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Substance Use Disorders and Addiction: Mechanisms, Trends, and Treatment Implications

Information & authors, metrics & citations, view options, insights into mechanisms related to cocaine addiction using a novel imaging method for dopamine neurons, treatment implications of understanding brain function during early abstinence in patients with alcohol use disorder, relatively low amounts of alcohol intake during pregnancy are associated with subtle neurodevelopmental effects in preadolescent offspring, increased comorbidity between substance use and psychiatric disorders in sexual identity minorities, trends in nicotine use and dependence from 2001–2002 to 2012–2013, conclusions, information, published in.

• Substance-Related and Addictive Disorders
• Addiction Psychiatry
• Transgender (LGBT) Issues

Competing Interests

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Clinical pharmacology: Current innovations and future challenges

Affiliations.

• 1 INSERM U1042, Université Grenoble Alpes, Grenoble, France.
• 2 CHU de Grenoble, Service de Pharmacologie - Pharmacosurveillance, CIC1406, Centre Régional de Pharmacovigilance, Grenoble, France.
• 3 INSERM, PARCC, Université de Paris, Paris, France.
• 4 CIC1418 and DMU CARTE, AP-HP, Hôpital Européen Georges-Pompidou, Paris, France.
• 5 Saint-Etienne, UMR1059, Université Jean-Monnet, Saint-Etienne, France.
• 6 CHU de Saint-Etienne, Unité de recherche clinique, Innovation et pharmacologie, Saint-Etienne, France.
• 7 Theranexus, Lyon, France.
• 8 Inserm, CHU Lille, U1172 - Degenerative & vascular cognitive disorders, Université Lille, Lille, France.
• 9 Inserm, CHU Lille, Clinical Investigation Center - CIC 1403, Université Lille, Lille, France.
• 10 CIC1401, INSERM U1045, University of Bordeaux, Bordeaux, France.
• 11 CHU de Bordeaux, CIC1401, Service de Pharmacologie Médicale, Bordeaux, France.
• PMID: 34954839
• DOI: 10.1111/fcp.12747

Clinical pharmacology is the study of drugs in humans, from first-in-human studies to randomized controlled trials (RCTs) and benefit-risk ratio assessment in large populations. The objective of this review is to present the recent innovations that may revolutionize the development of drugs in the future. On behalf of the French Society of Pharmacology and Therapeutics, we provide recommendations to address those future challenges in clinical pharmacology. Whatever the future will be, robust preliminary data on drug mechanism of action and rigorous study design will remain crucial prior to the start of pharmacological studies in human. At the present time, RCTs remain the gold standard to evaluate the efficacy of human drugs, although alternative designs (pragmatic trials, platform trials, etc.) are emerging. Innovations in healthy volunteers' studies and the contribution of new technologies such as artificial intelligence, machine learning, and internet-based trials have the potential to improve drug development. In the field of precision medicine, new disease phenotypes and endotypes will probably help to identify new pharmacological targets, responders to therapies, and patients at risk for drug adverse events. In such a moving landscape, the development of translational research through academic and private partnership, transparent sharing of clinical trial data and enhanced interactions between drug experts, patients, and the general public are priority areas for action.

Keywords: connected devices; drugs; machine learning; precision medicine; translational research; trials.

© 2021 Société Française de Pharmacologie et de Thérapeutique.

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Understanding Drug Use and Addiction DrugFacts

Many people don't understand why or how other people become addicted to drugs. They may mistakenly think that those who use drugs lack moral principles or willpower and that they could stop their drug use simply by choosing to. In reality, drug addiction is a complex disease, and quitting usually takes more than good intentions or a strong will. Drugs change the brain in ways that make quitting hard, even for those who want to. Fortunately, researchers know more than ever about how drugs affect the brain and have found treatments that can help people recover from drug addiction and lead productive lives.

What Is drug addiction?

Addiction is a chronic disease characterized by drug seeking and use that is compulsive, or difficult to control, despite harmful consequences. The initial decision to take drugs is voluntary for most people, but repeated drug use can lead to brain changes that challenge an addicted person’s self-control and interfere with their ability to resist intense urges to take drugs. These brain changes can be persistent, which is why drug addiction is considered a "relapsing" disease—people in recovery from drug use disorders are at increased risk for returning to drug use even after years of not taking the drug.

It's common for a person to relapse, but relapse doesn't mean that treatment doesn’t work. As with other chronic health conditions, treatment should be ongoing and should be adjusted based on how the patient responds. Treatment plans need to be reviewed often and modified to fit the patient’s changing needs.

Video: Why are Drugs So Hard to Quit?

What happens to the brain when a person takes drugs?

Most drugs affect the brain's "reward circuit," causing euphoria as well as flooding it with the chemical messenger dopamine. A properly functioning reward system motivates a person to repeat behaviors needed to thrive, such as eating and spending time with loved ones. Surges of dopamine in the reward circuit cause the reinforcement of pleasurable but unhealthy behaviors like taking drugs, leading people to repeat the behavior again and again.

As a person continues to use drugs, the brain adapts by reducing the ability of cells in the reward circuit to respond to it. This reduces the high that the person feels compared to the high they felt when first taking the drug—an effect known as tolerance. They might take more of the drug to try and achieve the same high. These brain adaptations often lead to the person becoming less and less able to derive pleasure from other things they once enjoyed, like food, sex, or social activities.

Long-term use also causes changes in other brain chemical systems and circuits as well, affecting functions that include:

• decision-making

Despite being aware of these harmful outcomes, many people who use drugs continue to take them, which is the nature of addiction.

Why do some people become addicted to drugs while others don't?

No one factor can predict if a person will become addicted to drugs. A combination of factors influences risk for addiction. The more risk factors a person has, the greater the chance that taking drugs can lead to addiction. For example:

• Biology . The genes that people are born with account for about half of a person's risk for addiction. Gender, ethnicity, and the presence of other mental disorders may also influence risk for drug use and addiction.
• Environment . A person’s environment includes many different influences, from family and friends to economic status and general quality of life. Factors such as peer pressure, physical and sexual abuse, early exposure to drugs, stress, and parental guidance can greatly affect a person’s likelihood of drug use and addiction.
• Development . Genetic and environmental factors interact with critical developmental stages in a person’s life to affect addiction risk. Although taking drugs at any age can lead to addiction, the earlier that drug use begins, the more likely it will progress to addiction. This is particularly problematic for teens. Because areas in their brains that control decision-making, judgment, and self-control are still developing, teens may be especially prone to risky behaviors, including trying drugs.

Can drug addiction be cured or prevented?

As with most other chronic diseases, such as diabetes, asthma, or heart disease, treatment for drug addiction generally isn’t a cure. However, addiction is treatable and can be successfully managed. People who are recovering from an addiction will be at risk for relapse for years and possibly for their whole lives. Research shows that combining addiction treatment medicines with behavioral therapy ensures the best chance of success for most patients. Treatment approaches tailored to each patient’s drug use patterns and any co-occurring medical, mental, and social problems can lead to continued recovery.

