Veterans Aging Cohort Study Index and Frailty: One Part of the Whole

Abstract

In HIV medicine, the Veterans Aging Cohort Study (VACS) index is associated to some geriatric syndromes and has also been recently used as a proxy of frailty. However, using it as a proxy for the frailty phenotype may inadvertently interchange two different concepts. The purpose of this study was to evaluate to what extent the frailty phenotype may be explained by the index. Cross-sectional analysis included 393 participants with HIV aged 50 or older. Somers' delta (d) was calculated, and a multinomial logistic regression model was run to determine to what extent the VACS index scores explained the probability of being prefrail or frail. Mean age was 57.6 (standard deviation 6.5) years and 87.3% men. A weak, but positive association between the VACS 2.0 index score and the frailty phenotype was established (Somers' d = 0.120, p < .001). The multinomial logistic regression showed that prefrail and frail participants had higher probabilities for greater VACS index scores [OR = 1.05, 95% confidence intervals (CI) 1.01–1.09; p = .006 and OR = 1.17, 95% CI 1.09–1.26; p < .001, respectively]; however, VACS index only explained <12% of the variability observed in the frailty phenotype. High VACS index scores were associated with a greater probability of being frail; however, with a weak association. Thus, we should be cautious when using the VACS index as a general proxy of frailty, particularly for the frailty phenotype. However, the VACS index may be used as a means to identify persons who might benefit from further comprehensive geriatric assessment.

Introduction

The construct of frailty has had many descriptions and evolving definitions over the last decade, and, even today, a harmonized characterization or measurement still arouses debate. Common ground among the most cited definitions of frailty is that it can be envisioned as a dynamic, age-related, nonlinear, and multidimensional condition associated with poor resilience and an increased risk for negative health-related outcomes. The development of this clinical syndrome of frailty may be the result of the interplay between cellular changes considered as the hallmarks of aging with other exaggerated age-related biologic changes that are modulated by numerous cofactors.^1
–3 A second way to consider the construct of frailty is that of an accumulation of deficits, encompassing the concept that the more physiologic systems that are out of balance, the more vulnerable an individual becomes.⁴

With the increasing proportion of adults aging with HIV, frailty has become increasingly recognized among even middle-aged persons with HIV, often at a much higher prevalence than compared to demographically similar populations without HIV.^5,6 The pathways underlying the early development of frailty are similar to those that contribute to frailty with advanced age. For example, chronic HIV infection triggers a state of chronic low-grade inflammation that may coexist and synergize with specific age-related changes of the immune system, as well as in the endocrine, and musculoskeletal systems among others.^7

–10 The early detection of these precursors to frailty, or conditions strongly linked to frailty, may prompt the implementation of preventive or therapeutic focused strategies.

The Veterans Aging Cohort Study (VACS) index is a composite marker tool originally developed and validated to estimate all-mortality risk in people with HIV.¹¹ More recently, the VACS has been modified, and both the VACS 1.0 and 2.0 have been used as a proxy of frailty in several studies.^12

–15 While the VACS correlates with certain geriatric outcomes, including frailty, utilization of the VACS index as a proxy for the construct of frailty or the frailty phenotype itself may inadvertently end-up interchanging two different concepts. To the best of our knowledge there is not a previous exploration of how much of the frailty state assessed by the frailty phenotype may be explained by the VACS index 2.0 score alone. Given the potential usefulness and feasibility of the VACS index in frailty detection, the purpose of the present study was to evaluate to what extent the frailty phenotype may be explained by VACS index scores among older adults with HIV (OAWH).

Materials and Methods

Study population

This was a cross-sectional analysis of a longitudinal study created to determine the correlates of disability among OAWH, including 393 adults aged 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 was completed among all participants by trained staff using standardized methods. The exclusion criteria were the presence of advanced conditions potentially associated with severe 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 IV–V 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.

The Local Ethics Committee reviewed and approved the study protocol, and written informed consent was obtained from all participants.

