{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T21:14:11Z","timestamp":1789593251804,"version":"build-2803163510"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"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><jats:title>Objective<\/jats:title><jats:p>Metabolic dysfunction-associated fatty liver disease (MAFLD) affects 25% of the population without approved drug therapy. According to the latest consensus, MAFLD is divided into three subgroups based on different diagnostic modalities, including Obesity, Lean, and Type 2 diabetes mellitus (T<jats:sub>2<\/jats:sub>DM) MAFLD subgroups. This study aimed to find out the optimum non-invasive metabolism-related indicators to respectively predict MAFLD and its subgroups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Design<\/jats:title><jats:p>1058 Chinese participants were enrolled in this study. Anthropometric measurements, laboratory data, and ultrasonography features were collected. 22 metabolism-related indexes were calculated, including fatty liver index (FLI), lipid accumulation product (LAP), waist circumference-triglyceride index (WTI), etc. Logistic regression analyzed the correlation between indexes and MAFLD. Receiver operating characteristics were conducted to compare predictive values among 22 indicators for screening the best indicators to predict MAFLD in different subgroups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>FLI was the best predictor with the maximum odds ratio (OR) values of overall MAFLD (OR: 6.712, 95%CI: 4.766-9.452, area under the curve (AUC): 0.879, <jats:italic>P &amp;lt;<\/jats:italic> 0.05) and T<jats:sub>2<\/jats:sub>DM MAFLD subgroup (OR: 14.725, 95%CI: 3.712-58.420, AUC: 0.958, <jats:italic>P &amp;lt;<\/jats:italic> 0.05). LAP was the best predictor with the maximum OR value of Obesity MAFLD subgroup (OR: 2.689, 95%CI: 2.182-3.313, AUC: 0.796, <jats:italic>P &amp;lt;<\/jats:italic> 0.05). WTI was the best predictor with the maximum OR values of Lean MAFLD subgroup (OR: 3.512, 95%CI: 2.286-5.395, AUC: 0.920, <jats:italic>P &amp;lt;<\/jats:italic> 0.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The best predictors of overall MAFLD, Obesity, Lean, and T<jats:sub>2<\/jats:sub>DM MAFLD subgroups were respectively FLI, LAP, WTI, and FLI.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2022.1035418","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T07:54:47Z","timestamp":1669881287000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Optimum non-invasive predictive indicators for metabolic dysfunction-associated fatty liver disease and its subgroups in the Chinese population: A retrospective case-control 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