Adoption of the BMI as an Index of Obesity

BMI Distribution in a Normal Population

WHO and the Categorization of BMIs Into Quartiles

Distribution of BMI in the General Population

BMI as a Determinant of Body Fat Mass

Trends in Body Weight and Height

Body Fat Location

The Life Cycle and Location of Accumulated Fat

Methods of Estimating Body Fat Mass and Location of the Fat

BMI and Morbidity and Mortality

More Explicit Problems in Relating the BMI to Medical Issues

The “Obesity Epidemic”

Is Being “Overweight” by BMI Criteria a Medical Issue?

Issues to be Resolved When Relating BMI With Health Determinants

SUMMARY AND CONCLUSION

Prevention and/or Treatment of BMI-Determined Overweight or Obesity

A Personal Perspective Regarding the Obesity Epidemic

Acknowledgments

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