Background: The accumulation of multiple chronic diseases (multimorbidity) is a major public health challenge. However, the patterns of disease accumulation over time and their underlying biological drivers remain poorly understood. We aimed to identify distinct multimorbidity trajectories and their associated baseline metabolomic signatures. Methods: We conducted a prospective analysis of 7,441 community-dwelling Japanese adults aged ≥40 years, free of cardiovascular disease and cancer at baseline. Using claims data from 2015 to 2021, we calculated the monthly cumulative Charlson Comorbidity Index (CCI) for each participant. We applied k-means clustering to 5-year CCI data to identify distinct multimorbidity trajectories. Baseline plasma metabolomic profiles were analyzed using ordinal logistic regression to identify metabolites associated with trajectory progression. Subsequently, we used adjusted Cox proportional hazards models to assess the association between these progression-associated metabolites and the risk of future incident diseases over a 5-year follow-up. Results: We identified 3 distinct multimorbidity trajectories among 7,256 participants with complete follow-up: Stable Low (n=6,367), Gradual Increase (n=620), and Rapid Increase (n=269). The Rapid Increase group was characterized by older age and higher baseline CCI. Ordinal logistic regression identified 38 baseline metabolites significantly associated with a higher odds of being in a more progressive trajectory (FDR < 0.05). These metabolites were primarily involved in urea cycle/arginine metabolism and fatty acid metabolism. In disease-specific analyses, several progression-associated metabolites predicted incident disease. For instance, higher baseline levels of Phenylalanine, a top predictor of trajectory progression, were associated with an increased risk of both incident congestive heart failure (HR: 1.30; 95% CI: 1.11-1.52) and chronic pulmonary disease (HR: 1.26; 95% CI: 1.09-1.45). Conversely, higher levels of the medium-chain fatty acid Hexanoate were associated with a reduced risk of congestive heart failure (HR: 0.76; 95% CI: 0.67–0.87). Conclusion: Distinct data-driven multimorbidity trajectories exist and can be predicted by a baseline metabolomic signature. This signature is also linked to the future risk of specific cardiometabolic diseases, suggesting a potential role for metabolomics in the early risk stratification and prevention of multimorbidity.

AIMS:The association between alcohol consumption and the risk of cardiovascular disease (CVD) varies according to the presence of underlying cardiovascular risk factors. Incorporating such risk factors may be important to effectively reduce harmful alcohol use through health guidance. However, whether the risk of alcohol consumption is affected due to renal impairment remains unclear. METHODS:A total of 10,583 community-dwelling Japanese adults (mean age 59.4 (SD 10.1) years; 46% men) were followed for 10 years. Alcohol consumption was categorized into five groups for men: never drinkers, former drinkers, light drinkers (＜20 g/day), moderate drinkers (20-39 g/day), and heavy drinkers (≥ 40 g/day), and four groups for women with moderate and heavy combined. Cox proportional hazards models estimated hazard ratios for incident CVD stratified by the presence or absence of reduced estimated glomerular filtration rate (eGFR) (＜60 mL/min/1.73m2) and proteinuria, with adjustment for confounders. RESULTS:Among men with proteinuria, alcohol consumption was associated with a higher risk of atherosclerotic CVD, whereas an inverse association was observed in men without proteinuria [hazard ratio (95% confidence interval): moderate drinkers without proteinuria, 0.58 (0.34-0.97); moderate drinkers with proteinuria, 3.49 (1.15-10.56)]. Moderate to heavy drinking increased the risk of intracerebral hemorrhage irrespective of the renal status. In women, moderate to heavy drinking was associated with an increased CVD risk only when proteinuria was present. In contrast, a reduced eGFR did not clearly affect the association in either sex. CONCLUSIONS:The CVD risk associated with alcohol consumption may differ according to the renal status, particularly depending on the presence or absence of proteinuria.

