{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T04:31:00Z","timestamp":1792816260919,"version":"4.5.0"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cardiovascular-kidney-metabolic (CKM) syndrome has emerged a major strain on healthcare systems worldwide, with marked disparities in incidence across different regions, which are partly attributed to social determinants of health (SDOH). While research has touched upon the link between SDOH and CKM, and isolated SDOH factor\u2019s influence on cardiovascular health has been probed in China, evaluation of how SDOH collectively affect CKM in Chinese population remains underexplored in a systematic fashion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The study drew on Shanghai community population (STONE cohort,\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f4,066) recruited via a three-phase stratified sampling scheme. Standardized interviews captured education, marital status, employment, household income, and neighborhood characteristics as SDOH exposures. CKM stages 0\u20134 were assigned using American Heart Association (AHA) criteria. Multinomial logistic regression quantified SDOH\u2013CKM relationships, with age-, sex-, and lifestyle-stratified analyses to pinpoint vulnerable subgroups.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Unfavorable SDOH profiles, including limited schooling (OR\u202f=\u202f8.18; 95% CI: 6.16\u201310.86), unemployment or retirement (OR\u202f=\u202f11.38; 95% CI: 8.59\u201315.08), and low income (OR\u202f=\u202f2.24; 95% CI: 1.78\u20132.81) were robustly linked to CKM advancement independent of classic metabolic risk factors. The effect was present only among adults aged 36\u201350\u202fyears (OR\u202f=\u202f2.45; 95% CI: 1.37\u20134.40). Women clear of alcohol displayed elevated odds with advanced CKM stages, whereas men did not show significant association with CKM progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Unfavorable SDOH, especially low education and unemployed or retired, and limited income, constitute independent determinants of CKM progression. Embedding SDOH metrics into routine CKM surveillance and designing targeted interventions for mid-life adults and women could transform primary prevention strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2025.1676833","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T06:41:07Z","timestamp":1765348867000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Low education and employment status drive cardiometabolic health: a social determinants perspective in Chinese 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