# Statistical Models of Alzheimer's Disease Pathological Cascade

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2020 · $409,375

## Abstract

Abstract
Enormous effort has been made to uncover the series of changes in biomarkers along Alzheimer’s disease
(AD) pathophysiological pathway and its later clinical manifestations. The most influential hypothetical model
proposed by Jack and colleagues has greatly shaped AD research in the past decade, whereas it remains a
hypothesis to be validated. The key challenge in the validation is the fact that AD pathophysiological process is
not directly observable. The temporal biomarker profile is therefore usually examined against discrete clinical
diagnoses, estimated years from clinical symptom onset or test score of cognitive impairment – neither is a
good measure of the AD pathogenic process, but merely clinical consequences that have been shown to vary
greatly among individuals and also to be affected by other diseases. In this proposal, we will tackle this topic in
the following aspects. (1) We will develop appropriate statistical models that directly address the unobservable
nature of the AD pathophysiological process and therefore provide the foundation to operationalize and
validate hypothetical AD biomarker models. (2) We will utilize data across multiple AD database to provide
data-based evidence on the AD biomarker cascade and its clinical manifestations, as well as inform the link
between the newly proposed biological AD definition in the 2018 NIA-AA research guideline and the current
syndromic AD definition. (3) We will develop a statistical framework for dynamic prediction of AD
pathophysiological progression trajectory and its clinical manifestations based on the history of a patient’s
biomarker profiles. (4) We will develop a web-based application that allows for expedited delivery of statistical
learning into practice. Although the scientific questions are focused, the proposed statistical model is
applicable to many observational studies with longitudinal, multivariate biomarker measures to capture an
unobservable structure, such as in aging or mental health studies.

## Key facts

- **NIH application ID:** 10026353
- **Project number:** 1R01AG068002-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Zheyu Wang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $409,375
- **Award type:** 1
- **Project period:** 2020-09-15 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10026353

## Citation

> US National Institutes of Health, RePORTER application 10026353, Statistical Models of Alzheimer's Disease Pathological Cascade (1R01AG068002-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10026353. Licensed CC0.

---

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