Abstract
Objective
To investigate associations between early-life diet trajectories and preclinical cardiovascular phenotypes and metabolic risk by age 12 years.
Methods
Participants were 1861 children (51% male) from the Longitudinal Study of Australian Children. At five biennial waves from 2–3 to 10–11 years: Every 2 years from 2006 to 2014, diet quality scores were collected from brief 24-h parent/self-reported dietary recalls and then classified using group-based trajectory modeling as ‘never healthy’ (7%), ‘becoming less healthy’ (17%), ‘moderately healthy’ (21%), and ‘always healthy’ (56%). At 11–12 years: During children’s physical health Child Health CheckPoint (2015–2016), we measured cardiovascular functional (resting heart rate, blood pressure, pulse wave velocity, carotid elasticity/distensibility) and structural (carotid intima-media thickness, retinal microvasculature) phenotypes, and metabolic risk score (composite of body mass index z-score, systolic blood pressure, high-density lipoproteins cholesterol, triglycerides, and glucose). Associations were estimated using linear regression models (n = 1100–1800) adjusted for age, sex, and socioeconomic position.
Results
Compared to ‘always healthy’, the ‘never healthy’ trajectory had higher resting heart rate (2.6 bpm, 95% CI 0.4, 4.7) and metabolic risk score (0.23, 95% CI 0.01, 0.45), and lower arterial elasticity (−0.3% per 10 mmHg, 95% CI −0.6, −0.1) and distensibility (−1.2%, 95% CI −1.9, −0.5) (all effect sizes 0.3–0.4). Heart rate, distensibility, and diastolic blood pressure were progressively poorer for less healthy diet trajectories (linear trends p ≤ 0.02). Effects for systolic blood pressure, pulse wave velocity, and structural phenotypes were less evident.
Conclusions
Children following the least healthy diet trajectory had poorer functional cardiovascular phenotypes and metabolic syndrome risk, including higher resting heart rate, one of the strongest precursors of all-cause mortality. Structural phenotypes were not associated with diet trajectories, suggesting the window to prevent permanent changes remains open to at least late childhood.
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References
Afshin A, Sur PJ, Fay KA, Cornaby L, Ferrara G, Salama JS, et al. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;393:1958–72.
Groner JA, Joshi M, Bauer JA. Pediatric precursors of adult cardiovascular disease: noninvasive assessment of early vascular changes in children and adolescents. Pediatrics. 2006;118:1683–91.
Kuh D, Shlomo YB . A life course approach to chronic disease epidemiology (No. 2). Oxford, UK: Oxford University Press; 2004.
Gopinath B, Flood VM, Wang JJ, Smith W, Rochtchina E, Louie JC, et al. Carbohydrate nutrition is associated with changes in the retinal vascular structure and branching pattern in children. Am J Clin Nutr. 2012;95:1215–22.
Gopinath B, Flood VM, Burlutsky G, Louie JCY, Baur L, Mitchell P. Dairy food consumption, blood pressure and retinal microcirculation in adolescents. Nutr Metab Cardiovasc Dis. 2014;24:1221–7.
Gopinath B, Moshtaghian H, Flood VM, Louie JCY, Liew G, Burlutsky G, et al. Pattern of omega-3 polyunsaturated fatty acid intake and fish consumption and retinal vascular caliber in children and adolescents: a cohort study. PLoS ONE. 2017;12:e0172109.
Lydakis C, Stefanaki E, Stefanaki S, Thalassinos E, Kavousanaki M, Lydaki D. Correlation of blood pressure, obesity, and adherence to the Mediterranean diet with indices of arterial stiffness in children. Eur J Pediatr. 2012;171:1373–82.
Ping-Delfos WL, Beilin LJ, Oddy WH, Burrows S, Mori TA. Use of the Dietary Guideline Index to assess cardiometabolic risk in adolescents. Br J Nutr. 2015;113:1741–52.
Cohen JFW, Lehnerd ME, Houser RF, Rimm EB. Dietary approaches to stop hypertension diet, weight status, and blood pressure among children and adolescents: National Health and Nutrition Examination Surveys 2003-12. J Acad Nutr Diet. 2017;117:1437–44.
Najafi A, Faghih S, Hojhabrimanesh A, Najafi M, Tangestani H, Atefi M, et al. Greater adherence to the Dietary Approaches to Stop Hypertension (DASH) dietary pattern is associated with lower blood pressure in healthy Iranian primary school children. Eur J Nutr. 2018;57:1449–58.
Eloranta AM, Schwab U, Venäläinen T, Kiiskinen S, Lakka HM, Laaksonen DE, et al. Dietary quality indices in relation to cardiometabolic risk among Finnish children aged 6–8 years–The PANIC study. Nutr Metab Cardiovasc Dis. 2016;26:833–41.
Asghari G, Yuzbashian E, Mirmiran P, Hooshmand F, Najafi R, Azizi F. Dietary Approaches to Stop Hypertension (DASH) dietary pattern is associated with reduced incidence of metabolic syndrome in children and adolescents. J Pediatr. 2016;174:178–84.
Hooshmand F, Asghari G, Yuzbashian E, Mahdavi M, Mirmiran P, Azizi F. Modified healthy eating index and incidence of metabolic syndrome in children and adolescents: Tehran lipid and glucose study. J Pediatr. 2018;197:134–9.
Bull CJ, Northstone K. Childhood dietary patterns and cardiovascular risk factors in adolescence: Results from the Avon longitudinal study of parents and children (ALSPAC) cohort. Public Health Nutr. 2016;19:3369–77.
Pahkala K, Hietalampi H, Laitinen TT, Viikari JS, Rönnemaa T, Niinikoski H, et al. Ideal cardiovascular health in adolescence: effect of lifestyle intervention and association with vascular intima-media thickness and elasticity (the STRIP Study). Circulation. 2013;127:2088–96.
Niinikoski H, Jula A, Viikari J, Rönnemaa T, Heino P, Lagström H, et al. Blood pressure is lower in children and adolescents with a low-saturated-fat diet since infancy. Hypertension. 2009;53:918–24.
Kaikkonen JE, Mikkilä V, Magnussen CG, Juonala M, Viikari JS, Raitakari OT. Does childhood nutrition influence adult cardiovascular disease risk? Insights from the Young Finns Study. Ann Med. 2013;45:120–8.
Herle M, De Stavola B, Hübel C, Ferreira DLS, Abdulkadir M, Yilmaz Z, et al. Eating behavior trajectories in the first 10 years of life and their relationship with BMI. Int J Obes. 2020;44:1766–75.
Herle M, Micali N, Abdulkadir M, Loos R, Bryant-Waugh R, Hübel C, et al. Identifying typical trajectories in longitudinal data: modelling strategies and interpretations. Eur J Epidemiol. 2020;35:205–22.
Kerr JA, Gillespie AN, Gasser CE, Mensah FK, Burgner D, Wake M. Childhood dietary trajectories and adolescent cardiovascular phenotypes: Australian community-based longitudinal study. Public Health Nutr. 2018;21:2642–53.
Castagné‚ R, Garès V, Karimi M, Chadeau-Hyam M, Vineis P, Delpierre C. et al. Allostatic load and subsequent all-cause mortality: which biological markers drive the relationship? Findings from a UK birth cohort. Eur J of epidemiol. 2018;33:1–18.
Gasser CE, Kerr JA, Mensah FK, Wake M. Stability and change in dietary scores and patterns across six waves of the Longitudinal Study of Australian Children. Br J Nutr. 2017;117:1137–50.
Clifford SA, Davies S, Wake M. Child Health CheckPoint: cohort summary and methodology of a physical health and biospecimen module for the Longitudinal Study of Australian Children. BMJ Open. 2019;9:3–22.
Sanson A, Nicholson J, Ungerer J, Zubrick S, Wilson K. Introducing the Longitudinal Study of Australian Children-LSAC discussion paper no. 1. Melbourne, Australia: Australian Institute of Family Studies; 2002.
Jones BL, Nagin DS. A Stata plugin for estimating group-based trajectory models. Pittsburgh: Carnegie Mellon University; 2012.
Andruff H, Carraro N, Thompson A, Gaudreau P, Louvet B. Latent class growth modelling: a tutorial. Tutor Quant Methods Psychol. 2009;5:11–24.
Gasser CE, Mensah FK, Kerr JA, Wake M. Early life socioeconomic determinants of dietary score and pattern trajectories across six waves of the Longitudinal Study of Australian Children. J Epidemiol Community Health. 2017;71:1152–60.
Gasser CE, Mensah FK, Clifford SA, Kerr JA, Wake M. Parental health behaviour predictors of childhood and adolescent dietary trajectories. Public Health Nutr. 2018;21:1874–85.
Daniels SR, Pratt CA, Hayman LL. Reduction of risk for cardiovascular disease in children and adolescents. Circulation. 2011;124:1673–86.
