Nature Aging

Quantitative measures for tracking functional health have generally been lacking. Intrinsic capacity (IC) has been proposed as an appropriate measure, but its metrics have been derived in small datasets and sparse longitudinal data. Using harmonized measures of cognition, locomotion, sensory function, vitality, and psychological well-being from 501,615 UK Biobank participants and followed for a median of 15.5 years, we derived domain-specific and composite IC scores. We examined associations with incident disease, cause-specific mortality, multimorbidity, lifestyle and socioeconomic factors, and multi-omic profiles from Olink proteomics, NMR metabolomics, clinical biochemistry, and blood-cell traits. We found that composite IC declined non-linearly with age, and within-person decline was steeper than the cross-sectional age measures. Participants with greater baseline morbidity, those who subsequently developed incident disease, and those who died earlier in follow-up showed lower IC trajectories across adulthood. The IC domains were only modestly correlated with one another, supporting multidimensionality, yet higher overall IC was associated with lower risk of most diseases examined. The dominant IC domain varied by endpoint, with cognition informative for dementia, sensory function for hearing loss, psychological capacity for depression, locomotion for osteoarthritis, and vitality for cardiometabolic outcomes. IC was also associated cross-sectionally with physical activity, insomnia, smoking, medication burden, and socioeconomic disadvantage. More proteins were found predictive for vitality, and enrichment converged on immune/inflammatory and metabolic pathways. Blood-based surrogates recapitulated part of the phenotypic signal, particularly for vitality. Overall, this IC framework captures longitudinal health trajectories and broad disease vulnerability in a large middle- to older-aged cohort and supports IC as a clinically meaningful, multidomain phenotype of aging and identifies blood-based correlates that may facilitate at-scale future monitoring of aging-related function declines.

Dementia incidence has been declining in Western societies for decades, but whether this reflects higher cognitive capacity entering old age, slower cognitive decline, or both remains unresolved. Analysing ~783,000 episodic memory assessments from ~219,000 individuals across five longitudinal cohorts, we find that later-born cohorts benefit from a double dividend: higher memory levels entering old age and slower rates of decline. The projected 20-year cohort advantage at age 80 is of sufficient magnitude to plausibly account for the observed 13% per-decade decline in dementia incidence reported in meta-analyses. Generational gains are disproportionately concentrated among the fastest-declining individuals, and are reflected in lower hippocampal atrophy rates in an independent sample. A formal bounding analysis shows that the double dividend is robust across a range of plausible period assumptions, consistent with environmental conditions operating across the lifespan having reshaped the architecture of human cognitive aging.

Background: The 2025/2026 COVID-19 vaccine season introduced updated formulations targeting the LP.8.1 lineage. This study assessed the absolute vaccine effectiveness (aVE) of mRNA-1283 and BNT162b2 on COVID-19 outcomes in adults aged [≥]65 years. Methods: Background: The 2025/2026 COVID-19 vaccine season introduced updated formulations targeting the LP.8.1 lineage. This study assessed the absolute vaccine effectiveness (aVE) of mRNA-1283 and BNT162b2 on COVID-19 outcomes in adults aged [≥]65 years. Methods: This retrospective study used linked electronic health record and administrative claims data through Jan 31, 2026. Adults [≥]65 years who received the mRNA-1283 or BNT162b2 2025/2026 COVID-19 vaccine were matched to unvaccinated individuals. Inverse probability of treatment weighting was applied to matched cohorts of each vaccine to balance covariates. Each vaccine was evaluated independently against its own unvaccinated comparator group. aVE against COVID-19 related hospitalization and medically-attended COVID-19 was estimated using Cox proportional hazards models; aVE = 100 x (1 - hazard ratio [HR]). Results: We identified 233,072 mRNA-1283 recipients and 422,610 BNT162b2 recipients [≥]65 years. The aVE (95% confidence interval) of mRNA-1283 against COVID-19 related hospitalization and medically-attended COVID-19 was 59.3% (39.0%, 72.9%) and 42.0% (35.0%, 48.3%) among adults [≥]65 years and 66.9% (45.9%, 79.8%) and 50.2% (42.1%, 57.2%) in [≥]75 years, respectively. The aVE of BNT162b2 against COVID-19 related hospitalization and medically-attended COVID-19 was 48.3% (32.4%, 60.5%) and 41.2% (36.2%, 45.8%) in [≥]65 years and 45.9% (26.0%, 60.4%) and 44.0% (37.8%, 49.6%) in [≥]75 years, respectively. Conclusions: This is the first real-world evidence showing that mRNA-1283 prevents COVID-19-related hospitalizations and medically attended events in vulnerable older adults at highest risk of severe disease. These findings support mRNA-1283 as an important public health tool for reducing the ongoing burden of COVID-19.Results: We identified 233,072 mRNA-1283 recipients and 422,610 BNT162b2 recipients [≥]65 years. The aVE (95% confidence interval) of mRNA-1283 against COVID-19 related hospitalization and medically-attended COVID-19 was 59.3% (39.0%, 72.9%) and 42.0% (35.0%, 48.3%) among adults [≥]65 years and 66.9% (45.9 %, 79.8%) and 50.2% (42.1%, 57.2%) in [≥]75 years, respectively. The aVE of BNT162b2 against COVID-19 related hospitalization and medically-attended COVID-19 was 48.3% (32.4%, 60.5%) and 41.2% (36.2%, 45.8%) in [≥]65 years and 45.9% (26.0%, 60.4%) and 44.0% (37.8%, 49.6%) in [≥]75 years, respectively. Conclusions: This is the first real-world evidence showing that mRNA-1283 prevents COVID-19-related hospitalizations and medically attended events in vulnerable older adults at highest risk of severe disease. These findings support mRNA-1283 as an important public health tool for reducing the ongoing burden of COVID-19.

