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PubMed2026

Plasma Proteomic Signatures of Physical Activity Provide Insights into Biological Impacts and its Protective Role against Dementia.

PURPOSE: Physical activity (PA) and sedentary behavior (SB) are associated with many diseases, including Alzheimer disease and all-cause dementia. However, the specific biological mechanisms through which PA protects against disease are not entirely understood. This study aims to address this gap, with a specific focus on all-cause dementia. METHODS: We first assessed the conventional observational associations of three self-reported and three device-based PA/SB measures with circulating levels of 2911 plasma proteins measured in the UK Biobank ( nmax = 39,160) and assessed functional enrichment of identified proteins. We then used bidirectional Mendelian randomization to further evaluate the evidence for causal relationships of PA/SB with protein levels. Finally, we performed mediation analyses to identify proteins that may mediate the relationship of PA with incident all-cause dementia. RESULTS: Our findings revealed 41 proteins consistently associated with all PA measures and 1027 proteins associated with at least one PA measure. Both conventional observational and Mendelian randomization study designs converged on proteins that appear to increase as a result of PA, including integrins such as ITGAV and ITGAM, as well as MXRA8, CLEC4A, CLEC4M, LPL, and ADGRG2; and on proteins that appear to decrease as a result of PA such as LEP, INHBC, CLMP, PTGDS, ADM, OGN, and PI3; and on proteins that are more responsive to high-intensity PA, such as CA14, CA6, CA4, KIT, and ANGPT2. Functional enrichment analyses revealed processes such as cell-matrix adhesion, integrin-mediated signaling, and collagen binding. Finally, GDF15, ITGAV, ITGAM, ITGA11, HPGDS, GFAP, ADM, AHNAK, and DPP4 were among 21 unique proteins found to mediate the relationship of PA with all-cause dementia, implicating processes such as synaptic plasticity, neurogenesis, and inflammation. CONCLUSIONS: Our results provide insights into how PA affects biological processes and protects against dementia, and provide avenues for future research into the health-promoting effects of PA.

Humans↗

PubMed2026

Cerebral Cortical Structural Variation and General Cognitive Ability: Evidence From Mendelian Randomization.

Understanding the cortical architecture underlying individual differences in general cognitive ability (GCA) remains a central question in cognitive neuroscience. Prior work has established associations between global brain size and GCA, yet the regional effects and directionality of these relationships remain debated. Using a genetically informed cortical parcellation in 11,289 UK Biobank participants, we examined associations between cortical surface area (SA), cortical thickness (CT), and GCA measured via verbal-numerical reasoning. Total SA showed a robust positive association with GCA. At the regional level, dorsolateral prefrontal and superior temporal SA exhibited the strongest positive associations, which persisted after adjustment for global SA. In contrast, CT showed comparatively modest associations. Using Mendelian randomization (MR) with genome-wide significant genetic instruments, we observed evidence consistent with a bidirectional relationship between total SA and GCA. At the regional level, dorsolateral prefrontal and temporal SA demonstrated evidence of MR-inferred directional effects on GCA, while GCA showed evidence of MR-inferred directional effects on total SA and perisylvian thickness. These findings support a polyregional SA architecture underlying GCA, with prominent contributions from prefrontal and temporal association cortices. Our results refine global brain-GCA models and highlight the value of genetically informed parcellation for identifying regional cortical contributions.

Humans↗

PubMed2026

Prostate Cancer, Genetic Susceptibility, and Risk of Chronic Non-Urological Complications.

