# Periodontal antibodies to predict Alzheimer's disease mortality

> **NIH NIH R21** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2020 · $401,420

## Abstract

Abstract
Early Alzheimer’s disease (AD) diagnosis may improve its management and slow disease progression.
Developing new biomarkers complementing existing diagnostic tools can potentially contribute to early AD
diagnosis and risk prediction. IgG antibodies against selected periodontal microorganisms remain elevated in
the blood for up to 15 years following exposure and precede the development of cognitive impairment by
several years. IgG antibodies against periodontal microorganisms may therefore be useful novel biomarkers of
AD. We have reported that empirically derived groups of IgG antibodies against 19 periodontal
microorganisms consistently predicted all-cause, cardiovascular and cancer mortality in the National Health
and Nutrition Examination Study 3 (NHANES 3) follow-up. In the present application we propose to evaluate
the association between empirically derived groups of 19 IgG antibodies against periodontal microorganisms
and Alzheimer’s disease mortality and cognitive impairment in the NHANES 3 data. The results of this study
will open up AD prevention strategies by identifying potentially novel biomarkers to predict AD years before it
develops and provide mechanistic insights linking the human microbiome to AD and cognition.

## Key facts

- **NIH application ID:** 10108050
- **Project number:** 1R21AG070449-01
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Anwar Merchant
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $401,420
- **Award type:** 1
- **Project period:** 2020-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10108050

## Citation

> US National Institutes of Health, RePORTER application 10108050, Periodontal antibodies to predict Alzheimer's disease mortality (1R21AG070449-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10108050. Licensed CC0.

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