Dementia affects tens of millions of people worldwide, yet disease-modifying treatments remain strikingly limited. Although the recombinant zoster vaccine Shingrix has been associated with reduced dementia incidence, its potential influence on individuals already living with dementia is unknown. Here, we followed a propensity-score matched cohort of 68,960 US dementia patients using a nationwide electronic health record network, comparing Shingrix recipients within two years of diagnosis to recipients of any other vaccine. Shingrix was associated with substantially reduced all-cause mortality across the first three years of follow-up (hazard ratios 0.74, 0.88, and 0.89; P[≤]0.006), robust across multiple sensitivity analyses. Furthermore, within-individual subgroup analyses of repeated Mini-Mental State Examinations conducted 3-6 years apart revealed significantly divergent cognitive decline rates across groups (time-by-group interaction P=0.002). Interval vaccination was associated with more stable cognition, contrasting with steeper declines in unvaccinated individuals. These findings support prospective evaluation of recombinant zoster vaccination as a potential strategy to improve outcomes in patients with established dementia.

Background. Detection of cerebral amyloid pathology currently requires amyloid PET imaging ($5,000-$8,000) or cerebrospinal fluid analysis via lumbar puncture, procedures that are inaccessible for population-level screening. The FDA-cleared Lumipulse G pTau217/Abeta1-42 plasma ratio test (May 2025) represents the first approved blood-based alternative; however, single-ratio approaches cannot distinguish Alzheimer's disease (AD) from non-AD neurodegeneration or provide multi-dimensional disease characterization. Methods. We developed Virtual Spectral Decomposition (VSD), a framework that decomposes plasma biomarker profiles into biologically interpretable diagnostic channels. Four plasma biomarkers - phosphorylated tau-217 (pTau217), amyloid-beta42/40 ratio, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) - were measured in 1,139 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Each biomarker was mapped to a VSD channel representing a distinct pathophysiological axis: tau/amyloid phosphorylation, amyloid clearance, neurodegeneration, and astrocytic activation. Channel weights were calibrated via logistic regression, and performance was evaluated against amyloid PET (UC Berkeley) using 10x5-fold repeated cross-validation. Results. VSD 4-channel fusion achieved AUC = 0.900 (+/-0.018), exceeding pTau217 alone (0.888+/-0.022). Optimal sensitivity was 89.7% with 78.1% specificity (NPV = 90.8%). The NfL channel received a negative weight (beta = -1.1), functioning as a disease-exclusion signal: elevated neurodegeneration without amyloid-tau coupling actively reduces the AD probability, distinguishing AD from non-AD neurodegeneration. Complementary CSF proteomics analysis (7,008 proteins, 533 participants) identified 17 amyloid-specific proteins (0.24% of the proteome), revealing a 49:1 tau-to-amyloid asymmetry that explains why blood-based tau markers outperform amyloid markers. Conclusions. Blood-based VSD provides an interpretable, multi-channel framework for amyloid detection that incorporates explicit disease-exclusion logic unavailable to single-biomarker approaches. The architecture extends to multi-disease screening, where the same blood specimen could be routed through disease-specific modules for AD, Parkinson's disease, and cancer.

