Higher Veterans Aging Cohort Study 2.0 Index Score Predicts Functional Decline Among Older Adults Living with HIV

Abstract

Living with HIV has been proposed as a risk factor for the early development of functional decline. Composite marker tools like the Veterans Aging Cohort Study (VACS) Index, which includes HIV-associated and non-HIV-related markers of disease may better reflect multiorgan system injury and potentially predict functional outcomes. Therefore, the objective of this work is to determine whether higher VACS 2.0 Index scores predicts functional decline among older adults living with HIV (OALWH). Longitudinal study, including 131 adults ages 50 or older who underwent a comprehensive geriatric assessment at baseline and follow-up, at least a year apart. Functional status was determined by the gait speed (seconds for a 4-m distance). Linear regression models were constructed to determine the relationship between VACS 2.0 Index at baseline with gait speed at follow-up adjusted for potential confounders. The median for age was 58.0 years (range 50–84), and 81.7% were male. At baseline, the median VACS 2.0 Index score was 50.4 (interquartile range 42.2–65.3). The adjusted linear regression analysis found that higher baseline VACS 2.0 Index scores were significantly associated with a decline in gait speed (p = .033) at follow-up. The results suggest that the VACS 2.0 Index works as a predictor of functional decline as showed by decline in gait speed and might serve as an easy tool to identify OALWH who might need additional resources or interventions to prevent it.

Introduction

Increased life expectancy among people living with the human immunodeficiency virus is experienced among a growing population of older adults living with HIV (OALWH).¹ These demographic changes will bring new challenges for the care of OALWH, including treatment and prevention for non-AIDS defining comorbidities. HIV infection has been proposed as a premature aging model rending the individual more susceptible to age-related comorbidities as well as to disability.^2
–4 Aging and HIV infection have a negative synergistic effect on functional status^4,5; the underlying mechanisms of such a relationship are unclear but seem to have a multisystemic origin. On the other hand, functional decline is a pivotal outcome researched in geriatric medicine as it may reflect a dynamic state in which, if an individual is left without and intervention or structured approaches for its determinants, the person may end up developing disability, depression, frailty, and institutionalization.^4
–6 Therefore, the preservation of function in older persons is essential to maintain a good health-related quality of life.

Early initiation of combined antiretroviral therapy (cART) significantly decreases the risk of AIDS-defining illnesses, serious non-AIDS illnesses, and all-cause mortality, particularly among adults ages 50 or older.⁷ Likewise, CD4⁺ cell count depletion and HIV-1 RNA viral load are considered hallmarks of HIV-associated disease and have been extensively studied as one of the best predictors of specific adverse health-related issues, such as geriatric syndromes.^8,9 However, in addition to the follow-up of HIV-related variables, it is also important to consider the incorporation of non-HIV-related markers of disease, as, in aggregate, they may better encompass multiorgan system injury and predict functional outcomes.^10,11

The Veterans Aging Cohort Study (VACS) Index, a composite marker tool (as it incorporates non-HIV-associated variables in addition to CD4⁺ cell counts, and viral load), was originally developed and validated to estimate all-mortality risk in people living with HIV. Additionally, higher VACS Index scores have also been associated with important outcomes for patients and health care providers working on the ever-growing field of aging with HIV, for example: cART adherence, fragility fractures, hospitalizations, cognitive decline, and functional decline.^{10,12

–16} Furthermore, the Index has recently been updated into a 2.0 version that provides improved discrimination for mortality risk over diverse populations living with HIV.¹⁷ However, little is known about this tool's ability to predict functional outcomes in OALWH. Simple ways to identify those OALWH at greater risk for functional decline can facilitate timely interventions or connect patients to resources that may attenuate or reverse functional decline trajectories, and its consequences. Hence, the purpose of this study is to determine whether higher VACS 2.0 Index scores predict subsequent functional decline, represented by a decline in gait speed, among OALWH.

