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PubMed2026

Genome-wide association study of estimated glomerular filtration rate using repeated measurements in the Taiwan Biobank.

BACKGROUND: Chronic kidney disease (CKD) is a major global public health issue, with genetic factors playing a significant role in kidney function. Although genome-wide association studies (GWAS) have identified numerous loci associated with estimated glomerular filtration rate (eGFR), most studies relied on a single time-point measurement, which limits the capacity to account for within-individual measurement variability. METHODS: We performed a repeated-measurement GWAS in the prospective Taiwan Biobank (Taiwanese ancestry; n = 25,004) using two repeated creatinine-based eGFR measurements. Repeated eGFR values were analyzed using a linear mixed-effects model with a subject-specific random intercept and time-varying covariates, providing a more precise estimate of eGFR level. Identified loci underwent functional annotation (expression quantitative trait locus, deleteriousness prediction, and epigenetic markers) and were compared with results from a single-measurement GWAS. RESULTS: Six loci associated with eGFR were identified, including four previously reported regions (1q22, 4q21.1, 11p14.1, and 17q21.2) and two additional loci (6p21.32 and 15q24.2). Functional annotation implicated several candidate genes-such as MUC1/EFNA1, SHROOM3, HLA-DQB1, MPPED2, NRG4, and PGAP3/FBXL20-in the regulation of kidney function. CONCLUSION: Incorporating repeated eGFR measurements into GWAS may improve phenotypic precision for identifying genetic associations with kidney function. This study identified eGFR-associated loci and biologically plausible candidate genes in a Taiwanese population, which require further replication and functional validation.

Chronic kidney disease↗

PubMed2026

Retinal microstructural alterations as early phenotypes of depression in radiogenomics analysis.

BACKGROUND: With the increasing prevalence of depression, there is an urgent clinical need for early screening in depression. The retina offers a promising window for early screening in depression due to its rapid, non-invasive, objective, eye-brain correlated characteristics, but previous research has yielded conflicting alterations in retinal microstructure in depression. METHODS: We screened retinal optical coherence tomography and brain magnetic resonance imaging data in the UK Biobank to enroll 23,225 participants for retinal study of depression occurrence, and 1475 participants for the eye-brain association study. We also used genetic data (ID: ebi-a-GCST90014267 and ukb-d-20,448) from the Integrative Epidemiology Unit Open Genome-Wide Association Study for Mendelian randomization analysis. We used Cox regression to assess the association between retinal microstructure and depression risk, Mendelian randomization to infer causality, and mediation analysis to explore retina-brain pathway association. RESULTS: The Cox regression analysis showed that retinal ganglion cell-inner plexiform layer (GCIPL) thickness remained a significant predictor of depression. The Mendelian randomization analysis indicated a positive statistical association between GCIPL thickness and depression. Moreover, there was a significant positive correlation (all p < 0.001) between the volume of specific depression-related brain regions and the GCIPL thickness. Adjusting for age, sex, and head size, the mediation analysis provided preliminary evidence for a potential anatomical pathway linking retinal GCIPL thickness to depression-related brain regions through primary visual cortex and secondary visual cortex volumes. CONCLUSION: Thickened retinal GCIPL is a potential early phenotype of depression and has a potential association pathway with depression-related brain regions using a radiogenomics approach.

Humans↗

PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗

PubMed2026

Apoptosis protein markers in comorbid type 2 diabetes mellitus and depression; relationships with cognitive performance, incident dementia, and white matter hyperintensities.

Type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) are reciprocal risk factors, and both elevate dementia risk. Dysregulation of programmed cell death is implicated in T2DM, MDD, and neurodegeneration, but proteomic markers of apoptosis have yet to be studied as dementia predictors in people with T2DM and/or MDD. This study examines apoptosis markers in comorbid T2DM and MDD, and their associations with cognitive, dementia, and neuroimaging outcomes. The retrospective sample (n = 15,765) consisted of UK Biobank participants (MDD only n = 1230; T2DM only n = 3644; comorbid T2DM + MDD n = 721). Individuals with T2DM + MDD comorbidity had poorer cognitive performance, and a higher 15-year dementia incidence (HR = 4.44, 95% CI = [3.23,6.11]). Among 60 apoptosis-related proteins identified by Kyoto Encyclopedia of Genes and Genomes pathway enrichment, 41 were significantly up-regulated in comorbid T2DM + MDD relative to controls, and 4 were higher in the comorbid group than both T2DM alone and MDD alone. Tumor necrosis factor ligand superfamily member 10 (TNFSF10), growth arrest and DNA damage-inducible protein GADD45 beta, tumor necrosis factor ligand superfamily member 6, and RAC-gamma serine/threonine-protein kinase were associated with dementia risk. Nine proteins (e.g. apoptosis-inducing factor 1, mitochondrial, caspase-2, mitogen-activated protein kinase kinase kinase 5, TNFSF10), were associated with white matter hyperintensity volumes in comorbid T2DM + MDD after FDR correction, but none were associated with cognitive performance, atrophy, or white matter microstructural changes. These findings identify peripheral apoptosis markers that were further elevated in comorbid T2DM + MDD compared to either alone, pointing to an important pathophysiological element underlying adverse outcomes in the context of mood and metabolic comorbidity.