BackgroundAccelerometer-derived behavioral phenotype captures multidimensional aspects of human behavior extending well beyond physical activity, encompassing light exposure, step counts, physical activity patterns, sleep, and circadian rhythms. Whether these five domains constitute a unified behavioral architecture underlying cancer risk and whether circadian organization and light exposure confer incremental predictive value beyond movement volume alone remains to be comprehensively established. MethodsWe conducted an accelerometer-wide association study (AWAS) encompassing the complete accelerometer-derived behavioral exposome across five behavioral domains in UK Biobank participants with valid wrist accelerometry data. Incident solid cancers were designated as the primary endpoint, with prespecified site-specific solid cancers and hematological malignancy as secondary outcomes. Cox proportional hazards models with age as the timescale were used. The minimal covariate set served as the primary reporting tier, followed by sensitivity analyses additionally adjusting for adiposity/metabolic factors, independent activity patterns, shift work history, and accelerometry measurement quality. Nominal statistical significance was defined as two-sided P < 0.05 ResultsAmong 89,080 participants, 6,598 incident solid cancer events were observed over a median follow-up of 8.39 years. In the minimally adjusted model, the pan-solid-tumor association atlas was dominated by signals from activity volume, inactivity fragmentation, and circadian rhythm. Higher overall acceleration (HR per SD: 0.91, 95% CI: 0.89-0.94) and higher daily step counts (HR: 0.93, 95% CI: 0.90-0.95) were independently associated with reduced solid cancer risk, while inactivity fragmentation metrics were consistently linked to higher risk. Notably, circadian rhythms, most prominently cosinor mesor (Midline Estimating Statistic of Rhythm under cosinor model), emerged as leading inverse risk signals, underscoring the independent contribution of circadian behavioral architecture. Site-specific analyses revealed pronounced heterogeneity across tumor sites. Lung cancer exhibited a robust inverse activity-risk gradient, while breast cancer showed reproducible associations with MVPA. Most strikingly, nocturnal light exposure demonstrated a tumor-site-specific association confined to pancreatic cancer, a signal absent across all other sites examined. Associations for uterine cancer were predominantly inactivity-related and substantially attenuated following adjustment for adiposity and metabolic factors. ConclusionsAcross five accelerometer-derived behavioral domains, solid cancers as a whole were most consistently associated with a high-movement, low-fragmentation, and circadian-coherent behavioral profile. While site-specific heterogeneity exists, the broad cancer risk landscape is dominated by movement volume, inactivity fragmentation, and circadian rhythmicity. Light exposure, although more localized in its contribution, demonstrates a potentially novel and specific association with pancreatic cancer risk. These findings support a five-domain behavioral exposome framework for cancer epidemiology and, importantly, position circadian rhythm integrity and nocturnal light exposure as critically understudied dimensions warranting dedicated mechanistic investigation.

Identifying individuals in the preclinical stages of Alzheimer's disease (AD) is necessary for inclusion into future prevention trials. AD pathology occurs in the brain 20 or more years before diagnosis. In a nested 1:1 matched case-control sample of 426 participants selected from 19,500 members of the EPIC-Oxford cohort, we found that higher blood-based brain-derived and total p-tau 181, 217, and 231, as well as GFAP, were associated with AD over up to 25 years of follow-up (median=19.4, interquartile range 16.8-21.9 years). Of these seven biomarkers, LASSO regression selected brain derived p-tau 217 as the strongest discriminator of AD cases from controls. The AUC for brain derived p-tau 217 accounting for age, sex, and time of blood draw was 0.80, which increased to 0.82, 0.83, 0.84, after further addition of 1) APOE-e4 carrier status, 2) sociodemographic and lifestyle factors, and 3) both, respectively. Blood-based biomarkers, including the novel brain-derived p-tau 217, could identify individuals at-risk of AD two decades pre-diagnosis.

