Novel predictors of Alzheimer's disease in Down syndrome identified using machine learning

Abstract

Background

Most adults with Down syndrome will develop Alzheimer's disease (AD) due to the triplication of the amyloid precursor protein in the 21^st chromosome. Predictors of condition onset are less known.

Objective

We used Medicaid and Medicare data and machine learning to identify which co-occurring conditions predict incident AD in United States adults with Down syndrome.

Methods

We examined adults with Down syndrome enrolled in Medicaid and/or Medicare between 2011 and 2019. We identified AD and other conditions using ICD 9 and 10 codes. We used a case-control design with risk set sampling to have that controls to mimic the distribution of times of incident AD. We trained gradient boosted trees to identify strongest predictors.

Results

The cohort had a mean age at entry of 44.6 years, 46.2% were male, and 73.7% were white non-Hispanic. 16,398 had incident AD diagnoses over the study period. The machine learning model had an area under the curve of 0.86 and high positive predictive value. Strongest predictors of increased probability of AD were age, dual Medicaid/Medicare enrollment; incident epilepsy or incident ulcer three years before index date; any hypothyroidism, schizophrenia, or hyperlipidemia. We found synergistic interaction between epilepsy and enrollment by age.

Conclusions

Predictors aligned with known predictors in the general population and characteristics that signal AD symptom onset. New onset epilepsy may be a relevant clinical sign. Identifying these predictors highlights areas for further etiologic inquiry and intervention.