The American Journal of Clinical Nutrition

Background: Many epidemiological studies rely on dietary exposures taken from baseline only. This limits our understanding of diet-disease associations because it requires assuming a level of temporal stability, either by individuals or dietary pattern composition. Objectives: This study aimed to evaluate these analytic assumptions of pattern structure consistency and baseline adherence using a cohort of US Black women with repeated measures of food frequency questionnaires (FFQ). Methods: Data from 6151 Black women aged 21-69 from the Black Women's Health Study with complete FFQ data in 1995, 2001, 2013, and 2021 were evaluated for temporal stability. Baseline dietary patterns were derived using an overfitted latent class model. Parameter estimates from the baseline model were then applied to subsequent waves to track individual transitions between existing patterns. Dietary patterns were also derived at each time point using an overfitted latent class model and assessed for changes in pattern composition over time. Results: Five baseline dietary patterns were identified in 1995. Only 18% of participants remained in the same baseline dietary pattern across all four time points, while all others transitioned to a different baseline-derived pattern. Dietary patterns derived independently at subsequent time points, yielded a different number of dietary patterns at each time point (2001: 6 patterns, 2013: 5 patterns, 2021: 4 patterns). Correlation strength of subsequent derived patterns and baseline patterns significantly weakened in strength after 2001 (40% pairings > 0.5), with no patterns correlated greater than 0.5 in 2021. Conclusion: Prospective studies that rely on baseline dietary exposure data cannot assume stability of pattern composition or individual pattern adherence over time, as it ignores changes in dietary habits and may bias our understanding of the diet-disease pathway.

Objective To develop and evaluate an automated large language model (LLM)-based framework for conducting meta-analyses of nutrition-related exposures and the risk of breast, ovarian, and uterine cancers. Design We developed MetaFemina, an automated evidence-synthesis pipeline for women's cancers that integrates keyword-based literature retrieval, LLM-assisted evidence extraction, and random-effects meta-analysis. We evaluated its performance against two recently published peer-reviewed meta-analyses and compared exposure-outcome associations across the three cancer types. Data sources PubMed articles identified through keyword-based searches of titles and abstracts. Methods MetaFemina was developed as a web platform that identifies relevant scientific articles, automatically extracts relevant information using LLMs, and synthesizes extracted evidence using random-effects meta-analysis. Additional analyses included assessment of heterogeneity, publication bias, and leave-one-out sensitivity analyses. The platform also provides sample size calculations based on synthesized effect sizes and generates visual summaries and plain-language interpretations. Results Compared with two recent peer-reviewed meta-analyses of folate and vitamin E intake in relation to breast cancer risk, MetaFemina demonstrated high sensitivity (81.82% and 80%, respectively) in identifying eligible studies and additionally retrieved relevant articles that had been missed by manual screening (27 and 13, respectively). Among 226 exposures considered, lutein and beta-carotene were significantly associated with lower risks of breast, ovarian, and uterine cancers. Vitamin D, antioxidants, and soy were significantly associated with lower risks of both breast and ovarian cancers, whereas calcium and folic acid were significantly associated with lower risks of both breast and uterine cancers. In contrast, iron, red meat, and copper were significantly associated with higher risks of both breast and uterine cancers. omega-6 fatty acids showed contrasting associations, being significantly associated with higher breast cancer risk but lower ovarian cancer risk. After restriction to dietary-intake studies, these cross-cancer significant associations remained statistically significant except for copper, which no longer met the two-study threshold for either breast or uterine cancer. Additionally, calcium became significantly associated with lower ovarian cancer risk, resulting in significant negative associations across all three cancer types, while vitamin E became significantly associated with lower breast cancer risk and remained significantly associated with lower ovarian cancer risk. Conclusions MetaFemina demonstrated high sensitivity for identifying relevant scientific literature, extracts key evidence, and performs statistically rigorous automated meta-analyses. The framework may facilitate more rapid evidence synthesis in nutritional epidemiology and may support researchers in study design, hypothesis generation, and interpretation of emerging evidence.

