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The current study tested whether poorer cardiometabolic health is associated with brain structure and microstructure development in 10 to 17 year old youth.\nMethods: Using the Adolescent Brain Cognitive Development Study, we analysed data from 3,527 participants with 4,433 observations across three waves (single wave: n = 2,745; two waves: n = 658 participants; three waves: n = 124 participants). 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.\nResults: Higher resting heart rate was associated with higher mean diffusivity, an association that strengthened over time. Higher waist circumference was associated with larger surface area. 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.\nConclusion: Across late childhood and adolescence, brain architecture appears largely insensitive to short term variation in cardiometabolic risk indices.<\/jats:p>","DOI":"10.1101\/2025.10.23.25338683","type":"posted-content","created":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T01:10:28Z","timestamp":1761354628000},"source":"Crossref","is-referenced-by-count":0,"title":["Cardiometabolic risk and structural brain development in a large community-based U.S. cohort"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0974-9304","authenticated-orcid":false,"given":"Dani","family":"Beck","sequence":"first","affiliation":[{"name":"University of Oslo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8644-956X","authenticated-orcid":false,"given":"Lars T.","family":"Westlye","sequence":"additional","affiliation":[{"name":"University of Oslo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian K.","family":"Tamnes","sequence":"additional","affiliation":[{"name":"University of Oslo"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025102704200652000_2025.10.23.25338683v1.1","doi-asserted-by":"crossref","first-page":"102949","DOI":"10.1016\/j.nicl.2022.102949","article-title":"Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults","volume":"33","year":"2022","journal-title":"NeuroImage Clin"},{"key":"2025102704200652000_2025.10.23.25338683v1.2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.25680"},{"key":"2025102704200652000_2025.10.23.25338683v1.3","doi-asserted-by":"crossref","first-page":"e26685","DOI":"10.1002\/hbm.26685","article-title":"Dissecting unique and common variance across body and brain health indicators using age prediction","volume":"45","year":"2024","journal-title":"Hum. 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