ArticleObesity (Silver Spring, Md.)2024
Heterogeneity in familial clustering of metabolic syndrome components in the multiethnic GENNID study.
Article in Obesity (Silver Spring, Md.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Can Polygenic Risk Scores for Metabolic Syndrome Contribute to Long-Term Syndrome Development in First-Episode Psychosis Patients? A Naturalistic 21-Year Follow-Up Study.Schizophrenia bulletin · 2026Article
- Quantifying Metabolic Syndrome Severity: Methodological Evolution, Clinical Validation, and Translational Perspectives.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
- Metabotype Risk Clustering Based on Metabolic Disease Biomarkers and Its Association with Metabolic Syndrome in Korean Adults: Findings from the 2016-2023 Korea National Health and Nutrition Examination Survey (KNHANES).Diseases (Basel, Switzerland) · 2025Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveMetabolic syndrome (MetS) is defined by clustering of cardiometabolic components, which may be present in different combinations. The authors evaluated clustering in individuals and extended families within and across ancestry groups.
methodsThe prevalence of different combinations of MetS components (high fasting glucose, low high-density lipoprotein cholesterol, high triglycerides, high blood pressure, and abdominal obesity) was estimated in 1651 individuals (340 families) self-reporting as European American (EA), Hispanic/Mexican American (MA), African American (AA), and Japanese American (JA). Odds ratios were estimated using logistic regression with generalized estimating equations comparing individual MetS components, number, and combinations of components for each ancestry group versus EA.
resultsClustering of all five components (Combination #16) was more prevalent in EA (29.9%) and MA (25.2%) individuals than in AA (18.7%) and JA (15.5%) individuals. Compared with EA individuals, AA individuals were 64% and 66% less likely to have high triglycerides and low high-density lipoprotein cholesterol, whereas JA individuals were 85% and 56% less likely to have abdominal obesity and high blood pressure, respectively. Compared with EA individuals, the odds of having two, four, or five components were at least 77% lower in JA individuals, whereas the odds of having three, four, or five components were at least 3.79 times greater in MA individuals.
conclusionsUnderstanding heterogeneity in MetS clustering may identify factors important in reducing health disparities.
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