Lifestyle patterns and endocrine, metabolic, and immunological biomarkers in European adolescents: The HELENA study

• Published in: Pediatric diabetes Vol. 20; no. 1; pp. 23 - 31
• Main Authors: Agostinis‐Sobrinho, César, Gómez‐Martínez, Sonia, Nova, Esther, Hernandez, Aurora, Labayen, Idoia, Kafatos, Antonios, Gottand, Frederic, Molnár, Dénes, Ferrari, Marika, Moreno, Luis A., González‐Gross, Marcela, Michels, Nathalie, Ruperez, Azahara, Ruiz, Jonatan R., Marcos, Ascensión
• Format: Journal Article
• Language: English
• Published: Former Munksgaard John Wiley & Sons A/S 01.02.2019; Wiley Subscription Services, Inc
• Subjects: Adolescents; Biomarkers; Body composition; cardiometabolic biomarkers; Cholesterol; Cluster analysis; Cortisol; Exercise; Girls; Immunology; Leptin; Lifestyles; Metabolism; Physical activity; Sedentary behavior; Sleep; sleep time; Teenagers; youth; cardiometabolic biomarkers; sedentary behavior; sleep time; youth; physical activity
• ISSN: 1399-543X; 1399-5448
• DOI: 10.1111/pedi.12802

Objective To evaluate the association of lifestyle patterns related to physical activity (PA), sedentariness, and sleep with endocrine, metabolic, and immunological health biomarkers in European adolescents. Methods The present cross‐sectional study comprised 3528 adolescents (1845 girls) (12.5‐17.5 years) enrolled in the Healthy Lifestyle in Europe by Nutrition in Adolescence Study. Cluster analysis was performed by including body composition, PA by accelerometry, self‐reported sedentary behaviors, and sleep duration. We also measured endocrine, metabolic, and immunological biomarkers. Results Three‐cluster solutions were identified: (a) light‐PA time, moderate‐vigorous‐PA time and sedentary time, (b) light‐PA time, moderate‐vigorous‐PA time, sedentary time and sleep time, (c) light‐PA time, moderate‐vigorous‐PA time, sedentary time and body composition. In addition, each cluster solution was defined as: “healthy,” “medium healthy,” and “unhealthy” according to the presented rating. Analysis of variance showed that overall the healthiest groups from the three clusters analyzed presented a better metabolic profile. A decision tree analysis showed that leptin had a strong association with cluster 3 in both boys and girls, high‐density lipoprotein cholesterol had the strongest association with clusters 1 and 3 in boys. Cortisol had the strongest association with cluster 1. HOMA index (homeostatic model assessment) and C3 showed a strong association with cluster 3 in girls. Conclusions Our results support the existence of different interactions between metabolic health and lifestyle patterns related to PA, sedentariness, and sleep, with some gender‐specific findings. These results highlight the importance to consider multiple lifestyle‐related health factors in the assessment of adolescents' health to plan favorable strategies.