Introduction: Multimorbidity, the co-occurrence of multiple chronic diseases, is associated with increased mortality and reduced quality of life. While cardiometabolic risk factors (CMRs) are strongly linked with cardiovascular events, their relationship with disease patterns across diverse organ systems remains unexplored. We examined associations between baseline CMRs and longitudinal chronic disease clustering patterns. Hypothesis: Baseline CMRs are associated with distinct multimorbidity patterns involving multiple disease domains. Methods: This study was performed with Tsuruoka Metabolomics Cohort Study, which included 11,002 participants (men 46.5%) at baseline in 2012. Of these, 3,396 participants (men 43.3%) who completed three surveys over six years were analyzed. Eighteen self-reported chronic diseases over six years were categorized into seven groups: inflammatory/tumorous gastrointestinal, ulcerative gastrointestinal, metabolic/renal, respiratory, ophthalmic, psychiatric, and skeletal/immune diseases. K-means clustering was applied to classify participants based on their prevalences of seven groups across the three surveys. Baseline CMRs included hypertension, hyperglycemia, dyslipidemia and overweight as defined by clinical guidelines. Multinomial logistic regression was performed to estimate odds ratios (ORs) for cluster membership versus the healthiest cluster, adjusting for age, sex, alcohol, and smoking. Results: A total of seven clusters were identified: one healthy and six disease-dominant clusters, including two inflammatory/tumorous gastrointestinal clusters (persistent and newly developed), metabolic/renal, ophthalmic with skeletal/immune, ulcerative gastrointestinal, and complex multimorbidity patterns. Baseline dyslipidemia showed elevated ORs in three clusters: persistent inflammatory/tumorous gastrointestinal (OR 1.59), metabolic/renal (OR 2.61), and complex multimorbidity (OR 1.81). The metabolic/renal cluster uniquely demonstrated multiple CMR associations with elevated ORs for hypertension (OR 1.60), dyslipidemia (OR 2.61), and overweight (OR 1.76). Conclusions: Baseline CMRs, particularly dyslipidemia, are associated with distinct multimorbidity patterns across diverse organ systems. These findings demonstrate that the impact of CMRs extends beyond the cardiovascular system, underscoring their relevance to multimorbidity across multiple domains. Integrated preventive strategies targeting CMRs may reduce multimorbidity burden.

OBJECTIVES:To investigate whether employment characteristics and psychosocial work factors are associated with the development of severe menopausal symptoms among working women. METHODS:We longitudinally analysed 648 working women aged 35-55 years without menopausal symptoms interfering with daily life at baseline (2012-2014). Employment characteristics included employment status, managerial position, shift work and working hours. Psychosocial work factors included workload, job control, job strain and effort-reward imbalance. At follow-up (2018-2020), women in the menopausal transition or postmenopause who reported menopausal symptoms that interfered with daily life during follow-up were classified as having developed severe menopausal symptoms. Associations between baseline work factors and the development of severe menopausal symptoms were examined using modified Poisson regression adjusted for age, follow-up duration, body mass index, smoking, drinking and educational attainment. Symptom domains (vasomotor, psychological and physical) were also evaluated. RESULTS:During 5.3±0.5 years of follow-up, 68 women (10.5%) developed severe menopausal symptoms. Per 1-SD increase, higher workload (risk ratio (RR) 1.34, 95% CI 1.04 to 1.73), job strain ratio (RR 1.30, 95% CI 1.10 to 1.54) and effort-reward ratio (RR 1.23, 95% CI 0.99 to 1.51) were associated with a higher risk, whereas higher job control was protective (RR 0.78, 95% CI 0.63 to 0.97). Results were similar after additional adjustment for menopausal, marital and parental status. Domain-specific analyses showed similar associations across symptoms, strongest for physical symptom severity. CONCLUSIONS:Psychosocial work factors, particularly high workload, low job control and high job strain, were associated with the development of severe menopausal symptoms. These findings highlight the importance of psychosocial work environments in supporting women during the menopausal transition.

CONTEXT:Previous metabolomics studies suggest potential associations between menopausal changes in lipids and an increased risk of metabolic syndrome (MetS). However, longitudinal data on other key metabolites, such as branched-chain amino acids (BCAAs) and homocysteine, remain limited, and most studies lack long-term follow-up across the menopause transition. OBJECTIVE:This study aimed to investigate longitudinal changes in circulating metabolites during menopause over a mean follow-up of 5 years and assess their associations with subsequent MetS development. METHODS:Premenopausal women from the Tsuruoka Metabolomics Cohort Study who participated in at least one follow-up survey were included. Menopausal status, data on MetS, and plasma metabolites profiled using capillary electrophoresis mass spectrometry were assessed at each visit. Thirty-one metabolites were examined for associations with menopausal status using mixed-effects models. The association of these menopause-related metabolites with MetS development was examined via logistic regression analysis adjusted for follow-up duration. RESULTS:Among 953 women (aged 43.8 ± 5.4 years), 316 (33.2%) reached menopause during follow-up (5.0 ± 1.1 years). Eighteen metabolites changed significantly with menopause, particularly those related to BCAA metabolism, urea cycle, and homocysteine metabolism. Of 695 women without MetS at baseline, 65 (9.4%) developed MetS. Glutamate (odds ratio [95% CI]: 1.95 [1.49-2.57]) was associated with higher MetS risk. Higher levels of glutamate, valine, leucine, and cystine were significantly associated with the development of hyperglycemia. CONCLUSION:Longitudinal changes in charged metabolites occur across the menopausal transition, with specific metabolites such as glutamate possibly contributing to the metabolic alterations underlying increased MetS risk.

