A prediction nomogram for metabolic syndrome in children: A retrospective study

Abstract

The aim of this retrospective study was to investigate and develop three screening nomogram models to facilitate early detection and prompt intervention for metabolic syndrome (MS) among children with obesity. We analyzed data from 657 pediatric patients at the Children’s Health Research Institute of Hunan Children’s Hospital, collected between March 1 and September 1, 2020. Participants were stratified into three age groups. Clinically independent predictors were identified using multivariate logistic regression, and then these statistically significant clinical characteristics were recruited to develop a nomogram. The models were designed to estimate individual risk of MS based on clinical characteristics, and subgroup analyses were conducted across age categories. Ten risk factors for MS were identified: rural residence, neck circumference, lymphocyte count, hemoglobin levels, uric acid, C-peptide, alanine aminotransferase, plateletocrit, serum cortisol and fatty liver parameters. In conclusion, rural residence, neck circumference, lymphocyte count, hemoglobin levels, uric acid, serum C-peptide, alanine aminotransferase, platelet count, serum cortisol and fatty liver parameters were identified as independent predictors of MS in children. For each patient, higher total score was associated with increased risk of MS.