Comparative Analysis of Metabolic Syndrome Diagnostic Criteria and Its Effects on Prevalence in a Multiethnic Population
Open accessAbstract
Background: Accurate prevalence measurement and diagnosis to prevent type 2 diabetes mellitus and cardiovascular disease cannot occur without consistent diagnostic criteria that can be applied to varying populations. Objective: The objective of this study was to determine the prevalence of metabolic syndrome in Caucasian, Filipino, Native Hawaiian, and Japanese populations utilizing different definitions. Methods: This study utilized cross-sectional study data from the Native Hawaiian/Multiethnic Health Research Project, collected from a population living in Kohala, Hawai‘i. The National Cholesterol Education Program–Adult Treatment Panel III (NCEP-ATPII), International Diabetes Federation (IDF), and World Health Organization (WHO) definitions were utilized, and each of the 1452 participants were evaluated on the criteria for metabolic syndrome based on all three definitions. Additionally, the average biomarker values associated with the diagnosis were taken for each ethnic group represented in the study and compared with Caucasians. Results: The overall prevalence of metabolic syndrome in this population varied from 22.31% to 39.05% using the different definitions. Ethnic disparities also occur, implying that certain populations are more prone to having severe abnormalities than others—shown when comparing the average biomarker values associated with metabolic syndrome diagnosis. Of all ethnic groups included in the study, Caucasians had the lowest prevalence of metabolic syndrome, while part-Hawaiians had the highest prevalence. Additionally, within the same ethnic group, the definitions yielded varying prevalence values. Conclusions: This implies that discrepancies exist among the criteria alone. Implications of this study revolve around not only the correct definition to apply to the population being studied but also the most accurate way to detect certain biomarker abnormalities to accurately assess the prevalence of metabolic syndrome in a multiethnic population.
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The discrepancy in external validation underscores the potential influence of region- and population-specific factors on model transferability.
“This discrepancy underscores the potential influence of region- and population-specific factors on model transferability, such as differences in dietary patterns, genetic susceptibility, and variations in metabolic phenotypes [38–41].”
From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [39]
The Discussion (page unavailable) says that differences between the China and Kohala findings may reflect the populations studied, and the Results report that diagnostic criteria performed differently by ethnicity. This supports the general relevance of population-specific factors, but the paper does not assess external validation or predictive-model transferability, or establish dietary patterns or genetic susceptibility as explanations. (LLM, conf=0.85)
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A comprehensive review on metabolic syndrome
Kaur, J. (2014) · Cardiology Research and Practice · doi:10.1155/2014/943162 · Cited by 1,889 · see citing works
RetractedAlberti, K. G. M. M., Eckel, R. H., Grundy, S. M., Zimmet, P. Z., Cleeman, J. I., Donato, K. A., Fruchart, J.-C., James, W. P. T., Loria, C. M., & Smith, S. C. (2009) · Circulation · doi:10.1161/circulationaha.109.192644 · Cited by 14,860 · see citing works
Grundy, S. M., Cleeman, J. I., Daniels, S. R., Donato, K. A., Eckel, R. H., Franklin, B. A., Gordon, D. J., Krauss, R. M., Savage, P. J., Smith, S. C., et al. (2005) · Circulation · doi:10.1161/circulationaha.105.169404 · Cited by 11,961 · see citing works
Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies
O’Neill, S., & O’Driscoll, L. (2014) · Obesity Reviews · doi:10.1111/obr.12229 · Cited by 1,550 · see citing works
Pathophysiology of the metabolic syndrome
McCracken, E., Monaghan, M., & Sreenivasan, S. (2017) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2017.09.004 · Cited by 730 · see citing works
A clinical perspective of obesity, metabolic syndrome and cardiovascular disease
Han, T. S., & Lean, M. E. (2016) · JRSM Cardiovascular Disease · doi:10.1177/2048004016633371 · Cited by 500 · see citing works
Asian Americans have greater prevalence of metabolic syndrome despite lower body mass index
Palaniappan, L. P., Wong, E. C., Shin, J., Fortmann, S. P., & Lauderdale, D. S. (2010) · International Journal of Obesity · doi:10.1038/ijo.2010.152 · Cited by 238 · see citing works
The evolving definitions and increasing prevalence of the metabolic syndrome.
Desroches, S., & Lamarche, B. (2007) · Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme · doi:10.1139/h06-095 · Cited by 183 · see citing works
Gurka, M. J., Filipp, S. L., & DeBoer, M. D. (2018) · Nutrition and Diabetes · doi:10.1038/s41387-018-0024-2 · Cited by 144 · see citing works
Kaholokula, J. K., Haynes, S. N., Grandinetti, A., & Chang, H. K. (2003) · Journal of Behavioral Medicine · doi:10.1023/a:1025772001665 · Cited by 60 · see citing works
Self-citationMetabolic syndrome in a multiethnic population in rural Hawaii.
