Nature Aging

Biological aging is heterogeneous across organ systems, yet whether CT-derived abdominal aging provides prognostic value beyond routine clinical data and whether organ decomposition adds beyond a unified estimate remains untested. We developed and evaluated organ-specific and ensemble biological age models from radiomic features across five abdominal organs in 68,675 CT scans from 32,883 subjects, evaluated on alignment with chronological age of healthy subjects (nested cross validation: MAE=3.68 years, R^2=0.90). In sequential analyses restricted to adults aged 20-60 years which is the stratum of strongest BAG-disease association, ensemble biological age gaps provided incremental prognostic value beyond demographic covariates for all-cause disease and mortality (Delta C-index=0.141, 0.051) and beyond routine blood biomarkers (Delta C-index=0.048), confirming CT-derived aging captures structural information beyond laboratory markers. Organ-specific biological age added incremental prognostic value beyond ensemble selectively for focal diseases: cardiovascular (aorta, Delta C-index=0.091) and hepato-pancreatic (pancreas, Delta C-index=0.096). These findings establish a hierarchical organization of CT-derived biological aging, positioning routine CT as a source that adds prognostic value to existing clinical biomarkers.

Can we decode Alzheimers disease (AD) heterogeneity into a few portable axes that capture how amyloid-{beta}, tau and neurodegeneration (A-T-N) spatially co vary in vivo? To answer this question, we built a pipeline that harmonizes longitudinal amyloid-{beta}/tau PET and T1 MRI (gray matter) from ADNI cohort (12,430 images) with mixed effects modeling and then derived stage specific multimodal axes (mVCs) using linked component analysis, with robustness tested in simulations and external validation in the OASIS cohort (4,958 images). We identified a small set of multimodal axes that (i) recapitulate early tau weighted variation in cognitively unimpaired (CU) individuals, AD like A-T-N coupling in cognitively impaired (CI) individuals and atypical CU and CI participants with posterior (precuneus/occipitoparietal) and fronto insular/frontal weighted patterns, (ii) map onto domain specific cognition, APOE e4, and blood/CSF biomarkers of neurodegeneration, neuroaxonal injury and astrocyte activation, (iii) predict clinical transitions, (iv) generalize in an independent cohort, and (v) demonstrate modelling robustness to missing data, high dimensionality, and cross-cohort variability, enabling direct application of the extracted axes to new datasets for biomarker discovery and stratification. Multimodal axes provide a portable, interpretable layer for quantifying amyloid-{beta}-tau-neurodegeneration coupling at the individual level, complementing current biomarker-based staging frameworks based on A-T-N status and tau PET topography, and can be computed on new datasets to aid clinical assessment and trial enrichment.

Understanding the regulatory consequences of genetic variation in the aging human brain requires molecular maps that span brain regions, cell types and regulatory modalities. We present the Alzheimer's Disease Sequencing Project Functional Genomics (FunGen-AD) xQTL Atlas, a harmonized resource of molecular quantitative trait loci from four postmortem brain studies, ROSMAP, MSBB, Knight-ADRC and MiGA. The atlas integrates histone acetylation, DNA methylation, gene expression, splicing and protein abundance QTLs across 14 brain regions, 7 major cell types and 17,566 samples, with standardized association, significance-filtered and fine-mapping outputs. To expand discovery beyond conventional 1-Mb cis windows, we include variants within Topologically Associating Domains (TAD) and their boundaries where appropriate, identifying on average 21% more variant-molecular-trait associations per dataset. Statistical fine-mapping reduced broad association sets by 95% into credible sets of candidate regulatory variants. Distributed through the NIAGADS xQTL portal and bulk-download services, the atlas provides a comprehensive functional-genomic foundation for interpreting genetic risk variants in Alzheimer's disease and aging-brain research.

