Background: A comprehensive, replicated atlas of circulating metabolites for incident coronary heart disease (CHD) across race diverse populations is lacking and metabolite signatures of early-onset CHD remain largely unidentified. Methods: We conducted a two-stage metabolome wide-association analysis using Cox regression model for incident CHD, with discovery analyses in 22,742 CHD-free individuals with 1245 blood metabolites profiled from 7 multi-ethnic cohorts (1,124 incident cases over 7.5~17.0 yrs of follow-up) in TOPMed, and replication analyses in 32,615 CHD-free individuals from 7 multi-ethnic cohorts (3,365 incident cases over 7.5~19.3 years) (Fig1.a). Random-effect meta-analysis was used to pool results from each cohort in these two stages. We further evaluated the associations of identified metabolites with incident CHD diagnosed at different ages. Results: We identified 189 metabolites (FDR<0.05) associated with incident CHD, with 127 metabolites (p<0.05) replicated (Fig1.b). Over 90% of these replicated metabolites showed positive associations, with the majority belonging to glycerolipids, phosphatidylethanolamine, fatty acids, lactoyl amino acid, histidine, aromatic amino acids, branched amino acids (Fig1.b). In the Study of Latinos (SOL, n=13,322), 14 out of these 127 metabolites were associated with incident CHD diagnosed before age 50 yrs (FDR<0.05; Fig1.c), including the known atherogenic metabolites (e.g., cholesterol, fibrinopeptide A), harmful microbial derived trimethylamine N−oxide, sugar sweeteners (e.g., mannitol/sorbitol, erythritol), markers of insulin resistance, inflammation and oxidative stress (e.g., mannose, erythronate, gluconate, suberoylcarnitine), and novel metabolites not previously linked to CHD (e.g., C−glycosyltryptophan, hydroxymalonate, and methyl glucopyranoside). Further, associations of these metabolites with CHD diagnosed at younger age tend to be stronger than those with late-onset cases (e.g., the hazard ratio per SD increase in mannose decreased from 4.4 for CHD diagnosed at age 45 to 1.5 for case diagnosed at age 65; Fig1.d). Adding metabolites to conventional risk factors improved AUC of CHD risk prediction from 0.78 to 0.84 (p<0.001) (Fig1.e). Conclusion: We provide the most comprehensive, replicated, multi-ethnic atlas of circulating metabolites for incident CHD, identify a set of early-onset CHD metabolite markers, and demonstrate significant gains in CHD risk prediction with identified metabolites.

Importance:Higher coffee intake has been associated with lower risk of type 2 diabetes (T2D), but the underlying biological pathways remain incompletely understood. Objective:To examine associations of coffee intake with insulin sensitivity, adiposity, and T2D risk, and assess whether coffee intake modifies associations between pathway-specific genetic susceptibility and incident T2D. Design Setting and Participants:Cross-sectional analyses among 806 participants without T2D in the VITamin D and OmegA-3 TriaL (VITAL) clinical sub-cohort, who underwent repeated dietary assessment, clinical phenotyping, and dual-energy X-ray absorptiometry imaging at baseline and year-2. Prospective analyses among 333,053 UK Biobank participants without T2D at baseline who had dietary and genetic data and were followed for a median of 13.3 years. Exposures:Coffee intake assessed by food frequency questionnaires. In UK Biobank, 12 pathway-specific polygenic scores (pPS) representing distinct T2D pathophysiological mechanisms were evaluated. Main Outcomes and Measures:The primary outcomes, in VITAL, were HbA1c, oral glucose tolerance test-derived measures of glucose response and insulin sensitivity, β-cell function, and overall, truncal, and visceral adiposity; in UK Biobank, was incident T2D. Results:In VITAL, higher coffee intake was associated with higher insulin sensitivity (standardized β per cup/day, 0.046; P = .004) and lower visceral adipose tissue mass (β, -0.047; P = .006), after adjusting for demographic, lifestyle, and clinical factors, including body mass index. In UK Biobank, higher coffee intake was associated with lower T2D incidence (hazard ratio per cup/day, 0.96; 95% CI, 0.95-0.97), lower triglyceride-to-HDL cholesterol ratio (β,-0.01; P = 2.51 × 10^-19), and lower visceral adipose tissue mass (β, -0.01; P = 4.28 × 10^-9). Associations of 3 pPS related to insulin resistance and fat distribution with incident T2D were attenuated among participants consuming higher amount of coffee than among non-consumers (P for interaction < .0043). Conclusions and Relevance:Higher coffee intake was associated with greater insulin sensitivity, lower visceral adiposity, and lower risk of T2D. Together with the attenuation of associations between pathway-specific genetic susceptibility and T2D risk among higher coffee consumers, these findings suggest that insulin resistance and visceral adiposity-related pathways may contribute to the association between coffee intake and T2D risk. Key Points:Question: Is coffee intake associated with specific insulin sensitivity and adiposity markers, and type 2 diabetes risk, and does it modify associations between pathway-specific genetic susceptibility and type 2 diabetes?Findings: In analyses repeated dietary, clinical, and imaging phenotyping in 806 VITAL participants and prospective data from 333,053 UK Biobank participants, higher coffee intake was associated with greater insulin sensitivity, lower visceral adiposity, and lower type 2 diabetes risk. Higher coffee intake also attenuated associations of three pathway-specific polygenic scores related to insulin resistance and fat distribution with type 2 diabetes risk.Meaning: These findings suggest that pathways related to insulin sensitivity and visceral adiposity may contribute to the associations between coffee intake and lower type 2 diabetes risk.