ImportanceAge-related eye diseases, such as cataract, glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy (DR), are leading causes of irreversible vision loss globally. Chronic inflammation is a shared pathogenic pathway, but the role of systemic inflammatory drivers like clonal hematopoiesis of indeterminate potential (CHIP) is unknown. ObjectiveTo investigate the association of CHIP, including its major genetic subtypes and clone sizes, with the risk of four major age-related eye diseases. Design, Setting, and ParticipantsThis was a prospective cohort study conducted using data from the UK Biobank, a large-scale, population-based cohort. A total of 436,469 participants free of the four eye diseases at baseline were included in the analysis. Data were collected from 2006 to 2010, with follow-up extending to March 2022. ExposuresCHIP status was ascertained from whole-exome sequencing data, defined by the presence of a somatic driver mutation with a variant allele fraction of 2% or greater. Main Outcomes and MeasuresThe primary outcomes were incident cases of cataract, glaucoma, AMD, and DR, identified through linked electronic health records. Associations were assessed using multivariable Cox proportional hazards regression models. ResultsOf 436,469 participants (mean [SD] age, 56.4 [8.1] years; 54.5% women), 14,110 (3.2%) had CHIP. Over a median follow-up of 13.1 years, CHIP was significantly associated with an increased risk of incident cataract (Hazard Ratio [HR], 1.08; 95% CI, 1.03-1.14), AMD (HR, 1.12; 95% CI, 1.04-1.21), and DR (HR, 1.41; 95% CI, 1.20-1.64). No significant association was found with glaucoma (HR, 1.08; 95% CI, 0.99-1.17). The risk for AMD was primarily associated with smaller clones (VAF <10%), while the risk for DR was highest with non-DNMT3A mutations. Systemic inflammation, particularly neutrophil count, partially mediated the associations. Conclusions and RelevanceIn this study, CHIP was independently associated with a higher risk of developing cataract, AMD, and DR, but not glaucoma. These findings establish a link between hematopoietic somatic mutations and the pathogenesis of several major age-related eye diseases, suggesting that CHIP-driven inflammation is a potential target for risk stratification and prevention. Key PointsO_ST_ABSQuestionC_ST_ABSIs clonal hematopoiesis of indeterminate potential (CHIP) associated with the risk of major age-related eye diseases? FindingsIn this cohort study of 436,469 participants, CHIP was associated with an increased risk of incident cataract (HR, 1.08; 95% CI, 1.03-1.14), age-related macular degeneration (HR, 1.12; 95% CI, 1.04-1.21), and diabetic retinopathy (HR, 1.41; 95% CI, 1.20-1.64), but not glaucoma. MeaningThese findings identify CHIP as an independent, non-ocular risk factor for cataract, AMD, and diabetic retinopathy, suggesting that systemic inflammation driven by CHIP contributes to the pathogenesis of these conditions and may represent a novel target for preventive strategies.

BackgroundWe explored the efficacy of AS01E-adjuvanted respiratory syncytial virus prefusion F protein-based vaccine (adjuvanted RSVPreF3) in subpopulations of participants with underlying medical conditions in the multi-country, phase 3 AReSVi-006 trial (conducted May/2021-May/2024). MethodsMedically stable [&ge;]60-year-olds were 1:1-randomised to receive one adjuvanted RSVPreF3 or placebo dose pre-RSV season 1. In exploratory post-hoc analyses in subgroups of participants with underlying conditions (including COPD, asthma, diabetes, obesity [BMI[&ge;]30 kg/m2]), we evaluated efficacy of one vaccine dose against RSV-related lower respiratory tract disease (RSV-LRTD), acute respiratory illness (RSV-ARI), and RSV-ARI-related complications (e.g., pneumonia, COPD/asthma exacerbation, cardiovascular events). We also evaluated (post-hoc) RSV-ARI-related systemic corticosteroid and antibiotics use in participants with COPD or asthma. ResultsThe efficacy analyses comprised 12,468 vaccine and 12,498 placebo recipients. Efficacy against RSV-LRTD over three RSV seasons was similar among participants with COPD (75.1%, 95% CI: 40.2-91.4), asthma (65.8%, 31.0-84.7), diabetes (69.8%, 37.5-87.1), and obesity (74.1%, 56.4-85.5) as in the overall study population (62.9%, 97.5% CI: 46.7-74.8). Efficacy was also observed against RSV-ARI in these subgroups. Efficacy against RSV-ARI-related complications was 74.4% (95% CI: 11.2-95.2) in participants with COPD and 60.8% (-9.9-88.7) in those with asthma. Among participants with COPD, 15.4% (1.9-45.4) of RSV-ARI episodes in vaccine vs 22.4% (12.5-35.3) in placebo recipients were treated with systemic corticosteroids, and 46.2% (19.2-74.9) vs 56.9% (43.2-69.8) with antibiotics. ConclusionsPost-hoc analyses of the AReSVi-006 trial suggest that adjuvanted RSVPreF3 may help prevent RSV-ARI, RSV-LRTD, and RSV-related complications in medically stable older adults with underlying medical conditions like COPD and asthma. Trial registrationClinicalTrials.gov: NCT04886596 SummaryPost-hoc analyses of the AReSVi-006 trial suggest that 1 dose of adjuvanted RSVPreF3 may help prevent RSV-related illness and complications over 3 consecutive RSV seasons in subgroups of [&ge;]60-year-olds with chronic medical conditions, e.g., COPD and asthma.