Age-related disease burden accumulates heterogeneously from later midlife to older age, but the biology underlying these divergent trajectories is poorly understood. We analysed 7199 adults aged 40 years and over in the Tsuruoka Metabolomics Cohort Study, Japan, with baseline fasting plasma metabolomics (94 metabolites measured by capillary electrophoresis–mass spectrometry) and linked health insurance claims. Monthly cumulative Charlson Comorbidity Index scores were constructed from aligned cohort entry to 60 months to capture accumulation of newly documented Charlson conditions after follow-up start. K-means clustering identified six trajectories of claims-recorded disease burden, and ordinal logistic regression related metabolites to ordered trajectory severity with adjustment for demographic and lifestyle factors. Six trajectories ranged from minimal accumulation to rapid progression. Nineteen metabolites were associated with greater trajectory severity after false discovery rate correction. Glutamate showed the strongest positive association (odds ratio, 1.18 per standard deviation; 95

Introduction: The co-occurrence of cardiometabolic risk factors (CMRFs) is strongly associated with all-cause mortality and incident disability. Although social determinants of health (SDoH), including lifestyle and psychosocial factors, have been recognized as important contributors to chronic diseases, their causal relationships with CMRF accumulation have not been sufficiently investigated. Hypothesis: SDoH are causally associated with CMRF accumulation. Methods: A total of 5,332 participants (2,362 men and 2,970 women, ≥40 years at T0) without cardiovascular disease completed initial (T0; 2012–2015) and six-year (T1) surveys in a Japanese population-based cohort. SDoH variables assessed at both timepoints included physical activity, sedentary time, sleep, alcohol, smoking, weight change, Kessler 6, Lubben Social Network Scale, living arrangement, occupational stress, and education. We analyzed cross-sectional associations between CMRFs scores (sum of hypertension, dyslipidemia, impaired glucose metabolism, and overweight, defined according to clinical guidelines) and SDoH at T1, using inverse probability of treatment weighting (IPTW) based on T0 data to estimate causal effects, applying multinomial logistic regression to estimate odds ratios comparing 1, 2, or ≥3 CMRFs versus 0 CMRFs adjusted for sex, age, and smoking. Results: Weight gain since age 20 showed the strongest association with CMRF accumulation, and recent weight gain within the past year was also positively associated with higher CMRF scores. Past smoking was associated with increased odds of CMRF accumulation, while current smoking showed a paradoxical inverse association, likely reflecting reverse causation whereby individuals with higher disease burden had already quit smoking. The cross-sectional association between current alcohol consumption and CMRFs observed in standard multinomial analysis disappeared after inverse probability of treatment weighting adjustment for T0 confounders. Additional modest associations included low weekday physical activity, high psychological distress, and short sleep duration. Conclusions: Both early adulthood weight gain and recent weight gain are the most robust modifiable risk factors for CMRF accumulation. These findings highlight weight management throughout life, warranting targeted interventions in health screening programs. Further investigation of other social determinants is needed to fully understand their contributions to CMRF development.

BACKGROUND:The role of the apoptosis inhibitor of macrophages (AIM) in human lipid metabolism remains unclear. OBJECTIVE:This study aimed to investigate the cross-sectional associations between serum AIM and plasma metabolites and to determine if baseline AIM concentrations predict incident dyslipidemia. METHODS:This study used a community-based cohort of 3139 participants for cross-sectional metabolomic analysis. Among them, 1292 participants without dyslipidemia at baseline were followed prospectively for up to 8.5 years. Cox proportional hazards models were used to estimate hazard ratios for incident high low-density lipoprotein cholesterol (LDL-C) and metabolic dyslipidemia (high triglycerides and/or low high-density lipoprotein cholesterol [HDL-C]) across AIM tertiles. RESULTS:Cross-sectionally, higher AIM concentrations were associated with elevated acylcarnitines and tricarboxylic acid cycle intermediates. Longitudinally, higher baseline AIM was associated with an increased risk of developing metabolic dyslipidemia in women (highest vs lowest tertile hazard ratio, 1.84) and showed a similar but smaller nonsignificant trend in men. The risk for high LDL-C was significantly increased in women but not in men. These associations remained robust after multivariable adjustment. CONCLUSION:Elevated baseline AIM concentrations were prospectively associated with the development of metabolic dyslipidemia in women, with a similar but nonsignificant trend in men, and with high LDL-C in women. The related metabolomic profile suggests enhanced lipolysis and altered lipid metabolism. Therefore, AIM may serve as a biomarker for future dyslipidemia, particularly for triglyceride and HDL-C dysregulation.

