Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort

Abstract

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.

Keywords: Alzheimer’s disease; Brain age; Machine learning; Mortality; Proteomics.

Fig. 1

Cumulative hazards of death by tertile (t1 ≤  − 0.75 years, − 0.75 < t2 < 1.75, t3 ≥ 1.75) of the BAG values for cognitively normal participants at visit 5 of the study. The Cox regression model was adjusted for age, sex, center-race, smoking, hypertension, education, diabetes, and intra-cranial volume

Fig. 2

BAG across cognitive status at visit 5 of the study

Fig. 3

Proteomic associations with BAG of all participants (left panel) and cognitively normal (right panel) participants. We fitted linear regression models for each protein at a time using the BAG values as the outcome. A Bonferroni correction (α < 0.05, corrected) for multiple comparisons was applied. The models were adjusted for age, sex, center-race, smoking, hypertension, education, diabetes, and intra-cranial volume. Red and green horizontal lines correspond to Bonferroni and FDR correction for multiple comparisons, respectively

Fig. 4

Biology of individual proteins. A The majority of BAG-associated proteins were implicated in one of eight biological pathways as identified by Gene Ontology (GO) terms. B Heatmap shows expression levels of genes encoding candidate proteins (cognate genes) across 76 available tissue types based on single-cell transcriptomics data sourced from the Human Protein Atlas. C Heatmap shows expression levels of genes encoding candidate proteins (cognate genes) across 18 different neurovascular cell types based on single-cell transcriptomics sourced from the Human BBB. Dendrograms reflect hierarchical clustering using Euclidean distances calculated from normalized transcripts per million (nTPM). nTPMs used to generate heatmaps were additionally standardized within cell types to improve interpretability. D Protein–protein interaction networks generated using STRING, with predicted conformations of proteins depicted in circular nodes