Predictors of Dementia in the Oldest Old

A Novel Machine Learning Approach

Abstract

Background: 

Incidence of dementia increases exponentially with age; little is known about its risk factors in the ninth and 10th decades of life. We identified predictors of dementia with onset after age 85 years in a longitudinal population-based cohort.

Methods: 

On the basis of annual assessments, incident cases of dementia were defined as those newly receiving Clinical Dementia Rating (CDR) ≥1. We used a machine learning method, Markov modeling with hybrid density-based and partition-based clustering, to identify variables associated with subsequent incident dementia.

Results: 

Of 1439 participants, 641 reached age 85 years during 10 years of follow-up and 45 of these became incident dementia cases. Using hybrid density-based and partition-based, among those aged 85+ years, probability of incident dementia was associated with worse self-rated health, more prescription drugs, subjective memory complaints, heart disease, cardiac arrhythmia, thyroid disease, arthritis, reported hypertension, higher systolic and diastolic blood pressure, and hearing impairment. In the subgroup aged 85 to 89 years, risk of dementia was also associated with depression symptoms, not currently smoking, and lacking confidantes.

Conclusions: 

An atheoretical machine learning method revealed several factors associated with increased probability of dementia after age 85 years in a population-based cohort. If independently validated in other cohorts, these findings could help identify the oldest-old at the highest risk of dementia.