Introduction: The association between alcohol consumption and cardiovascular events differs according to the presence or absence of cardiovascular risk factors such as hypertension or dyslipidemia. However, the effect of renal function on that association is unclear in general populations. Hypothesis: Renal function status modifies the association between alcohol consumption and cardiovascular disease (CVD) risk. Methods: We followed 10,515 Japanese community dwellers (4,824 men and 5,691 women) without a history of CVD, who participated in the baseline survey of a population-based cohort study conducted between 2012 and 2015, until June 2023. Participants were classified into five groups based on alcohol consumption status: G1 (non-drinkers), G2 (former drinkers), G3 (>0 to <20g/day), G4 (≥20 to <40g/day), and G5 (≥40g/day). Renal dysfunction was defined as estimated glomerular filtration rate (eGFR) <60mL/min/1.73m 2 or non-negative of semi-quantitative screening tests for urine protein. Hazard ratios (HRs) of alcohol consumption status for CVD, atherosclerotic cardiovascular disease (ASCVD), and intracerebral hemorrhage events were estimated using a Cox proportional hazard model adjusted for confounders. A stratified analysis was performed by renal function status. Results: During a 9.4-year follow-up, 321 CVD, 206 ASCVD, and 52 intracerebral hemorrhage events were identified. Compared to non-drinkers, alcohol consumption reduced the risk of both CVD and ASCVD. However, in participants with non-negative urine protein, alcohol consumption increased CVD and ASCVD risk, while in those with negative urine protein, it reduced the risk [HR (95% CI) for CVD: G2: 1.00 (0.61-1.66), G3: 0.67 (0.46-0.98), G4: 0.73 (0.48-1.10), G5: 0.66 (0.44-0.99) in negative urine protein; G2: 2.79 (1.04-7.44), G3: 1.19 (0.46-3.08), G4: 3.13 (1.29-7.59), G5: 2.29 (0.95-5.47) in non-negative urine protein]. Reduced eGFR did not affect the association between alcohol consumption and the risk of these events. For intracerebral hemorrhage, alcohol consumption increased risk regardless of renal function. Conclusions: Alcohol consumption was associated with lower CVD and ASCVD risk in Japanese community dwellers. However, it increased the risk in those with urinary protein and not in those without urinary protein. Reduced eGFR had no effect. These findings suggest that CVD risk from alcohol consumption should be evaluated considering renal dysfunction and the type of pathophysiology.

Plasma amino acids (AAs) have emerged as promising biomarkers for metabolic disorders, yet their causality remains unclear. We aimed to investigate the genetic determinants of AA levels in a cohort of 10,333 individuals and their causal effects on cardiometabolic traits using Mendelian randomization (MR). Plasma levels of 20 AAs were quantified using capillary electrophoresis mass spectrometry. Genome-wide association studies were conducted using BOLT-LMM and heritability estimation via LDSC analysis. Causal effects of AAs on 11 cardiometabolic traits were examined using two-sample MR analyses. We identified 85 locus-metabolite associations across 43 genes for 18 AAs, including 44 novel loci linked to metabolic genes. Heritability for AAs was estimated at 16%. MR analysis demonstrated cystine to positively associate with systolic blood pressure (SBP) (beta = 0.056, SE = 0.010), while serine indicated protective effects on SBP (beta = - 0.040, SE = 0.011), diastolic BP (beta = - 0.044, SE = 0.010), and coronary artery disease (odds ratio 0.888, SE = 0.028). We identified potentially novel genetic loci associated with AA levels and demonstrated robust causal associations between several AAs and cardiometabolic traits. These findings reinforce the importance of AAs as potential biomarkers and therapeutic targets in cardiometabolic health.

