Abstract P125: Patterns in Timing of Energy Intake and the Impact on Fat Mass in Adolescents

Abstract

Introduction: Pediatric obesity is excessively high, tracks into adulthood, and increases the risk of multiple chronic diseases (e.g., heart disease). There are dietary guidelines to help prevent childhood obesity, but they do not account for the timing of energy intake, which may be associated with obesity, independent of diet content. We aimed to determine: 1) energy intake timing patterns in adolescents, 2) if energy intake timing patterns differed by sociodemographic factors, and 3) if energy intake timing patterns associated with obesity-related outcomes. Methods: The Sleep and Growth Study (S-Grow) assessed diet in adolescents using three 24hr diet recalls in 8 th and 9 th grade. Time-stamped meals were analyzed using Nutrient Data System for Research software. We calculated the average energy (kcal) consumed for two-hour intervals during the 24-hour day (e.g., 00:00-01:59, 02:00-03:59, etc.) and input the 12 energy intake timing variables into a principal component model to identify energy intake timing patterns. Principal components (PC) with an eigen value 1.0 were retained. Linear mixed effect models were used to determine if sociodemographic factors associated with standardized PC loading scores and if the standardized PC loading scores associated with body mass index (BMI) and fat mass index (FMI, measured by dual energy X-ray absorptiometry), adjusting for sociodemographic factors. Results: Five PCs were identified explaining 58.6% of variance in timing of energy intake. Compared to females, male adolescents were more likely to have a higher PC1 score, corresponding to a “low overnight, moderate morning, high daytime and early evening” intake pattern (beta=0.57, 95% CI=0.20, 0.94). Adolescents living in households with $40-69K (compared to >$99K) annual income were more likely to have a higher PC4 score, corresponding to a “high later evening and overnight, moderate morning, low afternoon and early evening” intake pattern (beta=0.44, 95% CI=0.05, 0.83). Adolescents living in households with <$40K (compared to >$99K) annual income were more likely to have a higher PC5 score, corresponding to a “low overnight, high morning, moderate prolonged daytime and late evening” intake pattern (beta=-0.93, 95% CI=-1.66, -0.19). In addition, the PC5 loading score was associated with lower BMI (beta=-1.38, 95% CI=-2.18,-0.58) and lower FMI (beta=-0.94, 95% CI=-1.45,-0.43). Conclusion: We detected distinct energy intake timing patterns in adolescents. Demographic factors were associated with some of the energy intake patterns, and the energy intake pattern corresponding to “low overnight, high morning, moderate prolonged daytime and late evening” (PC5) was associated with obesity-related outcomes. These findings need to be replicated but point to future pediatric dietary guidelines considering the timing of energy intake for obesity prevention.