# Secondary Analysis of Cognitive Data from the Multi-Center AIDS Cohort Study (MACS) to Identify Longitudinal Change Phenotypes in HIV+ Individuals

> **NIH NIH R03** · JOHNS HOPKINS UNIVERSITY · 2021 · $81,875

## Abstract

PROJECT SUMMARY
Antiretroviral therapies (ART) have modified Human Immunodeficiency Virus (HIV) infection from a nearly
universal fatal disease to a manageable chronic condition, yet associated symptoms like cognitive impairments
(CI) persist at a higher rate than in comparable uninfected control populations. These impairments are
heterogeneous in terms of presentation (e.g. cognitive domains) and trajectory. Given that domain-specific
impairments are likely due to different pathological changes, studies that investigate CI based on a global
metric of impairment are likely to miss this. The application of advanced analysis methods, including machine
learning to interrogate complex diseases and biological processes, is beginning to transform our understanding
of the underlying mechanisms that contribute to complex diseases, including CI in the setting of HIV infection.
In order to identify and characterize factors that contribute to nonlinear patterns of cognitive change, we
propose to capitalize on over 20 years of data from the Multi-Center AIDS Cohort Study (MACS). MACS is
one of the largest and longest running studies of men infected with HIV, that includes a substudy with biannual
neurocognitive testing that began in 1988. A complete battery of longitudinal neurocognitive testing data is
available to us through this cohort, as well as concurrent datasets that include demographic, clinical,
psychiatric, lifestyle and biochemical data. The data set consists of over 3500 subjects with over 500,000 data
points. In Aim 1, we propose to use data-driven methods (k-means clustering for joint longitudinal trajectories
and dynamic time warping) to identify groups of individuals with distinct domain-specific patterns of cognitive
change. In Aim 2, we will use the vast amount of data collected on these subjects to identify subtype-specific
variables that contribute or predict group membership. By using advanced machine learning methods that are
unconstrained by preset statistical or biological assumptions, we are uniquely positioned to identify factors that
contribute to longitudinal patterns of change in specific cognitive functions.

## Key facts

- **NIH application ID:** 10116489
- **Project number:** 5R03MH123290-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Raha Maryam Dastgheyb
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $81,875
- **Award type:** 5
- **Project period:** 2020-03-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10116489

## Citation

> US National Institutes of Health, RePORTER application 10116489, Secondary Analysis of Cognitive Data from the Multi-Center AIDS Cohort Study (MACS) to Identify Longitudinal Change Phenotypes in HIV+ Individuals (5R03MH123290-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10116489. Licensed CC0.

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