Importance: Food and non-alcoholic beverage marketing drives children's dietary intake, yet updated evidence quantifying effects by marketing medium and sociodemographic factors is needed to inform policy. Objective: To quantify the effect of food marketing on dietary intake among children and adolescents (0-19 years) and examine variations by age, sex, socioeconomic position (SEP), weight status, marketing medium, and exposure duration. Data Sources: Nineteen electronic databases were searched for articles published from April 2020 to February 2026, complemented by World Health Organization-commissioned reviews covering 1970 to March 2020. Study Selection: Two reviewers independently selected peer-reviewed primary studies that assessed the association between food marketing and dietary intake, following PRISMA guidelines, with no language restrictions. Data Extraction and Synthesis: Two reviewers independently extracted data and assessed the risk of bias. Random-effects meta-analyses were conducted. The certainty of evidence was assessed using GRADE. Main Outcomes and Measures: Dietary intake (energy, quantity, or number of items consumed). Results: A total of 55 studies (N = 6,877; range 2-18 years) were included. Food marketing was associated with higher dietary intake (mean difference [MD], 34.8 kcal; 95% CI, 20.2-49.4) compared with no or less marketing. Unhealthy marketing via television (20 studies; MD, 44.5 kcal; 95% CI, 11.2-77.8), digital media (11 studies; MD, 37.5 kcal; 95% CI, 20.1-54.9), and packaging (11 studies; MD, 20.5 kcal; 95% CI, 0.7-40.3) all increased intake; the difference across media was significant (p < .001). Higher intake was observed in males (3 studies; MD, 51.9 kcal; 95% CI, 45.4-58.3) but not in females (MD, -6.8 kcal; 95% CI, -60.3-46.6); difference was not significant (p = .082). Differences by weight status (p = .012) were seen (5 studies; normal weight: MD, 55.6 kcal; 95% CI, -51.3-162.5; overweight/obese: 146.9 kcal; 95% CI, 34.1-259.7). Effects varied by age (p = .003) and by digital media exposure duration (p = .044). One study examined ethnicity; none studied SEP. Conclusions and Relevance: Food marketing is associated with increased dietary intake, with low certainty of evidence. Variations were observed across age, sex, weight status, and marketing medium. Further research is needed for adolescents and the role of SEP.

Background: The growing burden of lifestyle-related chronic diseases has increased the need for clinically interpretable decision-support tools capable of integrating artificial intelligence with evidence-based preventive nutrition. Although machine learning has shown considerable potential for health risk prediction, most existing approaches remain limited to isolated predictive models or conventional nutritional software, with little integration of multidimensional clinical assessment and personalized recommendations. Objective: To develop and internally validate NutrIA, a hybrid web-based Clinical Decision Support System (CDSS) that combines machine learning, validated clinical assessment, structured clinical reasoning and personalized nutritional recommendations for preventive medicine. Methods: NutrIA was developed using harmonized data from the National Health and Nutrition Examination Survey (NHANES, 1988 to 2018). A supervised machine learning model was trained to estimate 5-, 10- and 20-year all-cause mortality risk and subsequently integrated with an adaptive clinical questionnaire, validated screening instruments, nutritional indicators, dietary clustering, clinical phenotyping and a transparent rule-based recommendation engine within a unified web-based platform. Results: The predictive model achieved ROC-AUC values of 0.894, 0.914 and 0.923 for 5-, 10- and 20-year mortality prediction, respectively. The implemented CDSS incorporates an adaptive questionnaire (151 items), 39 validated clinical assessment instruments, 17 clinical phenotypes and 31 dietary clustering modules to generate individualized nutritional and lifestyle recommendations together with an automated clinical report. The integrated framework translates probabilistic risk estimates into clinically interpretable decision support for personalized preventive nutrition. Conclusions: NutrIA demonstrates the technical feasibility of integrating machine learning with knowledge-based clinical reasoning within a single web-based CDSS for preventive nutrition. Although external validation and prospective clinical evaluation are required before routine implementation, the proposed architecture represents a promising step toward clinically interpretable artificial intelligence for personalized nutritional care.

Plant-based diets may benefit planetary and human health. However, the molecular pathways linking plant-based diet quality to chronic diseases remain unclear. In 4,372 participants from the European Prospective Investigation into Cancer and Nutrition, we assessed 7,285 SomaScan-measured aptamers to identify circulating proteins associated with healthful (hPDI) and unhealthful (uPDI) plant-based dietary patterns using complementary statistical and machine-learning approaches. We then applied cis-pQTL Mendelian randomization (MR) and colocalization to prioritize diet-associated proteins with genetic evidence for associations with overall and site-specific cancers, type 2 diabetes (T2D), and cardiovascular disease (CVD). Among diet-associated proteins with MR evidence, 8 hPDI- and 12 uPDI-related protein-disease associations showed strong colocalization, including EGFR-breast cancer, MMP10-endometrial cancer, NCAN-T2D, and PCSK9-CVD. These findings identify candidate proteins that may link plant-based diet quality to chronic diseases and provide biological insights into the potential health benefits of healthful plant-based diets, which are increasingly relevant to public and planetary health.