The human metabolome reflects complex metabolic states affected by genetic and environmental factors. However, metabolites associated with type 2 diabetes (T2D) risk and their determinants remain insufficiently characterized. Here we integrated blood metabolomic, genomic and lifestyle data from up to 23,634 initially T2D-free participants from ten cohorts. Of 469 metabolites examined, 235 were associated with incident T2D during up to 26 years of follow-up, including 67 associations not previously reported across bile acid, lipid, carnitine, urea cycle and arginine/proline, glycine and histidine pathways. Further genetic analyses linked these metabolites to signaling pathways and clinical traits central to T2D pathophysiology, including insulin resistance, glucose/insulin response, ectopic fat deposition, energy/lipid regulation and liver function. Lifestyle factors-particularly physical activity, obesity and diet-explained greater variations in T2D-associated versus non-associated metabolites, with specific metabolites revealed as potential mediators. Finally, a 44-metabolite signature improved T2D risk prediction beyond conventional factors. These findings provide a foundation for understanding T2D mechanisms and may inform precision prevention targeting specific metabolic pathways.

Type 2 diabetes (T2D) is a heterogeneous disease shaped by genetic pathways related to insulin resistance and β-cell dysfunction, but how this heterogeneity is reflected molecularly remains unclear. We integrated partitioned polygenic scores (pPS) with proteomic and metabolomic profiling to define molecular signatures of T2D and their clinical relevance. We analyzed UK Biobank participants with genomic, proteomic, and metabolomic data. In a disease-free training subset, we used LASSO regression to identify multi-omic signatures associated with each pPS by jointly modeling proteins and metabolites. In an independent testing set, we constructed multi-omic scores and examined their associations with clinical traits and diabetes-related outcomes. Mediation analyses were used to investigate putative causal pathways. Key findings were evaluated in the Multi-Ethnic Study of Atherosclerosis (MESA). We identified distinct multi-omic signatures that capture the molecular architecture of T2D genetic risk across physiological subtypes. Compared with genetic scores alone, multi-omic pPS showed larger effect sizes and better disease discrimination. These scores recapitulated subtype-specific physiology and were associated with T2D risk. The Beta-Cell 2 multi-omic score showed marked stratification for insulin use, which was replicated in MESA, where it also predicted future insulin use. Mediation analyses implicated lipoprotein remodeling and fatty acid metabolism in the Lipodystrophy 1 cluster, accounting for 30-45% of the total effect of pPS on T2D risk. Integrating process-specific genetic risk with circulating multi-omic profiles reveals biologically distinct endotypes of T2D and supports a framework for improved patient stratification and risk assessment.

