Urinary organophosphate metabolites, DNA methylation aging, and heart disease mortality in middle-aged and older adults: an exploratory cohort study with in vitro evidence of biological plausibility

Organophosphate Pesticide Metabolites and Mortality in U.S. Adults: Age-Modified Dose–Response Associations from a National Cohort Study

Chronic OP exposure, reflected by these indirect urinary biomarkers, is associated with increased mortality, with heightened vulnerability in younger adults, and underscore the need for stricter pesticide regulation and targeted public health interventions.

Inflammation mediates the Pace of aging based on DNA methylation on mortality from NHANES 1999-2002: a national prospective cohort study.

BACKGROUND Global population aging underscores the urgent need for biomarkers quantifying biological aging trajectories. While DNA methylation-derived pace of aging (DunedinPoAm) measures individual differences, its generalizability across diverse populations and mechanistic links to systemic inflammation remain underexplored. This study aimed to systematically examine the longitudinal associations between the DunedinPoAm and all‑cause mortality in a multiethnic cohort, and to quantify the extent to which systemic inflammatory biomarkers mediate these associations using causal mediation analysis. METHODS For this cohort study, information on a nationally representative cohort of 21,004 U.S. adults was extracted from the National Health and Nutrition Examination Survey (NHANES) conducted from 1999 to 2002, along with the NHANES Linked Mortality File, which ascertained mortality through December 31, 2019. The exposures were Pace of aging (DunedinPoAm) and inflammation. The survival outcome measured was all-cause mortality. We employed Cox proportional hazards models, Kaplan-Meier survival curves, restricted cubic splines, and Bayesian mediation frameworks to evaluate mortality risk, explore non-linear dose-response relationships, and investigate inflammatory mediation. RESULTS Data were analyzed from 2,532 participants, with a mean follow-up duration of 18.5 ± 1.29 years. Higher DunedinPoAm quartiles exhibited graded mortality risks (Q4 vs. Q1: HR = 2.50, 95% CI 1.84-3.38), which persisted after multivariable adjustment. Restricted cubic splines revealed a non-linear association (P for overall < 0.001; P for nonlinearity < 0.001), indicating the presence of threshold effects. Systemic inflammation mediated 2.33-23.5% of the mortality risk associated with DunedinPoAm, driven by CD4 + T cells, B cells, CRP and comprehensive inflammatory indices. A significant interaction with diabetes (P for interaction = 0.026) underscored metabolic dysregulation as a vulnerability factor. CONCLUSION DunedinPoAm predicts all-cause mortality in a non-linearly manner across multiethnic populations, partially mediated by pathways associated with inflammaging. The observed diabetes-specific interactions and threshold effects indicate the potential for precision approaches targeting high-risk subgroups. These findings support the integration of DunedinPoAm into gerotherapeutic trials and public health strategies aimed at addressing disparities in aging.

Associations of Accelerated DNA Methylation Aging Algorithms with Chronic Liver Disease and All-Cause Mortality

Background: Chronic liver disease (CLD) represents a substantial global health challenge, with significant morbidity and mortality worldwide. Biological aging may be involved in CLD-related outcomes, but the associations of diverse DNA methylation (DNAm) aging algorithms with CLD phenotypes and long-term mortality remain incompletely characterized. Methods: Using a US nationally representative cohort, we analyzed 12 DNAm aging algorithms in 2522 adults aged ≥50. CLD was classified into viral, alcohol-related, metabolic syndrome (MetS)-related liver disease, or uncharacterized groups. Advanced fibrosis was defined by AST-to-platelet ratio index ≥0.7. Associations of algorithms with CLD and all-cause mortality (followed through 2019) were assessed using multivariable-adjusted regression and logistic regression models and Cox models, accounting for complex sampling. Results: Among participants with CLD, DNAm aging algorithms showed only nominal associations with APRI-defined advanced fibrosis. GrimAgeMortAcc, GrimAge2MortAcc, HannumAgeAcc, PhenoAgeAcc, DunedinPoAm, and HorvathTelo were significantly associated with all-cause mortality. GrimAge-based measures and PhenoAgeAcc showed the strongest associations with all-cause mortality across CLD-related phenotypes (HRs ranged from 1.31 to 1.82). HorvathTelo was inversely associated with mortality risk (HR = 0.71, 95% CI: 0.62–0.82). Conclusions: DNAm aging algorithms, particularly GrimAge-based measures, are strongly associated with long-term all-cause mortality among individuals with CLD-related phenotypes. Although associations with fibrosis-related outcomes varied according to fibrosis definitions, DNAm aging algorithms may provide additional biological information for mortality risk stratification among individuals with CLD-related phenotypes.

