AI Article Synopsis
- * A deep learning model was developed using data from 2,897 cases to predict cognitive function and brain atrophy based on age and basic blood tests, with key predictors being age, nutrition, and organ function.
- * The study supports using routine blood tests for assessing dementia risk and suggests personalized dietary interventions, aligning with traditional Chinese medicine's holistic approach to brain health.
Article Abstract
Recent research has linked systemic metabolic disorders to cognitive decline and dementia risk, including Alzheimer's. This is suspected to be due to lifestyle-related vascular impairments from atherosclerosis and other factors, such as malnutrition and anaemia. Applying deep learning using 2897 cases from a rehabilitation hospital and health screenings, we trained a model to predict cognitive function [mini-mental state examination (MMSE) scores] and brain atrophy [Brain Healthcare Quotient (BHQ) scores] from basic blood tests and age. The deep learning model accurately estimated MMSE and BHQ from these inputs, with age, nutritional information, and organ function indicators being top predictors. These findings highlight the relationship of dementia with systemic metabolic disorders and suggest the potential of using routine blood tests for dementia risk assessment. Furthermore, personalised dietary interventions could be tailored based on blood test anomalies. This holistic view mirrors traditional Chinese medicine, which considers brain disorders systemic, that is related to vital organs but not the brain itself.
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| http://dx.doi.org/10.1007/978-3-031-67458-7_44 | DOI Listing |
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