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Pathophysiology of atherosclerosis.

1. Introduction

2. endothelium, 3. atherosclerosis initiation and fatty streak formation, 3.1. endothelial dysfunction in atherosclerosis development, 3.1.1. hemodynamic forces and endothelial dysfunction, 3.1.2. the role of nitric oxide in endothelial dysfunction, 3.2. ldl infiltration, ldl modifications in the intima, 3.3. endothelial activation, 3.4. monocyte recruitment and foam cell formation, 3.5. contribution of vsmcs to foam cell population, 4. fibrous plaque development, 4.1. fibrous cap, 4.2. necrotic core, 4.3. plaque calcification, 5. plaque stability and rupture, 5.1. vulnerable plaque, 5.2. plaque rupture and thrombus formation, 5.3. clinical complications, 6. inflammation in atherosclerosis, 7. post-transcriptional regulation of atherosclerotic plaques, 7.1. mirnas, 7.1.1. mirnas in atherosclerotic plaque initiation and progression, 7.1.2. mirnas and atherosclerotic plaque rupture, 7.1.3. distinct mirna regulation of vsmc and ec function, 7.2. lncrnas, 8. microbiota, 9. sex as an important risk factor in atherosclerosis, 9.1. impact of plaque size and morphology between sexes, 9.2. clinical implications, 10. cigarette-smoking-induced atherosclerosis, molecular mechanisms underlying clinical smoking-induced atherosclerosis, 11. conclusions, author contributions, institutional review board statement, informed consent statement, conflicts of interest.

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Jebari-Benslaiman, S.; Galicia-García, U.; Larrea-Sebal, A.; Olaetxea, J.R.; Alloza, I.; Vandenbroeck, K.; Benito-Vicente, A.; Martín, C. Pathophysiology of Atherosclerosis. Int. J. Mol. Sci. 2022 , 23 , 3346. https://doi.org/10.3390/ijms23063346

Jebari-Benslaiman S, Galicia-García U, Larrea-Sebal A, Olaetxea JR, Alloza I, Vandenbroeck K, Benito-Vicente A, Martín C. Pathophysiology of Atherosclerosis. International Journal of Molecular Sciences . 2022; 23(6):3346. https://doi.org/10.3390/ijms23063346

Jebari-Benslaiman, Shifa, Unai Galicia-García, Asier Larrea-Sebal, Javier Rekondo Olaetxea, Iraide Alloza, Koen Vandenbroeck, Asier Benito-Vicente, and César Martín. 2022. "Pathophysiology of Atherosclerosis" International Journal of Molecular Sciences 23, no. 6: 3346. https://doi.org/10.3390/ijms23063346

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CASE REPORT article

Case report: coronary atherosclerosis in a patient with long-standing very low ldl-c without lipid-lowering therapy.

• 1 Department of Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, United States
• 2 Division of Cardiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States
• 3 Department of Radiology & Biomedical Imaging, Section of Vascular & Interventional Radiology, Yale School of Medicine, New Haven, CT, United States

Background: ApoB-containing lipoproteins including low-density lipoprotein cholesterol (LDL-C) are necessary for the development of atherosclerosis, and lifelong exposure to low serum levels of LDL-C have been associated with a substantial reduction of cardiovascular risk. Although plaque regression has been observed in patients with serum LDL-C less than 70–80 mg/dl on lipid-lowering therapy, an LDL-C level under which atherosclerosis cannot develop has not been established.

Case presentation: In this case we describe a 60-year-old man with well-controlled diabetes mellitus and hypertension who presented to the hospital after an acute stroke likely due to an atrial myxoma discovered on imaging. A coronary computed tomography angiography scan performed in preparation for the planned surgical myxoma resection revealed an anomalous origin of the right coronary artery as well as evidence of nonobstructive coronary atherosclerosis in the right coronary and non-anomalous left coronary system. Despite not having ever been on any lipid-lowering therapy, this patient was found to have low LDL-C levels (<40 mg/dl) during this admission and on routine laboratory data collected over the prior 16 years. His family history strongly suggested heterozygous familial hypobetalipoproteinemia as a possible diagnosis.

Conclusions: This case illustrates that even long-standing, very low levels of LDL-C may be insufficient to completely prevent atherosclerosis and emphasizes the importance of primordial prevention of all cardiovascular risk factors.

Introduction

While the biological mechanisms underlying the pathogenesis of atherosclerosis involve numerous factors, apolipoprotein B (ApoB)-containing lipoproteins inclusive of low-density lipoprotein cholesterol (LDL-C) are necessary for its development ( 1 ). Mendelian randomization studies and prospective studies of individuals with genetic variants leading to naturally low LDL-C levels have demonstrated that lifelong exposure to low LDL-C results in substantial reductions in risk of atherosclerotic cardiovascular disease (ASCVD) even in the presence of other risk factors ( 2 ). Atherosclerosis detected by coronary calcification can also occur in asymptomatic middle-aged adults without other risk factors and at serum LDL-C levels below 100 mg/dl ( 3 ). Reduction in atherogenic lipoproteins early in life is therefore central to primary prevention of ASCVD, though the serum levels of ApoB or LDL-C at which atherosclerosis fails to develop has not been firmly established and likely varies among individuals. It has been hypothesized that lifelong LDL-C of less than 30 mg/dl would be sufficient to completely prevent atherosclerosis ( 4 ), though studies of patients with established ASCVD have demonstrated plaque regression with serum LDL-C less than 70–80 mg/dl on lipid-lowering therapy ( 5 ).

Case description

A 60-year-old man presented to his local hospital with new-onset weakness, ataxia and paresthesia in the left upper extremity and was found to have an acute ischemic stroke based on MRI imaging showing multiple areas of acute cerebral and cerebellar infarction. A transthoracic echocardiogram was performed which showed a 4.3 × 2.0 cm mass in the left atrium ( Supplementary Video S1 ), consistent with an atrial myxoma which was thought to be the cause of his acute stroke. He was subsequently transferred to a tertiary medical center for further work-up and evaluation for cardiothoracic surgery. Prior to surgery for surgical myxoma resection, inpatient cardiology was consulted for preoperative evaluation. Aside from mild residual weakness, ataxia and paresthesia in his left upper extremity, the patient was asymptomatic and had been physically active. On physical exam, he was noted to have mild weakness of his left upper extremity (4+/5 strength in shoulder abduction) and trace dysmetria of the left hand. His cardiac exam was notable for a low-pitched sound heard early in diastole with no associated murmurs. His physical exam was otherwise unremarkable.

The patient reported being diagnosed with hypertension and type 2 diabetes mellitus approximately 7 years prior to admission. He was currently taking amlodipine and losartan and reported his systolic blood pressure was typically less than 130 mmHg at all recent clinic visits. At the time of diabetes diagnosis, his hemoglobin A1c (HbA1c) was 12.3 and he reported his diet contained high amounts of processed foods, saturated fat, and sugar-sweetened beverages. He was prescribed metformin and subsequently stopped drinking sugar-sweetened beverages, made other healthful changes to his diet and started exercising on a regular basis. He subsequently lost 20 pounds and his HbA1c after one year was markedly improved at 5.0. On admission his HbA1c was 6.2 with a body mass index of 27.9 Kg/m 2 . He was also noted to have hepatic steatosis on an outpatient liver ultrasound prior to admission. He denied ever using tobacco or drugs and rarely consumed alcohol.

On admission the patient's standard lipid panel showed a serum LDL-C of 32 mg/dl and a non-HDL-C of 46 mg/dl. The patient had never been on lipid-lowering therapy and reported being previously told he had naturally very low blood cholesterol levels, which was confirmed on outpatient labs over the prior 16 years ( Table 1 ). He recalled being told his mother and maternal grandmother had very low levels of LDL-C. He had no family history of premature ASCVD. Further testing during his hospitalization revealed an ApoB level of 35 mg/dl, directly measured LDL-C of 48 mg/dl, and lipoprotein(a) of 36 nmol/L.

Table 1 . Patient laboratory tests for Serum lipids and HbA1c.

As part of pre-operative planning for cardiac surgery, the patient underwent coronary computed tomography angiography (CCTA) which demonstrated an anomalous right coronary artery (RCA; Figures 1A,B ) at the origin of the left sinus with mild atherosclerotic plaques in the RCA. Mild atherosclerotic plaques were also noted in the left anterior descending artery (LAD) and left circumflex artery (LCx; Figures 1C,D ), with normal LAD and LCx origins. To assess for ischemia due to the anomalous RCA, pharmacologic myocardial perfusion imaging with positron emission tomography was performed which showed a small-sized, mild-intensity, reversible perfusion defect in the apical inferior wall which was thought to be clinically insignificant due to lack of any exertional symptoms.

Figure 1 . Patient's coronary computed tomographic angiography (CCTA). Anomalous origin of the right coronary artery (RCA; A ). Multiplanar reconstructions of the RCA ( B ), the left anterior descending artery (LAD; C ) and the left circumflex artery (LCx; D ).

The patient underwent successful robotic-assisted resection of the left atrial mass (confirmed to be a myxoma on pathology) with no intervention performed to the RCA. He was discharged on post-operative day 4 with rosuvastatin and metoprolol. He was also recommended for consideration of outpatient genetic screening for hypobetalipoproteinemia.

Despite chronic and presumably life-long levels of LDL-C typically less than 40 mg/dl, the patient presented here was found to have mild atherosclerosis in multiple coronary arteries by CCTA. Genetic testing was not performed in this patient, though his family history, long-standing low blood cholesterol levels and hepatic steatosis suggest his low LDL-C is likely due to heterozygous familial hypobetalipoproteinemia, a genetic cause of low LDL-C with a ∼1:1000 prevalence in most populations ( 6 ). Patients with familial hypobetalipoproteinemia typically have LDL-C levels between 20 and 50 mg/dl and though the APOB mutations often associated with this condition lead to substantially lower risk of ASCVD ( 7 ), prevalence of coronary atherosclerosis specifically in this population has not been well described.

Importantly, this case demonstrates that coronary atherosclerosis can occur even with long-standing LDL-C less than 40 mg/dl when other risk factors such as hypertension and diabetes are present and otherwise well controlled. Reports of atherosclerosis in individuals with lifelong very low LDL-C are extremely rare ( 8 ). The patient in this instance did have uncontrolled diabetes mellitus on initial diagnosis with a HbA1c of 12.3 in 2015, and due to lack of regular follow-up it is unclear how long that level of hyperglycemia may have occurred and contributed to plaque development. His serum ApoB was not assessed at that time and his LDL-C and non-HDL-C were higher than on all his other laboratory checks, though still at very low levels. Notably, his diabetes rapidly improved with only lifestyle changes and metformin. At the time of hospitalization, the patient's serum ApoB was concordantly low and his lipoprotein(a) level was also low, and therefore these factors are unlikely to explain development of atherosclerosis. There is little evidence available on whether anomalous coronary artery origin directly contributes to atherosclerosis; it has previously been shown that anomalous right-sided arteries with a retroaortic course may develop atherosclerosis earlier than non-anomalous arteries within the same patient. However, the case patient was also observed to have atherosclerosis in the non-anomalous left coronary system ( 9 ). To our knowledge, this is the first case report showing calcified coronary plaque on CCTA in a patient with long-standing LDL-C less than 40–50 mg/dl while not on lipid-lowering therapy.

This case indicates that even lifelong LDL-C at very low levels, as could theoretically be achieved with early initiation of statins and PCSK9 inhibition with medications or gene editing ( 10 – 12 ), may be insufficient to completely prevent atherosclerosis when other risk factors are present. Therefore, primordial prevention of risk factors, rather than only managing them after they have developed, remains critical to reducing prevalence of atherosclerosis and ASCVD across populations. However, atherosclerosis specifically in patients with familial hypobetalipoproteinemia is not well understood, and there may be unique factors that promote development in this population. Given that our observations were made from a single patient, definitive conclusions about coronary plaque development cannot be made from this report. Further study will be needed to determine what specific factors drive development of atherosclerosis in the setting of low LDL-C, including in patients with familial hypobetalipoproteinemia.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author.

Ethics statement

Additional written informed consent was not required because we had obtained verbal informed consent from the patient to publish this case report prior to submission. Informed consent for publication was documented in the patient's medical record.

Author contributions

GM: Conceptualization, Data curation, Visualization, Writing – original draft, Writing – review & editing. FW: Conceptualization, Data–curation, Writing – review & editing. HM: Conceptualization, Visualization, Writing – review & editing. KF: Conceptualization, Supervision, Visualization, Writing – original draft, Writing – review editing, Funding acquisition.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

HM is a consultant for Inari Medical and receives an educational grant from the same company.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1272944/full#supplementary-material

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Keywords: LDL-C, atherosclerosis, familial hypobetalipoproteinemia (FHBL), primordial prevention of CVD, coronary artery disease

Citation: Mottola G, Welty FK, Mojibian HR and Faridi KF (2023) Case report: Coronary atherosclerosis in a patient with long-standing very low LDL-C without lipid-lowering therapy. Front. Cardiovasc. Med. 10:1272944. doi: 10.3389/fcvm.2023.1272944

Received: 4 August 2023; Accepted: 8 September 2023; Published: 19 September 2023.

Reviewed by:

© 2023 Mottola, Welty, Mojibian and Faridi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Kamil F. Faridi [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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A 52-Year-Old Man With Atherosclerosis

A 52-year-old executive was referred to our clinic for risk factor management after undergoing coronary computed tomography angiography (CTA) as part of an Executive Physical. He has no history of coronary artery disease and exercises regularly without experiencing anginal symptoms.

His family history is notable for a myocardial infarction (MI) in his father at the age of 52 years. He is a lifelong non-smoker. He does not take medications.

His blood pressure was 110/75. His exam was notable for being overweight with a BMI of 27, but was otherwise unremarkable.

His total cholesterol is 206 mg/dL, HDL-C is 46 mg/dL, triglycerides are 178 mg/dL, calculated LDL-C is 124 mg/dL, and non HDL-C is 160 mg/dL. His fasting glucose is 86 mg/dL. His Hgb A1c is 5.6%.

His 10-year risk based on the 2013 ACC/AHA pooled ASCVD risk estimator is 3.7%.

