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PubMed2026

Proteomics of multimorbidity progression across cardiometabolic diseases and cancer in a multinational cohort.

BACKGROUND: Multimorbidity, defined here as the co-occurrence of cardiovascular disease (CVD), type 2 diabetes (T2D), and/or cancer is a major public health challenge. However, its underlying biological mechanisms remain unclear, limiting progress toward identifying shared interventional targets. METHODS: We applied large-scale plasma proteomics (SomaScan 7k; 7,289 aptamers) in 13,270 European Prospective Investigation into Cancer and Nutrition (EPIC) participants to identify protein signatures of multimorbidity. We modelled multimorbidity progression as sequential disease transitions, i.e., from the disease-free state at baseline to a first disease and from the first disease to a second disease. Using weighted multivariable Cox regression, we estimated hazard ratios (HR) and 95% confidence intervals (CI) for risk of cancer, CVD, and T2D. Risk associations were replicated using Olink proteomics in UK Biobank (N&#x2009;=&#x2009;44,567). RESULTS: We identified 422 aptamers associated with more than one disease (FDR-corrected P&#x2009;<&#x2009;0.05), e.g., 265 aptamers were shared between CVD and T2D. Thirty-eight aptamers were associated with multimorbidity progression. Among these, 27 aptamers showed consistent positive associations across sequential disease transitions, including SEMA6A (disease-free to cancer HR: 1.14; 95% CI 1.05, 1.23; cancer to T2D HR: 2.61; 95% CI 1.76, 3.80). Four aptamers showed consistent inverse associations, including NLGN1 (disease-free to T2D HR: 0.72; 95% CI 0.61, 0.84; T2D to cancer HR: 0.57; 95% CI 0.43, 0.75). Nineteen of the identified proteins were also measured in UK Biobank, with broadly consistent associations. CONCLUSIONS: This study identifies candidate proteins that may indicate molecular pathways to multimorbidity of cardiometabolic diseases and cancer. Future studies should evaluate the causal roles of these proteins for targeted interventions and risk stratification.

Humans↗
PubMed2026

Discrimination, chronic stress, and multimorbidity in cohort of Black and Latina transgender women with HIV: Longitudinal findings from the LITE Plus study.

Black and Latina transgender women with HIV (BLTWH) are exposed to repeated, intersecting discrimination based on race, gender, and serostatus. Minority stress theory conceptualizes discrimination as minority-specific stressors that drive health inequities. Allostatic load theory posits a pathway between discrimination and chronic disease through multisystem physiological dysregulation caused by chronic stress. To test this pathway, a longitudinal cohort of 108 BLTWH, enrolled December 2020 - June 2022 in Boston, New York City, and Washington, DC, were followed for 24 months, with biomarkers measured at baseline, 12, and 24 months. Questionnaires administered every 6 months assessed anticipated discrimination, everyday discrimination, perceived stress, and other psychosocial factors. Multimorbidity was measured via self-reported non-HIV chronic conditions. In mixed-effects mediation models, allostatic load did not mediate relationships between multimorbidity outcomes and anticipated discrimination (&#x3b2;: -0.0004 [95% CI: -0.004, 0.003]) nor everyday discrimination (&#x3b2;: -0.002 [95%CI: -0.009, 0.003]). Perceived stress demonstrated indirect effects on multimorbidity in unadjusted models of anticipated discrimination (&#x3b2;: 0.024, [95% CI: 0.015, 0.081]) and everyday discrimination (&#x3b2;: 0.021 [95%CI: 0.016, 0.077]). Indirect effects remained significant, with attenuated effects (&#x3b2;: 0.020 for anticipated discrimination; &#x3b2;: 0.017 for everyday discrimination) after adjusting for social support, community connection, and resilient coping. Total effects were only significant for the adjusted model of everyday discrimination (&#x3b2;: 0.056 [0.014, 0.099]). Findings suggest discrimination impacted health through specific psychosocial pathways. Alongside efforts to eliminate intersectional discrimination, stress-lowering interventions and increased access to social support and community connection may be effective approaches to reducing multimorbidity in this highly marginalized group.

Humans↗
PubMed2026

Adolescent depression as a systemic multimorbidity catalyst: integrated genetic and metabolic pathway analysis.