Emerging evidence links the gut microbiome to sleep quality, yet measuring sleep at scale remains challenging. Commercial wearables, such as Fitbit, capture objective sleep and activity data in naturalistic settings. We integrated Fitbit data from a large, deeply-phenotyped cohort with paired lifestyle and health questionnaires. Wearable-derived measures aligned well with self-reported sleep, activity, and happiness. We identified dozens of covariate-adjusted associations between Fitbit-derived sleep features, lifestyle factors, and multi-omic data. Among molecular feature sets, the gut microbiome showed the greatest number of associations with sleep quality: butyrate-producing genera were positively associated with sleep and amplified the benefits of physical activity. Oscillospira, in particular, was consistently associated with better sleep. In blood, insulin, omega-3, and cortisol correlated with poorer sleep, whereas lower alcohol intake and mineral supplements correlated with better sleep. These robust, covariate-adjusted findings advance mechanistic understanding of the gut-sleep axis and broader molecular and lifestyle determinants of sleep quality.

Background: Community-acquired pneumonia (CAP) remains a major global health burden disproportionately affecting older adults and people with comorbidities, with Streptococcus pneumoniae as one of the leading bacterial causes in Europe. The Hungarian Occurrence and Burden of PnEumonia (Hungarian-HOPE) study examined the incidence, hospitalization rates, and mortality of CAP between 2016 and 2020 in Hungary. Methods: The National Health Insurance Fund database was used to identify adult CAP patients (all-cause) based on ICD-10 codes J10-18. Outcomes included CAP incidence, 0-15-day hospitalization, and 0-30-day mortality after hospitalization, stratified by age, sex, and comorbidities (chronic obstructive pulmonary disease [COPD], asthma, cardiovascular disease [CVD], and type 1 and 2 diabetes [T1DM, T2DM]). Risk maps visualized relative risk gradients across population strata. Results: During the pre-pandemic period (2016-2019), over 100,000 CAP cases and more than 50,000 hospitalizations were recorded annually. In 2020, recorded cases fell to approximately 98,000, while hospitalizations increased to 66,200. Hospitalization rates increased from 25.1% in 2016 to 29.1% in 2019, then increased to 43.1% in 2020. The 30-day mortality among hospitalized patients rose from 22.7% in 2016 to 23.6% in 2019. Incidence, hospitalization, and mortality all increased with age. Relative to healthy males aged 30-39 years, CAP risk escalated steeply in the [≥]80 years cohort (incidence 5-15-fold; hospitalization >3-fold; mortality 11-24-fold) and was further amplified by COPD, CVD, or T2DM, with a lesser effect for T1DM. Conclusions: The results highlight the substantial age- and comorbidity-driven CAP burden in Hungary and support prioritization of preventive strategies including pneumococcal vaccination for older adults and high-risk groups.

Using a longitudinal cohort of 633 Gambian children (IHAT-GUT, NCT02941081), we resolve two mechanistically distinct ecological pathways linking Prevotella stercorea to infection risk. Its abundance positively predicts gut microbiome richness, consistent with community-level colonisation resistance for enteric outcomes. However, its association with reduced acute respiratory infection (ARI) persists unchanged after richness adjustment, identifying a species-autonomous pathway independent of community diversity. Weight-for-age z-score (WAZ) is uncorrelated with microbiome richness within strata, supporting WAZ as a proxy for host immune-metabolic reserve rather than a determinant of microbiome composition. In Low-WAZ children, P. stercorea at Day 1 associates with suppressed CRP, whereas in higher-WAZ children, elevated Day 1 inflammation predicts subsequent P. stercorea colonisation at Day 85, consistent with host-context-dependent immune selection. ARI and fever protection is richness-independent and concentrated in Low-WAZ children. P. copri does not retain an independent protective association when modelled jointly. These findings have direct implications for microbiome-directed interventions.