His coronary artery calcium (CAC) score is 120, which places him in the 87th percentile for his age, gender, and ethnicity.

His coronary CTA shows the following in the proximal LAD:

In addition to maximizing therapeutic lifestyle changes (exercise, weight loss), what is the next step in this patient’s management? A. There is nothing more to do since his estimated 10-year risk is low. B. Maximize risk factor modification by starting a low-dose aspirin and statin therapy. C. Pursue stress testing. D. Pursue coronary catheterization. E. B and C. Show Answer The correct answer is: B. Maximize risk factor modification by starting a low-dose aspirin and statin therapy. Atherosclerosis is necessary for nearly all coronary events. The development of atherosclerosis is multifactorial. There is significant heterogeneity in the contribution of common traditional modifiable risk factors including apolipoprotein B (apoB)-containing lipoproteins, smoking, diabetes, hypertension, and sedentary lifestyle to atherogenesis. 1 In participants from the Multi-Ethnic Study of Atherosclerosis (MESA) without atherosclerosis as measured by coronary artery calcium (CAC), even the presence of multiple modifiable risk factors was associated with low event rates (3.1%). In contrast, in those with elevated CAC and no modifiable risk factors, the event rates were significantly higher at nearly 11%. 1 While risk estimators are improving in accuracy, 2,3 the presence of subclinical atherosclerosis (primarily by CAC) has consistently been an additive predictor of coronary events in individuals and further discriminates between those at higher and lower risk for events. 4-6 The case patient is at elevated risk because of the burden of subclinical atherosclerosis and, therefore, answer choice A is incorrect. The recent 2013 ACC/AHA guidelines on cholesterol treatment take a risk-based approach to recommendations for statin therapy. 7 The patient in this case has a low estimated 10-year risk at 3.7%. The current guidelines suggest a risk discussion in this case based on the patient's family history of premature coronary heart disease (CHD). Subclinical atherosclerosis imaging by CAC scanning can help with this discussion. 8 Recently, eight-year follow-up from the Dallas Heart Study showed that among participants with a family history of MI, those without CAC experienced a significantly lower CHD event rate of 1.9% compared to 8.8% in those with any CAC. 9 The case patient has a CAC score >100 that places him above the 75th percentile for his age, gender, and ethnicity. We would recommend moderate- or high-intensity statin therapy during a risk discussion based on an estimated 10-year event rate that exceeds 7.5%. Furthermore, recent data support the use of aspirin in those with CAC >100. Therefore, answer choice B is the correct answer. Subclinical atherosclerosis imaging with CAC scanning has been endorsed by several committees to assist with risk assessment. The 2010 ACC/AHA risk assessment guidelines gave CAC scanning a IIA recommendation in those deemed to be at intermediate risk for CHD events. 11 In the 2010 appropriate use criteria for cardiac CT endorsed by multiple societies, CAC scanning was deemed appropriate among low-risk asymptomatic patients with a family history of premature CHD in addition to those at intermediate risk. 12 Most recently, the 2013 ACC/AHA risk assessment guidelines gave CAC scoring a IIB recommendation. Specifically, the committee suggests that if there is uncertainty about whether to start pharmacotherapy after risk estimation, then CAC scoring could be considered. 2 An important consideration in this case is the use of CTA to identify subclinical atherosclerosis. While coronary CTA is a more specific and sensitive test for atherosclerosis, there is no evidence that CTA adds significantly to CAC scanning in an asymptomatic population. However, CTA may identify vulnerable features of plaque that are not picked up by CAC scanning, such as those present in the case example. 13 Motoyama et al. identified high-risk features for acute coronary syndromes (ACS) in asymptomatic subjects on CTA including positive remodeling and low-attenuation plaque, in addition to spotty calcification in those presenting with ACS. 14,15 A small preliminary study suggested a benefit of statin therapy on plaque volume and the amount of low-attenuation plaque. 16 The role of CTA in asymptomatic, primary prevention patients is actively under investigation. 17 The benefits of CTA should be weighed against exposure to contrast, expense, and the need to train readers. With rapidly advancing technology, CTAs can now be performed with the equivalent radiation exposure of two mammograms. 18 Currently, the use of CTA is useful in appropriate symptomatic patients, particularly in chest pain protocols in the emergency department. 19 Many of these patients have mild or moderate, non-obstructive atherosclerosis that is unlikely to be the cause of their presenting symptoms, but should be managed with aggressive preventive therapies. In symptomatic patients from the CONFIRM registry, there is an increased hazard of mortality in those with non-obstructive atherosclerosis on CTA compared to those without atherosclerosis on CTA. 20 Some have advocated for stress testing in those with CAC scores > 400; however, the patient in this case does not meet this criteria. 21,22 Therefore, answer choices C and D are incorrect as the patient is asymptomatic. Silverman MG, Blaha MJ, Krumholz HM, et al. Impact of coronary artery calcium on coronary heart disease events in individuals at the extremes of traditional risk factor burden: the Multi-Ethnic Study of Atherosclerosis. Eur Heart J. 2013 Dec 13 [Epub ahead of print]. Goff DC, Jr., Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2014;63:2935-59. Muntner P, Colantonio LD, Cushman M, et al. Validation of the atherosclerotic cardiovascular disease Pooled Cohort risk equations. JAMA 2014;311:1406-15. Polonsky TS, McClelland RL, Jorgensen NW, et al. Coronary artery calcium score and risk classification for coronary heart disease prediction. JAMA 2010;303:1610-6. Erbel R, Möhlenkamp S, Moebus S, et al. Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf Recall study. J Am Coll Cardiol 2010;56:1397-406. Yeboah J, McClelland RL, Polonsky TS, et al. Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. JAMA 2012;308:788-95. Stone NJ, Robinson J, Lichtenstein AH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2014;63:2889–934. Nasir K, Budoff MJ, Wong ND, et al. Family history of premature coronary heart disease and coronary artery calcification: Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 2007;116:619-26. Paixao AR, Berry JD, Neeland IJ, et al. Coronary artery calcification and family history of myocardial infarction in the Dallas Heart Study. JACC Cardiovasc Imaging . 2014 June [Epub ahead of print]. Miedema MD, Duprez DA, Misialek JR, et al. Use of coronary artery calcium testing to guide aspirin utilization for primary prevention: estimates from the multi-ethnic study of atherosclerosis. Circ Cardiovasc Qual Outcomes 2014;7:453-60. Greenland P, Alpert JS, Beller GA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2010;56:e50-103. Taylor AJ, Cerqueira M, Hodgson JM, et al. ACCF/SCCT/ACR/AHA/ASE/ASNC/NASCI/SCAI/SCMR 2010 appropriate use criteria for cardiac computed tomography. a report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, the Society of Cardiovascular Computed Tomography, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the American Society of Nuclear Cardiology, the North American Society for Cardiovascular Imaging, the Society for Cardiovascular Angiography and Interventions, and the Society for Cardiovascular Magnetic Resonance. J Am Coll Cardiol 2010;56:1864-94. Voros S, Rinehart S, Qian Z, et al. Coronary atherosclerosis imaging by coronary CT angiography: current status, correlation with intravascular interrogation and meta-analysis. JACC Cardiovasc Imaging 2011;4:537-48. Motoyama S, Sarai M, Harigaya H, et al. Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. J Am Coll Cardiol 2009;54:49-57. Motoyama S, Kondo T, Sarai M et al. Multislice computed tomographic characteristics of coronary lesions in acute coronary syndromes. J Am Coll Cardiol 2007;50:319-26. Inoue K, Motoyama S, Sarai M, et al. Serial coronary CT angiography-verified changes in plaque characteristics as an end point: evaluation of effect of statin intervention. JACC Cardiovasc Imaging 2010;3:691-8. U.S. National Institutes of Health.Detection of Subclinical Atherosclerosis in Asymptomatic Individuals (Decide CTA). (ClinicalTrials.gov website). 2009-2014. Available at: http://clinicaltrials.gov/ct2/show/NCT00862056?term=DECIDE-CTA . Accessed June 22, 2014. Achenbach S, Marwan M, Ropers, D et al. Coronary computed tomography angiography with a consistent dose below 1 mSv using prospectively electrocardiogram-triggered high-pitch spiral acquisition. Eur Heart J 2010;31:340-6. Hoffmann U, Truong QA, Schoenfeld DA, et al. Coronary CT angiography versus standard evaluation in acute chest pain. N Engl J Med 2012;367:299-308. Min JK, Dunning A, Lin FY, et al. Age- and sex-related differences in all-cause mortality risk based on coronary computed tomography angiography findings results from the International Multicenter CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry) of 23,854 patients without known coronary artery disease. J Am Coll Cardiol 2011;58:849-60. Hendel RC, Berman DS, Di Carli MF, et al. ACCF/ASNC/ACR/AHA/ASE/SCCT/SCMR/SNM 2009 appropriate use criteria for cardiac radionuclide imaging: a report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, the American Society of Nuclear Cardiology, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the Society of Cardiovascular Computed Tomography, the Society for Cardiovascular Magnetic Resonance, and the Society of Nuclear Medicine. J Am Coll Cardiol 2009;53:2201-29. Berman DS, Hachamovitch R, Shaw LJ, et al. Roles of nuclear cardiology, cardiac computed tomography, and cardiac magnetic resonance: Noninvasive risk stratification and a conceptual framework for the selection of noninvasive imaging tests in patients with known or suspected coronary artery disease. J Nucl Med 2006;47:1107-18. You must be logged in to save to your library. Jacc journals on acc.org. 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View all journals Explore content About the journal Publish with us Sign up for alerts Open access Published: 14 August 2024 Nonlinear dynamics of multi-omics profiles during human aging Xiaotao Shen   ORCID: orcid.org/0000-0002-9608-9964 1 , 2 , 3   na1 , Chuchu Wang   ORCID: orcid.org/0000-0003-2015-7331 4 , 5   na1 , Xin Zhou   ORCID: orcid.org/0000-0001-8089-4507 1 , 6 , Wenyu Zhou 1 , Daniel Hornburg   ORCID: orcid.org/0000-0002-6618-7774 1 , Si Wu 1 & Michael P. Snyder   ORCID: orcid.org/0000-0003-0784-7987 1 , 6   Nature Aging ( 2024 ) Cite this article 110k Accesses 2637 Altmetric Metrics details Biochemistry Systems biology Aging is a complex process associated with nearly all diseases. Understanding the molecular changes underlying aging and identifying therapeutic targets for aging-related diseases are crucial for increasing healthspan. Although many studies have explored linear changes during aging, the prevalence of aging-related diseases and mortality risk accelerates after specific time points, indicating the importance of studying nonlinear molecular changes. In this study, we performed comprehensive multi-omics profiling on a longitudinal human cohort of 108 participants, aged between 25 years and 75 years. The participants resided in California, United States, and were tracked for a median period of 1.7 years, with a maximum follow-up duration of 6.8 years. The analysis revealed consistent nonlinear patterns in molecular markers of aging, with substantial dysregulation occurring at two major periods occurring at approximately 44 years and 60 years of chronological age. Distinct molecules and functional pathways associated with these periods were also identified, such as immune regulation and carbohydrate metabolism that shifted during the 60-year transition and cardiovascular disease, lipid and alcohol metabolism changes at the 40-year transition. Overall, this research demonstrates that functions and risks of aging-related diseases change nonlinearly across the human lifespan and provides insights into the molecular and biological pathways involved in these changes. Similar content being viewed by others Personal aging markers and ageotypes revealed by deep longitudinal profiling Considerations for reproducible omics in aging research Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention Aging is a complex and multifactorial process of physiological changes strongly associated with various human diseases, including cardiovascular diseases (CVDs), diabetes, neurodegeneration and cancer 1 . The alterations of molecules (including transcripts, proteins, metabolites and cytokines) are critically important to understand the underlying mechanism of aging and discover potential therapeutic targets for aging-related diseases. Recently, the development of high-throughput omics technologies has enabled researchers to study molecular changes at the system level 2 . A growing number of studies have comprehensively explored the molecular changes that occur during aging using omics profiling 3 , 4 , and most focus on linear changes 5 . It is widely recognized that the occurrence of aging-related diseases does not follow a proportional increase with age. Instead, the risk of these diseases accelerates at specific points throughout the human lifespan 6 . For example, in the United States, the prevalence of CVDs (encompassing atherosclerosis, stroke and myocardial infarction) is approximately 40% between the ages of 40 and 59, increases to about 75% between 60 and 79 and reaches approximately 86% in individuals older than 80 years 7 . Similarly, also in the United States, the prevalence of neurodegenerative diseases, such as Parkinson’s disease and Alzheimer’s disease, exhibits an upward trend as well as human aging progresses, with distinct turning points occurring around the ages of 40 and 65, respectively 8 , 9 , 10 . Some studies also found that brain aging followed an accelerated decline in flies 11 and chimpanzees 12 that lived past middle age and advanced age. The observation of a nonlinear increase in the prevalence of aging-related diseases implies that the process of human aging is not a simple linear trend. Consequently, investigating the nonlinear changes in molecules will likely reveal previously unreported molecular signatures and mechanistic insights. Some studies examined the nonlinear alterations of molecules during human aging 13 . For instance, nonlinear changes in RNA and protein expression related to aging have been documented 14 , 15 , 16 . Moreover, certain DNA methylation sites have exhibited nonlinear changes in methylation intensity during aging, following a power law pattern 17 . Li et al. 18 identified the 30s and 50s as transitional periods during women’s aging. Although aging patterns are thought to reflect the underlying biological mechanisms, the comprehensive landscape of nonlinear changes of different types of molecules during aging remains largely unexplored. Remarkably, the global monitoring of nonlinear changing molecular profiles throughout human aging has yet to be fully used to extract basic insights into the biology of aging. In the present study, we conducted a comprehensive deep multi-omics profiling on a longitudinal human cohort comprising 108 individuals aged from 25 years to 75 years. The cohort was followed over a span of several years (median, 1.7 years), with the longest monitoring period for a single participant reaching 6.8 years (2,471 days). Various types of omics data were collected from the participants’ biological samples, including transcriptomics, proteomics, metabolomics, cytokines, clinical laboratory tests, lipidomics, stool microbiome, skin microbiome, oral microbiome and nasal microbiome. The investigation explored the changes occurring across different omics profiles during human aging. Remarkably, many molecular markers and biological pathways exhibited a nonlinear pattern throughout the aging process, thereby providing valuable insight into periods of dramatic alterations during human aging. Most of the molecules change nonlinearly during aging We collected longitudinal biological samples from 108 participants over several years, with a median tracking period of 1.7 years and a maximum period of 6.8 years, and conducted multi-omics profiling on the samples. The participants were sampled every 3–6 months while healthy and had