Liu RS, Dunn S, Grobler AC, Lange K, Becker D, Goldsmith G, et al. Carotid artery intima–media thickness, distensibility and elasticity: population epidemiology and concordance in Australian children aged 11–12 years old and their parents. BMJ Open. 2019;9:23–33.
Ellul S, Wake M, Clifford SA, Lange K, Würtz P, Juonala M, et al. Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents. BMJ Open. 2019;9:106–17.
Kahn FK, Wake M, Lycett K, Clifford S, Burgner DP, Goldsmith G, et al. Vascular function and stiffness: population epidemiology and concordance in Australian children aged 11–12 years and their parents. BMJ Open. 2019;9:34–43.
Dascalu J, Liu M, Lycett K, Grobler AC, He M, Burgner DP, et al. Retinal microvasculature: population epidemiology and concordance in Australian children aged 11–12 years and their parents. BMJ Open. 2019;9:44–52.
Lycett K, Juonala M, Magnussen CG, Norrish D, Mensah FK, Liu R, et al. Body mass index from early to late childhood and cardiometabolic measurements at 11 to 12 years. Pediatrics 2020; e-pub ahead of print July 2020; https://doi.org/10.1542/peds.2019-3666.
Gurka MJ, Ice CL, Sun SS, Deboer MD. A confirmatory factor analysis of the metabolic syndrome in adolescents: an examination of sex and racial/ethnic differences. Cardiovasc Diabetol. 2012;11:128.
Ellul S, Mensah F, Grobler A, Carlin JB. Technical paper 1: development and use of CheckPoint sample weights. Melbourne: Murdoch Children’s Research Institute; 2017.
Perneger TV. What’s wrong with Bonferroni adjustments. BMJ. 1998;316:1236–8.
Australian Institute of Health and Welfare. A picture of overweight and obesity in Australia 2017. Cat. no. PHE216. Canberra: Australian Institute of Health and Welfare; 2017.
Blakemore T, Strazdins L, Gibbings J. Measuring family socioeconomic position. Australian Social Policy. 2009;8:121–68.
Kaldor J, Clayton D. Latent class analysis in chronic disease epidemiology. Stat Med. 1985;4:327–35.
Liberali R, Kupek E, de Assis MAA. Dietary patterns and childhood obesity risk: a systematic review. Child Obes. 2019;16:70–85.
Johnson BJ, Bell LK, Zarnowiecki D, Rangan AM, Golley RK. Contribution of discretionary foods and drinks to Australian children’s intake of energy, saturated fat, added sugars and salt. Children. 2017;4:104.
Golley RK, Hendrie GA, McNaughton SA. Scores on the Dietary Guideline Index for children and adolescents are associated with nutrient intake and socio-economic position but not adiposity. J Nutr. 2011;141:1340–7.
Liu RS, Mensah FK, Carlin J, Edwards B, Ranganathan S, Cheung M, et al. Socioeconomic position is associated with carotid intima–media thickness in mid‐childhood: the Longitudinal Study of Australian Children. J Am Heart Assoc. 2017;6:e005925.
Mackenbach JP, Kulhánová I, Artnik B, Bopp M, Borrell C, Clemens T, et al. Changes in mortality inequalities over two decades: register based study of European countries. BMJ. 2016;353:i1732.
Mente A, Dehghan M, Rangarajan S, McQueen M, Dagenais G, Wielgosz A, et al. Association of dietary nutrients with blood lipids and blood pressure in 18 countries: a cross-sectional analysis from the PURE study. Lancet Diabetes Endocrinol. 2017;5:774–87.
Littlejohns TJ, Sudlow C, Allen NE, Collins R. UK Biobank: opportunities for cardiovascular research. Eur Heart J. 2017;40:1158–66.
Sarganas G, Rosario AS, Neuhauser HK. Resting heart rate percentiles and associated factors in children and adolescents. J Pediatr. 2017;187:174–81.
Ostchega Y, Porter KS, Hughes J, Dillon CF, Nwankwo T. Resting pulse rate reference data for children, adolescents, and adults: United States, 1999-2008. US Natl Center for Health Stat. 2011;41:1–16.
Vazir A, Claggett B, Cheng S, Skali H, Shah A, Agulair D, et al. Association of resting heart rate and temporal changes in heart rate with outcomes in participants of the Atherosclerosis Risk in Communities Study. JAMA Cardiol. 2018;3:200–6.
Malik S, Wong ND, Franklin SS, Kamath TV, L’Italien GJ, Pio JR, et al. Impact of the metabolic syndrome on mortality from coronary heart disease, cardiovascular disease, and all causes in United States adults. Circulation. 2004;110:1245–50.
van Sloten TT, Schram MT, van den Hurk K, Dekker JM, Nijpels G, Henry RMA, et al. Local stiffness of the carotid and femoral artery is associated with incident cardiovascular events and all-cause mortality: the Hoorn Study. J Am Coll Cardiol. 2014;63:1739–47.