Abstract Importance: Irregular sleep-wake patterns have been associated with poor health and cognitive outcomes, yet evidence linking 24-hour sleep-wake regularity to cognitive decline or dementia remains inconsistent. Particularly, regularity can be measured as regularity of rest-wake, sleep-wake or overall 24-hour activity, but it is unclear which aspects are most relevant for cognitive aging. Objective: To assess associations of rest-wake, sleep-wake, and 24-hour activity regularity with cognitive decline and dementia risk. Design: Observational prospective study comprised of six US and European cohorts: MrOS (sleep study between 2003-2005, mean follow-up: 7.1 years), Rotterdam Study (2004-2007, 11.6 years), MESA (2010-2013, 8.2 years), MAP (2005-2018, 7.2 years), Whitehall II (2012-2013, 6.9 years), and UKB (2013-2015, 7.9 years). Setting: Cohort-specific estimates were pooled using random-effects meta-analysis. Analyses were done between June 2025 and March 2026. Participants 74,733 dementia-free adults with multi-day actigraphy were included across cohorts: MrOS (age: 67-96 years, female:0%), MESA (54-95y, female:54.6%), Rotterdam Study (46-98y, female:55.0%), MAP (56-100y, female:77.1%), Whitehall II (59-83y, female:25.9%), and UKB (55-78y, female:55.5%). Exposure: Day-to-day rest-wake regularity (Rest Regularity Index, RRI), day-to-day sleep-wake regularity (Sleep Regularity Index, SRI), and 24-hour activity regularity (Interdaily Stability, IS) were derived from multi-day actigraphy. Main Outcome: Outcomes were risk of dementia and changes in global cognition. Results: Across six cohorts, 1,906 dementia cases occurred among 74,733 participants. After adjusting for demographics, health behaviors, depressive symptoms and cardiovascular comorbidities, each 1-SD higher regularity score was associated with an 9-14% lower dementia risk (pooled hazard ratios: RRI 0.86 95%CI: [0.79-0.95]; SRI 0.87[0.79-0.97]; IS: 0.91[0.88-0.95]). Associations were approximately linear. Age-stratified analyses showed directionally stronger associations among adults aged < 65, although meta-regression did not support an interaction(p > 0.55). Greater regularity was associated with modestly slower decline in global cognition (pooled {beta} per 1-SD higher score of RRI per year: 0.003, 95%CI [0.001-0.006]). Conclusions & Relevance: Greater regularity of rest-wake, sleep-wake, and 24-hour activity rhythms was associated with lower dementia risk and modestly slower global cognitive decline. These findings suggest that 24-hour sleep-wake regularity is a relevant behavioral marker of cognitive aging and may inform future efforts to identify or intervene on early risk.

Importance: Little is known about the impact of returning Alzheimer disease (AD) biomarkers to cognitively unimpaired (CU) research participants. Objective: Does return of research results (RoRR) negatively impact longitudinal symptoms of depression and cognition. Design: Randomized, noninferiority, delayed-start clinical trial, 2021-2025 Setting: AD biomarker research results offered to CU participants in a longitudinal study of aging Participants: CU participants age 65+ were offered research AD biomarker results (APOE genotype and either plasma AB42/40 or amyloid PET and MRI hippocampal volume) with an estimated 5-year risk of symptomatic AD. Intervention(s) (for clinical trials) or Exposure(s) (for observational studies): 147 participants were randomized to receive results either soon after consent (RoRR arm, N=73) or one year later (delayed-start arm, N=74). Main Outcome(s) and Measure(s): Longitudinal change in Geriatric Depression Scale (GDS), Clinical Dementia Rating sum of boxes (CDR-SB), and global cognitive composite. Outcomes were measured at annual assessments for a longitudinal study of aging. Results: 187 participants received results: 70 in RoRR arm (average age 75, 60% female), 66 in delayed-start arm (average age 73, 53% female). The observed changes in annual measures did not differ between arms in both those with elevated amyloid (AB+) and in those without elevated amyloid (AB-) for GDS (AB+ difference 0.7, 95% CI 0.0-1.3; AB- difference -0.1, 95% CI -0.7-0.5; clinically significant decline >4.0), CDR-SB (AB+ difference 0.0, 95% CI -0.1-0.1; AB difference 0.0, 95% CI 0.0-0.1; clinically significant decline >0.5), and cognitive composite (AB+ difference -0.10, 95% CI -0.25-0.06; AB- difference -0.05, 95% CI -0.17-0.07; clinically significant decline < -0.26). Secondary analyses found no evidence of association between RoRR and proximity to follow-up testing. Conclusions and Relevance: In the first randomized, delayed-start clinical trial of returning AD research results to CU older-adult participants, no effect was seen on longitudinal changes in symptoms of depression or cognition. This supports evidence that there are no harms to returning AD research results, although the results may not apply to more diverse populations not included in this study. Trial Registration: NCT04699786