diverse ethnic backgrounds and ages ranging from 25 years to 75 years (median, 55.7 years). The participants’ body mass index (BMI) ranged from 19.1 kg m −2 to 40.8 kg m −2 (median, 28.2 kg m −2 ). Among the participants, 51.9% were female (Fig. 1a and Extended Data Fig. 1a–d ). For each visit, we collected blood, stool, skin swab, oral swab and nasal swab samples. In total, 5,405 biological samples (including 1,440 blood samples, 926 stool samples, 1,116 skin swab samples, 1,001 oral swab samples and 922 nasal swab samples) were collected. The biological samples were used for multi-omics data acquisition (including transcriptomics from peripheral blood mononuclear cells (PBMCs), proteomics from plasma, metabolomics from plasma, cytokines from plasma, clinical laboratory tests from plasma, lipidomics from plasma, stool microbiome, skin microbiome, oral microbiome and nasal microbiome; Methods ). In total, 135,239 biological features (including 10,346 transcripts, 302 proteins, 814 metabolites, 66 cytokines, 51 clinical laboratory tests, 846 lipids, 52,460 gut microbiome taxons, 8,947 skin microbiome taxons, 8,947 oral microbiome taxons and 52,460 nasal microbiome taxons) were acquired, resulting in 246,507,456,400 data points (Fig. 1b and Extended Data Fig. 1e,f ). The average sampling period and number of samples for each participant were 626 days and 47 samples, respectively. Notably, one participant was deeply monitored for 6.8 years (2,471 days), during which 367 samples were collected (Fig. 1c ). Overall, this extensive and longitudinal multi-omics dataset enables us to examine the molecular changes that occur during the human aging process. The detailed characteristics of all participants are provided in the Supplementary Data . For each participant, the omics data were aggregated and averaged across all healthy samples to represent the individual’s mean value, as detailed in the Methods section. Compared to cross-sectional cohorts, which have only a one-time point sample from each participant, our longitudinal dataset, which includes multiple time point samples from each participant, is more robust for detecting complex aging-related changes in molecules and functions. This is because analysis of multi-time point samples can detect participants’ baseline and robustly evaluate individuals’ longitudinal molecular changes. a , The demographics of the 108 participants in the study are presented. b , Sample collection and multi-omics data acquisition of the cohort. Four types of biological samples were collected, and 10 types of omics data were acquired. c , Collection time range and sample numbers for each participant. The top x axis represents the collection range for each participant (read line), and the bottom x axis represents the sample number for each participant (bar plot). Bars are color-coded by omics type. d , Significantly changed molecules and microbes during aging were detected using the Spearman correlation approach ( P  < 0.05). The P values were not adjusted ( Methods ). Dots are color-coded by omics type. e , Differential expressional molecules/microbes in different age ranges compared to baseline (25–40 years old, two-sided Wilcoxon test, P  < 0.05). The P values were not adjusted ( Methods ). f , The linear changing molecules comprised only a small part of dysregulated molecules in at least one age range. g , Heatmap depicting the nonlinear changing molecules and microbes during human aging. We included samples only from healthy visits and adjusted for confounding factors (for example, BMI, sex, insulin resistance/insulin sensitivity (IRIS) and ethnicity; Extended Data Fig. 1a–d ), allowing us to discern the molecules and microbes genuinely associated with aging ( Methods ). Two common and traditional approaches, linear regression and Spearman correlation, were first used to identify the linear changing molecules during human aging 5 . The linear regression method is commonly used for linear changing molecules. As expected, both approaches have very high consistent results for each type of omics data (Supplementary Fig. 1a ). For convenience, the Spearman correlation approach was used in the analysis. Interestingly, only a small portion of all the molecules and microbes (749 out of 11,305, 6.6%; only genus level was used for microbiome data; Methods ) linearly changed during human aging (Fig. 1d and Supplementary Fig. 1b ), consistent with our previous studies 5 ( Methods ). Next, we examined nonlinear effects by categorizing all participants into distinct age stages according to their ages and investigated the dysregulated molecules within each age stage compared to the baseline (25–40 years old; Methods ). Interestingly, using this approach, 81.03% of molecules (9,106 out of 11,305) exhibited changes in at least one age stage compared to the baseline (Fig. 1e and Extended Data Fig. 2a ). Remarkably, the percentage of linear changing molecules was relatively small compared to the overall dysregulated molecules during aging (mean, 16.2%) (Fig. 1f and Extended Data Fig. 2b ). To corroborate our findings, we employed a permutation approach to calculate permutated P values, which yielded consistent results ( Methods ). The heatmap depicting all dysregulated molecules also clearly illustrates pronounced nonlinear changes (Fig. 1g ). Taken together, these findings strongly suggest that a substantial number of molecules and microbes undergo nonlinear changes throughout human aging. Clustering reveals nonlinear multi-omics changes during aging Next, we assessed whether the multi-omics data collected from the longitudinal cohort could serve as reliable indicators of the aging process. Our analysis revealed a substantial correlation between a significant proportion of the omics data and the ages of the participants (Fig. 2a ). Particularly noteworthy was the observation that, among all the omics data examined, metabolomics, cytokine and oral microbiome data displayed the strongest association with age (Fig. 2a and Extended Data Fig. 3a–c ). Partial least squares (PLS) regression was further used to compare the strength of the age effect across different omics data types. The results are consistent with the results presented above in Fig. 2a ( Methods ). These findings suggest the potential utility of these datasets as indicators of the aging process while acknowledging that further research is needed for validation 4 . As the omics data are not accurately matched across all the samples, we then smoothed the omics data using our previously published approach 19 ( Methods and Supplementary Fig. 2a–c ). Next, to reveal the specific patterns of molecules that change during human aging, we then grouped all the molecules with similar trajectories using an unsupervised fuzzy c-means clustering approach 19 ( Methods , Fig. 3b and Supplementary Fig. 2d,e ). We identified 11 clusters of molecular trajectories that changed during aging, which ranged in size from 638 to 1,580 molecules/microbes (Supplementary Fig. 2f and Supplementary Data ). We found that most molecular patterns exhibit nonlinear changes, indicating that aging is not a linear process (Fig. 2b ). Among the 11 identified clusters, three distinct clusters (2, 4 and 5) displayed compelling, straightforward and easily understandable patterns that spanned the entire lifespan (Fig. 2c ). Most molecules within these three clusters primarily consist of transcripts (Supplementary Fig. 2f ), which is expected because transcripts dominate the multi-omics data (8,556 out of 11,305, 75.7%). Cluster 4 exhibits a relatively stable pattern until approximately 60 years of age, after which it shows a rapid decrease (Fig. 2c ). Conversely, clusters 2 and 5 display fluctuations before 60 years of age, followed by a sharp increase and an upper inflection point at approximately 55–60 years of age (Fig. 2c ). We also attempted to observe this pattern of molecular change during aging individually. The participant with the longest follow-up period of 6.8 years (Fig. 1c ) approached the age of 60 years (range, 59.5–66.3 years; Extended Data Fig. 1g ), and it was not possible to identify obvious patterns in this short time window (Supplementary Fig. 2g ). Tracking individuals longitudinally over longer periods (decades) will be required to observe these trajectories at an individual level. a , Spearman correlation (cor) between the first principal component and ages for each type of omics data. The shaded area around the regression line represents the 95% confidence interval. b , The heatmap shows the molecular trajectories in 11 clusters during human aging. The right stacked bar plots show the percentages of different kinds of omics data, and the right box plots show the correlation distribution between features and ages ( n  = 108 participants). c , Three notable clusters of molecules that exhibit clear and straightforward nonlinear changes during human aging. The top stacked bar plots show the percentages of different kinds of omics data, and the top box plots show the correlation distribution between features and ages ( n  = 108 participants). The box plot shows the median (line), interquartile range (IQR) (box) and whiskers extending to 1.5 × IQR. Bars and lines are color-coded by omics type. Abs, absolute. a , Pathway enrichment and module analysis for each transcriptome cluster. The left panel is the heatmap for the pathways that undergo nonlinear changes across aging. The right panel is the pathway similarity network ( Methods ) ( n  = 108 participants). b , Pathway enrichment for metabolomics in each cluster. Enriched pathways and related metabolites are illustrated (Benjamini–Hochberg-adjusted P  < 0.05). c , Four clinical laboratory tests that change during human aging: blood urea nitrogen, serum/plasma glucose, mean corpuscular hemoglobin and red cell distribution width ( n  = 108 participants). The box plot shows the median (line), interquartile range (IQR) (box) and whiskers extending to 1.5 × IQR. Although confounders, including sex, were corrected before analysis ( Methods ), we acknowledge that the age range for menopause in females is typically between 45 years and 55 years of age 20 , which is very close to the major transition points in all three clusters (Fig. 2c ). Therefore, we conducted further investigation into whether the menopausal status of females in the dataset contributed to the observed transition point at approximately 55 years of age (Fig. 2c ) by performing separate clustering analyses on the male and female datasets. Surprisingly, both the male and female datasets exhibited similar clusters, as illustrated in Extended Data Fig. 4a . This suggests that the transition point observed at approximately 55 years of age is not solely attributed to female menopause but, rather, represents a common phenomenon in the aging process of both sexes. This result is consistent with previous studies 14 , 15 , further supporting the notion that this transition point is a major characteristic feature of human aging. Moreover, to investigate the possibility that the transcriptomics data might skew the results toward transcriptomic changes as age-related factors, we conducted two additional clustering analyses—one focusing solely on transcriptomic data and another excluding it. Interestingly, both analyses yielded nearly identical three-cluster configurations, as observed using the complete omics dataset (Extended Data Fig. 4b ). This reinforces the robustness of the identified clusters and confirms that they are consistent across various omics platforms, not just driven by transcriptomic data. Nonlinear changes in function and disease risk during aging To gain further insight into the biological functions associated with the nonlinear changing molecules within the three identified clusters, we conducted separate functional analyses for transcriptomics, proteomics and metabolomics datasets for all three clusters. In brief, we constructed a similarity network using enriched pathways from various databases (Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome) and identified modules to eliminate redundant annotations. We then used all modules from different databases to reduce redundancy further using the same approach and define the final functional modules ( Methods , Extended Data Fig. 4c and Supplementary Data ). We identified some functional modules that were reported in previous studies, but we defined their more accurate patterns of change during human aging. Additionally, we also found previously unreported potential functional modules during human aging ( Supplementary Data ). For instance, in cluster 2, we identified a transcriptomic module associated with GTPase activity (adjusted P  = 1.64 × 10 −6 ) and histone modification (adjusted P   =  6.36 × 10 −7 ) (Fig. 3a ). Because we lack epigenomic data in this study, our findings should be validated through additional experiments in the future. GTPase activity is closely correlated with programmed cell death (apoptosis), and some previous studies showed that this activity increases during aging 21 . Additionally, histone modifications have been demonstrated to increase during human aging 22 . In cluster 4, we identified one transcriptomics module associated with oxidative stress; this module includes antioxidant activity, oxygen carrier activity, oxygen binding and peroxidase activity (adjusted P  = 0.029) (Fig. 3a ). Previous studies demonstrated that oxidative stress and many reactive oxygen species (ROS) are positively associated with increased inflammation in relation to aging 23 . In cluster 5, the first transcriptomics module is associated with mRNA stability, which includes mRNA destabilization (adjusted P   =  0.0032), mRNA processing (adjusted P   =  3.2 × 10 −4 ), positive regulation of the mRNA catabolic process (adjusted P   =  1.46 × 10 −4 ) and positive regulation of the mRNA metabolic process (adjusted P   =  0.00177) (Fig. 3a ). Previous studies showed that mRNA turnover is associated with aging 24 . The second module is associated with autophagy (Fig. 3a ), which increases during human aging, as demonstrated in previous studies 25 . In addition, we also identified certain modules in the clusters that suggest a nonlinear increase in several disease risks during human aging. For instance, in cluster 2, where components increase gradually and then rapidly after age 60, the phenylalanine metabolism pathway (adjusted P   =  4.95 × 10 −4 ) was identified (Fig. 3b ). Previous studies showed that aging is associated with a progressive increase in plasma phenylalanine levels concomitant with cardiac dysfunction, and dysregulated phenylalanine catabolism is a factor that triggers deviations from healthy cardiac aging trajectories 26 . Additionally, C-X-C motif chemokine 5 (CXCL5 or ENA78) from proteomics data, which has higher concentrations in atherosclerosis 27 , is also detected in cluster 2 ( Supplementary Data ). The clinical laboratory test blood urea nitrogen, which provides important information about kidney function, is also detected in cluster 2 (Fig. 3c ). This indicates that kidney function nonlinearly decreases during aging. Furthermore, the clinical laboratory test for serum/plasma glucose, a marker of type 2 diabetes (T2D), falls within cluster 2. This is consistent with and supported by many previous studies demonstrating that aging is a major risk factor for T2D 28 . Collectively, these findings suggest a nonlinear escalation in the risk of cardiovascular and kidney diseases and T2D with advancing age, particularly after the age of 60 years (Fig. 2c ). The identified modules in cluster 4 also indicate a nonlinear increase in disease risks. For instance, the unsaturated fatty acids biosynthesis pathway (adjusted P   =  4.71 × 10 −7 ) is decreased in cluster 4. Studies have shown that unsaturated fatty acids are helpful in reducing CVD risk and maintaining brain function 29 , 30 . The pathway of alpha-linolenic acid and linolenic acid metabolism (adjusted P   =  1.32 × 10 −4 ) can reduce aging-associated diseases, such as CVD 31 . We also detected the caffeine metabolism pathway (adjusted P   =  7.34 × 10 −5 ) in cluster 4, which suggests that the ability to metabolize caffeine decreases during aging. Additionally, the cytokine MCP1 (chemokine (C-C motif) ligand 2 (CCL2)), a member of the CC chemokine family, plays an important immune regulatory role and is also in cluster 4 ( Supplementary Data ). These findings further support previous observations and highlight the nonlinear increase in age-related disease risk as individuals age. Cluster 5 comprises the clinical tests of mean corpuscular hemoglobin and red cell distribution width (Fig. 3c ). These tests assess the average hemoglobin content per red blood cell and the variability