Phenotype of frailty

Frailty was defined according to the phenotype proposed by Fried et al, which has been validated for Mexican population:^16
–18 (1) weight loss defined as self-report of unintentional loss of >10 lb. within the previous year; (2) exhaustion defined by two questions from the Center for Epidemiological Studies-Depression scale; (3) slowness determined by the 4-m gait speed test adjusted for sex and height; (4) weakness established when there was decreased dynamometer-measured hand-grip strength adjusted for sex and body mass index; and (5) low physical activity determined by the Spanish version of the Physical Activity Scale for the Elderly, where participants who were in the lowest sex-adjusted quintile were categorized as positive for this frailty criterion. As recommended, participants meeting three or more criteria were classified as frail, one or two as prefrail, and those meeting none as nonfrail.

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 a potential multiorgan system injury.¹² The index has recently been updated into a version with enhanced discrimination for risk of mortality. Variables included in the index are age, body mass index (weight in kilograms/height in meters²), CD4⁺ cell counts, HIV-1 RNA viral load, hemoglobin, platelets, white blood cell count, albumin, aspartate and alanine transaminases (AST, ALT), creatinine, and hepatitis C virus infection status. The FIB-4 index or liver fibrosis index, which included 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)], and the estimated glomerular filtration rate that was calculated according to the Modified Diet in Renal Disease group (MDRD) were also considered for the calculation of the index.

The obtained score is weighted to indicate increasing all-cause mortality risk, as well as diverse negative health-related outcomes among people with HIV.

Other variables

Sociodemographic variables included age, sex, employment, and educational level. Participants were asked whether they had a physician's diagnosis of hypertension, diabetes, dyslipidemia, history of 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. Smoking status (current and former vs. never) and alcohol intake (current and former vs. never) were also included.

Two domains of disability were investigated, disability for activities of daily living (ADL) and instrumental activities of daily living (IADL). For ADL, participants were asked about their ability to carry out the seven tasks evaluated by the Barthel ADL index; bathing, dressing, transferring from bed to chair, climbing stairs, toileting, continence, and feeding.¹⁹ For IADL, participants reported their ability to perform the following eight activities: using the telephone, shopping, grooming, housekeeping, doing laundry, using transportation, handling medications, and handling finances.²⁰ For each domain of disability, if participants had a Barthel index <85 or they indicated that they were unable to perform at least one of the IADL without help, they were considered as having ADL or IADL disability, respectively. Current CD4⁺ T lymphocyte counts were measured by means of flow cytometry, and cell counts were treated as a dichotomous variable (≤200 vs. >200). HIV-RNA levels (viral load) were measured using real time PCR either on the Roche TaqMan^® Analyzer 48 or the Abbott m2000 Realtime System with a lower limit of detection of <40 copies/mL and was treated as a dichotomous variable (suppressed when <40 copies/mL and not suppressed when ≥40 copies/mL were found). Year of HIV diagnosis was obtained from clinical records. Time on combined antiretroviral therapy (cART), in years, represents the time from the first (cART) treatment administrated until the date of clinical evaluation.

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 means and standard deviations (SDs) or frequencies and proportions. Chi square and ANOVA tests were also used with descriptive purposes. Somers' delta (d) was calculated to quantify the strength of association between the VACS 2.0 index score and the frailty phenotype.

To determine how VACS 2.0 index scores alone explain the probability of being prefrail or frail, a multinomial logistic regression model was run, including VACS index 2.0 as predictor, and the Nagelkerke pseudo R ² was established. All comparisons were evaluated using 95% confidence intervals (CI). Statistical analyses were performed in SPSS software for Windows^® (version 20.0; SPSS, Inc., Chicago, IL) and R Software, version 4.0.3.

Results

The study sample comprised 393 participants with complete data. Mean age was 57.6 (SD 6.5) years, and 87.3% were men. The most frequent chronic diseases were dyslipidemia (53.2%), hypertension (26.9%), and diabetes (16.3%). Disability for at least one ADL and IADL was 23.9% and 15.0%, respectively. Only 5.3% of the participants had CD4⁺ cell counts under 200, 6.9% had a detectable viral load, and the mean time under cART was of 5.5 (SD 6.8) years. The prevalence of frailty was 4.3%, 31.0% for prefrailty, and 64.6% for nonfrail participants.

Sociodemographic characteristics and health status by frail status are presented in Table 1. In comparison with nonfrail participants, frail persons were older (p < .001), mostly men (p = .003), and had lower education level (p < .001). In addition, frail participants had shorter times under cART (p = .002) and more disabilities for ADL (p < .001) and IADL (p < .001) compared with their nonfrail counterparts. Lower CD4⁺ cell counts and detectable viral load were also more frequent among frail persons (Table 1).