Humans↗

PubMed2026

Menopause in the All of Us Research Program: a descriptive summary of electronic health record and survey response across sociodemographic characteristics.

OBJECTIVES: Menopause is a significant physiological transition with implications for health outcomes (eg, cardiometabolic disease), yet gaps remain in understanding this transition, including how menopause timing and type influence health outcomes. Large-scale cohort studies in midlife (age=40-60) females, including the All of Us Research Program (AoURP), provide opportunities to study menopause across diverse populations and data modalities. We characterized menopause-related data in AoURP, focusing on age distributions and concordance between electronic health record (EHR) diagnosis codes and survey responses. METHODS: We analyzed menopause-related surveys, EHR diagnostic codes, and genomic data among ~396,000 AoURP female participants. We summarized menopause-related variables across data sources, evaluated overlap between survey, EHR, and genomic data sets, and described age distributions overall and across sociodemographic characteristics. RESULTS: Among ~396,000 females, survey responses captured ~193,000 menopause observations, nearly seven times more than EHR diagnoses (~28,000), suggesting under-ascertainment in EHR data. Nearly all females (~99%) with an EHR menopause diagnosis reported menopause in the survey. Approximately 22,000 participants had overlapping menopause-related EHR, survey, and genomic data. Survey age patterns matched expectations, with participants predominantly <40 years reporting premenopausal status and those >60 years reporting postmenopausal status. A small subset with age >70 years (N≈1,700; 4%) reported no menopause, suggesting response or recall bias. EHR menopause codes were concentrated after age 45 years, with a notable spike at age 65. Modest differences in survey-based menopause age distributions were observed across sociodemographic characteristics (eg, race and ancestry). CONCLUSIONS: These findings inform sampling strategies, power calculations, phenotype definition, and study design for menopause research using AoURP data.

Age↗

PubMed2026

Height variation independent of known genetic variants and health in later life: a cohort study.

BACKGROUND: Adult-attained height is associated with later-life health, but it reflects both genetic and nongenetic influences. The health implications of height variation not explained by known common height-associated genetic variants remain unclear. OBJECTIVES: This study aimed to examine associations of residual height (height variation independent of known genetic variants) with multiple disease incidence and all-cause mortality in later life. METHODS: In this cohort study of 407,366 adults of European ancestry (aged 40-70 y) in the United Kingdom Biobank (2006-2010), sex- and age-specific genetically predicted height was estimated from 9863 height-associated variants, adjusted for 30 principal components of ancestry. Residual height was calculated as the difference between observed and genetically predicted height. Plasma proteomics (2054 proteins; Olink Explore) were profiled. Deaths and 49 incident diseases were ascertained through national registries. Multivariable Cox models estimated associations of residual height and related proteins with disease incidence and mortality. RESULTS: Higher residual height [mean (standard deviation, SD), 0.0 (4.8)] was associated with more favorable self-reported preadulthood exposures (e.g., later birth years, no maternal smoking around birth, being breastfed as an infant, no adoption experience, and lower childhood adversity scores) and lower hazard ratios (HRs) of 32 out of 49 diseases (median follow-up = ∼12.5 y). Using participants with residual height within ±0.5 SDs from the mean as reference, those with residual height < -2 SDs had higher adjusted HRs of mortality [1.61; 95% confidence interval (CI): 1.50, 1.72], multimorbidity (1.28; 95% CI: 1.12, 1.46), cardiovascular disease (1.45; 95% CI: 1.32, 1.60), psychiatric/neurological disease (1.38; 95% CI: 1.28, 1.48), and other disease categories (e.g., diabetes, digestive, and musculoskeletal diseases). In contrast, higher genetically predicted height was associated with a higher incidence of 19 diseases, including subtypes of cancer, non-atherosclerotic cardiovascular diseases, and musculoskeletal diseases, as well as higher all-cause mortality. We identified 806 plasma proteins related to inflammation, immune response, and autophagy via tumor necrosis factor, Nuclear factor-kappa B, phosphoinositide-3 kinase/protein kinase B, and Janus kinase/signal transducer and activator of transcription signaling pathways, which were associated with residual height and multiple diseases and mortality. CONCLUSIONS: Higher residual height is associated with lower disease incidence and mortality, with associations that are distinct from those for genetically predicted height.

