The results support a prioritized genomic map with substantial instrument- and model-specific uncertainty, and disease-to-clock signals were richer than clock-to-disease signals, GrimAA shared polygenic architecture with osteoarthritis, and selected loci showed strong shared-variant evidence.
Abstract
Background: The directional relationship between epigenetic age acceleration (EAA) and musculoskeletal disease remains unresolved. This study integrated bidirectional Mendelian randomization (MR) with multi-layer genomic evidence to evaluate directionality, shared genetic architecture, and robustness to instrument definition. Methods: Four EAA clocks (IEAA, PhenoAA, HannumAA, and GrimAA) and ten musculoskeletal phenotypes were analyzed in a 10 × 4 bidirectional two-sample MR design. EAA instruments underwent GRCh37 functional annotation, genome-wide-significant external-association screening for the index variants and European linkage-disequilibrium proxies, pair-specific Steiger filtering, and conservative Set A/B/C sensitivity analyses. The juvenile-arthritis reverse models underwent instrument-flow reconstruction, strength assessment, liability-scale directionality testing, and minimum-detectable-effect analysis. Additional analyses comprised LD score regression (LDSC), PLACO+ cross-trait locus mapping, Bayesian colocalization, multivariable MR (MVMR) with exact-SNP matched univariable comparators, and integrated evidence synthesis. Results: Forward MR yielded two nominal HannumAA associations. The inverse HannumAA–spondyloarthritis estimate remained directionally consistent across the original, Steiger-filtered, and conservative external-association-filtered sets, whereas the HannumAA–pain-in-thoracic-spine estimate lost nominal significance in the conservative set; no forward result survived correction across 40 tests. GrimAA forward estimates were sensitive to use of the fallback instrument threshold. Reverse MR identified ten nominal associations. For juvenile arthritis, three harmonized instruments had F statistics of 51.25–102.35; liability-scale Steiger comparisons supported the tested direction under all 16 outcome-by-prevalence combinations, although the 788-case discovery GWAS and possible winner’s curse remained important limitations. LDSC identified FDR-significant positive genetic correlations of GrimAA with hip osteoarthritis (r_g = 0.267, p = 8.49 × 10−5, q = 0.0019) and knee osteoarthritis (r_g = 0.269, p = 9.52 × 10−5, q = 0.0019). PLACO+ identified 738 genome-wide-significant cross-trait variants and 65 independent loci; six of 37 evaluable loci showed strong colocalization. Of 96 MVMR models, 43 had primary-exposure conditional F ≥ 10, and 32 also had candidate-trait conditional F ≥ 10. After exact-SNP matching, the 43 primary-strength models were operationally classified as 35 partially attenuated and eight independent-signal models, with no fully attenuated model; no adjusted association survived multiplicity correction. Conclusions: The results support a prioritized genomic map with substantial instrument- and model-specific uncertainty. Disease-to-clock signals were richer than clock-to-disease signals, GrimAA shared polygenic architecture with osteoarthritis, and selected loci showed strong shared-variant evidence, while the MR and MVMR findings remained unsuitable for definitive causal or mediation claims.
Background Endometriosis (EMS) and inflammatory bowel disease (IBD) are both chronic inflammatory disorders with overlapping clinical features, suggesting a potentially shared etiology. Although epidemiological studies have reported an association between the two conditions, the genetic basis of this relationship remains poorly understood. This study is aimed at characterizing the shared genetic architecture of EMS and IBD, identify pleiotropic loci implicated in both conditions, and explore the potential causal relationships and underlying biological mechanisms. Methods We analyzed large‐scale GWAS summary statistics to investigate the genetic relationship between EMS and IBD. Linkage‐disequilibrium score regression (LDSC) was used to estimate genetic correlation. The PLACO method was applied to identify pleiotropic loci, which were further evaluated by Bayesian colocalization. Functional implications were assessed using MAGMA gene‐set analysis, tissue enrichment analysis, and functional annotation. Bidirectional two‐sample Mendelian randomization (MR) was performed to examine causal inference, accompanied by leave‐one‐out sensitivity analyses and statistical power calculations. Results A statistically significant but modest positive genetic correlation was observed between EMS and IBD (rg = 0.0957, p = 0.0042). Twenty‐four genomic loci showed evidence of pleiotropic effects, and six of these carried strong evidence of a shared causal variant (posterior probability: PP.H4 > 0.7), including loci at 2p23.3 (ADCY3/DNAJC27), 4q12, 5q23.3, 7p15.2, 11q13.1 (CCDC88B), and 1q22. Functional analyses consistently implicated eight pleiotropic genes across multiple platforms: ADCY3, CCDC88B, GREB1, NOD2, RSPO3, SYNE1, THADA, and TRAIP. Pathway enrichment highlighted “positive regulation of gene expression” as a key shared mechanism (p < 0.05). Tissue‐specific analyses revealed strong signals in whole blood, spleen, and colon, consistent with the immunoinflammatory etiology of both diseases. Bidirectional MR did not support a causal relationship in either direction (IBD on EMS: OR = 1.007, 95% CI: 0.986–1.028, p = 0.543; EMS on IBD: OR = 1.054, 95% CI: 0.991–1.122, p = 0.092). Leave‐one‐out analyses confirmed that no single variant drove these estimates, and power analysis indicated adequate power to detect moderate causal effects. Conclusions The findings provide evidence for a shared, albeit modest, and genetic basis between EMS and IBD that is likely mediated through immune regulation, hormonal signaling, and tissue repair. The absence of significant causal effects in MR suggests that the observed comorbidity reflects common genetic susceptibility rather than a direct causal link. These results offer new insight into the mechanisms underlying the co‐occurrence of the two conditions and may inform future investigation of shared therapeutic targets.
