medRxiv : the preprint server for health sciences · 2025
Cardiometabolic risk and structural brain development in a large community-based U.S. cohort.
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Beck Dani, Westlye Lars T, Tamnes Christian K
Аннотация
OBJECTIVE: Cardiometabolic risk factors are already detectable in childhood and adolescence, but their relations to the developing brain remains unclear. The current study tested whether poorer cardiometabolic health is associated with brain structure and microstructure development in 10-17-year-old youth.
METHODS: Using the Adolescent Brain Cognitive Development Study, we analysed data from 3,527 participants with 4,433 observations across three waves. We related anthropometric (body-mass index, waist circumference), cardiovascular (systolic and diastolic blood pressure, resting heart rate), and metabolic (haemoglobin A1c, high-density lipoprotein cholesterol) indices to global cortical thickness and surface area, and to white matter fractional anisotropy and mean diffusivity. Bayesian multilevel models were fitted to estimate main and time-interaction effects, and sensitivity analyses tested within-person change, prospective prediction to the next wave, and replaced chronological age with puberty status.
RESULTS: Higher body-mass index was associated with thinner cortex, and higher resting heart rate was associated with higher mean diffusivity, an association that strengthened over time. Other cardiometabolic measures favoured the null, and sensitivity analyses provided little evidence that wave-to-wave changes in cardiometabolic health tracked contemporaneous brain change or predicted subsequent brain structure.
CONCLUSION: Across late childhood and adolescence, brain architecture appears largely insensitive to variation in cardiometabolic risk indices.
METHODS: Using the Adolescent Brain Cognitive Development Study, we analysed data from 3,527 participants with 4,433 observations across three waves. We related anthropometric (body-mass index, waist circumference), cardiovascular (systolic and diastolic blood pressure, resting heart rate), and metabolic (haemoglobin A1c, high-density lipoprotein cholesterol) indices to global cortical thickness and surface area, and to white matter fractional anisotropy and mean diffusivity. Bayesian multilevel models were fitted to estimate main and time-interaction effects, and sensitivity analyses tested within-person change, prospective prediction to the next wave, and replaced chronological age with puberty status.
RESULTS: Higher body-mass index was associated with thinner cortex, and higher resting heart rate was associated with higher mean diffusivity, an association that strengthened over time. Other cardiometabolic measures favoured the null, and sensitivity analyses provided little evidence that wave-to-wave changes in cardiometabolic health tracked contemporaneous brain change or predicted subsequent brain structure.
CONCLUSION: Across late childhood and adolescence, brain architecture appears largely insensitive to variation in cardiometabolic risk indices.