Humans↗

PubMed2026

Exploring China's Clean Air Act and associated cardiovascular disease risk: a prospective, quasi-experimental, and causal inference modelling study.

BACKGROUND: Substantial improvements in air quality have been recorded following the implementation of China's Clean Air Act (CCAA) in 2013. However, the association between CCAA implementation and individual-level cardiovascular disease (CVD) risk remains unclear. We aimed to examine the long-term association between CCAA implementation and individual-level predicted CVD risk. METHODS: In this prospective, quasi-experimental study, we used data from the China Kadoorie Biobank, a prospective cohort study that recruited participants from five urban and five rural areas across China between 2004 and 2008, with three resurveys conducted after the baseline survey (in 2008, 2013-14, and 2020-21). We included 34 862 individuals (mean age 51·3 years) who participated in at least one resurvey and had no history of CVD at baseline. Participants were classified into intervention (n=25 497) and control (n=9365) groups based on the local government's targets for particulate matter reduction. We estimated the 10-year risk of incident CVD morbidity or mortality using a validated risk prediction model. We used a difference-in-difference model to assess the long-term association between CCAA implementation and predicted risk, with adjustments made for regional confounders and individual-level characteristics, including demographics, lifestyle factors, medical history, and indoor air pollution exposure. The relationship between changes in long-term exposure to PM2·5, PM10, and O3 and predicted risk after CCAA implementation was analysed using a linear model. The estimated risk differences associated with air pollutant changes were estimated based on the magnitude of changes and their corresponding effect sizes. FINDINGS: After the CCAA was implemented, PM2·5 and PM10 concentrations declined in both groups, but O3 concentrations increased. The intervention group showed a 3·95% (95% CI 3·18-4·72%) lower increase in predicted risk than the control group, with larger estimated differences under stricter enforcement. Between 2013 and 2021, each 10 μg/m3 change in PM2·5 concentration was positively associated with a 1·80 (1·34-2·27) percentage point change in predicted CVD risk, whereas each 10 μg/m3 change in PM10 concentration was associated with a 1·24 (0·84-1·63) percentage point change and each 10 μg/m3 change in O3 concentration with a 0·58 (0·33-0·83) percentage point change. Overall, the observed changes in air pollutants during the study period were associated with an average 6·6 percentage point reduction in predicted CVD risk. INTERPRETATION: The CCAA and improved air quality were associated with a slower increase in predicted CVD risk, supporting the necessity for stricter, multipollutant air quality policies to maximise public health benefits. FUNDING: National Natural Science Foundation of China, Kadoorie Charitable Foundation, Noncommunicable Chronic Diseases-National Science and Technology Major Project, National Key R&D Program of China, Chinese Ministry of Science and Technology, and UK Wellcome Trust.

Journal Article↗

PubMed2026

Multi-Omics Genome-Wide to Explore the Formation and Development Targets for Intracranial Aneurysms.

Intracranial aneurysms (IAs) represent a significant and potentially life-threatening category of disease, and there is currently a lack of effective treatment options aimed at preventing the progression of the disease. Accordingly, this study is dedicated to exploring and identifying effective drug targets that can help in the prevention of both the formation and rupture of IAs, along with a detailed examination of the underlying potential mechanisms involved in these processes. The data related to IAs for this research was obtained from the ISGC Biobank and UK Biobank. Then, we investigated the possible biological functions and unintended consequences of targeting the specific genes that were highlighted in IAs by using mediation analysis, virtual knockout experiments, and PW-MR studies. A total of 5 unique potential drug targets for IAs (FKTN, MAP3K1, PSMA4, SLC22A4, ADAM17), 4 unique potential drug targets for SAH (PSMA4, ADAM17, GPR160, SLC22A4), and 2 unique potential drug targets for UIA (SLC22A4, PRCP) were identified across brain or blood samples. Among the various candidates identified, SLC22A4 has emerged as a promising potential drug target, showing significant expression levels in both blood and brain tissues. Additionally, phenome-wide MR of SLC22A4 across 32 selected phenotypes did not identify statistically significant adverse associations after FDR correction. Virtual knockout (KO) experiments on SLC22A4 revealed that SLC22A4 KO disrupted 81 genes, all of which are involved in IAs-related pathways. Besides, we recognized BRD-K85337334 as potential candidates for targeting SLC22A4. This research indicates that an increase in SLC22A4 gene expression within the blood or brain is directly linked to a heightened risk of IAs rupture, which will aid in prioritizing the development of drugs for IAs.

