Redefining Aging in HIV Infection Using Phenotypes

Stoff, David M.; Goodkin, Karl; Jeste, Dilip V.; Marquine, María J.

doi:10.1007/s11904-017-0364-x

Cited by 22 publications

(16 citation statements)

References 112 publications

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“…Our findings contribute to the literature on sex-based differences with HIV infection and aging-related comorbidities or phenotype development in aging populations of PLWH. Moreover, cohort studies report that females are better controllers of HIV, although the mechanisms have been unclear [9,11,36]. Sex-specific mechanisms of protection must also be explored in future studies as females generally demonstrate phenotypes of viral control [37].…”

Section: Discussionmentioning

confidence: 99%

“…The presence of multiple comorbidities, observed in HIV, include characteristics of aging-associated phenotypes such as disability and frailty [6]. However, phenotypes are studied far more in the aging field, which is not specific to HIV [7-9]. Poor health outcomes such as disability and frailty increase the risk of poor functional status, which complicates access to care and disrupts disease self-management, resulting in increased medical resource utilization [9].…”

Section: Introductionmentioning

confidence: 99%

“…However, phenotypes are studied far more in the aging field, which is not specific to HIV [7-9]. Poor health outcomes such as disability and frailty increase the risk of poor functional status, which complicates access to care and disrupts disease self-management, resulting in increased medical resource utilization [9]. Therefore, effective interventions require the identification of narrower phenotypes with greater clinical validity [7,10,11].…”

Section: Introductionmentioning

confidence: 99%

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Understanding Comorbidities and Their Contribution to Predictors of Medical Resource Utilization for an Age- and Sex-Matched Patient Population Living With HIV: Cross-Sectional Study

Odlum¹,

Yoon²

2019

JMIR Aging

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Background More than 60% of people aging with HIV are observed to have multiple comorbidities, which are attributed to a variety of factors (eg, biological and environmental), with sex differences observed. However, understanding these differences and their contribution to medical resource utilization remains challenging as studies conducted exclusively and predominantly among males do not translate well to females, resulting in inconsistent findings across study cohorts and limiting our knowledge of sex-specific comorbidities. Objective The objective of the study was to provide further insight into aging-related comorbidities, their associated sex-based differences, and their contribution to medical resource utilization, through the analysis of HIV patient data matched by sex. Methods International Classification of Disease 9/10 diagnostic codes that comprise the electronic health records of males (N=229) and females (N=229) were categorized by individual characteristics, chronic and mental health conditions, treatment, high-risk behaviors, and infections and the codes were used as predictors of medical resource utilization represented by Charlson comorbidity scores. Results Significant contributors to high Charlson scores in males were age (beta=2.37; 95% CI 1.45-3.29), longer hospital stay (beta=.046; 95% CI 0.009-0.083), malnutrition (beta=2.96; 95% CI 1.72-4.20), kidney failure (beta=2.23; 95% CI 0.934-3.52), chemotherapy (beta=3.58; 95% CI 2.16-5.002), history of tobacco use (beta=1.40; 95% CI 0.200-2.61), and hepatitis C (beta=1.49; 95% CI 0.181-2.79). Significant contributors to high Charlson scores in females were age (beta=1.37; 95% CI 0.361-2.38), longer hospital stay (beta=.042; 95% CI 0.005-0.078), heart failure (beta=2.41; 95% CI 0.833-3.98), chemotherapy (beta=3.48; 95% CI 1.626-5.33), and substance abuse beta=1.94; 95% CI 0.180, 3.702). Conclusions Our findings identified sex-based differences in medical resource utilization. These include kidney failure for men and heart failure for women. Increased prevalence of comorbidities in people living long with HIV has the potential to overburden global health systems. The development of narrower HIV phenotypes and aging-related comorbidity phenotypes with greater clinical validity will support intervention efficacy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding Comorbidities and Their Contribution to Predictors of Medical Resource Utilization for an Age- and Sex-Matched Patient Population Living With HIV: Cross-Sectional Study