Materials and Methods

Study population

This was a secondary analysis of a longitudinal study created to determine the correlates of disability among OALWH, including 131 adults ages 50 or older receiving care for HIV at a tertiary care, university-affiliated hospital in Mexico City. Participants were recruited between March 2013 and February 2019, and a comprehensive geriatric assessment, including functional assessment was completed among all participants by trained staff at baseline and follow-up using standardized methods. To isolate the impact of VACS without severe comorbidities, exclusion criteria were the presence of conditions potentially associated with functional impairment; class III–IV heart failure by the New York Heart Association functional classification; those with class III–IV chronic obstructive pulmonary disease according to the Global Initiative for Chronic Obstructive Lung Disease classification; stage III–IV rheumatoid arthritis according to the American College of Rheumatology classification; stage 4–5 Parkinson's disease according to the Hoehn and Yahr scale; patients with history of ischemic and/or embolic cerebrovascular disease with motor sequelae; myocardial ischemia in the previous 3 months; amputation of any upper and/or lower limb; and severe dementia. All eligible persons were invited to participate in the study during a scheduled visit for HIV care. Two evaluations were made, at least a year apart. The Local Ethics Committee reviewed and approved the study protocol (approval 698), and written informed consent was obtained from all participants.

Outcome

The proxy used for functional status was the time to walk 4 m (gait speed). Gait speed was measured twice, at baseline and at the follow-up, at a usual pace; participants could use a walking aid if necessary, or if they used it on a regular basis. The fastest time of the two trials was recorded and expressed as seconds over 4 m. Thus, the best time (in seconds) to walk 4 m at follow-up was the dependent variable.

The VACS 2.0 Index

The VACS 2.0 Index is a multivariable risk assessment tool that creates a score by summing prespecified points for age, biochemical markers of organ system injury, and HIV-related indicators of disease, thus, reflecting the potential multiorgan involvement of HIV.¹⁸ The index was originally developed to predict all-cause mortality in people living with HIV (and has recently been updated into a version with enhanced discrimination for this outcome), but it can also predict cause-specific mortality, and other outcomes.¹⁷ The analyzed factors to create a score include age, body mass index (weight in kilograms/height in meters²), CD4⁺ cell counts, HIV-1 RNA viral load (CD4⁺ cell counts, and viral load determinations were measured within 1 month of the clinical evaluation at the latest), hemoglobin, platelets, white blood cell count, albumin, aspartate and alanine transaminases (AST, ALT), creatinine, and hepatitis C virus infection status. Two compound markers are also considered for the calculation of the VACS Index. The FIB-4 index or liver fibrosis index, including ALT and AST, as well as platelet count and age, to estimate the probability of liver fibrosis [FIB 4: (age × AST)/(platelet count × ALT^1/2)].¹⁹ Estimated glomerular filtration rate was calculated according to the Modified Diet in Kidney Disease group (MDRD). The VACS 2.0 Index scores were calculated with data from the baseline visit, and what is presented in the constructed models is the change in gait speed (in seconds) at follow-up for a five-point change in the VACS 2.0 Index.

Covariates

Sociodemographic variables included age, sex, employment, and educational level. Participants were asked whether they had a physician's diagnosis of hypertension, diabetes, dyslipidemia, cancer, stroke, myocardial ischemia, chronic obstructive pulmonary disease, or osteoarthritis. The presence of each of these diseases was summed up in a score ranging from 0 to 8. The number of medications currently used was also recorded. Smoking status (current and former vs. never) and alcohol intake (current and former vs. never) were also included.

Depressive symptoms were assessed using the 15-item Geriatric Depression Scale (GDS), where a score of >5 points indicated the presence of clinically significant depressive symptoms.²⁰ The Mini-Mental State Examination (MMSE) (score ranging from 0 to 30) was used to assess global cognitive function, and a score of <24 points was used to define cognitive impairment.²¹

Time from HIV diagnosis and time on cART, both in years, were used as continuous covariates. CD4⁺ cell nadir values (the lowest measure registered) and the history of at least one opportunistic infection were also considered for analysis.

