Sex differences in predictors and regional patterns of brain age gap estimates

Abstract

The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.

Keywords: aging; brainAGE; human connectome project; machine learning; sex differences; structural MRI; young adults.

FIGURE 1

Sex differences in L‐brainAGE. T‐value overlay of statistically significant sex differences in LbrainAGE (PFWE < .05 with familywise error‐correction). Red/yellow: Females > males; blue: Males > females. Images are displayed in neurological orientation with MNI coordinates

FIGURE 2

Predictors of G‐brainAGE in females and males. Relative importance of predictors derived from random forest regression based on mean decrease in prediction accuracy when removed from the model, scaled to range from 0–100 (values do not reflect percentage of variance explained); predictors are displayed in descending order of importance; (a) in females, there was a positive relationship with GbrainAGE for systolic blood pressure and poor sleep quality, and a negative relationship for education level; (b) in males, there was a positive relationship with G‐brainAGE for non‐white self‐identified racial categories, poor sleep quality, and times used illicit drugs, and a negative relationship for number of childhood conduct problems and emotional support

FIGURE 3

Scatter plots of the association of G‐brainAGE and functional indicators. Results are shown for (a) endurance, (b) gait speed, (c) grip strength, (d) fluid cognition composite score, and (e) crystalized cognition composite score; green = males; purple = females. None of these associations were statistically significant