Practical risk score for 5-, 10-, and 20-year prediction of dementia in elderly persons: Framingham Heart Study.

INTRODUCTION: With a rapidly aging population, general practitioners are confronting the challenge of how to determine those who are at greatest risk for dementia and potentially need more specialized follow-up to mitigate symptoms early in its course. We created a practical dementia risk score and provided individualized estimates of future dementia risk. METHODS: Using the Framingham Heart Study data, we built our prediction model using Cox proportional hazard models and developed a point system for the risk score and risk estimates. RESULTS: The score system used total points ranging from -1 to 31 and stratifies individuals into different levels of risk. We estimated 5-, 10-, and 20-year dementia risk prediction and incorporated these into the points system. DISCUSSION: This risk score system provides a practical tool because all included predictors are easy to assess by practitioners. It can be used to estimate future probabilities of dementia for individuals.

Assessment of the Mid-Life Demographic and Lifestyle Risk Factors of Dementia Using Data from the Framingham Heart Study Offspring Cohort.

BACKGROUND: Dementia is the leading cause of dependence and disability in the elderly population worldwide. However, currently there is no effective medication for dementia treatment. Therefore, identifying lifestyle-related risk factors including some that are modifiable may provide important strategies for reducing risk of dementia. OBJECTIVE: This study aims to highlight associations between easily obtainable lifestyle risk factors in mid-life and dementia in later adulthood. METHODS: Using data from the Framingham Heart Study Offspring cohort, we leveraged well-known classification models (decision tree classifier and random forests) to associate demographic and lifestyle behavioral data with dementia status. We then evaluated model performance by computing area under receiver operating characteristic (ROC) curve. RESULTS: As expected, age was strongly associated with dementia. The analysis also identified 'widowed' marital status, lower BMI, and less sleep at mid-life as risk factors of dementia. The areas under the ROC curves were 0.79 for the decision tree, and 0.89 for the random forest model. CONCLUSION: Demographic and lifestyle factors that are non-invasive and inexpensive to implement can be assessed in midlife and used to potentially modify the risk of dementia in late adulthood. Classification models can help identify associations between dementia and midlife lifestyle risk factors. These findings inform further research, in order to help public health officials develop targeted programs for dementia prevention.