INTRODUCTION: Cognitive trajectories may clarify how type 2 diabetes (T2D) and impaired fasting glucose (IFG) relate to dementia risk, but longitudinal associations remain unclear, particularly in the context of stroke. METHODS: Data from 5,631 dementia- and stroke-free older adults (mean age 75 years) from 7 international population-based cohorts were analyzed. Linear mixed-effects models estimated cognitive trajectories during stroke-free and post-stroke follow-up. Glucose status was defined by fasting glucose and prior T2D diagnosis. RESULTS: Over 6.6 years of follow-up (4.5% with incident stroke), T2D was associated with lower baseline cognitive performance compared with normal fasting glucose (-0.14 SD, 95% CI -0.21 to -0.07), but not with faster cognitive decline during stroke-free or post-stroke follow-up. IFG was not associated with lower cognitive performance or faster decline. DISCUSSION: In older adults, T2D was associated with persistently lower cognitive performance but not faster decline, suggesting adverse cognitive effects may be established before late life.

Atherosclerosis is a systemic vascular process linked to cardiovascular, cognitive and renal outcomes. DNA methylation (DNAm)-based scores of atherosclerosis may capture cumulative biological processes underlying vascular aging. Here, we examined associations of DNAm scores for coronary artery calcification (DNAm-CAC) and carotid plaque (DNAm-cPlaque), derived from a large study of imaging-based subclinical atherosclerosis, with prevalent and incident outcomes in two population-based cohorts of older adults: the Health and Retirement Study (HRS; n = 3,875) and The Irish Longitudinal Study on Ageing (TILDA; n = 487). Higher DNAm scores were associated with adverse cardiometabolic profiles and socioeconomic indicators. In HRS, higher DNAm-CAC was associated with prevalent cardiovascular disease (odds ratio per SD, 1.16; 95% confidence interval (CI), 1.07-1.26), lower cognitive function ({beta} = -0.50, 95% CI -0.68 to -0.32) and lower estimated glomerular filtration rate (eGFR; -1.7 ml min-1 1.73 m-2, 95% CI -2.6 to -0.8) in unadjusted models. After adjustment for demographic and clinical risk factors, DNAm-CAC ({beta} = -0.29, 95% CI -0.46 to -0.13) and DNAm-cPlaque ({beta} = -0.24, 95% CI -0.42 to -0.06) remained associated with lower cognitive function, and DNAm-cPlaque was associated with incident cognitive impairment or dementia (hazard ratio per SD, 1.16; 95% CI, 1.01-1.32). Associations were attenuated after further adjustment for race/ethnicity and socioeconomic indicators. In TILDA, higher DNAm-cPlaque was associated with worse cognitive performance (incidence rate ratio, 1.11; 95% CI, 1.01-1.21), increased risk of incident cardiovascular disease (hazard ratio, 1.18; 95% CI, 1.00-1.42) and lower eGFR, with consistent associations observed for DNAm-CAC. These findings suggest that DNAm-based scores of atherosclerosis capture systemic vascular processes linked to multiple age-related outcomes across populations. Further work is needed to clarify the biological pathways reflected by these scores and their relation to cumulative and socially patterned vascular risk.

