{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:56:28Z","timestamp":1777294588418,"version":"3.51.4"},"reference-count":44,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Diabetes"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The genetic basis of cardiovascular\u2010kidney\u2010metabolic syndrome (CKMs) involves complex pleiotropy, necessitating analytical approaches capable of dissecting shared genetic architectures across multiple cardiometabolic traits.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We employed genomic structural equation modeling (genomic SEM) to integrate summary statistics from six cardiometabolic traits. The model was assessed using standard fit indices. Genome\u2010wide association analyses were performed on 1,862,425 SNPs under stringent quality control measures, with LD Score regression applied to evaluate polygenic heritability and confounding bias. Novel loci were identified using GWAS\u2010by\u2010Subtraction. Functional characterization included transcriptome\u2010wide association analysis (TWAS), fine\u2010mapping, pathway enrichment analysis, and cell\u2010type specificity analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The genomic SEM model demonstrated excellent fit (CFI\u2009=\u20090.99, SRMR\u2009=\u20090.14). Quality control metrics confirmed that genomic inflation (Lambda GC\u2009=\u20091.591) was primarily attributable to polygenic heritability (h\n                      <jats:sup>2<\/jats:sup>\n                      =\u20090.3286 \u00b1\u20090.0135) rather than population stratification (Intercept\u2009=\u20091.0176 \u00b1\u20090.0169). This framework identified 2,212 significantly associated variants, encompassing 32 novel loci discovered via GWAS\u2010by\u2010Subtraction. Functional annotation indicated that the majority of these loci were located in intronic (57.92%) or intergenic (30.6%) regions. TWAS and fine\u2010mapping nominated 188 high\u2010confidence genes; SENP2 (TWAS Z\u2009=\u200912.5), KIF11 (Z\u2009=\u200911.5), and JAZF1 (Z\u2009=\u200911.38) exhibited the strongest positive associations with CKMs risk, whereas TCF7L2 (Z = \u221213.4) demonstrated the strongest inverse association. Pathway enrichment implicated lipid balance and ubiquitin\u2010mediated proteolysis, and cell\u2010type specificity analysis prominently localized genetic signals to pancreatic islet cells. Fine\u2010mapping prioritized 18 causal variants with posterior probability \u27e9 0.95, including key signals within GCKR (rs1260333, rs780093) and INADL (rs2481665). Chromosomal regions 7 and 12 exhibited significant contributions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our study unravels the shared genetic architecture of CKMs, revealing novel risk loci and pathogenic mechanisms. The results establish a direct cellular link between the pleiotropic genetic basis of CKMs and endocrine metabolic regulation within pancreatic islets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1753-0407.70225","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:30:17Z","timestamp":1776130217000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Genomic Structural Equation Modeling Reveals Cardiovascular\u2010Kidney\u2010Metabolic Syndrome Genetic Architecture"],"prefix":"10.1111","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2137-6853","authenticated-orcid":false,"given":"Chuanlong","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Vascular Surgery, The Second Hospital Shanxi Medical University  Taiyuan 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