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Int J Sports Med 2023; 44(12): 889-895
DOI: 10.1055/a-2108-6594
Behavioural Sciences

Moderation of Physical Activity between Cardiometabolic Risk and Adiponectin in Adolescents

Maiara Cristina Tadiotto 
1   Departamento de Educação Física, Universidade Federal do Paraná, Curitiba, Brasil
,
Patricia Ribeiro Paes Corazza
1   Departamento de Educação Física, Universidade Federal do Paraná, Curitiba, Brasil
,
Francisco Jose de Menezes Junior
1   Departamento de Educação Física, Universidade Federal do Paraná, Curitiba, Brasil
,
Frederico Bento de Moraes Junior
1   Departamento de Educação Física, Universidade Federal do Paraná, Curitiba, Brasil
,
Maria de Fátima Aguiar Lopes
1   Departamento de Educação Física, Universidade Federal do Paraná, Curitiba, Brasil
,
Tatiana Aparecida Affornali Tozo
1   Departamento de Educação Física, Universidade Federal do Paraná, Curitiba, Brasil
,
Kátia Sheylla Malta Purim
2   Departamento de Medicina, Universidade Positivo, Curitiba, Brazil
,
Jorge Mota
3   Center in Physical Activity, Health and Leisure, Universidade do Porto Faculdade de Desporto, Porto, Portugal
,
Neiva Leite
1   Departamento de Educação Física, Universidade Federal do Paraná, Curitiba, Brasil
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Abstract

The aims of the study were to examine the moderating role of physical activity in the relationship between cardiometabolic risk factors and adiponectin concentration in adolescents. This is a cross-sectional study conducted with 96 adolescents of both sexes, between 11 and 17 years old. Body mass, height, fat mass (FM), fat-free mass, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein (LDL-c), triglycerides, insulin, adiponectin, C-reactive protein, and level of physical activity (energy expenditure questionnaire) were measured. Body mass index (BMI), triponderal mass index (TMI), homeostasis model to assessment insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI) were calculated. Macro-PROCESS for SPSS was used for moderation analyses. Direct interactions were found for BMI, TMI, FM, insulin, and HOMA-IR and inverse for LDL-c, and QUICKI. Protection against cardiometabolic risk was found when the PA-coeff was completed above 1.57 coeff (BMI), 1.62 coeff (TMI), 1.55 coeff (FM), 1.41 coeff (LDL-c)1.60 coeff (insulin), 1.59 coeff (HOMA-IR) and 1.35 coeff (QUICKI). We conclude that physical activity was a moderator in the relationship with adiposity, insulin resistance and sensitivity, LDL-c, and adiponectin. In this context, we evidenced a relevant clinical impact on the health of adolescents, demonstrating the interaction between anthropometrics variables and physical activity.

