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Background: Aging characteristics in people living with HIV (PLWH) are heterogeneous, and the identification of risk factors associated with aging-related comorbidities such as neurocognitive impairment (NCI) and frailty is important. We evaluated predictors of novel aging markers, phenotypic age (PhenoAge) and phenotypic age acceleration (PAA) and their association with comorbidities, frailty, and NCI.
Methods: In a cohort of PLWH and age- and sex-matched HIV-negative controls, we calculated PhenoAge using chronological age and 9 biomarkers from complete blood counts, inflammatory, metabolic-, liver- and kidney-related parameters. PAA was calculated as the difference between chronological age and PhenoAge. Multivariate logistic regression models were used to identify the factors associated with higher (>median) PAA. Area under the receiver operating characteristics curve (AUROC) was used to assess model discrimination for frailty.
Results: Among 333 PLWH and 102 HIV-negative controls (38% female), the median phenotypic age (49.4 vs. 48.5 years, p = 0.54) and PAA (- 6.7 vs. -7.5, p = 0.24) was slightly higher and PAA slightly less in PLWH although this did not reach statistical significance. In multivariate analysis, male sex (adjusted odds ratio = 1.68 [95%CI = 1.03-2.73]), current smoking (2.74 [1.30-5.79]), diabetes mellitus (2.97 [1.48-5.99]), hypertension (1.67 [1.02-2.72]), frailty (3.82 [1.33-10.93]), and higher IL-6 levels (1.09 [1.04-1.15]), but not HIV status and NCI, were independently associated with higher PAA. PhenoAge marker discriminated frailty better than chronological age alone (AUROC: 0.75 [0.66-0.85] vs. 0.65 [0.55-0.77], p = 0.04). In the analysis restricted to PLWH, PhenoAge alone predicted frailty better than chronological age alone (AUROC: 0.7412 vs. 0.6499, P = 0.09) and VACS index (AUROC: 0.7412 vs. 0.6811, P = 0.34) despite not statistically significant.
Conclusions: While PLWH did not appear to have accelerated aging in our cohort, the phenotypic aging marker was significantly associated with systemic inflammation, frailty, and cardiovascular disease risk factors. This simple aging marker could be useful to identify high-risk PLWH within a similar chronological age group.
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http://dx.doi.org/10.1186/s12877-022-03720-1 | DOI Listing |
Mar Environ Res
August 2025
Southern Seas Ecology Laboratories, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia.
Otolith chemical approaches are widely used to inform fisheries management, supporting the identification of population structure, connectivity, and natal origins. Chemical transects combined with fish age and growth data can reveal individual life histories, highlighting movement patterns and environmental influences within populations. Scaling these distinct variations to the population-level through novel chronological approaches could further boost our understanding of long-term physiological and environmental processes, and their interactions across regions and species.
View Article and Find Full Text PDFPlant Cell Environ
September 2025
Max-Planck Institute for Biogeochemistry, Jena, Germany.
The time elapsed between carbon fixation into nonstructural carbohydrates (NSC) and their use to grow tree structural tissues can be estimated by C ages. Reported C-ages indicate that NSC used to grow root tissues (growth NSC) can vary from < 1 year to decades. To understand the controls of this variability, we compared C-ages of leaf, branch, and root tissues from two conifers (Larix decidua, Pinus mugo) in a control valley site and an alpine treeline ecotone where low temperatures restrict tree growth.
View Article and Find Full Text PDFNPJ Metab Health Dis
September 2025
ATLAS Molecular Pharma, Parque Tecnológico de Bizkaia, Ed. 800, 48160, Derio, Spain.
Molecular aging clocks estimate biological age from molecular biomarkers and often outperform chronological age in predicting health outcomes. Types include epigenetic, transcriptomic, proteomic, and metabolomic clocks. NMR-based metabolomic clocks provide a non-invasive, high-throughput platform to assess metabolic health.
View Article and Find Full Text PDFBrain Commun
August 2025
Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, F-75014 Paris, France.
Brain age, as distinct from chronological age, may reveal post-stroke recovery mechanisms, but longitudinal studies tracking brain age are lacking. We explored longitudinal change of brain age post-stroke and its relation to upper limb sensorimotor outcome. T-weighted MRI at baseline (∼3 weeks) and follow-up (3-7 months) post-stroke was used to estimate brain age.
View Article and Find Full Text PDFAnn Med Surg (Lond)
September 2025
Department of internal medicine, Kist medical college, Nepal.
Introduction And Importance: Age-inconsistent brain atrophy refers to brain shrinkage that is not proportional to chronological age. This case report is first to report a young patient who developed age-inconsistent brain atrophy due to post cardiac arrest brain injury (PCABI). Due to limitations in the available data, we report our experience and novel magnetic resonance (MR) imaging changes in the brain over the course of 2 months.
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