Purpose: Iron deficiency impairs sports performance, and female athletes are particularly vulnerable. High-dose iron supplements, commonly used to prevent iron depletion and performance impairments, may cause gastrointestinal side effects and disrupt the gut microbiota. Whether lower doses can improve iron status without adverse effects remains unclear. The aim of this study was to characterize iron intake and iron status in female soccer players during the competitive season and investigate effects of low-dose iron supplementation on iron status, safety-related outcomes and gut microbiota. Methods: In a two-arm parallel randomized controlled trial, female soccer players (median age 21) were randomized to an intervention group (n=12) receiving 3-month low-dose iron supplementation (27 mg elemental iron/day) or a control group (n=11) without supplementation. Blood/fecal samples were collected at baseline and 3-month follow-up. Dietary intake was estimated using 7-day food diaries. Between-group differences were analyzed per protocol (n=18) using ANCOVA with baseline adjustment. Results: The players had inadequate baseline iron intake (median 11.2 mg/day) and 43% had serum ferritin indicating iron depletion (<35 g/L). At follow-up, no significant between-group difference was found for serum ferritin, but fewer athletes in the intervention group experienced decreases from baseline to follow-up (P<0.05). Moreover, serum iron was higher in the intervention group (Pgroup=0.05). No between-group differences were observed for gastrointestinal symptoms or liver damage biomarkers. On the contrary, the intervention led to lower IL-6 (Pgroup=0.04) and higher gut microbial -diversity (Pgroup=0.01) compared to controls. Conclusions: The low-dose iron supplementation was well tolerated, attenuated decreases in iron stores, increased gut microbial diversity and attenuated systemic inflammation in female soccer players with suboptimal dietary iron intake. However, potential adverse effects of long-term exposure cannot be excluded.

Reduced nicotinamide mononucleotide (NMNH) is a reduced NAD precursor with reported NAD- augmenting activity in preclinical models; however, controlled human data remain limited. This was a randomized, double-blind, placebo-controlled, parallel-group phase I trial evaluating oral NMNH-Ca in healthy adults aged 40-65 years. Eighty participants received placebo or NMNH-Ca 125, 250, or 500 mg once daily for 90 days. The primary objective was safety and tolerability. Whole-blood NAD was assessed as the key pharmacodynamic endpoint, including a 24-hour post-dose substudy, with biomarker-derived blood phenotypic age, treadmill-based six-minute walk distance, body mass index, and SF-36 domains analyzed as exploratory outcomes. NMNH-Ca was well tolerated at all doses, with no serious adverse events, treatment-related adverse events, or discontinuations. In the acute substudy, whole-blood NAD increased after single-dose NMNH-Ca, with peak mean concentrations at 12 hours. Over 90 days, NAD increased in a dose-related pattern; Day 90 mean changes from baseline were 2.33 {+/-} 18.53 M with placebo and 8.22 {+/-} 10.25, 15.85 {+/-} 11.16, and 39.90 {+/-} 14.11 M with NMNH-Ca 125, 250, and 500 mg, respectively. Exploratory analyses showed hypothesis-generating favorable signals in blood phenotypic age, treadmill-based six-minute walk distance, and health-related quality of life, most consistently at 500 mg. Oral NMNH-Ca was safe and pharmacodynamically active over 90 days, supporting larger and longer confirmatory trials with prespecified geroscience endpoints and tissue-relevant NAD metabolomics.

Background: Pro-inflammatory and high-environmental-impact diets both threaten population and planetary health, but whether the two objectives align or conflict across countries is unresolved. We tested whether a supply-based dietary inflammatory index (sDII) is coupled to greenhouse-gas (GHG), land and freshwater footprints, and whether a nutrition-feasible reallocation can lower both simultaneously. Methods: From FAO Food Balance Sheets we built sDII (12 inflammatory-weighted components; construct validity r=0.9999) and five per-capita footprints using three independent life-cycle inventories for 182 national food-supply series. For each country, constrained optimisation reallocated 13 food-group supplies under isoenergetic, protein-preserving and food-group-bound constraints, minimising sDII and GHG jointly (Pareto frontier). Health burden was estimated via pooled relative-risk meta-analysis and 2023 World Bank population data. Results: sDII was only weakly associated with GHG (Spearman rho=0.14), land (rho=0.13) and freshwater (rho=0.28) in 2023. The balanced-Pareto reallocation lowered both sDII and GHG in 182/182 series (100% synergy): population-weighted delta sDII=-0.235, GHG -37.5%, land -49.3%, water -17.2%, i.e. 4.28 Gt CO2e/yr avoided. The associated reduction in metabolic-syndrome burden was directionally consistent but modest (~1.1% of the prevalent pool, ~2.84 million cases). Results were robust to three life-cycle inventories and three feasibility-bound regimes. Conclusions: Anti-inflammatory and low-carbon goals are decoupled rather than conflicting, and an isoenergetic, protein-preserving reallocation reconciles them in every country. Environmental gains are large and robust; health gains are directionally consistent but modest--triangulation, not a causal claim.