OBJECTIVE:This study aimed to identify metabolomic profiles associated with type 2 diabetes and insulin resistance (HOMA-IR) and relate them to the risk of total mortality. METHODS:A longitudinal study was conducted in a subset of participants from a diabetes case-cohort study (mean age, 66.5 years; 62% women; 176/699 with incident T2D) within the PREDIMED trial. Plasma metabolites were analyzed using LC-MS/MS methods at baseline (discovery sample) and 1-year follow-up (validation sample). Multi-metabolite profile scores for type 2 diabetes and HOMA-IR, respectively, were derived using elastic net regression. Cox proportional hazards were fitted to assess the association between metabolomic profiles and total mortality, adjusting for potential confounders. External validation was performed in the NHS/HPFS cohorts. RESULTS:A total of 31 metabolites were associated with type 2 diabetes and 105 with HOMA-IR. Both metabolomic profiles were significantly associated with a higher risk of total mortality (type 2 diabetes HR = 1.52, 95%CI: 1.04-2.25; HOMA-IR HR = 1.33, 95%CI: 1.00-1.75) in the PREDIMED cohort. Shared metabolites between both metabolomic signatures, including glycine, SDMA, DMGV, and phosphocreatine, were associated with mortality. These associations were replicated in a pooled analysis of three independent American cohorts (type 2 diabetes HR = 1.09, 95%CI: 1.05-1.13; HOMA-IR HR = 1.04, 95%CI: 1.00-1.09). CONCLUSIONS:In an older population at high cardiometabolic risk, metabolomic scores of type 2 diabetes and insulin resistance were associated with total mortality risk, potentially explaining some mechanisms behind the increased risk of mortality observed in epidemiological studies for individuals with glycemic dysregulations.

Objective: Testosterone influences urogenital development during embryogenesis and metabolic–cardiovascular homeostasis in adult women. Observational links between testosterone abnormalities and cardiovascular disease (CVD) are inconclusive due to confounding and reverse causation. We aimed to assess the genetically predicted associations of endogenous sex hormones with CVD risk in women and to explore potential gut microbiota involvement. Methods: Two-sample Mendelian randomization (MR) was conducted to assess associations of bioavailable testosterone (BioT), total testosterone (TotalT), free testosterone (FreeT), and estradiol levels with seven prespecified cardiovascular outcomes. Instruments were derived from published genome-wide association studies (GWASs) in women of European ancestry (sample sizes: 158,089–188,507). Inverse-variance weighted (IVW) MR was the primary approach, complemented by MR-Egger, weighted median, and sensitivity analyses. Two-step mediation MR and multivariable MR (MVMR) were performed using GWAS data on 119 gut microbial genera to explore microbiome-related pathways. Genetic correlations were estimated using linkage disequilibrium score regression (LDSC). Results: Higher BioT levels were associated with lower risks of CVD (OR = 0.935, p = 0.034) and coronary heart disease (CHD; OR = 0.886, p = 0.009). TotalT levels were inversely associated with CVD (OR = 0.949, p = 0.039), and FreeT levels with CHD (OR = 0.940, p = 0.043). Mediation MR and MVMR analyses suggested partial microbiome involvement: Haemophilus mediated 10.6% of the TotalT–CVD association, and Holdemanella mediated 10.1% of the BioT–CVD association. LDSC revealed significant negative genetic correlations of BioT levels with CHD and CVD (all p < 1 × 10 -5 ) and of TotalT and FreeT levels with secondary right heart disease ( p < 0.05). Estradiol levels were not significantly associated with the studied cardiovascular outcomes. Conclusion: Genetically predicted testosterone-related traits showed inverse associations with CVD risk in women and evidence of shared genetic architecture with several cardiovascular outcomes. Exploratory mediation analyses suggested that the gut microbiome may partly mediate these relationships, warranting further validation.

