{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T19:29:24Z","timestamp":1789154964593,"version":"build-2803163510"},"reference-count":36,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Public Health Emergency Project Nutrition Health and Healthy Diet Campaign","award":["102393220020070000012"],"award-info":[{"award-number":["102393220020070000012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>Background: Remnant cholesterol (RC) is highly associated with several chronic diseases. However, the relationship between RC and Metabolic syndrome (MetS) remains unclear. The study\u2019s objective is to illustrate the relationship of RC to MetS. Methods: The data were collected from the Chinese Nutrition and Health Surveillance (2015\u20132017), which included personal, household and dietary information. A total of 65,618 residents aged 20 years or older from 31 provinces in mainland China were included in this study. RC was calculated by the equation RC = TC \u2212 (LDL-C + HDL-C). The criteria for MetS were based on the 2020 Chinese Type 2 Diabetes Prevention and Treatment Guidelines. Logistic regression models were used to analyse the relationship between RC and MetS and every MetS component. The receiver operating characteristic (ROC) curve was established to evaluate the accuracy of RC in identifying MetS, and the area under the curve (AUC) and the best threshold were calculated. Results: The weighted RC level of Chinese residents aged 20 years or older was 0.48 mmol\/L. Participants with high RC levels were likely to be elderly, have a higher prevalence of MetS, higher total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FBG), hba1c, and lower high-density lipoprotein cholesterol (HDL-C). Sex, body mass index (BMI), education status, household yearly income per capita, marital status, area of the country, residence location, smoking status, fruit intake and sleep time had statistical differences in the RC group (p &lt; 0.05). The OR of MetS gradually increased with an increase in the RC quartile (p &lt; 0.01), and higher quartiles of RC (Q4) suggested the highest MetS risk. The prevalence of each MetS component gradually increased with an increase in the RC quartile. The ROC curve found that to identify MetS, the AUC and best threshold of RC were 0.71 and 0.52 mmol\/L, respectively. Conclusions: RC had a positive association with MetS and each MetS component. The accuracy in identifying MetS was higher in RC than in other indexes. The current study could provide new scientific evidence for the early prevention and control of MetS.<\/jats:p>","DOI":"10.3390\/nu16193275","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T11:19:33Z","timestamp":1727435973000},"page":"3275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Association between Remnant Cholesterol and Metabolic Syndrome among Chinese Adults: Chinese Nutrition and Health Surveillance (2015\u20132017)"],"prefix":"10.3390","volume":"16","author":[{"given":"Fusheng","family":"Li","sequence":"first","affiliation":[{"name":"National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongtao","family":"Yuan","sequence":"additional","affiliation":[{"name":"National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuya","family":"Cai","sequence":"additional","affiliation":[{"name":"National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Piao","sequence":"additional","affiliation":[{"name":"National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China"},{"name":"NHC Key Laboratory of Public Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Nan","sequence":"additional","affiliation":[{"name":"National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxiang","family":"Yang","sequence":"additional","affiliation":[{"name":"National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyun","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China"},{"name":"NHC Key Laboratory of Public Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongmei","family":"Yu","sequence":"additional","affiliation":[{"name":"National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China"},{"name":"NHC Key Laboratory of Public Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"ref_1","unstructured":"(2021). 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