{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T06:09:49Z","timestamp":1772431789060,"version":"3.50.1"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"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>Objective<\/jats:title>\n                    <jats:p>To investigate the interaction between circadian rhythm disorders and related gene polymorphisms (SNPs) in the risk of developing type 2 diabetes (T2DM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Cross-sectional study included 4,070 coal miners who underwent occupational health examinations between 2017 and 2018. We constructed comprehensive indicators of circadian rhythm disorder (CICRD) using factor analysis. In case-control analysis, 424 cases and 464 controls were randomly selected from 3,878 male coal miners. Logistic regression models were used to examine the relationship between CICRD, selected SNPs, and T2DM. Gene-gene and gene-environment interactions were evaluated using log-linear models and the generalized multifactor dimensionality reduction (GMDR) method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The CICRD captures 79.771% of seven circadian rhythm disorder assessment indicators. Higher CICRD and variants at rs10830963 (MTNR1B), rs7958822 (BMAL2), and rs11605924 (CRY2) were associated with an increased risk of T2DM (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05).A CICRD score \u2265 0.2782 with each high-risk SNP (rs10830963, rs1387153, rs7958822, rs11605924) significantly increased T2DM risk (P &amp;lt; 0.05).The five-factor interaction model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) based on the GMDR method significantly increased T2DM risk in the full dataset (P &amp;lt; 0.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The interaction between circadian rhythm disruption and high-risk SNP genotypes further amplifies the risk of T2DM among coal miners. Notably, the five-factor interaction model (rs10830963-rs7950226-rs7958822-rs11605924-CICRD) provides a standardized basis for assessing circadian rhythm disruption, screening high-risk populations, and identifying that high-risk genetic combinations are unsuitable for shift work, offering scientific evidence for the precision prevention of T2DM in coal miners.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1567465","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T05:21:27Z","timestamp":1772428887000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Association of circadian rhythms, MTNR1B, BMAL1, BMAL2, and CRY2 gene polymorphisms and their interactions with type 2 diabetes in coal miners"],"prefix":"10.3389","volume":"17","author":[{"given":"Qinglin","family":"Li","sequence":"first","affiliation":[{"name":"Department of Public Health, Tianjin Union 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