Introduction: Carbohydrate quality is associated with type 2 diabetes (T2D) risk, but whether intakes of specific carbohydrates interact with genetic susceptibility remains unclear. Hypothesis: The associations between lower carbohydrate quality and higher T2D risk may be modified by global and pathway-specific polygenic risk scores (PRS). Methods: We analyzed up to 36 years of longitudinal data from 39,540 participants in the Nurses’ Health Studies and Health Professionals Follow-Up Study, free of diabetes, cardiovascular disease, and cancer at baseline. Diet was assessed every 4 years using validated food frequency questionnaires. We calculated cumulatively averaged alternative Carbohydrate Quality Index (aCQI; based on cereal fiber, whole fruit carbohydrates, glycemic index, sugar from sugar-sweetened beverages [SSB], and whole grain carbohydrates), with a lower score indicating poorer long-term carbohydrate quality. We calculated global and 12 pathway-specific PRS based on 650 genetic variants reflecting distinct T2D mechanisms. Cox regression was used to examine associations between aCQI (and secondarily, its components), PRS, and their interactions with T2D risk. Results: We identified 5,116 incident T2D cases. The global-PRS, and 11 out of 12 pathway-specific PRS (except for bilirubin metabolism) robustly predicted T2D risk. In multivariable analysis, a lower aCQI was associated with higher T2D risk (HR per IQR: 1.20, 95% CI: 1.14-1.26, P <0.001). A significant additive interaction was observed, among participants older than 65 yrs but not younger, between global-PRS and aCQI, with T2D risk (relative excess risk due to interaction =0.21, P int =0.025, Fig. A ). In secondary analysis of aCQI components in those ≥65 yrs, nominally significant interactions were noted between global-PRS with low whole fruit carbohydrates and low whole grain carbohydrates for T2D risk ( P int ≤0.011 ; Fig. A ). Further analysis on pathway-specific PRS suggested potential additive interactions between whole fruit carbohydrates and PRS reflecting proinsulin, hyper insulin, and obesity-mediated insulin resistance pathways, and between sugar from SSB and PRS for obesity-mediated insulin resistance ( P int ≤0.038; Fig. B ). Conclusions: Our data suggest that the association between lower carbohydrate quality and T2D risk may be stronger in older adults with higher genetic risk. Replication studies are needed to examine how specific carbohydrates may interact with genetic risk through specific pathways.

Abstract Background Obesity increases risks of chronic liver disease and liver cancer, but risks across obesity phenotypes and related molecular signatures remain incompletely characterized. We evaluated associations of obesity phenotypes and obesity-related omics signatures with adverse liver outcomes. Methods We analyzed 451 463 UK Biobank participants aged 40-69 years. General and central obesity were defined by body mass index and waist circumference. Metabolically unhealthy obesity was defined as obesity plus at least 1 metabolic abnormality. Liver outcomes (metabolic dysfunction–associated steatotic liver disease, cirrhosis, liver cancer, severe liver disease, and chronic liver disease mortality) were ascertained from hospital inpatient records, cancer and death registries. Cox models estimated hazard ratios (HRs) for obesity measures, and internally validated nuclear magnetic resonance metabolomic and Olink proteomic signatures were derived using elastic-net regression. Results During a median 11-year follow-up, general obesity and central obesity were associated with higher risks of adverse liver outcomes (HR = 1.53-3.29), with generally stronger associations for metabolically unhealthy obesity (HR = 1.69-3.68). Participants with both general and central obesity had the highest risks. Omics signatures of metabolically unhealthy obesity were more strongly associated with liver outcomes than general or central obesity signatures (HR = 1.58-6.13 vs 1.43-4.72). Nuclear magnetic resonance metabolomic biomarkers (albumin, glutamine, triglycerides) and proteomic biomarkers (FABP4, CDHR2, TGFBR2, IL1RN) were consistently associated with multiple liver outcomes. Conclusion Obesity phenotypes, particularly metabolically unhealthy obesity, and obesity-related omics signatures were associated with higher risks of liver cancer and other adverse liver outcomes. Findings highlight metabolic dysfunction beyond adiposity as an important dimension of obesity-related liver risk and suggest that metabolomic and proteomic profiles may provide molecular insight.

AIM:Despite growing evidence linking better oxidative balance to improved cardiometabolic health, metabolite signatures reflecting oxidative status remain still poorly characterized. We aimed to identify a plasma metabolite signature of the oxidative balance score (OBS) and to examine its association with incident cardiovascular disease (CVD) and type 2 diabetes (T2D). METHODS:The discovery population included 1732 participants at high cardiovascular risk from the PREDIMED study with available plasma metabolomics using LC-MS and OBS data at baseline. The OBS was calculated at baseline and after 1-year of follow-up based on 12 a priori selected pro- and antioxidant dietary and non-dietary lifestyle factors, with higher scores indicating a more favorable antioxidant balance. A set of metabolites predicting OBS was selected from 388 candidate metabolites using elastic net regression. Multivariable Cox models were used to examine the associations between the OBS metabolite signature and incident CVD and T2D. RESULTS:A subset of 21 metabolites was consistently selected (amino acids, vitamins, nucleotides, lipid species, xenobiotics, and others). The metabolite signature was inversely associated with incident CVD in the baseline sample (HR per SD 0.70; 95% CI 0.61-0.81), but not in the 1-year sample (HR 0.94; 95% CI 0.81-1.09). In addition, baseline (HR 0.70; 95% CI 0.60-0.80) and 1-year (HR 0.84; 95% CI 0.72-0.97) OBS metabolite signatures were inversely associated with T2D risk. CONCLUSIONS:A plasma metabolite signature reflecting oxidative balance-related exposures was inversely associated with CVD and T2D risk at baseline. The association with T2D was also observed when the signature was applied to 1-year measurements and in an independent external cohort, whereas the association with CVD was not replicated at 1 year. Most metabolites showed biologically plausible patterns, correlating with specific pro- and antioxidant exposures and including metabolites previously implicated in oxidative stress-related processes. CLINICAL TRIAL REGISTRATION:This trial was registered at controlled-trials.com as ISRCTN35739639.