Key words

adiponectin - adiposity - risk factors - physical activity


Publikationsverlauf

Eingereicht: 13. Dezember 2023

Angenommen: 24. Mai 2023

Artikel online veröffentlicht:
09. August 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 World Health Organization. WHO guidelines on physical activity and sedentary behaviour. Geneva: World Health Organization; 2020
    Suche in Google Scholar
  • 2 Cureau FV, da Silva TL, Bloch KV. et al. ERICA: leisure-time physical inactivity in Brazilian adolescents. Rev Saude Publica 2016; 50: 1-4
    CrossrefPubMedSuche in Google Scholar
  • 3 Du Y, Liu B, Sun Y. et al. Trends in adherence to the physical activity guidelines for americans for aerobic activity and time spent on sedentary behavior among US adults, 2007 to 2016. JAMA Netw Open 2019; 2: 197597
    CrossrefPubMedSuche in Google Scholar
  • 4 Yang L, Cao C, Kantor ED. et al. Trends in sedentary behavior among the US population, 2001–2016. JAMA 2019; 321: 1587-1597
    CrossrefPubMedSuche in Google Scholar
  • 5 Cabanas-Sánchez V, Martínez-Gómez D, Izquierdo-Gómez R. et al. Association between clustering of lifestyle behaviors and health-related physical fitness in youth: the UP&DOWN Study. J Pediatr 2018; 199: 41-48
    CrossrefPubMedSuche in Google Scholar
  • 6 World Health Organization. World health statistics 2022: monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization; 2022
    Suche in Google Scholar
  • 7 Bibiloni MD, Salas R, De la Garza YE. et al. Serum lipid profile, prevalence of dyslipidaemia, and associated risk factors among northern Mexican adolescents. J Pediatr Gastroenterol Nutr 2016; 63: 544-549
    CrossrefPubMedSuche in Google Scholar
  • 8 Tozo TA, Pereira BO, Menezes Junior FJ. et al. Hypertensive measures in schoolchildren: risk of central obesity and protective effect of moderate-to-vigorous physical activity. Arq Bras Cardiol 2020; 115: 42-49
    PubMedSuche in Google Scholar
  • 9 Lee JH, Kim HC, Kang DR, Suh I. The 23-year tracking of blood lipids from adolescence to adulthood in Korea: The Kangwha study. Lipids Health Dis 2017; 16: 221
    CrossrefPubMedSuche in Google Scholar
  • 10 Ouchi N, Parker JL, Lugus JJ, Walsh K. Adipokines in inflammation and metabolic disease. Nat Rev Immunol 2011; 11: 85-97
    CrossrefPubMedSuche in Google Scholar
  • 11 Ghadge AA, Khaire AA, Kuvalekar AA. Adiponectin: A potential therapeutic target for metabolic syndrome. Cytokine Growth Factor Rev 2018; 39: 151-158
    CrossrefPubMedSuche in Google Scholar
  • 12 Cristi-Montero C, Chillón P, Labayen I. et al. Cardiometabolic risk through an integrative classification combining physical activity and sedentary behavior in European adolescents: HELENA study. J Sport Health Sci 2019; 8: 55-62
    CrossrefPubMedSuche in Google Scholar
  • 13 Martinez-Gomez D, Eisenmann JC, Gomez-Martinez S. et al. Associations of physical activity and fitness with adipocytokines in adolescents: the AFINOS Study. Nutr Metab Cardiovasc Dis 2012; 22: 252-259
    CrossrefPubMedSuche in Google Scholar
  • 14 Joensuu L, Kujala UM, Kankaanpää A. et al. Physical fitness development in relation to changes in body composition and physical activity in adolescence. Scand J Med Sci Sports 2021; 31: 456-464
    CrossrefPubMedSuche in Google Scholar
  • 15 Belanger M, Katapally TR, Barnett TA. et al. Link between physical activity type in adolescence and body composition in adulthood. Med Sci Sports Exerc 2018; 50: 709-714
    CrossrefPubMedSuche in Google Scholar
  • 16 Emken BA, Richey J, Belcher B. et al. Objectively measured physical activity is negatively associated with plasma adiponectin levels in minority female youth. Int J Pediatr Endocrinol 2010; 2010: 846070
    CrossrefPubMedSuche in Google Scholar
  • 17 Metcalf BS, Jeffery AN, Hosking J. et al. Objectively measured physical activity and its association with adiponectin and other novel metabolic markers: a longitudinal study in children (EarlyBird 38). Diabetes Care 2009; 32: 468-473
    CrossrefPubMedSuche in Google Scholar
