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However, biomarker-based CKM staging is difficult to implement in large population surveys where laboratory data are unavailable. We aimed to develop a self-report\u2013based Metabolic Multimorbidity Burden Index (MMBI) for quantifying survey-accessible CKM-related metabolic multimorbidity burden, evaluate its internal anchor-aligned performance, and examine whether combining MMBI with body mass index (BMI) helps characterize self-rated health (SRH) and health-related quality of life (HRQoL) risk heterogeneity among US adults. We used pooled 2017 and 2019 Behavioral Risk Factor Surveillance System (BRFSS) data from 677,784 US adults. MMBI was constructed using an outcome-anchored, survey-weighted logistic model based on five self-reported CKM-related conditions: diabetes, hypertension, hypercholesterolemia, chronic kidney disease, and cardiovascular disease. Poor SRH was used as the anchoring outcome. BMI categories were cross-classified with weighted MMBI tertiles to create a 3\u2009\u00d7\u20093 joint-exposure matrix. The primary outcome was poor SRH; secondary HRQoL outcomes were frequent physical distress and frequent mental distress. In the held-out test set, the anchor-derived MMBI showed stable internal performance for poor SRH, with a survey-weighted area under the receiver operating characteristic curve (AUC) of 0.815 and good calibration. The joint-exposure analysis revealed risk heterogeneity not captured by BMI alone. Compared with adults with normal weight and Low MMBI, adults with normal weight and High MMBI had markedly higher odds of poor SRH (odds ratio [OR], 4.19; 95% confidence interval [CI], 3.89\u20134.51), exceeding the estimate observed for adults with obesity but Low MMBI (OR, 1.76; 95% CI, 1.63\u20131.90). Adults with both obesity and High MMBI had the highest odds of poor SRH (OR, 6.06; 95% CI, 5.69\u20136.47). Statistical interaction between BMI and MMBI was observed. MMBI is a scalable, self-report\u2013based measure of CKM-related metabolic multimorbidity burden for population-surveillance settings where laboratory biomarkers are unavailable. Integrating MMBI with BMI may help characterize HRQoL-oriented risk heterogeneity. Given its reliance on poor SRH anchoring and self-reported diagnosed conditions, MMBI should be interpreted as a survey-based burden measure for population surveillance and hypothesis generation.<\/jats:p>","DOI":"10.1038\/s41598-026-57886-4","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T11:15:12Z","timestamp":1783422912000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint burden of obesity and CKM-related metabolic multimorbidity in US adults: development of a survey-based MMBI"],"prefix":"10.1038","volume":"16","author":[{"given":"Xiaoqing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiufang","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"57886_CR1","doi-asserted-by":"publisher","first-page":"4063","DOI":"10.1093\/eurheartj\/ehae508","volume":"45","author":"KC Koskinas","year":"2024","unstructured":"Koskinas, K. 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