Background: A high-sodium, low-potassium diet promotes hypertension and cardiovascular disease (CVD). Intricate interactions between the gut microbiota and minerals, encompassing absorption, transformation, and signaling, point to an underexplored role of gut bacteria in this diet-cardiometabolic disease association. Aim: To characterize microbial features in response to sodium and potassium, map related shifts in circulating metabolites, and evaluate associations between the identified metabolites and incident cardiometabolic events. Design: In 264 men from the Men's Lifestyle Validation Study, we examined the gut taxonomic and functional compositions in response to 24-hour urinary sodium and potassium excretion. We further assessed plasma metabolomic profiles responsive to the identified gut microbial features and their associations with coronary heart disease (CHD), CVD, and type 2 diabetes (T2D) among 12,225 participants from Nurses' Health Study (NHS), NHSII, and the Health Professionals Follow-up Study. Results: We identified 25 sodium-responsive species, including Collinsella , Desulfovibrio , and Roseburia species, along with 4 potassium- and 4 sodium-to-potassium ratio-responsive species. Microbial scores correlated strongly with urinary sodium, potassium, and their ratio (Pearson r = 0.37–0.57). High-sodium and low-potassium microbiome scores were linked to a favorable plasma metabolomic profile, characterized by higher imidazole propionate, and lower hippuric acid and 3-indolepropionic acid levels. These metabolomic alterations were associated with an increased risk of developing CHD (Quintile5 vs. Quintile1: HR=2.14 [95%CI: 1.53, 2.99]; p-trend < 0.0001), CVD (HR=1.56 [1.34, 1.83]; p-trend < 0.0001), and T2D (HR=3.03 [2.56, 3.60]; p-trend < 0.0001). Conclusions: Our findings linked high sodium and low potassium intake to a specific gut microbial pattern that predicted an adverse metabolomic profile precipitating an elevated risk of developing cardiometabolic conditions. Overall, these data suggest that the human gut microbiome can actively participate in the etiological processes linking high salt intake with adverse health outcomes.

Objective This study aimed to identify metabolomic profiles associated with type 2 diabetes and insulin resistance (HOMA-IR) and relate them to the risk of total mortality. Methods A longitudinal study was conducted in a subset of participants from a diabetes case-cohort study (mean age, 66.5 years; 62% women; 176/699 with incident T2D) within the PREDIMED trial. Plasma metabolites were analyzed using LC-MS/MS methods at baseline (discovery sample) and 1-year follow-up (validation sample). Multi-metabolite profile scores for type 2 diabetes and HOMA-IR, respectively, were derived using elastic net regression. Cox proportional hazards were fitted to assess the association between metabolomic profiles and total mortality, adjusting for potential confounders. External validation was performed in the NHS/HPFS cohorts. Results A total of 31 metabolites were associated with type 2 diabetes and 105 with HOMA-IR. Both metabolomic profiles were significantly associated with a higher risk of total mortality (type 2 diabetes HR = 1.52, 95%CI: 1.04–2.25; HOMA-IR HR = 1.33, 95%CI: 1.00–1.75) in the PREDIMED cohort. Shared metabolites between both metabolomic signatures, including glycine, SDMA, DMGV, and phosphocreatine, were associated with mortality. These associations were replicated in a pooled analysis of three independent American cohorts (type 2 diabetes HR = 1.09, 95%CI: 1.05–1.13; HOMA-IR HR = 1.04, 95%CI: 1.00–1.09). Conclusions In an older population at high cardiometabolic risk, metabolomic scores of type 2 diabetes and insulin resistance were associated with total mortality risk, potentially explaining some mechanisms behind the increased risk of mortality observed in epidemiological studies for individuals with glycemic dysregulations.