More good news is that drug use and addiction are preventable. Results from NIDA-funded research have shown that prevention programs involving families, schools, communities, and the media are effective for preventing or reducing drug use and addiction. Although personal events and cultural factors affect drug use trends, when young people view drug use as harmful, they tend to decrease their drug taking. Therefore, education and outreach are key in helping people understand the possible risks of drug use. Teachers, parents, and health care providers have crucial roles in educating young people and preventing drug use and addiction.

Points to Remember

• Drug addiction is a chronic disease characterized by drug seeking and use that is compulsive, or difficult to control, despite harmful consequences.
• Brain changes that occur over time with drug use challenge an addicted person’s self-control and interfere with their ability to resist intense urges to take drugs. This is why drug addiction is also a relapsing disease.
• Relapse is the return to drug use after an attempt to stop. Relapse indicates the need for more or different treatment.
• Most drugs affect the brain's reward circuit by flooding it with the chemical messenger dopamine. Surges of dopamine in the reward circuit cause the reinforcement of pleasurable but unhealthy activities, leading people to repeat the behavior again and again.
• Over time, the brain adjusts to the excess dopamine, which reduces the high that the person feels compared to the high they felt when first taking the drug—an effect known as tolerance. They might take more of the drug, trying to achieve the same dopamine high.
• No single factor can predict whether a person will become addicted to drugs. A combination of genetic, environmental, and developmental factors influences risk for addiction. The more risk factors a person has, the greater the chance that taking drugs can lead to addiction.
• Drug addiction is treatable and can be successfully managed.
• More good news is that drug use and addiction are preventable. Teachers, parents, and health care providers have crucial roles in educating young people and preventing drug use and addiction.

For information about understanding drug use and addiction, visit:

• www.nida.nih.gov/publications/drugs-brains-behavior-science-addiction/drug-abuse-addiction

For more information about the costs of drug abuse to the United States, visit:

• www.nida.nih.gov/related-topics/trends-statistics#costs

For more information about prevention, visit:

• www.nida.nih.gov/related-topics/prevention

For more information about treatment, visit:

• www.nida.nih.gov/related-topics/treatment

To find a publicly funded treatment center in your state, call 1-800-662-HELP or visit:

• https://findtreatment.samhsa.gov/

This publication is available for your use and may be reproduced in its entirety without permission from NIDA. Citation of the source is appreciated, using the following language: Source: National Institute on Drug Abuse; National Institutes of Health; U.S. Department of Health and Human Services.

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New Journal of Chemistry

Development of drug-induced gastrointestinal injury models based on ann and svm algorithms and their applications in the field of natural products †.

* Corresponding authors

a School of Chinese Materia Medica, Tianjin Key Laboratory of Therapeutic Substance University of Traditional Chinese Medicine, Tianjin 301617, China E-mail: [email protected] , [email protected] Fax: +86-022-59591974 Tel: +86-022-59591974

The broad use of natural products and the accompanied incidences of gastrointestinal injury have attracted considerable interest in investigating the responsible toxic ingredients. Computer models are efficient tools to predict toxicity, but research on drug-induced gastrointestinal injury (DIGI) related to the use of natural products remains lacking. In the present study, a total of 1295 compounds were retrieved from SIDER and AdisInsight databases to investigate whether they cause diseases such as colitis, intestinal perforation, intestinal obstruction, irritable bowel syndrome, intestinal bleeding, inflammatory bowel disease, colon cancer, colorectal cancer and duodenal ulcer as datasets. The ANN and SVM algorithms were evaluated to construct a series of classification prediction models, and finally, a computer model was built based on an ANN algorithm to rapidly screen DIGI induced by natural products. A dataset containing 201 toxic components was established, and the ANN model was used to screen 104 potential DIGI ingredients. Finally, based on molecular docking and CCK-8 methods, the intestinal injury effects of veratramine, emodin and euphobiasteroid were verified. The results of the molecular docking showed that these three components could bind well with the intestinal injury targets PIK3CA, SLC9A3, ACTG2 and HSP90AA1. According to NCM-460 cell experiments, the IC50 values of veratramine, emodin and euphobiasteroid were 75.13, 340.9 and 339.6 μmol L −1 , respectively. The study findings further proved the accuracy of the ANN model in screening DIGI components caused by natural products.

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Development of drug-induced gastrointestinal injury models based on ANN and SVM algorithms and their applications in the field of natural products

W. Zhang, M. Zhou, X. Yan, S. Chen, W. Qian, Y. Zhang, X. Zhang, G. Jia, S. Zhao, Y. Yao and Y. Li, New J. Chem. , 2024, Advance Article , DOI: 10.1039/D4NJ02680B

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• Open access
• Published: 17 September 2024

Association between non-injection drug use and hepatitis C infection among HIV-negative men who have sex with men

• Jing Zhao 1 ,
• Charles Green 2 ,
• Christine Markham 3 ,
• Kayo Fujimoto 3 ,
• Alan G. Nyitray 4 &
• Lu-Yu Hwang 5  

BMC Infectious Diseases volume  24 , Article number:  985 ( 2024 ) Cite this article

Metrics details

Prior research predominantly examined the association between HIV-positive men who have sex with men (MSM) or those using injection drugs and hepatitis C virus (HCV) infection. However, limited attention has been given to understanding the association among HIV-negative MSM who do not inject drugs. This gap leaves apportion of the population unexamined, potentially overlooking important factor that may contribute to the transmission and prevalence of HCV. This study aims to investigate the relationship between non-injection drug use and HCV infection in this population.

In this cross-sectional study, we analyzed data on 118 MSM who reported use of non-injection drugs. The participants were recruited from two inner-city communities in Houston, TX, between 2004 and 2007 and were negative for both HIV and hepatitis B virus infection. Latent class analysis (LCA) was used to identify drug use latent classes. Multinomial logistic regression analysis was used to evaluate the association between drug use latent class and HCV infection.

Four distinct latent classes of drug use were identified: class 1, persons ≥ 42 years of age who used only crack cocaine; class 2, persons approximately 42 years of age who used > 2 drugs; class 3, persons < 42 years of age who used > 5 drugs; and class 4, persons ≥ 42 years of age who used > 6 drugs. Class 4 was significantly associated with HCV infection. The odds of HCV infection in members of class 4 was 17 times higher than in class 2 members (adjusted odds ratio [aOR] = 16.9, 95% confidence interval [CI]: 1.4–205.4) and almost 22 times higher than in class 3 members (aOR = 21.8, 95% CI: 1.5–322.8).

Conclusions

Among MSM with non-injection drug use, the subgroup of individuals who were ≥ 42 years of age and used multiple drugs (including heroin, speedball, methamphetamine, crack cocaine, and marijuana) had a high probability of HCV infection. Public health and education programs, as well as drug treatment and rehabilitation programs, should be developed for this high-risk subgroup of individuals to prevent HCV acquisition and transmission.

Peer Review reports

Compared to the general population, men who have sex with men (MSM) are disproportionately affected by various infectious diseases, including HIV, syphilis, and other sexually transmitted infections (STIs) [ 9 ]. Additionally, there is evidence suggesting that hepatitis C virus (HCV) infections may also affect MSM disproportionately. Reports of an HCV epidemic or outbreaks among MSM have been emerging since 2000 [ 6 , 16 , 41 , 42 , 44 , 45 ]. Most of these studies have focused on MSM who were HIV positive, used injection drugs, or were HIV positive and used injection drugs. However, there have been fewer studies specifically targeting HIV-negative MSM who do not use injection drugs [ 14 ].