BACKGROUND: Chronic nonurological complications are common among prostate cancer (PCa) survivors; however, their spectrum, magnitude, and genetic contribution remain poorly characterized. METHODS: We evaluated 15 commonly reported nonurological complications and tested their associations with exposure to PCa and disease-specific polygenic risk scores (PRS) in the UK Biobank (UKB; N = 219,133). Analyses were conducted using cause-specific Cox proportional-hazards models within a full-cohort framework with time-updated PCa status, delayed entry at study recruitment, and age as the underlying time scale. RESULTS: After recruitment, incident PCa was diagnosed in 13,780 men, of which 1,656 (12.02%) had metastatic PCa (mPCa). Risks for seven complications were higher among men with PCa, adjusting for genetic background (all p < 0.05), including osteoporosis, venous thromboembolism, depression, and four primary cancers (bladder, kidney, colorectal, and pancreatic). Risks were consistently stronger among men exposed to mPCa than non-mPCa; for example, the hazard ratio (HR) (95% confidence interval) for osteoporosis was 1.88 (1.63-2.18) for any PCa, 4.67 (3.11-7.01) for mPCa, and 1.75 (1.50-2.04) for non-mPCa. Elevated risks for three additional complications (coronary artery disease, type 2 diabetes and chronic obstructive pulmonary disease) were observed only among men with mPCa. The risks for these ten complications were further increased among men with higher disease-specific PRS; for example, the HR for osteoperosis in mPCa patients in the top PRS quartile was 13.57 (8.24-22.34) compared with men without PCa (p < 0.001). CONCLUSION: PCa diagnosis and inherited genetic susceptibility jointly contribute to increased risks of multiple chronic non-urological complications among survivors.

Humans↗

PubMed2026

Big data and psychiatry: advances, constraints and future directions.

Early work in psychiatry research, often involving single sites, small samples, and limited variables, has shifted to contemporary research involving multiple sites, large samples, and many variables. Such research raises important questions, including concerns about data quality and methodological rigor, uncertainty about its key lessons, issues regarding clinical relevance, and questions about how to optimize future advances. Here we consider these questions and concerns against the context of big data work on community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials. The development of large datasets allowing well-powered analyses is a major milestone, but sample size alone does not guarantee more precise estimates, and ongoing attention to the quality and rigor of big data collation and analysis is needed. Big data research has fostered trans-disciplinarity and given insights into mechanisms underlying psychiatric disorders, but also emphasizes the intricacy, heterogeneity and variability of such mechanisms, and the importance of triangulating between large-scale and small-scale research. The complexity of psychiatric phenotypes and psychobiological mechanisms contributes to the difficulty in bridging from big data to clinical application; big data research reinforces the importance of holding our diagnoses of psychiatric disorders lightly and providing explanations of these conditions humbly; and future work needs to be more attentive to clinical issues. There is enormous scope for further building databases relevant to psychiatry, but advances in conceptual models and asking the right questions are equally valuable. The full impact of big data, including artificial intelligence analyses, remains to be seen, but overenthusiastic support should be tempered by a better understanding of its strengths and limitations. At its best, such work will contribute in an iterative and integrative way to advancing our knowledge of psychiatric disorders and mental health.

Big data↗

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

Shared inheritance reveals landscape of somatic and germline cancer risk in TP53.

Pathogenic variants in TP53, the key tumor suppressor gene underlying Li-Fraumeni syndrome (LFS), are among the best-established causes of inherited cancer predisposition. However, large-scale sequencing has revealed that many apparently pathogenic TP53 variants detected in blood are the result of somatic clonal expansions, complicating risk interpretation. Using blood-derived whole-exome data from 469,391 UK Biobank participants, we combined the variant allele fraction (VAF) with haplotype-sharing analysis to distinguish germline and somatic TP53 variants. Germline variants were concentrated at sites linked to partial loss of p53 function and lower disease penetrance, whereas classic LFS alleles appeared to be predominantly somatically acquired. Classic LFS alleles at high VAF conferred markedly increased risk of hematological malignancy but not solid tumors, indicating an important contribution from large TP53-mutant clonal expansions. The prevalence of somatic clonal expansion also correlated with missense variant pathogenicity, suggesting that somatic activity provides an informative in vivo proxy for functional impact. These results provide new insights into TP53-associated cancer risk at the population level, demonstrate that somatic rather than germline risk predominates in middle-aged healthy adults, and provide a scalable framework for variant classification in large-scale population genomics.