Humans↗

PubMed2026

Metagenome-scale modeling to assess microbiome metabolic complementarity for precision microbiota transplantation therapies.

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remain limited, in part because the role of donor‒recipient metabolic interactions in shaping the post-FMT community remains poorly understood. Here, we leverage metagenome-scale metabolic modeling to quantify metabolic niche complementarity between donor and recipient microbiomes and predict post-FMT community composition. Using MICOM-derived metabolic models, we show that donor genomes whose metabolic flux profiles are more dissimilar from the recipient community colonize at significantly higher rates in a murine FMT model. In a human IBS trial, the same metric predicted post-FMT community composition via leave-one-out cross-validation and captured known disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to evaluate personalized donor screening, identifying super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition. Together, these results support metabolic niche complementarity as a potential determinant of post-FMT community composition and provide a mechanistic basis for evaluating donor-recipient metabolic compatibility. This framework offers a scalable approach for generating testable hypotheses for personalized donor selection.

Fecal Microbiota Transplantation↗

PubMed2026

Genetic risk stratification of common diseases in breast cancer survivors: a population-based cohort study.

IMPORTANCE: Patients diagnosed with breast cancer (BCa) are at increased risk of multiple common diseases; however, the spectrum of these diseases and the contribution of inherited genetic susceptibility remain incompletely characterized. METHODS: We evaluated 15 common diseases and tested their associations with BCa exposure and disease-specific polygenic risk scores (PRS) in the UK Biobank (UKB; N = 254,736). Analyses were performed using cause-specific Cox proportional hazards models within a full-cohort framework, with time-updated BCa status, delayed entry at study recruitment, and age as the underlying time scale. RESULTS: After recruitment, incident BCa was diagnosed in 11,386 women (4.47%), including 2,742 (24.08%) with metastatic BCa. Patients with BCa had an increased risk of nine diseases spanning cardiovascular, metabolic, and neuropsychiatric domains (P<0.003, Bonferroni-corrected). Elevated risks were generally observed among patients with both early staged and advanced BCa. Inherited susceptibility further stratified disease risk, with the highest risks observed among patients with BCa with elevated disease-specific PRS. For example, compared with women without BCa, the hazard ratio (HR; 95% CI) for osteoporosis was 2.33 (2.15-2.52) among women with any BCa, 2.38 (2.18-2.59) among those with non-metastatic BCa, and 2.12 (1.78-2.54) among those with metastatic BCa; the HR was 4.48 (3.99-5.02) among patients with BCa in the highest quartile of osteoporosis-specific PRS (all P<0.001). In contrast, BCa was not significantly associated with risk of coronary artery disease. CONCLUSION: BCa and inherited genetic susceptibility jointly contribute to increased risk of multiple common diseases, supporting the integration of genetic risk stratification into survivorship care.

Complications↗

PubMed2026

Sugar rationing during the first 1000 days and early onset cancer: a natural experiment.

BACKGROUND: The "first 1000 days" of life is a critical window for metabolic programming, while the long-term oncological consequences of nutritional exposures during this period remain understudied. OBJECTIVES: We aimed to evaluate whether restricted sugar intake in utero and during early childhood reduces risk of early onset cancer diagnosis and mortality in adulthood, utilizing a natural experiment. METHODS: We analyzed 63,819 United Kingdom Biobank participants born between October 1951 and March 1956, spanning the end of United Kingdom sugar rationing (September 1953). Leveraging a quasi-experimental birth cohort design, we compared participants exposed to sugar rationing in utero and during infancy with those unexposed. Early onset cancer incidence (≤50 y) and mortality were ascertained via integrated national Cancer Registry and hospital inpatient records. Multivariable Cox proportional hazards models (including Gompertz distribution) were used to estimate hazard ratios (HRs), with exploratory site-specific analyses. RESULTS: Among 63,819 participants (56.3% female), 40,397 were exposed to rationing and 23,422 were unexposed. Early life sugar restriction significantly reduced early onset cancer risk (HR: 0.66; 95% confidence interval: 0.53, 0.81; P < 0.001). A dose-response relationship was observed, with peak protection in individuals exposed for ≤24 mo postnatally. This protection was observed systemically across solid tumors, independent of specific cancer sites. Specificity was corroborated by null associations with negative controls (herpes zoster and cataract). No significant difference was found for cancer-specific mortality. CONCLUSIONS: Restricting sugar intake during the first 1000 days is associated with a reduced risk of early onset cancer, extending the disease-free lifespan. The divergence between reduced incidence and unchanged mortality suggests early life metabolic environments primarily influence tumor latency rather than biological aggressiveness. These findings highlight the potential long-term public health implications of early life dietary guidelines against the rising burden of early onset cancer.

Humans↗