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People living with HIV (PLWH) experience chronic immune activation despite effective antiretroviral therapy, contributing to non-communicable diseases and geriatric syndromes such as frailty. The biological mechanisms driving frailty in older PLWH remain poorly defined. This study sought to identify serum proteomic signatures associated with frailty in this population. Older PLWH from the Correlates of Healthy Aging in Geriatric (CHANGE) HIV study were categorized as frail ( n = 44), pre-frail ( n = 70), or non-frail ( n = 18) using Fried’s frailty phenotype. Serum proteins were quantified using the Olink Reveal assay, which measures 1,034 proteins per participant. Associations between protein abundances and frailty status were examined using multinomial regression. Least Absolute Shrinkage and Selection Operator (LASSO) regression, adjusted for demographic, behavioural, clinical, and HIV-related covariates, was used to select predictive proteins, followed by multinomial logistic modelling. A total of 68 proteins were differentially abundant between non-frail and pre-frail PLWH. Key upregulated proteins included IL18, TNFRSF9, NCR1, ACP6, IL12B, and MMP13, while CTSV, ERBB4, ENTPD5, PAEP, and CES2 were among the most downregulated. Comparing non-frail to frail individuals revealed upregulation of XCL1, CD28, SPRY2, CDH3, and FGF5, with downregulation of CTSV, CES2, CLEC4A, GP2, and TLR4. In pre-frail versus frail PLWH, proteins such as CDH3, TNFRSF9, CLEC7A, NCR1, and MMP13 were upregulated, whereas CTSV, CES2, ENTPD5, TMEM106A, CLEC4A, and CES3 were downregulated. Enrichment analysis indicated involvement of inflammatory, coagulation, cell-growth, immune-response, and infectious-disease pathways. Several distinct proteomic signatures were found to be associated with frailty in older PLWH. Validating these biomarkers could facilitate their translation into predictive tools for early triage and clinical decision-making, with the potential to transform frailty care by enabling timely prevention and targeted monitoring, ultimately reducing adverse outcomes and optimizing resource allocation. Not-applicable.
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Massy et al. (2026) studied this question.
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