COVID-19 risk scores developed during the pandemic relied on measurements contemporaneous with infection, leaving unresolved whether the metabolic and inflammatory vulnerability they capture pre-existed as a stable trait or was triggered by acute illness. Here, using 501,946 UK Biobank participants whose blood was drawn between 2006 and 2010---at least ten years before SARS-CoV-2 emerged---we show that baseline proteomic and metabolic profiles predict both COVID-19 hospitalization (2,783 events; C-statistic =0.676 [0.666--0.686]) and COVID-19 mortality (1,564 deaths; C-statistic =0.730 [0.701--0.760]) from parsimonious, regularized feature sets. The IL-1 pathway index (xIL1, +0.093) was independently selected for hospitalization but not mortality, while the IL-6 trans-signaling index (xIL6, + 0.040) was selected for mortality but not hospitalization---a differential pathway weighting corroborated by independent LightGBM/SHAP analysis and mirroring the subsequent success of tocilizumab (anti-IL-6R) and the limited efficacy of anakinra (anti-IL-1R) in reducing COVID-19 mortality in randomized trials conducted years later. The mortality model was additionally characterized by central adiposity (waist-hip ratio, +0.386), a respiratory compromise index (xRSP, +0.149), and prodromal cardiovascular disease (pCVD, +0.246). These findings establish that vulnerability to a novel pathogen is, in substantial part, a pre-existing and measurable prodromal state, with implications for pandemic preparedness and population-level risk stratification.

Alzheimer's disease (AD) is characterized by complex alterations in synaptic, glial, neuronal and inflammatory markers. Given its emerging role at the interface of synaptic dysfunction and inflammation, the astrocytic marker GFAP may represent a cross-domain hub linking synaptic, neuronal and inflammatory alterations. Using multivariate and network-based analyses we examined the relationships among cerebrospinal fluid (CSF) biomarkers of astrocytic activation and synaptic failure, inflammation, and neurodegeneration in biologically confirmed AD patients and healthy controls (HC). We studied 60 AD patients and 40 HC. CSF concentrations of Neurogranin, SNAP-25, CAP2, NfL, GFAP, IL-1 , IL-1{beta}, IL-8, MCP-1, TNF were measured. Associations were assessed using Spearman correlations, LASSO regression, and network analysis to characterize multivariate dependency structures. Compared with controls, AD patients showed significantly higher CSF levels of Neurogranin, SNAP-25, CAP2, NfL, GFAP, IL-1{beta}, TNF- .. In AD, synaptic biomarkers were strongly intercorrelated and associated with astroglial activation, inflammatory markers, and tau-related pathology. Network analysis identified GFAP as a cross-domain hub linking synaptic, inflammatory, and neurodegenerative domains in AD. In controls, GFAP was mainly associated with neuronal injury markers. Network-based modelling revealed a disease-related reorganization of biomarker connectivity in AD, with GFAP occupying a central cross-domain position, supporting a systems-level view of AD pathophysiology.

Yang et al. recently published a systematic comparison of genetic effects on disease susceptibility and disease-specific mortality across nine common diseases and seven biobanks, concluding that susceptibility and survival architectures overlap only modestly. This is an important resource, but we argue that the current mortality genome-wide association studies (GWAS) require explicit power calibration before limited overlap can be interpreted biologically. Using two-sample Mendelian randomization (MR) with positive-control exposures, we show that even a well-powered positive control, body mass index (BMI), instrumented by 855 genome-wide-significant variants, produces a clearly detectable effect for heart failure (HF) mortality, with only weaker evidence for chronic kidney disease (CKD) mortality. However, when BMI instruments were stratified into quartiles by exposure-association strength, the heart failure association remained nominally significant only in the two strongest quartiles and was not significant in the two weakest quartiles. Further, using household income as a weakly instrumented socio-economic contrast has insufficient power to detect moderate effects on any disease mortality outcome. These analyses indicate that current disease mortality GWAS may be insufficiently powered to detect shared effects. In contrast, the same BMI instrument set produced large and directionally coherent effects when applied to case-control GWAS of the matched six diseases, with the HF and prostate cancer associations preserved under a within-family BMI sensitivity analysis, and nominal support for CKD. The HF mortality association was also preserved in a within-family BMI sensitivity analysis. Similarly, genetically proxied household income was associated with HF risk in the case-control GWAS despite null associations with disease-specific mortality, consistent with limited power in the mortality GWAS. These findings indicate that the limited BMI-mortality evidence across several outcomes is unlikely to reflect a weak BMI instrument or dynastic artefacts alone and instead supports limited effective power in current disease-mortality GWAS.

