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Continuous Metabolic Syndrome Scores for Children Using Salivary Biomarkers

BACKGROUND: Binary definitions of the metabolic syndrome based on the presence of a particular number of individual risk factors are limited, particularly in the pediatric population. To address this limitation, we aimed at constructing composite and continuous metabolic syndrome scores (cmetS) to r...

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Detalles Bibliográficos
Autores principales: Shi, Ping, Goodson, J. Max, Hartman, Mor-Li, Hasturk, Hatice, Yaskell, Tina, Vargas, Jorel, Cugini, Maryann, Barake, Roula, Alsmadi, Osama, Al-Mutawa, Sabiha, Ariga, Jitendra, Soparkar, Pramod, Behbehani, Jawad, Behbehani, Kazem, Welty, Francine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4587796/
https://www.ncbi.nlm.nih.gov/pubmed/26418011
http://dx.doi.org/10.1371/journal.pone.0138979
Descripción
Sumario:BACKGROUND: Binary definitions of the metabolic syndrome based on the presence of a particular number of individual risk factors are limited, particularly in the pediatric population. To address this limitation, we aimed at constructing composite and continuous metabolic syndrome scores (cmetS) to represent an overall measure of metabolic syndrome (MetS) in a large cohort of metabolically at-risk children, focusing on the use of the usual clinical parameters (waist circumference (WC) and systolic blood pressure (SBP), supplemented with two salivary surrogate variables (glucose and high density lipoprotein cholesterol (HDLC). Two different approaches used to create the scores were evaluated in comparison. METHODS: Data from 8,112 Kuwaiti children (10.00 ± 0.67 years) were used to construct two cmetS for each subject. The first cmetS (cmetS-Z) was created by summing standardized residuals of each variable regressed on age and gender; and the second cmetS (cmetS-PCA) was defined as the first principal component from gender-specific principal component analysis based on the four variables. RESULTS: There was a graded relationship between both scores and the number of adverse risk factors. The areas under the curve using cmetS-Z and cmetS-PCA as predictors for severe metabolic syndrome (defined as the presence of ≥3 metabolic risk factors) were 0.935 and 0.912, respectively. cmetS-Z was positively associated with WC, SBP, and glucose, but inversely associated with HDLC. Except for the lack of association with glucose, cmetS-PCA was similar to cmetS-Z in boys, but had minimum loading on HDLC in girls. Analysis using quantile regression showed an inverse association of fitness level with cmetS-PCA (p = 0.001 for boys; p = 0.002 for girls), and comparison of cmetS-Z and cmetS-PCA suggested that WC and SBP were main contributory components. Significant alterations in the relationship between cmetS and salivary adipocytokines were demonstrated in overweight and obese children as compared to underweight and normal-weight children. CONCLUSION: We have derived continuous summary scores for MetS from a large-scale pediatric study using two different approaches, incorporating salivary measures as surrogate for plasma measures. The derived scores were viable expressions of metabolic risk, and can be utilized to study the relationships of MetS with various aspects of the metabolic disease process.