Abstract 051: Association Between Body Mass Index Trajectories From Early To Middle Childhood And Biomarkers Of Cardiometabolic Risk Among Children Living In Low-income And Racially/ethnically Diverse Households

Abstract

Introduction: Substantial cross-sectional evidence exists supporting an association between high body mass index (BMI) during childhood and increased cardiometabolic risk factors. However, less is known about longitudinal associations of child BMI trajectories with biomarkers of cardiometabolic risk. The objective of this study was to (1) identify distinct BMI trajectories during childhood from ages 2-11 years and (2) evaluate the association of these BMI trajectories with biomarkers of cardiometabolic risk at ages 7-11 years. Methods: The analytic sample included 338 children living in low-income and racially/ethnically diverse households who participated in the NET-Works randomized intervention trial and the NET-Works 2 prospective follow-up study. BMI was measured at baseline (when children were 2-4 years of age) and again at 1-, 2-, 3- and 5-year follow-up visits. Percent of the 95 th percentile of BMI (%BMIp95) was the BMI metric calculated at each time point. At the 5-year follow-up visit, children (aged 7 to 11 years) completed assessments for carotid to anterior tibial artery pulse wave velocity (PWV), C-reactive protein (CRP), leptin, adiponectin, and oxidized low-density lipoprotein (oxLDL). Group-based trajectory modeling was used to identify child %BMIp95 trajectory groups. Adjusted linear regressions evaluated the associations between %BMIp95 trajectory groups and PWV, CRP, leptin, adiponectin, and oxLDL. Results: We identified two trajectory groups: (1) 75% of children had a moderate-decreasing trajectory (baseline BMI was below the 95th percentile of BMI and followed a moderate-decreasing trajectory over time), and (2) 25% had a marked-increasing trajectory (baseline BMI was above the 95th percentile of BMI and followed a steep increase over time). Relative to children in the moderate-decrease trajectory group, children in the marked-increase trajectory group had higher mean adjusted CRP (3.3 mg/L, 95% CI [1.6, 4.9]) and leptin (62.5 ng/mL, 95% CI [44.0, 80.9]), and lower levels of adiponectin (-1.4 μg/mL, 95% CI [-2.5, -0.1]). Differences in PWV and oxLDL were small and not statistically significant. Conclusions: Two unique BMI trajectories were observed among children living in low-income and racially/ethnically diverse households. Compared to children with steady BMI decrease over time, those with steady BMI increase over time had a higher baseline BMI and adverse inflammatory and adipokine profiles later in childhood. However, we found no evidence of differences in arterial stiffness, which could develop later in adolescence or adulthood. This study provides additional objective evidence supporting the importance of early-life tracking of BMI using the %BMIp95 metric and lifestyle interventions to reduce the risk of obesity and emerging cardiometabolic risk during childhood.