BACKGROUND: Although adolescent depression has been linked to individual chronic conditions, its broader role in shaping multimorbidity risk remains understudied. METHODS: A total of 87,562 UK Biobank participants were included, of whom 18,851 had documented adolescent depression. Cox proportional hazards models were applied to evaluate associations between adolescent depression and 24 chronic diseases, followed by stratified analyses by sex and age. Two-sample Mendelian randomization (MR) was then conducted to infer causality for diseases showing significant associations. Genomic colocalization analyses were performed using relevant GWAS data to identify shared causal variants. Mediation analyses were performed to detect possible mediating factors, including the frailty index, KDM biological age acceleration, allostatic load and 30 circulating biomarkers. RESULTS: Adolescent depression was associated with elevated risk for 12 chronic diseases, with strongest associations for hypothyroidism (HR&#xa0;=&#xa0;1.29 [1.18-1.42]), diabetes (HR&#xa0;=&#xa0;1.25 [1.13-1.38]) and chronic obstructive pulmonary disease (COPD) (HR&#xa0;=&#xa0;1.74 [1.50-2.01]). Risks were notably higher among females and younger adults. MR confirmed likely causal relationships for hypothyroidism (OR&#xa0;=&#xa0;1.45 [1.03-2.05]), diabetes (OR&#xa0;=&#xa0;1.01 [1.01-1.02]) and COPD (OR&#xa0;=&#xa0;1.04 [1.02-1.06]). Genomic colocalization revealed a shared genetic signal at the CDSN/PSORS1C1 locus between adolescent depression and hypothyroidism. Mediation analyses revealed disease-specific pathways: creatinine for hypothyroidism, testosterone for diabetes, KDM biological ageing for COPD and frailty index across all three conditions. CONCLUSIONS: Adolescent depression confers systemic vulnerability through genetic and metabolic mechanisms, with amplified risks in females and individuals aged &#x2264;55&#xa0;years. These findings support early, integrated interventions to mitigate long-term multimorbidity.

Humans↗
PubMed2026

MULTIPREVENT: Integrated screening for smoking-related multimorbidity using low-dose chest computed tomography.

OBJECTIVES: Tobacco consumption, combined with individual genetic predispositions, contributes to an age-dependent risk not only for lung cancer but also for other non-communicable diseases (NCDs) such as cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), osteoporosis, and diabetes. The MULTIPREVENT project aims to validate whether low-dose computed tomography (LDCT) of the chest, combined with simple biomarkers, functional tests, and genomic profiling, can serve as an effective tool for comprehensive health assessment and risk prediction of multimorbidity in adults. STUDY DESIGN: The study is based on a prospective epidemiological design involving 3000 participants from the MOLTEST-BIS lung cancer screening cohort (2016-2018). These participants, aged 50-79 years (during MOLTEST-BIS) and with a smoking history of at least 30 pack-years, will undergo two follow-up assessments in 2025-2027 and 2030-2032. METHODS: Each follow-up includes LDCT, spirometry, standardized blood pressure measurement, anthropometric evaluation, biomarker assessment (lipid profile, lipoprotein(a), glycated haemoglobin), and health-related questionnaires. Genetic profiling will be performed using the Illumina Infinium Global Screening Arrays approach to identify inherited predispositions to major NCDs. All data, clinical, imaging (including radiomics), molecular, and genetic, will be integrated through machine learning algorithms to develop AI-based risk prediction models. RESULTS: The MULTIPREVENT study is expected to generate a wide range of scientific, clinical, and infrastructural results that will serve as a foundation for future public health initiatives in integrated prevention. CONCLUSIONS: By linking imaging and biochemical markers, genetic susceptibility, and clinical parameters within a longitudinal design, MULTIPREVENT will establish data-driven, AI-supported prevention strategies aimed at reducing morbidity and mortality among adults exposed to tobacco. The project will also serve as a model for population-based multimorbidity prevention programs.

Humans↗
PubMed2026

Cardiometabolic Multimorbidity Increases the Risk of Hip Fracture: A Longitudinal Cohort Study Based on CHARLS.

