Frailty,Physical Function Impairment,and Pulmonary Function in Aging Men With and Without HIV from the Multicenter AIDS Cohort Study

Abstract

People aging with HIV (PAWH) experience greater impairment in physical and pulmonary function than individuals aging without HIV. We examined whether baseline physical function was associated with subsequent pulmonary impairments. Associations of frailty and physical function (gait speed [m/s], grip strength [kg]) with pulmonary function (<80% predicted diffusing capacity for carbon monoxide [DL_CO] and forced expiratory volume [FEV₁]) 3 years later were modeled; age, HIV status, and smoking were assessed as effect modifiers. Among 1,024 men (54% PAWH, 10% frail, 51% prefrail), mean (SD) age = 53 (12) years, cumulative smoking = 12 (19) pack-years, gait speed = 1.1 (0.2) m/s, and grip strength = 36.6 (9.2) kg. Frailty, prefrailty, and weak grip strength were associated with higher odds of subsequent impaired DL_CO and FEV₁. Slow gait speed was associated with higher odds of DL_CO impairment but not FEV₁. No statistically significant modifications were found. Interventions to improve physical function may help preserve pulmonary function.

Keywords

physical function pulmonary function HIV aging

The life expectancy for people aging with HIV (PAWH) has increased due to effective antiretroviral therapy.¹ PAWH are at higher risk of developing earlier frailty and physical function impairments with greater severity.^2,3 Frailty and pulmonary disease may co-occur due to shared risk factors and pathophysiological mechanisms, and a bidirectional association has been shown in the general population.⁴ In our prior study, we showed that pulmonary impairment is associated with increased subsequent frailty risk, decreased gait speed, and weaker grip strength among men with and without HIV.⁵ Building on our prior study, we hypothesize that frailty and physical function impairments are associated with subsequent impaired pulmonary function and that associations would be greater among PAWH, older adults, and smokers.

The Multicenter AIDS Cohort Study (MACS), now part of the Multicenter AIDS Cohort Study/Women’s Interagency HIV Combined Cohort Study (MWCCS),⁶ began in 1984 and enrolled men with or at risk of HIV at four study centers (Baltimore/Washington DC, Chicago, Pittsburgh/Columbus, and Los Angeles) and three enrollment cohorts (before 1995, between 2001 and 2009, and 2010 and later). Institutional review board approval was obtained at each site. MACS details have been published elsewhere; in brief, participants completed semiannual visits for demographic, clinical, and laboratory assessments.⁷ Pulmonary function tests (PFTs) were conducted from 2017 to 2019. Participants were invited to undergo PFTs; those who consented, completed PFTs, and had measurements that passed quality control standards were included here.^8,9 Participants performed spirometry before and after inhaling 360 µg of albuterol via metered-dose inhaler, followed by single-breath DL_CO testing (ndd EasyOne Pro, Zurich/Switzerland). Spirometry and DL_CO tests underwent central quality control, and only acceptable-quality tests were included (Grades A, B, or C).¹⁰ Predicted values were calculated using National Health and Nutrition Examination Survey (NHANES) equations for spirometry¹¹ and DL_CO¹² accounting for factors such as age, sex, race, and height. Percent predicted DL_CO and postbronchodilator percent predicted FEV₁ in 1 s (FEV₁) were categorized with <80% indicating impairment, except for models with an age interaction where absolute continuous DL_CO and FEV₁ were utilized (since percent predicted values are already adjusted for age). DL_CO was corrected for hemoglobin (Hb) and carboxyhemoglobin.¹³ The visit 3 years prior to PFTs was considered baseline for frailty, gait, and grip assessments, and if missing, then the next visit (up to 1 year prior to PFTs) was used. Participants who seroconverted during follow-up were excluded (n = 13).

Age, HIV serostatus, and cumulative pack-years of smoking were examined as potential effect modifiers on the associations of frailty and physical function with pulmonary function.

Baseline HIV serostatus, enrollment center, race, age, cumulative pack-years of smoking, and enrollment cohort were included in all models. In models examining interactions of age with DL_CO or FEV₁ (using absolute measurement), height (in cm) was also included. Potential confounders included education level, weight, body mass index, cardiovascular risk (Framingham coronary heart disease 10-year risk score %), diabetes defined as HbA1c ≥6.5% or fasting glucose ≥126 mg/dL or self-reported diagnosis of diabetes with medication use, use of cholesterol lowering medication, treatment for depression, current or prior confirmed diagnosis of kidney disease, hepatitis C seropositivity, hepatitis B (surface antigen positive or resolved vs. negative), and any use since last visit of alcohol, marijuana, cocaine, and/or heroin. Potential confounders were included in the model if their addition resulted in a ≥10% change in the primary exposure coefficient and excluded otherwise.^14,15 Statistically significant precision variables that did not meet the confounder definition were also included in the final model.

