# Disentangling Alzheimer's disease and HIV-Associated Neurocognitive Disorder: Identifying unique neuropsychological profiles with distinct biomarker and neuropathological signatures

> **NIH NIH RF1** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $71,597

## Abstract

PROJECT SUMMARY/ABSTRACT
 Persons living with HIV (PLWH) are living longer with antiretroviral therapy (ART). Unfortunately, older
PLWH also appear to be at increased risk for HIV-associated neurocognitive disorders (HAND). In parallel, as
a person ages, there is risk of developing neurodegenerative disorders of late life, including Alzheimer’s
disease (AD) or its precursor, amnestic mild cognitive impairment (aMCI). A current challenge lies in
disentangling HAND from the more deleterious and progressive aMCI diagnosis. Distinguishing between aMCI
and HAND is critical in order to provide the most appropriate life planning, intervention and treatment options
for patients and to properly identify biological mechanisms of HAND. Studies that propose to identify biological
mechanisms of HAND, without careful recognition that some HAND cases may either additionally have aMCI
and or may have aMCI misdiagnosed as HAND, may lead to problematical mechanistic examinations of an
unclear phenotype. We believe that our proposal is particularly innovative in its approach because it first
attempts to clarify the phenotype of aMCI among PLWH and then examine its neurobiologic underpinnings.
 Participants will include PLWH who were characterized neuropsychologically in life and also have plasma,
CSF, and/or neuropathologic samples available for additional characterization. Samples and data come from
the National NeuroAIDS Tissue Consortium (of which the California NeuroAIDS Tissue Network at UCSD is
one of four contributing banks), the UCSD HIV Neurobehavioral Research Program (HNRP) and the UCSD
Shiley-Marcos Alzheimer’s Disease Research Center (ADRC). To find aMCI among PLWH, the aims use both
1) an empirically-based neuropsychological diagnostic approach, and 2) a data-driven latent class analysis
(LCA) approach, which will allow us to better identify neuropsychological and biological characteristics
distinguishing HAND from aMCI. Specifically, Aim 1 will use the neuropsychological diagnostic approach to
identify aMCI among PLWH and compare profiles of aMCI- and HAND-associated biological markers and
neurocognitive trajectories among HIV+ and HIV- diagnostic groups. Aim 2 will use a data-driven latent class
analysis (LCA) approach to classify PLWH as aMCI, HAND or cognitively normal, and similarly compare
profiles of aMCI- and HAND-associated biological markers and neurocognitive trajectories among LCA groups
and HIV- diagnostic groups. Aim 3 will compare and validate the diagnostic and LCA classification methods.
 The public health benefits of our project would be significant if it is able to identify PLWH who have aMCI
and reliably distinguish it from HAND. This capability would: 1) allow us to apply the approach to all PLWH to
aid in identification of those at increased risk for progression to AD dementia, 2) allow for appropriate
treatments to be implemented, e.g., HAND-specific treatments or aMCI/AD-specific treatments, and for these
to be applied as early...

## Key facts

- **NIH application ID:** 11061528
- **Project number:** 3RF1AG061070-01S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Mark W Bondi
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $71,597
- **Award type:** 3
- **Project period:** 2018-09-30 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11061528

## Citation

> US National Institutes of Health, RePORTER application 11061528, Disentangling Alzheimer's disease and HIV-Associated Neurocognitive Disorder: Identifying unique neuropsychological profiles with distinct biomarker and neuropathological signatures (3RF1AG061070-01S1). Retrieved via AI Analytics 2026-06-10 from https://api.ai-analytics.org/grant/nih/11061528. Licensed CC0.

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