Biological age prediction in schizophrenia using brain MRI, gut microbiome and blood data.
Authors
Affiliations (14)
Affiliations (14)
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China. Electronic address: [email protected].
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China. Electronic address: [email protected].
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China. Electronic address: [email protected].
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China. Electronic address: [email protected].
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China. Electronic address: [email protected].
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China. Electronic address: [email protected].
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China. Electronic address: [email protected].
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China. Electronic address: [email protected].
- Psychiatric service of the Centro Hospitalar Conde de São Januário, Macao 999078, China.
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China; School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China. Electronic address: [email protected].
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA. Electronic address: [email protected].
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China. Electronic address: [email protected].
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou 510370, China. Electronic address: [email protected].
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China; Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou 510006, China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan. Electronic address: [email protected].
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.