# Stratifying dementia risk factors: A prediction model and hypothesis‐driven analysis

## TL;DR

This study compares different methods to identify key risk factors for dementia, finding that age, depression, and low education are major contributors, while higher BMI is unexpectedly protective.

## Contribution

The study directly compares hypothesis- and data-driven approaches to dementia risk stratification, revealing convergent key predictors in a real-world cohort.

## Key findings

- Age, depression, and low education are consistently identified as major dementia risk factors.
- Higher body mass index (BMI) was unexpectedly found to be protective against dementia conversion.
- Multimorbidity requires simultaneous evaluation of multiple risk factors for accurate dementia risk assessment.

## Abstract

Most older adults present multimorbidity, but dementia risk factors are typically analyzed individually. Direct methodological comparisons evaluating simultaneous multiple risk factors are essential to provide the realistic effects of multimorbidity. We aimed to compare hypothesis‐ and data‐driven approaches for dementia risk stratification in a real‐world cohort.

We analyzed 9606 participants from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (2005–2023) using machine learning with interpretability analysis and survival models to simultaneously evaluate 13 risk factors for incident dementia conversion.

A total of 877 participants (9%) developed dementia over (mean ± SD, 6 ± 4.2) years of follow‐up. Both approaches consistently identified four key predictors: age, depression, low education, and body mass index (protective). Convergent findings across methodologies demonstrated robust factor identification despite different analytical paradigms.

In this direct methodological comparison, age, depression, and low education emerge as major dementia risk factors regardless of analytical approach. Convergent interpretability of these approaches support simultaneous multifactorial risk assessment in clinical practice.

Data‐ and hypothesis‐driven approaches identified convergent key risk factorsAge, depression, and low education are major risk factors for dementiaHigher body mass index was unexpectedly protective against dementia conversionMultimorbidity requires simultaneous evaluation of multiple risk factorsReal‐world analysis reveals complex interactions between dementia risks

Data‐ and hypothesis‐driven approaches identified convergent key risk factors

Age, depression, and low education are major risk factors for dementia

Higher body mass index was unexpectedly protective against dementia conversion

Multimorbidity requires simultaneous evaluation of multiple risk factors

Real‐world analysis reveals complex interactions between dementia risks