Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV
About This Grant
PROJECT SUMMARY Despite advances in antiretroviral therapy that have led to widespread viral suppression, nearly half of older people with HIV (PWH) still suffer from neurocognitive impairment (NCI). The elevated rates of NCI in this population are primarily due to the deleterious synergistic effects of HIV and aging on the brain, and exacerbating inflammatory, genetic, environmental, and lifestyle influences. Metabolomics, which quantifies small molecule byproducts of metabolic processes from biospecimens to provide a comprehensive view of intrinsic and extrinsic influences on an individual’s health status, may serve as the optimal tool for studying the complex heterogeneity of NCI in PWH, but this emerging technology is still critically underutilized in neuroHIV research. For example, recent NIH initiatives advocating for advanced data-driven methods for modeling biotypes of HIV brain health disorders have yet to incorporate metabolomics data, despite the unprecedented amount of biological information that can be obtained from this approach. Thus, the current K23 study proposes cross-disciplinary integration between the metabolomics and neuroHIV fields by leveraging cutting-edge metabolomics and machine learning methods to discover and classify novel metabolite biomarkers of NCI in older PWH. For Specific Aim 1, I will leverage untargeted metabolomics data from fecal samples of 403 older PWH enrolled in cohort studies at the UC San Diego HIV Neurobehavioral Research Center (HNRC) to first identify candidate biomarkers of NCI, then validate them using CSF and plasma metabolomics data from 598 PWH enrolled in the CNS HIV Antiretroviral Therapy Effects Research (CHARTER) study. For Specific Aim 2, I will then leverage the best-in-class analytic approach established by the Multimodal Integrated Analysis and Assessment Development for NeuroHIV Outcomes (MIAAD-NHIV) initiative and conduct machine learning models to identify and characterize metabolite biomarker subgroups. This K23 is well-supported by my mentorship team of world-renowned scientists with expertise in areas related to metabolomics (Dr. Pieter Dorrestein [primary]; pioneer of metabolomics technology and data repositories), biological mechanisms underlying neuroHIV (Dr. Ronald Ellis [co-primary]; HNRC and CHARTER PI), HIV and aging (Dr. David Moore; HNRC PI), and brain-based biotypes (Dr. Robert Paul; MIAAD-NHIV investigator). The proposed study strongly aligns with the 2020-2025 National Institute on Aging Strategic Directions (e.g., Goal D- 2: “Identify the genetic, molecular, and cellular mechanisms underlying the pathogenesis of AD/ADRD and other neurodegenerative disorders of aging” and Goal D-4: “Translate basic discovery into effective treatment and/or prevention strategies”). Results will provide proof of concept and critical preliminary data to inform a future R01 application that will examine the mechanistic and causal pathways of the metabolite biomarkers identified in this K23 with NCI through in vivo and in vitro experiments, and thus facilitate my independence as a clinical and translational scientist.
Grant Summary
Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV is a NIA - National Institute on Aging grant providing up to $164K for university, nonprofit, healthcare org. Applications are due 2031-04-30 (open). Check eligibility and apply with FindGrants.
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Up to $164K
2031-04-30
- 1Confirm your organization is eligible for Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV from NIA - National Institute on Aging, checking organization type, location, and any population or project requirements.
- 2Gather the required documents and information, including your organization details, project plan, and budget figures.
- 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
- 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NIA - National Institute on Aging before the deadline.
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Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV: Frequently Asked Questions
Who is eligible for the Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV?
Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV is offered by NIA - National Institute on Aging and is generally open to university, nonprofit, healthcare org. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.
How much funding does the Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV provide?
Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV provides up to $164K per award from NIA - National Institute on Aging. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.
When is the Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV deadline?
Applications for Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV are due 2031-04-30 (open). Because deadlines can change, verify the date with the funder, NIA - National Institute on Aging, and give yourself enough time to prepare a complete, competitive application before the close date.
How do you apply for the Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV?
To apply for Discovery and Machine Learning Classification of Metabolite Biomarkers of Neurocognitive Impairment in Older People with HIV, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NIA - National Institute on Aging.