Statistical analyses

Variables were described using medians and interquartile ranges, or frequencies and proportions as appropriate. A univariate linear regression model—with the best time for the 4-m walk at follow-up as the dependent variable—was performed to establish the relationship between the VACS 2.0 Index, at baseline, and the gait speed in seconds. Afterward, a multivariate linear regression analysis was run adjusting for potential confounders not included into the VACS 2.0 Index (educational level, living alone, eight chronic diseases, time from HIV diagnosis, CD4⁺ cell nadir counts, time on cART, depressive symptoms, cognitive performance, history of opportunistic infection, and smoking status), as well as the gait speed at baseline with the purpose of considering its fixed effect on the outcome. To test for an interaction of VACS 2.0 Index and chronic diseases, the interaction term (VACS*8CD) was introduced into the model and statistical significance was tested using the backward selection procedure at the p < .05 level. Additionally, with the objective of knowing the individual and collective contribution of HIV-related variables not included on the VACS 2.0 Index (time from diagnosis, time on cART, history of opportunistic infections, CD4⁺ cell nadir, and number of treatment regimens), linear regression models were also constructed separately to determine the variability of the gait speed in seconds at follow-up attributable to those variables. With the purpose of verifying linear regression assumptions, the natural logarithm of gait speed in seconds at follow-up was also used; however, when the models were performed, no differences were found with the model that included the conventional expression of the dependent variable. Thus, to facilitate its interpretation we decided to present those results. Finally, residual and diagnostic analyses, including Studentized residuals, Standardized residuals, Cook's distance, and leverage h, were performed to assure that no outliers were present, and that the data followed the assumptions underlying multiple linear regression analysis. All comparisons were evaluated using 95% confidence intervals (CIs). Statistical analyses were performed in SPSS software for Windows^® (SPSS, Inc., Chicago, IL, version 20.0).

Results

The study sample included 131 participants, all receiving cART. Median age was 58.0 (range 50 to 84) years and 81.7% (n = 107) were men. Dyslipidemia (n = 83; 63.4%) and hypertension (n = 49; 37.4%) were the most frequent chronic diseases. Median time from HIV diagnosis was 10.4 [interquartile range (IQR) 4.9–15.6] years, and median time on cART was 7.4 (IQR 3.2–12.7) years. Only four participants (3.1%) had both detectable VL and low CD4⁺ cell counts (<200 CD4⁺ cells). The median VACS 2.0 Index score at baseline was 50.4 (IQR 42.2–65.3). Table 1 shows the sociodemographic characteristics and health status of participants at baseline. The mean time for the participant's follow-up was 29.7 (standard deviation 9.1) months.

Table 1.

Characteristics of Participants at Baseline

Variable	All n = 131
Male, n (%)	107 (81.7)
Age, median (IQR), years	58 (53.0–63.0)
Not living alone, n (%)	37 (29.1)
Educational level, median (IQR), years	12.0 (9.0–16.0)
High blood pressure, n (%)	49 (37.4)
Dyslipidemia, n (%)	83 (63.4)
Diabetes, n (%)	28 (21.4)
Cancer, n (%)	12 (9.2)
Ischemic cardiomyopathy, n (%)	7 (5.3)
Stroke, n (%)	2 (1.5)
COPD, n (%)	6 (4.6)
Osteoarthritis, n (%)	11 (8.4)
Hepatitis C, n (%)	3 (2.7)
Number of chronic diseases,^a median (IQR)	1.5 (1–2)
Gait speed, median (IQR), m/s	4.2 (3.6–4.8)
Current or former smoker, n (%)	33 (26.0)
Current or former drinker, n (%)	36 (28.3)
Cognitive impairment, n (%)	14 (10.7)
Depressive symptoms, n (%)	14 (10.7)
Time from HIV diagnosis, median (IQR), years	10.4 (4.9–15.6)
Time on cART (median (IQR), years	7.4 (3.1–12.7)
Nadir CD4⁺ cell count,^b media (IQR), cell/m³	142.0 (41.0–234.0)
Current detectable viral load, n (%)	8 (6.1)
Current CD4⁺ cell count, media (IQR), cell/m³	469 (337.0–652.0)
History of at least one opportunistic infection, n (%)	43 (35.2)
Hemoglobin, media (IQR), g/dL	15.3 (14.1–16.3)
Platelets, median (IQR), 10⁹/L	198 (166.5–230.0)
AST, median (IQR), U/L	25 (21.0–33.5)
ALT, median (IQR), U/L	26 (19.5–37.0)
Creatinine, median (IQR), mg/dL	0.9 (0.7–1.0)
eGFR, median (IQR), mL/min/1.73 m²	85.5 (70.5–97.2)
Fibrosis-4 (FIB-4) index, median (IQR)	1.6 (1.2–1.9)
Leucocytes/1,000, median (IQR), × 10⁹/L	6.1 (4.8–7.0)
Albumin, median (IQR), g/dL	4.5 (4.0–4.7)
Body mass index, median (IQR)	24.9 (22.0–27.4)
VACS 2.0 Index, median (IQR)	50.4 (42.2–65.3)