The hidden effects of metabolism-disrupting agents on alcohol-related liver disease risk: an integrated epidemiological, machine learning, and network toxicology Analysis

Individual susceptibility to alcohol-related liver disease (ALD) varies substantially despite similar alcohol consumption patterns. Emerging evidence suggests metabolism-disrupting agents (MDAs) may synergistically amplify alcohol-induced hepatotoxicity, yet population-level evidence remains limited. This study investigated associations between 40 MDAs and ALD risk using integrated analytical approaches. We analyzed 13,472 National Health and Nutrition Examination Survey participants (2005–2016). Forty MDAs spanning per- and polyfluoroalkyl substances, phenolic compounds, phthalate metabolites, polycyclic aromatic hydrocarbon metabolites, and volatile organic compound metabolites were measured using standardized protocols. Multivariable logistic regression assessed MDA-ALD associations. Restricted cubic splines characterized dose-response relationships. Subgroup analyses identified vulnerable populations by sex, age, race/ethnicity, body mass index, and hyperlipidemia status. Machine learning algorithms including LightGBM were developed with nested cross-validation (to prevent data leakage during feature selection) to identify predictive biomarkers. Network toxicology integrated computationally predicted MDA targets with ALD gene expression data (GEO GSE28619), followed by pathway enrichment analyses. Three MDAs demonstrated robust positive associations: benzylmercapturic acid (BMA; OR: 1.56, 95% CI: 1.31–1.86), perfluorohexanesulfonic acid (PFHxS; OR: 1.56, 95% CI: 1.28–1.90), and benzophenone-3 (BP-3; OR: 1.40, 95% CI: 1.15–1.72). BMA and PFHxS exhibited linear dose-response relationships without thresholds, while ATCA showed an inverted U-shaped pattern and BP-3 displayed a plateau pattern. Phenolic compounds and phthalates demonstrated stronger associations in females. LightGBM achieved optimal performance (cross-validation AUC: 0.760, test AUC: 0.706), identifying N-acetyl-S-(3-hydroxypropyl)-L-cysteine, N-acetyl-S-(N-methylcarbamoyl)-L-cysteine, and mono-carboxyoctyl phthalate as key predictive biomarkers, including compounds not significant in regression. Network analysis identified 192 shared genes with enrichment in neuroactive ligand-receptor interactions and nuclear receptor pathways (PPARα/γ, PXR, FXR). Specific MDAs demonstrated significant ALD associations through complementary approaches. Evidence suggests MDAs may function as signal disruptors within neuro-endocrine-immune networks, with pathways paralleling alcohol-induced hepatotoxicity. Findings underscore incorporating environmental assessments into ALD risk stratification and highlight needs for prospective validation and mechanistic studies.

DNA Methylation Biomarkers Capture Residual Biological Risk Beyond PREVENT

The interplay of air pollution with plasma neurodegenerative markers and metabolome for dementia, Parkinson's disease and all-cause mortality risks and transitions: The UK Biobank study.

We investigated the joint associations of circulating neurodegeneration markers-neurofilament light (NfL) and glial fibrillary acidic protein (GFAP)-ambient air pollution, and plasma metabolomic profiles with transitions from a healthy state to dementia, Parkinson's disease (PD), and all-cause mortality in the UK Biobank. The analytic sample included 19,645 participants aged ≥ 50 years with complete proteomic, metabolomic, and environmental data. Time-to-event analyses used Cox proportional hazards and multistate Weibull models to evaluate associations and statistical interactions across health transitions. Higher NfL concentrations were associated with increased risks of transitions from healthy to PD, dementia, and death, whereas higher GFAP concentrations were specifically associated with dementia (HR = 2.65, 95% CI: 2.17-3.25). At the nominal level, particulate matter (PM2.5, PM10) showed positive statistical interactions with NfL for mortality, while nitrogen oxides (NO₂/NOx) showed negative interactions with GFAP. There was little evidence of interaction between PM2.5 and either biomarker for dementia. Metabolomic principal components reflecting lipid, amino acid, and energy pathways were associated with variation in the relationships of NfL and GFAP with dementia and mortality and interacted with PM2.5 for PD and dementia. A branched-chain amino acid-related component showed a negative interaction with GFAP for dementia, indicating weaker associations at higher levels. These findings highlight complex relationships linking air pollution, metabolic dysregulation, and neurodegeneration, and support integrative multi-omics approaches to identify pathways relevant to prevention of neurodegenerative diseases and premature mortality.