  • 18 Nielsen MS, Quist JS, Chaput JP. et al. Physical activity, sedentary time, and sleep and the association with inflammatory markers and adiponectin in 8- to 11-year-old Danish Children. J Phys Act Health 2016; 13: 733-739
    CrossrefPubMedSuche in Google Scholar
  • 19 Rubin DA, McMurray RG, Harrell JS. et al. Vigorous physical activity and cytokines in adolescents. Eur J Appl Physiol 2008; 103: 495-500
    CrossrefPubMedSuche in Google Scholar
  • 20 Brand C, Sehn AP, Gaya AR. et al. Physical fitness as a moderator in the relationship between adiposity and cardiometabolic risk factors in children and adolescents. J Sports Med Phys Fitness 2020; 60: 1567-1575
    CrossrefPubMedSuche in Google Scholar
  • 21 Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Champaign, Illinois: Human Kinetics; 1988
    Suche in Google Scholar
  • 22 Onis M, Onyango AW, Borghi E. et al. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ 2007; 85: 660-667
    CrossrefPubMedSuche in Google Scholar
  • 23 Houtkooper LB, Going SB, Lohman TG. et al. Bioelectrical impedance estimation of fat-free body mass in children and youth: a cross-validation study. J Appl Physiol 1992; 72: 366-373
    CrossrefPubMedSuche in Google Scholar
  • 24 Matthews DR, Hosker JP, Rudenski AS. et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985; 28: 412-419
    CrossrefPubMedSuche in Google Scholar
  • 25 Katz A, Nambi SS, Mather K. et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 2000; 85: 2402-2410
    CrossrefPubMedSuche in Google Scholar
  • 26 Moore SA, McKay HA, MacDonald H. et al. Enhancing a somatic maturity prediction model. Med Sci Sports Exerc 2015; 47: 1755-1764
    CrossrefPubMedSuche in Google Scholar
  • 27 Bouchard C, Tremblay A, Leblanc C. et al. A method to assess energy expenditure in children and adults. Am J Clin Nutr 1983; 37: 461-467
    CrossrefPubMedSuche in Google Scholar
  • 28 Fasshauer M, Blüher M. Adipokines in health and disease. Trends Pharmacol Sci 2015; 36: 461-470
    CrossrefPubMedSuche in Google Scholar
  • 29 Sabbatini AR, Faria AP, Barbaro NR. et al. Deregulation of adipokines related to target organ damage on resistant hypertension. J Hum Hypertens 2014; 28: 388-392
    CrossrefPubMedSuche in Google Scholar
  • 30 Von Frankenberg AD, Reis AF, Gerchman F. Relationships between adiponectin levels, the metabolic syndrome, and type 2 diabetes: a literature review. Arch Endocrinol Metab 2017; 61: 614-622
    CrossrefPubMedSuche in Google Scholar
  • 31 Martins J, Marques A, Loureiro N. et al. Trends and age-related changes of physical activity among Portuguese adolescent girls from 2002–2014: highlights from the health behavior in school-aged children study. J Phys Act Health 2019; 16: 281-287
    CrossrefPubMedSuche in Google Scholar
  • 32 Ortega FB, Konstabel K, Pasquali E. et al. Objectively measured physical activity and sedentary time during childhood, adolescence and young adulthood: a cohort study. PLoS One 2013; 8: 60871
    CrossrefPubMedSuche in Google Scholar
  • 33 Brown T, Smith S, Bhopal R. et al. Diet and physical activity interventions to prevent or treat obesity in South Asian children and adults: a systematic review and meta-analysis. Int J Environ Res Public Health 2015; 12: 566-594
    CrossrefPubMedSuche in Google Scholar
  • 34 Bray GA, Heisel WE, Afshin A. et al. The Science Of Obesity Management: An Endocrine Society Scientific Statement. Endocr Rev 2018; 39: 79-132
    CrossrefPubMedSuche in Google Scholar
  • 35 Malina RM. Top 10 research questions related to growth and maturation of relevance to physical activity, performance, and fitness. Res Q Exerc Sport 2014; 85: 157-173
    CrossrefPubMedSuche in Google Scholar
  • 36 Mirwald RL, Baxter-Jones ADG, Bailey DA. et al. An assessment of maturity from anthropometric measurements. Med Sci Sports Exerc 2002; 34: 689-694
    PubMedSuche in Google Scholar
  • 37 Kozieł SM, Malina RM. Modified maturity offset prediction equations: validation in independent longitudinal samples of boys and girls. Sports Med 2018; 48: 221-236
    CrossrefPubMedSuche in Google Scholar