# Predicting post-kidney transplant dementia/Alzheimer's Disease risk in older patients

> **NIH NIH F32** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $94,892

## Abstract

PROJECT SUMMARY/ABSTRACT
Kidney transplantation (KT) is increasing for older adults (≥50) with ESRD. In 2021, older adults received
roughly 60% of all KTs and are at increased risk of dementia/Alzheimer’s disease (AD). KT recipients who
develop dementia/AD post-transplant have a 2.4-fold increased risk of mortality and a 1.5-fold increased risk of
graft loss. Of older KT recipients who are diagnosed with dementia/AD, 88.6% die within 10 years. These
deaths may be due to inability to perform self-care, inadequate nutrition, or medication non-adherence.
Despite these risks, predicting who will develop post KT dementia/AD is not part of pre-KT evaluation.
Furthermore, factors routinely measured at pre-KT evaluation (age, sex, comorbidities, etc.) have only
moderate predictive power for post-KT dementia/AD. Predicting post-KT dementia/AD risk can help identify
older candidates who would benefit from interventions such as cognitive prehabilitation or post-KT surveillance.
Predicting post-KT dementia/AD risk at transplant evaluation provides enough time to intervene prior to KT.
To design a geriatric-specific model that can predict post-KT dementia/AD risk utilizing machine learning, we
will leverage an ongoing NIA-funded R01 prospective longitudinal cohort study of frailty among older KT
candidates to accomplish the following aims: (1) To identify dementia/AD cases and possible subtypes among
KT recipients and quantify the cumulative incidence of AD/dementia in KT recipients in this ongoing cohort
study; (2) To identify clinical, geriatric, and ESRD-specific risk factors that are associated with post-KT
dementia/AD; and (3) To design a model with the aid of machine learning that successfully predicts the risk of
post-KT dementia/AD in older patients undergoing KT evaluation. Our group’s expertise in frailty and
dementia/AD and access to the ongoing Frailty Assessment in Renal Disease (FAIR) cohort, along with Dr.
Long’s training interests in machine learning and regression, provide a unique opportunity to build prediction
models that could identify older candidates at highest risk of post-KT dementia/AD.
We hypothesize that a risk prediction tool that incorporates traditional clinical, geriatric, and ESRD-specific risk
factors that are commonly measured at KT evaluation, will improve post-KT dementia/AD risk prediction. If the
proposed aims are achieved, we will improve our ability to identify older patients at increased risk of developing
post-KT dementia/AD, who will need additional interventions to improve post-KT outcomes.

## Key facts

- **NIH application ID:** 10974021
- **Project number:** 5F32AG082486-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Jane J Long
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $94,892
- **Award type:** 5
- **Project period:** 2023-08-02 → 2026-08-01

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10974021

## Citation

> US National Institutes of Health, RePORTER application 10974021, Predicting post-kidney transplant dementia/Alzheimer's Disease risk in older patients (5F32AG082486-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10974021. Licensed CC0.

---

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