Colorectal cancer (CRC) is the second leading cause of cancer death globally and the number one cause of cancer death in people under 50 years old. The reasons for the rise of early-onset CRC are unknown, and while anatomically distinct subtypes of CRC have substantial clinical and molecular associations, the etiology of region-specific disease, such as early-onset CRC's enrichment in the distal colon, remains unclear. Understanding regional mutagenesis may identify risk factors for this public health concern and CRC more broadly. To evaluate mutational dynamics across the premalignant colon, we performed whole-genome sequencing of 125 individual colon crypts taken from six standardized regions biopsied during colonoscopy, collected from 11 donors without polyps and 10 with polyps. We observed mutation spectra and accumulation rates consistent with previous whole-organ studies, with greater subclonal mutation capture enabled by experimental design. T>[A,C,G] mutations, which are associated with colibactin genotoxicity from pks+ Escherichia coli, were significantly enriched in the rectum of donors with and without polyps (adjusted p-values < 0.01). Moreover, when comparing findings to crypts from individuals with CRC and sequenced CRC tumors, we observed consistent enrichment of the colibactin-associated mutational signature "ID18" in the rectum in both normal colon crypts and CRC tumors, without significant difference in colibactin-specific single nucleotide variant or insertion-deletion burden in crypts across the three clinical groups (i.e., no polyp, polyp, and CRC). These findings argue against a causal or prognostic role for colibactin in CRC, instead indicating that the proposed association with early-onset disease reflects anatomic specificity rather than cancer-specific clinical relevance.

Background: Among cardiac measures, diastolic parameters demonstrate the earliest and most consistent age-related changes. This can be leveraged to develop a continuous left ventricular (LV) Diastolic Age from routine echocardiographic parameters. Analogous to how epigenetic clocks weight molecular markers against mortality risk, we calibrated Diastolic Age by weighting echocardiographic features against the validated PREVENT-Heart Failure (HF) risk score. Methods: We analyzed 1,952 participants from the Project Baseline Health Study (median age 50 [36-64] years, 54% female). The measure was derived using partial least-squares regression anchored on PREVENT-HF and calibrated within a healthy reference subgroup. External validation was performed in the WASE (n=1,708) and Stanford Cardiovascular Aging (n=313) cohorts. Associations with ASE-defined LV diastolic dysfunction (LVDD), epigenetic clocks, and major adverse cardiovascular events (MACE) were examined. Results: Diastolic Age correlated strongly with chronological age (r=0.78) with robust external validation (WASE r=0.76; Stanford r=0.82; calibration slopes {approx}1.0). It increased progressively across grades of diastolic dysfunction and discriminated LVDD with an AUC of 0.89 (95% CI 0.87-0.92), and was independently associated with hypertension, diabetes, and elevated C-reactive protein. While correlated with the Levine (r=0.76) and Horvath (r=0.41) epigenetic clocks, residual analyses indicated that Diastolic Age captures a distinct cardiac-specific dimension of biological aging. Over median follow-up of 4.2 years, it independently predicted MACE (HR 2.30, 95% CI 1.70-3.18), with accelerated diastolic aging across all age groups among those with events. Discrimination was comparable to ASE-defined LVDD (C-index 0.83 vs. 0.82). Conclusion: Diastolic Age provides a continuous, echocardiography-derived measure of cardiac biological aging that complements categorical diastolic grading and epigenetic aging clocks, and independently predicts cardiovascular outcomes.

