Association of Phenotypic Aging Marker with comorbidities, frailty and inflammatory markers in people living with HIV

Abstract

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.

Biological aging markers in blood and brain tissue indicate age acceleration in alcohol use disorder

“…PhenoAge is supposed to capture CpG sites that change methylation levels not only with age, but also due to environmental and disease processes (Levine et al., 2018). Accelerated PhenoAge is known to be associated with pro‐inflammatory and interferon pathways (Han et al., 2022; Levine et al., 2018). Our findings from the present study might therefore point toward increased inflammation in the brains of individuals with AUD, which is in line with our previous findings in these cohorts (Witt et al., 2020; Zillich, Frank, Streit, et al., 2022).…”

Forecasting and validating fat mass ratio models through anthropometric measurements and health-related factors among people with HIV: a cross-sectional investigation

“…About the use of ART, longer exposure times are associated with higher insulin resistance, leading to lipotoxicity and lipodystrophy, which contribute to comorbidities and cardiovascular pathogenesis ( 41 ). PWH using ART also have a higher presence of markers of accelerated ageing, such as systemic inflammation, frailty, and cardiovascular risk factors ( 42 ). Therefore, in addition to the high accuracy of model 6, we can diagnose lipodystrophy by monitoring body changes in PWH, paying attention to possible abnormalities that can lead to poor outcomes in this population.…”

Association of Physical Activity with Phenotypic Age among Populations with Different Breakfast Habits

“…This selection was made from a total of forty-two biomarkers and chronological age [ 3 , 4 ]. PhenoAge was developed to facilitate the identification of individuals at risk for all-cause mortality, cause-specific mortality, physical functioning, and cognitive performance measures among individuals of similar chronological age [ 18 ]. The final equation to calculate PhenoAge is provided below [ 3 ]: where …”