Vitamins A and D regulate numerous genes through intersecting metabolic pathways, influencing lung development and asthma. This article aimed to examine the impact of vitamins A and D on lung function, epigenetic ageing and regulatory epigenetics in children and adults with asthma. Two asthma cohorts, GACRS (Genetic Epidemiology of Asthma in Costa Rica Study; 1165 children) and ODOLLFA (Omic Determinants of Longitudinal Lung Function in Asthma; 1041 adults) were included. Serum miRNA profiles, blood DNA methylation and plasma/serum vitamin A and D levels were measured. Associations between vitamin levels and lung function (forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC), miRNA expression and DNA methylation were evaluated. In adults, mediation analysis assessed whether methylation status and miRNAs mediated the relationship between vitamins and lung function or epigenetic ageing. In children, higher vitamin A was associated with higher FEV1 (β=2.5, p=4.9 × 10-3) and FVC (β=7.6, p=7.7 × 10-14) but lower FEV1/FVC (β=-3.9, p=2.0 × 10-16); vitamin D showed no association. In adults, both vitamins were positively associated with FEV1 (vitamin A: β=4.7, p=9.3 × 10-3, vitamin D: β=0.16, p=8.6 × 10-3) and FVC (vitamin A: β=3.4, p=3.0 × 10-2, vitamin D: β=0.18, p=4.6 × 10-4); only vitamin A was associated with FEV1/FVC (vitamin A: β=2.5, p=3.9 × 10-2). Vitamin D sufficiency corresponded to lower epigenetic ageing in adults. Higher vitamin levels were associated with IRF5 regulatory cytosine hypomethylation at cytosine-guanine dinucleotides (CpG), corresponding to better lung function and lower ageing. Distinct vitamin-associated miRNAs were identified, with shared targets enriched in immune and cell-cycle pathways. Mediation analyses indicated statistical partial effects through methylation and miRNAs. Vitamin A, but not vitamin D, in children was associated with better lung function and lower epigenetic ageing in asthma, with effects mediated by DNA methylation and miRNA regulation.

Proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors are potent lipid-lowering therapies, however, concerns regarding their neurocognitive safety persist because of conflicting evidence. We applied an integrated approach combining real-world pharmacovigilance, genetic epidemiology, and preclinical models. Disproportionality analysis was performed using the FDA Adverse Event Reporting System (FAERS) data to assess cognitive adverse events. Two-sample Mendelian randomization (MR) was used to assess the causal effects of apolipoprotein B (ApoB) lowering and PCSK9 inhibition on Alzheimer's disease (AD) using UK Biobank and FinnGen data, with a factorial MR design to evaluate interactions stratified by ApoB and PCSK9 polygenic risk. Preclinically, hepatocyte-specific knockout and pharmacological inhibition of PCSK9 in AD-transgenic mice were assessed through cognitive testing and neuropathological assessment. FAERS analysis showed no increased reporting signal for cognitive adverse events with PCSK9 inhibitors. For AD, the ROR was below unity (ROR = 0.62, 95% CI 0.45-0.87), whereas no statistically significant signal was observed for dementia (ROR = 0.92, 95% CI 0.79-1.08). In contrast, statins showed significant disproportionality signals for AD (ROR = 2.86, 95% CI 2.51-3.26) and dementia (ROR = 1.51, 95% CI 1.36-1.67). MR analysis revealed no causal association between genetically proxied PCSK9 inhibition and AD risk (OR = 1.00, 95% CI 0.93-1.07), or between ApoB and AD risk (OR = 0.79, 95% CI 0.56-1.11). In AD-transgenic mice, PCSK9 knockout reduced low-density lipoprotein-cholesterol by 54% without impairing cognition or neuronal integrity, or increasing amyloid-β plaques and astrocytes. Pharmacological inhibition of PCSK9 similarly showed no cognitive deficits. Integrated evidence from pharmacovigilance, genetic epidemiology, and preclinical models robustly supports the neurocognitive safety of PCSK9 inhibitors, including in individuals with high ApoB levels. These findings affirm their long-term neurocognitive safety profile in the management of atherosclerotic cardiovascular disease.