Humans↗

PubMed2026

Can Psychiatric Genetics Advance Without Incorporating a Life Course Perspective?

Psychiatric disorders unfold over the life course; however, genomic studies of these conditions overwhelmingly rely on phenotypes collected at a single time point, often in adulthood. Therefore, genome-wide association studies (GWASs) of psychiatric conditions may miss genetic variants with time-varying relevance to etiology, prevention, and treatment, such as those that influence trajectories of symptoms and behaviors, age at onset, course of treatment response, and the co-evolution of comorbidities. With recent advances in longitudinal biobanks and analytic tools, we posit that incorporating a life course perspective in psychiatric genetics will enable critically relevant insights into each of these areas of investigation. We propose that the current inconsistent portability of polygenic scores across age groups can be reconciled through the design of carefully considered longitudinal GWASs in age-diverse samples. Pioneering longitudinal GWASs in psychiatry have revealed novel genomic signals associated with time-dependent phenotypes that are distinct from those influencing lifetime diagnosis, suggesting that the study of longitudinal phenotypes will complement cross-sectional approaches and empower biological and therapeutic discoveries. Advances in post-GWAS functional annotation resources and analytic approaches now enable us to contextualize the genetic contributions to psychiatric disorders as dynamic age- and exposure-dependent processes. Although longitudinal GWASs pose unique challenges with regard to data availability, selection bias, and missing data, integrating temporality into psychiatric genetics at scale is now attainable and promises to reveal novel biology and therapeutic opportunities for psychiatric conditions.

Cohort study↗

PubMed2026

Proteomic signatures for sudden cardiac death and related intermediate phenotypes.

BACKGROUND: Novel markers for sudden cardiac death (SCD) are needed. OBJECTIVE: This study aimed to explore whether a protein risk score derived from a large-scale proteomics dataset improves risk prediction of SCD in the general population. METHODS: A total of 52,705 individuals with 1459 unique plasma protein measurements were included from the UK Biobank Pharma Proteomics Project. A protein risk score was developed using lasso-penalized Cox regression on 40,722 participants enrolled at the English centers and validated on 11,983 participants enrolled at the remaining centers. RESULTS: The protein risk score formula developed from the derivation set comprised 64 unique plasma proteins including latent-transforming growth factor beta-binding protein 2, protein tyrosine phosphatase receptor sigma, and spondin-1. In the test set, a per standard deviation increase in protein risk score was associated with a hazard ratio of 2.60 (95% confidence interval [CI] 2.12-3.18) for SCD. Adding a protein risk score to SCD clinical risk factors resulted in a concordance index increase of 0.063 (95% CI 0.037-0.105) for SCD. For ventricular arrhythmia-mediated SCDs, an increase in concordance index when a protein risk score was added to SCD clinical risk factors was 0.070 (95% CI 0.010-0.188). A protein risk score added to SCD clinical risk factors resulted in a risk reclassification of 16.9% (95% CI 9.0-24.7) at a 10-year risk threshold of 5%. A protein risk score was significantly associated with intermediate phenotypes of SCD including corrected QT prolongation, an increase in left ventricular mean myocardial thickness, and a decrease in left ventricular global longitudinal strain. CONCLUSION: A protein risk score derived from a single plasma sample significantly improved risk prediction of SCD and related intermediate phenotypes.

Humans↗

PubMed2026

Cross-Ancestry Proteogenomic Analyses Identified New Therapeutic Insights for Ischemic Heart Disease.