BACKGROUND: Cardiometabolic Multimorbidity (CMM) is defined as the co-occurrence of two or more conditions among heart disease, diabetes mellitus, stroke, and hypertension. Previous studies have shown associations between cardiometabolic diseases and fragility fractures; however, the relationship between CMM and hip fractures remains unclear in the Chinese population. This study therefore aims to investigate this association in a Chinese cohort to inform fracture prevention strategies. METHODS: This prospective cohort study used data from the China Health and Retirement Longitudinal Study (CHARLS) collected from 2011 to 2020. Participants from the 2011 baseline survey cohort were initially included. Subsequently, individuals were sequentially excluded if they were under 45&#x2009;years of age, had incomplete baseline CMM information, had a history of hip fracture, lost to follow-up, or had missing data on confounders. Kaplan-Meier survival analysis, Cox proportional hazards regression, subgroup analyses, and sensitivity analyses were performed to evaluate the association between CMM and the risk of hip fracture. RESULTS: A total of 6314 participants aged 45&#x2009;years and older were included, of whom 544 had CMM. Over a 9-year follow-up period, 287 incident hip fractures (4.55%) were identified. Among these, 36 participants had been diagnosed with CMM at baseline, whereas 251 had not. The incidence of hip fracture was significantly higher in participants with CMM than in those without CMM (13% vs. 8%, p&#x2009;=&#x2009;0.015). After full adjustment for confounders, multivariable Cox regression showed that CMM was associated with a 70% increased risk of hip fracture (HR&#x2009;=&#x2009;1.70, 95% CI: 1.318-2.47; p&#x2009;=&#x2009;0.005). Subgroup analyses indicated that age and history of falls were significant effect modifiers. The association between CMM and hip fracture was more pronounced in participants under 60&#x2009;years old (P for interaction&#x2009;=&#x2009;0.048) and those with a history of falls (P for interaction&#x2009;=&#x2009;0.014). CONCLUSION: These findings suggest that CMM increases the risk of hip fracture, particularly among relatively younger individuals and those with a history of falls.

Humans↗
PubMed1976

[Diabetes mellitus and femoral fractures in close proximity to the hip joint in multimorbid patients of old age (author's transl)].

From a great number of patients with femoral fractures in close proximity to the hip joint with an average age of 78.3 years, all diabetics were compared to persons with a normal metabolism in terms of concomitant diseases, causes of death, complications, and fracture healing. Although no differences with regard to fracture healing and subsequent functional results in later years could be established in patient put on the correct diet and insulin dose, as compared to the control group, the clinical complication rate and the mortality rate were significantly higher among patients of old age with Diabetes mellitus. To bring about a possible change, operating techniques providing the possibility of an early mobilisation and optimal physiotherapeutical after--care are recommended.

Aged↗
PubMed2026

Unraveling 'F' factor: towards a genetic-clinical framework for the musculoskeletal-heart crosstalk in metabolic aging.

BACKGROUND: The rising co-occurrence of cardiometabolic diseases and musculoskeletal degeneration poses a critical challenge to healthy aging, yet the shared biological mechanisms underlying this multimorbidity remain poorly defined. This study aimed to establish an integrative clinical-genetic framework to elucidate the common frailty factor, the 'F' factor, that captures the systemic vulnerability linking cardiometabolic multimorbidity (CMM) and musculoskeletal aging. METHODS: Utilizing the prospective China Health and Retirement Longitudinal Study (CHARLS) cohort, we developed and validated novel Frailty-Integrated Indices for CMM risk prediction, evaluated with machine learning models interpreted via SHapley Additive exPlanations (SHAP). Independently, we applied genomic structural equation modeling (Genomic-SEM) to integrate genome-wide association data from six traits-coronary artery disease, type 2 diabetes, hypertension, bone mineral density, frailty, and telomere length-to model a shared latent genetic factor ('F' factor). This was followed by multivariate GWAS, fine-mapping, transcriptome-wide association study (TWAS), gene-based analysis, and functional annotation to prioritize causal genes, pathways, and cell types. RESULTS: Clinically, several Frailty-Integrated Indices significantly improved CMM risk prediction, with the optimal model achieving an AUC of 0.727. Genetically, we modeled a significant shared latent genetic factor ('F' factor), pinpointing novel risk loci and implicating key genes such as APOE and SLC22A3. These genes were enriched in pathways including cellular senescence and cholesterol metabolism and showed specific expression patterns in developmental brain stages and across multi-organ endothelial cells. CONCLUSION: Our findings provide converging evidence for Musculoskeletal&#x2011;Heart crosstalk of metabolic aging and inferred the 'F' factor as a genetic correlate of a transdiagnostic state, which links genetic predisposition to metabolic dysregulation, and systemic functional decline. This work provides a multi-level biological characterization of multimorbidity liability, informing early-risk detection and preventive strategies for complex aging-related comorbidities.

Humans↗
PubMed2026

Cardiovascular risks in psychiatric disorders and psychiatric risks in cardiovascular disorders: implications for prevention and clinical management - a large-scale umbrella review encompassing 76 meta-analyses.