Mendelian randomization (MR) analysis is widely used to estimate causal relationships between risk factors and outcomes of interest. Two-sample MR approaches have gained increasing attention in genetic epidemiology due to the growing availability of Genome-Wide Association Study (GWAS) summary statistics from public databases. A critical step in two-sample MR is the selection of genetic variants as instrumental variables (IVs). Although genome-wide significant variants are typically preferred, the inclusion of variants with weaker association p-values is considered, as they may potentially improve power through an increased instrument number of instruments, while they may introduce weak instrument bias and attenuate effect estimates towards the null. Our simulation results show that even modest levels of pleiotropy substantially increase the variability of causal effect estimates, while the inclusion of weak IVs does not substantially affect the direction and variability of causal effect estimates in most cases. In real data analyses, we used two released versions of FinnGen GWAS summary statistics with different sample sizes as exposure GWASs to assess the influence of weak IVs. Here, the inclusion of IVs with higher exposure-association p-values resulted in weakened estimated effect sizes, particularly when the exposure GWAS sample size was small. These findings suggest that incorporating weak IVs is reasonable when the exposure GWAS sample size is large, but it poses a risk of falsely concluding null associations when the exposure GWAS sample size is small.

Despite major advances in prevention and treatment, cardiovascular disease remains a leading cause of death and disability worldwide. Cardiovascular genetic epidemiology has moved from family-based and candidate-gene studies to a genome-wide discipline supported by genome-wide association studies (GWAS), population-scale biobanks, whole-genome sequencing, and multi-omics resources. This narrative review examines how the field is shifting from locus discovery toward a continuous but still incomplete evidence chain that links variants to genes, cellular context, causal pathways, functional validation, and clinical use. We emphasize a central tension: cardiovascular genetics has been exceptionally successful at discovering associations, but robust mechanistic resolution and implementation-ready clinical translation remain uneven across diseases, populations, and use cases. We synthesize progress in coronary artery disease, blood pressure traits, atrial fibrillation, stroke, heart failure, inherited cardiovascular disorders, and intermediate phenotypes, and we distinguish established applications from promising but still emerging approaches such as polygenic risk scores, spatial omics, EHR-linked implementation, and genetically informed target prioritization. We also highlight persistent bottlenecks, including ancestry imbalance, noncoding locus interpretation, limited functional validation, imperfect polygenic score portability, data-governance constraints, and the need for privacy-preserving analytical frameworks. Overall, the next phase of cardiovascular genetic epidemiology will depend less on the mere accumulation of loci and more on rigorous evidence triangulation, context-specific functional testing, ancestry equity, and feasible clinical implementation pathways.

Impact of co-occurrent assortative mating and vertical cultural transmission on measures of genetic associations.

Metformin is increasingly recognised for its vasculoprotective effects, primarily through activation of AMP-activated protein kinase (AMPK). We combined population-based genetic epidemiology, with an ex vivo vascular model, to investigate the beneficial vascular effects of AMPK activation by metformin. We used genetically proxied AMPK activation (GP-AMPK), based on 20 genome-wide significant single-nucleotide polymorphisms (SNPs), to assess associations with pulse wave velocity (PWV) and carotid intima-media thickness (cIMT). A two-sample Mendelian randomisation (MR) was performed to test the effect of GP-AMPK on a panel of inflammatory markers. We also developed a porcine coronary artery model of endothelial dysfunction to test the restorative effects of metformin on vascular function and related molecular signalling pathways. Higher GP-AMPK activation was associated with lower PWV, but not cIMT, in our epidemiological analysis (mean age 64.3 years, 43.8% female). Ex vivo, metformin restored endothelial relaxation in Angiotensin (Ang) II-treated vessels via endothelium-dependent hyperpolarisation (EDH) mechanisms, an effect abolished by AMPK inhibition. Western blotting confirmed the activation of AMPK (alpha 1 subunit) with metformin treatment. Inhibitor studies demonstrated a shift from NO-mediated to EDH-mediated relaxation with metformin. Molecular analyses showed reduced Ang II-induced expression of matrix remodelling (MMP1, MMP2) and senescence (P21) markers, as well as suppression of pro-inflammatory cytokines (Il-1β, Il-6, TNF-α), consistent with MR results. Metformin restored endothelial function and reduced vascular stiffness through an AMPK-EDH axis, highlighting a novel, potential therapeutic mechanism for early vascular dysfunction independent of glycaemic effects.

