Distinct metabolic syndrome profiles across Asian American subpopulations

Metabolic Syndrome (MetS) is a cluster of conditions increasing risks for cardiovascular diseases. Existing research on Asian Americans has been limited by aggregate data reporting, which masks critical intra-group health variations. In this study, we conducted a comprehensive disaggregated analysis of MetS prevalence across diverse Asian American ethnic groups, revealing nuanced health disparities that challenge monolithic health narratives. We analyzed National Health and Nutrition Examination Survey data from 2011 to 2016. The Analysis sample consisted of five Asian American ethnic groups: Chinese (n = 652), Asian Indian (n = 409), Filipino (n = 262), Vietnamese (n = 243), and Korean (n = 215). Logistic regression assessed associations between MetS and demographic factors. Significant variations in MetS prevalence emerged across subpopulations and sexes. For example, among women with BMI < 23, Filipino women showed significantly higher MetS prevalence (31.70%) compared to Chinese women (14.45%). Among men with BMI 23-27.4, Asian Indian men exhibited higher prevalence (50.80%) than Vietnamese men (22.66%). Our findings also shed light on the nuanced associations between modifiable lifestyle factors and MetS risks in these Asian subpopulations. This study underscores the critical importance of data disaggregation in understanding health disparities. The findings support developing targeted interventions that address the unique metabolic health profiles within Asian American communities.

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Publication Details

Journal
Scientific Reports
Published
2025-08-12
DOI
https://doi.org/10.1038/s41598-025-15183-6
Citations
1
Primary Topic
Diabetes, Cardiovascular Risks, and Lipoproteins
Type
article
Field-Weighted Citation Impact
1.29

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article

Distinct metabolic syndrome profiles across Asian American subpopulations

X. Grace, Lin Zhu, Aisha Bhimla, Loretta Hsueh et al.
1 citations
Scientific Reports
Diabetes, Cardiovascular Risks, and Lipoproteins
1.29
article

Distinct metabolic syndrome profiles across Asian American subpopulations

X. Grace, Lin Zhu, Aisha Bhimla, Loretta Hsueh, Allison E. Mann
article en
1 citations

Abstract

Metabolic Syndrome (MetS) is a cluster of conditions increasing risks for cardiovascular diseases. Existing research on Asian Americans has been limited by aggregate data reporting, which masks critical intra-group health variations. In this study, we conducted a comprehensive disaggregated analysis of MetS prevalence across diverse Asian American ethnic groups, revealing nuanced health disparities that challenge monolithic health narratives. We analyzed National Health and Nutrition Examination Survey data from 2011 to 2016. The Analysis sample consisted of five Asian American ethnic groups: Chinese (n = 652), Asian Indian (n = 409), Filipino (n = 262), Vietnamese (n = 243), and Korean (n = 215). Logistic regression assessed associations between MetS and demographic factors. Significant variations in MetS prevalence emerged across subpopulations and sexes. For example, among women with BMI < 23, Filipino women showed significantly higher MetS prevalence (31.70%) compared to Chinese women (14.45%). Among men with BMI 23-27.4, Asian Indian men exhibited higher prevalence (50.80%) than Vietnamese men (22.66%). Our findings also shed light on the nuanced associations between modifiable lifestyle factors and MetS risks in these Asian subpopulations. This study underscores the critical importance of data disaggregation in understanding health disparities. The findings support developing targeted interventions that address the unique metabolic health profiles within Asian American communities.

Scientific ReportsVol. 15(1)
University of Illinois Chicago (US), Temple University (US)
American Heart Association, National Cancer Institute
Openalex Percentile: Top 16%
Diabetes, Cardiovascular Risks, and Lipoproteins
1.29
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Distinct metabolic syndrome profiles across Asian American subpopulations — X. Grace, Lin Zhu, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS