Assessment of Biochemical Liver Markers, Physical Activity, Fitness and Body Mass Index For a Cardiometabolic Risk Model in Childhood

A Konidari, M K H Auth, MH Murphy, C Cunningham, L Foweather, R Gobbi, L E F Graves, N D Hopkins, G Stratton, L M Boddy

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

AimTo investigate clustered cardiometabolic risk scores in healthy 10 to 12-yearolds using anthropometric characteristics, measurements of cardiorespiratory fitness (CRF) and physical activity and blood markers of metabolic disease. We also evaluated how including markers of liver cell injury would affect the clustered cardiometabolic risk assessment model.MethodsThis cross-sectional study focused on 99 children aged 10 to 12 years. The main outcome included assessing participants with increased and low cardiometabolic risk factors using a clustered risk score model that incorporated markers implicated in metabolic syndrome pathogenesis. Two clustered risk scores were calculated, one incorporating markers of liver cell injury.ResultsChildren classified as ‘increased risk’ exhibited significantly lower CRF and higher body mass index Z-scores than their ‘low risk’ peers. No significant differences in physical activity were observed. This trend remained unchanged when markers of liver injury were included in the clustered risk assessment model.ConclusionThe clustered risk score model is a scientifically robust method of cardiometabolic risk assessment, which reiterates the importance of weight reduction and CRF promotion in childhood. Liver injury markers did not make a significant contribution to our study and further research is needed to evaluate their effect on cardiometabolic risk stratification in childhood.
LanguageEnglish
JournalActa Paediatrica
Volume0
Early online date11 Feb 2014
DOIs
Publication statusE-pub ahead of print - 11 Feb 2014

Fingerprint

Physical Fitness
Body Mass Index
Biomarkers
Exercise
Liver
Wounds and Injuries
Metabolic Diseases
Weight Loss
Cross-Sectional Studies
Research
Cardiorespiratory Fitness

Keywords

  • cardiorespiratory fitness
  • markers of liver cell injury
  • metabolic syndrome
  • obesity
  • physical activity