in the size and volume of red blood cells, respectively. These findings align with the aforementioned transcriptomic data, which suggest a nonlinear reduction in the oxygen-carrying capacity associated with the aging process. Aside from these three distinct clusters (Fig. 2c ), we also conducted pathway enrichment analysis across all other eight clusters, which displayed highly nonlinear trajectories, employing the same method (Fig. 2b and Supplementary Data ). Notably, cluster 11 exhibited a consistent increase up until the age of 50, followed by a decline until the age of 56, after which no substantial changes were observed up to the age of 75. A particular transcriptomics module related to DNA repair was identified, encompassing three pathways: positive regulation of double-strand break repair (adjusted P   =  0.042), peptidyl−lysine acetylation (adjusted P   =  1.36 × 10 −5 ) and histone acetylation (adjusted P   =  3.45 × 10 −4 ) (Extended Data Fig. 4d ). These three pathways are critical in genomic stability, gene expression and metabolic balances, with their levels diminishing across the human lifespan 32 , 33 , 34 . Our findings reveal a nonlinear alteration across the human lifespan in these pathways, indicating an enhancement in DNA repair capabilities before the age of 50, a marked reduction between the ages of 50 and 56 and stabilization after that until the age of 75. The pathway enrichment results for all clusters are detailed in the Supplementary Data . Altogether, the comprehensive functional analysis offers valuable insights into the nonlinear changes observed in molecular profiles and their correlations with biological functions and disease risks across the human lifespan. Our findings reveal that individuals aged 60 and older exhibit increased susceptibility to CVD, kidney issues and T2D. These results carry important implications for both the diagnosis and prevention of these diseases. Notably, many clinically actionable markers were identified, which have the potential for improved healthcare management and enhanced overall well-being of the aging population. Uncovering waves of aging-related molecules during aging Although the trajectory clustering approaches described above effectively identify nonlinear changing molecules and microbes that exhibit clear and compelling patterns throughout human aging, it may not be as effective in capturing substantial changes that occur at specific chronological aging periods. In such cases, alternative analytical approaches may be necessary to detect and characterize these dynamics. To gain a comprehensive understanding of changes in multi-omics profiling during human aging, we used a modified version of the DE-SWAN algorithm 14 , as described in the Methods section. This algorithm identifies dysregulated molecules and microbes throughout the human lifespan by analyzing molecule levels within 20-year windows and comparing two groups in 10-year parcels while sliding the window incrementally from young to old ages 14 . Using this approach and multiomics data, we detected changes at specific stages of lifespan and uncovered the sequential effects of aging. Our analysis revealed thousands of molecules exhibiting changing patterns throughout aging, forming distinct waves, as illustrated in Fig. 3a . Notably, we observed two prominent crests occurring around the ages of 45 and 65, respectively (Fig. 4a ). Notably, too, these crests were consistent with findings from a previous study that included only proteomics data 14 . Specifically, crest 2 aligns with our previous trajectory clustering result, indicating a turning point at approximately 60 years of age (Fig. 2c ). a , Number of molecules and microbes differentially expressed during aging. Two local crests at the ages of 44 years and 60 years were identified. b , c , The same waves were detected using different q value ( b ) and window ( c ) cutoffs. d , The number of molecules/microbes differentially expressed for different types of omics data during human aging. To demonstrate the significance of the two crests, we employed different q value cutoffs and sliding window parameters, which consistently revealed the same detectable waves (Fig. 4b,c and Supplementary Fig. 4a,b ). Furthermore, when we permuted the ages of individuals, the crests disappeared (Supplementary Figs. 3a and 4c ) ( Methods ). These observations highlight the robustness of the two major waves of aging-related molecular changes across the human lifespan. Although we already accounted for confounders before our statistical analysis, we took additional steps to explore their impact. Specifically, we investigated whether confounders, such as insulin sensitivity, sex and ethnicity, differed between the two crests across various age ranges. As anticipated, these confounders did not show significant differences across other age brackets (Supplementary Fig. 4d ). This further supports our conclusion that the observed differences in the two crests are attributable to age rather than other confounding variables. The identified crests represent notable milestones in the aging process and suggest specific age ranges where substantial molecular alterations occur. Therefore, we investigated the age-related waves for each type of omics data. Interestingly, most types of omics data exhibited two distinct crests that were highly robust (Fig. 3b and Supplementary Fig. 4 ). Notably, the proteomics data displayed two age-related crests at ages around 40 years and 60 years. Only a small overlap was observed between our dataset and the results from the previous study (1,305 proteins versus 302 proteins, with only 75 proteins overlapping). The observed pattern in our study was largely consistent with the previous findings 14 . However, our finding that many types of omics data, including transcriptomics, proteomics, metabolomics, cytokine, gut microbiome, skin microbiome and nasal microbiome, exhibit these waves, often with a similar pattern as the proteomics data (Fig. 4d ), supports the hypothesis that aging-related changes are not limited to a specific omics layer but, rather, involve a coordinated and systemic alteration across multiple molecular components. Identifying consistent crests across different omics data underscores the robustness and reliability of these molecular milestones in the aging process. Next, we investigated the roles and functions of dysregulated molecules within two distinct crests. Notably, we found that the two crests related to aging predominantly consisted of the same molecules (Supplementary Fig. 6 ). To focus on the unique biological functions associated with each crest and eliminate commonly occurring molecules, we removed background molecules present in most stages. To explore the specific biological functions associated with each type of omics data (transcriptomics, proteomics and metabolomics) for both crests, we employed the function annotation approach described above ( Methods ). In brief, we constructed a similarity network of enriched pathways and identified modules to remove redundant annotations (Supplementary Fig. 6 and Extended Data Fig. 5a,b ). Furthermore, we applied the same approach to all modules to reduce redundancy and identify the final functional modules ( Methods and Extended Data Fig. 6a ). Our analysis revealed significant changes in multiple modules associated with the two crests (Extended Data Fig. 6b–d ). To present the results clearly, Fig. 5a displays the top 20 pathways (according to adjusted P value) for each type of omics data, and the Supplementary Data provides a comprehensive list of all identified functional modules. a , Pathway enrichment and biological functional module analysis for crests 1 and 2. Dots and lines are color-coded by omics type. b , The overlapping of molecules between two crests and three clusters. Interestingly, the analysis identifies many dysregulated functional modules in crests 1 and 2, indicating a nonlinear risk for aging-related diseases. In particular, several modules associated with CVD were identified in both crest 1 and crest 2 (Fig. 5a ), which is consistent with the above results (Fig. 3b ). For instance, the dysregulation of platelet degranulation (crest 1: adjusted P   =  1.77 × 10 −30 ; crest 2: adjusted P   =  1.73 × 10 −26 ) 35 , 36 , complement cascade (crest 1: adjusted P   =  3.84 × 10 −30 ; crest 2: adjusted P   =  2.02 × 10 −28 ), complement and coagulation cascades (crest 1: adjusted P   =  1.78 × 10 −46 ; crest 2: adjusted P   =  2.02 × 10 −28 ) 37 , 38 , protein activation cascade (crest 1: adjusted P   =  1.56 × 10 −17 ; crest 2: adjusted P   =  1.61 × 10 −8 ) and protease binding (crest 1: adjusted P   =  2.7 × 10 −6 ; crest 2: adjusted P   =  0.0114) 39 have various effects on the cardiovascular system and can contribute to various CVDs. Furthermore, blood coagulation (crest 1: adjusted P   =  1.48 × 10 −28 ; crest 2: adjusted P   =  9.10 × 10 −17 ) and fibrinolysis (crest 1: adjusted P   =  2.11 × 10 −15 ; crest 2: adjusted P   =  1.64 × 10 −10 ) were also identified, which are essential processes for maintaining blood fluidity, and dysregulation in these modules can lead to thrombotic and cardiovascular events 40 , 41 . We also identified certain dysregulated metabolic modules associated with CVD. For example, aging has been linked to an incremental rise in plasma phenylalanine levels (crest 1: adjusted P   =  9.214 × 10 −4 ; crest 2: adjusted P   =  0.0453), which can contribute to the development of cardiac hypertrophy, fibrosis and dysfunction 26 . Branched-chain amino acids (BCAAs), including valine, leucine and isoleucine (crest 1: adjusted P : not significant (NS); crest 2: adjusted P   =  0.0173), have also been implicated in CVD development 42 , 43 and T2D, highlighting their relevance in CVD pathophysiology. Furthermore, research suggests that alpha-linolenic acid (ALA) and linoleic acid metabolism (crest 1: adjusted P : NS; crest 2: adjusted P   =  0.0217) may be protective against coronary heart disease 44 , 45 . Our investigation also identified lipid metabolism modules that are associated with CVD, including high-density lipoprotein (HDL) remodeling (crest 1: adjusted P   =  1.073 × 10 −8 ; crest 2: adjusted P   =  2.589 × 10 −9 ) and glycerophospholipid metabolism (crest 1: adjusted P : NS; crest 2: adjusted P   =  0.0033), which influence various CVDs 46 , 47 , 48 . In addition, the dysregulation of skin and muscle stability was found to be increased at crest 1 and crest 2, as evidenced by the identification of numerous modules associated with these processes (Fig. 5a,b ). This suggests that the aging of skin and muscle is markedly accelerated at crest 1 and crest 2. The extracellular matrix (ECM) provides structural stability, mechanical strength, elasticity and hydration to the tissues and cells, and the ECM of the skin is mainly composed of collagen, elastin and glycosaminoglycans (GAGs) 49 . Phosphatidylinositols are a class of phospholipids that have various roles in cytoskeleton organization 50 . Notably, the dysregulation of ECM structural constituent (crest 1: adjusted P   =  3.32 × 10 −8 ; crest 2: adjusted P   =  1.61 × 10 −8 ), GAG binding (crest 1: adjusted P   =  1.805 × 10 −8 ; crest 2: adjusted P   =  4.093 × 10 −6 ) and phosphatidylinositol binding (crest 1: adjusted P   =  3.391 × 10 −6 ; crest 2: adjusted P   =  7.832 × 10 −6 ) were identified 51 , 52 . We also identified cytolysis (crest 1: adjusted P   =  2.973 × 10 −5 ; crest 2: adjusted P : NS), which can increase water loss 53 . The dysregulated actin binding (crest 1: adjusted P   =  3.536 × 10 −8 ; crest 2: adjusted P   =  3.435 × 10 −9 ), actin filament organization (crest 1: adjusted P   =  8.406 × 10 −9 ; crest 2: adjusted P   =  1.157 × 10 −9 ) and regulation of actin cytoskeleton (crest 1: adjusted P   =  0.00090242; crest 2: adjusted P   =  6.788 × 10 −4 ) were identified, which affect the structure and function of various tissues 54 , 55 , 56 , 57 , 58 . Additionally, cell adhesion is the attachment of a cell to another cell or to ECM via adhesion molecules 59 . We identified the positive regulation of cell adhesion (crest 1: adjusted P   =  3.618 × 10 −5 ; crest 2: adjusted P   =  8.272 × 10 −9 ) module, which can prevent or delay skin aging 60 , 61 . Threonine can affect sialic acid production, which is involved in cell adhesion 62 . We also identified the glycine, serine and threonine metabolism (crest 1: adjusted P : NS; crest 2: adjusted P   =  0.00506) 62 . Additionally, scavenging of heme from plasma was identified (crest 1: adjusted P   =  1.176 × 10 −11 ; crest 2: adjusted P   =  1.694 × 10 −8 ), which can modulate skin aging as excess-free heme can damage cellular components 63 , 64 . Rho GTPases regulate a wide range of cellular responses, including changes to the cytoskeleton and cell adhesion (RHO GTPase cycle, crest 1: adjusted P   =  9.956 × 10 −10 ; crest 2: adjusted P   =  1.546 × 10 −5 ) 65 . In relation to muscle, previous studies demonstrated that muscle mass decreases by approximately 3–8% per decade after the age of 30, with an even higher decline rate after the age of 60, which consistently coincides with the observed second crest 66 . Interestingly, we identified dysregulation in the module associated with the structural constituent of muscle (crest 1: adjusted P   =  0.00565; crest 2: adjusted P   =  0.0162), consistent with previous findings 66 . Furthermore, we identified the pathway associated with caffeine metabolism (crest 1: adjusted P   =  0.00378; crest 2: adjusted P   =  0.0162), which is consistent with our observations above (Fig. 2b ) and implies that the capacity to metabolize caffeine undergoes a notable alteration not only around 60 years of age but also around the age of 40 years. In crest 1, we identified specific modules associated with lipid and alcohol metabolism. Previous studies established that lipid metabolism declines with human aging 67 . Our analysis revealed several modules related to lipid metabolism, including plasma lipoprotein remodeling (crest 1: adjusted P   =  2.269 × 10 −9 ), chylomicron assembly (crest 1: adjusted P   =  9.065 × 10 −7 ) and ATP-binding cassette (ABC) transporters (adjusted P   =  1.102 × 10 −4 ). Moreover, we discovered a module linked to alcohol metabolism (alcohol binding, adjusted P   =  8.485 × 10 −7 ), suggesting a decline in alcohol metabolization efficiency with advancing age, particularly around the age of 40, when it significantly diminishes. In crest 2, we observed prominent modules related to immune dysfunction, encompassing acute-phase response (adjusted P   =  2.851 × 10 −8 ), antimicrobial humoral response (adjusted P   =  2.181 × 10 −5 ), zymogen activation (adjusted P   =  4.367 × 10 −6 ), complement binding (adjusted P   =  0.002568), mononuclear cell differentiation (adjusted P   =  9.352 × 10 −8 ), viral process (adjusted P   =  5.124 × 10 −7 ) and regulation of hemopoiesis (adjusted P   =  3.522 × 10 −7 ) (Fig. 5a ). Age-related changes in the immune system, collectively known as immunosenescence, have been extensively documented 68 , 69 , 70 , and our results demonstrate a rapid decline at age 60. Furthermore, we also identified modules associated with kidney function (glomerular filtration, adjusted P   =  0.00869) and carbohydrate metabolism (carbohydrate binding, adjusted P   =  0.01045). Our previous findings indicated a decline in kidney function around the age of 60 years (Fig. 3c ), as did the present result of this observation. Previous studies indicated the influence of carbohydrates on aging, characterized by the progressive decline of physiological functions and increased susceptibility to diseases over time 71 , 72 . In summary, our analysis identifies many dysregulated functional modules identified in both crest 1 and crest 2 that underlie the risk for various diseases and alterations of biological functions. Notably, we observed an overlap of dysregulated functional modules among clusters 2, 4 and 6 because they overlap at the molecular level, as depicted in Fig. 5b . This indicates that certain molecular components are shared among these clusters and the identified crests. However, it is important to note that numerous molecules are specific to each of the two approaches employed in our study. This suggests that these two approaches complement each other in identifying nonlinear changes in molecules and functions during human aging. By using both approaches, we were able to capture a more comprehensive understanding of the molecular alterations associated with aging and their potential implications for diseases. Analyzing a longitudinal multi-omics dataset involving 108 participants, we successfully captured the dynamic and nonlinear molecular changes that occur during human aging. Our