# Machine learning to distinguish HAND from Alzheimer's disease in HIV over age 60

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $1,124,685

## Abstract

The CDC estimated that one-quarter of Americans living with HIV were over the age of 55 in 2012. By
next year, they will be over age 60, entering into the age demographic where Alzheimer's disease (AD)
becomes a distinct differential for clinicians. Because up to one-half of people living with HIV experience
cognitive impairment from HIV or related factors along, the likelihood for delayed diagnosis of early AD is
substantial. Differentiating HIV-associated Neurocognitive Disorder (HAND) from the Mild Cognitive
Impairment stage of AD (MCI-AD) is one of the most pressing issues in geriatric neuroHIV. Current
HAND nosology does not provide guidance on this issue. Published work suggests the likelihood for
distinct phenotypes that would facilitate diagnostic sorting with commonly available inputs from
neuropsychological testing and structural imaging.
In this application, we will use a new approach that leverages computational machine learning with inputs
from structural imaging, neuropsychological testing, motor examination and affective/behavioral
assessments to determine the factors that most accurately discriminate HAND from MCI-AD. Our
preliminary examinations using this novel technique demonstrate a likelihood that this approach will
provide diagnostic sorting that exceeds 90% accuracy. We will examine tightly characterized phenotypes
using HIV tests to exclude HAND and PET amyloid scanning to exclude AD among 75 HIV+/amyloid
marker negative participants with HAND to 50 HIV-negative/amyloid+ cases with MCI (MCI-AD group),
all age, sex and disease severity matched and all over age 60, the population of interest due to dual risk.
Our methodology will iterate the most distinctive aspects of each disease's phenotype to inform sorting
and subsequently, guidelines. We will validate the identified inputs that most clearly contribute to the
algorithm though clinical correlations and through the ability of the determined clusters (e.g. diagnostic
group) to predict the meaningful outcomes of disease progression. The long-term goal of this work is to
inform clinical guidelines, thus, the modalities examined are readily available in clinical care. This work
will also extend our understanding of neuropathology in older HIV patients and may identify factors that
shift paradigms because our novel approach does not rely on a priori assumptions to inform
neuropsychological abnormalities and brain structural alterations linked to HAND in older age. In an
exploratory aim, we extend this examination of HAND neuropathogenesis with the added examination of
diffusion tensor imaging (DTI) and a monocyte associated inflammatory marker, soluable CD163
(sCD163), two measures tightly linked to HAND in published work among virally suppressed patients in
the current era.

## Key facts

- **NIH application ID:** 9848011
- **Project number:** 5R01MH113406-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Kilian Maria Pohl
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,124,685
- **Award type:** 5
- **Project period:** 2017-05-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9848011

## Citation

> US National Institutes of Health, RePORTER application 9848011, Machine learning to distinguish HAND from Alzheimer's disease in HIV over age 60 (5R01MH113406-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9848011. Licensed CC0.

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