Background: Regular exercise is a highly effective yet underutilized strategy to reduce cardiometabolic disease burden. Whether brief structured exercise programs confer lasting cardiometabolic benefits remains unclear. The STRRIDE-Prediabetes Reunion study examined legacy effects of exercise training on cardiorespiratory fitness, body composition, and cardiometabolic health. Methods: Seventy-three participants (71.3 {+/-} 7.2 years; 64% women; 77% White) completed Reunion assessments ~11 years after completing one of four 6-month interventions differing in exercise amount, intensity, and inclusion of diet-induced weight loss. Linear mixed effects models evaluated longitudinal trajectories; secondary analyses examined baseline-adjusted associations among short-term intervention response and Reunion outcomes. Results: Abdominal adiposity improved across all groups from baseline to Reunion, with waist circumference decreasing ~3 cm over the follow-up period. In contrast, cardiorespiratory fitness and fat-free mass declined significantly. A significant group by time interaction was observed for total fat mass (p=0.01), with continued fat mass reductions observed in women randomized to high amount exercise. After baseline adjustment, greater short-term intervention response was associated with more favorable Reunion outcomes across fitness, body composition, and cardiometabolic domains; fat-free mass showed the strongest association ({beta}=0.84, p<0.0001). Conclusions: In older adults with prediabetes, the STRRIDE-Prediabetes interventions produced several legacy health effects persisting more than a decade later. Legacy effects differed by sex and exercise dose, and short-term intervention response relative to baseline was associated with long-term outcomes, supporting targeted exercise strategies to preserve cardiometabolic health and functional independence with aging.

Background: Circadian rest-activity rhythms weaken with age, but whether sleep disorders modify this trajectory is unknown. Methods: We analyzed wrist accelerometry data from 4,386 participants aged 6-80 years in the 2011-2012 National Health and Nutrition Examination Survey (NHANES). Circadian features were extracted using cosinor analysis and nonparametric methods; a Circadian Disruption Index (CDI) was constructed from five standardized components. Survey-weighted regression with natural cubic splines and Wald F-tests tested age-by-sleep-disorder interactions using Taylor series linearization for variance estimation. Results: Doctor-diagnosed sleep disorder (N = 360, 8.2%) was associated with significantly different age-related trajectories of amplitude (F(2,17) = 11.24, p = 0.0008) and MESOR (F(2,17) = 8.22, p = 0.0032), both surviving Bonferroni correction (p < 0.006). CDI was higher in those with a sleep disorder (0.290 vs. 0.131, p < 0.001) and was independently associated with higher BMI (beta = 1.33 kg/m2, p < 0.001), higher HbA1c (beta = 0.089%, p = 0.004), greater diabetes prevalence (beta = 3.8 percentage points, p < 0.001), and worse depressive symptoms (beta = 0.43 PHQ-9 points, p = 0.020). Sensitivity analyses using a broader sleep problem exposure did not replicate these interactions. Conclusions: Doctor-diagnosed sleep disorders are associated with an altered age-related decline in circadian amplitude and mean activity level. CDI was independently linked to cardiometabolic and depressive outcomes, supporting a mechanistic connection between clinically significant sleep pathology and circadian disruption across the lifespan.

