The gap between predicted brain age and chronological age could serve as biomarker for early-stage neurodegeneration and as potentially as a risk indicator for dementia. We assess the utility of this age gap as a risk biomarker for incident dementia in a general elderly population. The brain age is estimated from longitudinal brain magnetic resonance imaging (MRI) data using deep learning models. From the population-based Rotterdam Study, 5656 dementia-free and stroke-free participants (mean age 64.67±9.82, 54.73% women) underwent brain MRI at 1.5T, including three-dimensional (3D) T1-weighted sequence, between 2006 and 2015. All participants were followed for incident dementia until 2016. During 6.66±2.46 years of follow-up, 159 subjects developed dementia. The entire dataset was split into control (N=5497) and incident dementia (N=159) groups. We then built a convolutional neural network (CNN) model trained on the control group to predict brain age based on brain MRI. Model prediction performance was measured in mean absolute error MAE=4.45±3.59 years of brain age prediction. Reproducibility of prediction was tested using the intra-class correlation coefficient ICC=0.97 (95% confidence interval CI=0.96-0.98), computed on a subset of 80 subjects. Hereafter, we investigated the gap between model predicted age and chronological age of the incident dementia group data, compared to control group. Logistic regressions and Cox proportional hazards models were used to assess the association of the age gap with incident dementia, adjusted for years of education, ApoE4 allele carriership, GM and intracranial volume. These models showed that the age gap was significantly related to incident dementia (odds ratio OR=1.11 and 95% confidence intervals CI=1.05-1.16; hazard ratio HR=1.11 and 95% CI=1.06-1.15, respectively). Additionally, we computed the attention maps of CNN, which shows the importance of brain regions for age prediction. These were particularly focused on the amygdalae and hippocampi. We show that the gap between predicted and chronological brain age is a biomarker, associated with a risk of dementia development. This suggests that it can potentially be used as a complimentary biomarker for early-stage dementia risk screening.

Depression, a leading cause of disability worldwide, is challenging to diagnose due to its reliance on subjective clinical evaluations. Metabolomics, which analyzes small molecules to reflect physiological and pathological states, holds promise for enhancing the diagnosis and identifying biomarkers for depression, potentially leading to better understanding and treatment options. Despite the complexity of metabolomics data, deep learning methods have not been extensively explored due to issues with interpretability, which are crucial for gaining insights into biological mechanisms. This study evaluates a biologically interpretable deep neural network, MetaboNet, trained on metabolomics data, for predicting depression and identifying key metabolites and biochemical pathways relevant to the condition. Our results demonstrate that MetaboNet outperforms logistic regression, though the overall classification performance remains modest. Notably, the classification results revealed sex-related differences, with better performance observed in females. Our findings do not support the capability of MetaboNet to identify biologically relevant individual metabolites. However, MetaboNet shows promise in identifying biochemical sub-pathways and super-pathways relevant to depression, which are validated by existing literature.