# Screening ADNI patient cohorts to identify a sub-population that are AD early progressors in validating the prognostic ability of the Alzosure Predict blood test, 6 years in advance of current methods

> **NIH NIH R44** · DIADEM US, INC. · 2023 · $859,175

## Abstract

ABSTRACT
AD is a progressive dementia with a disease-severity gradient from normal cognition to irreversible cognitive
impairment, and accounts for 60 to 80% of all dementias. There are 10 million new cases per year, and 50 million
total cases worldwide, including 5.8 million people in the United States alone living with AD in 2019. Furthermore,
these figures are expected to significantly escalate in the next decade or two. The AD socioeconomic burden is
substantial; worldwide dementia-related costs increased 35% between 2010 and 2015, and by 2030 are
estimated to reach 2 trillion USD. At present, disease management is symptomatic (cholinesterase inhibitors or
NMDA antagonists) as definitive care options have not yet been established. Given the lack of remedial or
curative therapies for AD, the best option for improving clinical outcomes is early detection and prevention to
enable treatment early-on, maximizing physical, mental, and behavioral wellbeing. Current methods of diagnosis
are time consuming, ambiguous or inconclusive, invasive, and expensive, often requiring advanced cognitive
scanning tests or even requiring examination of the cerebrospinal fluid (CSF) for specific disease biomarkers.
However, evidence suggests AD pathophysiological features occur decades prior to initial symptomatic
presentation, and as such, there is a crucial need for minimally-invasive early disease detection.
The p53 protein plays a well-defined role several diseases as cancer, and its dysregulation also contributes to
comorbidities such as diabetes, obesity, and aging-associated neurodegenerative disorders. Specifically, p53
misfolding/ unfolding (U-p53) was observed in sporadic AD subjects’ peripheral fibroblasts, but was not noted in
age-matched non-AD subjects. The presence of altered p53 in AD pathogenesis is well documented
Based on more than 20 years of R&D and developed by Diadem, AlzoSure Predict is a minimally invasive, cost
effective, blood-based test able to detect U-p53AZ.To address the gap in time and cost-effective early diagnosis
of AD samples as plasma, brain tissue and CSF from individuals at different stages of cognitive decline due to
AD progression and recruited by the well characterized retrospective and longitudinal cohort of ADNI 1,2,3 will
be analyzed by AlzoSure® Predict. The availability and the analysis of different biological samples from the same
individuals will provide additional knowledge about occurrence of the biomarker unfolding process at the brain
and/or systemic level. The main goal is the identification of the subpopulation of early progressing individuals to
AD within the longitudinal cohorts. This will be accomplished in a number of study aims, particularly: Specific
Aim 1, will validate the ability of the biomarker to discriminate the different stage of cognitive decline; Specific
Aim 2, will also allow to establish the specificity of the biomarker toward AD compared to other dementias;
Specific Aim 3 will confirm the cl...

## Key facts

- **NIH application ID:** 10688164
- **Project number:** 5R44AG078051-02
- **Recipient organization:** DIADEM US, INC.
- **Principal Investigator:** Shmuel Agus
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $859,175
- **Award type:** 5
- **Project period:** 2022-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10688164

## Citation

> US National Institutes of Health, RePORTER application 10688164, Screening ADNI patient cohorts to identify a sub-population that are AD early progressors in validating the prognostic ability of the Alzosure Predict blood test, 6 years in advance of current methods (5R44AG078051-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10688164. Licensed CC0.

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