study’s strength lies in the comprehensive nature of the dataset, which includes multiple time point samples for each participant. This longitudinal design enhances the reliability and robustness of our findings compared to cross-sectional studies with only one time point sample for each participant. The first particularly intriguing finding from our analysis is that only a small fraction of molecules (6.6%) displayed linear changes throughout human aging (Fig. 1d ). This observation is consistent with previous research and underscores the limitations of relying solely on linear regression to understand the complexity of aging-related molecular changes 5 . Instead, our study revealed that a considerable number of molecules (81.0%) exhibited nonlinear patterns (Fig. 1e ). Notably, this nonlinear trend was observed across all types of omics data with remarkably high consistency (Fig. 1e,g ), highlighting the widespread functionally relevant nature of these dynamic changes. By unveiling the nonlinear molecular alterations associated with aging, our research contributes to a more comprehensive understanding of the aging process and its molecular underpinnings. To further investigate the nonlinear changing molecules observed in our study, we employed a trajectory clustering approach to group molecules with similar temporal patterns. This analysis revealed the presence of three distinct clusters (Fig. 2c ) that exhibited clear and compelling patterns across the human lifespan. These clusters suggest that there are specific age ranges, such as around 60 years old, where distinct and extensive molecular changes occur (Fig. 2c ). Functional analysis revealed several modules that exhibited nonlinear changes during human aging. For example, we identified a module associated with oxidative stress, which is consistent with previous studies linking oxidative stress to the aging process 23 (Fig. 3a ). Our analysis indicates that this pathway increases significantly after the age of 60 years. In cluster 5, we identified a transcriptomics module associated with mRNA stabilization and autophagy (Fig. 3a ). Both of these processes have been implicated in the aging process and are involved in maintaining cellular homeostasis and removing damaged components. Furthermore, our analysis uncovered nonlinear changes in disease risk across aging. In cluster 2, we identified the phenylalanine metabolism pathway (Fig. 3b ), which has been associated with cardiac dysfunction during aging 26 . Additionally, we found that the clinical laboratory tests blood urea nitrogen and serum/plasma glucose increase significantly with age (cluster 2; Fig. 3c ), indicating a nonlinear decline in kidney function and an increased risk of T2D with age, with a critical threshold occurring approximately at the age of 60 years. In cluster 4, we identified pathways related to cardiovascular health, such as the biosynthesis of unsaturated fatty acids and caffeine metabolism (Fig. 3b ). Overall, our study provides compelling evidence for the existence of nonlinear changes in molecular profiles during human aging. By elucidating the specific functional modules and disease-related pathways that exhibit such nonlinear changes, we contribute to a better understanding of the complex molecular dynamics underlying the aging process and its implications for disease risk. Although the trajectory clustering approach proves effective in identifying molecules that undergo nonlinear changes, it may not be as proficient in capturing substantial alterations that occur at specific time points without exhibiting a consistent pattern in other stages. We then employed a modified version of the DE-SWAN algorithm 14 to comprehensively investigate changes in multi-omics profiling throughout human aging. This approach enabled us to identify waves of dysregulated molecules and microbes across the human lifespan. Our analysis revealed two prominent crests occurring around the ages of 40 years and 60 years, which were consistent across various omics data types, suggesting their universal nature (Fig. 4a,e ). Notably, in the proteomics data, we observed crests around the ages of 40 years and 60 years, which aligns approximately with a previous study (which reported crests at ages 34 years, 60 years and 78 years) 14 . Due to the age range of our cohort being 25–75 years, we did not detect the third peak. Furthermore, the differences in proteomics data acquisition platforms (mass spectrometry versus SomaScan) 14 , 73 resulted in different identified proteins, with only a small overlap (1,305 proteins versus 302 proteins, of which only 75 were shared). This discrepancy may explain the age variation of the first crest identified in the two studies (approximately 10 years). However, despite the differences in the two proteomics datasets, the wave patterns observed in both studies were highly similar 14 (Fig. 4a ). Remarkably, by considering multiple omics data types, we consistently identified similar crests for each type, indicating the universality of these waves of change across plasma molecules and microbes from various body sites (Fig. 4e and Supplementary Fig. 3 ). The analysis of molecular functionality in the two distinct crests revealed the presence of several modules, indicating a nonlinear increase in the risks of various diseases (Fig. 5a ). Both crest 1 and crest 2 exhibit the identification of multiple modules associated with CVD, which aligns with the aforementioned findings (Fig. 3b ). Moreover, we observed an escalated dysregulation in skin and muscle functioning in both crest 1 and crest 2. Additionally, we identified a pathway linked to caffeine metabolism, indicating a noticeable alteration in caffeine metabolization not only around the age of 60 but also around the age of 40. This shift may be due to either a metabolic shift or a change in caffeine consumption. In crest 1, we also identified specific modules associated with lipid and alcohol metabolism, whereas crest 2 demonstrated prominent modules related to immune dysfunction. Furthermore, we also detected modules associated with kidney function and carbohydrate metabolism, which is consistent with our above results. These findings reinforce our previous observations regarding a decline in kidney function around the age of 60 years (Fig. 3c ) while shedding light on the impact of dysregulated functional modules in both crest 1 and crest 2, suggesting nonlinear changes in disease risk and functional dysregulation. Notably, we identified an overlap of dysregulated functional modules among clusters 2, 4 and 6, indicating molecular-level similarities between these clusters and the identified crests (Fig. 5b ). This suggests the presence of shared molecular components among these clusters and crests. However, it is crucial to note that there are also numerous molecules specific to each of the two approaches employed in our study, indicating that these approaches complement each other in identifying nonlinear changes in molecules and functions during human aging. The present research is subject to certain constraints. We accounted for many basic characteristics (confounders) of participants in the cohort; but because this study primarily reflects between-individual differences, there may be additional confounders due to the different age distributions of the participants. For example, we identified a notable decrease in oxygen carrier activity around age 60 (Figs. 2c and 3a ) and marked variations in alcohol and caffeine metabolism around ages 40 and 60 (Fig. 3a ). However, these findings might be shaped by participants’ lifestyle—that is, physical activity and their alcohol and caffeine intake. Regrettably, we do not have such detailed behavioral data for the entire group, necessitating validation in upcoming research. Although initial BMI and insulin sensitivity measurements were available at cohort entry, subsequent metrics during the observation span were absent, marking a study limitation. A further constraint is our cohort’s modest size, encompassing merely 108 individuals (eight individuals between 25 years and 40 years of age), which hampers the full utilization of deep learning and may affect the robustness of the identification of nonlinear changing features in Fig. 1e . Although advanced computational techniques, including deep learning, are pivotal for probing nonlinear patterns, our sample size poses restrictions. Expanding the cohort size in subsequent research would be instrumental in harnessing the full potential of machine learning tools. Another limitation of our study is that the recruitment of participants was within the community around Stanford University, driven by rigorous sample collection procedures and the substantial expenses associated with setting up a longitudinal cohort. Although our participants exhibited a considerable degree of ethnic age and biological sex diversity (Fig. 1a and Supplementary Data ), it is important to acknowledge that our cohort may not fully represent the diversity of the broader population. The selectivity of our cohort limits the generalizability of our findings. Future studies should aim to include a more diverse cohort to enhance the external validity and applicability of the results. In addition, the mean observation span for participants was 626 days, which is insufficient for detailed inflection point analyses. Our cohort’s age range of 25–70 years lacks individuals who lie outside of this range. The molecular nonlinearity detected might be subject to inherent variations or oscillations, a factor to consider during interpretation. Our analysis has not delved into the nuances of the dynamical systems theory, which provides a robust mathematical framework for understanding observed behaviors. Delving into this theory in future endeavors may yield enhanced clarity and interpretation of the data. Moreover, it should be noted that, in our study, the observed nonlinear molecular changes occurred across individuals of varying ages rather than within the same individuals. This is attributed to the fact that, despite our longitudinal study, the follow-up period for our participants was relatively brief for following aging patterns (median, 1.7 years; Extended Data Fig. 1g ). Such a timeframe is inadequate for detecting nonlinear molecular changes that unfold over decades throughout the human lifespan. Addressing this limitation in future research is essential. Lastly, our study’s molecular data are derived exclusively from blood samples, casting doubt on its direct relevance to specific tissues, such as the skin or muscles. We propose that blood gene expression variations might hint at overarching physiological alterations, potentially impacting the ECM in tissues, including skin and muscle. Notably, some blood-based biomarkers and transcripts have demonstrated correlations with tissue modifications, inflammation and other elements influencing the ECM across diverse tissues 74 , 75 . In our future endeavors, the definitive confirmation of our findings hinges on determining if nonlinear molecular patterns align with nonlinear changes in functional capacities, disease occurrences and mortality hazards. For a holistic grasp of this, amalgamating multifaceted data from long-term cohort studies covering several decades becomes crucial. Such data should encompass molecular markers, comprehensive medical records, functional assessments and mortality data. Moreover, employing cutting-edge statistical techniques is vital to intricately decipher the ties between these nonlinear molecular paths and health-centric results. In summary, the unique contribution of our study lies not merely in reaffirming the nonlinear nature of aging but also in the depth and breadth of the multi-omics data that we analyzed. Our study goes beyond stating that aging is nonlinear by identifying specific patterns, inflection points and potential waves in aging across multiple layers of biological data during human aging. Identifying specific clusters with distinct patterns, functional implications and disease risks enhances our understanding of the aging process. By considering the nonlinear dynamics of aging-related changes, we can gain insights into specific periods of significant changes (around age 40 and age 60) and the molecular mechanisms underlying age-related diseases, which could lead to the development of early diagnosis and prevention strategies. These comprehensive multi-omics data and the approach allow for a more nuanced understanding of the complexities involved in the aging process, which we think adds value to the existing body of research. However, further research is needed to validate and expand upon these findings, potentially incorporating larger cohorts to capture the full complexity of aging. The participant recruitment, sample collection, data acquisition and data processing were documented in previous studies conducted by Zhou et al. 76 , Ahadi et al. 5 , Schüssler-Fiorenza Rose et al. 77 , Hornburg et al. 78 and Zhou et al. 79 . Participant recruitment Participants provided informed written consent for the study under research protocol 23602, which was approved by the Stanford University institutional review board. This study adheres to all relevant ethical regulations, ensuring informed consents were obtained from all participants. All participants consented to publication of potentially identifiable information. The cohort comprised 108 participants who underwent follow-up assessments. Exclusion criteria encompassed conditions such as anemia, kidney disease, a history of CVD, cancer, chronic inflammation or psychiatric illnesses as well as any prior bariatric surgery or liposuction. Each participant who met the eligibility criteria and provided informed consent underwent a one-time modified insulin suppression test to quantify insulin-mediated glucose uptake at the beginning of the enrollment 76 . The steady-state plasma glucose (SSPG) levels served as a direct indicator of each individual’s insulin sensitivity in processing a glucose load. We categorized individuals with SSPG levels below 150 mg dl −1 as insulin sensitive and those with levels of 150 mg dl −1 or higher as insulin resistant 80 , 81 . Thirty-eight participants were missing SSPG values, rendering their insulin resistance or sensitivity status undetermined. We also collected fasting plasma glucose (FPG) data for 69 participants at enrollment. Based on the FPG levels, two participants were identified as having diabetes at enrollment, with FPG levels exceeding 126 mg dl −1 ( Supplementary Data ). Additionally, we measured hemoglobin A1C (HbA1C) levels during each visit, using it as a marker for average glucose levels over the past 3 months: 6.5% or higher indicates diabetes. Accordingly, four participants developed diabetes during the study period. At the beginning of the enrollment, BMI was also measured for each participant. Participants received no compensation. Comprehensive sample collection was conducted during the follow-up period, and multi-omics data were acquired (Fig. 1b ). For each visit, the participants self-reported as healthy or non-healthy 76 . To ensure accuracy and minimize the impact of confounding factors, only samples from individuals classified as healthy were selected for subsequent analysis. Transcriptomics Transcriptomic profiling was conducted on flash-frozen PBMCs. RNA isolation was performed using a QIAGEN All Prep kit. Subsequently, RNA libraries were assembled using an input of 500 ng of total RNA. In brief, ribosomal RNA (rRNA) was selectively eliminated from the total RNA pool, followed by purification and fragmentation. Reverse transcription was carried out using a random primer outfitted with an Illumina-specific adaptor to yield a cDNA library. A terminal tagging procedure was used to incorporate a second adaptor sequence. The final cDNA library underwent amplification. RNA sequencing libraries underwent sequencing on an Illumina HiSeq 2000 platform. Library quantification was performed via an Agilent Bioanalyzer and Qubit fluorometric quantification (Thermo Fisher Scientific) using a high-sensitivity dsDNA kit. After normalization, barcoded libraries were pooled at equimolar ratios into a multiplexed sequencing library. An average of 5–6 libraries were processed per HiSeq 2000 lane. Standard Illumina pipelines were employed for image analysis and base calling. Read alignment to the hg19 reference genome and personal exomes was achieved using the TopHat package, followed by transcript assembly and expression quantification via HTseq and DESeq2. In the realm of data pre-processing, genes with an average read count across all samples lower than 0.5 were excluded. Samples exhibiting an average read count lower than 0.5 across all remaining