Although overall HCV prevalence rates are comparable in HIV-negative MSM and the general United States (U.S.) population [ 4 , 36 ], individuals who use non-injection drugs have a higher rate of HCV infection (2.3% to 35.3%) than the general population (1%) [ 37 ]. Furthermore, non-injection drug use is higher in MSM than in heterosexual men, with past-month prevalence rates of 16.3% versus 9.9% [ 11 ]. These results therefore suggest that HIV-negative MSM with non-injection drug use may have a higher rate of HCV infection than the general population.

People who use drugs are typically heterogeneous with regard to the type of drug, as a number of different drug types are available, and a person may choose multiple drug types at the same or different times. Studying the isolated effects of individual drugs may not fully capture the complexity of using multiple drugs concurrently, which could potentially limit its relevance to real-world scenarios. To simultaneously analyze drug use variables, latent class analysis (LCA) [ 8 , 25 , 30 , 38 , 47 ] is a widely applied and highly effective approach. The authors of one U.S. internet-based MSM sample used LCA to identify a distinct multiple drug use group [ 27 ]. Another study recruited a similar sample of MSM and found that individuals in the “high polydrug” subgroup (identified using LCA) were more likely to report unprotected anal intercourse and STIs [ 48 ]. In a Malaysian internet-based MSM sample, LCA identified an “amphetamine-type stimulant use” latent class, which was associated with a higher likelihood of high-risk sexual behavior, HIV infection, and STIs, compared with a low-risk drug use group [ 26 ].

Furthermore, previous research has highlighted specific factors that may facilitate HCV transmission among individuals who use non-injection drugs, including the sharing of pipes when smoking drugs and having cracked lips [ 21 ]. These findings underscore the need to investigate the potential mechanisms behind HCV transmission in this context.

While there have been some studies on HCV infection among MSM, Fitzpatrick et al. [ 14 ] conducted a study on acute hepatitis C in HIV-uninfected men who have sex with men and do not report injecting drug use. However, their study did not specifically focus on latent class analysis or the identification of latent classes among this population. To address this gap and provide a more comprehensive understanding, we employed LCA to identify latent classes among HIV-negative MSM reporting non-injection drug use in our study and examined the association between these latent classes and HCV infection in this population. Subsequently, we examined the association between these latent classes and HCV infection in this specific group of men. The results of this study may provide important insights for HCV prevention and health education programs targeting HIV-negative MSM who use non-injection drugs.

Study design and participants

Data for this study were collected from the Drugs, AIDS, STDs, and Hepatitis (DASH) project, a community-based intervention study focused on preventing HIV, HBV, and HCV infections [ 23 ]. Participants were untreated drug users recruited from two highly endemic drug-using urban neighborhoods in Houston, Texas, USA, from February 2004 to October 2007. Participants were recruited by outreach workers using a chain referral approach. Eligibility criteria included being 18 years or older, residing locally, self-reported use of illegal or non-medically prescribed drugs (including cocaine or heroin) in the past 48 h, and the presence of drug metabolites confirmed by urinalysis (OnTrak Varian Testik, Palo Alto, CA). Individuals who tested negative for HIV and HBV were enrolled in the baseline study.

Data collection

Enrollment interviews were conducted using verbally administered questionnaires via computer-assisted personal interview (CAPI, QDS, Bethesda, MD). Baseline data were obtained from the enrollment interview. All data collection procedures and laboratory protocols were approved by the Committee for the Protection of Human Subjects at the University of Texas Health Science Center at Houston.

Variables and measurements

Information was collected on the following sociodemographic characteristics age, race/ethnicity, sexual orientation, education level, marital status, working status, income level, living arrangement, previous incarceration for more than 24 h, and drug treatment history.

Sexual behavior variables included number of male sexual partners in the past 30 days, frequency of condom use, trading sex for money or drugs in the past 30 days, and trading money or drugs for sex in the past 30 days.

Disease-related data included self-reported histories of sexually transmitted infections (STIs) such as gonorrhea, herpes, chlamydia, and trichomoniasis, as well as the participants' HIV, HBV, and HCV infection statuses. Screening tests for HIV1/2 antibodies, hepatitis B surface antigen (HBsAg), and antibodies to HCV (anti-HCV) were conducted using the Combo test (Core Combo HIV-HBsAg-HCV, Core Diagnostics, United Kingdom). Confirmatory tests for HIV were performed using the Microparticle Enzyme Immunoassay test (Abbott Laboratories, Chicago, IL) [ 23 ]. HCV infection was defined as a positive HCV antibody test.

For the collection of drug use variables, participants were asked if they had ever used the following drugs: crack cocaine, methamphetamine, marijuana, alcohol, fry (embalming fluid and phencyclidine [PCP]-laced cigarettes or marijuana sticks), powder cocaine, heroin, speedball (mixture of heroin and cocaine), and codeine syrup. Drug use indicators were recorded as “never used” (0) or “have ever used” (1). Age was also considered an indicator variable, as it is associated with the type of drug use [ 5 , 17 ]. Based on the median age of 42 years, participants were categorized as “ < 42 years old” (0) or “ ≥ 42 years old” (1).

Statistical analysis

Latent Class Analysis (LCA) was used to identify subgroups of drug use. LCA models use a maximum likelihood approach to identify subgroups or classes of individuals with similar patterns of responses to a set of indicator variables [ 28 , 46 ]. Drug use variables and age were used as indicator variables. We began with a 1-class model and increased the number of classes up to 6-class models, using 5,000 random starts to obtain global maxima for each model. Model selection was based on the Bayesian Information Criterion (BIC), parametric bootstrap likelihood ratio test (BLRT), and Lo-Mendell-Rubin adjusted likelihood ratio test (LMR). We also used entropy, a measure of classification accuracy, with higher values indicating better classification.

The final latent class solution was based on statistical significance and epidemiological interpretation of drug use patterns. After identifying latent classes, multinomial logistic regression models were conducted to examine associations between class membership and HCV status, sociodemographic characteristics, sexual behaviors, and STI history. We used the AUXILIARY (r) option [ 31 ] for multinomial logistic regression estimation, which incorporates posterior probabilities of membership into the estimation procedure [ 34 ]. Bivariate associations between latent class and each independent variable were analyzed. Variables with a p-value < 0.25 were included in the joint model to evaluate the adjusted relationships between class membership and HCV infection. LCA model building, and logistic regression analysis were conducted using Mplus 6.1 (Muthén & Muthén, CA), and data management was performed using SAS 9.4 (Cary, NC).

In the DASH parental project study, the prevalence of HCV was 36.1% (1011 out of 2800) among 2,800 drug users contacted for HIV/HBV/HCV screening. [ 22 ]. Among 273 MSM who reported non-injection drug use, 40 individuals tested HCV positive, resulting in an HCV prevalence of 14.7%. In these individuals, only age was significantly associated with HCV infection. The odds of HCV infection was 2.1 (95% confidence interval [CI]: 1.4–3.0) times higher in participants ≥ 42 years of age than in those < 42 years old (Supplementary Table 1).