Background: Large-scale prevention and population health monitoring require measurement approaches that are both feasible and informative. Although several self-measurable anthropometric and fitness indicators have been associated with cardiometabolic risk, it remains unclear whether combining multiple measurements provides meaningful improvements over simpler approaches. We evaluated whether a parsimonious set of self-measurable indicators can achieve classification performance comparable to a full candidate set and quantified the incremental value of additional measurements. Methods: Using data from 8,275 adults in the NHANES 1999-2004 cohorts, we evaluated a predefined minimal set of four self-measurable anthropometric and fitness indicators (body mass index (BMI), waist-to-height ratio (WHtR), mid-upper arm circumference (MUAC), and VO2max (as a proxy for the 6-minute walk test) as candidate indicators of cardiometabolic risk. Their ability to reflect underlying clinical risk factors related to adiposity, glucose and lipid metabolism, and physical fitness was assessed using nested logistic regression models, likelihood ratio tests, discrimination metrics, and decision tree analyses. Results: WHtR consistently showed the strongest discriminative performance, with {Delta}PR-AUC values for BMI versus WHtR ranging from -0.002 to -0.037, and emerged as the primary splitting variable. Adding BMI to WHtR resulted in small gains in PR-AUC for most outcomes, ranging from 0.000 to 0.008, except for triglycerides where the gain was larger ({Delta}PR-AUC=0.039). Further inclusion of MUAC and VO2max provided limited additional value overall, with evidence of variation across outcomes and sex stratified analyses. Conclusion: Most classification performance was achieved using a limited number of simple self-measurable indicators, with little additional benefit from incorporating further measurements. These findings suggest that parsimonious measurement strategies may provide a feasible approach for cardiometabolic risk classification in population health and prevention settings while reducing measurement burden.

Background: Cardiometabolic health typically declines with age and is worse among individuals living with obesity. Weight loss medications have modified the potential for weight loss across the life course, but it remains unclear whether weight reduction in later midlife contributes to improved cardiometabolic health, or if continuing to gain weight may continue to worsen cardiometabolic health. Methods: Using the nationally representative 1958 National Child Development Study (NCDS), a British birth cohort, associations were examined using lagged linear regression between weight change between ages 50-55 and health outcomes at age 62 (n=6,309 high-density lipoprotein (HDLc) and low-density lipoprotein (LDLc) cholesterol, systolic and diastolic blood pressure (SBP and DBP), heart rate, triglycerides, C-reactive protein (CRP), and glycated haemoglobin (HbA1c). Models accounted for prior biomarker levels at age 44. We also explored impacts of weight change on subsequent body composition. Results: Those who gained weight into or within obesity had less favourable cardiometabolic profiles and experienced faster deterioration of cardiometabolic markers between the ages of 44 and 62 than those remaining in healthy weight (e.g. SBP: 5.726, 95% CI: 2.660 to 8.793, p < 0.001; CRP: 0.802, 95% CI: 0.409 to 1.196, p < 0.001). Those who lost weight from obesity had similar rates of cardiometabolic biomarker deterioration to the healthy weight group (SBP: 0.947, 95%CI: -6.605 to 8.499, p=0.806; CRP: 0.140, 95% CI: -0.774 to 1.055, p= 0.764). Conclusion: Weight change in midlife tends towards increasing obesity and associated adverse cardiometabolic risk. Those who lose weight experienced improved cardiometabolic profiles. By viewing midlife as a modifiable stage of the life course, this study highlights opportunities to promote cardiometabolic health, and limit the speed of health decline.