Cite this

Konidari, A ; Auth, M K H ; Murphy, MH ; Cunningham, C ; Foweather, L ; Gobbi, R ; Graves, L E F ; Hopkins, N D ; Stratton, G ; Boddy, L M. / Assessment of Biochemical Liver Markers, Physical Activity, Fitness and Body Mass Index For a Cardiometabolic Risk Model in Childhood. In: Acta Paediatrica. 2014 ; Vol. 0.
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title = "Assessment of Biochemical Liver Markers, Physical Activity, Fitness and Body Mass Index For a Cardiometabolic Risk Model in Childhood",
abstract = "AimTo investigate clustered cardiometabolic risk scores in healthy 10 to 12-yearolds using anthropometric characteristics, measurements of cardiorespiratory fitness (CRF) and physical activity and blood markers of metabolic disease. We also evaluated how including markers of liver cell injury would affect the clustered cardiometabolic risk assessment model.MethodsThis cross-sectional study focused on 99 children aged 10 to 12 years. The main outcome included assessing participants with increased and low cardiometabolic risk factors using a clustered risk score model that incorporated markers implicated in metabolic syndrome pathogenesis. Two clustered risk scores were calculated, one incorporating markers of liver cell injury.ResultsChildren classified as ‘increased risk’ exhibited significantly lower CRF and higher body mass index Z-scores than their ‘low risk’ peers. No significant differences in physical activity were observed. This trend remained unchanged when markers of liver injury were included in the clustered risk assessment model.ConclusionThe clustered risk score model is a scientifically robust method of cardiometabolic risk assessment, which reiterates the importance of weight reduction and CRF promotion in childhood. Liver injury markers did not make a significant contribution to our study and further research is needed to evaluate their effect on cardiometabolic risk stratification in childhood.",
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author = "A Konidari and Auth, {M K H} and MH Murphy and C Cunningham and L Foweather and R Gobbi and Graves, {L E F} and Hopkins, {N D} and G Stratton and Boddy, {L M}",
note = "Reference text: 1. Eckel RH, Alberti KG, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2010; 375:181-3 2. Friedemann C, Heneghan C, Mahtani K, Thompson M, Perera R, Ward AM. Cardiovascular disease risk in healthy children and its association with body mass index: systematic review and meta-analysis. BMJ. 2012; 345:e4759 3. Mattsson N, Ronnemaa T, Juonala M, Viikari JS, Raitakari OT. Childhood predictors of the metabolic syndrome in adulthood. The Cardiovascular Risk in Young Finns Study. Ann. of Med. 2008; 40:542-52 4. Andersen LB, Sardinha LB, Froberg K, Riddoch CJ, Page AS, Anderssen SA. Fitness, fatness and clustering of cardiovascular risk factors in children from Denmark, Estonia and Portugal: the European Youth Heart Study. Int. J of Ped Obesity obesity. 2008; 3 Suppl 1:58- 66 5. Ekelund U, Anderssen SA, Froberg K, Sardinha LB, Andersen LB, Brage S. Independent associations of physical activity and cardiorespiratory fitness with metabolic risk factors in children: the European youth heart study. Diabetologia. 2007; 50:1832-40 6. Alberti KG, Zimmet P, Shaw J. The metabolic syndrome--a new worldwide definition. Lancet. 2005; 366:1059-62 7. Earnest CP, Artero EG, Sui X, Lee DC, Church TS, Blair SN. Maximal estimated cardiorespiratory fitness, cardiometabolic risk factors, and metabolic syndrome in the aerobics center longitudinal study. Mayo Clin. Proc. 2013; 88:259-70 8. Andersen LB, Bugge A, Dencker M, Eiberg S, El-Naaman B. The association between physical activity, physical fitness and development of metabolic disorders. Int. J of Ped obesity. 