Remnant cholesterol - The transition from biomarker to risk factor.

BackgroundThe genetic epidemiology of carbapenem-resistant Klebsiella pneumoniae (CRKP) is geographically distinct. This study aimed to investigate the association of capsular types and plasmid-mediated factors with mortality in patients with CRKP bacteraemia.MethodsPatients with CRKP bacteraemia were enrolled at two medical centres between 2017 and 2021. Capsular typing, antimicrobial susceptibility testing, polymerase chain reaction (PCR) for carbapenemase-encoding genes in all strains, and whole-genome sequencing (WGS) of selected strains were performed.ResultsAmong 402 patients with monomicrobial CRKP bacteraemia, 238 (59.2%) strains were carbapenemase-producing. Of the K47 strains (n = 188), 89.9% carried blaKPC; of the K64 (n = 44) strains, 43.2% carried blaKPC or blaOXA-48-like. In a multivariate Cox analysis, blaKPC was a significant predictor of mortality specifically in K64 (aHR = 3.64; p = 0.004) but not in K47. Other carbapenemase genes did not show significant association with their capsular types. WGS identified the 85 selected isolates as ST11 or ST11-1LV; among them, K64-blaKPC(+) strains (n = 11) clustered separately from K64-blaKPC(-) (n = 33) and K47-blaKPC(+) strains (n = 41). Plasmid structure analysis revealed the distinctness of K64-associated blaKPC plasmids which were generally larger than those in K47 and characterized by the presence of a 57-kb region encoding a T4SS module along with Escherichia coli virulence genes traT and traJ. Among 188 KPC-producing K. pneumoniae, independent mortality predictors included Pitt score (aHR = 1.27; p < 0.001), pneumonia (aHR = 1.84; p = 0.01), and ceftazidime-avibactam treatment (aHR = 0.38, p = 0.002). Capsular type was significantly prognostic (p = 0.02), with K47 showing a reduced risk of death (aHR = 0.33; p = 0.009) compared to other types. Additionally, traT was identified as a potential marker for fatal outcomes (aHR = 3.12; p = 0.05).ConclusionsCapsular types and the structural characteristics of blaKPC plasmids are associated with clinical outcomes in CRKP bacteraemia. Although traT is a putative marker for high-risk lineages, clinical severity remains the primary predictor of mortality. The capsular type–specific distribution of conjugative plasmids and the co-carriage of traT with blaKPC suggest a potential route for the concurrent dissemination of resistance and lineage-associated genetic features.Supplementary InformationThe online version contains supplementary material available at 10.1186/s41182-026-00987-7.

