# Predictors of Severity in Alzheimer's Disease

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2021 · $1,059,931

## Abstract

PROJECT SUMMARY/ABSTRACT
The ability to predict the length of time from disease onset to major disease outcomes in individual patients with
Alzheimer's disease (AD) has important implications for patient care, the development of interventions and
public health. The major aim of the Predictors Study is to further the understanding of AD progression in order
to develop predictor algorithms to address this issue. Over the past funding periods, we have followed two
clinic-based cohorts of AD patients recruited from three major medical centers, and have made major progress
in characterizing the natural history of AD and identifying predictors of disease course. While the Predictors
study has had a major impact on our understanding of AD and its progression, the patient cohorts are clinic-
based and ethnically homogenous, and the true date of disease onset was unknown. We therefore have
assembled and initiated follow-up a new, well-characterized, population-based cohort of ethnically diverse
elders with AD. Many of these individuals were followed from a point prior to the onset of AD, so the onset date
of clinical AD is known. Another portion are at-risk for AD, allowing us to track the disease process from it
preclinical state. We propose to continue intensive follow-up of this cohort in order to validate our previous
Predictors study findings in this population-based cohort and to implement new research questions based on
novel predictor and outcome variables. We will use linkage to Medicare and Medicaid data to understand
correlates of the economic impact of AD in this multiethnic community cohort, focusing on costs associated with
transition to dementia, medication utilization, the relation of dementia status to the cost of comorbid conditions,
end of life costs, and lifetime economic burden based on predicted disability-free and disabled survival. We will
also continue to refine a unique predictive approach that uses longitudinal Grade of Membership (L-GoM)
modeling to accurately summarize the AD process. We will validate and apply an updated L-GoM model to
external datasets, extend it to include the pre-dementia phase, explore the genetic correlates of heterogeneity
of disease course, incorporate AD biomarkers, determine the extent to the model refines analysis of AD
treatment effects by applying it to data from recent AD clinical trials, and develop and support software for
calculation of the L-GoM model predictions in individual patient data.

## Key facts

- **NIH application ID:** 10164685
- **Project number:** 5R01AG007370-30
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** YAAKOV STERN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,059,931
- **Award type:** 5
- **Project period:** 1989-02-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10164685

## Citation

> US National Institutes of Health, RePORTER application 10164685, Predictors of Severity in Alzheimer's Disease (5R01AG007370-30). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10164685. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*