Background: Obesity, a leading risk factor for coronary artery disease (CHD) and other chronic diseases, is a multifactorial condition with heterogenous etiologies and comorbidity profiles. Hypothesis: Circulating metabolome can capture metabolic states associated with obesity trajectory and inter-person variation in obesity-related disease risk. Methods: We analyzed up to 40-yr of longitudinal data of 10754 participants from the Nurses’ Health Studies and Health Professionals Follow-Up Study. Baseline plasma levels of 288 metabolites were profiled using LC-MS. Body mass index (BMI) was collected biennially, and its trajectory was estimated using function principal component (FPC) analysis. We categorize participants as having early- (<60y) or late-onset (>70y) obesity-related diseases based on age of first onset of 14 chronic diseases (Fig A). Linear regression was used to examine metabolites-BMI trajectory associations; elastic net regression to derive metabolomic signatures for BMI trajectory; Cox model to examine association with disease risk; and Mendelian randomization (MR) analysis to infer potential causal relationships. Results: The FPC1 of BMI trajectory accounted 81% of variation. We identified extensive associations between baseline metabolites with BMI-FPC1 (240 at FDR<0.05; Fig B). Further stratified analysis identified 63 metabolites, including glycine, alanine and C52:2 TAG, showing stronger associations with BMI-FPC1 among participants with early-onset vs late-onset of obesity-related diseases (Fig C). In MR analysis, genetically predicted levels of 26 metabolites were associated with at least one of these diseases (e.g., C4-OH carnitine with CHD; Fig D). We identified a metabolomic signature for BMI-FPC1, which was associated with risk of any chronic disease in multivariable-adjusted analysis (HR=1.99, p=4e-47). A second metabolomic signature, derived from the 63 metabolites differentially associated with BMI-FPC1 between two disease groups, was associated with disease risk after adjusting for the BMI-FPC1 signature (HR=1.2, p=5e-10). The two signatures showed an additive effect (p-interaction=6e-4), with participants in the highest vs. lowest quartiles of both signatures having a 11.3-fold higher disease risk (p=3e-50; Fig E). Conclusions: We identified metabolomic profiles reflecting metabolic states related to long-term BMI trajectory and inter-individual variation in obesity-related disease risk, which may facilitate personalized intervention.

The polygenic risk scores (PRS) have emerged as a transformative approach for quantifying inherited predisposition to complex diseases, leveraging the unprecedented expansion of genome-wide association studies (GWAS) and advances in statistical genetics. By aggregating the marginal effects of millions of common variants, PRS provide a single metric of genetic liability that can achieve predictive performance comparable to traditional clinical risk factors. Current methodologies are undergoing a paradigm shift, moving beyond simple linear additive models to incorporate complex linkage disequilibrium (LD) structures, multi-ancestry frameworks, and functional genomic landscapes. In particular, the integration of regulatory annotations, including expression quantitative trait loci (eQTL), chromatin accessibility, and cell-type-specific enhancers, has enhanced both the biological interpretability and predictive robustness of these scores.This review synthesizes the rapid methodological evolution of PRS, encompassing Bayesian shrinkage frameworks, machine learning algorithms, and functionally informed strategies designed to mitigate the persistent Eurocentric biases in current datasets. We critically evaluate the evidence supporting the integration of PRS into clinical workflows, focusing on cardiovascular diseases, oncology, and neuropsychiatric disorders, where genetic stratification can enhance preventive interventions and diagnostic precision. Despite this progress, we identify significant challenges to widespread adoption, including the reduced portability of scores across diverse populations, the lack of standardized clinical thresholds, and complex ethical considerations related to health equity.Finally, we propose a multidisciplinary roadmap for the future of PRS, emphasizing the necessity of global biobank diversity, dynamic risk modeling that incorporates temporal and environmental factors, and the seamless integration of genomic insights into electronic health records. Collectively, these advancements are essential for transitioning PRS from a powerful research tool into an equitable and actionable component of the precision medicine toolkit.