BackgroundEmphysema progression is spatially heterogeneous; individuals withchronic obstructive pulmonary disease (COPD) may show similar overalldeclines in lung density but different regional patterns of diseaseprogression. However, there are currently no tools available to predictlobe-specific density changes to quantify and forecast emphysemaprogression over time.PurposeTo develop and evaluate a deep learning prognostic model using chest CTscans to help predict lobar lung density decline and assess lobaremphysema progression in participants with COPD.Materials and MethodsIn this prospective study, 5823 participants with and without COPD whounderwent serial chest CT were included from the Genetic Epidemiology ofChronic Obstructive Pulmonary Disease (COPDGene) and Evaluation of COPDLongitudinally to Identify Predictive Surrogate End-points (ECLIPSE)studies. A foundational model was trained on CT scans from a subset of2136 participants from the COPDGene training set to identify localradiographic features indicative of emphysema progression. Thelobe-based model was designed to leverage a global attention mechanismto capture lobar tissue destruction and was evaluated in an internaltest set of 2718 participants from the COPDGene study and an externaltest set of 969 participants from the ECLIPSE study. CT scans wereharmonized to a 20 HU noise profile, consistent with the COPDGenehigh-dose protocol. Predicted annualized change in volume-adjusted lungdensity (ΔALD, g/L per year) values were compared with observedvalues using root mean squared error and Pearson correlation(r), with agreement assessed using Bland-Altmanplots.ResultsThis study included 5823 participants (mean age, 60 years ± 8.6[SD]; 3003 men). Model predictions of ΔALD showed a positivecorrelation with observed lobar decline in the COPDGene internal testset (mean Pearson correlation coefficient across lobes,r = 0.433 ± 0.054) and in the ECLIPSEexternal test set (mean Person r across lobes = 0.471± 0.036). The model performed similarly in identification oflobes with accelerated emphysema progression based on predictedΔALD values in both the COPDGene internal test set (mean lobararea under the receiver operating characteristic curve [AUC], 0.70± 0.013) and the ECLIPSE external test set (mean AUC, 0.70± 0.009) across ΔALD risk values.ConclusionA CT-based prognostic model of lobe density decline and emphysemaprogression was developed and evaluated in participants with COPD atchest CT, and model predictions showed positive correlations withobserved values.ClinicalTrials.gov registration nos. NCT00608764 andNCT0029255210© The Author(s) 2026. Published by the Radiological Society ofNorth America under a CC BY 4.0 license.Supplementalmaterial is available for this article.See also the editorial by Marrocchio and Lynch in this issue.

Record linkage projects provide powerful opportunities to enrich cohort data with administrative and clinical information from national registries. The Netherlands Twin Register (NTR) is a large population-based twin-family cohort with longitudinal data collection that has established multiple record linkage initiatives to expand research on genetic and environmental determinants of health, disease, behaviour, development, and fertility. In this article we summarize and discuss NTR record linkage projects. Clinical and pathology registries have provided essential data on chorionicity, reproductive history, neonatal thyroid function, and cancer diagnoses, facilitating studies on prenatal environment, disease heritability, and polygenic risk prediction for diseases. Linkage with health insurance data has enabled validation of medication use and health research. Collaborations with Statistics Netherlands allow linkage to nationwide population-based data via secure infrastructures such as Open Data Infrastructure for Social Science and Economic Innovations (ODISSEI), supporting genomewide association studies of, for example, healthcare expenditure. Finally, linkage to environmental exposure datasets has permitted exposomewide analyses of health and wellbeing. Together, these projects illustrate the feasibility, scientific value, and challenges of record linkage in the Dutch context, highlighting its role in advancing twin research, genetic epidemiology, and population health studies.

ObjectiveObservational studies have reported an association between glaucoma and stroke risk; however, whether this relationship reflects causality remains unclear. This study aimed to comprehensively evaluate the association between glaucoma and stroke by integrating population‐based cross‐sectional analysis with Mendelian randomization (MR) to assess both observational and genetic evidence.MethodsA cross‐sectional analysis was conducted using data from the National Health and Nutrition Examination Survey (NHANES) 2005–2008 cycles. Multivariable logistic regression models were applied to examine the association between glaucoma and stroke after adjustment for demographic, socioeconomic, and cardiovascular risk factors. In parallel, a two‐sample MR analysis was performed using summary statistics from large‐scale genome‐wide association studies, evaluating overall glaucoma as well as primary open‐angle and neovascular glaucoma in relation to stroke and ischemic stroke subtypes. Causal estimates were primarily derived using the inverse‐variance weighted (IVW) method, with multiple sensitivity analyses to assess robustness.ResultsIn the NHANES analysis (n = 1312), glaucoma was not significantly associated with stroke in crude or fully adjusted models. In MR analyses, genetically predicted glaucoma showed no evidence of a causal association with stroke or ischemic stroke subtypes, including large artery atherosclerotic, cardioembolic, and small vessel stroke. These null findings were consistent across MR‐Egger, weighted median, and weighted mode methods. Sensitivity analyses accounting for heterogeneity, pleiotropy, and outlier instruments did not materially alter the results.ConclusionUsing complementary observational and genetic approaches, this study found no evidence supporting a causal relationship between glaucoma and stroke, providing new insights into their genetic epidemiology.

