ArticlePediatric research2021
Alternative pediatric metabolic syndrome definitions impact prevalence estimates and socioeconomic gradients.
Article in Pediatric research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- ObMetrics: A Shiny app to assist in metabolic syndrome assessment in paediatric obesity.Pediatric obesity · 2025Observational
- Health inequity in metabolic disorders and lifestyle behaviors in under-resourced adolescents: a cross-sectional study from E-DUCASS Program.American journal of preventive cardiology · 2025Article
- Metabolic syndrome therapy in pediatric age - between classic and modern. From diets to pipeline drugs.Frontiers in nutrition · 2024Review
- Prevalence of metabolic syndrome in adolescents based on three diagnostic definitions: a cross-sectional study.Archives of endocrinology and metabolism · 2023Article
- Socioeconomic inequalities in paediatric metabolic syndrome: mediation by parental health literacy.European journal of public health · 2023Article
- Comparison of the ability of HOMA-IR, VAI, and TyG indexes to predict metabolic syndrome in children with obesity: a cross-sectional study.BMC pediatrics · 2023Article
- Metabolic Syndrome Prevalence among High School First-Year Students: A Cross-Sectional Study in Taiwan.Nutrients · 2022Article
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Abstract
backgroundThere is no consensus regarding the definition of pediatric metabolic syndrome (MetS). This study assessed the impact of alternative definitions on the prevalence, children identified, and association with socioeconomic status (SES).
methodsData were from the prospective multigenerational Dutch Lifelines Cohort Study. At baseline, 9754 children participated, and 5085 (52.1%) with average follow-up of 3.0 (SD = 0.75) years were included in the longitudinal analyses; median ages were 12 (IQR = 10-14) and 14 years (IQR = 12-15), respectively. We computed MetS prevalence according to five published definitions and measured the observed proportion of positive agreement. We used logistic regression to assess the SES-MetS association, adjusted for age and sex. Longitudinal models were also adjusted for baseline MetS.
resultsMetS prevalence and positive agreement varied between definitions, from 0.7 to 3.0% and from 0.34 (95% CI: 0.28; 0.41) to 0.66 (95% CI: 0.58; 0.75) at baseline, respectively. We consistently found a socioeconomic gradient; in the longitudinal analyses, each additional year of parental education reduced the odds of having MetS by 8% (95% CI: 1%; 14%) to 19% (95% CI: 7%; 30%).
conclusionsAlternative MetS definitions had differing prevalence estimates and agreed on 50% of the average number of cases. Additionally, regardless of the definition, low SES was a risk factor for MetS. IMPACT: Little is known about the impact of using different definitions of pediatric metabolic syndrome on study results. Our study showed that the choice of pediatric metabolic syndrome definition produces very different prevalence estimates. We also showed that the choice of definition influences the socioeconomic gradient. However, low socioeconomic status was consistently a risk factor for having pediatric metabolic syndrome. In conclusion, studies using different definitions of metabolic syndrome could be reasonably compared when investigating the association with socioeconomic status but not always validly when comparing prevalence studies.
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