Background The study aimed to estimate healthcare costs associated with malnutrition in Swedish older adults. Methods We conducted a cohort study using data from the population-based Swedish National Study on Aging and Care in Kungsholmen (SNAC-K, N = 2982), a geriatric inpatient cohort of complex patients (N = 7680), and a cohort of individuals with cognitive impairment from the Swedish Register of Cognitive/Dementia Disorders (SveDem, N = 64192). At risk of malnutrition and malnutrition were ascertained by the Mini-Nutritional Assessment in SNAC-K and the geriatric inpatient cohort. In SveDem, body mass index was used for identifying malnutrition. Healthcare resource use was derived from regional and national registers. Associations between malnutrition and healthcare costs in 2024 Swedish kronor (SEK) were analyzed using two-part models and generalized linear regression models, adjusting for demographic and clinical factors. Findings In the community, at risk of malnutrition and malnutrition were associated with an increase in annual healthcare costs of 2267 SEK (95% CI: 64,4469) and 1846 SEK (95% CI: -6802,10493), respectively. In geriatric patients, healthcare costs over 6 months in individuals at risk of malnutrition and individuals with malnutrition were 60205 SEK (45613,74798) and 86619 SEK (68362,104875) higher than those without malnutrition. In people with cognitive impairment, malnutrition was associated with higher annual healthcare costs (22170 SEK, 95% CI: 15152,29188). Interpretation Both at risk of malnutrition and malnutrition are associated with higher healthcare costs in Swedish older adults. The study findings are important for informing future economic evaluations of malnutrition interventions in Swedish older adults.

Objective: To systematically evaluate pathway-informed polygenic risk score (PRS) strategies and determine which approaches most effectively leverage biological annotations for risk prediction, using brain amyloid-beta positivity as a case study. Methods: We systematically benchmarked approaches for integrating pathway information into PRS construction to predict brain A{beta} positivity. Using two cohorts, the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 969) and Australian Imaging, Biomarkers and Lifestyle (AIBL, n = 251), we compared Apolipoprotein E (APOE) genetic risk score (GRS), clumping and thresholding (C+T) PRS, pathway-guided single nucleotide polymorphism (SNP) selection PRS, and pathway-specific PRSs ensembled via machine learning. Pathways were derived from manually curated literature or from pathway databases via Functional Mapping and Annotation (FUMA). Results: In cross-validation on the ADNI cohort, pathway-informed PRS using a narrow-set of pathways to guide SNP selection (PathPRS-SNPLit without APOE locus) significantly outperformed the standard PRS model (median AUC = 0.742, p = 0.006) and the APOE locus model (median AUC = 0.736, p = 5.1 x 10-5) based on the Mann-Whitney U test, achieving a median AUC of 0.763. This model showed enhanced ability to identify subgroups within the 10% lowest- and highest-risk groups compared to the current standard of APOE locus alone (odds ratio = 0.67, 95% CI: 0.56-0.81; and OR = 13.23, 95% CI: 10.23-17.11), highlighting its clinical potential. Using a focused set of literature-curated pathways outperformed using a broader set of database-derived pathways across configurations. When contrasting strategies for aggregating information across pathways, we observed that using pathways to guide selection of SNPs and then building a single PRS performed comparably to building PRS for each pathway and using machine learning (ML) to aggregate these, though the latter enabled pathway-level interpretability. Similar trends were observed in the external AIBL validation dataset. Interpretation: Pathway-informed PRS can meaningfully improve genetic risk enrichment for A{beta} positivity beyond APOE and standard C+T approaches, provided pathway definitions are carefully curated. The choice of pathway source has the strongest impact on predictive performance, with aggregation strategies or ML model choice having far less impact. Our findings highlight the utility of literature-curated, pathway-informed PRSs for A{beta} prediction and offer practical guidance for pathway-informed PRS construction in other polygenic traits.