BACKGROUND: Most drugs target proteins, and proteome-wide genetic analyses in diverse populations could discover potential novel and repurposed targets for improved prevention and treatment of ischemic heart disease (IHD) beyond statin therapy. OBJECTIVES: The purposes of this study were to use cis-acting single nucleotide polymorphisms (cis-pQTLs) identified for plasma proteins in East Asians and Europeans to discover and validate potential drug targets for IHD. METHODS: We measured plasma levels of 9,520 (Olink/SomaScan: 2,923/7,297) proteins in a case-cohort study of IHD (1,976 incident cases and 2,001 subcohort controls) in statin-free individuals in the prospective China Kadoorie Biobank (CKB). Genome-wide association studies identified 2,895 (Olink/SomaScan: 1,301/1,594) cis-pQTLs for these proteins in CKB. Two-sample Mendelian randomization (MR) and colocalization analyses assessed associations of all available cis-pQTLs for these proteins with IHD in East Asians (n = 29,319 cases), with further replication in Europeans (n = 181,522 cases) and comparison with findings in previous MR studies. RESULTS: In CKB observational analyses, a total of 959 (Olink/SomaScan: 426/533) proteins were associated at false discovery rate-corrected P < 0.05 with IHD after adjusting for major IHD risk factors. Two-sample MR analyses provided genetic support for 54 unique (Olink/SomaScan: 36/28) proteins in IHD etiology. Colocalization analyses confirmed shared gene-protein-IHD associations (posterior probability of hypothesis 4 [PPH4] ≥0.8) for 15 unique (Olink/SomaScan: 10/10) proteins, including 8 lipid-related, 3 inflammation-related, 1 blood pressure-related, and 3 alcohol-related proteins in East Asians. In Europeans, MR analyses of 12 non-alcohol-related proteins showed directionally concordant results for 8 proteins, with 5 having strong colocalization evidence of shared gene-protein-IHD associations (PPH4 ≥0.8), including 4 lipid-related (proprotein convertase subtilisin/kexin type 9, LPA, APOE, cadherin-1) and 1 systolic blood pressure-related (fibroblast growth factor 5) protein. However, 4 proteins showed directionally discordant MR results, including 2 lipid-related (APOA5, SORT1) and 1 inflammation-related (transforming growth factor beta 1) proteins with strong colocalization evidence of shared gene-protein-IHD associations (PPH4 ≥0.8). Comparison with previous MR studies revealed little consistency across studies in the number and identity of target proteins for IHD beyond well-established lipid-related (low-density lipoprotein cholesterol, lipoprotein(a), and triglycerides) or inflammation-related (interleukin-6) protein targets. CONCLUSIONS: The findings support a role for lipid-driven chronic inflammation in IHD etiology, and treatment strategies simultaneously targeting multiple lipid and inflammation pathways should be prioritized for further research to improve drug treatment of IHD beyond statin therapy.

Aged↗

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

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

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

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

Biological Mechanisms Underlying the Cardiovascular Effects of Branched-Chain Amino Acids: A Proteome-Wide Mendelian Randomization Study.

BACKGROUND: Ischemic heart disease (IHD) is the leading cause of morbidity and mortality. Branched-chain amino acids (BCAAs) are associated with higher IHD risk, but the underlying biological pathways remain unclear. OBJECTIVES: This study aims to explore these pathways using 2-step proteome-wide Mendelian randomization. METHODS: We examined the associations between genetic proxies for BCAAs and 2922 proteins in the United Kingdom Biobank Pharma Proteomics Project, supplemented by a meta-analysis with data from deCODE to identify proteins associated with BCAAs. Next, we tested their effects on IHD risk using Coronary Artery Disease Genome-wide Replication and Meta-analysis plus Coronary Artery Disease Genetics Consortium (122,733 cases and 424,528 controls) and replicated in FinnGen (31,640 cases and 187,152 controls). We conducted sensitivity analyses using genetic instruments from deCODE. Proteins associated with IHD risk and, in a consistent direction, with genetically predicted BCAAs were considered potential mediators. RESULTS: Genetic proxies for BCAAs were associated with 40 proteins. Among these, 6 proteins showed consistent evidence of mediation, including complement C1s subcomponent, coagulation factor II, granulin, proprotein convertase subtilisin/kexin type 9, sex hormone-binding globulin, and V-set and transmembrane domain-containing protein 2-like. These proteins are involved in inflammation, coagulation, lipid metabolism, and cellular stress response. All associations were robust across different analytical methods and replicated in independent datasets. Mediation analysis showed that these proteins accounted for 6.5% to 32.1% of the association between BCAAs and IHD risk. CONCLUSIONS: This study identified 6 proteins that potentially link BCAAs to IHD, implicating pathways related to inflammation, coagulation, lipid metabolism, and cellular stress responses. To our knowledge, these findings provide novel mechanistic insights into the BCAA-IHD relationship and highlight potential protein targets for future prevention and intervention strategies.