Statistical analyses utilized SAS v9.4 (Cary, NC). Logistic regression models determined associations of frailty and physical function with pulmonary function. Exploratory analyses examined if frailty components other than gait speed or grip strength (i.e., weight loss, exhaustion, or low physical activity) were associated with pulmonary function impairment.

Interaction terms between frailty or physical function with age, HIV serostatus, or smoking were considered separately. A minimal clinically important difference was determined based on the literature and/or clinical expertise (Supplementary Data).

Of 1,133 men with PFTs, 1,024 and 1,007 met inclusion criteria for DL_CO and FEV₁ analyses, respectively. Overall, 54% and 55% of the DL_CO and FEV₁ participants, respectively, were PAWH. Cohort characteristics are reported in Table 1. Briefly, 16 and 14 participants with DL_CO and FEV₁ measures, respectively, were missing frailty measurement. On average, participants had 2.7 years (SD = 0.5) of follow-up between frailty and PFT measurements.

Table 1.

Baseline Characteristics of the Cohort (Diffusing Capacity for Carbon Monoxide and Forced Expiratory Volume)

Variable	DL_CO cohort overall(n = 1,024)	FEV₁ cohort overall(n = 1,007)
Age (years) mean (SD)	52.8 (12.0)	52.8 (12.1)
Race
White	656 (64%)	640 (64%)
Black	278 (27%)	273 (27%)
Other	90 (9%)	94 (9%)
Center of enrollment
Baltimore/Washington DC	208 (20%)	206 (20%
Chicago	257 (25%)	243 (24%
Pittsburgh/Columbus	312 (31%)	311 (31%)
Los Angeles	247 (24%)	247 (25%)
Cohort of enrollment
Before 1995	469 (46%)	454 (45%)
2001–2010	400 (39%)	397 (39%)
2010+	155 (15%)	156 (16%)
Education level
12th grade or less	216 (21%)	219 (22%)
At least 1 year of college but no degree	275 (27%)	271 (27%)
Four-year college degree or more	533 (52%)	517 (51%)
Participant with HIV	552 (54%)	552 (55%)
CD4 count cells/mm³ (for participants with HIV) mean (SD)	692.1 (294.0)	698.7 (303.7)
Viral load ≤200 copies/mL (for participants with HIV)	476 (86%)	478 (87%)
Smoking status
Never smoker	328 (32%)	321 (32%)
Former smoker	440 (43%)	438 (44%)
Current smoker	254 (25%)	246 (24%)
Cumulative pack years of smoking mean (SD)	12.0 (18.7)	12.1 (19.1)
Weight (kg) mean (SD)	84.4 (17.1)	84.2 (17.0)
Body mass index (BMI, kg/m²) mean (SD)	26.9 (4.9)	26.9 (5.0)
Alcohol use
None	187 (18%)	191 (19%
1–3 drinks/week	495 (49%)	488 (49%
4–13 drinks/week	248 (24%)	235 (23%)
More than 13 drinks/week	87 (9%)	87 (9%)
Framingham coronary heart disease 10-year risk (%) mean (SD)	8.0 (7.3)	8.1 (7.3)
Framingham hard coronary heart disease 10-year risk (%) mean (SD)	8.6 (6.0)	8.6 (6.0)
Diabetes status	136 (14%)	139 (15%)
Treatment for depression	140 (14%)	139 (14%)
On cholesterol-lowering medication	350 (34%)	338 (34%)
Heroin use	18 (2%)	17 (2%)
Cocaine use	83 (8%)	83 (8%)
Injection drug use	18 (2%)	19 (2%)
Hash/Marijuana use	324 (32%)	312 (31%)
Kidney disease (current or prior)	21 (2%)	25 (3%)
Hepatitis B (active or resolved)	524 (52%)	504 (50%)
Hepatitis C seropositivity	54 (5%)	48 (5%)
Gait speed (m/s) mean (SD)	1.1 (0.2)	1.1 (0.2)
Grip strength (kg) mean (SD)	36.6 (9.2)	36.3 (9.4)
Frailty
Nonfrail	397 (39%)	385 (39%)
Prefrail	510 (51%)	491 (50%)
Frail	100 (10%)	114 (11%)
DL_CO impaired (<80% predicted)	342 (33%)	—
FEV₁ impaired (<80% predicted)	—	128 (13%)

DL_CO, diffusing capacity for carbon monoxide; FEV₁, forced expiratory volume.