Micronutrient deficiencies are prevalent in Madagascar, where diets rely heavily on starchy staples and access to animal-source foods is limited. Small dried fish (SDF) are widely available, yet their nutritional value and health risks remain poorly documented. We combined market surveys, taxonomic identification, and micronutrient and heavy metal analyses of nine SDF types collected along National Road 7. The samples encompassed 33 fish families, were dominated by small pelagic species (Clupeidae and Engraulidae), and were appreciated by consumers. A daily portion (5 g for infants; 10 g for young children and women of childbearing age) contributed substantially to Recommended Nutrient Intakes (RNIs). Across samples and groups, SDF were rich (>30% of RNI) in selenium and, for infants and young children, in calcium. All samples were a source of (>15% of RNI), or rich in, phosphorus, whereas iron contributions were more variable but often substantial. Several samples exceeded 100% of RNIs for selenium, calcium, iron, or manganese in infants and young children, and some were also sources of magnesium and, less frequently, zinc. Vitamin A was absent from sun-dried samples but detected in a smoked freshwater type. Heavy metal concentrations varied markedly, and portions of several types led to estimated exposures to inorganic arsenic or cadmium exceeding reference values, whereas freshwater species and some pelagic types showed a more favorable nutrition-risk balance. Overall, SDF are affordable, nutrient-dense foods with strong potential to alleviate micronutrient deficiencies in Madagascar, while highlighting the need for type-specific guidance to balance nutritional benefits and contamination risks.

Background: Cardiovascular health is affected by health behaviors, but postpartum behavioral influences are not well understood. We examined whether intrinsic motivation (IM) is longitudinally associated with long-term postpartum health behaviors (healthy eating, physical activity, self-weighing) and cardiovascular health (Life's Essential 8 [LE8] scores). Methods: The prospective Pregnancy, Lifestyle and Environment Study-2 (PETALS-2) followed women enrolled in the PETALS study at Kaiser Permanente Northern California during pregnancy (N=311). Data were collected via validated self-report surveys and objective measurements during pregnancy and 6-24 months postpartum (2017-2021). Health behaviors were dichotomized by sample-specific 75th percentiles (P75) or pre-specified thresholds (attaining guideline-recommended moderate-to-vigorous physical activity [MVPA, {greater than or equal to}150 minutes/week]; self-weighing regularly [{greater than or equal to}once/week]). Separate analyses lagged IM by timepoint to assess longitudinal associations between behavior-specific IM and immediate subsequent health behaviors; and between an IM composite and immediate subsequent LE8 scores. Results: Each one-unit higher IM score was associated with greater likelihood of Healthy Eating Index-2015 scores {greater than or equal to}P75 at 24 months postpartum (RR=1.42; 95% CI=1.07, 1.88); attaining MVPA guidelines at 6 (1.48; 1.03, 2.12), 12 (1.85; 1.26, 2.71), and 24 months postpartum (1.66; 1.22, 2.27); and regular self-weighing at 6 (1.53; 1.03, 2.27) and 12 months postpartum (1.65; 1.15, 2.36). Each one-unit higher composite IM score was associated with higher LE8 scores at 6, 18, and 24 months postpartum (18-month mean estimate=2.34; 95% CI=0.67, 4.02). Conclusions: Greater IM was associated with healthier behaviors and cardiovascular health through 24 months postpartum. Future research should test whether interventions targeting IM improve health behaviors and long-term maternal cardiovascular health.

Background and Objective. Missing data frequently occurs in health databases and can bias analyses if not correctly dealt with. Using real-world data, we compared complete-case and multiple-imputation methods for recovering true parameters of a multivariable logistic regression model for the association between maternal glucose levels during pregnancy and child excess weight at preschool age, where missing values were present in as much as 30% of our sample. Methods. This study utilized a cohort of 130,424 children with complete preschool-age body mass index (BMI) measurements from the Calgary and Edmonton health regions of Alberta, Canada. In the complete BMI data, we introduced missingness through deletion following three distinct mechanisms: missing completely at random (MCAR), at random (MAR), and not at random (MNAR). To handle the missing data created, we employed complete-case and multiple-imputation methods. Maternal glucose levels during pregnancy were categorized into five groups and its association with child excess weight at pre-school age was determined based on a logistic regression model using the full observed data (yielding true values), observed data that was not deleted (complete-case estimates), and imputed data (multiple-imputation estimates). The accuracy of complete-case and multiple-imputation estimates were evaluated against the true values. Finally, we conducted a sensitivity analysis for the MNAR mechanism using pattern-mixture models with an additive shift. Results. Under MCAR and MAR, multiple-imputation generally outperformed complete-case, yielding smaller absolute and relative bias. Both methods achieved high significance ([&ge;] 0.96) for most effects. Mean squared errors for multiple-imputation and complete-case were similar missing completely at random, missing at random, and coverage was consistently high ([&ge;] 0.99). Under MNAR, both complete-case and multiple-imputation showed poor performance regarding bias and statistical significance. Sensitivity analysis using pattern-mixture models indicated performance varied by specific effect. Conclusions. Under MCAR and MAR, multiple-imputation introduced higher bias but demonstrated superior overall performance based on mean squared error and restored statistical power. Conversely, both methods failed under MNAR, where pattern-mixture modeling sensitivity analyses revealed highly variable, effect-specific performance due to unverifiable shift assumptions. When faced with missing data, researchers should assess missingness mechanisms, report both complete-case and multiple-imputation estimates under MCAR/MAR while accounting for power-versus-bias tradeoffs, and employ pattern-mixture sensitivity analyses to test robustness when MNAR is plausible.