Yuan C, Wang J, Ying M. Predictive value of carotid distensibility coefficient for cardiovascular diseases and all-cause mortality: a meta-analysis. PLoS ONE. 2016;11:e0152799.
Chen X, Wang Y. Tracking of blood pressure from childhood to adulthood: a systematic review and meta–regression analysis. Circulation. 2008;117:3171–80.
Palatini P, Julius S. Elevated heart rate: a major risk factor for cardiovascular disease. Clin Exp Hypertens. 2004;26:637–44.
Woo KS, Chook P, Yu CW, Sung RYT, Qiao M, Leung SSF, et al. Effects of diet and exercise on obesity-related vascular dysfunction in children. Circulation. 2004;109:1981–6.
Meyer AA, Kundt G, Lenschow U, Schuff-Werner P, Kienast W. Improvement of early vascular changes and cardiovascular risk factors in obese children after a six-month exercise program. J Am Coll Cardiol. 2006;48:1865–70.
Santiprabhob J, Limprayoon K, Aanpreung P, Charoensakdi R, Kalpravidh RW, Phonrat B, et al. Impact of a group-based treatment program on adipocytokines, oxidative status, inflammatory cytokines and arterial stiffness in obese children and adolescents. J Pediatr Endocrinol Metab. 2018;31:733–42.
Touboul PJ, Hennerici MG, Meairs S, Adams H, Amarenco P, Bornstein N, et al. Mannheim carotid intima-media thickness and plaque consensus (2004–6–2011). Cerebrovasc Dis. 2012;34:290–6.
Knudtson MD, Lee KE, Hubbard LD, Wong TY, Klein R, Klein BE. Revised formulas for summarizing retinal vessel diameters. Curr Eye Res. 2003;27:143–9.
Acknowledgements
This paper uses data from Growing Up in Australia, the Longitudinal Study of Australian Children (LSAC). The study is conducted in partnership between the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The authors thank all families, senior researchers, research assistants, students, and interns who assisted in LSAC and CheckPoint data collection and management. The findings and views reported in this paper are those of the authors and should not be attributed to DSS, AIFS, or the ABS. REDCap (Research Electronic Data Capture) electronic data capture tools were used in this study. More information about this software can be found at: www.project-redcap.org. We thank the LSAC and CheckPoint study participants, staff, and students for their contributions.
Funding
The Child Health CheckPoint was supported by the National Health and Medical Research Council (NHMRC) of Australia (Project Grants 1041352, 1109355), The Royal Children’s Hospital Foundation (2014-241), the Murdoch Children’s Research Institute (MCRI), The University of Melbourne, the National Heart Foundation of Australia (100660), Financial Markets Foundation for Children (2014-055, 2016-310) and the Victorian Deaf Education Institute. The following authors were supported by the NHMRC: MW (Principal Research Fellowship 1160906), DB (Senior Research Fellowship 1064629); FKM (Career Development Fellowship 1111160); KL (Early Career Fellowship 1091124). The following authors were supported by the National Heart Foundation of Australia: Honorary Future Leader Fellowship to DB (100369); Postdoctoral Fellowship to KL (101239). The MCRI administered the research grants for the study and provided infrastructural support to its staff and the study, but played no role in the conduct or analysis of the study. DSS played a role in study design; however, no other funding bodies had a role in the study design and conduct; data collection, management, analysis and interpretation; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.
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JAK planned and conducted the analyses, drafted the initial manuscript, and reviewed and revised the manuscript. CEG created the diet trajectories, and reviewed and revised the manuscript. FKM, DB, MJ, TO, RS, LG, PA, BE, and TD are study investigators involved in the conception and oversight of the Child Health CheckPoint, and provided expert advice and critical review of this manuscript. RSL, KL, and SAC and ANG and ML are study staff, students, and postdoctoral fellows and contributed to data creation and critical review of the manuscript. MW is the principal investigator of the Child Health CheckPoint and provided critical review of this manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
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Kerr, J.A., Liu, R.S., Gasser, C.E. et al. Diet quality trajectories and cardiovascular phenotypes/metabolic syndrome risk by 11–12 years. Int J Obes 45, 1392–1403 (2021). https://doi.org/10.1038/s41366-021-00800-x
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DOI: https://doi.org/10.1038/s41366-021-00800-x
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