RESUMO
OBJECTIVE: The impact of age of diabetes diagnosis on dementia risk across the life course is poorly characterized. We estimated the lifetime risk of dementia by age of diabetes diagnosis. RESEARCH DESIGN AND METHODS: We included 13,087 participants from the Atherosclerosis Risk in Communities Study who were free from dementia at age 60 years. We categorized participants as having middle age-onset diabetes (diagnosis <60 years), older-onset diabetes (diagnosis 60-69 years), or no diabetes. Incident dementia was ascertained via adjudication and active surveillance. We used the cumulative incidence function estimator to characterize the lifetime risk of dementia by age of diabetes diagnosis while accounting for the competing risk of mortality. We used restricted mean survival time to calculate years lived without and with dementia. RESULTS: Among 13,087 participants, there were 2,982 individuals with dementia and 4,662 deaths without dementia during a median follow-up of 24.1 (percentile 25-percentile 75, 17.4-28.3) years. Individuals with middle age-onset diabetes had a significantly higher lifetime risk of dementia than those with older-onset diabetes (36.0% vs. 31.0%). Compared with those with no diabetes, participants with middle age-onset diabetes also had a higher cumulative incidence of dementia by age 80 years (16.1% vs. 9.4%), but a lower lifetime risk (36.0% vs. 45.6%) due to shorter survival. Individuals with middle age-onset diabetes developed dementia 4 and 1 years earlier than those without diabetes and those with older-onset diabetes, respectively. CONCLUSIONS: Preventing or delaying diabetes may be an important approach for reducing dementia risk throughout the life course.
RESUMO
Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.