BACKGROUND People with type 2 diabetes mellitus (T2DM) are at higher risk of cerebral small vessel disease and left ventricular hypertrophy (LVH), potentially contributing to cognitive decline and dementia. We aimed to describe brain volume and cognitive trajectories over 2 years in a cohort of people with T2DM and to determine whether LVH causes increased brain atrophy and cognitive decline. METHODS Diabetes and Dementia (D2) study is a multicentre observational cohort study in Melbourne, Australia. Participants aged >50 years were recruited via 2 hospital outpatient clinics, 3 private clinics, and study advertisements. Participants with pre-existing cognitive impairment, life-limiting medical illness, and severe chronic renal impairment were excluded. Participants attended study visits for brain MRI, transthoracic echocardiography (TTE), and cognitive testing at baseline and 2 years. The exposure was LVH determined on baseline TTE. Pre-specified outcomes were total brain volume (TBV) change and cognitive decline (z-score change?-1 in any cognitive domain) over 2 years. Regression analyses examined associations between baseline variables and outcomes. A causal inference approach was utilized using inverse probability of treatment weighting to standardize for confounding covariates, excluding participants for non-positivity on age and baseline TBV. RESULTS Participants were recruited 20May2016 to 20March2020: 2378 screened, 702 eligible, 196 consented, 150 baseline and 123 2-year assessments with complete MRI, TTE, and cognitive data (17.4% attrition). At baseline, LVH was associated with female sex, older age, lower educational attainment, lower mood, hypertension, obesity, beta-blocker use, and smaller TBV. Participants with baseline cognitive impairment exhibited greater brain atrophy. Lower educational attainment, hypertension, and lower baseline cognitive scores were associated with cognitive decline. Causal inference analysis included 62 participants with no LVH (20(32%) women; mean [SD]=66.9[5.9] years), and 31 with LVH (17(55%) women, 67.4[5.4] years). LVH caused lower TBV change: standardized mean difference (95% CI) 6.3 (0.1, 12.5) cm3, P=.048. LVH had no effect on cognitive decline. CONCLUSIONS Brain atrophy and cognitive decline were associated with baseline cognitive impairment. LVH caused less brain atrophy and cognitive decline in people with T2DM. We conclude that guideline-directed LVH therapies such as beta-blockers have both cardioprotective (remodelling) and neuroprotective effects. TRIAL REGISTRATION ACTRN12616000546459 UTN: U1111-1181-6659

Abstract Background The gut microbiome has been widely studied in the context of obesity, and yet the reported associations vary widely across populations and analytical approaches. In Asian populations where the prevalence of obesity is rapidly rising, the extent to which gut microbiome features could associate with adiposity in a robust and generalizable manner remains unclear. Methods Population-scale shotgun metagenomic data was generated for adults (n=871) from the Health for Life in Singapore (HELIOS) cohort, comprising ethnic Chinese, Malay, and Indian participants. Integrated taxonomic, functional, and machine-learning-based analyses were used to assess associations between gut microbiome features and obesity, adjusting for demographic covariates and evaluating for robustness across multiple statistical frameworks. Results Global microbiome structure exhibited weak separation by body mass index (BMI), with enterotype-like clustering providing limited discriminatory power for obesity status. Differential abundance analyses identified a small number of method-dependent taxa and pathways, with only limited recurrence across methods. Supervised machine learning models trained on taxonomic profiles achieved modest predictive performance, particularly for intermediate BMI classes, and did not reveal robust microbial signatures beyond those detected by univariate analyses. Conclusions Our study highlights the importance of large-scale, multi-framework analyses for distinguishing robust microbiome-phenotype associations from weak, method-dependent signals. Together, our findings emphasize that obesity-associated microbiome signatures may be too weak, diffuse, and insufficient to explain adiposity in Asian populations.