Table 1.

Sociodemographic Characteristics and Health Status by Frail Status

	Total, N = 393	Nonfrail (n = 254), 64.6%	Prefrail (n = 122), 31.0%	Frail (n = 17), 4.3%	p
Male, %	87.3	90.6	83.6	64.7	.003
Age, mean (SD)	57.6 (6.5)	57.0 (5.8)	58.2 (7.0)	64.1 (8.6)	<.001
Education, years, mean (SD)	13.2 (5.0)	14.0 (4.7)	11.9 (5.3)	10.2 (6.3)	<.001
Chronic diseases,^a mean (SD)	1.5 (1.2)	1.5 (1.1)	1.4 (1.3)	2.0 (1.4)	.560
Diabetes, %	16.3	14.2	17.2	41.2	.013
Hypertension, %	26.0	24.8	23.8	58.8	.007
Dyslipidemia, %	53.2	56.3	47.5	47.1	.246
Non-AIDS cancer, %	9.4	8.7	8.2	29.4	.015
Ischemia, %	5.12	4.7	4.9	11.8	.439
Alcohol, %	34.7	38.9	30.9	7.7	.047
Smoking status, %	25.3	26.3	24.7	11.4	.676
Barthel index <85, %	23.9	16.1	34.4	64.7	<.001
Disability for at least one IADL, %	15.0	8.3	23.8	52.9	<.001
Time under cART, mean (SD)	5.5 (6.8)	10.3 (6.8)	8.8 (6.7)	4.2 (4.3)	.003
Time since HIV diagnosis, mean (SD)	12.9 (7.7)	13.5 (7.7)	12.1 (7.5)	10.1 (8.5)	.085
CD4⁺ cell count <200, %	5.3	2.6	4.2	50.0	<.001
Detectable viral load, %	6.9	6.5	4.2	33.3	.028
Opportunistic infection ≥1, %	33.8	29.5	37.9	50.0	.177
VACS 2.0 index, mean (SD)	54.8 (9.0)	53.3 (8.5)	56.5 (8.2)	66.5 (12.0)	<.001

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

cART, combined antiretroviral therapy; IADL, instrumental activities of daily living; SD, standard deviation; VACS, Veterans Aging Cohort Study.

We found weak positive association between the VACS 2.0 index score and the frailty phenotype, which was statistically significant (Somers' d = 0.120, p < .001).

The unadjusted multinomial logistic regression analyses demonstrated that in comparison to nonfrail participants, those participants who were prefrail and frail had a higher probability of having higher VACS 2.0 index scores (OR = 1.05, 95% CI 1.01–1.09; p = .006 and OR = 1.17, 95% CI 1.09–1.26; p < .001, respectively); however, the Nagelkerke pseudo R ² was 0.116. Hence, the contribution of the VACS 2.0 index explained only 12% of the variability observed in the frailty phenotype, as assessed by the model's R ².

Discussion

The results of the present work suggest that while higher VACS 2.0 index scores are associated with increased frailty, these scores only explain a small component of the frailty phenotype. Although both constructs intend to detect a population with greater vulnerability to adverse events, our results suggest that these constructs may describe two different processes. Frailty in geriatrics is commonly conceptualized as an age-related, nonlinear (often dynamic) multidimensional condition that is often linked to a series of cumulative defects at the molecular, cellular, and system levels.^1,21,22 Many of the potential drivers of frailty at the physiological level are captured in the VACS index; however, the concept of frailty also encompasses a further global state of vulnerability that expands beyond of what is captured in the VACS index.^1,21,23 Thus, we should be cautious about using frailty and high VACS index scores interchangeably, or using the VACS index as a proxy for the phenotype of frailty, as it is often conceptualized in geriatrics.