2011; 6 Suppl 1:29-34 9. Ndumele CE, Nasir K, Conceicao RD, Carvalho JA, Blumenthal RS, Santos RD. Hepatic steatosis, obesity, and the metabolic syndrome are independently and additively associated with increased systemic inflammation. Arterioscler, Thromb. Vasc. Biol. 2011; 31:1927-32 10. Klunder-Klunder M, Flores-Huerta S, Garcia-Macedo R, Peralta-Romero J, Cruz M. Adiponectin in eutrophic and obese children as a biomarker to predict metabolic syndrome and each of its components. BMC Pub. Health. 2013; 13:88 11. Trilk JL, Ortaglia A, Blair SN, Bottai M, Church TS, Pate RR. Cardiorespiratory fitness, waist circumference, and alanine aminotransferase in youth. Med.Sci. Sports and Exerc. 2013; 45:722-7 12. Schwimmer JB, Pardee PE, Lavine JE, Blumkin AK, Cook S. Cardiovascular risk factors and the metabolic syndrome in pediatric nonalcoholic fatty liver disease. Circulation. 2008; 118:277-83 13. Houston EL, Baker JS, Buchan DS, Stratton G, Fairclough SJ, Foweather L, et al. Cardiorespiratory fitness predicts clustered cardiometabolic risk in 10-11.9-year-olds. Eur.J. Pediatr. 2013; 14. Gortmaker SL, Swinburn BA, Levy D, Carter R, Mabry PL, Finegood DT, et al. Changing the future of obesity: science, policy, and action. Lancet. 2011; 378:838-47 15. Cole TJ, Freeman JV, Preece MA. Body mass index reference curves for the UK, 1990. Arch Dis Child. 1995; 73:25-9 16. Hopkins ND, Stratton G, Tinken TM, McWhannell N, Ridgers ND, Graves LE, et al. Relationships between measures of fitness, physical activity, body composition and vascular function in children. Atherosclerosis. 2009; 204:244-9 17. Keenan T, Blaha MJ, Nasir K, Silverman MG, Tota-Maharaj R, Carvalho JA, et al. Relation of uric acid to serum levels of high-sensitivity C-reactive protein, triglycerides, and high-density lipoprotein cholesterol and to hepatic steatosis. The Am. J. Cardiol.. 2012; 110:1787-92 18. Andersen LB, Harro M, Sardinha LB, Froberg K, Ekelund U, Brage S, et al. Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet. 2006; 368:299-304 19. Catellier DJ, Hannan PJ, Murray DM, Addy CL, Conway TL, Yang S, et al. Imputation of missing data when measuring physical activity by accelerometry. Med. Sci. in Sports and Exerc. 2005; 37:S555-62 20. Mattocks C, Ness A, Leary S, Tilling K, Blair SN, Shield J, et al. Use of accelerometers in a large field-based study of children: protocols, design issues, and effects on precision. Journal of Physical Activity & Health. 2008; 5 Suppl 1:S98-111 21. Hopkins N, Stratton G, Maia J, Tinken TM, Graves LE, Cable TN, et al. Heritability of arterial function, fitness, and physical activity in youth: a study of monozygotic and dizygotic twins. J. Pediatr. 2010; 157:943-8 22. Treuth MS, Schmitz K, Catellier DJ, McMurray RG, Murray DM, Almeida MJ, et al. Defining accelerometer thresholds for activity intensities in adolescent girls. Med. Sci. Sports and Exerc. 2004; 36:1259-66 23. Bailey DP, Boddy LM, Savory LA, Denton SJ, Kerr CJ. Associations between cardiorespiratory fitness, physical activity and clustered cardiometabolic risk in children and adolescents: the HAPPY study. Eur J Pediatr. 2012; 171:1317-23 24. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000; 320:1240-3 25. Lopez-Martinez S, Sanchez-Lopez M, Solera-Martinez M, Arias-Palencia N, Fuentes-Chacon RM, Martinez-Vizcaino V. Physical activity, fitness, and metabolic syndrome in young adults. International Journal of Sport Nutrition and Exercise Metabolism. 2013; 23:312-21",
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Konidari, A, Auth, MKH, Murphy, MH, Cunningham, C, Foweather, L, Gobbi, R, Graves, LEF, Hopkins, ND, Stratton, G & Boddy, LM 2014, 'Assessment of Biochemical Liver Markers, Physical Activity, Fitness and Body Mass Index For a Cardiometabolic Risk Model in Childhood', Acta Paediatrica, vol. 0. https://doi.org/10.1111/apa.12591