Background: Large-scale biobanks have advanced genomic and epidemiologic research, but many rely on infrequent biological sampling and limited digital phenotyping. The Yamanashi Multi-omics Cohort (YMoC) was established to support longitudinal assessment of molecular, clinical, and behavioural changes in a screening-defined cohort of adults at elevated metabolic risk without diagnosed diabetes. Methods: YMoC is a longitudinal cohort of 215 adults aged 30-70 years in Yamanashi Prefecture, Japan, who met prespecified glycaemic eligibility criteria at health check-up, including fasting plasma glucose 100-125 mg/dL (5.6-6.9 mmol/L) and HbA1c <6.5%. Participants underwent three in-person visits over six months. Measurements include 75-g oral glucose tolerance testing with serial sampling, clinical biochemistry, anthropometry, liver elastography, and collection of blood, urine, stool, and saliva for multi-omics profiling. Between visits, participants wore a Fitbit Inspire 3 and completed daily app-based questionnaires using the Taohealth app. Current molecular data include genome-wide single nucleotide polymorphism array genotyping and longitudinal plasma proteomics in a subset. Conclusions: YMoC is designed to evaluate within-person molecular and phenotypic trajectories in a screening-defined metabolic-risk cohort. The cohort provides a dense longitudinal resource linking clinical assessments, biospecimens, omics assays, and digital phenotyping, including analyses of insulin-resistance-related markers such as homeostasis model assessment of insulin resistance (HOMA-IR).

Background Data-driven type 2 diabetes (T2D) subtypes differ in their risks of dementia and stroke. We examined whether their metabolomic profiles also differed and whether subtype-related metabolic patterns were associated with dementia, stroke, and all-cause mortality. Methods We analyzed NMR-based metabolomic profiles across previously defined T2D subtypes in the UK Biobank. Subtype-related metabolites were summarized using principal component analysis (PCA), and their associations with incident dementia, stroke, and all-cause mortality were examined using Cox models. Attenuation analyses and two-sample Mendelian randomization further assessed subtype-outcome relationships and the potential causal relevance of outcome-associated metabolites. Results Among 7,671 individuals (mean age 59.85 years; 37% female), the first five PCs explained 76.7% of variance in subtype-related metabolites and mainly reflected lipid and lipoprotein signatures. After adjustment for T2D subtype and confounders, the HDL-remodeling PC increased risks of all-cause dementia (HR 1.17, 95% CI 1.08-1.27), VaD (HR 1.18, 95% CI 1.05-1.32), and all-cause mortality (HR 1.16, 95% CI 1.13-1.19). Lower scores on the LDL cholesterol-enriched axis increase risks of all-cause dementia (HR 0.75, 95% CI 0.62-0.91) and mortality (HR 0.76, 95% CI 0.69-0.83). The VLDL/LDL-enriched PC was inversely associated with mortality (HR 0.93, 95% CI 0.88-0.98). No significant stroke results were observed. Adjustment for the PCA-derived metabolomic patterns generally attenuated subtype-outcome associations, MR analyses identified 197 metabolite-outcome associations that remained significant after FDR correction. Conclusions Metabolomic profiling showed that the metabolic signatures differed across data-driven T2D subtypes and highlighted lipid and lipoprotein remodeling as a major metabolic feature associated with dementia, stroke, and all-cause mortality.

BACKGROUND: Undernutrition is the leading risk factor for tuberculosis (TB), yet evidence on programmatic nutritional support during treatment is limited. Benin and Togo are neighboring West African counties. Benin provides in-kind food support to all people with drug-susceptible TB; neighbouring Togo does not. This created the opportunity for a natural experiment. METHODS: We conducted a prospective cohort study at 13 sites in Benin and Togo (September 2023-June 2024). We compared recipients of nutritional support with non-recipients, using Beninese non-recipients as an internal comparison. Primary outcomes were [&ge;]5% weight gain at month 2, change in 6-minute walk test (6MWT) distance, and pill-count adherence. We used multivariable regression adjusted for pre-specified covariates. RESULTS: Of 769 participants, 450 received nutritional support and 319 did not. Recipients had higher odds of [&ge;]5% weight gain at month 2 (adjusted odds ratio [aOR] 1.57, 95% CI 1.13-2.19) and [&ge;]10% at month 6 (aOR 1.92, 1.35-2.74), greater 6MWT improvement (adjusted {beta} 40.6 m, 26.5-54.6), and higher adherence (aOR 3.43, 1.81-6.51). Mortality was lower among recipients (aOR 0.32, 0.11-0.93). Sputum conversion and treatment success did not differ. Beninese non-recipients resembled Togolese participants across outcomes. CONCLUSION: Programmatic nutritional support was associated with improved weight gain, functional recovery, adherence, and lower mortality during TB treatment, supporting its integration into national TB programmes.