Replication of acne susceptibility loci and gene\u2013environment interactions with screen time in Singapore and Malaysia Chinese population

Cyclin-dependent kinase 4/6 inhibitors improve outcomes in hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer, but their toxicity profiles may differ in clinically meaningful ways. We aimed to compare the neuropsychiatric and systemic toxicity patterns of abemaciclib and palbociclib and to explore pharmacokinetic and molecular features that might contribute to these differences. We conducted a triangulation study integrating pharmacovigilance, Mendelian randomization, explainable machine learning, transcriptomics, and molecular docking. We analyzed 15,215 reports from the US Food and Drug Administration Adverse Event Reporting System and retained 5524 matched patients after propensity score matching. In the matched cohort, abemaciclib showed a toxicity profile more consistent with central nervous system involvement, with reporting enrichment for headache, dizziness, and memory impairment, and a shorter median time to onset than palbociclib (27.5days vs 37.0days). By contrast, palbociclib showed a greater reporting burden of fatigue and anxiety and a higher fatal outcome reporting rate than abemaciclib (12.46% vs 6.52%). Mendelian randomisation showed an association between genetically predicted lower CDK6 levels and reduced cognitive performance (odds ratio 0.991, 95% CI 0.983-1.000; p=0.040), but no association with C-reactive protein (odds ratio 0.999, 95% CI 0.987-1.012; p=0.914). The machine learning model achieved an area under the curve of 0.641 for prediction of central nervous system toxicity. Transcriptomic analysis showed broader transcriptional reprogramming with abemaciclib than with palbociclib, with 5450 versus 265 differentially expressed genes, while docking analysis identified more favorable predicted binding of abemaciclib than palbociclib to GSK-3β and ABCB1 in this in silico setting, providing structural support for but not proving the proposed mechanistic interpretation. Abemaciclib and palbociclib were associated with distinct neuropsychiatric and systemic toxicity patterns. These findings argue against a uniform class effect and support further prospective evaluation of drug-specific survivorship toxicity profiles.

Emerging biobank resources allow large-scale integration of eye-specific phenotypes with clinical, genomic, and multiomic data. This convergence enables unprecedented opportunities to systematically dissect the genetic architecture, epidemiology, and mechanistic pathways of both rare monogenic and common polygenic diseases. The review aims to critically examine how contemporary data extraction, multiomics, and analytic methodologies are reshaping disease classification, genetic discovery, and translational research in ophthalmology, while highlighting the associated challenges in leveraging these advanced tools. Recent literature demonstrates the utility of genome-wide and phenome-wide association studies, transcriptomic analyses, and artificial intelligence in uncovering novel risk loci, endophenotypes, and biomarkers relevant to eye diseases. Furthermore, advances in multiancestry sampling show substantial population-specific genetic variation, enriching disease models for conditions such as glaucoma and age-related macular degeneration. Finally. integrative approaches, including Mendelian randomization and enrichment analyses, are helping elucidate shared genetic architecture between ocular and systemic diseases, informing therapeutic target identification, and refining risk prediction models. The convergence of biobank-derived multimodal data and sophisticated analytic techniques is catalyzing a path to personalized medicine in ophthalmology. For these approaches to fully translate into clinical practice ensuring scientifically robust and equitable outcomes, future research must address cohort diversity, mechanistic validation, and practical cost-effectiveness.

Plasma proteome mendelian randomization and network pharmacology reveal therapeutic targets for thyroid disorders.