genes were likewise removed. For subsequent global variance and correlation assessments, genes with an average read count of less than 1 were eliminated. Plasma sample tryptic peptides were fractionated using a NanoLC 425 System (SCIEX) operating at a flow rate of 5 μl min −1 under a trap-elute configuration with a 0.5 × 10 mm ChromXP column (SCIEX). The liquid chromatography gradient was programmed for a 43-min run, transitioning from 4% to 32% of mobile phase B, with an overall run time of 1 h. Mobile phase A consisted of water with 0.1% formic acid, and mobile phase B was formulated with 100% acetonitrile and 0.1% formic acid. An 8-μg aliquot of non-depleted plasma was loaded onto a 15-cm ChromXP column. Mass spectrometry analysis was executed employing SWATH acquisition on a TripleTOF 6600 system. A set of 100 variable Q1 window SWATH acquisition methods was designed in high-sensitivity tandem mass spectrometry (MS/MS) mode. Subsequent data analysis included statistical scoring of peak groups from individual runs via pyProphet 82 , followed by multi-run alignment through TRIC60, ultimately generating a finalized data matrix with a false discovery rate (FDR) of 1% at the peptide level and 10% at the protein level. Protein quantitation was based on the sum of the three most abundant peptide signals for each protein. Batch effect normalization was achieved by subtracting principal components that primarily exhibited batch-associated variation, using Perseus software v.1.4.2.40. Untargeted metabolomics A ternary solvent system of acetone, acetonitrile and methanol in a 1:1:1 ratio was used for metabolite extraction. The extracted metabolites were dried under a nitrogen atmosphere and reconstituted in a 1:1 methanol:water mixture before analysis. Metabolite profiles were generated using both hydrophilic interaction chromatography (HILIC) and reverse-phase liquid chromatography (RPLC) under positive and negative ion modes. Thermo Q Exactive Plus mass spectrometers were employed for HILIC and RPLC analyses, respectively, in full MS scan mode. MS/MS data were acquired using quality control (QC) samples. For the HILIC separations, a ZIC-HILIC column was used with mobile phase solutions of 10 mM ammonium acetate in 50:50 and 95:5 acetonitrile:water ratios. In the case of RPLC, a Zorbax SBaq column was used, and the mobile phase consisted of 0.06% acetic acid in water and methanol. Metabolic feature detection was performed using Progenesis QI software. Features from blanks and those lacking sufficient linearity upon dilution were excluded. Only features appearing in more than 33% of the samples were retained for subsequent analyses, and any missing values were imputed using the k -nearest neighbors approach. We employed locally estimated scatterplot smoothing (LOESS) normalization 83 to correct the metabolite-specific signal drift over time. The metid package 84 was used for metabolite annotation. Cytokine data A panel of 62 human cytokines, chemokines and growth factors was analyzed in EDTA-anticoagulated plasma samples using Luminex-based multiplex assays with conjugated antibodies (Affymetrix). Raw fluorescence measurements were standardized to median fluorescence intensity values and subsequently subjected to variance-stabilizing transformation to account for batch-related variations. As previously reported 76 , data points characterized by background noise, termed CHEX, that deviate beyond five standard deviations from the mean (mean ± 5 × s.d.) were excluded from the analyses. Clinical laboratory test The tests encompassed a comprehensive metabolic panel, a full blood count, glucose and HbA1C levels, insulin assays, high-sensitivity C-reactive protein (hsCRP), immunoglobulin M (IgM) and lipid, kidney and liver panels. Lipid extraction and quantification procedures were executed in accordance with established protocols 78 . In summary, complex lipids were isolated from 40 μl of EDTA plasma using a solvent mixture comprising methyl tertiary-butyl ether, methanol and water, followed by a biphasic separation. Subsequent lipid analysis was conducted on the Lipidyzer platform, incorporating a differential mobility spectrometry device (SelexION Technology) and a QTRAP 5500 mass spectrometer (SCIEX). Immediately after arrival, samples were stored at −80 °C. Stool and nasal samples were processed and sequenced in-house at the Jackson Laboratory for Genomic Medicine, whereas oral and skin samples were outsourced to uBiome for additional processing. Skin and oral samples underwent 30 min of beads-beating lysis, followed by a silica-guanidinium thiocyanate-based nucleic acid isolation protocol. The V4 region of the 16S rRNA gene was amplified using specific primers, after which the DNA was barcoded and sequenced on an Illumina NextSeq 500 platform via a 2 × 150-bp paired-end protocol. Similarly, stool and nasal samples were processed for 16S rRNA V1–V3 region amplification using a different set of primers and sequenced on an Illumina MiSeq platform. For data processing, the raw sequencing data were demultiplexed using BCL2FASTQ software and subsequently filtered for quality. Reads with a Q-score lower than 30 were excluded. The DADA2 R package was used for further sequence data processing, which included filtering out reads with ambiguous bases and errors, removing chimeras and aligning sequences against a validated 16S rRNA gene database. Relative abundance calculations for amplicon sequence variants (ASVs) were performed, and samples with inadequate sequencing depth (<1,000 reads) were excluded. Local outlier factor (LOF) was calculated for each point on a depth-richness plot, and samples with abnormal LOF were removed. In summary, rigorous procedures were followed in both the collection and processing stages, leveraging automated systems and specialized software to ensure the quality and integrity of the microbiome data across multiple body sites. Statistics and reproducibility For all data processing, statistical analysis and data visualization tasks, RStudio, along with R language (v.4.2.1), was employed. A comprehensive list of the packages used can be found in the Supplementary Note . The Benjamini–Hochberg method was employed to account for multiple comparisons. Spearman correlation coefficients were calculated using the R functions ‘cor’ and ‘cor.test’. Principal-component analysis (PCA) was conducted using the R function ‘princomp’. Before all the analyses, the confounders, such as BMI, sex, IRIS and ethnicity, were adjusted using the previously published method 19 . In brief, we used the intensity of each feature as the dependent variable (Y) and the confounding factors as the independent variables (X) to build a linear regression model. The residuals from this model were then used as the adjusted values for that specific feature. All the omics data were acquired randomly. No statistical methods were used to predetermine the sample size, but our sample sizes are similar to those reported in previous publications 5 , 76 , 77 , 78 , 79 , and no data were excluded from the analyses. Additionally, the investigators were blinded to allocation during experiments and outcome assessment to the conditions of the experiments. Data distribution was assumed to be normal, but this was not formally tested. The icons used in figures are from iconfont.cn, which can be used for non-commercial purposes under the MIT license ( https://pub.dev/packages/iconfont/license ). Cross-sectional dataset generation The ‘cross-sectional’ dataset was created by briefly extracting information from the longitudinal dataset. The mean value was calculated to represent each molecule’s intensity for each participant. Similarly, the age of each participant was determined by calculating the mean value of ages across all sample collection time points. Linear changing molecule detection We detected linear changing molecules during human aging using Spearman correlation and linear regression modeling. The confounders, such as BMI, sex, IRIS and ethnicity, were adjusted using the previously published method 19 . Our analysis revealed a high correlation between these two approaches in identifying such molecules. Based on these findings, we used the Spearman correlation approach to showcase the linear changing molecules during human aging. The permutation test was also used to get the permutated P values for each feature. In brief, each feature was subjected to sample label shuffling followed by a recalculation of the Spearman correlation. This process was reiterated 10,000 times, yielding 10,000 permuted Spearman correlations. The original Spearman correlation was then compared against these permuted values to obtain the permuted P values. Dysregulated molecules compared to baseline during human aging To depict the dysregulated molecules during human aging compared to the baseline, we categorized the participants into different age stages based on their ages. The baseline stage was defined as individuals aged 25–40 years. For each age stage group, we employed the Wilcoxon test to identify dysregulated molecules in comparison to the baseline, considering a significance threshold of P  < 0.05. Before the statistical analysis, all the confounders were corrected. Subsequently, we visualized the resulting dysregulated molecules at different age stages using a Sankey plot. The permutation test was also used to get the permutated P values for each feature. In brief, we shuffled the sample labels and recalculated the absolute mean difference between the two groups, against which the actual absolute mean difference was benchmarked to derive the permuted P values. To identify the molecules and microbes that exhibited significant changes at any given age stage, we adjusted the P values for each feature by multiplying them by 6. This adjustment adheres to the Bonferroni correction method, ensuring a rigorous evaluation of statistical significance. Evaluation of the age reflected by different types of omics data To assess whether each type of omics data accurately reflects the ages of individuals in our dataset, we conducted a PCA. Subsequently, we computed the Spearman correlation coefficient between the ages of participants and the first principal component (PC1). The absolute value of this coefficient was used to evaluate the degree to which the omics data reflect the ages (Fig. 2a ). PLS regression was also used to compare the strength of the age effect to the different omics data types. In brief, the ‘pls’ function from the R package mixOmics was used to construct the regression model between omics data and ages. Then, the ‘perf’ function was used to assess the performance of all the modules with sevenfold cross-validation. The R 2 was extracted to assess the strength of the age effect on the different omics data types. To accommodate the varying time points of biological and omics data, we employed the LOESS approach. This approach allowed us to smooth and predict the multi-omics data at specific time points (that is, every half year) 14 , 85 . In brief, for each molecule, we fitted a LOESS regression model. During the fitting process, the LOESS argument ‘span’ was optimized through cross-validation. This ensured that the LOESS model provided an accurate and non-overfitting fit to the data (Supplementary Fig. 2a,b ). Once we obtained the LOESS prediction model, we applied it to predict the intensity of each molecule at every half-year time point. Trajectory clustering analysis To conduct trajectory clustering analysis, we employed the fuzzy c-means clustering approach available in the R package ‘Mfuzz.’ This approach was previously described in our publication 19 . The analysis proceeded in several steps. First, the omics data were auto-scaled to ensure comparable ranges. Next, we computed the minimum centroid distances for a range of cluster numbers, specifically from 2 to 22, in step 1. These minimum centroid distances served as a cluster validity index, helping us determine the optimal cluster number. Based on predefined rules, we selected the optimal cluster number. To refine the accuracy of this selection, we merged clusters with center expression data correlations greater than 0.8 into a single cluster. This step aimed to capture similar patterns within the data. The resulting optimal cluster number was then used for the fuzzy c-means clustering. Only molecules with memberships above 0.5 were retained within each cluster for further analysis. This threshold ensured that the molecules exhibited a strong association with their assigned cluster and contributed considerably to the cluster’s characteristics. Pathway enrichment analysis and functional module identification Transcriptomics and proteomics pathway enrichment. Pathway enrichment analysis was conducted using the ‘clusterProfiler’ R package 86 . The GO, KEGG and Reactome databases were used. The P values were adjusted using the Benjamini–Hochberg method, with a significance threshold set at <0.05. To minimize redundant enriched pathways and GO terms, we employed a series of analyses. First, for enriched GO terms, we used the ‘Wang’ algorithm from the R package ‘simplifyEnrichment’ to calculate the similarity between GO terms. Only connections with a similarity score greater than 0.7 were retained to construct the GO term similarity network. Subsequently, community analysis was performed using the ‘igraph’ R package to partition the network into distinct modules. The GO term with the smallest enrichment adjusted P value was chosen as the representative within each module. The same approach was applied to the enriched KEGG and Reactome pathways, with one slight modification. In this case, the ‘jaccard’ algorithm was used to calculate the similarity between pathways, and a similarity cutoff of 0.5 was employed for the Jaccard index. After removing redundant enriched pathways, we combined all the remaining GO terms and pathways. Subsequently, we calculated the similarity between these merged entities using the Jaccard index. This similarity analysis aimed to capture the overlap and relationships between the different GO terms and pathways. Using the same approach as before, we performed community analysis to identify distinct biological functional modules based on the merged GO terms and pathways. Identification of functional modules First, we used the ‘Wang’ algorithm for the GO database and the ‘jaccard’ algorithm for the KEGG and Reactome databases to calculate the similarity between pathways. The enriched pathways served as nodes in a similarity network, with edges representing the similarity between two nodes. Next, we employed the R package ‘igraph’ to identify modules within the network based on edge betweenness. By gradually removing edges with the highest edge betweenness scores, we constructed a hierarchical map known as a dendrogram, representing a rooted tree of the graph. The leaf nodes correspond to individual pathways, and the root node represents the entire graph 87 . We then merged pathways within each module, selecting the pathway with the smallest adjusted P value to represent the module. After this step, we merged pathways from all three databases into modules. Subsequently, we repeated the process by calculating the similarity between modules from all three databases using the ‘jaccard’ algorithm. Once again, we employed the same approach described above to identify the functional modules. Metabolomics pathway enrichment To perform pathway enrichment analysis for metabolomics data, we used the human KEGG pathway database. This database was obtained from KEGG using the R package ‘massDatabase’ 88 . For pathway enrichment analysis, we employed the hypergeometric distribution test from the ‘TidyMass’ project 89 . This statistical test allowed us to assess the enrichment of metabolites within each pathway. To account for multiple tests, P values were adjusted using the Benjamini–Hochberg method. We considered pathways with Benjamini–Hochberg-adjusted P values lower than 0.05 as significantly enriched. Modified DE-SWAN The DE-SWAN algorithm 14 was used. To begin, a unique age is selected as the center of a 20-year window. Molecule levels in individuals younger than and older than that age are compared using the Wilcoxon test to assess differential expression. P values are calculated for each molecule, indicating the significance of the observed differences. To ensure sufficient sample sizes for statistical analysis in each time window, the initial window ranges from ages 25 to 50. The left half of this window covers ages 25–40, whereas the right half spans ages 41–50. The window then moves in one-year steps; this is why Fig. 4 displays an age range of 40–65 years. To account for multiple comparisons, these P values are adjusted using Benjamini–Hochberg correction. To evaluate the robustness and relevance of the DE-SWAN results, the algorithm is tested with various parcel widths, including 15 years, 20 years, 25 years and 30 years. Additionally, different q value thresholds, such as <0.0001, <0.001, <0.01 and <0.05, are applied. By comparing the results obtained with these different parameters to results obtained by chance, we can assess the significance of the findings. To generate random results for comparison, the phenotypes of the individuals are randomly permuted, and the modified DE-SWAN algorithm is applied to the permuted dataset. This allows us to determine whether the observed results obtained with DE-SWAN are statistically significant and not merely a result of chance. Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Data availability The raw data used in this study can be accessed without any restrictions on the National Institutes of Health Human Microbiome 2 project site ( https://portal.hmpdacc.org ). Both the raw and processed data are also available on the Stanford iPOP site ( http://med.stanford.edu/ipop.html ). Researchers and interested individuals can visit these websites to access the data. For further details and inquiries about the study, we recommend contacting the corresponding author, who can provide additional information and address any specific questions related to the research. Code availability The statistical analysis and data processing in this study were performed using R v.4.2.1, along with various base packages and additional packages. Detailed information about the specific packages used can be found in the Supplementary Note , which accompanies the manuscript. 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Stainton for their valuable administrative assistance. Additionally, we are deeply grateful to A.T. Brunger’s support for this work. This work was supported by National Institutes of Health (NIH) grants U54DK102556 (M.P.S.), R01 DK110186-03 (M.P.S.), R01HG008164 (M.P.S.), NIH S10OD020141 (M.P.S.), UL1 TR001085 (M.P.S.) and P30DK116074 (M.P.S.) and by the Stanford Data Science Initiative (M.P.S.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Author information These authors contributed equally: Xiaotao Shen, Chuchu Wang. Authors and Affiliations Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA Xiaotao Shen, Xin Zhou, Wenyu Zhou, Daniel Hornburg, Si Wu & Michael P. Snyder Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore Xiaotao Shen School of Chemistry, Chemical Engineering and Biotechnology, Singapore, Singapore Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA Chuchu Wang Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA Xin Zhou & Michael P. Snyder You can also search for this author in PubMed   Google Scholar Contributions X.S. and M.P.S. conceptualized and designed the study. X.Z. and W.Z. prepared the microbiome data. D.H. and W.S. prepared the lipidomics data. X.S. and C.W. conducted the data analysis. X.S. and C.W. prepared the figures. X.S., C.W. and M.P.S. contributed to the writing and revision of the manuscript, with input from other authors. M.S. and X.S. supervised the overall study. Corresponding author Correspondence to Michael P. Snyder . Ethics declarations Competing interests. M.P.S. is a co-founder of Personalis, SensOmics, Qbio, January AI, Filtricine, Protos and NiMo and is on the scientific advisory boards of Personalis, SensOmics, Qbio, January AI, Filtricine, Protos, NiMo and Genapsys. D.H. has a financial interest in PrognomIQ and Seer. All other authors have no competing interests. Peer review Peer review information. Nature Aging thanks Daniel Belsky and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Extended data Extended data fig. 1 demographic data of all the participants in the study.. a , The ages positively correlate with BMI. The shaded area around the regression line represents the 95% confidence interval. b , Gender with age. c , Ethnicity with age. d , Insulin response with age. e , biological sample collection for all the participants. f , Overlap of the different kinds of omics data. g , The age range for each participant in this study. Extended Data Fig. 2 Most of the molecules change nonlinearly during human aging. a , Differential expressional microbes in different age ranges compared to baselines (25 – 40 years old, two-sided Wilcoxon test, p -value < 0.05). b , Most of the linear changing molecules and microbiota are also included in the molecules/microbes that significantly dysregulated at least one age range. Extended Data Fig. 3 Omics data can represent aging. PCA score plot of metabolomics data ( a ), cytokine ( b ), and oral microbiome ( c ). Extended Data Fig. 4 Functional analysis of molecules in different clusters. a , The Jaccard index between clusters from different datasets. b , The overlap between clusters using different types of omics data. c , Functional module detection and identification. d , Functional analysis of nonlinear changing molecules for all clusters. Extended Data Fig. 5 Function annotation for significantly dysregulated molecules in crest 1 and 2. a , Transcriptomics data. b , Proteomics data. c , Metabolomics data. Extended Data Fig. 6 Pathways enrichment results for crest 1 and 2. a , The final functional modules identified for Crest 1 and 2. b , The pathway enrichment analysis results for transcriptomics data. c , The pathway enrichment analysis results for proteomics data. d , The pathway enrichment results for metabolomics data. Supplementary information Supplementary figs. 1–6, reporting summary, supplementary data analysis results of the study., rights and permissions. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . Reprints and permissions About this article Cite this article. Shen, X., Wang, C., Zhou, X. et al. Nonlinear dynamics of multi-omics profiles during human aging. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00692-2 Download citation Received : 09 December 2023 Accepted : 22 July 2024 Published : 14 August 2024 DOI : https://doi.org/10.1038/s43587-024-00692-2 Share this article Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Quick links Explore articles by subject Guide to authors Editorial policies Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Pardon Our Interruption As you were browsing something about your browser made us think you were a bot. There are a few reasons this might happen: You've disabled JavaScript in your web browser. You're a power user moving through this website with super-human speed. You've disabled cookies in your web browser. 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Advanced Search Journal List Arq Bras Cardiol v.103(3); 2014 Sep Case 4/2014 - A 66-Year-Old Man with Acute Myocardial Infarction and Death in Asystole after Primary Coronary Angioplasty A 66-year-old man sought medical care at the hospital due to severe chest pain lasting for 24 hours. The patient was aware of being hypertensive and was a smoker. Without any prior symptom, he started to have severe chest pain and sought emergency medical care after about 24 hours, due to pain persistence. At physical examination (August 13, 2005, 10 PM) he had a heart rate of 90 bpm and blood pressure of 110/70 mmHg. Lung examination showed no alterations. Heart assessment showed a systolic murmur in the lower left sternal border and mitral area. The initial electrocardiogram (August 13, 2005, 22 h) showed HR of 100 bpm, sinus rhythm, 1 st -degree atrioventricular block (PR 240 ms), low-voltage QRS complexes in the frontal plane, QRS complex electrical alternans and extensive ongoing anterior wall infarction (QS V1 to V6, ST elevation in the same leads and QS in the inferior wall, II, III and aVF) ( Figure 1 ). ECG: low QRS voltage in the frontal plane, electrical alternans of QRS complexes, electrically inactive lower wall area and extensive ongoing myocardial infarction. Acetylsalicylic acid by oral route and 5 mg of intravenous metoprolol were administered. The patient had bradycardia and cardiorespiratory arrest in pulseless electrical activity, reversed after five minutes. He developed hypotension and peripheral hypoperfusion and was transferred to InCor (The Heart Institute). On admission he had received heparin and continuous intravenous norepinephrine. BP was 60/30 mmHg. The ECG (August 13, 2005, 11:36 PM) disclosed heart rate of 116 bpm, junctional escape rhythm with sinus arrest and atrial extrasystoles); low-voltage QRS complex in the frontal plane, extensive ongoing anterior acute myocardial infarction, inactive area in the inferior wall; presence of ST elevation at V1 to V5 and ST depression in leads I, II and aVF; ST elevation in aVR ( Figure 2 ). ECG: low QRS voltage in the frontal plane, electrically inactive lower wall area and anterior myocardial infarction with increased ST elevation, still with positive T waves, "hyperacute phase of myocardial infaction". Coronary angiography was indicated, which disclosed anterior interventricular branch occlusion and images suggestive of intracoronary thrombus, lesion of 70% in the circumflex artery, 50% in the right coronary artery and 70% in the ostium of the right posterior descending branch. Angioplasty was performed with stent implant in the anterior interventricular artery, but distal flow was not restored. This was followed by cardiac arrest in asystole, which did not respond to treatment and the patient died. Clinical Aspects This clinical case reports on a 66-year-old hypertensive patient, long-term smoker, who sought medical care due to acute chest pain. The main diagnostic hypothesis for this clinical case is of acute coronary syndrome. Chest pain is one of the most common reasons for seeking emergency care and remains a challenge for the clinician, due to the difficulty in differentiating between non-emergency diagnoses and those of high morbidity and mortality, such as acute coronary syndrome (ACS), aortic dissection and pulmonary thromboembolism. In the assessment of acute chest pain, there are three basic parameters for its management: clinical examination (clinical history and physical examination), electrocardiogram (ECG) and myocardial necrosis markers. They should be analyzed together to provide a safer approach to the patient, especially when it is necessary ruling out ACS. Chest radiography, chest Computed Tomography Angiography (CTA), echocardiography and other tests may be useful in the differential diagnosis. Approximately 15-25% of patients presenting in the ER with chest pain are diagnosed with acute coronary syndrome, and this represents its more frequent clinical manifestation 1 . Therefore, in the first step of the evaluation, which is the clinical examination, the greater determinant of an ischemic etiology is the characteristic presence of angina. Angina is often described as a burning or compression sensation or difficulty breathing, located in the precordial region or any other region of the chest, radiating to the neck, shoulder and left arm. It usually increases in intensity within minutes and may be accompanied by symptoms such as nausea and sweating. It can be triggered by physical or emotional stress and relieved by rest or use of nitrates. It should also be remembered that ACS can occur without obvious precipitating factors and be asymptomatic or present as ischemic equivalent, especially in the elderly and diabetic patients with autonomic dysfunction (dyspnea, syncope and pre-syncope). On the other hand, there are characteristics of pain that make the diagnosis of ACS unlikely, such as pleuritic pain (reproduced by respiratory movements) located with the fingertip, pain in meso/hypogastrium region and reproduction of pain with local palpation or movement. These features raise the suspicion of other differential diagnoses such as pericarditis, pleuritis, gastrointestinal or musculoskeletal diseases. In the present case report, the patient presented with prolonged chest pain, which does not rule out acute coronary syndrome (ACS), but raises the possibility of some condition associated with this coronary picture, such as pericarditis or mechanical complications. Among the most important risk factors for atherosclerotic disease risk are dyslipidemia, diabetes mellitus, hypertension, male gender, older age, obesity/metabolic syndrome, smoking, sedentary lifestyle, chronic kidney disease, depression and stress. This patient had some risk factors that contributed to the development of coronary artery disease: age, male gender, hypertension and smoking. Patients with chest pain and ACS often have a nonspecific physical examination, with less than 20% of them showing significant alterations in the initial evaluation 2 . This becomes important by helping in the detection of differential diagnoses (e.g., pericardial friction rub in pericarditis) or by inferring the presence of risk factors for coronary artery disease (abdominal or carotid murmur, among others). However, when findings resulting from an ACS are present, they indicate a worse prognosis due to mechanical complications or due to a large area of myocardium at risk and ventricular dysfunction (hypotension, tachycardia, pulmonary edema and mitral regurgitation murmur secondary to ischemia). The electrocardiogram is important in the diagnostic, prognostic and therapeutic approach and must be obtained within 10 minutes after the presentation of patients with ongoing chest pain 2 . A normal electrocardiographic tracing does not exclude the possibility of ACS and a serial ECG is indicated, which increases its sensitivity and helps differentiating between acute and chronic alterations. The patient reported in this clinical case had, at the admission ECG performed at another service, ST-segment elevation in the anterior wall, suggesting the hypothesis of acute coronary syndrome with ST-segment elevation. However, this ECG also showed low voltage and electrical alternans of the QRS complex, which suggests large pericardial effusion or even cardiac tamponade. The main hypothesis for this pericardial effusion is a mechanical complication of myocardial infarction: left ventricular free wall rupture. It occurs within 24 hours after infarction or between the third and fifth day, has an incidence of 0.8 to 6.2% and is more common in an extensive myocardial infarction, in the elderly, women and hypertensive patients. Its clinical course is variable 3 and may be acute and severe, leading to sudden death or subacute, with nonspecific clinical manifestations. Other mechanical complications that may be present in myocardial infarction are papillary muscle and interventricular septum rupture. These complications do not present with significant pericardial effusion and normal pulmonary auscultation in this patient also makes the diagnosis of papillary muscle rupture less likely. This clinical condition presents with pulmonary congestion due to volume overload secondary to acute mitral regurgitation. Another diagnostic hypothesis for this patient presenting with chest pain and pericardial effusion would be aortic dissection. Pain, in these cases, is usually of sudden onset and strong intensity since the beginning (unlike angina pain, which often increasingly escalates). It is often described as excruciating and its location reflects the site and progression of the dissection. Autonomic signs (pallor, profuse sweating) are greatly associated. In aortic dissection, physical examination may disclose hypertensive crisis, differences between limb pulses, signs of pleural and pericardial effusion, diastolic murmur of aortic regurgitation, different from the systolic murmur detected in this clinical case. The extension of the dissection to other vessels can lead to other symptoms corresponding to ischemia of the organs irrigated by them: cerebrovascular accident, acute myocardial infarction, mesenteric ischemia, etc. Another diagnostic hypothesis for the clinical case is pulmonary embolism. The absence of pulmonary symptoms, mainly dyspnea, makes this hypothesis less likely. It is the most common symptom of this disease, occurring in over 78% of the patients 4 . Sudden chest pain of sudden onset and very often pleuritic, affects up to 44% of patients 4 . Cough and hemoptysis may also occur. Additionally, there was no mention is made on admission at the other service, of right ventricular dysfunction manifestations, such as jugular stasis and hypotension. The patient, an hour and 36 minutes after his admission at the Heart Institute, was submitted to coronary angiography with left anterior descending artery angioplasty. However, he developed asystole and cardiac arrest. The main diagnoses for the final clinical picture are cardiogenic shock and/or distributive shock due to cardiac tamponade, discussed below. The hypothesis of