Amino Acids, Branched-Chain↗

PubMed2026

Can we identify people with Alzheimer's disease from examination of the eye? A bidirectional Mendelian randomization (MR) study.

BACKGROUND: Neurodegeneration in Alzheimer's disease (AD) is thought to be driven by amyloid-beta and tau deposition in the cerebral vasculature and brain. As the eye is an extension of the central nervous system, this study aimed to determine which neurovascular and neuroretinal changes in the eye are caused by AD rather than associations of the disease. METHODS: Bidirectional two-sample univariable and multivariable Mendelian randomization (MR) methods were applied. Instrumental variables were derived from genome-wide association studies (GWAS) of AD and the following ocular features: thickness measurements of central macula (MT), retinal nerve fibre layer (mRNFL), ganglion cell-inner plexiform layer (mGCIPL), outer nuclear layer (ONL), inner segment layer (IS), and outer segment (OS) from macular region OCT scans; arteriolar tortuosity (AT), venular tortuosity (VT), venular width (VW), fractal dimension (FD), vertical cup-to-disc ratio (VCDR), optic cup area (OCA), and optic disc area (ODA) derived from other imaging methods. RESULTS: There was strong evidence that genetic liability to AD affected the retinal vasculature by specifically increasing AT (β = 0.007;95%CI=0.002,0.011;p-value=0.005) in UK Biobank participants (n=52,798). AD may influence the mRNFL (β=-0.047,95%CI=-0.119,0.023,p-value=0.18) and mGCIPL (β=-0.061;95%CI=-0.14,0.025,p-value=0.16) of the inner retina and OS layer (β = 0.044;95%CI=-0.0001,0.08;p-value=0.05) but the evidence was weak. Multivariable MR analysis showed that a causal relationship between optic disc area and AD (OR=0.76;95%CI=0.62,0.93,p-value=0.009) was probably mediated by refractive error. CONCLUSION: Early cerebrovascular signs of AD may be detected by examination of the eye. Further investigation is required to determine the clinical utility of eye screening for dementia.

Humans↗

PubMed2026

Genetic heterogeneity affects the risk of incident depression, comorbidity, and response to environment: A prospective trajectory study.

BACKGROUND: Depression exhibits significant heterogeneity in its genetic underpinnings. The role of genetic components in the development of depression and its comorbidities remains insufficiently explored. METHODS: First, depression risk loci from a large-scale genome-wide meta-analysis were annotated to Gene Ontology (GO) terms by functional enrichment. GO-based polygenic risk scores (GO-PRS) were then calculated for individuals in the UK Biobank. Principal component analysis (PCA) was applied for dimensionality reduction, followed by cluster analysis to identify genetic subtypes of depression. Multistate models were applied to assess the impact of genetic patterns on the trajectory from healthy status to incident depression, and depression to 26 subsequent diseases, as well as the associations between environmental factors and disease trajectories across genetic subtypes. RESULTS: Participants were categorized into three genetic subtypes: immune-dominant, neuro-dominant, and comprehensive-risk. Significant differences in risk of depression and subsequent diseases, and susceptibility to environmental factors were observed across subtypes. Comprehensive-risk subtype showed higher risks of depression compared to immune-dominant (HR: 1.10, 95% CI: 1.05-1.15) and neuro-dominant subtype (HR: 1.12, 95% CI: 1.08-1.16). Comprehensive-risk subtype exhibited higher risks of transition from depression to subsequent diseases, such as anemia compared to immune-dominant subtype, and diseases of the digestive system compared to neuro-dominant subtype. Environmental factors were more strongly associated with the transition from depression to subsequent diseases in immune-dominant and comprehensive-risk subtypes, including cardiovascular, respiratory, and metabolic diseases. CONCLUSIONS: Our findings highlight the genetic heterogeneity of depression and comorbidities, and shed light on how genetic components modulate responses to environmental factors.