Assessment of Biochemical Liver Markers, Physical Activity, Fitness and Body Mass Index For a Cardiometabolic Risk Model in Childhood. / Konidari, A; Auth, M K H; Murphy, MH; Cunningham, C; Foweather, L; Gobbi, R; Graves, L E F; Hopkins, N D; Stratton, G; Boddy, L M.

In: Acta Paediatrica, Vol. 0, 11.02.2014.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Assessment of Biochemical Liver Markers, Physical Activity, Fitness and Body Mass Index For a Cardiometabolic Risk Model in Childhood

AU - Konidari, A

AU - Auth, M K H

AU - Murphy, MH

AU - Cunningham, C

AU - Foweather, L

AU - Gobbi, R

AU - Graves, L E F

AU - Hopkins, N D

AU - Stratton, G

AU - Boddy, L M

N1 - Reference text: 1. Eckel RH, Alberti KG, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2010; 375:181-3 2. Friedemann C, Heneghan C, Mahtani K, Thompson M, Perera R, Ward AM. Cardiovascular disease risk in healthy children and its association with body mass index: systematic review and meta-analysis. BMJ. 2012; 345:e4759 3. Mattsson N, Ronnemaa T, Juonala M, Viikari JS, Raitakari OT. Childhood predictors of the metabolic syndrome in adulthood. The Cardiovascular Risk in Young Finns Study. Ann. of Med. 2008; 40:542-52 4. Andersen LB, Sardinha LB, Froberg K, Riddoch CJ, Page AS, Anderssen SA. Fitness, fatness and clustering of cardiovascular risk factors in children from Denmark, Estonia and Portugal: the European Youth Heart Study. Int. J of Ped Obesity obesity. 2008; 3 Suppl 1:58- 66 5. Ekelund U, Anderssen SA, Froberg K, Sardinha LB, Andersen LB, Brage S. Independent associations of physical activity and cardiorespiratory fitness with metabolic risk factors in children: the European youth heart study. Diabetologia. 2007; 50:1832-40 6. Alberti KG, Zimmet P, Shaw J. The metabolic syndrome--a new worldwide definition. Lancet. 2005; 366:1059-62 7. Earnest CP, Artero EG, Sui X, Lee DC, Church TS, Blair SN. Maximal estimated cardiorespiratory fitness, cardiometabolic risk factors, and metabolic syndrome in the aerobics center longitudinal study. Mayo Clin. Proc. 2013; 88:259-70 8. Andersen LB, Bugge A, Dencker M, Eiberg S, El-Naaman B. The association between physical activity, physical fitness and development of metabolic disorders. Int. J of Ped obesity. 2011; 6 Suppl 1:29-34 9. Ndumele CE, Nasir K, Conceicao RD, Carvalho JA, Blumenthal RS, Santos RD. Hepatic steatosis, obesity, and the metabolic syndrome are independently and additively associated with increased systemic inflammation. Arterioscler, Thromb. Vasc. Biol. 2011; 31:1927-32 10. Klunder-Klunder M, Flores-Huerta S, Garcia-Macedo R, Peralta-Romero J, Cruz M. Adiponectin in eutrophic and obese children as a biomarker to predict metabolic syndrome and each of its components. BMC Pub. Health. 2013; 13:88 11. Trilk JL, Ortaglia A, Blair SN, Bottai M, Church TS, Pate RR. Cardiorespiratory fitness, waist circumference, and alanine aminotransferase in youth. Med.Sci. Sports and Exerc. 2013; 45:722-7 12. Schwimmer JB, Pardee PE, Lavine JE, Blumkin AK, Cook S. Cardiovascular risk factors and the metabolic syndrome in pediatric nonalcoholic fatty liver disease. Circulation. 2008; 118:277-83 13. Houston EL, Baker JS, Buchan DS, Stratton G, Fairclough SJ, Foweather L, et al. Cardiorespiratory fitness predicts clustered cardiometabolic risk in 10-11.9-year-olds. Eur.J. Pediatr. 2013; 14. Gortmaker SL, Swinburn BA, Levy D, Carter R, Mabry PL, Finegood DT, et al. Changing the future of obesity: science, policy, and action. Lancet. 2011; 378:838-47 15. Cole TJ, Freeman JV, Preece MA. Body mass index reference curves for the UK, 1990. Arch Dis Child. 1995; 73:25-9 16. Hopkins ND, Stratton G, Tinken TM, McWhannell N, Ridgers ND, Graves LE, et al. Relationships between measures of fitness, physical activity, body composition and vascular function in children. Atherosclerosis. 2009; 204:244-9 17. Keenan T, Blaha MJ, Nasir K, Silverman MG, Tota-Maharaj R, Carvalho JA, et al. Relation of uric acid to serum levels of high-sensitivity C-reactive protein, triglycerides, and high-density lipoprotein cholesterol and to hepatic steatosis. The Am. J. Cardiol.. 2012; 110:1787-92 18. Andersen LB, Harro M, Sardinha LB, Froberg K, Ekelund U, Brage S, et al. Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet. 2006; 368:299-304 19. Catellier DJ, Hannan PJ, Murray DM, Addy CL, Conway TL, Yang S, et al. Imputation of missing data when measuring physical activity by accelerometry. Med. Sci. in Sports and Exerc. 2005; 37:S555-62 20. Mattocks C, Ness A, Leary S, Tilling K, Blair SN, Shield J, et al. Use of accelerometers in a large field-based study of children: protocols, design issues, and effects on precision. Journal of Physical Activity & Health. 2008; 5 Suppl 1:S98-111 21. Hopkins N, Stratton G, Maia J, Tinken TM, Graves LE, Cable TN, et al. Heritability of arterial function, fitness, and physical activity in youth: a study of monozygotic and dizygotic twins. J. Pediatr. 2010; 157:943-8 22. Treuth MS, Schmitz K, Catellier DJ, McMurray RG, Murray DM, Almeida MJ, et al. Defining accelerometer thresholds for activity intensities in adolescent girls. Med. Sci. Sports and Exerc. 2004; 36:1259-66 23. Bailey DP, Boddy LM, Savory LA, Denton SJ, Kerr CJ. Associations between cardiorespiratory fitness, physical activity and clustered cardiometabolic risk in children and adolescents: the HAPPY study. Eur J Pediatr. 2012; 171:1317-23 24. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000; 320:1240-3 25. Lopez-Martinez S, Sanchez-Lopez M, Solera-Martinez M, Arias-Palencia N, Fuentes-Chacon RM, Martinez-Vizcaino V. Physical activity, fitness, and metabolic syndrome in young adults. International Journal of Sport Nutrition and Exercise Metabolism. 2013; 23:312-21