OBJECTIVE: Psychiatric and cardiovascular disorders often co-occur, complicating their assessment and management. No umbrella review(UR) has summarized the meta-analytic evidence on the co-occurrence of psychiatric and cardiovascular disorders and assessed its credibility. METHODS: Meta-analytic systematic reviews of observational studies documenting the prevalence, risk factors, and outcomes associated with the co-occurrence of cardiovascular and psychiatric disorders, indexed from inception through March.16.2026, and meeting established diagnostic criteria, were included. Meta-analytic association and prevalence estimates were recalculated and graded based on established or adapted criteria. The AMSTAR-2 assessed the quality of the meta-analyses, while several subgroup analyses and meta-regressions aimed to explain the heterogeneity. RESULTS: We included 76 meta-analyses yielding 131 meta-analytic estimates. Based on pre-existing meta-analytic evidence, 22/24 prevalence estimates (91.7%) met moderate/strong credibility criteria. Strong credibility emerged for: orthostatic hypotension in Lewy body(58%;95%C.I.&#xa0;=&#xa0;50-66%) and Alzheimer's dementias(28.0%&#xa0;=&#xa0;95%C.I.&#xa0;=&#xa0;17.0-40.0%); pericardial effusion in anorexia nervosa(25.0%;95%C.I.&#xa0;=&#xa0;17.0-34.0%); in heart failure(HF): major depressive disorder(MDD)(41.9%;95%C.I.&#xa0;=&#xa0;36.7-47.1%), mild cognitive impairment(MCI)(41.4%;95%C.I.&#xa0;=&#xa0;38.3-45.6%), anxiety(32.0%;95%C.I.&#xa0;=&#xa0;26.5-37.6%), MDD&#xa0;+&#xa0;anxiety(24.7%;95%C.I.&#xa0;=&#xa0;17.9-34.3%), and dementia(19.8%;95%C.I.&#xa0;=&#xa0;12.9-27.8%); in atrial fibrillation(AF): MCI(26.0%;95%C.I.&#xa0;=&#xa0;21.0-30.0%), anxiety in patients undergoing pulmonary vein isolation(PVI)(25.0%;95%C.I.&#xa0;=&#xa0;12.0-46.0%), MDD in PVI patients (20.0%;95%C.I.&#xa0;=&#xa0;13.0-29.0%); in coronary artery disease: MDD&#xa0;+&#xa0;anxiety(19.8%;95%C.I.&#xa0;=&#xa0;16.0-24.6%): in schizophrenia spectrum disorders: clozapine-associated-cardiomyopathy(0.6%;95%C.I.&#xa0;=&#xa0;0.2-2.3%); clozapine-associated-cardiomyopathy absolute death rates (0.0003;95%C.I.&#xa0;=&#xa0;0.0001-0.0012); clozapine-associated-cardiomyopathy case fatality rate (0.078;95%C.I.&#xa0;=&#xa0;0.018-0.285). Several additional disorders were multimorbid in>5% of people, yet with a lower credibility rating. No re-pooled risk factors/outcomes reached strong credibility criteria. CONCLUSIONS: The present study provides an atlas of cardiovascular and psychiatric multimorbidity across varying levels of credibility, reinforcing the need for an integrated, multidisciplinary approach to patient care and for more research on actionable risk/protective factors and outcomes.

Humans↗
PubMed2026

Medication safety in older adults in India: an integrative PhD synthesis of direct evidence and contextual implementation evidence.

BACKGROUND: Unsafe medication practices among older adults are an important global health concern, particularly in low- and middle-income countries where multimorbidity, fragmented care, self-medication, and informal healthcare provision intersect. OBJECTIVE(S): To synthesize direct evidence on medication safety among older adults in India and contextual evidence on deprescribing and community-level provider interventions relevant to safer medication use. METHODS: This PhD synthesis integrates four studies: a record-based cross-sectional study on polypharmacy and cardiovascular autonomic function in Kolkata; a six-city community study of 600 Indian older adults; a systematic review and meta-analysis on deprescribing preventive medications in frail or end-of-life older adults; and a systematic review of informal healthcare provider interventions in low- and middle-income countries. Studies I-II provided direct Indian older-adult evidence, while Studies III-IV provided indirect contextual evidence for their optimization and implementation. RESULTS: Polypharmacy was associated with higher anticholinergic burden and numerically higher cardiac autonomic neuropathy although residual confounding limits causal interpretation. In the multicity study, one-third had polypharmacy, while potentially inappropriate medications, prescribing omissions, and self-medication were common. Risks were higher with multimorbidity, recent hospitalization, care transitions, or living alone. Deprescribing showed no statistically significant increase in mortality, hospitalization, or major cardiovascular events, but heterogeneity was high and certainty low to very low. Informal-provider interventions showed the potential to improve knowledge, referral, case management, and medication-related practices. CONCLUSIONS: Medication safety among older adults in India requires an integrated continuum approach, but direct evidence supports only some components and implementation strategies that need prospective evaluation.