The final analysis included 118 HIV and HBV negative participants who reported male-to-male sex and used only non-injection drugs. Of these, 21 (17.8%) were infected with HCV. Table 1 presents the sociodemographic characteristics and behavioral variables of the analytical sample. The age of these participants ranged from 19 to 61 years (mean: 39.6 years, interquartile range: 35–46 years), 83% were African American, 83% reported sexual orientation as bisexual or homosexual, 76% completed only high school or had less than a high school education, 65% were single, 50% worked < 14 days in the past month, 50% had an income < $400 dollars in the past month, 46% were homeless at least once in the past, 76% had been arrested and spent > 24 h in jail, and 35% never received drug treatment. Regarding sexual risk behaviors, 41% of participants had 0 or 1 male sexual partners in the past month, approximately two-thirds used condoms < 50% of the time while having sex, approximately two-thirds traded sex for money or drugs in the past month, and > 50% traded money for drugs or sex in the past month. Regarding disease history, 45% were previously diagnosed with STI(s). The majority of participants had used multiple drugs (defined as ever using > 2 drugs); the most prevalent drug types were crack cocaine (98%), marijuana (89%), and alcohol (86%). The prevalence rates for other types of drugs were 57% for powder cocaine, 22% for codeine, 21% for fry, 14% for methamphetamine, 6.8% for heroin, and 3.4% for speedball.

Table 2 presents the results of statistics and entropy for LCA models ranging from 1-class to 6-class solutions. While the 2-class model had the lowest Bayesian Information Criterion (BIC), other statistical criteria favored the 3-class and 4-class models. Specifically, the Lo-Mendell-Rubin (LMR) test showed significance for the 2-class, 3-class, 4-class, and 5-class models (P < 0.05), and the Bootstrapped Likelihood Ratio Test (BLRT) supported the use of the 2-class through 4-class models (P < 0.05). Notably, all models from 3-class to 6-class demonstrated satisfactory precision with entropy values exceeding 0.8. Considering statistical significance and practical utility, we opted for the 4-class model as the best-fit model. This decision was substantiated by the comprehensive evaluation of statistical criteria, where the 4-class model exhibited the significant LMR and BLRT values, and an Entropy value closer to 1, collectively affirming its suitability for our analytical framework.

Figure  1 shows the estimated probabilities for each indicator variable in our 4-class model. Participants in class 1 (accounting for 6.5% of the sample) had a high probability (> 95%) of using only crack cocaine, the lowest probability of using all other types of drugs, and 75% probability of being ≥ 42 years of age. We thus referred to class 1 as “persons  ≥  42 years of age who used only crack cocaine”. Class 2 members accounted for 70.3% of the sample and had a high probability (> 90%) of using crack cocaine and marijuana, moderate probability (50%) of using powder cocaine, and 50% probability of being ≥ 42 years of age. We therefore referred to class 2 as “persons approximately 42 years of age who used  >  2 drugs”. Class 3 members accounted for 20.1% of the participants; had a high probability (> 90%) of using crack cocaine, marijuana, and powder cocaine; had the highest probability of using fry and codeine, compared to other groups; and had an only 35% probability of being ≥ 42 years of age. We thus referred to class 3 as “persons  <  42 years of age who used  >  5 drugs”. Individuals in class 4 accounted for 3.2% of the sample; had a high probability of using all types of drugs except fry and codeine, with the probability of using methamphetamine, heroin, and speedball being the highest of all classes; and had a very high probability (> 99%) of being ≥ 42 years of age. We referred to class 4 as “persons  ≥  42 years of age who used  >  6 drugs”.

Estimated probability for each indicator variable in each drug use class in the 4-latent class model (Meth represents methamphetamine). Class 1 (6.5%): persons ≥ 42 years of age who used only crack cocaine. Class 2 (70.3%): persons approximately 42 years of age who used > 2 drugs. Class 3 (20.1%): persons < 42 years of age who used > 5 drugs. Class 4 (3.2%): persons ≥ 42 years of age who used > 6 drugs

Table 3 presents the results of our bivariate multinomial logistic regression analysis. We found that only HCV status was significantly associated with the drug use latent class. Compared with members of the other classes, class 4 members had the highest odds of HCV infection. The odds of HCV infection in class 4 members was 14 times higher (crude odds ratio [cOR] = 14.2, 95% CI: 1.3–157.4) than the odds of having HCV infection among individuals in class 2 and 20 times higher (cOR = 20.5, 95% CI: 1.4–291.7) than the odds of HCV infection among MSM in class 3. The probability of HCV was also higher in class 4 members than in class 1 members, although the difference was not statistically significant (cOR = 7.8, 95% CI: 0.5–134.7). Associations between drug use classes and other variables, such as sociodemographic characteristics, sexual behaviors, self-reported STI history, blood transfusion history, and occupational blood exposure history, were not statistically significant.

Table 4 presents the results of our multivariable regression model. We entered drug treatment history, self-reported STI history, and trading money or drugs for sex in the past month (variables with p-values < 0.25 in bivariate analysis) into the model to adjust for these factors when examining the association between drug use class and HCV infection. The results showed that HCV infection was significantly associated with drug use class. The odds of HCV infection in class 4 members was almost 17 times higher than in class 2 members (adjusted OR = 16.9, 95% CI: 1.4–205.4) and almost 22 times higher than in class 3 members (adjusted OR = 21.8, 95% CI: 1.5–322.8), when controlling for drug treatment history, self-reported STI history, and trading money or drugs for sex in the past month.

In this study, we applied LCA to identify latent classes among HIV-negative MSM who used non-injection drugs. We found four distinct latent classes: class 1, persons ≥ 42 years of age who used only crack cocaine; class 2, persons approximately 42 years of age who used > 2 drugs; class 3, persons < 42 years of age who used > 5 drugs; and class 4, persons ≥ 42 years of age who used > 6 drugs. We also found associations between certain latent classes of drug use and HCV infection. After adjusting for drug treatment history, self-reported STI history, and the behavior of trading money or drugs for sex in the past month, we found that persons ≥ 42 years of age who used > 6 drugs had an almost 17 times higher odds of HCV infection, compared with persons approximately 42 years old who used > 2 drugs, and an almost 22 times higher odds of HCV infection, compared with persons < 42 years of age who used > 5 drugs.

Among participants aged 42 years or older, drug use latent class membership was polarized. Members in one class (class 1) used only crack cocaine, whereas members in the other class (class 4) used multiple drugs, including crack cocaine, marijuana, powder cocaine, methamphetamine, heroin, and speedball. Individuals in class 4 had a higher probability of HCV infection than those in class 1, although the difference did not reach statistical significance. The lack of significance may have resulted from the relatively small number of individuals in these classes: they accounted for only 3.2% and 6.5% of the sample, respectively, thus limiting the statistical power of the study to detect differences between these two classes.

