{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T12:14:34Z","timestamp":1754482474696,"version":"3.41.2"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T00:00:00Z","timestamp":1662595200000},"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><jats:title>Background<\/jats:title><jats:p>Understanding renal function state transition risk and associated factors in community residences is vital for appropriate preventive and care actions. We aim to investigate factors affecting renal function state transitions through 10-year longitudinal community screening surveys.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The prospective cohort study included participants who attended the screening program \u22652 times from 2001 to 2009 and were divided into two cohorts: those with baseline estimated glomerular filtration rate (eGFR) \u226560 (<jats:italic>n<\/jats:italic> = 46,278) and those with eGFR 59\u201330 mL\/min\/1.73 m<jats:sup>2<\/jats:sup> (<jats:italic>n<\/jats:italic> = 4,656). We applied the illness-death model to identify associated factors with eGFR &amp;lt;60 and death for the cohort with baseline eGFR \u226560 and eGFR &amp;lt;30 and death for that with baseline eGFR \u226559\u201330.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among the followed-up participants, 3,018 (6.5%) in the cohort of baseline eGFR \u226560 mL\/min\/1.73 m<jats:sup>2<\/jats:sup> and 322 (6.9%) in the cohort of eGFR 59\u201330 mL\/min\/1.73 m<jats:sup>2<\/jats:sup> experienced renal function state transition during a median over 7-year follow-up. Besides eGFR and grade of proteinuria, diabetes mellitus (adding nearly 50% hazard rate) is the main factor associated with both state transitions. Other early-phase eGFR state transition risk factors were metabolic syndrome score, triglyceride, uric acid, fasting blood sugar, and high-density lipoprotein cholesterol. Males, poor hemoglobin, high triglyceride, and high low-density lipoprotein cholesterol were all linked with the late-phase eGFR state transition hazard rate.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The study developed the state transition functions for community participants with varying renal function levels. Further actions to develop precision screening plans and services that incorporate personal risk factors and state transition risks are necessary.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2022.930798","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T07:18:56Z","timestamp":1662621536000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Factors associated with renal function state transitions: A population-based community survey in Taiwan"],"prefix":"10.3389","volume":"10","author":[{"given":"Ming-Hsien","family":"Tsai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming-Yen","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen-Yang","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amy 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