Humans↗
PubMed2026

Height variation independent of known genetic variants and health in later life: a cohort study.

BACKGROUND: Adult-attained height is associated with later-life health, but it reflects both genetic and nongenetic influences. The health implications of height variation not explained by known common height-associated genetic variants remain unclear. OBJECTIVES: This study aimed to examine associations of residual height (height variation independent of known genetic variants) with multiple disease incidence and all-cause mortality in later life. METHODS: In this cohort study of 407,366 adults of European ancestry (aged 40-70 y) in the United Kingdom Biobank (2006-2010), sex- and age-specific genetically predicted height was estimated from 9863 height-associated variants, adjusted for 30 principal components of ancestry. Residual height was calculated as the difference between observed and genetically predicted height. Plasma proteomics (2054 proteins; Olink Explore) were profiled. Deaths and 49 incident diseases were ascertained through national registries. Multivariable Cox models estimated associations of residual height and related proteins with disease incidence and mortality. RESULTS: Higher residual height [mean (standard deviation, SD), 0.0 (4.8)] was associated with more favorable self-reported preadulthood exposures (e.g., later birth years, no maternal smoking around birth, being breastfed as an infant, no adoption experience, and lower childhood adversity scores) and lower hazard ratios (HRs) of 32 out of 49 diseases (median follow-up = &#x223c;12.5 y). Using participants with residual height within &#xb1;0.5 SDs from the mean as reference, those with residual height < -2 SDs had higher adjusted HRs of mortality [1.61; 95% confidence interval (CI): 1.50, 1.72], multimorbidity (1.28; 95% CI: 1.12, 1.46), cardiovascular disease (1.45; 95% CI: 1.32, 1.60), psychiatric/neurological disease (1.38; 95% CI: 1.28, 1.48), and other disease categories (e.g., diabetes, digestive, and musculoskeletal diseases). In contrast, higher genetically predicted height was associated with a higher incidence of 19 diseases, including subtypes of cancer, non-atherosclerotic cardiovascular diseases, and musculoskeletal diseases, as well as higher all-cause mortality. We identified 806 plasma proteins related to inflammation, immune response, and autophagy via tumor necrosis factor, Nuclear factor-kappa B, phosphoinositide-3 kinase/protein kinase B, and Janus kinase/signal transducer and activator of transcription signaling pathways, which were associated with residual height and multiple diseases and mortality. CONCLUSIONS: Higher residual height is associated with lower disease incidence and mortality, with associations that are distinct from those for genetically predicted height.

Humans↗
PubMed2026

NMR metabolomics and glycomics for cancer detection in patients with non-specific symptoms: a prospective observational cohort study.

BACKGROUND: Early cancer diagnosis in patients with non-specific symptoms is limited by the lack of discriminatory tests. Within the Oxfordshire Suspected CANcer (SCAN) pathway, exploratory biomarker work showed that serum 1H NMR-based metabolomics can identify cancer with high accuracy. SCAN2 evaluated whether integrating metabolomics with glycomics provides complementary molecular information and improves discrimination in a clinically complex, real-world population. METHODS: Serum from 369 SCAN patients (59 cancers) was analysed using AXINON&#xae; System-derived NMR metabolomics and HPLC-MS glycomics. Machine-learning models were trained to predict cancer status, with performance assessed by receiver operating characteristic (ROC) analysis of pooled cross-validated predictions. To place cancer risk in a broader clinical context, a second classifier modelling alternative non-cancer diagnosis was incorporated, and mean predicted probabilities from both models were jointly projected into a two-dimensional space, maintaining strict separation of training and test data. FINDINGS: In the full cohort, integration of glycomics with metabolomics achieved an AUC of 0.814 (95% CI 0.808-0.820). In a refined sub-cohort excluding major comorbidities and selected cancer types (32 cancers, 277 non-cancers), performance improved to an AUC of 0.884 (95% CI 0.879-0.890). Discriminatory features included cancer-associated biantennary fucosylated glycans alongside amino acid metabolites (glutamate, histidine) and lipoprotein-related measures. A classifier distinguishing metastatic from non-metastatic disease (n = 29 vs. 30) achieved an AUC of 0.80. Joint probability analysis in the full cohort preserved cancer-associated signatures across comorbidity burden, with projection-based classification achieving an accuracy of 89.2% (95% CI 85.7-92.6). INTERPRETATION: These findings validate the SCAN1 metabolomic signature in a more clinically complex cohort and indicate that integrating glycomics with metabolomics provides complementary biological information for cancer discrimination. Joint probability analysis provides an interpretable framework for cancer risk stratification within multimorbid diagnostic pathways, supporting the clinical potential of scalable multi-omics blood testing. FUNDING: EPSRC, EU Horizon 2020, Wellcome/MLSTF, Novo Nordisk Foundation.

