Ambient PM2.5 exposure and sleep quality: Evidence from circadian DNA methylation signatures in a population-based study.

CLOCK rs1801260 and MTNR1B rs10830963 Polymorphisms Modulate the Association between Cholesterol Levels and Circadian Rhythms in Arctic Residents

Long-term exposure to air pollution, proteomic aging clocks, and liver-related outcomes in Chinese adults.

Although air pollution is recognized as a contributor to accelerated biological aging, its associations with clinically significant liver-related outcomes and the potential role of proteomic aging remain unclear. We aimed to evaluate the associations of key air pollutants with liver-related outcomes and the potential mediating role of proteomic aging clocks. Among 504,006 China Kadoorie Biobank participants without liver disease or cancer, we used time-varying Cox regression to assess associations of PM2.5, PM10, NO2, and warm-season O3, and their mixtures with liver-related events (LRE) and liver-related mortality (LRM), and mediation analyses to examine the role of proteomic aging clocks (i.e., ProtAge). Long-term exposure to PM2.5, PM10, NO2, and warm-season O3 showed significant positive associations with LRE, while PM2.5, PM10, and NO2 were also positively associated with LRM. A 1-unit increment in the weighted pollutant score was associated with an 81% higher risk of LRE (hazard ratio [HR]=1.81 [1.65-1.98]) and a 17% higher risk of LRM (HR=1.17 [1.05-1.31]). Acceleration in ProtAge was associated with higher risks of LRE (HR=1.86 [1.16-2.97]) and LRM (HR=4.43 [3.02-6.50]). ProtAge accounted for an estimated 7.1% and 12.5% of the associations of pollutant mixtures with LRE and LRM, respectively. Among proteins in ProtAge, CD248, GDF15, TNFRSF6B, and XG showed concordant associations in pollutant mixture-protein and protein-LRE analyses. Long-term air pollution was associated with higher risks of clinically significant liver-related outcomes. These findings suggest a possible role of proteomic aging in the association between air pollution and liver-related outcomes, warranting further investigation.

Association Between Sleep Patterns and the Risk of Cardiometabolic Diseases or Cardiometabolic Multimorbidity and the Mediating Role of Metabolic Biomarkers: A Prospective Study of the UK Biobank Cohort

Aim: To identify the relationship between joint sleep patterns and the incidence of cardiometabolic diseases (CMDs) and multimorbidity (CMM) as well as the mediating effects of plasma metabolites. Methods: This prospective study included 190,827 participants from the UK Biobank. The joint sleep pattern was evaluated by the healthy sleep score according to five sleep factors (chronotype, sleep duration, insomnia, snoring, and excessive daytime sleepiness). Cox proportional hazards models were utilized to evaluate the relationship between sleep-related metabolites and the incidence of CMM and CMDs. Mediation analyses were conducted to quantify the potential mediating effects of circulating metabolites. Results: After a median follow-up of 13.5 years, 24,602 incident cases of CMDs and 2740 incident cases of CMM were identified. Per 1-decrement of sleep score was associated with 10.5% and 15.1% increased risks of CMDs and CMM, respectively. However, sleep was not associated with the progression from the first disease to CMM. A total of 152 metabolites were identified to be associated with sleep patterns, among which lipoproteins emerged as the predominant class. Most of the metabolites showed mediation effects on the association between sleep and outcomes. Conclusions: Poor sleep pattern was associated with increased risk of CMDs and CMM. Differences in metabolites, especially lipoproteins, might partially explain the association between sleep pattern and cardiometabolic health.