BackgroundLung adenocarcinoma (LUAD), the most prevalent and clinically heterogeneous form of lung cancer, lacks robust biomarkers for prognosis and immunotherapy response prediction. This study aimed to identify key prognostic genes and develop a predictive framework to guide personalized treatment strategies.MethodsWe integrated summary data-based Mendelian randomization (SMR) with machine learning to identify LUAD-associated genes. RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed. A prognostic model was constructed with 101 combinations of 10 machine learning algorithms and validated via receiver operating characteristic (ROC) curve and concordance index analyses. Single-cell RNA-sequencing and spatial transcriptomic data were further examined to clarify the therapeutic implications of the identified genes.ResultsSMR identified 316 LUAD-associated genes, among which 33 were associated with prognosis according to univariate Cox regression. The optimal model combining random survival forest and ridge regression achieved superior predictive accuracy, with the area under the curves for 1-, 3-, and 5-year survival being 0.757, 0.707, and 0.686, respectively. High-risk patients exhibited distinct immune microenvironment suppression, reduced tumor mutation burden, and diminished immunotherapy responsiveness as compared to their low-risk counterparts. Single-cell and spatial transcriptomic analyses identified interactions between cancer cells and stromal cells as potential therapeutic targets.ConclusionsThe integrative approach employed in this study identified prognostic biomarkers and immunotherapy response predictors for patients with LUAD, offering a machine learning-driven framework for risk stratification and precision oncology. The findings underscore the clinical utility of combining genetic epidemiology with advanced analytics to optimize therapeutic strategies.

ObjectivesIn light of a VREfm spread in a large tertiary hospital in China, this study aimed to explore the transmission dynamics and genetic characteristics of these isolates within a global context.MethodsA total of 27 VREfm were collected from a hospital in Guangdong province, China. Antimicrobial susceptibility testing, whole-genome sequencing, and transmission dynamics analysis were performed. Additionally, global sequence type 80 (ST80) data were gathered for genetic comparison, and complete plasmid sequences were reconstructed to characterize the genetic environment surrounding the vanA genes.ResultsThe study identified VREfm ST80 as the main agent of the patient infection, with the exception of four ST789 isolates identified on environmental surfaces within one bed. The genomes displayed high clustering, with similar profiles of virulence factors and antimicrobial resistance genes. The study observed that genetically similar VREfm strains were distributed across different beds. Moreover, VREfm ST80 from the hospital clustered with other Chinese isolates and exhibited a higher number of VFs compared to other lineages. Although vanA genes were present in different STs, the vanA-carrying plasmids shared highly similar backbone structure, with Tn1546-associated elements detected in each.ConclusionThis study sheds light on the genetic epidemiology of a VREfm spread in a Chinese hospital through comparison with global isolates. To control the spread of VREfm within hospitals, the implementation of active screening programs and rigorous infection control measures is essential.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13756-026-01755-2.

BackgroundAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by progressive motor neuron loss. The most common genetic cause of ALS is the hexanucleotide repeat expansion in the C9orf72 gene, which is associated with earlier disease onset, faster progression, and an increased frequency of cognitive and psychiatric involvement. Data on population-specific characteristics of C9orf72-associated ALS remains limited in Central and Eastern Europe.MethodsBetween 2011 and 2024, a total of 959 ALS patients fulfilling established diagnostic criteria were screened for C9orf72 repeat expansions at two Hungarian centers. Hexanucleotide repeat expansions were analyzed using repeat-primed long-read PCR. Repeat numbers exceeding 30 were considered pathogenic. Clinical, demographic, and disease course data were retrospectively collected and analyzed.ResultsPathogenic C9orf72 repeat expansions were identified in 63 of 959 patients, corresponding to a prevalence of 6.57% among Hungarian ALS patients. Bulbar onset was the most common presentation and was associated with faster progression and shorter survival (mean survival: 27.8 months). Cognitive impairment and psychiatric comorbidities were present in a substantial proportion of patients and were associated with slower functional decline. Regional differences in survival were observed, likely reflecting disparities in healthcare access rather than biological factors.ConclusionsThis study provides the first comprehensive national characterization of C9orf72 repeat expansion–associated ALS in Hungary, based on a genetically defined cohort assembled over 13 years. Despite limitations related to retrospective data collection and cohort size, this ethnically homogeneous dataset offers valuable insight into population-specific clinical and epidemiological features and complements larger international studies. Systematic characterization and longitudinal follow-up of genetically defined, trial-ready ALS cohorts will be essential as targeted therapies for C9orf72-associated ALS approach clinical implementation.