Background: Contemporary cardiovascular disease (CVD) risk equations may not fully capture cumulative biological aging or long-term exposure burden. DNA methylation (DNAm) biomarkers may capture aging- and exposure-related biology, but their incremental prognostic value beyond clinical risk-factor models like PREVENT remains uncertain. To our knowledge, no prior study has benchmarked DNAm-based biomarkers with PREVENT. Methods: In a population-based cohort study, we analyzed NHANES 1999-2002 participants with DNAm biomarkers and mortality follow-up. We derived a DNAmScore from candidate DNAm biomarkers using elastic-net Cox regression with repeated nested cross-validation. A PREVENT-like clinical model was defined as a Cox model fit in NHANES using PREVENT predictors. Weighted Cox models estimated the association between DNAmScore and mortality after adjustment for PREVENT-like clinical predictors. We then compared the PREVENT-like clinical model, DNAmScore alone, and a combined model (PREVENT-like clinical predictors plus DNAmScore) using cross-fitted C-index, time-dependent AUC, calibration, and Brier score. Results: Our cohort included 2,282 participants; 597 and 937 deaths occurred by 10 and 15 years, respectively. After adjustment for PREVENT-like clinical predictors, the cross-fitted DNAmScore was strongly associated with all-cause mortality (HR per 1-SD increase, 2.43; 95% CI, 1.97?2.99). At 10 years, AUCs were 0.791 for the PREVENT-like model, 0.791 for DNAmScore, and 0.803 for the combined model. At 15 years, corresponding AUCs were 0.825, 0.822, and 0.835. Compared with the PREVENT-like model, the combined model improved AUC by 0.013 (95% CI, 0.006?0.020) at 10 years and 0.010 (95% CI, 0.004?0.015) at 15 years. The combined model had lower Brier scores at all three horizons with similar calibration. DNAmScore remained associated with CVD mortality after clinical adjustment. Conclusions: DNAmScore identified residual biological risk beyond PREVENT-like clinical predictors, with strong independent mortality associations and modest, consistent improvements in cross-fitted prediction performance. These findings support development and external validation of CVD-specific DNAm biomarkers.

Background: The retail food environment is a widely used exposure in behavioural-nutrition and obesity research, on the premise that nearby food retailers shape diet and obesity risk. Over the past quarter-century, grocery stores have declined across rural and small-town America while limited-assortment discount ("dollar") stores have proliferated. Standard food-environment indices classify retailers as healthy or less-healthy but typically exclude dollar stores, now the fastest-growing food-retail format. As a result, a single classification decision may alter how the food environment is measured and the conclusions drawn from it. We develop a dollar-aware index, quantify how counting dollar stores changes the measured exposure, and derive a longitudinal trajectory typology. Methods: Using establishment-level data from Data Axle for all 878 Mississippi census tracts (1997-2024), we classified food retailers into five mutually exclusive categories using a previously validated approach and calculated the modified Retail Food Environment Index (mRFEI) in both its standard and dollar-aware forms, with the latter counting dollar stores as less-healthy outlets. We fitted Nagin-style group-based trajectory models to the tract-level dollar-aware index, related class membership to the Social Vulnerability Index (SVI) and urbanicity with multinomial regression, and characterised spatial clustering (Getis-Ord Gi*, join-counts) and grocery access. Results: Grocery stores fell from 1,616 to 716 while dollar stores rose from 315 to 1,005, intersecting in 2018. Counting dollar stores lowered the index by a margin that widened over time, and a growing number of tracts had only dollar-store retail, undefined under the standard index. Six trajectory classes emerged: stable adequate (5.6% of tracts), steady decline (13.1%), early collapse (11.1%), late collapse (6.7%), persistently constrained (34.1%) and chronic desert (29.3%); only the stable-adequate class (5.2% of children) stayed adequate throughout. Constrained and steady-decline membership rose steeply with vulnerability (RRR 11.7 and 9.9); chronic desert was urban (RRR 5.2, a food-swamp pattern); collapse classes had no cross-sectional social signature. Conclusions: In the US state with the highest adult obesity prevalence, a single retailer-classification decision substantially changes the measured food environment. The dollar-aware index and trajectory typology offer a transferable, time-varying exposure for behavioural-nutrition and obesity research and establish a foundation for future childhood-obesity studies.