The mean age of class 3 members was slightly lower than that of class 2 members. Class 3 members had a higher probability of using fry and codeine than class 2 members, which is consistent with the results of previous reports of fry [ 29 , 33 ] and codeine abuse [ 13 , 32 ] in the 1990s, especially among teenagers. Nevertheless, in the current study, using fry and codeine in addition to crack cocaine and marijuana did not increase the likely of HCV infection, compared with using crack cocaine and marijuana alone. One explanation for this finding may relate to their modes of use. Fry is generally smoked, and codeine is usually consumed orally in the form of syrup, pills, or drinks (mixed with soda); both routes of administration have a low likelihood of blood exposure. Although studies have reported increased high-risk sexual behavior among fry or codeine drug users [ 32 ], these studies were not restricted to MSM. In the present study, which involved only MSM, use of fry or codeine was not associated with sexual risk behaviors.

By comparing latent classes of drug use with different ages, we found an interaction between age and drug use types on the probability of HCV infection. This indicates that both age and drug use types were associated with HCV infection, and that differences in age were linked to different preferences for type of drug use. Participants who used multiple types of drugs (heroin, speedball, and methamphetamine, in addition to the commonly used crack cocaine and marijuana) were all ≥ 42 years of age and formed a latent class with a much higher HCV infection probability than that of other latent classes. Some studies have reported that people born between 1945 and 1965 have a higher HCV infection rate than other individuals, suggesting that age alone contributes to the higher probability of HCV infection in people 42 years or older. However, other studies have indicated that use of heroin, speedball, and methamphetamine may increase the risk of HCV infection for several reasons. First, repeated intranasal use of heroin, cocaine (speedball is heroin mixed with cocaine), and methamphetamine may cause mucosal trauma and hyperemia [ 2 , 3 , 35 , 40 ], and HCV has been detected in the nasal secretions [ 1 ] of people with HCV infection. Second, drug use paraphernalia are often shared among people who use drugs, and HCV RNA may remain in the paraphernalia for up to 16 h [ 24 ]. Third, people who use heroin, speedball, and methamphetamine may be exposed to social networks with a higher HCV infection rate than those who use other drugs because a proportion of people who use heroin, speedball, and methamphetamine inject these drugs, and 40%-90% of people who use injection drugs are infected with HCV [ 15 , 19 ]. Nevertheless, some studies have found no increased risk of HCV infection in people who share straws or dollar bills when snorting drugs but do not use injection drugs [ 18 , 20 ]. More research is required to determine whether sharing equipment for non-injection drug use is a transmission route for HCV.

We cannot directly compare the present LCA findings with the results of previous studies using LCA because of differences in recruitment strategies, indicator variables, and disease of interest between studies. However, our findings are consistent with the results of previous studies demonstrating high rates of infectious diseases, including HIV, among multiple drug users [ 7 , 10 , 12 , 43 ]. Previous LCA studies in MSM have also demonstrated that multiple drug use is associated with increased transmission of STIs by promoting disinhibition and subsequent high-risk sexual behavior [ 26 , 48 ],however, we found no association between multiple drug use and STIs in the current study. One reason for this lack of association may be that individuals with HIV and/or HBV infection were excluded from the baseline data collection in the DASH project. This may have resulted in the exclusion of individuals also coinfected with STIs and thus led to an underestimation of the effects of multiple drug use on STIs and high-risk sexual behavior in our sample of MSM.

This study had several limitations. First, the limited sample size within certain drug use classes raises concerns about the precision of our results. The small number of HCV-infected individuals, particularly within latent classes, necessitates extreme caution in interpretating the results. Wide confidence intervals further underscore variability. Future studies should include larger sample sizes within specific non-injection drug use groups. Second, there is potential for misclassification of drug use behaviors, especially past injection drug use, which may not be accurately recalled or reported. This could affect the association between non-injection drug use and HCV infection. Third, the unspecified timeframe for reporting non-injection drug use may lead to variability in reports, complicating the distinction between lifetime and recent behaviors. Fourth, self-report and injection mark identification used to confirm injection drug use are imperfect. Some individuals hide injection marks, and those who stopped injecting long ago might no longer display them. Participants concealing their injection drug use could lead us to overestimate the risk of HCV infection in the target population. Fifth, information on drug use routes and equipment sharing was not collected, which could provide crucial insights into HCV transmission [ 39 ]. Sixth, drug use types and sexual risk behaviors were self-reported, potentially leading to underreporting despite lab verification of drug use. Seventh, the study's age categories were based on a median age of 42 years, potentially introducing bias and it may not fully capture the complexity of age-related factors. Future research should use predefined age categories. Eighth, the cross-sectional design does not permit conclusions about the temporality of risk behaviors and HCV infection. Ninth, data from 2004 to 2007 do not account for newer drugs like MDMA and LSD. An updated study is needed for more current and comprehensive evidence. Lastly, the study did not collect information on drug administration routes, equipment sharing, or specific sexual behaviors related to HCV transmission. Future studies should include these aspects, and a longitudinal design could clarify the temporality of risk behaviors and HCV infection.

Despite these limitations, this study has several strengths. To our knowledge, this is the first study to evaluate the association between latent class of non-injection drug use and HCV infection among HIV-negative MSM using LCA. LCA provided a valuable tool for categorizing participants based on their age and patterns of drug usage, allowing us to uncover nuanced association between these factors and HCV infection. By reducing the dimension of drug use types, LCA enabled us to explore the interaction between age and multiple drug use types on the likelihood of HCV infection. This approach, which has been relatively underutilized in previous studies, allowed us to gain deeper insights into the complex relationship between drug use behavior and HCV infection risk within this specific population. In addition, we excluded individuals with HIV and/or HBV infection from this study. Although it led to a smaller sample size, it allowed us to demonstrate that even in the absence of HIV and HBV infection, the interaction of age and multiple drug use types was associated with HCV infection.

Additionally, it is crucial to emphasize the broader implications of our findings in the context of HCV infection among MSM populations. Hepatitis C virus (HCV) infection represents a significant public health concern, and our study sheds light on a specific at-risk subgroup within the MSM community. The identification of latent classes of drug use and their association with HCV infection provides valuable insights for targeted intervention strategies. Understanding the intersecting factors of age and drug use in driving HCV transmission is not only relevant for this specific study population but also contributes to the broader understanding of infectious disease dynamics among marginalized communities. This knowledge can inform public health efforts aimed at reducing the prevalence of HCV and improving the overall health and well-being of MSM individuals.

In conclusion, our study reveals essential insights into drug use patterns among MSM, identifying four distinct latent classes and emphasizing the heightened risk of HCV infection among individuals aged 42 years or older who use multiple drugs. Tailored interventions, including health education, promotion, and drug treatment programs, are imperative for this specific subgroup to raise awareness, increase testing, and reduce the transmission of HCV. Our research contributes to the understanding of the intricate interplay between age, drug use, and infectious diseases within the MSM community, providing a foundation for targeted public health strategies. Future research should further investigate transmission mechanisms and social networks. This study underscores the urgency of addressing HCV infection within the MSM population and offers valuable insights for effective public health interventions.

Availability of data and materials

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

Abbreviations

• Hepatitis C virus
• Men who have sex with men
• Latent class analysis

Sexually transmitted infection

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Acknowledgements

We thank the participants who volunteered biological specimens and data, the DASH staff who collected the data, and Dr. Dimpy Shah, who managed the data. I would like to thank my previous affiliations, UTHealth and Baylor College of Medicine, for their support and resources during the research for this article. I especially appreciate the guidance and assistance of the faculty and staff at these institutions.