Parkinsons disease (PD) exhibits well-established sex differences in prevalence and clinical phenotypes, yet the underlying molecular mechanisms remain largely elusive. Here, we conducted a comprehensive sex-stratified multi-omic integration to identify sex-specific causal proteins and biological pathways in PD. We performed gene-based association analysis, transcriptome-wide association studies (TWAS), and proteome-wide Mendelian randomization (PWMR) with colocalization analysis using GWAS summary statistics from the International PD Genetics Consortium (IPDGC; 12,054 male cases/11,999 controls; 7,384 female cases/12,389 controls) for sex-stratified analyses and Global Parkinsons Genetics Program (GP2; 34,933 cases/31,009 controls) for sex-combined analyses. Prioritized candidates were further evaluated through MR with brain expression quantitative trait loci (eQTLs) from MetaBrain and differential protein abundance analysis using the Global Neurodegeneration Proteomics Consortium (GNPC; 704 PD cases/5,629 controls in plasma; 78 cases/1,411 controls in cerebrospinal fluid). Additionally, pathway enrichment analysis was performed for prioritized molecules. Integration across three analytical layers prioritized 102 molecular candidates across 31 unique loci, significant from multiple analyses. Of these, eleven genes reached significance across all three layers, including SNCA, MAPT, and CTSB significant in both sexes; CD160, GPNMB, and LRRC37A2 as male-predominant; STX4 and PRSS53 as female-predominant; and BST1, SCARB2, and LGALS3 significant only in sex-combined analysis. In males, CD160 emerged as a novel candidate with convergent evidence across all three analyses and colocalization, while L3MBTL2 was identified as a novel risk gene from gene-based association and TWAS analyses. In females, STX4 and PRSS53 at the 16p11.2 locus showed female-predominant associations. Pathway enrichment analysis revealed innate immune and SUMOylation pathways in males, with CD160 and L3MBTL2 as key contributors respectively, contrasting with WDR5-mediated chromatin remodeling in females. Brain eQTL-based MR confirmed significant associations for 69 of 86 testable candidates (80.2%) in at least one tissue. Protein abundance analysis confirmed sex-specific patterns, and several candidates showed discordant directions between genetically predicted causal effects and observed protein abundance -- including male-specific plasma elevation of CD160 and female-specific patterns for STX4 -- underscoring the distinction between causal risk mechanisms and disease-state molecular changes. These findings demonstrate that PD is a molecularly heterogeneous disorder with sexually dimorphic pathogenic drivers. While shared axes such as lysosomal dysfunction and vesicle trafficking disruption exist, the divergence into male-specific immune dysregulation and female-specific chromatin remodeling suggests that the primary triggers of neurodegeneration differ by sex. Our results underscore the necessity of sex-stratified approaches in biomarker discovery and the development of precision therapeutic strategies for PD.

Intermuscular adipose tissue (IMAT) expansion is closely associated with cardiometabolic disease, yet its cellular organization and regulatory mechanisms remain poorly defined. Here, we define a human IMAT gene signature using bulk transcriptomics and identify candidate regulators for IMAT function including adipogenic transcription factor early B-cell factor 2 (EBF2). To determine how these programs are organized in situ, we mapped this signature in a mouse model of diet-induced CMD using spatial transcriptomics. We found that IMAT expansion occurs within discrete stromal niches surrounding muscle fibers, characterized by coordinated activation of adipogenic, extracellular matrix, inflammatory, and metabolic pathways. Spatial analyses showed that fibro-adipogenic progenitor (FAP) abundance does not predict adipocyte formation, supporting a model of localized and context-dependent lineage transitions. Cross-species comparison revealed partial conservation of human IMAT gene programs, validating the mouse model and highlighting species-specific features. Functional experiments in human primary myoblasts showed that EBF2 is sufficient to induce adipogenic reprogramming. Our findings establish IMAT as an active, spatially organized remodeling niche and identify lineage plasticity as a central mechanism driving its expansion in metabolic disease

The transcription coregulator OCA-B promotes CD4+ T cell memory recall responses and autoimmunity. OCA-B T cell deletion prevents spontaneous type-1 diabetes (T1D) onset in non-obese diabetic (NOD) mice and blunts T1D in a subset of more aggressive models. However, the role of OCA-B in diabetes induced by treatment with immune checkpoint inhibitors (ICIs), and the role of OCA-B in the control of tumors with and without ICI treatment, has not been studied. Here we show that islet and pancreatic lymph node T cells from T1D individuals express measurable POU2AF1 mRNA. Deletion of OCA-B in T cells fully insulates 8-week-old non-obese diabetic (NOD) mice against ICI-induced diabetes and partially protects 12-week-old mice. Salivary and lacrimal gland infiltration and inflammation were also reduced. Protection was associated with a block in the differentiation of progenitor exhausted CD8+ T cells (TPEX) into terminally exhausted CD8+ T cells (TEX). We show that OCA-B T cell loss preserves anti-tumor immune responses following PD-1 blockade in different tumors and mouse strains. These findings point to a potential therapeutic window in which pharmaceuticals targeting OCA-B could be used to block the emergence of both spontaneous and ICI-induced autoimmunity while sparing anti-tumor immunity. We develop first-in-class small molecule inhibitors of Oct1/OCA-B transcription complexes and show that administration into NOD mice also blocks diabetes emergence following PD-1 blockade. These results identify OCA-B as a promising therapeutic target for the prevention of autoimmunity and immune-related adverse events (irAEs).

