Parental status and markers of brain and cellular age: A 3D convolutional network and classification study.
Level 3 - non-randomized controlled study
Large cross-sectional observational cohort study
PubMed 38636355 · doi:10.1016/j.psyneuen.2024.107040
What was done
Analysis of 36,323 UK Biobank participants (age range 44.57-82.06 years; 52% female) evaluating how parental status and number of children born or fathered associate with markers of brain and cellular aging. Brain age was estimated from T1-weighted MRI using a 3D convolutional neural network on a held-out test set. Cortical and subcortical volumes were extracted via FreeSurfer and grouped with hierarchical clustering. Leukocyte telomere length (LTL) from DNA served as a marker of cellular aging. Linear regression assessed associations and sex interactions, and binary classification models evaluated whether these markers predicted parental status.
What was found
A higher number of children was associated with younger brain age in both females and males, with stronger associations in females. Volume analyses identified maternal-specific effects in striatal and limbic regions that were absent in fathers. No association was found between the number of children and LTL. Classification of parental status yielded an Area under the ROC Curve (AUC) of 0.57 using brain age and 0.52 using regional brain volumes and LTL. Numerical regression coefficients and effect sizes were not reported in the abstract.
Why it matters
In a very large population cohort, having children was linked to subtle markers of younger structural brain age in late adulthood, but not to telomere length.
Limits
The observational design precludes causal inference. Predictive performance was marginally above chance (AUC 0.52-0.57). The abstract reports no regression effect sizes, confidence intervals, or p-values.