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Lifestyle Clusters and Cardiometabolic Risks in Adolescents: A Chinese School-Based Study Using a Latent Class Analysis Approach

Overview
Journal Front Pediatr
Specialty Pediatrics
Date 2022 Jan 3
PMID 34976884
Citations 5
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Abstract

Unhealthy dietary and lifestyle behaviors are associated with a higher prevalence of non-communicable chronic diseases and higher mortality in adults. However, there remains some uncertainty about the magnitude of the associations between lifestyle behaviors and cardiovascular factors in adolescents. We conducted a school-based cross-sectional study of 895 Chinese adolescents aged 15-19 years. They participated in a questionnaire survey, physical examination, and blood sample collection. Latent class analysis (LCA) was used to identify heterogeneous subgroups of lifestyle behaviors. A set of 12 latent class indicators, which reflected lifestyle behaviors including dietary habits, physical activity, sleep duration, screen time, and pressure perception, were included in the analysis. Logistic regression analysis was performed to determine whether the derived classes were related to a cardiometabolic risk. In total, 13.7 and 5.6% of the participants were overweight and obese, respectively, and 8.4 and 14.1% reported having pre-hypertension and hypertension, respectively. A two-class model provided the best fit with a healthy lifestyle pattern (65.8%) and a sub-healthy lifestyle pattern (34.2%). There were more female participants with a healthy lifestyle (56.2 vs. 43.8%), whereas there were more males with a sub-healthy lifestyle (45.4 vs. 54.6%), (all = 0.002). Increased risk of cardiometabolic abnormality (BMI categories, blood pressure and lipids) was not significant across lifestyle patterns, except for waist circumference (70.5 vs 69.1 cm, = 0.044). There was no significant difference in physical activity and intake of fruit and vegetable between the two patterns. Primary prevention based on lifestyle modification should target patterns of behaviors at high risk in adolescents. Due to the complex effect of lifestyle clusters on cardiometabolic risks, well-designed and prospective studies in adolescents are needed in the future.

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References
1.
Navarro P, Shivappa N, Hebert J, Mehegan J, Murrin C, Kelleher C . Predictors of the dietary inflammatory index in children and associations with childhood weight status: A longitudinal analysis in the Lifeways Cross-Generation Cohort Study. Clin Nutr. 2019; 39(7):2169-2179. DOI: 10.1016/j.clnu.2019.09.004. View

2.
Poorolajal J, Sahraei F, Mohamdadi Y, Doosti-Irani A, Moradi L . Behavioral factors influencing childhood obesity: a systematic review and meta-analysis. Obes Res Clin Pract. 2020; 14(2):109-118. DOI: 10.1016/j.orcp.2020.03.002. View

3.
Bigazzi R, Zagato L, Lanzani C, Fontana S, Messaggio E, Delli Carpini S . Hypertension in High School Students: Genetic and Environmental Factors: The HYGEF Study. Hypertension. 2019; 75(1):71-78. DOI: 10.1161/HYPERTENSIONAHA.119.13818. View

4.
Dumuid D, Olds T, Martin-Fernandez J, Lewis L, Cassidy L, Maher C . Academic Performance and Lifestyle Behaviors in Australian School Children: A Cluster Analysis. Health Educ Behav. 2017; 44(6):918-927. DOI: 10.1177/1090198117699508. View

5.
Parker K, Salmon J, Costigan S, Villanueva K, Brown H, Timperio A . Activity-related behavior typologies in youth: a systematic review. Int J Behav Nutr Phys Act. 2019; 16(1):44. PMC: 6524235. DOI: 10.1186/s12966-019-0804-7. View