{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T04:16:13Z","timestamp":1789186573651,"version":"build-2803163510"},"reference-count":43,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["No.2020JDRC0022"],"award-info":[{"award-number":["No.2020JDRC0022"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The strong association between obesity and chronic kidney disease (CKD) has been empirically validated, yet traditional measures like the Body Mass Index (BMI) fail to accurately assess the extent of obesity due to CKD\u2019s characteristics, such as reduced muscle mass and increased visceral fat. This study investigates the association between CKD and several anthropometric indices, including A Body Shape Index (ABSI), Body Roundness Index (BRI), Waist-to-Height Ratio (WHtR), and the Conicity Index (C-index), to determine their predictive capabilities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Based on the datasets from the National Health and Nutrition Examination Survey (NHANES) 2009\u20132018, weighted multivariable regression analyses were carried out to examine the independent relationship between two anthropometric indices and CKD. Also, subgroup analyses, restricted cubic spline regression (RCS), and receiver operating characteristic curve analysis were conducted for further data analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 24,162 participants were enrolled in this study. After adjusting for confounding factors, ABSI, BRI, WHtR, and the C-index were significantly associated with an increased risk of CKD, while BMI was not. Height showed a protective effect against CKD. ABSI and the C-index demonstrated the highest areas under the curve (AUCs), indicating superior predictive capabilities compared to traditional measures like BMI and waist circumference (WC). Subgroup analyses revealed significant interactions between the anthropometric indices and factors such as age, disease status, dietary intake, and physical activity levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study highlights the significant associations between various anthropometric indices (including ABSI, BRI, WHtR, and C-index) and the risk of CKD. ABSI and the C-index demonstrated the strongest predictive capabilities for CKD, with the highest AUC values.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0311547","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T13:29:07Z","timestamp":1739539747000},"page":"e0311547","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":18,"title":["Association between anthropometric indices and chronic kidney disease: Insights from NHANES 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