Osteoarthritis (OA) is a prevalent musculoskeletal disorder and a leading cause of global disability. Although meniscal damage is a major risk factor of OA pathogenesis, genetic regulatory studies have remained largely confined to articular cartilage. Here, we establish the first comprehensive expression quantitative trait locus (eQTL) map integrating whole-genome sequencing and bulk transcriptomics from human meniscus (n=112) and cartilage (n=113). Supported by single-nucleus multiomics (cartilage: 56,549 nuclei; meniscus: 34,343 nuclei), we uncovered highly tissue-specific genetic risk architectures. Colocalization with OA GWAS identified 27 meniscus-specific, 28 shared, and 20 cartilage-specific causal genes. Chromatin-informed fine-mapping and deconvolution elucidated distinct pathogenic mechanisms; notably, meniscus-specific signals converged on VEGFA via rare promoter variants and an enhancer in fibrochondrocyte progenitors, alongside a shared eQTL for CLEC18A. Exploratory analysis suggested candidate compounds to reverse pathogenic gene expression. Our findings underscore the meniscus as a distinct genetic driver, molecularly reinforcing OA as an entire joint organ failure.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest human cancers. The current largest published PDAC Genome-Wide Association Study (GWAS) identified 23 genetic risk signals, but most lack sufficient characterization. This study aimed to functionally characterize the chr13q12.2 (PLUT/PDX1) PDAC GWAS risk locus. Fine-mapping, luciferase reporter assays, and electrophoretic mobility shift assays implicated rs9581943, a PDX1 promoter SNP, as a functional variant underlying this GWAS signal. GTEx expression QTL analyses identified rs9581943 as a significant PDX1 eQTL in pancreas, and CRISPR/Cas9 editing in PDAC-derived cell lines confirmed a functional relationship. PDX1 is a transcription factor involved in early pancreas development and {beta}-cell homeostasis, but its role in exocrine pancreatic cells is unclear. Single-nucleus RNA-seq analyses of pancreatic acinar and ductal cells from neonatal, adult, and chronic pancreatitis donors suggested PDX1 activity alleviates high secretory load and ER-stress in acinar and biases ducts toward homeostatic phenotypes. Similarly, scRNA-seq analyses of pancreatic tumors suggested PDX1 activity reduces biosynthetic and inflammatory stress and promotes epithelial differentiation. Our study therefore implicates rs9581943 as a causal variant for the chr13q12.2 PDAC GWAS signal wherein the risk allele reduces PDX1 expression, eroding PDX1's capacity to buffer stress and stabilize epithelial cell fate in the exocrine compartment.

BackgroundPlasma biomarkers demonstrate strong within-cohort performance for identifying cerebral amyloid pathology, but their real-world clinical utility depends on generalization across populations and assay platforms. The impact of cross-cohort deployment on clinically actionable metrics such as negative predictive value (NPV) remains poorly characterized. ObjectiveTo evaluate the performance and portability of plasma biomarker-based machine learning models for amyloid PET prediction across independent cohorts, with emphasis on calibration and clinically relevant predictive values. MethodsData from ADNI (n=885) and A4 (n=822) were analyzed. Machine learning models were trained within each cohort to predict amyloid PET status and continuous amyloid burden (centiloids). Performance was assessed using ROC AUC, accuracy, R{superscript 2}, and RMSE. Cross-cohort generalizability was evaluated using bidirectional transfer without retraining. Calibration, predictive values, and decision curve analysis were used to assess clinical utility. ResultsWithin-cohort discrimination was high (AUC up to 0.913 in ADNI and 0.870 in A4), with moderate performance for centiloid prediction (R{superscript 2} up to 0.628 and 0.535, respectively). Cross-cohort deployment resulted in modest attenuation of AUC ([~]4-7%) but substantially greater degradation in clinically actionable performance. NPV declined from 0.831 to 0.644 under ADNI[-&gt;]A4 transfer ([~]19 percentage points) despite preserved discrimination. Calibration analyses demonstrated systematic probability misestimation, and decision curve analysis showed reduced net clinical benefit. Biomarker distribution differences across cohorts were consistent with dataset shift. ConclusionPlasma biomarker models retain discrimination across cohorts but exhibit clinically meaningful degradation in predictive value under deployment. Calibration instability and prevalence differences critically affect NPV, highlighting the need for cross-cohort validation, calibration assessment, and assay harmonization before clinical implementation.

Amyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disease with a heterogeneous clinical presentation, complicating early diagnosis and therapeutic monitoring. To identify disease-specific biomarkers, we performed an unbiased cerebrospinal fluid (CSF) proteomic analysis in 87 ALS patients, 89 healthy controls, and 61 neurological controls using mass spectrometry. Across all quantified proteins, 399 were significantly dysregulated in ALS, including established neurodegeneration (NEFL, NEFM, UCHL1) and neuroinflammatory (CHIT1, CHI3L1, CHI3L2) markers. Correlation and pathway analyses uncovered dysregulation of immune, synaptic, and metabolic processes, with aberrant complement activation emerging as a hallmark. Complement proteins increased progressively with declining ALS Functional Rating Scale-Revised and longer disease duration, whereas early-stage markers (CLSTN3, CHAD, RELN) indicated pre symptomatic neuronal and synaptic disruptions. Machine learning identified a minimal five protein CSF panel (MB, ITLN1, YWHAG, FCGR3A, PGAM1) that accurately distinguished ALS patients from healthy controls, capturing disease-specific pathophysiology beyond general neurodegeneration. Our findings define a robust ALS-specific CSF proteomic signature, reveal prognostic protein candidates across disease stages, and provide a framework for diagnostic biomarker development, enabling earlier intervention and monitoring.

Large language models are increasingly used for clinical guidance while their parent companies introduce advertising. We tested whether pharmaceutical ads embedded in the prompts of 12 models from OpenAI, Anthropic, and Google shift drug recommendations across 258,660 API calls and four experiments probing distinct epistemic conditions. When two drugs were both guideline appropriate, advertising shifted selection of the advertised drug by +12.7 percentage points (P < 0.001), with some model scenario pairs shifting from 0% to 100%. Google models were the most susceptible (+29.8 pp), followed by OpenAI (+10.9 pp), while Anthropic models showed minimal change (+2.0 pp). When the advertised product lacked evidence or was clinically suboptimal, models resisted. This reveals a structured vulnerability: advertising does not override medical knowledge but fills the space where clinical evidence is underdetermined. An open response sub analysis (2,340 calls across three representative models) confirmed that advertising restructures free-text clinical reasoning: models echoed ad claims at 2.7 times the baseline rate while maintaining high stated confidence and rarely disclosing the ad. Susceptibility was provider dependent (Google: +29.8 pp; OpenAI: +10.9 pp; Anthropic: +2.0 pp). Because this bias operates within clinically correct answers, it is invisible to accuracy based evaluation, identifying a class of AI safety vulnerability that standard testing cannot detect.

Background and aims: Pancreatic ductal adenocarcinoma (PDAC) surveillance is limited to individuals with familial or genetic risk although most future cases arise outside these groups. In a retrospective study, PRISM, an electronic health record (EHR)-based PDAC risk model, identified individuals in the general population at elevated near-term risk of PDAC. We aimed to prospectively evaluate whether PRISM can identify high-risk individuals beyond current surveillance groups across U.S. health systems. Methods: We performed a prospective multicenter cohort study after deployment of PRISM in April 2023 across 44 U.S. health care organizations. Eligible adults aged [&ge;]40 years without prior PDAC received a single baseline risk score and were assigned to prespecified risk tiers. Patients were followed for incident PDAC for 30 months. We estimated tier-specific 30-month cumulative incidence (positive predictive value, PPV), number needed to screen (NNS), standardized incidence ratios (SIRs), and time from deployment and first high-risk flag to diagnosis. Results: Among 6,282,123 adults assigned a PRISM score, 5,058,067 had follow-up; 3,609 developed PDAC. The highest-risk tier had 30-fold higher PDAC incidence than the study population. At the SIR 5 threshold, 30-month cumulative incidence was 0.35% (NNS, 284.2); at SIR 16, 1.14% (NNS, 87.4); and at SIR 30, 2.19% (NNS, 45.7). Median time from deployment to PDAC diagnosis was 9.5 months, and median time from first high-risk flag to diagnosis at SIR 5 was 3.5 years. Shapley additive explanations (SHAP) analyses supported patient- and tier-level interpretability. Conclusions: Prospective deployment of PRISM across multiple U.S. health care organizations identified individuals at elevated near-term risk for PDAC, with substantial risk enrichment and lead time before diagnosis. These findings support the real-world scalability and generalizability of EHRbased risk stratification for risk-adapted early detection. ClinicalTrials.gov identifier NCT05973331