Compared to nonfrail participants, the odds of impaired DL_CO (<80% predicted) were 1.94 [(95% CI: 1.42, 2.65); p < .001] times higher for prefrail participants and 2.53 [(1.54, 4.15); p < .001] times higher for frail participants (Fig. 1 and Supplementary Table S1a). Similarly, compared with nonfrail participants, the odds of impaired FEV₁ were 1.44 times higher for prefrail participants and 2.32 times higher for frail participants compared with nonfrail ([95% CI 0.90, 2.32]; p = .13; [95% CI: 1.24, 4.32]; p = .008, respectively, Fig. 1 and Supplementary Table S1b). Every 0.05 m/s decrease in gait speed was associated with greater odds of DL_CO impairment (OR = 1.07 [95% CI: 1.03, 1.12]; p < .001, Supplementary Fig. S2 and Table S2a) but not with FEV₁ impairment (OR = 0.99 [95% CI: 0.94, 1.05]; p = .82, Supplementary Fig. S2 and Table S2b). Every 4 kg decrease in grip strength was associated with greater odds of both DL_CO (OR = 1.25 [95% CI: 1.16, 1.34]; p < .001, Supplementary Fig. S2 and Table S3a) and FEV₁ impairment (OR = 1.21 [95% CI: 1.10, 1.33]; p < .001, Supplementary Fig. S2 and Table S3b).

FIG. 1.

Odds ratios and 95% confidence intervals from adjusted logistic regression models for the association of frailty with subsequent impairment in DL_CO* and FEV1**.*DL_CO models adjusted for age, cohort of enrollment, center of enrollment, race, HIV serostatus, and cumulative pack-years of smoking** FEV₁ models adjusted for age, cohort of enrollment, center of enrollment, race, HIV serostatus, cumulative pack-years of smoking, and Framingham hard coronary 10-year risk score.

Inconclusive results were found when exploring age, HIV status, or smoking modification of frailty on DL_CO and FEV₁ and age modification of grip strength on FEV₁ (Supplementary Tables S4, S5, S6, S7, S8, S9, S10, S11, and S12). Therefore, primary results were presented without these interactions.

Neither age, HIV, nor smoking modified associations between gait speed or grip strength and DLCO or between gait speed and FEV₁ (Supplementary Tables S13, S14, S15, S16, S17, S18, S19, S20, and S21). Neither HIV nor smoking modified the association between grip strength and FEV₁ (Supplementary Tables S22, and S23).

In exploratory analyses, participants reporting low physical activity had higher odds of DL_CO and FEV₁ impairment compared with participants not reporting low physical activity (OR = 1.76 [95% CI: 1.32, 2.34]; p < .01, OR = 1.66 [95% CI: 1.07, 2.57]; p = .02, respectively, Supplementary Fig. S3). Unintentional weight loss and exhaustion were not significantly associated with subsequent pulmonary function (Supplementary Fig. S3).

Our results expand on the literature by showing associations between frailty and subsequent pulmonary function across multiple measures. Surprisingly, associations between frailty, gait speed, and grip strength with DL_CO and FEV₁ were generally similar regardless of age, HIV serostatus, and smoking history. Considering this and our previous study,⁵ we propose a bidirectional association between pulmonary function and physical function impairment or frailty, as has been suggested in the general population.⁴

We found no differences in associations between physical function or frailty and pulmonary function by HIV serostatus or age. We did observe a higher nonsignificant percentage of PAWH with impaired DL_CO (36% vs. 31%) and frailty (10.3% vs. 9.4%). The lack of modification by HIV serostatus could result from the majority of the PAWH having limited disease progression, with 86% having a viral load ≤200 copies/mL and 74% having a CD4⁺ count >500 cells/mm³. The lack of modification by age could be due to one-time pulmonary function testing or a relatively narrow age range.

This study has several strengths. It is a multicenter study with a large sample size including men with and without HIV. We used well-validated objective physical function measures of gait speed and grip strength and lung function. The extensive demographic and health-related data allowed us to adjust for numerous covariates. This study also has limitations. Although the MACS has more recently combined with the Women’s Interagency HIV Study (comparable data not yet available), this study only included U.S. men, >90% of whom were White or Black, and study results may not be generalizable to women, other racial/ethnic groups, or non-U.S. populations. We looked at single measurements for both physical function/frailty and pulmonary function (at different time points); so, we could not examine changes over time. We acknowledge that participants with impaired pulmonary function at the 3-year follow-up may have had pre-existing impairments, and thus, there remains uncertainty about the directionality of the association. Future longitudinal studies are needed to confirm. Our interpretation of pulmonary function relied on percent-predicted values calculated using NHANES/NHANES III reference equations. Because these equations were developed from general-population samples, they may not fully capture normative lung function in the MACS cohort and could lead to some misclassification of impairment, thereby limiting generalizability.