Chronic diseases: diabetes, hypertension, dyslipidemia, cancer, myocardial ischemia, stroke, chronic obstructive pulmonary disease, and osteoarthritis.

Lowest CD4⁺ cell count measurement registered.

ALT, alanine transaminase; AST, aspartate transaminase; cART, combined antiretroviral therapy; COPD, chronic obstructive pulmonary disease; eGFR, Estimated glomerular filtration rate; IQR, interquartile range; VACS, Veterans Aging Cohort Study.

The unadjusted linear regression analysis showed a positive relationship between baseline VACS 2.0 Index scores and an increase in the time needed to walk 4 m (p < .001) (Table 2). After adjusting for covariates mentioned above, including the fixed effect of gait speed at baseline, the relationship between the VACS 2.0 Index remained a statistically significant predictor of a decline in gait speed at follow-up among OALWH. The gait speed at follow-up was 0.194 s slower by each increase in five points of the VACS 2.0 Index (p = .033). The final adjusted model explained 33.1% of the variability in the gait speed, and the total contribution of VACS 2.0 Index to this variability was of 10.5%. The interaction term VACS*8CD was not statistically significant. Finally, the linear regression models constructed to know the individual and collective contribution of the HIV-related variables not included on the VACS 2.0 Index (time from HIV diagnosis, nadir CD4⁺ cell counts, time on cART, and history of opportunistic infections) showed that those variables explained <4% of the variability of the gait speed at follow-up, as assessed by the model's R ² (data not shown). Residual and diagnostic analyses did not show violation of the assumptions underlying multiple regression analysis.

Table 2.

Linear Regression of Gait Speed at Follow-Up According to the Veterans Aging Cohort Study 2.0 Index

	Gait speed in seconds at T1
	B coefficient^a	95% CI	p
Unadjusted
VACS 2.0/5 Index	0.243	0.117 to 0.370	<.001
Adjusted^b
VACS 2.0/5 Index	0.194	0.016 to 0.372	.033

The beta coefficient represents the gain in gait speed at follow-up (in seconds), for each increase in five points of the VACS 2.0 Index. For the adjusted analysis, the beta coefficient also considers the effect of gait speed at baseline.

Adjusted by educational level, living alone, number of eight chronic diseases, time from HIV diagnosis, nadir CD4⁺ cell count, time on cART, depressive symptoms, cognitive impairment, history of opportunistic infections, smoking status, and gait speed at baseline.

CI, confidence interval.

Discussion

In a cohort of OALWH, we have shown that a simple measure of readily available clinical laboratory values (the VACS 2.0 Index) is associated with the development of subsequent functional impairment, as measured by the gait speed. The observed relationship remained robust even after accounting for additional measures associated with functional decline, including cognitive impairment, depression, comorbidities, gait speed at baseline, and additional HIV factors.

A number of previous publications have shown cross-sectional associations between HIV infection, its effect on the immune system, and its particular inflammatory profile with reduced physical performance, even among those under a successful cART.^5,22
–24 In addition, other studies have shown cross-sectionally that in OALWH, HIV-related parameters (low CD4⁺ cell counts and detectable viral load) were associated with both disability for activities of daily living (ADL) as well as instrumental ADL.^4,25,26 However, it seems that only using HIV-related parameters may not reflect the potential multisystemic nature of HIV, or the determinants of functional decline, particularly among people under cART.