This study was funded by The National Institute on Drug Abuse (NIDA# 1R01DA017505). This work was funded (in part) by a Research Training Award for Cancer Prevention Post-Graduate Training Program in Integrative Epidemiology from the Cancer Prevention & Research Institute of Texas, grant number RP160097 (PI: M. Spitz) and the Systems Epidemiology of Cancer Training (SECT) Program (RP210037; PI: A. Thrift).

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JZ analyzed the data and wrote the manuscript, with support from CM and AGN. CG and KF verified the analytical methods. LYH helped supervise the project. All authors read and approved the final manuscript.

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Zhao, J., Green, C., Markham, C. et al. Association between non-injection drug use and hepatitis C infection among HIV-negative men who have sex with men. BMC Infect Dis 24 , 985 (2024). https://doi.org/10.1186/s12879-024-09685-3

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The death was confirmed by his daughter Carol Clements.

In 1949, Dr. Clements was fresh out of Cornell University Medical College (now Weill Cornell Medical College) and working for the Army as a physiologist when he became intrigued by the miraculous mechanics of human breathing.

How could the millions of tiny air sacs in the lungs deflate when a person breathes out, but not collapse like a balloon? Dr. Clements theorized that there must be some chemical relaxing the surface tension of the air sacs, and he went on to identify the substance as a surfactant, a class of lubricants that work like household detergents.

In a 1956 paper, based on research done with a crude instrument he built himself, Dr. Clements demonstrated the presence of a surfactant in the lungs.

His work led to a breakthrough three years later by two Harvard researchers whom Dr. Clements advised: Pulmonary surfactant, they found , was absent in premature babies with undeveloped lungs who died of respiratory distress syndrome, or R.D.S.

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Use of Antidepressants among Patients Diagnosed with Depression: A Scoping Review

Nirmal raj marasine.

1 Western Health Science Academy, Pokhara, Kaski, Nepal

2 Pharmaceutical Sciences Program, School of Health and Allied Sciences, Pokhara University, Kaski, Nepal

Sabina Sankhi

Rajendra lamichhane.

3 Department of Public Health, Asian College for Advance Studies, Lalitpur, Nepal

Nabin Raj Marasini

4 Department of Public Health, La Grande International College, Kaski, Nepal

Nim Bahadur Dangi

Associated data.

The raw data used to support the findings of this study are made available from the corresponding author upon reasonable request.

Introduction

Depression is a major global health problem with a relatively high lifetime prevalence and significant disability. Antidepressants are the most effective medications used for the treatment of depression. Hence, this study is aimed at summarizing the studies on antidepressant use among patients diagnosed with depression.

PubMed, Embase, Web of Science, Scopus, and Google Scholar were searched for literature (2000-2019) using keywords such as depression, drug utilization, antidepressants, prescription, serotonin reuptake inhibitor, serotonin and norepinephrine reuptake inhibitor, tricyclic antidepressants, and atypical antidepressants.

Antidepressant users were mostly females, married people, housewives, lower-income people, employees, and highly educated people, as they were found to be more prone to develop depression than their counterparts. Selective serotonin reuptake inhibitors (SSRIs), such as sertraline, were most commonly prescribed among depressive patients.

Our study suggested that out of five major antidepressant drugs available for the treatment of depression, selective serotonin reuptake inhibitors are preferred over others because of their better side effects and tolerability profile.

1. Introduction

Depression is a common mental disorder and a major cause of functional disability [ 1 , 2 ]. According to the World Health Organization (WHO), by 2020, it will be the second-highest known cause of worldwide disability [ 3 , 4 ]. Depression is characterized by a sad mood, pessimistic thought, lowered interest in day-to-day activities, poor concentration, insomnia or increased sleep, significant weight loss or gain, decreased energy, continuous feelings of guilt and worthlessness, decreased libido, and suicidal thoughts occurring for at least two weeks [ 5 , 6 ]. Depressed patients can be of any gender, age, or background. Due to fear of stigmatization associated with mental disorders, patients lack to seek medical treatment in their early stages [ 7 – 9 ]. To maintain normal human health in patients, drugs play a crucial role. Antidepressant drugs are the most widely used and are most effective in the treatment of depression [ 10 , 11 ]. For many years, tricyclic antidepressants (TCAs) have been the drug of choice for treating depression in patients [ 12 – 14 ]. Many new antidepressants with better tolerance and broader indications have been discovered because of an increase in the prevalence of depression throughout the world [ 15 ]. This results in the gradual replacement of conventional drugs such as TCAs and monoamine oxidase inhibitors (MAOIs) by selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and atypical antidepressants [ 7 , 16 , 17 ]. The most appropriate antidepressants should be selected according to symptoms and patient characteristics, with adequate dose and duration of therapy, to enhance the treatment success rate [ 18 , 19 ]. However, other factors, such as adverse effect profiles, cost, safety profile, history of prior medication treatment, and patient preference, are important in the initial selection of antidepressants and should be considered by physicians [ 20 , 21 ]. The optimal use of antidepressants could reduce individual distress, along with the social burden of depression [ 22 ]. The aim of the analysis of prescribing patterns is to evaluate the prescription habits of medical practitioners and to suggest necessary modifications if required to make drug therapy rational and cost-effective [ 6 , 16 ]. The use of antidepressants has increased within the last two decades [ 23 , 24 ]. As a result of the increased prescription and use of antidepressants, the need for information regarding the actual prescribing practices has become vital to maintain patient safety as well as to ensure that the optimal therapeutic outcome is achieved, especially when the nature of the side effects of these drugs is considered. Hence, this study is aimed at summarizing the studies on antidepressant use among patients diagnosed with depression.

2.1. Search Strategy

Databases such as PubMed, Embase, Web of Science, Scopus, and Google Scholar were searched for studies published between January 2000 and December 2019. The main Medical Subject Heading (MeSH) terms used for literature search within the databases were depression, drug utilization, antidepressants, and prescription. Other keywords used were serotonin reuptake inhibitor, serotonin and norepinephrine reuptake inhibitor, tricyclic antidepressants, and atypical antidepressants. For the second round of search, the keyword “antidepressant” was combined with “drug utilization”, “prescription”, “depression”, “depressive patients”, “systematic reviews”, and “narrative reviews”. The bibliographies of relevant articles were also searched for more studies that were not identified in the original database search. All the abstracts and studies were screened for their relevance and discarded those that did not fit our selection criteria. Only those studies that were identified as potentially relevant to our study title were retrieved and fully reviewed.

2.2. Study Selection

2.2.1. inclusion criteria.

Inclusion criteria are the following: (1) literatures of varying methodologies such as observational, cross-sectional, and retrospective studies, survey, and case reports, (2) studies conducted on all patients aged ≥18 years with a primary diagnosis of depression and prescription of at least one antidepressant drug, (3) studies that mainly focused on antidepressant utilization or antidepressant prescription pattern among patients with depression, and (4) full-text articles, published in peer-reviewed journals, in years lying between 2000 and 2019 and available in English language.