cardiogenic shock should be considered, as the patient had an extensive acute myocardial infarction without culprit artery reperfusion even after percutaneous revascularization attempt. However, this diagnosis cannot fully explain the patient's clinical condition, such as the absence of pulmonary congestion, which usually follows an acute myocardial failure. Considering the patient's history, late cardiac tamponade seems to have been the main precipitating factor of the final clinical picture in this case. The electrocardiographic findings commonly observed in cardiac tamponade are low voltage and electrical alternans of the QRS complex, observed in the case. Although physical examination made no reference to clinical findings suggestive of tamponade, such as jugular stasis or muffled heart sounds, we cannot exclude this diagnostic hypothesis. An echocardiography could have been performed to confirm this diagnosis, which is the most widely used noninvasive method for diagnostic investigation of this pathology. The ventriculography in this context would not be informative, as it was a free wall rupture with cardiac tamponade and thus, it would not allow the visualization of contrast leakage into the pericardial cavity. This is a patient with myocardial infarction that came at the emergency room more than 24 hours after the onset of the event and who probably had a mechanical complication of myocardial infarction: ventricular free wall rupture. Most deaths from myocardial infarction occur in the first hours of disease onset, with 40-65% occurring within the first hour and approximately 80% in the first 24 hours 5 , 6 . The recently implemented therapies for MI treatment have been proven to modify patient evolution and prognosis. However, the effectiveness of most of these measures is time-dependent and delay in seeking medical care may have been the factor that likely contributed to the clinical outcome of the patient in this case report. (Dr. Wilma Noia Ribeiro, Dr. Alice Tatsuko Yamada) Diagnostic hypotheses: Acute myocardial infarction with mixed shock (cardiogenic - distributive) by mechanical complication - free wall rupture with tamponade (Dr. Wilma Noia Ribeiro, Dr. Alice Tatsuko Yamada) The heart weighed 414 g. The myocardium of the left ventricular anterosseptal wall and right ventricular anterior wall was softened, slightly yellowish in color, characterizing extensive transmural acute myocardial infarction. There was an obvious narrowing of the affected anterosseptal wall, with ventricular septum rupture in the anterior region of its mid portion, with a ventricular septal defect measuring 10 mm in its longest axis. The other left ventricular walls showed to be slightly hypertrophic and there was a small area of fibrosis in the postero-inferior region of the ventricular septum. There was also moderate right ventricular dilation ( Figure 3 ). Cross-section of the ventricles showing left ventricular transmural infarction of the anterosseptal wall (asterisks) and of the right ventricular anterior wall (arrows). The explorer shows the VSD secondary to septal rupture. Observe the ventricular wall thinning and the small area of fibrosis in the ventricular septum (arrowhead). Histological analysis confirmed the presence of myocardial infarction, with marked neutrophil infiltration, confirming histological dating of 24-48 hours of onset. Another small ongoing microinfarction was observed in the posterior region of the ventricular septum, in addition to the previously described small area of fibrosis (healed infarction), compatible with approximately 7-10 days of evolution. There was superficial fibrin deposition in the epicardium, with the presence of reactive inflammatory infiltrate. Examination of the initial segment of the left anterior descending artery showed fatty atherosclerotic plaques with areas of marked thinning of the fibrous cap that covered the lipid core and 80% of obstruction. There were also areas of plaque rupture and hemorrhage, with acute thrombosis in the first and second centimeters of that artery ( Figs. 4 and ​ and5 5 ). Histological section of the first centimeter of the left anterior descending artery showing large lipid-core atherosclerotic plaque, with internal area of fibrin deposition and hemorrhage (asterisk). Observe the area with marked thinning of the fibrous cap of the lipid core plaque (arrow), site of potential rupture and thrombosis. Hematoxylin-eosin, 2.5×. Histological section of the second centimeter of the anterior interventricular artery showing large lipid-core atherosclerotic plaque with ruptured area (arrows) and occlusive luminal thrombosis (asterisk). Hematoxylin-eosin, 2.5×. The lungs weighed 1,208 g together and showed alveolar edema. The kidneys showed irregular surface and retention cysts, with hyaline arteriolosclerosis on histological examination. The aorta showed mild / moderate degree of atherosclerosis. (Dr. Luiz Alberto Benvenuti) Anatomopathological diagnoses. Coronary atherosclerosis; acute myocardial infarction involving the left ventricular anterosseptal wall and the right ventricular anterior wall; rupture of the ventricular septum, with VSD; acute pulmonary edema (cause of death) (Dr. Luiz Alberto Benvenuti) . This is the case report of a 66-year-old man with systemic hypertension and a chronic smoker that presented with acute severe chest pain. After medical assessment, he was diagnosed with acute myocardial infarction and the patient underwent coronary angiography, which disclosed proximal occlusion of the left anterior descending artery with images suggesting the presence of thrombi. He was submitted to balloon-angioplasty in the affected segment without restoration of distal coronary flow (unsuccessful procedure) and the patient developed irreversible cardiac arrest and died. The autopsy confirmed acute myocardial infarction, which was very extensive, affecting the left ventricular anterosseptal wall and the right ventricular anterior wall. Histological dating was 24-48 hours of onset, consistent with the clinical history. It is noteworthy the fact that the detailed examination of the ventricular septum showed the presence of two previous microinfarctions, an old (healed) one and an ongoing one. The presence of atherosclerosis of the coronary arteries was identified, with massive plaques in the proximal segment of the left anterior descending artery, which resulted in chronic obstruction of 80% of the lumen. The fatty plaques had extensive lipid cores and there were areas of marked thinning of the fibrous cap that covered the cores, as well as areas of rupture associated with acute thrombosis of the remaining lumen in the first two centimeters of the left anterior descending artery. It is known that acute coronary occlusions with luminal thrombosis are usually associated with large lipid-core plaques, which undergo rupture due to the instability of their thin fibrous cap 7 , as observed in this case. Aside from the great extent of the infarcted area, the patient developed an important mechanical complication of acute myocardial infarction, the occurrence of ventricular septal rupture with the establishment of VSD 8 - which certainly aggravated his hemodynamic condition, progressing to cardiogenic shock -, acute pulmonary edema and death. It should be emphasized that the patient had two classic risk factors for atherosclerosis and myocardial infarction: systemic hypertension and chronic smoking 9 . (Dr. Luiz Alberto Benvenuti) Section Editor: Alfredo José Mansur ( rb.psu.rocni@rusnamja ) Associated Editors: Desidério Favarato ( rb.psu.rocni@otaravaflcd ) Vera Demarchi Aiello ( rb.psu.rocni@arevpna ) 496 COMMENTS 21-Year-old male with severe coronary atherosclerosis The patient received a coronary artery bypass surgery at just 21 years of age. He exhibits traditional risk factors of CHD; hypertension, dyslipidemia, cigarette smoking, and obesity. CHD may be more prevalent in the younger population than once was thought, and it is important that risk factor reduction starts in early in life.>. 21-Year-old male with severe coronary atherosclerosis The patient received a coronary artery bypass surgery at just 21 years of age. He exhibits traditional risk factors of CHD; hypertension, dyslipidemia, cigarette smoking, and obesity. CHD may be more prevalent in the younger population than once was thought, and it is important that risk factor reduction starts in early in life.>. Case report: Coronary atherosclerosis in a patient with long-standing Importantly, this case demonstrates that coronary atherosclerosis can occur even with long-standing LDL-C less than 40 mg/dl when other risk factors such as hypertension and diabetes are present and otherwise well controlled. Reports of atherosclerosis in individuals with lifelong very low LDL-C are extremely rare . The patient in this instance ... Case Report: Multiple atherosclerotic plaques at its extreme in This case is an important reminder to all clinicians that atherosclerosis often involves multiple arterial territories. The weaknesses of the case report include significant omitted details regarding the history, physical examination, and objective findings. The timeline of the patient's management is also unclear. A Prospective Natural-History Study of Coronary Atherosclerosis Between October 29, 2004, and June 8, 2006, a total of 697 patients with acute coronary syndromes were enrolled after they had undergone successful percutaneous coronary intervention ( Table 1 ... PDF Pathophysiology and treatment of atherosclerosis Atherosclerosis is a chronic condition in which arteries harden through build-up of plaques. Main classical risk factors for atherosclerosis include dyslipoproteinaemia, di-abetes, cigarette smoking, hypertension and genetic abnor-malities. In this review, we present an update on the patho-physiology of atherosclerosis and related current and ... (PDF) EC CARDIOLOGY Mini Case Study Atherosclerosis Presenting as Case Report A 65 year old asymptomatic female was suspected to have a distal ascending aortic aneurysm and a moderate Aortic Regurgitation with some aortic sclerosis on routine health checkup. Progression of Early Subclinical Atherosclerosis (PESA) Study: Introduction. Cardiovascular disease (CVD) is the leading cause of mortality and morbidity worldwide and represents the highest proportion of costs to health care systems (1,2).Global CVD prevalence is growing, and if this trend continues, it will threaten the ability of health care systems to cope with its consequences ().Most forms of CVD originate in atherosclerosis, which is thus the main ... Arteriosclerosis and Atherosclerosis Arteriosclerosis and Atherosclerosis 1215. 3. Sawabe M, Takahashi R, Matsushita S, Ozawa T, Arai T, Hamamatsu A, Nakahara K, Chida K, Yamanouchi H, Murayama S, Tanaka N. Aortic pulse wave velocity and the degree of atherosclerosis in the elderly: a pathological study based on 304 autopsy cases. Atherosclerosis. 2005; 179:345-351. (PDF) A Multiscale and Patient-specific Computational Framework of A Multiscale and Patient-specific Computational Framework of Atherosclerosis Formation and Progression: A Case Study in the Aorta and Peripheral Arteri es.pdf Content available from CC BY-NC-ND 4.0: IJMS Atherosclerosis is the main risk factor for cardiovascular disease (CVD), which is the leading cause of mortality worldwide. Atherosclerosis is initiated by endothelium activation and, followed by a cascade of events (accumulation of lipids, fibrous elements, and calcification), triggers the vessel narrowing and activation of inflammatory pathways. The resultant atheroma plaque, along with ... Frontiers Importantly, this case demonstrates that coronary atherosclerosis can occur even with long-standing LDL-C less than 40 mg/dl when other risk factors such as hypertension and diabetes are present and otherwise well controlled. Reports of atherosclerosis in individuals with lifelong very low LDL-C are extremely rare . The patient in this instance ... Coronary Artery Disease Discovery of these common variants comes at the heel of large genome-wide association studies now being conducted on an increasingly larger scale. 2,3 As the name of these ... Atherosclerosis. 2011;219:799-806. doi: 10.1016/j.atherosclerosis ... a prospective case-control study. Lancet Diabetes Endocrinol. 2015;3:507-513. doi: 10. ... Atherosclerosis: Process, Indicators, Risk Factors and New Hopes Atherosclerosis is the major cause of morbidities and mortalities worldwide. In this study we aimed to review the mechanism of atherosclerosis and its risk factors, focusing on new findings in atherosclerosis markers and its risk factors. Furthermore, the role of antioxidants and medicinal herbs in atherosclerosis and endothelial damage has ... A Prospective Natural-History Study of Coronary Atherosclerosis Between October 29, 2004, and June 8, 2006, a total of 697 patients with acute coronary syn-dromes were enrolled after they had undergone successful percutaneous coronary intervention (Table 1 ... Cholesterol and Atherosclerotic Cardiovascular Disease: A Lifelong In this issue of the Journal of the American Heart Association (JAHA), Duncan et al apply the technique of trajectory analysis to a more‐contemporary cohort of the Framingham Heart Study, followed for 35 years to determine the relationship of lifelong exposure to elevated LDL cholesterol and low high‐density lipoprotein cholesterol to ASCVD and total mortality. 6 For LDL cholesterol, they ... Clinical case: The development of atherosclerosis in a patient 28 years Aim: The purpose of the study was to determine the presence of cardiovascular disease in young patient with a rare monogenic form of diabetes mellitus (MODY12). Clinical case: The development of atherosclerosis in a patient 28 years old with 12 mody diabetes - Atherosclerosis A 52-Year-Old Man With Atherosclerosis His family history is notable for a myocardial infarction (MI) in his father at the age of 52 years. He is a lifelong non-smoker. He does not take medications. His blood pressure was 110/75. His exam was notable for being overweight with a BMI of 27, but was otherwise unremarkable. His total cholesterol is 206 mg/dL, HDL-C is 46 mg/dL ... Atherosclerosis Atherosclerosis is a chronic inflammatory disease of the arteries and is the underlying cause of about 50% of all deaths in westernized society. It is principally a lipid-driven process initiated by the accumulation of low-density lipoprotein and remnant lipoprotein particles and an active inflammatory process in focal areas of arteries particularly at regions of disturbed non-laminar flow at ... Study on Correlation of Homocysteine Level and Ischemic Stroke Epidemiologic studies have identified hyperhomocysteinemia as a possible risk factor for atherosclerosis. We determined the risk of carotid-artery atherosclerosis in relation to both plasma ... Nonlinear dynamics of multi-omics profiles during human aging Aging is a complex and multifactorial process of physiological changes strongly associated with various human diseases, including cardiovascular diseases (CVDs), diabetes, neurodegeneration and ... Updates on Approaches for Studying Atherosclerosis Many animal models have been used to study atherosclerosis, as summarized in the recent American Heart Association Scientific Statement, 3 which provide suggestions on selecting appropriate animal models for a specific atherosclerosis study. Given its value and feasibility as a proof-of-principle model, mouse models have continued to be the most common species to study atherosclerosis. Case 5/2015 The microscopic study of the coronary arteries revealed recent partial thrombosis on the seventh cm of the right coronary artery, in addition to atherosclerosis with 80% obstruction. The anterior interventricular and circumflex branches of the left coronary artery had stents placed several years before and were submitted to special processing ... Nervous Tissue-Case Study (pdf) Nervous System case study Name:_____ Going Under the Knife: A Case on Membrane Structure and Function Twenty-year-old Kevin groaned and clutched his abdomen as he lay on the emergency room gurney. He had just been diagnosed with acute appendicitis and was waiting to be taken to the operating room (OR). Although he desperately wanted the pain to stop, Kevin was terrified of having general ... Case 4/2014 This is the case report of a 66-year-old man with systemic hypertension and a chronic smoker that presented with acute severe chest pain. After medical assessment, he was diagnosed with acute myocardial infarction and the patient underwent coronary angiography, which disclosed proximal occlusion of the left anterior descending artery with ... assignment essay homework paper phd report resume review speech thesis