However, both the frailty phenotype and the VACS index have been applied and demonstrated remarkable benefit for the detection of multiple adverse health-related outcomes in people with HIV.^24

–28

In contrast, the other most commonly used model to assess frailty, including in people with HIV, is the frailty index (FI).⁴ While the frailty phenotype is based on physical components (e.g., weight loss, weakness, or slowing), the FI is a model in which various deficits ranging from deviations in vital signs to diseases, and even disability, accumulate, and when a certain threshold is exceeded, the person is considered as frail.⁴ Therefore, it has been proposed that the FI, rather than being a marker of aging, could represent a marker of the impact that different diseases have on a person.²⁹ Like the frailty phenotype, the FI has robust evidence supporting it as an excellent predictor of multiple adverse outcomes; however, when comparing the two models in the general population it has been observed that the correlation between them is also weak.^30,31 Many of the VACS elements (multisystem dysfunction) may be represented in the FI, and both represent an accumulation of deficits. Thus, the VACS index may be closer to a cumulative deficit approach of frailty.

Nevertheless, contrasts between the FI and the VACS index are that the VACS has been more extensively evaluated in large populations of people with HIV, it remains the same across these different studies, uses variables that are routinely collected in the follow-up of people living with HIV, and applies a weighting system for variables that have greater contribution to mortality.¹²

When incorporating the VACS index into the construct of frailty, we believe that the VACS index can have an important role in the detection of the physiological component of frailty, particular in a population such as OAWH in which many of its physiological drivers can present at an earlier age.^5,7
–9 Likewise, as the VACS index is easy to apply, readily available, and weakly correlates with the frailty phenotype, we could use high VACS scores as a marker of increased risk of frailty or a quick screen to identify those who might benefit from further frailty assessment and a geriatric consultation. Such collaboration might not only help to detect the other components participating in the state of increased vulnerability observed in the person but also to improve the quality of care and the fourth “90.”³² The latest guidelines of the European AIDS Clinical Society propose that comprehensive geriatric assessments should be performed in OAWH to improve the detection and management of frailty.³³ As most clinics do not have the time or resources to routinely conduct comprehensive geriatric assessments in all patients, the VACS 2.0 index could be used to identify those that might most benefit. Finally, other results that deserve a mention were the observed associations between frailty, age, educational level, and time under cART. Associations between frailty and age, as well as educational level, are well documented for the general population, and similar findings are being replicated in studies focusing on OAWH.^34,35 However, some works suggest that the association with educational level may be modulated by factors as social inequalities and should be interpreted with caution.³⁶

Studies specifically focused on factors associated with phenotypical frailty in OAWH have found that both chronological age (particularly being ≥65 years old), as well as HIV-related factors such as the CD4⁺/CD8⁺ lymphocyte ratio, can also be associated with frailty, thus, other HIV-related variables like time under cART should be considered in future studies.^35,37

This study has some limitations, including its cross-sectional design, a relatively small sample size, mostly men, and being a single center study. Exclusion of advanced comorbidities may have made our population healthier than the general population of people aging with HIV. And finally, the study included people with potentially higher frailty risks, as participants with adverse immunological and viral markers. However, our data add to the diversity of literature on geriatric outcomes in OAWH. Strengths as the use of different statistical approaches to explore the possible associations between both constructs and to relevant geriatric outcomes within a population of interest as OAWH can be underlined.

Conclusions and Implications

In summary, we found that high VACS 2.0 index scores were associated with a greater risk of phenotypical frailty among Mexican OAWH; however, these two constructs were only weakly correlated, and VACS index scores explained <12% of variability for the frailty phenotype. Thus, we caution using both constructs interchangeably, particularly when describing frailty as a phenotype as conceptualized in geriatric medicine. Nonetheless, and given its components, the VACS index may be an excellent discriminant for the physiologic/physical component of frailty and more closely related to the concept of frailty as an accumulation of deficits. Hence, the VACS index may be used as a means to identify those who might most benefit from further comprehensive geriatric assessment in OAWH. Given the growing population of OAWH, tailored strategies that can be implemented in the clinical setting are needed to maximize the health span and ensure the highest quality of care.

Footnotes

Authors' Contributions

V.A.H.-R. and J.A.A.-F. designed, drafted the article, and directly accessed and verified the underlying data reported in the article. K.M.E. and H.A. contributed equally to critical analysis, data interpretation, and edition.

Data

The dataset presented in this article is not readily available as it is property of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán. Requests to access the dataset should be directed to: alberto.avilaf@incmnsz.mx

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This research did not receive any funding from agencies in the public, commercial, or not-for-profit sectors. Virgilio A. Hernández-Ruiz was supported by the National Council for Science and Technology (CONACYT) in Mexico.

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