Xinyue Cui, Jian-Hong Wang, Jingjing Chen et al.· BioMed Research Internationa...· 0 citations
GSMAP-based spatial enrichment provides a hypothesis-generating framework for understanding vascular, meningeal, and myelin-related pathways linking hypertension to increased brain age gap.
Xiaoyang Zhu, Shuaiqi Zhang, Zhiyuan Liu et al.· Biogerontology (Dordrecht)· 0 citations
These findings provide a hypothesis-generating reframing of the traditional comorbidity model, suggesting that divergent molecular programs may converge on shared pathways and offer a preliminary foundation for exploring therapeutic strategies at the mood–metabolism interface.
Xingpei Li, Chunling Chen, Huibin Li et al.· Frontiers in Genetics· 0 citations
Background Facial aging reflects genetic, metabolic, and environmental influences. Although obesity has been associated with older perceived facial age, the shared genetic basis and direction of the BMI-facial aging relationship remain uncertain. Objective To evaluate genetic correlations between facial aging and 15 metabolic traits, assess bidirectional associations by Mendelian randomization (MR), and conduct exploratory locus, gene, and compound prioritization. Methods Public genome-wide association study (GWAS) summary statistics were analyzed using linkage disequilibrium score regression (LDSC) and bidirectional MR with inverse-variance weighted (IVW), weighted median, MR-Egger, and MR-PRESSO methods. Because BMI showed the most consistent signal, related loci were evaluated by fine-mapping, colocalization, ANNOVAR, MAGMA, and GCTA-fastBAT. DGIdb screening and molecular docking were used for hypothesis generation. Results BMI showed the strongest genetic correlation with facial aging (rg = 0.215; FDR-adjusted P = 3.94×10−33). After MR-PRESSO outlier removal, higher genetically predicted BMI was associated with greater odds of appearing older (IVW OR = 1.053, 95% CI 1.044–1.063; P = 1.80×10−28), although heterogeneity remained and reverse-direction estimates were less robust. Of four retained variants, three met the prespecified colocalization threshold. JAZF1, RAD52, and PPARG were prioritized by positional and gene-based analyses. Docking of five natural compounds did not establish target engagement or efficacy. Limitations The facial-aging phenotype was perception-based, the GWAS datasets were predominantly of European ancestry and may have partially overlapping samples, and all downstream analyses were computational. Conclusion Higher BMI may contribute to perceived facial aging, but these findings do not show that BMI reduction or any candidate compound improves facial aging. Metabolic health is the more clinically actionable implication, whereas the prioritized genes and compounds remain exploratory and require functional validation.
Yuan Hu, Kehan Li, Ming-Jie He et al.· Clinical, Cosmetic and Inves...· 0 citations
This study indicates a genetic correlation and common risk genes, linking adiposity-related traits to PE-related diseases, and offers novel insights into the biological mechanisms underlying this comorbidity.
Yuping Shan, Hong Hu, Chong Liu et al.· Journal of Obstetrics and Gy...· 0 citations
Objective This study aims to systematically elucidate the shared and specific genetic basis of osteoarthritis (OA) and obesity by integrating large-scale genome-wide association study (GWAS) summary statistics, cross-tissue quantitative trait loci (QTLs), and single-cell and spatial transcriptomic data. Method The research employed a multi-omics integrative analysis pipeline. First, a meta-analysis was conducted on GWAS data for OA and obesity. Next, tissue- and spatial-specific enrichment analyses were performed using methods such as QTLEnrich, MAGMA, and gsMap. Key steps included the application of single-cell analysis, Cell-stratified mendelian randomization (csMR), and the ECLIPSER/CELLECT framework to identify specific cell types. Finally, hub genes were identified using hdWGCNA. Results The results revealed significant enrichment of genetic risk signals for OA and obesity in brain tissues, including the cortex and pituitary gland. At the cellular level, T cells were identified as the highest-priority shared cell type for both diseases. Hub genes—GSN, CALD1, EBF1, LHFPL6, and TIMP3—were identified through co-expression network analysis. Spatial transcriptomic analysis further mapped the genetic risk signals to brain regions during embryonic development. Conclusion This study precisely anchors the genetic risk of OA and obesity to specific brain regions, cell types, and developmental time windows, providing a novel perspective for understanding the pathological mechanisms of OA.
Zehong Lin, Jihu Wei, Honghai Zhou· Osteoarthritis and Cartilage...· 0 citations
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