Biological age prediction in schizophrenia using brain MRI, gut microbiome and blood data

Abstract

The study of biological age prediction using various biological data has been widely explored. However, single biological data may offer limited insights into the pathological process of aging and diseases. Here we evaluated the performance of machine learning models for biological age prediction by using the integrated features from multi-biological data of 140 healthy controls and 43 patients with schizophrenia, including brain MRI, gut microbiome, and blood data. Our results revealed that the models using multi-biological data achieved higher predictive accuracy than those using only brain MRI. Feature interpretability analysis of the optimal model elucidated that the substantial contributions of the frontal lobe, the temporal lobe and the fornix were effective for biological age prediction. Notably, patients with schizophrenia exhibited a pronounced increase in the predicted biological age gap (BAG) when compared to healthy controls. Moreover, the BAG in the SZ group was negatively and positively correlated with the MCCB and PANSS scores, respectively. These findings underscore the potential of BAG as a valuable biomarker for assessing cognitive decline and symptom severity of neuropsychiatric disorders.