Humans↗
PubMed2026

Ovarian aging and systemic health: Mechanisms and emerging intervention strategies.

Ovarian aging may contribute to systemic aging via the ovarian-systemic axis. This review outlines intrinsic ovarian cellular defects such as genomic instability, epigenetic shifts, and mitochondrial and proteostasis damage, which may trigger senescence-associated secretory phenotype (SASP)-related inflammaging, fibrosis, and distal pro-aging signals. Ovarian-derived endocrine disruption, especially estrogen decline, broadly affects bodily physiology. We summarize emerging multimodal interventions, including senolytics, metabolic reprogramming, regenerative medicine, and systemic approaches, and we discuss their dual potential to preserve fertility and intercept ovarian contributions to systemic aging. Ovarian aging is possibly associated with female age-related multimorbidity. Ovary-targeted prevention may extend healthspan, as assessed by combined reproductive and systemic clinical evaluations.

Humans↗
PubMed2026

Determinants of functional burden pleiotropy and gene dosage responses across human traits.

Pleiotropic and monotonic effects of gene dosage are central to understanding comorbidities in developmental pediatric and psychiatric disorders, yet the underlying biological processes are not well characterized. Here we develop a functional burden analysis to investigate the association of all protein-coding copy-number variants, genome-wide, with 43 complex traits in approximately 500,000 UK Biobank participants. We test variant associations disrupting 172 tissue or cell-type gene sets, finding associations for all traits, which we replicate in the All of Us cohort. Functional burden pleiotropy, defined as the number of traits significantly associated with a gene set, correlates with genetic constraint and is higher for brain than non-brain functions, even after normalizing for genetic constraint. Levels of pleiotropy, measured by burden correlation, are similar in deletions and loss-of-function single-nucleotide variants, and higher than in common variants and duplications. Most gene dosage responses are non-monotonic, with deletions and duplications showing same-direction effects, and monotonic responses decrease with genetic constraint. We observe associations between functional gene sets and traits for either deletions or duplications, but rarely both, with negatively correlated effect sizes. Together, these results link genetic constraint and brain-specific mechanisms to the whole-body multimorbidity of neurodevelopmental and psychiatric conditions.

Humans↗
PubMed2026

Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.

Electronic health records (EHRs) contain extensive multidimensional patient data, presenting challenges for the discovery of novel and meaningful clinical patterns. Unsupervised clustering of high-dimensional clinical data holds great potential for identifying novel clinical patterns. Here, we performed unsupervised clustering and characterized 100,272 patients in the Electronic Medical Records and GEnomics (eMERGE) Network. We identified 70 clusters defined by distinct comorbidity patterns. Meanwhile, age and sex are also strongly associated with patient stratification, influencing phenotype prevalence and onset time. Notably, phenotype onset time accurately predicted chronological age and was significantly associated with overall mortality risk. Besides age and sex, we assessed the contribution of genetic variation to phenotype development and observed evidence of cross-phenotype associations influencing cluster membership and comorbidity patterns. However, the role of genetics recedes during aging. We also identified several high-risk clusters with elevated Charlson Comorbidity Index (CCI) scores and validated these findings in an independent cohort. Further analysis of these clusters revealed phenotypes linked to premature aging and highlighted a survival selection among older participants in observational studies. Overall, this study enables phenome-wide unsupervised patient stratification for multimorbidity discovery in largely unannotated clinical data, offering valuable insights into patient stratification, comorbidity analysis, aging, and health outcomes.

Journal Article↗