ABSTRACT: Dementia is one of the most burdensome illnesses in elderly populations worldwide. However, the literature about multiple risk factors for dementia is scant.To develop a simple, rapid, and appropriate predictive tool for the clinical quantitative assessment of multiple risk factors for dementia.A population-based cohort study.Based on the Taiwan National Health Insurance Research Database, participants first diagnosed with dementia from 2000 to 2009 and aged ?65 years in 2000 were included.A logistic regression model with Bayesian supervised learning inference was implemented to evaluate the quantitative effects of 1- to 6-comorbidity risk factors for dementia in the elderly Taiwanese population: depression, vascular disease, severe head injury, hearing loss, diabetes mellitus (DM), and senile cataract, identified from a nationwide longitudinal population-based database.This study enrolled 4749 (9.5%) patients first diagnosed as having dementia. Aged, female, urban residence, and low income were found as independent sociodemographic risk factors for dementia. Among all odds ratios (ORs) of 2-comorbidity risk factors for dementia, comorbid depression and vascular disease had the highest adjusted OR of 6.726. The 5-comorbidity risk factors, namely depression, vascular disease, severe head injury, hearing loss, and DM, exhibited the highest OR of 8.767. Overall, the quantitative effects of 2 to 6 comorbidities and age difference on dementia gradually increased; hence, their ORs were less than additive. These results indicate that depression is a key comorbidity risk factor for dementia.The present findings suggest that physicians should pay more attention to the role of depression in dementia development. Depression is a key cormorbidity risk factor for dementia. It is the urgency of evaluating the nature of the link between depression and dementia; and further testing what extent controlling depression could effectively lead to the prevention of dementia.