PY - 2014/2/11

Y1 - 2014/2/11

N2 - AimTo investigate clustered cardiometabolic risk scores in healthy 10 to 12-yearolds using anthropometric characteristics, measurements of cardiorespiratory fitness (CRF) and physical activity and blood markers of metabolic disease. We also evaluated how including markers of liver cell injury would affect the clustered cardiometabolic risk assessment model.MethodsThis cross-sectional study focused on 99 children aged 10 to 12 years. The main outcome included assessing participants with increased and low cardiometabolic risk factors using a clustered risk score model that incorporated markers implicated in metabolic syndrome pathogenesis. Two clustered risk scores were calculated, one incorporating markers of liver cell injury.ResultsChildren classified as ‘increased risk’ exhibited significantly lower CRF and higher body mass index Z-scores than their ‘low risk’ peers. No significant differences in physical activity were observed. This trend remained unchanged when markers of liver injury were included in the clustered risk assessment model.ConclusionThe clustered risk score model is a scientifically robust method of cardiometabolic risk assessment, which reiterates the importance of weight reduction and CRF promotion in childhood. Liver injury markers did not make a significant contribution to our study and further research is needed to evaluate their effect on cardiometabolic risk stratification in childhood.

AB - AimTo investigate clustered cardiometabolic risk scores in healthy 10 to 12-yearolds using anthropometric characteristics, measurements of cardiorespiratory fitness (CRF) and physical activity and blood markers of metabolic disease. We also evaluated how including markers of liver cell injury would affect the clustered cardiometabolic risk assessment model.MethodsThis cross-sectional study focused on 99 children aged 10 to 12 years. The main outcome included assessing participants with increased and low cardiometabolic risk factors using a clustered risk score model that incorporated markers implicated in metabolic syndrome pathogenesis. Two clustered risk scores were calculated, one incorporating markers of liver cell injury.ResultsChildren classified as ‘increased risk’ exhibited significantly lower CRF and higher body mass index Z-scores than their ‘low risk’ peers. No significant differences in physical activity were observed. This trend remained unchanged when markers of liver injury were included in the clustered risk assessment model.ConclusionThe clustered risk score model is a scientifically robust method of cardiometabolic risk assessment, which reiterates the importance of weight reduction and CRF promotion in childhood. Liver injury markers did not make a significant contribution to our study and further research is needed to evaluate their effect on cardiometabolic risk stratification in childhood.

KW - cardiorespiratory fitness

KW - markers of liver cell injury

KW - metabolic syndrome

KW - obesity

KW - physical activity

U2 - 10.1111/apa.12591

DO - 10.1111/apa.12591

M3 - Article

VL - 0

JO - Acta Paediatrica

T2 - Acta Paediatrica

JF - Acta Paediatrica

SN - 0803-5253

ER -