229 - Patterns of cardiometabolic risk among children 3 to 12 years of age in Canada: A longitudinal cohort study

Publication Number: 2220.229

Xuedi Li, The Hospital for Sick Children, Toronto, ON, Canada; Charles DG. Keown-Stoneman, Unity Health Toronto, Toronto, ON, Canada; Marium Kiwan, University of Toronto Temerty Faculty of Medicine, Oakville, ON, Canada; Erica N. Stone, The Hospital for Sick Children, Toronto, ON, Canada; Laura Anderson, McMaster University, Hamilton, ON, Canada; Joseph Jamnik, The Hospital for Sick Children, Toronto, ON, Canada; Jonathon maguire, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Catherine Birken, The Hospital for Sick Children, toronto, ON, Canada

Background: Cardiometabolic measures in children including waist circumference, blood pressure, glucose, and lipids are important indicators of future cardiometabolic risk. The cardiometabolic risk (CMR) score, a composite measure incorporating these components, is commonly used to monitor CMR in children. Few studies have examined cardiometabolic patterns across childhood.

Objective: To identify trajectories of CMR score and individual cardiometabolic measures among healthy children aged 3 to 12 years.

Design/Methods: A longitudinal cohort study between 2008 and 2020 was conducted among healthy children age 3 to 12 years participating in the TARGet Kids! primary care practice-based research network in Canada (www.targetkids.ca). Waist circumference (WC), systolic blood pressure (SBP), glucose, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), non-HDL-C, and total cholesterol were measured during well-child visits. A total CMR z-score was calculated using age- and sex-standardized WC, SBP, glucose, TG, and HDL-C values. Latent class mixed models were used to estimate distinct trajectories of CMR score and the seven individual measures adjusting for potential confounders.

Results: 2801 children had 4712 total CMR scores with complete data on all five components (mean age 5.9 years, 46.1% female; Table 1). For individual cardiometabolic measures, 6553 children had 16883 WC observations; 5984 children had 15653 SBP observations; 3161 children had 5363 glucose and lipids (TG, HDL-C, non-HDL-C, and TC) measurements (Table 2). Two distinct trajectories of total CMR score were identified. 99% of children were classified into a stable trajectory, while a small subgroup followed a high stable trajectory (Figure 1). Similar patterns were observed for most of the seven individual cardiometabolic measures, with approximately 95% of children in each measure following a stable trajectory. The remaining small subgroups, when present, exhibited high stable or rapidly increasing/decreasing trajectories.

Conclusion(s): There was little variation in cardiometabolic patterns across childhood in this longitudinal cohort of healthy children, with most children following a stable trajectory. Our dense data in early childhood may have enabled better characterization of early patterns. However, we may be underpowered to detect divergent trajectories later in childhood due to reduced data density at older ages. Given the observed limited heterogeneity in cardiometabolic patterns, future research could evaluate whether single early measurements can reliably identify children at risk to reduce the need for repeated testing.

Table 1. Characteristics of children aged 3-12 years participating in the TARGet Kids! cohort between 2008 and 2020 with a total cardiometabolic risk (CMR) score, compared to those with at least 1 cardiometabolic measure, and those without bloodwork (glucose and lipids).


Table 2. Descriptive statistics of total cardiometabolic risk (CMR) score and individual cardiometabolic measures.


Figure 1. Trajectories of total cardiometabolic risk (CMR) score in children 3 to 12 years (2,801 children, 4,712 observations). Trajectory 1, the high trajectory (n=29, 1.0%); Trajectory 2, the stable (reference) trajectory (n=2772, 99.0%).