Background: The Planetary Health Diet Index (PHDI) quantifies adherence to the planetary health diet, aiming to promote human health and environmental sustainability. While self-reported PHDI is linked to lower cardiometabolic risk, the underlying biological pathways remain unclear. We aimed to identify a metabolomic signature of PHDI and evaluate its association with cardiovascular disease (CVD) and mortality. Methods: In this prospective cohort study, we included 11,190 adults from three US cohorts (Nurses’ Health Study [NHS], NHS II, and Health Professionals Follow-up Study) with up to 30 years of follow-up. Self-reported PHDI and covariates, including body-mass index (BMI), sociodemographic, and lifestyle factors, were derived from the two questionnaires closest to blood draw. Plasma metabolites were profiled using liquid chromatography-tandem mass spectrometry. We used elastic net regression to identify a metabolomic signature of the PHDI. Associations with incident CVD (1,861 events) and mortality (3,788 deaths) were assessed using Cox proportional hazards models. Findings were replicated in the Women’s Health Initiative (WHI; n=1,711). Findings: We identified an 84-metabolite signature of PHDI (Pearson r=0·46), characterised by higher long-chain highly unsaturated lipids and plant-related amino acids and lower carnitines and saturated lipid species, reflecting favourable lipid remodelling and oxidative stress modulation. Higher metabolomic PHDI was associated with lower risks of CVD (hazard ratio [HR] per 1-SD increment 0·95 [95% CI 0·90-1·00]) and total mortality (0·93 [0·89-0·96]), independently of self-reported PHDI, BMI, and other conventional risk factors. The signature was replicated in WHI for CVD (0·92 [0·84-1·00]). Interpretation: We identified and validated a metabolomic signature of PHDI that captures biological pathways linking adherence to a sustainable dietary pattern with cardiovascular health. These findings provide mechanistic insight into the potential cardiometabolic benefits of the planetary health diet and identify metabolic pathways that could inform future prevention strategies.

BACKGROUND:The effect of long-term daily multivitamin-mineral (MVM) supplementation on Coronavirus Disease 2019 (COVID-19) prevention and symptom severity remains unclear. OBJECTIVES:We tested the hypothesis that a daily MVM reduced COVID-19 incidence or symptom severity among generally healthy older adults. METHODS:We conducted a secondary analysis in the COcoa Supplement and Multivitamin Outcomes Study, a randomized, double-blinded, placebo-controlled trial (June 2015 to December 2020), evaluating daily MVM and cocoa extract supplementation for the prevention of cardiovascular disease and cancer among 21,442 United States adults. The primary outcome was incident COVID-19 between 1 January, 2020 and 31 December, 2020, defined as the first occurrence of a self-reported positive test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, a physician's diagnosis, hospitalization, or death due to COVID-19. The prespecified secondary outcomes were symptomatic COVID-19 and symptom count among nonfatal cases through 31 August, 2020, when detailed symptoms were collected. We conducted intention-to-treat (primary) and per-protocol (secondary) analyses among consistently adherent participants. RESULTS:The final year of the COcoa Supplement and Multivitamin Outcomes Study intervention phase from January 2020 to December 2020 coincided with the start of the COVID-19 pandemic. Of the 18,205 participants who remained in the trial and were adherent to study pills as of 1 January, 2020, 382 were randomly assigned to MVM and 404 to MVM placebo (no MVM) and reported a COVID-19 infection through 31 December, 2020. In intention-to-treat analysis, the hazard ratio for MVM compared with placebo was 0.93 for COVID-19 incidence [95% confidence interval (CI): 0.81, 1.07]. Among the 338 cases with detailed symptom data, the odds ratio for symptomatic COVID-19 comparing MVM with placebo was 0.70 (95% CI: 0.44, 1.11). In per-protocol analyses, among participants compliant with study pills during 2020, the odds ratio for symptomatic COVID-19 was 0.60 (95% CI: 0.37, 0.99). CONCLUSIONS:A daily MVM supplement, compared with placebo, does not significantly reduce COVID-19 incidence among older adults but shows a promising signal lowering the odds of symptomatic COVID-19 illness. The Cocoa Supplement and Multivitamin Outcomes Study was registered at clinicaltrials.gov as NCT02422745.

