{"title":"Overlap prevalence and interaction effect of cardiometabolic risk factors for metabolic dysfunction-associated steatotic liver disease.","authors":"Dongying Zhao, Xiaoyan Zheng, Liwei Wang, Yujie Xie, Yan Chen, Yongjun Zhang","doi":"10.1186/s12986-025-00903-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Cardiometabolic risk factors (CMRFs) related to metabolic dysfunction-associated steatotic liver disease (MASLD) comprised overweight/obesity, impaired glucose metabolism, hypertension, hypertriglyceridemia and low high-density lipoprotein cholesterol. We aimed to describe the overlap prevalence and synergistic interaction of the five CMRFs on MASLD and liver fibrosis.</p><p><strong>Methods: </strong>Using data of 2017-2020 National Health and Nutrition Examination Survey, we included non-pregnant participants aged ≥ 20 years who completed vibration-controlled transient elastography examinations and had sufficient information to determine their metabolic status. Logistic and generalized linear regression models were performed to assess synergistic interaction between CMRFs on MASLD and identify the contributions to liver fibrosis.</p><p><strong>Results: </strong>The overall estimated prevalence of MASLD was about 33.1%. More than 80% of patients had three or more CMRFs. For MASLD, synergistic interaction between pairs of overweight/obesity and other four CMRFs were higher than it between other CMRFs' pairs [attributable proportion(AP): 40-50% vs 20-30%]. For liver fibrosis, overweight/obesity and impaired glucose metabolism or hypertension had significant synergistic interactions (AP: 50% or 30%, respectively). We identified 27 out of 31 possible CMRF combinations. Combinations including dyslipidemia were more frequent in men than women (77% vs 59%). Combinations including hypertension were less in Mexican Americans than other ethnicities (25% vs 45-57%). Most combinations with three or more CMRFs, regardless of overlap type, had significant associations with elevated liver stiffness value.</p><p><strong>Conclusions: </strong>CMRF overlap was quite common and had additive interaction in patients with MASLD. Overlapping number may be more important than combination type in liver fibrosis development.</p>","PeriodicalId":19196,"journal":{"name":"Nutrition & Metabolism","volume":"22 1","pages":"10"},"PeriodicalIF":3.9000,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11817221/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nutrition & Metabolism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12986-025-00903-6","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Cardiometabolic risk factors (CMRFs) related to metabolic dysfunction-associated steatotic liver disease (MASLD) comprised overweight/obesity, impaired glucose metabolism, hypertension, hypertriglyceridemia and low high-density lipoprotein cholesterol. We aimed to describe the overlap prevalence and synergistic interaction of the five CMRFs on MASLD and liver fibrosis.
Methods: Using data of 2017-2020 National Health and Nutrition Examination Survey, we included non-pregnant participants aged ≥ 20 years who completed vibration-controlled transient elastography examinations and had sufficient information to determine their metabolic status. Logistic and generalized linear regression models were performed to assess synergistic interaction between CMRFs on MASLD and identify the contributions to liver fibrosis.
Results: The overall estimated prevalence of MASLD was about 33.1%. More than 80% of patients had three or more CMRFs. For MASLD, synergistic interaction between pairs of overweight/obesity and other four CMRFs were higher than it between other CMRFs' pairs [attributable proportion(AP): 40-50% vs 20-30%]. For liver fibrosis, overweight/obesity and impaired glucose metabolism or hypertension had significant synergistic interactions (AP: 50% or 30%, respectively). We identified 27 out of 31 possible CMRF combinations. Combinations including dyslipidemia were more frequent in men than women (77% vs 59%). Combinations including hypertension were less in Mexican Americans than other ethnicities (25% vs 45-57%). Most combinations with three or more CMRFs, regardless of overlap type, had significant associations with elevated liver stiffness value.
Conclusions: CMRF overlap was quite common and had additive interaction in patients with MASLD. Overlapping number may be more important than combination type in liver fibrosis development.
期刊介绍:
Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects.
The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases.
Key areas we wish to encourage submissions from include:
-how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes;
-the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components;
-how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved;
-how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.