AIMS/INTRODUCTION:Obesity is a known risk factor for several chronic diseases, including type 2 diabetes mellitus, which results from increased insulin resistance and impaired insulin secretion. However, the association between obesity and insulin resistance in Asian populations has not yet been fully elucidated. Therefore, we aimed to investigate the causal relationship between body mass index (BMI) and glycemic traits using Mendelian randomization (MR). MATERIALS AND METHODS:We performed individual-level MR analyses using genetic risk scores based on BMI-related variants in 3,745 individuals without diabetes mellitus from a Japanese cohort. We examined heterogeneity through subgroup analyses based on potential modifiers and determined the shape of the causal relationship using nonlinear MR analyses to further assess the impact of BMI on the homeostasis model assessment of insulin resistance (HOMA-IR). RESULTS:MR analyses revealed a significant positive association between BMI and HOMA-IR (β = 0.077; 95% confidence interval, 0.014-0.141; P = 0.016; outcome variable was log-transformed and standardized). Additional analyses revealed heterogeneity among subgroups differentiated by age, sex, lifestyle habits, and cardiometabolic traits. Nonlinear MR analyses suggested a potential J-shaped causal relationship between BMI and HOMA-IR. CONCLUSIONS:Our findings demonstrated that obesity and low BMI may contribute to increased insulin resistance. Furthermore, the impact of BMI on insulin resistance could vary owing to effect modification. Managing BMI is crucial in individuals at high risk of increased insulin resistance and may have important implications for preventing type 2 diabetes, especially given the low insulin secretory capacity observed in East Asian populations.

Studies examining long-term longitudinal metabolomic data and their reliability in large-scale populations are limited. Therefore, we aimed to evaluate the reliability of repeated measurements of plasma metabolites in a prospective cohort setting and to explore intra-individual concentration changes at three time points over a 6-year period. The study participants included 2999 individuals (1317 men and 1682 women) from the Tsuruoka Metabolomics Cohort Study, who participated in all three surveys—at baseline, 3 years, and 6 years. In each survey, 94 plasma metabolites were quantified for each individual and quality control (QC) sample. The coefficients of variation of QC, intraclass correlation coefficients, and change rates of QC were calculated for each metabolite, and their reliability was classified into three categories: excellent, fair to good, and poor. Seventy-six percent (71/94) of metabolites were classified as fair to good or better. Of the 39 metabolites grouped as excellent, 29 (74%) in men and 26 (67%) in women showed significant intra-individual changes over 6 years. Overall, our study demonstrated a high degree of reliability for repeated metabolome measurements. Many highly reliable metabolites showed significant changes over the 6-year period, suggesting that repeated longitudinal metabolome measurements are useful for epidemiological studies.

Aims: Nonalcoholic fatty liver disease (NAFLD) is known to be associated with atherosclerosis. This study focused on upstream changes in the process by which NAFLD leads to atherosclerosis. The study aimed to confirm the association between NAFLD and the cardio-ankle vascular index (CAVI), an indicator of subclinical atherosclerosis, and explore metabolites involved in both by assessing 94 plasma polar metabolites. Methods: A total of 928 Japanese community-dwellers (306 men and 622 women) were included in this study. The association between NAFLD and CAVI was examined using a multivariable regression model adjusted for confounders. Metabolites commonly associated with NAFLD and CAVI were investigated using linear mixedeffects models in which batch numbers of metabolite measurements were used as a random-effects variable, and false discovery rate-adjusted p-values were calculated. To determine the extent to which these metabolites mediated the association between NAFLD and CAVI, mediation analysis was conducted. Results: NAFLD was positively associated with CAVI (coefficients [95% Confidence intervals (CI)]=0.23 [0.09-0.37]; p=0.001). A total of 10 metabolites were involved in NAFLD and CAVI, namely, branched-chain amino acids (BCAAs; valine, leucine, and isoleucine), aromatic amino acids (AAAs; tyrosine and tryptophan), showed that BCAAs mediated more than 20% of the total effect in the association between NAFLD and CAVI. Conclusions: NAFLD was associated with a marker of atherosclerosis, and several metabolites related to insulin resistance, including BCAAs and AAAs, could be involved in the process by which NAFLD leads to atherosclerosis.

