{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:49:33Z","timestamp":1782452973207,"version":"3.54.5"},"reference-count":57,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Lower educational attainment is associated with obesity and type 2 diabetes (T2D), but prospective evidence, external validation, mediator patterns, and genetic triangulation have rarely been integrated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>UK Biobank analyses included 501,932 participants at baseline and 474,659 participants without baseline diabetes prospectively. Logistic and Cox models estimated associations of higher educational attainment with prevalent and incident diabetes. T2D pathway analyses evaluated adiposity, health behaviors, and cardiometabolic biomarkers. NHANES 2011\u20132018 provided weighted cross-sectional validation. Public GWAS summary statistics were used for genetic correlation, Mendelian randomization (MR), tissue enrichment, candidate-gene analyses, shared-locus analyses, and S-PrediXcan transcriptome-wide association analyses (TWAS).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In fully adjusted UK Biobank models, higher educational attainment was associated with lower odds of prevalent T2D (OR 0.76, 95% CI 0.72-0.79) and lower risk of incident T2D (HR 0.70, 95% CI 0.68-0.72). Detailed qualification categories showed lower prevalent T2D odds for college\/university degree versus no qualifications (OR 0.69, 95% CI 0.65-0.72). In NHANES, college graduation or above was associated with lower prevalent T2D odds (OR 0.69, 95% CI 0.61-0.78; n=20,502). Adiposity, smoking, alcohol use, lipids, blood pressure, and C-reactive protein attenuated the education-T2D association. Genetically predicted educational attainment was inversely associated with BMI and T2D, and BMI\/T2D brain TWAS identified 19 shared multi-SNP gene signals, including NPC1, HSD17B12, MAP2K5, DHX36, BHMT, and LEPROT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Higher educational attainment was consistently associated with lower T2D risk across UK Biobank, NHANES, and genetic analyses. The results highlight modifiable metabolic and behavioral pathways relevant to T2D prevention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1867159","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:43:56Z","timestamp":1782452636000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Educational attainment and diabetes risk: triangulation evidence from UK Biobank prospective cohort, NHANES 2011-2018, and cross-trait genomics analyses"],"prefix":"10.3389","volume":"17","author":[{"given":"Guannan","family":"Geng","sequence":"first","affiliation":[{"name":"Department of Endocrinology, The First Affiliated Hospital of Harbin Medical University","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shizheng","family":"Qiu","sequence":"additional","affiliation":[{"name":"Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology","place":["Harbin, China"]},{"name":"Key Laboratory of Biological Bigdata, Ministry of Education, Harbin Institute of Technology","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhishuai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology","place":["Harbin, China"]},{"name":"Key Laboratory of Biological Bigdata, Ministry of Education, Harbin Institute of Technology","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinru","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Stomatology, The Fourth Hospital of Harbin Medical University","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, The First Affiliated Hospital of Harbin 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