Introduction: Motor function decline and increase in frailty accelerate with aging and are associated shorter lifespan. These functional changes have been associated with age-related changes in brain activity, but the precise molecular pathways involved remain unclear. Hypothesis: Motor function and frailty trajectories are associated with specific brain proteins in older adults. Methods: Our study included 813 adults from the Religious Orders Study and the Rush Memory and Aging Project (mean age=80.7 yr at baseline and 89.4 yr at death). Antemortem global motor function was assessed objectively by 10 performances, and frailty was assessed based on BMI, fatigue, gait, grip strength, and physical activity, with up to 26 repeated annual assessments ( Fig. A ). Slopes of changes in motor function and frailty were estimated using linear mixed-effects models. Proteomic profiling of dorsolateral prefrontal cortex tissues was conducted using isobaric tandem mass tag (TMT) peptide labeling coupled with LC-MS. Diet was assessed annually using a food frequency questionnaire. Linear regression was used to assess associations between diet, brain proteins, and motor function and frailty slopes. Results: Of the 8780 brain proteins measured, multivariable analysis identified 53 associated with motor function decline (P-adjusted<0.05; Fig. B ). These proteins are enriched in pathways such as mitochondrial dysfunction, mTOR signaling, and estrogen receptor signaling for motor-associated proteins, leading by proteins including PRKAB2, MAPK1, and NDUFB3 ( Fig. B-C ). In addition, frailty progression was associated with 176 brain proteins (P-adjusted<0.05; Fig. D ), leading by NRN1, MACROD1, and PPP1CB, with pathway enrichment in respiratory electron transport, sirtuin signaling, and mitochondrial dysfunction ( Fig. D-E ). Furthermore, higher consumptions of fish and EPA+DPA were inversely associated with frailty slope (P<0.05). Among frailty-associated brain proteins, fish and DPA intake were associated with Paralemmin-3 (FDR=0.03). Conclusions: Motor function decline and increase in frailty are associated with alterations of brain proteins enriched in mitochondrial, mTOR, and energy metabolism-related signaling pathways. Fish intake, which is related to less frailty, is associated with Paralemmin-3 (involved in brain mitochondrial function).

Background and ObjectivesIschemic stroke (IS) accounts for 87% of all strokes and is a leading cause of disability worldwide. Women face higher lifetime IS risk and worse functional outcomes, yet predictive biomarkers remain limited. Moreover, inflammation is increasingly recognized as a contributor to IS pathogenesis, with inflammatory markers such as C-reactive protein (CRP) positively associated with IS. Yet, the metabolic pathways linking chronic inflammation to IS risk are poorly understood. We aimed to identify a metabolomic signature reflecting systemic inflammation and evaluate its association with incident IS in women.MethodsThis study used nested case-control designs within the Nurses' Health Study (NHS), a prospective cohort of US female registered nurses aged 30-55 at enrollment. Using elastic net regression in a derivation cohort with inflammatory biomarker (high-sensitive CRP, interleukin 6, tumor necrosis factor receptor 2, adiponectin) and metabolomic data, we developed a metabolomic signature index of inflammation (i-MSI). The i-MSI's association with incident IS was examined in an independent NHS nested case-control study using conditional logistic regression, adjusting for cardiovascular risk factors. Generalizability to atherosclerotic disease was evaluated in a coronary heart disease (CHD) nested case-control study from the Women's Health Initiative (WHI).ResultsThe derivation cohort included 1,699 women (mean age 58 years, 94% White). The i-MSI comprised 102 metabolites, with lysophosphatidylcholine species-promoters of endothelial activation, vascular inflammation, and plaque instability-contributing most significantly. In the independent IS case-control study (454 cases, 454 controls; mean age 66 years), women in the highest compared with lowest i-MSI quartile had a multivariable-adjusted odds ratio (OR) of 1.76 (95% CI 1.02-3.03) for IS, whereas each 1-SD increase in the i-MSI was associated with an OR of 1.35 (95% CI 1.09-1.67). In the WHI study (793 cases, 795 controls; mean age 67 years), each SD increase in the i-MSI was associated with an OR of 1.20 (95% CI 1.05-1.37) for CHD.DiscussionAn inflammatory metabolomic signature was associated with higher IS risk, independent of traditional cardiovascular disease risk factors, with consistent findings for CHD. Future studies should replicate these findings in other populations and evaluate whether these metabolites can improve risk stratification and serve as biomarkers for atherosclerotic cardiovascular diseases.