BACKGROUND:Heated tobacco products (HTPs) have gained global popularity, but their health risks remain unclear. Therefore, the current study aimed to identify plasma metabolites associated with smoking and HTP use in a large Japanese population to improve health risk assessment. METHODS:Metabolomics data from 9,922 baseline participants of the Tsuruoka Metabolomics Cohort Study (TMCS) were analyzed to determine the association between smoking habits and plasma metabolites. Moreover, alterations in smoking-related metabolites among HTP users were examined based on data obtained from 3,334 participants involved from April 2018 to June 2019 in a follow-up survey. RESULTS:Our study revealed that cigarette smokers had metabolomics profiles distinct from never smokers, with 22 polar metabolites identified as candidate biomarkers for smoking. These biomarker profiles of HTP users were closer to those of cigarette smokers than those of never smokers. The concentration of glutamate was higher in cigarette smokers, and biomarkers involved in glutamate metabolism were also associated with cigarette smoking and HTP use. Network pathway analysis showed that smoking was associated with the glutamate pathway, which could lead to endothelial dysfunction and atherosclerosis of the vessels. CONCLUSION:Our study showed that the glutamate pathway is affected by habitual smoking. These changes in the glutamate pathway may partly explain the mechanism by which cigarette smoking causes cardiovascular disease. HTP use was also associated with glutamate metabolism, indicating that HTP use may contribute to the development of cardiovascular disease through mechanisms similar to those in cigarette use.

Background: The application of metabolomics-based profiles in environmental epidemiological studies is a promising approach to refine the process of health risk assessment. We aimed to identify potential metabolomics-based profiles in urine and plasma for the detection of relatively low-level cadmium (Cd) exposure in large population-based studies. Method: We analyzed 123 urinary metabolites and 94 plasma metabolites detected in fasting urine and plasma samples collected from 1,412 men and 2,022 women involved in the Tsuruoka Metabolomics Cohort Study. Regression analysis was performed for urinary N-acetyl-beta-D-glucosaminidase (NAG), plasma, and urinary metabolites as dependent variables, and urinary Cd (U-Cd, quartile) as an independent variable. The multivariable regression model included age, gender, systolic blood pressure, smoking, rice intake, BMI, glycated hemoglobin, low-density lipoprotein cholesterol, alcohol consumption, physical activity, educational history, dietary energy intake, urinary Na/K ratio, and uric acid. Pathway-network analysis was carried out to visualize the metabolite networks linked to Cd exposure. Result: Urinary NAG was positively associated with U-Cd, but not at lower concentrations (Q2). Among urinary metabolites in the total population, 45 metabolites showed associations with U-Cd in the unadjusted and adjusted models after adjusting for the multiplicity of comparison with FDR. There were 12 urinary metabolites which showed consistent associations between Cd exposure from Q2 to Q4. Among plasma metabolites, six cations and one anion were positively associated with U-Cd, whereas alanine, creatinine, and isoleucine were negatively associated with U-Cd. Our results were robust by statistical adjustment of various confounders. Pathway-network analysis revealed metabolites and upstream regulator changes associated with mitochondria (ACACB, UCP2, and metabolites related to the TCA cycle). Conclusion: These results suggested that U-Cd was associated with metabolites related to upstream mitochondrial dysfunction in a dose-dependent manner. Our data will help develop environmental Cd exposure profiles for human populations.

The Tsuruoka Metabolomics Cohort Study (TMCS) is an ongoing population-based cohort study being conducted in the rural area of Yamagata Prefecture, Japan. This study aimed to enhance the precision prevention of multi-factorial, complex diseases, including non-communicable and aging-associated diseases, by improving risk stratification and prediction measures. At baseline, 11,002 participants aged 35-74 years were recruited in Tsuruoka City, Yamagata Prefecture, Japan, between 2012 and 2015, with an ongoing follow-up survey. Participants underwent various measurements, examinations, tests, and questionnaires on their health, lifestyle, and social factors. This study used an integrative approach with deep molecular profiling to identify potential biomarkers linked to phenotypes that underpin disease pathophysiology and provide better mechanistic insights into social health determinants. The TMCS incorporates multi-omics data, including genetic and metabolomic analyses of 10,933 participants and comprehensive data collection ranging from physical, psychological, behavioral, and social to biological data. The metabolome is used as a phenotypic probe because it is sensitive to changes in physiological and external conditions. The TMCS focuses on collecting outcomes for cardiovascular disease, cancer incidence and mortality, disability, functional decline due to aging and disease sequelae, and the variation in health status within the body represented by omics analysis that lies between exposure and disease. It contains several sub-studies on aging, heated tobacco products, and women's health. This study is notable for its robust design, high participation rate (89%), and long-term repeated surveys. Moreover, it contributes to precision prevention in Japan and East Asia as a well-established multi-omics platform.