In conclusion, our findings support a bidirectional association between pulmonary function and physical function and frailty. A better understanding of mechanistic pathways underlying both frailty and pulmonary functions is needed to develop improved treatments to reduce declines in physical and pulmonary function and improve overall quality of life.

Authors’ Contributions

M.A.: Conceptualization, methodology, software, formal analysis, writing—original draft, and visualization. K.M.K., A.M., D.C., G.D., K.C., and V.S.: Writing: review and editing. M.A.-M., C.D., T.T.B., and S.M.: Conceptualization, visualization, data analysis and interpretation, writing: review and editing. S.M.: Conceptualization, visualization, data analysis and interpretation, validation, supervision, and writing—review and editing. K.M.E.: Conceptualization, visualization, data analysis and interpretation, supervision, and writing—review and editing.

Footnotes

Acknowledgments

Data in this article were collected by the MACS/WIHS Combined Cohort Study (MWCCS). The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). MWCCS (Principal Investigators): Atlanta CRS (Ighovwerha Ofotokun, Anandi Sheth, and Gina Wingood), U01-HL146241; Baltimore CRS (Todd Brown and Joseph Margolick), U01-HL146201; Bronx CRS (Kathryn Anastos, David Hanna, and Anjali Sharma), U01-HL146204; Brooklyn CRS (Deborah Gustafson and Tracey Wilson), U01-HL146202; Data Analysis and Coordination Center (Gypsyamber D’Souza, Stephen Gange, and Elizabeth Topper), U01-HL146193; Chicago-Cook County CRS (Mardge Cohen, Audrey French, and Ryan Ross), U01-HL146245; Chicago-Northwestern CRS (Steven Wolinsky, Frank Palella, and Valentina Stosor), U01-HL146240; Northern California CRS (Bradley Aouizerat, Jennifer Price, and Phyllis Tien), U01-HL146242; Los Angeles CRS (Roger Detels and Matthew Mimiaga), U01-HL146333; Metropolitan Washington CRS (Seble Kassaye and Daniel Merenstein), U01-HL146205; Miami CRS (Maria Alcaide, Margaret Fischl, and Deborah Jones), U01-HL146203; Pittsburgh CRS (Jeremy Martinson and Charles Rinaldo), U01-HL146208; UAB-MS CRS (Mirjam-Colette Kempf, James B. Brock, and Deborah Konkle-Parker), U01-HL146192; and UNC CRS (M. Bradley Drummond and Michelle Floris-Moore), U01-HL146194. The MWCCS is funded primarily by the National Heart, Lung, and Blood Institute (NHLBI), with additional cofunding from the Eunice Kennedy Shriver National Institute Of Child Health & Human Development (NICHD), National Institute On Aging (NIA), National Institute Of Dental & Craniofacial Research (NIDCR), National Institute Of Allergy And Infectious Diseases (NIAID), National Institute Of Neurological Disorders And Stroke (NINDS), National Institute Of Mental Health (NIMH), National Institute On Drug Abuse (NIDA), National Institute Of Nursing Research (NINR), National Cancer Institute (NCI), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Deafness and Other Communication Disorders (NIDCD), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute on Minority Health and Health Disparities (NIMHD), and in coordination and alignment with the research priorities of the National Institutes of Health, Office of AIDS Research (OAR). MWCCS data collection is also supported by UL1-TR000004 (UCSF CTSA), UL1-TR003098 (JHU ICTR), UL1-TR001881 (UCLA CTSI), P30-AI-050409 (Atlanta CFAR), P30-AI-073961 (Miami CFAR), P30-AI-050410 (UNC CFAR), P30-AI-027767 (UAB CFAR), P30-MH-116867 (Miami CHARM), UL1-TR001409 (DC CTSA), KL2-TR001432 (DC CTSA), and TL1-TR001431 (DC CTSA).

The authors gratefully acknowledge the contributions of the study participants and dedication of the staff at the MWCCS sites.

Author Disclosure Statement

The authors have no conflicts of interest to declare for this specific study.

Funding Information

M.A. was supported by the Integrative Physiology of Aging Training Grant T32 AG000279-18. T.T.B. was supported in part by K24 AI120834. K.M.E. was supported in part by K24 AG082527. This material is also the result of work supported with resources and the use of facilities at the Minneapolis Veterans Affairs Medical Center, Minneapolis, the United States, and Veterans Affairs Puget Sound, Seattle, USA.

Supplemental Material

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