Abstract

PURPOSE: Cardiometabolic disease (CMD) risk begins early in life, with lifestyle behaviors (e.g. activity, diet) playing a prominent role. However, it is unclear how these lifestyle behaviors associate with each other in preadolescents and whether certain behaviors are more or less important to CMD risk than others. Further, prior studies have mostly examined singular CMD risk factors, combined risk factors as a linear sum, or investigated lifestyle behaviors in isolation. Thus, the purpose of this cross-sectional study was to simultaneously examine the associations among lifestyle behaviors (sedentary minutes, social jetlag, handgrip strength, cardiorespiratory fitness, processed food pattern, fruit/veg pattern) with CMD risk in preadolescent children. METHODS: A data-driven approach (factor analysis), using 11 variables, identified four cardiometabolic risk factors: blood pressure, cholesterol, vascular, and carbohydrate-metabolic. Multivariable analyses, adjusted for age, sex, ethnicity, and socioeconomic status identified associations between exposure (lifestyle behaviors) and outcome (cardiometabolic factors) variables. RESULTS: Sedentary minutes, social jetlag, and fruit/veg pattern associated with the cholesterol factor (β = 0.001, -0.20 and -0.08, respectively; all p < 0.05); sedentary minutes and processed food pattern associated with the vascular factor (β = 0.001 and 0.08, respectively; both p < 0.05); and cardiorespiratory fitness and handgrip strength associated with the carbohydrate-metabolic factor (β = -0.08 and 0.07, respectively; both p < 0.001). No exposure variables associated with the blood pressure factor. CONCLUSIONS: With respect to cardiovascular risk, sedentary minutes may be an especially critical behavior, followed by diet quality. In terms of metabolic control, physical fitness may be key. This study was a necessary starting point to gain an initial understanding of multiple lifestyle behaviors and their effects on CMD risk in preadolescents. Future longitudinal studies are now needed to identify optimal CMD risk prevention strategies in this population.

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