Humans↗

PubMed2026

Schizophrenia and bipolar disorder: a comparative analysis of genetic and brain network connectivity.

BACKGROUND: Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions with overlapping clinical presentations, genetic risk factors, and brain network dysfunction. Whether alterations in large-scale intrinsic brain networks reflect shared or disorder-specific genetic influences remains poorly understood. Clarifying this distinction is essential for refining etiological models and improving diagnostic precision. METHODS: Genome-wide inferred statistics (GWIS) were applied to decompose the genetic architecture of SCZ and BD into shared and unique components. Using resting-state network (RSN) data from the UK Biobank, functional connectivity (FC) and structural connectivity (SC) were extracted as neuroimaging phenotypes. Causal inference approaches were subsequently employed to infer potential directional relationships between brain network connectivity and each disorder. RESULTS: Analyses revealed both common and distinct patterns of brain network connectivity associated with SCZ and BD. Notably, SC within the default mode network (DMN) exhibited opposing effects across the two disorders, suggesting divergent structural underpinnings despite clinical overlap. Additionally, SC within the limbic network (LN) and frontotemporal control network demonstrated potential causal relationships with both conditions, implicating these circuits astransdiagnostic neural substrates. CONCLUSION: These findings illuminate the shared and disorder-specific genetic and neural architecture underlying SCZ and BD. Integrating genome-wide genetic methods with large-scale neuroimaging data offers a powerful framework for disentangling psychiatric comorbidity and may inform more targeted diagnostic criteria and individualized treatment strategies.

Humans↗

PubMed2026

Adolescent depression as a systemic multimorbidity catalyst: integrated genetic and metabolic pathway analysis.

BACKGROUND: Although adolescent depression has been linked to individual chronic conditions, its broader role in shaping multimorbidity risk remains understudied. METHODS: A total of 87,562 UK Biobank participants were included, of whom 18,851 had documented adolescent depression. Cox proportional hazards models were applied to evaluate associations between adolescent depression and 24 chronic diseases, followed by stratified analyses by sex and age. Two-sample Mendelian randomization (MR) was then conducted to infer causality for diseases showing significant associations. Genomic colocalization analyses were performed using relevant GWAS data to identify shared causal variants. Mediation analyses were performed to detect possible mediating factors, including the frailty index, KDM biological age acceleration, allostatic load and 30 circulating biomarkers. RESULTS: Adolescent depression was associated with elevated risk for 12 chronic diseases, with strongest associations for hypothyroidism (HR = 1.29 [1.18-1.42]), diabetes (HR = 1.25 [1.13-1.38]) and chronic obstructive pulmonary disease (COPD) (HR = 1.74 [1.50-2.01]). Risks were notably higher among females and younger adults. MR confirmed likely causal relationships for hypothyroidism (OR = 1.45 [1.03-2.05]), diabetes (OR = 1.01 [1.01-1.02]) and COPD (OR = 1.04 [1.02-1.06]). Genomic colocalization revealed a shared genetic signal at the CDSN/PSORS1C1 locus between adolescent depression and hypothyroidism. Mediation analyses revealed disease-specific pathways: creatinine for hypothyroidism, testosterone for diabetes, KDM biological ageing for COPD and frailty index across all three conditions. CONCLUSIONS: Adolescent depression confers systemic vulnerability through genetic and metabolic mechanisms, with amplified risks in females and individuals aged ≤55 years. These findings support early, integrated interventions to mitigate long-term multimorbidity.

Humans↗