[Relationship between skeletal muscle mass and strength with metabolic syndrome in children].

<b>Objective:</b> To explore the correlation between skeletal muscle mass and strength with metabolic syndrome in children. <b>Methods:</b> This cross-sectional study was conducted involving 383 children aged 10 to 15 years who visited the Department of Child Health Care, Children's Hospital of Nanjing Medical University from June 2021 to December 2022. Their height, weight, waist circumference, body composition, grip strength and blood pressure were measured. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were calculated. The levels of fasting blood glucose, lipids and insulin were tested. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated. Children were divided into the metabolic syndrome group and the non-metabolic syndrome group. Independent <i>t</i> test or Mann-Whitney <i>U</i> test etc. was used to compare the difference between groups. Spearman correlation analysis and binary Logistic regression were used to investigate the correlation between skeletal muscle mass and strength and metabolic syndrome. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to compare the accuracy of the index of skeletal muscle in predicting metabolic syndrome in children. <b>Results:</b> Among the 383 children, 282 (73.6%) were male, at the age of 11.4 (10.6, 12.5) years. There were 216 children (56.4%) diagnosed with obesity and 90 children (23.5%) diagnosed with metabolic syndrome. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index of the metabolic syndrome group were all lower than those in the non-metabolic syndrome group (all <i>P<</i>0.001). After adjusting for sex and age, relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were all negatively correlated with body mass index (<i>r</i>=-0.84, -0.38, -0.63), waist circumference (<i>r</i>=-0.76, -0.36, -0.70), systolic blood pressure (<i>r</i>=-0.42, -0.21, -0.38), diastolic blood pressure (<i>r</i>=-0.33, -0.18, -0.24), triglycerides (<i>r</i>=-0.29, -0.13, -0.23), fasting insulin (<i>r</i>=-0.28, -0.20, -0.29), and HOMA-IR (<i>r</i>=-0.26, -0.18, -0.26) (all <i>P</i><0.05), and positively correlated with high-density lipoprotein cholesterol (<i>r=</i>0.38, 0.13, 0.31, all <i>P<</i>0.01). After adjusting for sex and age, high relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index all decreased the risks of metabolic syndrome (<i>OR</i>=0.80, 0.55, 0.90), obesity (<i>OR</i>=0.53, 0.64, 0.82), hypertension (<i>OR</i>=0.86, 0.58, 0.92), low high-density lipoprotein cholesterol (<i>OR</i>=0.83, 0.62, 0.92), hypertriglyceridemia (<i>OR</i>=0.88, 0.78, 0.96). After adjusting for sex and age, high relative skeletal muscle mass and high grip strength-to-body weight index all decreased the risks of hyperglycemia (<i>OR</i>=0.93 and 0.95, all <i>P</i><0.05). ROC curve analysis showed that the relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index all had good predictive accuracy of metabolic syndrome in children (AUC=0.79, 0.71, 0.76), with optimal cutoff values of 40%, 1.2, and 35%, respectively. <b>Conclusions:</b> High relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index are all protective factors for metabolic syndrome in children. Regular measurement of skeletal muscle mass and grip strength can aid in the early identification and prevention of obesity and metabolic syndrome during childhood<b>.</b>

Authors

Institutions

Publication Details

Journal
PubMed
Published
2025-05-02
DOI
https://doi.org/10.3760/cma.j.cn112140-20250106-00018
Primary Topic
Nutrition and Health in Aging
Type
article
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

[Relationship between skeletal muscle mass and strength with metabolic syndrome in children].

X N Li, L Wang, Wu Yan, L T Peng et al.
1 links
PubMed
Nutrition and Health in Aging
article

[Relationship between skeletal muscle mass and strength with metabolic syndrome in children].

X N Li, L Wang, Wu Yan, L T Peng, Ming Zhao, Y N Shi, Miao Cao, Shangkai Gao, Q Q Zheng
article en

Abstract

<b>Objective:</b> To explore the correlation between skeletal muscle mass and strength with metabolic syndrome in children. <b>Methods:</b> This cross-sectional study was conducted involving 383 children aged 10 to 15 years who visited the Department of Child Health Care, Children's Hospital of Nanjing Medical University from June 2021 to December 2022. Their height, weight, waist circumference, body composition, grip strength and blood pressure were measured. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were calculated. The levels of fasting blood glucose, lipids and insulin were tested. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated. Children were divided into the metabolic syndrome group and the non-metabolic syndrome group. Independent <i>t</i> test or Mann-Whitney <i>U</i> test etc. was used to compare the difference between groups. Spearman correlation analysis and binary Logistic regression were used to investigate the correlation between skeletal muscle mass and strength and metabolic syndrome. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to compare the accuracy of the index of skeletal muscle in predicting metabolic syndrome in children. <b>Results:</b> Among the 383 children, 282 (73.6%) were male, at the age of 11.4 (10.6, 12.5) years. There were 216 children (56.4%) diagnosed with obesity and 90 children (23.5%) diagnosed with metabolic syndrome. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index of the metabolic syndrome group were all lower than those in the non-metabolic syndrome group (all <i>P<</i>0.001). After adjusting for sex and age, relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were all negatively correlated with body mass index (<i>r</i>=-0.84, -0.38, -0.63), waist circumference (<i>r</i>=-0.76, -0.36, -0.70), systolic blood pressure (<i>r</i>=-0.42, -0.21, -0.38), diastolic blood pressure (<i>r</i>=-0.33, -0.18, -0.24), triglycerides (<i>r</i>=-0.29, -0.13, -0.23), fasting insulin (<i>r</i>=-0.28, -0.20, -0.29), and HOMA-IR (<i>r</i>=-0.26, -0.18, -0.26) (all <i>P</i><0.05), and positively correlated with high-density lipoprotein cholesterol (<i>r=</i>0.38, 0.13, 0.31, all <i>P<</i>0.01). After adjusting for sex and age, high relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index all decreased the risks of metabolic syndrome (<i>OR</i>=0.80, 0.55, 0.90), obesity (<i>OR</i>=0.53, 0.64, 0.82), hypertension (<i>OR</i>=0.86, 0.58, 0.92), low high-density lipoprotein cholesterol (<i>OR</i>=0.83, 0.62, 0.92), hypertriglyceridemia (<i>OR</i>=0.88, 0.78, 0.96). After adjusting for sex and age, high relative skeletal muscle mass and high grip strength-to-body weight index all decreased the risks of hyperglycemia (<i>OR</i>=0.93 and 0.95, all <i>P</i><0.05). ROC curve analysis showed that the relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index all had good predictive accuracy of metabolic syndrome in children (AUC=0.79, 0.71, 0.76), with optimal cutoff values of 40%, 1.2, and 35%, respectively. <b>Conclusions:</b> High relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index are all protective factors for metabolic syndrome in children. Regular measurement of skeletal muscle mass and grip strength can aid in the early identification and prevention of obesity and metabolic syndrome during childhood<b>.</b>

PubMedVol. 63(5)
Nanjing Children's Hospital (CN), Nanjing Maternity and Child Health Care Hospital (CN)
Nutrition and Health in Aging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.