{"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.
背景:与代谢功能障碍相关的脂肪变性肝病(MASLD)相关的心脏代谢危险因素(CMRFs)包括超重/肥胖、糖代谢受损、高血压、高甘油三酯血症和低高密度脂蛋白胆固醇。我们的目的是描述5种CMRFs在MASLD和肝纤维化中的重叠患病率和协同相互作用。方法:使用2017-2020年全国健康与营养调查数据,我们纳入了年龄≥20岁的非怀孕参与者,他们完成了振动控制的瞬态弹性成像检查,并有足够的信息来确定其代谢状态。采用Logistic和广义线性回归模型来评估CMRFs对MASLD的协同相互作用,并确定对肝纤维化的贡献。结果:MASLD的总体患病率约为33.1%。超过80%的患者有三次或更多的CMRFs。对于MASLD,超重/肥胖对与其他4种CMRFs对之间的协同相互作用高于其他CMRFs对之间的协同相互作用[归因比例(AP): 40-50% vs 20-30%]。对于肝纤维化,超重/肥胖和糖代谢受损或高血压具有显著的协同作用(AP分别为50%或30%)。我们确定了31种可能的CMRF组合中的27种。包括血脂异常的组合在男性中比女性更常见(77%对59%)。包括高血压的组合在墨西哥裔美国人中少于其他种族(25% vs 45-57%)。大多数合并三个或更多cmrf的组合,无论重叠类型如何,都与肝脏硬度值升高有显著关联。结论:CMRF重叠在MASLD患者中相当普遍,并具有叠加性相互作用。在肝纤维化的发展过程中,重叠数可能比组合型更重要。
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.