2.2.2. Exclusion Criteria

Exclusion criteria are the following: (1) reviews, clinical trials, descriptive studies, pilot studies, editorials, case series, conference abstracts, letters, commentaries, posters, qualitative interviews, and book chapters; (2) studies conducted on patients younger than 18 years with no depression and not prescribed with antidepressants; pregnant or lactating mothers; those with a history of psychotic, bipolar disorder, or drug abuse; and those with cognitive impairment; (3) studies that did not focus on antidepressant utilization or antidepressant prescription pattern among patients with depression; and (4) studies published in non-peer-reviewed journals before 2000 and after 2019 and available in languages other than English.

2.2.3. Study Outcomes

Our study outcomes were the demographic factors and types of antidepressants used among patients with depression.

The literature search process is summarized in Figure 1 . A total of 51 studies were included in our review; only 13 original articles were reviewed. The studies were conducted in India [ 25 ], Bangladesh [ 26 ], Malaysia [ 27 ], Nigeria [ 28 ], Singapore [ 29 ], China [ 30 ], Italy [ 31 ], Saudi Arabia [ 32 ], USA [ 33 ], Australia [ 34 ], Germany [ 35 ], Canada [ 36 ], and Netherlands [ 37 ]. The summarized main findings of the studies are presented as follows ( Table 1 ).

Data screening and extraction.

Study, sample, methods, and major findings of the studies.

4. Discussion

We identified thirteen studies through this review, and the majority of the patients were in the economically productive age group of 40-50 years [ 25 – 30 , 32 – 37 ]. Conversely, the findings of a study from Italy showed that the mean age of the patients receiving antidepressant prescriptions was more than 50 years [ 31 ]. Our review showed that the majority of the patients receiving antidepressants for the treatment of their depression were females [ 26 , 28 – 41 ]. This could be due to hormones that are associated with the regulation of the menstruation cycle and pregnancy affecting mood in females. These alterations in hormonal regulation cause dysregulation of the stress response, which makes them more sensitive to depression and often shows magnified neuroendocrine responses to even low levels of stress [ 28 , 38 , 39 , 42 ]. Women play multiple roles in family and society, such as homemakers, spouses, mothers, professionals, and caregivers. These multiple responsibilities may be the source of increased stress that might have led to depression in them [ 43 , 44 ]. In many societies, until today women are not given equal respect, they are considered less powerful with low status, they cannot make a choice, and they are sexually abused, which all results in the development of depression in them [ 5 , 42 ]. In contrast, a study from our neighboring country, India, depicted more depressive males than females, which could be due to more stress at work, a monotonous lifestyle with no entertainment, low income, and economic burden of family [ 25 ]. Evidence suggests that depression is associated with various psychological factors, such as loneliness, lack of family care and affection, poor family support, insufficient time with children, high use of emotional coping, low level of spirituality, stressful incidents, poor health, and dependency [ 25 , 38 ]. Sedentary lifestyle, lack of physical exercise, lack of hobby, irregular dietary habits, smoking, and taking alcoholic beverages or substance abuse are also interconnected with depression [ 25 , 30 , 35 ]. Continuous arguments, stressful daily routines, unsupportive spouses, continuous discouragement, lack of family time or husbands or wives going to other countries for employment, and ignorance from family members may be the reason for more married, housewives, and lower-income people being vulnerable to depression [ 25 , 26 , 28 , 29 , 31 , 35 , 36 , 45 ]. One study showed that a spouse's weekly working hours are greatly associated with the partner's risk of developing depression and suicidal thoughts [ 46 ]. This means that long working hours not only affect individuals' own mental health but also affect their spouses [ 46 ]. Unsatisfactory job, lower income, high level of physical activity, time pressure, lack of encouragement, promotion, and job security are associated with lowering self-esteem and hence could be the reason for taking antidepressants by a high number of employees involved in paid works [ 25 , 29 , 35 ]. Similarly, being concerned about more profit or suffering a continuous loss in business may also lead to depression in people involved in self-employed business [ 28 ]. Our review showed that education is another source of depression in many people. People with a higher education background becomes the victim of depression when they do not get a job equivalent to their qualification. On the other hand, in the job they got involved, they have to work as instructed with unsatisfactory payment and no opportunity to implement their knowledge and skills due to which they feel lack of challenges in their work along with lack of intellectual growth in them [ 25 , 29 , 31 , 36 ]. However, other studies have displayed less educated people as victims of depression [ 26 , 35 ]. These people work as machine operators, laborers, farmers, and unskilled manual workers, where there is more physical and psychological-related stress along with less respect from other employees.

The prescribing pattern of antidepressants for patients with depression varies across different countries. This could be due to differences in availability and antidepressant prices as well as variations in recommendations in each country's national guidelines [ 28 , 36 ]. Medical treatment of depression not only improves the mental health of patients but also increases their physical and social performance, making them optimistic and encouraged towards life [ 32 ]. Our review revealed that SSRIs are the dominant antidepressants prescribed over TCAs, SNRIs, and other atypical antidepressants for the treatment of depression [ 25 – 27 , 29 – 37 ]. The preference of psychiatrists for SSRI prescription over other antidepressants could be because of the advantages they offer to the patients. Antidepressants other than SSRIs nonselectively inhibit the reuptake of norepinephrine, dopamine, and serotonin into presynaptic vesicles and affect adrenergic, cholinergic, postsynaptic serotonin, and histaminic receptors in the brain, which are unrelated to depression, leading to intolerable adverse effects [ 47 ]. SSRIs do not cause life-threatening adverse effects, such as overdose-related cardiotoxicity and CNS toxicity, as they do not show receptor antagonism [ 48 ]. Additionally, they can be administered once daily, require less dose titration than TCAs, are safer, and show fewer side effects compared to other antidepressants [ 26 , 31 , 33 – 37 , 49 ]. Hence, it could be safer and effective for many patients. In contrast, a study showed TCAs as the most commonly prescribed antidepressants despite SSRIs being more advantageous [ 28 ]. This could be due to the affordability and easy availability of TCAs over SSRIs. In developing countries, the affordability of drugs plays an important role in the continuation of treatment because many low-income families cannot afford expensive medicines. The majority of the population has to rely on government insurance policies to obtain drugs for their treatment. Many people buy their prescribed antidepressants from the government hospital as they are available at cheaper prices than in retail pharmacies. Such regional differences along with cultural differences and country economy also create huge differences in the prescription of antidepressants [ 50 , 51 ]. Our review showed that sertraline was the most frequently prescribed SSRI, followed by others such as escitalopram, fluoxetine, paroxetine, and fluvoxamine. Amitriptyline was commonly prescribed among TCAs, venlafaxine and duloxetine among SNRIs, and mirtazapine and bupropion among atypical antidepressants.

4.1. Limitation and Strength of the Study

There are certain limitations to our study. Literature published in languages other than English was excluded, which might be associated with language bias. The data used were observational, cross-sectional, retrospective, survey, and case reports only. This does not provide direct insight into the changing trends of prescribing behaviors of physicians over time in patients and may reflect a bias. Likewise, based on clinical setting and physician variables, prescription pattern varies. This study does not provide information on such variables and clinical appropriateness of antidepressants used. However, systematic search strategy and review of types of studies included which are of about two decades are the strength of this study. Additionally, a number of characteristics associated with antidepressant prescription such as age, gender, education, marital state, socioeconomic status, and all other sociodemographic factors are identified. Hence, the findings of this study are expected to have a good impact on the education of psychopharmacology.