Diverse conditions have been postulated to interact and potentially influence the development of functional decline in OALWH. For instance, chronic activation of the immune system as well as the presence of an underlying independent “signature” of inflammation in aging (inflammaging) have been part of the postulated mechanisms responsible for the development of an “accelerated” aging process in HIV. An increased concentration of immune activation markers, including interleukin 6, tumor necrosis factor alpha, and lipopolysaccharide can directly cause breakdown of muscle, resulting in loss of muscle function and subsequent disability.^22,27 Even low-level viremia may have a detrimental effect on inflammation, as observed in men with suppressed HIV-RNA but who reported <100% cART adherence.²⁸

Following this biological premise, some studies have shown that VACS Index scores correlate with those biomarkers of senescence, and chronic inflammation, thus potentially linking it with one of the determinants of functional decline in OALWH under cART.²⁹ Furthermore, accelerated gait speed decline has been reported in OALWH, and likewise, slower gait speed has been associated to diverse negative health-related outcomes such as cognitive impairment in PLWH, as well as it is a known predictor of disability, falls, and mortality in older persons (and middle-aged PLWH),^30
–32 which highlights the potential usefulness of the VACS Index to identify patients at risk for functional decline and other geriatric outcomes.^33,34 The present study adds to the existing literature by demonstrating the longitudinal effect of high VACS Index scores, particularly the recent updated version, as a subsequent predictor of functional decline in people aging with HIV. This has clinical relevance as the index may be easily applied in outpatient consultations and depending on the scenario, trigger specialized models of care (early comprehensive geriatric assessment).

Having only two measurements of the VACS 2.0 Index scores, at baseline and at the end of the follow-up, is the main limit of the present study because this prevents the analysis of the variability in markers of disease control on the main outcome. A small sample size may also limit the results. However, the study has several strengths such as its longitudinal design, robust analyses, the use of an objective tool to assess functional status, including an updated and validated multicomposite marker that accounts for HIV-associated, and non-HIV-associated factors, and having only participants ages 50 or older from a region of the world that faces with particular attention, the increase of OALWH.

From the lens of Geriatric medicine, disability may be seen as the result of multiple accumulated effects and impairments, rarely from a single cause. By this standpoint, we could visualize that both HIV-related, and non-HIV-related factors may contribute to disability, and integrating, or using simple tools that contemplate both, and correlate with “geriatric outcomes” could facilitate the early detection of certain conditions or vulnerable individuals that would profit from tailored approaches. As a consequence of these results, our perspective is to emphasize on the strategies that we know will benefit them such as treatment adherence, exercise, nutritional interventions, sleep quality, and polypharmacy, to further prevent functional decline.

It seems that in addition to cART, multicomponent strategies need to be considered in a case-by-case scenario to prevent functional decline in OALWH. One of the enduring questions is to know whether this tailored approach will really help on its prevention, the question remains of great importance considering the increase in the survival of this population, and the potential underlying benefit of reducing the burden that this health problem entails.

Sponsor's Role

The study sponsors had no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; or the preparation, review, or approval of the article.

Footnotes

Authors' Contributions

C.G.H.-F.: developed conceptualization and design of this study; contributed to interpretation of data, and she wrote the article. She had full access to all the data in the study. V.A.H.-R.: contributed to conception, design, and interpretation of data. He wrote the article. O.Y.B.-C.: contributed to statistical analyses and drafting the article. B.C.-R.: contributed to interpretation of data and revised the article. J.S.-M.: contributed to interpretation of data and revised the article. H.A.: contributed to interpretation of data and revised the article. K.M.E.: contributed to interpretation of data and revised the article. J.A.A.-F.: developed conceptualization and design of this study and contributed to interpretation of data. He wrote the article; he had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Author Disclosure Statement

J.S.-M. has received Grants from: BMS, Pfizer, MSD, Gilead; is a Speaker for: MSD, Gilead, Stendhal, Viiv; and is a Consultant for: MSD, Pfizer, Gilead, Viiv. K.M.E. has received a Grant from Gilead. The remaining authors have no disclosures.

Funding Information

All authors state no financial interest, stock, or derived direct financial benefit. V.A.H.-R. was supported by the Mexican National Council of Science and Technology (CONACYT).

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