This study aimed to investigate the association and potential causal relationship between body fat percentage (BFP) and hypertension, with a focus on sex-specific thresholds and clinical implications for prevention. We integrated data from the National Health and Nutrition Examination Survey (1999-2018; n = 25,957) and summary-level genome-wide association studies (GWAS) from United Kingdom Biobank and Finnish Genetic Epidemiology Consortium. Multivariate logistic regression, restricted cubic spline, and threshold effect analyses were used to evaluate the nonlinear association between BFP and hypertension in the observational cohort. Two-sample Mendelian randomization (MR) employed single-nucleotide polymorphisms as instrumental variables, with inverse-variance weighted as the primary method; sensitivity analyses (MR-Egger, leave-one-out, heterogeneity tests) assessed robustness. Hypertension prevalence increased progressively from adipopenia (6.5%) to obesity (43.1%). Restricted cubic spline revealed a nonlinear positive association between BFP and hypertension in both sexes (P < .001 for nonlinearity). Sex-specific inflection points were identified: 31.32% in males and 41.82% in females. Below these thresholds, each 1% increase in BFP conferred higher odds of hypertension (males: odds ratio [OR] = 1.10; females: OR = 1.16); the effect attenuated above the thresholds. MR analyses confirmed a causal effect of genetically predicted BFP on hypertension (United Kingdom Biobank: OR = 1.003, 95% confidence interval: 1.001-1.005, P < .001; Finnish Genetic Epidemiology Consortium: OR = 1.846, 95% confidence interval: 1.587-2.146, P < .001), with no evidence of heterogeneity or horizontal pleiotropy. Higher BFP is robustly associated with and likely causally contributes to increased hypertension risk, with distinct sex-specific thresholds. These findings support incorporating BFP (beyond body mass index) into hypertension risk assessment and personalized prevention strategies.

BackgroundPostmenopausal osteoporosis (PMO) develops as a result of pathological cross‐tissue interactions. However, current experimental paradigms are constrained by their single‐tissue focus, hindering efforts to discover systemwide regulatory genes.ObjectiveWe aimed to discover conserved genetic regulators of PMO by integrating cross‐tissue transcriptomic profiles in humans and to characterize their biological functions via combined genetic epidemiology and experimental studies using integrated analytical strategies.MethodsOur analytical framework encompassed transcriptome profiles from human peripheral blood mononuclear cells, bone marrow, and bone tissue. We adopted a tiered strategy involving differential expression analysis, weighted gene coexpression network construction, and machine learning with 108 algorithm combinations for candidate gene selection. A two‐sample Mendelian randomization was used to inform causal gene–disease relationships, while the key results were validated in an ovariectomized mouse model of osteoporosis. Mechanistic studies included single‐cell transcriptomics, functional enrichment, and immune microenvironment profiling.ResultsCross‐tissue analysis identified 97 consistently dysregulated genes between tissues, which were further refined to 64 high‐confidence candidates. TGFBR3 was significantly protective against PMO (IVW OR = 0.675, 95% CI: 0.466–0.977, p = 0.037). Osteoporotic mice exhibited considerable downregulation of TGFBR3 expression, which was positively correlated with bone mineral density and mechanical properties as well as bone formation markers and negatively correlated with resorption markers. Cellular localization showed enrichment of TGFBR3 in bone marrow mesenchymal stem cells and T cells from human and mouse bone marrow. Functional analyses suggested that its protective effects involve the modulation of osteogenic differentiation pathways and regulation of the immune microenvironment.ConclusionThis is the first study to identify TGFBR3 as a novel cross‐tissue protective regulator of PMO. Our integrated approach covering genomic discovery, causal inference, and experimental validation offers strong support to the hypothesis that TGFBR3 deficiency constitutes a fundamental feature of PMO pathogenesis, while shedding light on its multilevel protective mechanisms.