Urinary Metabolomic Alterations Associated with Contrasting Ambient Air Pollution Exposure in Thai Adults: An Untargeted 1H-NMR Study

Ambient fine particulate matter (PM2.5) is associated with oxidative stress, metabolic dysregulation, and cardiometabolic disease. However, systemic metabolic responses to contrasting real-world ambient air pollution exposure environments remain poorly characterized. In this cross-sectional study, untargeted proton nuclear magnetic resonance (1H-NMR)-based urinary metabolomics was used to investigate metabolic signatures associated with contrasting ambient air pollution exposure environments in Thailand. Adults residing in Chiang Mai (high-exposure region; n = 51) and Songkhla (low-exposure region; n = 52) were recruited during a period of elevated regional air pollution, with long-term residence serving as a proxy for differential exposure to ambient air pollution environments. Partial least squares-discriminant analysis (PLS-DA) demonstrated separation between exposure groups (R2 = 0.873, Q2 = 0.447), indicating good model fit but only modest predictive ability. Eight urinary metabolites differed significantly (p < 0.05), implicating pathways related to tryptophan metabolism, nucleotide metabolism, energy metabolism, and host–microbial co-metabolism. L-arginine and L-cystathionine showed lower relative abundance in the high-exposure group, and six metabolites remained significant after Benjamini–Hochberg false discovery rate correction. Following covariate adjustment, L-tryptophan, hippuric acid, xanthine, and 5-hydroxyindoleacetic acid (5-HIAA) remained significantly associated with the high-exposure group. Contrasting ambient air pollution exposure environments, indexed by regional PM2.5 concentrations, were associated with coordinated urinary metabolic alterations. Because long-term regional residence served as a proxy for individual-level exposure and diet and lifestyle differences between regions were not fully controlled, these findings should be interpreted as associations with contrasting regional exposure environments. Metabolite annotations remain putative and require validation in longitudinal studies with comprehensive pollutant characterization and individual-level exposure assessment.

Circadian syndrome, subjective cognitive decline, and plasma biomarkers: Sex-specific patterns and non-linear biomarkers associations.

CircS is associated with SCD severity, while exploratory analyses suggest possible nonlinear associations between CircS score and selected plasma biomarkers and male participants showed a stronger association with SCD-domain scores.

Epigenetic Clock Trajectories and Brain Health in Midlife

Background: Accelerated biological aging can be assessed with DNA methylation (DNAm)- based epigenetic clocks. Research suggests that greater DNAm is associated with faster cognitive decline and risk of Alzheimer disease (AD) and other dementias. However, most studies have relied on single-time-point measurements of clocks, rather than evaluating dynamic changes over time. We examined the association between 15-year epigenetic aging trajectories and brain health outcomes in midlife. Methods: We analyzed 2,833 middle-aged adults (mean baseline age 40 years, 59% female and 44% Black) with [≥]3 DunedinPACE (a recently developed epigenetic clock) measurements, collected over 15 years. Using mixed-effects modeling, we derived individual-specific slopes of epigenetic aging trajectories and categorized participants as Fast Agers (slopes > 1 SD above the mean), Slow Agers (slopes < 1 SD below the mean), or Typical Agers (within &plusmn1 SD of the mean). We examined associations between trajectory group and cognition on five cognitive domains as well as on plasma AD biomarkers (NfL, p-tau217, A{beta}42/A{beta}40), all assessed 15-20 years post-baseline. Models were adjusted for demographics, education, physical activity and APOE*{varepsilon}4 carrier status (with additional adjustments for eGFRcr for biomarker outcomes). Results: Epigenetic aging trajectories were associated with multiple domains of cognition and AD biomarkers (Figure 1). Compared to Typical Agers, Fast Agers showed worse processing speed, memory, executive function, and global cognition (all p<0.05), with no difference in verbal fluency. Slow Agers had better performance on memory and global cognition (both p < 0.05). Fast Agers also exhibited significantly lower A{beta}42/A{beta}40 levels (p = 0.011) compared to Typical agers; no significant associations with p-tau217 or NfL were observed in either group. Conclusion: Middle-aged adults with faster 15-year epigenetic aging trajectories demonstrated worse cognitive performance, whereas those with slower biological aging trajectories exhibited cognitive resilience and more favorable AD biomarker profiles. By examining long-term trajectories rather than single timepoints, these findings identify individuals at differential risk for brain health outcomes.