5. Conclusion

Our study revealed that the majority of antidepressant users were aged between 40 and 50 years, females, married, housewives, lower income, and highly educated people. SSRIs were found to be highly prescribed over TCAs, SNRIs, MAOIs, and atypical antidepressants. Among the prescribed SSRIs, sertraline was the dominant SSRI. The result of this study suggests the further need for high-quality studies, which may consider the use of data sources like clinical files and patient self-reports, and also includes reports on whether antidepressants were prescribed to treat physical or mental symptoms.

Acknowledgments

We acknowledge all the authors of the retrieved original articles and surveys.

Data Availability

We would like to pledge that the aforementioned manuscript has been published as a preprint with doi: https://10.21203/rs.3.rs-65197/v1 .

Conflicts of Interest

The authors declare that they have no competing interests.

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• 19 September 2024

Obesity-drug pioneers win prestigious Lasker Award for medical science

• Mariana Lenharo

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Joel Habener (from left), Svetlana Mojsov and Lotte Bjerre Knudsen have won a 2024 Lasker Award for developing a class of drugs that treats obesity, diabetes and more. Credits: Joel Habener, Lori Chertoff for The Rockefeller University, Soren Svendsen

Three scientists involved in developing the blockbuster anti-obesity drugs that are currently changing the health-care landscape are among the winners of this year’s prestigious Lasker Awards. The prizes, which honour important advances in medical research, are often considered an indicator of whether a specific advance or scientist will win a Nobel Prize — and some are speculating that this could soon be the case for the weight-loss treatments.

The ‘breakthrough’ obesity drugs that have stunned researchers

Joel Habener, Svetlana Mojsov and Lotte Bjerre Knudsen each contributed to the creation of the popular anti-obesity drugs, which mimic a hormone called glucagon-like peptide 1 (GLP-1), involved in lowering blood-sugar levels and controlling appetite. The trio, recognized with a Lasker in the clinical-research category, will share a US$250,000 prize.

Biomedical scientists are enthusiastic about the increasing recognition of GLP-1 research, which was initially aimed at treating diabetes. “I’ve been working on this for 30 years, and for a long time nobody cared,” says Randy Seeley, an obesity specialist at the University of Michigan in Ann Arbor. “Over the last several years, the situation has changed so much. We now have therapies that are actually helping people.”

Other recipients of this year’s Lasker Awards include Zhijian ‘James’ Chen at UT Southwestern Medical Center in Dallas, Texas, who was honoured in the basic-research category for discovering how DNA triggers immune and inflammatory responses. In the public-service category, Salim Abdool Karim and Quarraisha Abdool Karim, both at the Centre for AIDS Programme of Research in South Africa, in Durban, were recognized for developing life-saving approaches to prevent and treat HIV infections.

Inside the science

Habener, an endocrinologist at Massachusetts General Hospital in Boston, was a leader in discovering the GLP-1 hormone in the 1980s. He was interested in understanding the hormones involved in type 2 diabetes, a condition characterized by high blood-sugar levels, in which the body either doesn’t produce enough insulin or has trouble using it to absorb sugar from the blood.

Habener zeroed in on glucagon, a hormone that increases blood-sugar levels. After cloning the gene for glucagon, he discovered that the gene also encoded a related hormone — later named GLP-1 — that stimulates the pancreas to produce insulin 1 .

Obesity drugs have another superpower: taming inflammation

“This was interesting because, rather than having to give injections of insulin to people with diabetes to control blood sugar, giving GLP-1 would theoretically prompt the body to make its own insulin,” Habener says.

Around that time, Mojsov, a biochemist who directed a facility producing synthetic proteins at Massachusetts General Hospital, identified the sequence of amino acids making up the biologically active form of GLP-1. Eventually, she would demonstrate that this active form could stimulate insulin release from a rat pancreas 2 — a necessary step on the path to a human treatment.

Now at Rockefeller University in New York City, Mojsov spoke out last year about the lack of recognition for her contribution to the field. Since then, she has received awards such as the VinFuture Prize . “I’m happy that I’m getting awards, but what makes me even happier is that people are actually reading my work,” she says.

Ozempic keeps wowing: trial data show benefits for kidney disease

After the initial discoveries about GLP-1, researchers realized that there was a significant obstacle to its therapeutic use: the hormone was rapidly metabolized, lasting only a few minutes in the blood. That’s where the work of Knudsen, a scientist at pharmaceutical firm Novo Nordisk, in Copenhagen, came in. She and her team realized that regular GLP-1 was not going to work as a medicine, Knudsen says. Instead, the researchers came up with a way to modify GLP-1 by attaching a fatty acid to it — an alteration that allowed the molecule to remain active in the body for an extended period before degrading 3 .

The work resulted in liraglutide, the first long-lasting GLP-1-based drug, approved by the US Food and Drug Administration in 2010 for type 2 diabetes. In the meantime, researchers were already exploring the drugs’ weight-loss potential, and in 2014, liraglutide became the first molecule in its class to be approved for treating obesity. Today, newer variants, including semaglutide and tirzepatide, sold as Wegovy and Zepbound, are important obesity treatments.

“I really hope to inspire young people so that they can see that you can do great science also in the pharmaceutical industry,” Knudsen says.

Nobel ahead?

GLP-1-based drugs don’t just treat obesity and diabetes. Studies have shown they can help with cardiovascular disease , sleep apnea and kidney disease , among other conditions. These benefits are thought to arise from the drugs’ effects on the brain, as well as their anti-inflammatory potential .

Meet the unsung scientists behind the Nobel for quantum dots

Owing to the shake-up these drugs are causing in health care, some think they might soon win science’s top prize — the Nobel. Winning a Lasker often precedes winning a Nobel prize: since 1945, 95 Lasker laureates have also received that top honour. “This raises the spectre that the Nobel committee will take [GLP-1 research] seriously,” Seeley says. The Nobel prizes will be announced next month.

Each prize in a science discipline is limited to no more than three winners, and the challenge will be to select the most deserving recipients. Several other scientists involved in the research behind GLP-1-based drugs have been recognized by other awards, including Jens Juul Holst at the University of Copenhagen, Daniel Drucker at the University of Toronto in Canada, and Richard DiMarchi at Indiana University in Bloomington.

“It’s 10,000 ants that move the anthill, and we’re trying to pick out the three ants that made the most difference,” Seeley says. “You could come up with a dozen names of people, at least, who have made seminal contributions to the field.”

doi: https://doi.org/10.1038/d41586-024-03078-x

Kieffer, T. J. & Habener J. F. Endocr. Rev. 20 , 876–913 (1999).

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Mojsov, S., Weir, G. C. & Habener, J. F. J. Clin. Invest. 79 , 616–619 (1987).

Knudsen, L. B. & Lau, J. Front. Endocrinol. 10 , 155 (2019).

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