Grandinetti, A., Chang, H. K., Theriault, A., & Mor, J. M. (2005) · PubMed · Cited by 50 · see citing works
Self-citationCheng, L., Yan, W., Zhu, L., Chen, Y., Liu, J., Xu, Y. J., Ji, L., & He, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0189046 · Cited by 19 · see citing works
Alberti (1998) · doi:10.1002/(sici)1096-9136(199807)15:7
Flagged first, then by citations
Research works printed but not linked: 2
- [6]6. Grundy SM, Becker D, Clark LT, et al. Detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). Circulation 2002;106:3143-3421.
- [11]11. Alberti KGMM, Aschner P, Assal JP, et al. Definition, diagnosis, and classification of diabetes mellitus and its complications. Diabet Med 1998;15:1-59.
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Sorted by citations
Huang, Y., Chen, Z., Wang, X., Zheng, C., Shao, L., Tian, Y., Cao, X., Tian, Y., Gao, R., Zhang, L., et al. (2022) · Metabolites · doi:10.3390/metabo13010012 · Cited by 18 · see citing works
San-Cristóbal, R., de Toro‐Martín, J., & Vohl, M. (2022) · Current Nutrition Reports · doi:10.1007/s13668-022-00430-3 · Cited by 16 · see citing works
Alrabbaie, H., Al-Wardat, M. S., Etoom, M., Beauchamp, M. K., Goldstein, R. S., & Brooks, D. (2025) · Chronic Respiratory Disease · doi:10.1177/14799731251346194 · Cited by 10 · see citing works
He, Y., Chen, Z., Tang, Z., Qin, Y., & Wang, F. (2025) · BMC Public Health · doi:10.1186/s12889-025-23858-y · Cited by 5 · see citing works
Williams, J., Rogers, K., Southworth, J., Hum, R. M., Ho, P., & Zhao, S. S. (2025) · Rheumatology International · doi:10.1007/s00296-025-05970-9 · Cited by 4 · see citing works
Vasylenko, O. O., & Tykhonova, T. M. (2022) · Actual problems of modern medicine · doi:10.26565/2617-409x-2022-9-08 · Cited by 4 · see citing works
López‐González, Á. A., Rifá, E. M.-A., Paublini, H., Sánchez, C. M., López, P. J. T., & Ramírez‐Manent, J. I. (2025) · Medicina de Familia SEMERGEN · doi:10.1016/j.semerg.2025.102455 · Cited by 2 · see citing works
Rowan, K. (2019) · Europe PMC (PubMed Central) · Cited by 1 · see citing works
(2022) · Actual problems of modern medicine · doi:10.26565/2617-409x-2022-9
谢, 思., Liu, H., Liu, Y., Xu, C., Zhang, T., Wang, Q., Li, J., Huang, Z., Li, S., Hao, M., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3195875/v1
Chen, N., Hu, R., Wu, J., Ma, W., Cai, Y., Yue, N., Yu, Y., Zhang, X., & Hu, Q. (2026) · Medicine · doi:10.1097/md.0000000000047012
- Authors
- Asato, C. B. H., Nelson-Hurwitz, D. C., Lee, T., & Grandinetti, A.
- Affiliations
- University of Hawaiʻi at Mānoa (US)
- Corresponding author
- Asato, Chloe B.H.
- Venue
- Metabolic Syndrome and Related Disorders
- Publisher
- SAGE Publications
- Published
- 2021-08-01
- Volume
- 19
- Issue
- 6
- Pages
- 347-351
- ISSN
- 1540-4196 (ISSN-L)
- 1557-8518
- PMC
- PMC8380796
- OpenAlex
- W3134211635
- Cited by
- 11
- References
- 14
- Citations by year
- 2026: 1 · 2025: 4 · 2023: 1 · 2022: 4 · 2019: 1
- FWCI
- 0.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Metabolic Syndrome and Related Disorders (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Center for Research Resources (G12RR003061)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Health Professions › General Health Professions › Indigenous Health and Education
- Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
- Keywords
- metabolic syndrome, diagnostic criteria, NCEP ATP III criteria, IDF criteria, WHO criteria, prevalence, ethnic disparities, multi-ethnic population, Native Hawaiians, Filipinos, Japanese Americans, Caucasians, biomarkers, type 2 diabetes prevention, cardiovascular risk
- MeSH terms
- Adult, Cross-Sectional Studies, Ethnicity, Humans, Prevalence, Cultural Diversity, Metabolic Syndrome
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