A Cross‐Cohort Study Examining the Associations of Metabolomic Profile and Subclinical Atherosclerosis in Children and Their Parents: The Child Health CheckPoint Study and Avon Longitudinal Study of Parents and Children

Background High‐throughput nuclear magnetic resonance profiling of circulating metabolites is suggested as an adjunct for cardiovascular risk evaluation. The relationship between metabolites and subclinical atherosclerosis remains unclear, particularly among children. Therefore, we examined the associations of metabolites with carotid intima‐media thickness (cIMT) and arterial pulse wave velocity (PWV). Methods and Results Data from two independent population‐based studies was examined; (1) cross‐sectional associations with cIMT and PWV in 1178 children (age 11–12 years, 51% female) and 1316 parents (mean age 45 years, 87% female) from the CheckPoint study (Australia); and (2) longitudinal associations in 4249 children (metabolites at 7–8 years, PWV at 10–11 years, 52% female), and cross‐sectional associations in 4171 of their mothers (mean age 48 years, cIMT data) from ALSPAC (The Avon Longitudinal Study of Parents and Children; UK). Metabolites were measured by the same nuclear magnetic resonance platform in both studies, comprising of 69 biomarkers. Biophysical assessments included body mass index, blood pressure, cIMT and PWV. In linear regression analyses adjusted for age, sex, body mass index, and blood pressure, there was no evidence of metabolite associations in either children or adults for cIMT at a 10% false discovery threshold. In CheckPoint adults, glucose was positively, and some high‐density lipoprotein‐cholesterol derived measures and amino acids (glutamine, histidine, tyrosine) inversely associated with PWV. Conclusions These data suggest that in children circulating metabolites have no consistent association with cIMT and PWV once adjusted for body mass index and blood pressure. In their middle‐aged parents, some evidence of metabolite associations with PWV were identified that warrant further investigation.

[1]  J. Carlin,et al.  Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents , 2019, BMJ Open.

[2]  Melissa Wake,et al.  Child Health CheckPoint: cohort summary and methodology of a physical health and biospecimen module for the Longitudinal Study of Australian Children , 2019, BMJ Open.

[3]  F. Huang,et al.  Carotid–Femoral Pulse Wave Velocity in the Prediction of Cardiovascular Events and Mortality: An Updated Systematic Review and Meta-Analysis , 2018, Angiology.

[4]  D. Lawlor,et al.  Metabolic characterization of menopause: cross-sectional and longitudinal evidence , 2017, bioRxiv.

[5]  L. Liang,et al.  Comprehensive Metabolomic Profiling and Incident Cardiovascular Disease: A Systematic Review , 2017, Journal of the American Heart Association.

[6]  M. Jarvelin,et al.  Association of pre-pregnancy body mass index with offspring metabolic profile: Analyses of 3 European prospective birth cohorts , 2017, PLoS medicine.

[7]  J. Carlin,et al.  Socioeconomic Position Is Associated With Carotid Intima–Media Thickness in Mid‐Childhood: The Longitudinal Study of Australian Children , 2017, Journal of the American Heart Association.

[8]  M. Ward,et al.  Increasing proportion of ST elevation myocardial infarction patients with coronary atherosclerosis poorly explained by standard modifiable risk factors , 2017, European journal of preventive cardiology.

[9]  Debbie A Lawlor,et al.  Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies , 2017, American journal of epidemiology.

[10]  D. Lawlor,et al.  Metabolic signatures of birthweight in 18 288 adolescents and adults , 2016, International journal of epidemiology.

[11]  M. Pirinen,et al.  Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA , 2016, Nature Communications.

[12]  J. Kals,et al.  Metabolomic signature of arterial stiffness in male patients with peripheral arterial disease , 2015, Hypertension Research.

[13]  T. Spector,et al.  Metabolomic study of carotid–femoral pulse-wave velocity in women , 2015, Journal of hypertension.

[14]  Tom R. Gaunt,et al.  Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. , 2015, Circulation.

[15]  Pasi Soininen,et al.  Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. , 2015, Circulation. Cardiovascular genetics.

[16]  Samuli Ripatti,et al.  Metabolic Signatures of Adiposity in Young Adults: Mendelian Randomization Analysis and Effects of Weight Change , 2014, PLoS medicine.

[17]  Toomas Haller,et al.  Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons , 2014, PLoS medicine.

[18]  B. Edwards.  Growing Up in Australia: The Longitudinal Study of Australian Children: Entering adolescence and becoming a young adult , 2014 .

[19]  Melissa Wake,et al.  Introducing growing up in Australia's child health checkpoint: A physical health and biomarkers module for the longitudinal study of Australian children , 2014 .

[20]  Mark Butlin,et al.  Carotid-femoral pulse wave velocity assessment using novel cuff-based techniques: comparison with tonometric measurement , 2013, Journal of hypertension.

[21]  Thomas J. Wang,et al.  A diabetes-predictive amino acid score and future cardiovascular disease. , 2013, European heart journal.

[22]  D. Lawlor,et al.  Cohort Profile: The ‘Children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children , 2012, International journal of epidemiology.

[23]  D. Lawlor,et al.  Cohort Profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort , 2012, International journal of epidemiology.

[24]  Reino Laatikainen,et al.  High-throughput quantification of circulating metabolites improves prediction of subclinical atherosclerosis. , 2012, European heart journal.

[25]  T. Lehtimäki,et al.  Characterization of systemic metabolic phenotypes associated with subclinical atherosclerosis. , 2011, Molecular bioSystems.

[26]  T. Laitinen,et al.  Pediatric Metabolic Syndrome Predicts Adulthood Metabolic Syndrome, Subclinical Atherosclerosis, and Type 2 Diabetes Mellitus but Is No Better Than Body Mass Index Alone: The Bogalusa Heart Study and the Cardiovascular Risk in Young Finns Study , 2010, Circulation.

[27]  D. Lawlor,et al.  Determinants of vascular phenotype in a large childhood population: the Avon Longitudinal Study of Parents and Children (ALSPAC). , 2010, European heart journal.

[28]  T. Lehtimäki,et al.  Lifetime Risk Factors and Arterial Pulse Wave Velocity in Adulthood: The Cardiovascular Risk in Young Finns Study , 2010, Hypertension.

[29]  O. Raitakari,et al.  Epidemiology and Prevention Influence of Age on Associations Between Childhood Risk Factors and Carotid Intima-Media Thickness in Adulthood , 2010 .

[30]  Matthias W. Lorenz,et al.  Prediction of Clinical Cardiovascular Events With Carotid Intima-Media Thickness: A Systematic Review and Meta-Analysis , 2007, Circulation.

[31]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[32]  James C. Beyer,et al.  Coronary Disease Among United States Soldiers Killed in Action in Korea: Preliminary Report , 1953 .