Odlum¹,

Yoon²

2019

JMIR Aging

View full text Add to dashboard Cite

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“…The National HIV/AIDS Strategy identified the pressing need to facilitate successful aging of PLWH [5]. As those with long-standing HIV infection age, comorbidities are becoming increasingly common [16, 24]. To support successful aging, it is essential to improve our understanding of the contribution of HIV infection to the presence of comorbidities and associated clinical outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Aging phenotype development has improved the understanding of physical and cognitive decline in populations of aging adults with multiple comorbidities [16]. Several positive aging phenotypes are characterized (e.g., physical and social functioning) to allow investigators to study avenues for healthier aging outcomes [17].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Comorbidities and their Contribution to Medical Resource Utilization for Matched HIV-Infected and Uninfected Individuals: A Cross-Sectional Analysis

Odlum

Yoon

2019

Preprint

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HIV long-term survivorship is characterized by higher rates of comorbidities compared to uninfected groups. Aging with HIV involves complex interactions of factors (e.g., Individual Characteristics, Infections) that result in a 20% increase in comorbidity risk. With over half of the 1.1 million people living with HIV in the US age 50 and over, the need exists to further understand this interplay and differences in aging-related outcomes. Electronic health record data was analyzed for HIV infected (N=208) and uninfected (N=208) adult inpatients, propensity score matched by age and gender. Diagnostic codes were extracted that comprise the factors of Individual Characteristics, High Risk Behaviors, Chronic Conditions, Mental Health Conditions and Infections. Identified codes were assessed for their contributions to medical resource utilization, based on Charlson Comorbidity scores. Significant contributors to high Charslon scores for HIV infected patients were age (β=0.116; [95% CI 0.077, 0.155]) and admission frequency (β=0.159; [95% CI 0.114, 0.205]) in addition to the comorbidities of acute kidney failure (β=3.27; [95% CI 1.76, 4.78]), hypertension (β= −1.77; [95% CI −2.99, −0.551]). Significant contributors for HIV uninfected patients were age (β=0.110; [95% CI 0.087, 0.133]), length of hospital stay (β=0.006; [95% CI 0.003, 0.009]), acute kidney failure (β=1.556; [95% CI 0.611, 2.50]), heart failure (β= 1.713; [95% CI 0.717, 2.71]), and diabetes mellitus II (β= 1.385; [95% CI 0.634, 2.14]). Our findings enhance the understanding of the contributions to medical resource utilization based on HIV status and can inform intervention efficacy for improved HIV aging outcomes.

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Identification of Youthful Neurocognitive Trajectories in Adults Aging with HIV: A Latent Growth Mixture Model

et al. 2021

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Despite the neurocognitive risks of aging with HIV, initial cross-sectional data suggest a subpopulation of older people with HIV (PWH) possess youthful neurocognition (NC) characteristic of SuperAgers (SA). Here we characterize longitudinal NC trajectories of older PWH and their convergent validity with baseline SA status, per established SuperAging criteria in PWH, and baseline biopsychosocial factors. Growth mixture modeling (GMM) identified longitudinal NC classes in 184 older (age ≥ 50-years) PWH with 1–5 years of follow-up. Classes were defined using ‘peak-age’ global T-scores, which compare performance to a normative sample of 25-year-olds. 3-classes were identified: Class 1Stable Elite (n = 31 [16.8%], high baseline peak-age T-scores with flat trajectory); Class 2Quadratic Average (n = 100 [54.3%], intermediate baseline peak-age T-scores with u-shaped trajectory); Class 3Quadratic Low (n = 53 [28.8%], low baseline peak-age T-scores with u-shaped trajectory). Baseline predictors of Class 1Stable Elite included SA status, younger age, higher cognitive and physiologic reserve, and fewer subjective cognitive difficulties. This GMM analysis supports the construct validity of SuperAging in older PWH through identification of a subgroup with longitudinally-stable, youthful neurocognition and robust biopsychosocial health.

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Redefining Aging in HIV Infection Using Phenotypes

Cited by 22 publications

References 112 publications

Understanding Comorbidities and Their Contribution to Predictors of Medical Resource Utilization for an Age- and Sex-Matched Patient Population Living With HIV: Cross-Sectional Study

Understanding Comorbidities and Their Contribution to Predictors of Medical Resource Utilization for an Age- and Sex-Matched Patient Population Living With HIV: Cross-Sectional Study

A Comparison of Comorbidities and their Contribution to Medical Resource Utilization for Matched HIV-Infected and Uninfected Individuals: A Cross-Sectional Analysis

Identification of Youthful Neurocognitive Trajectories in Adults Aging with HIV: A Latent Growth Mixture Model

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