Clonal hematopoiesis of indeterminate potential (CHIP) represents the age-related expansion of hematopoietic stem cells with preleukemic mutations. However, its association with all-cause and cause-specific mortality has not been well characterized in older adults. We aimed to evaluate whether CHIP is associated with all-cause and cause-specific mortality in a population of older women in the United States. Our study included 6,704 participants in the Women?s Health Initiative Long Life Study (WHI-LLS) without hematologic malignancy. The co-primary exposures were any CHIP (variant allele frequency [VAF] [&ge;] 2%) and large CHIP (VAF [&ge;] 10%), and the primary outcome was all-cause mortality. Multivariable-adjusted Cox proportional hazards models tested the associations of CHIP and CHIP subtypes with all-cause and cause-specific mortality. Any CHIP and large CHIP were independently associated with all-cause mortality, with multivariable-adjusted hazard ratios (aHRs) of 1.12 (95% confidence interval [CI] 1.04-1.21; P = 0.003) and 1.28 (95% CI 1.15-1.43; P < 0.001), respectively. In gene-specific analyses, non-DNMT3A CHIP was associated with all-cause mortality (aHR: 1.22 [95% CI: 1.12-1.34], P < 0.001), while DNMT3A CHIP was not (aHR: 1.07 [95% CI: 0.98-1.18], P = 0.13). Furthermore, large CHIP was associated with cardiovascular (aHR: 1.29 [95% CI: 1.08-1.55], P = 0.006), cancer (aHR: 1.49 [95% CI: 1.11-2.02], P = 0.009), and neurologic (aHR: 1.40 [95% CI: 1.07-1.84], P = 0.02) death. In this cohort of older women, CHIP, particularly large clones and non-DNMT3A CHIP, was associated with all-cause and cause-specific mortality. These findings suggest that clonal size and subtype may differentially influence mortality risk.

Non-coding variants are important contributors to human traits and diseases but linking them to molecular mechanisms and phenotypes at scale remains challenging. G-quadruplexes (G4s) are four-stranded structures formed by guanine-rich sequences and have emerged as key functional elements within the non-coding genome. G4s are enriched in regulatory regions and can modulate gene expression at both the DNA and RNA levels, influencing transcription, replication, and RNA processing, positioning them as key mediators linking non-coding variation to complex biological traits. Here, we profile putative G4s across five regulatory regions in 459,449 UK Biobank genomes and perform phenome-wide association analyses spanning 2,941 plasma protein abundances, 13,321 binary traits, and 1,682 quantitative traits. We show that putative G4-modifying variants are depleted under purifying selection despite elevated local mutability and drive large, bidirectional associations with plasma proteins and clinical traits, including associations not captured by coding variants. Using a mechanism-aware collapsing strategy that groups rare non-coding variants by their predicted impact on G4 stability, we achieved stronger gene-level signals than those obtained with standard rare-variant collapsing approaches. Integrating non-coding and protein-truncating variants (PTVs) increases discovery power, revealing 843 significant associations missed by the PTV-only model. Replication in the Alliance for Genomic Discovery cohort demonstrates cross-cohort robustness. Our study suggests G4s as widespread mediators of non-coding regulation and provides a framework for mechanism-informed target discovery and prioritization across the non-coding genome.

Most routine clinical markers are interpreted using population-based reference intervals, despite being regulated around patient-specific homeostatic setpoints. This mismatch obscures physiologic shifts, inhibiting detection of early disease signatures. Here, we develop a novel Bayesian inference method that adaptively constructs personalized reference intervals using each patients existing health records. In analysis of >100 million lab tests in >800,000 patients, these personalized intervals can be accurately constructed with only minimal prior data, meaning this method can be applied near universally. We show that across 43 common lab markers, patient setpoints are strongly associated with future morbidity, with signal strength increasing as more test data is collected. Deviation from personalized reference intervals provides strong and novel risk signatures across diverse disease states, including hypothyroidism, hematologic cancers, kidney disease, and pregnancy complications. Importantly, personalized reference intervals capture a different risk signature to existing population-based approaches, with the highest risk patients being those who deviate from both intervals simultaneously. In a targeted clinical use case study of iron infusion, use of personalized reference intervals greatly improved prediction of treatment efficacy and allowed precise tracking of treatment responses. Our results illustrate how existing health records can be used to construct personalized benchmarks for nearly all common clinical tests, driving a new paradigm for precision laboratory medicine.