Abstract
Sarcopenia, age-related low muscle mass and function, is a well-established independent risk factor for bone fracture in the geriatric population but is understudied in older people living with HIV (PLWH). The objective of this cross-sectional study was to investigate in older PLWH the relationship between muscle mass and bone mineral density (BMD). Sedentary PLWH who were ≥50 years of age, receiving antiretroviral therapy, and enrolled in an exercise intervention trial were included. Established definitions for sarcopenia and osteopenia/osteoporosis were applied to muscle mass data and BMD collected by dual-energy X-ray absorptiometry before exercise training. Participants were 93% male and 33% Caucasian race with median age 61 years, and median CD4 lymphocytes 707 cells/μL. The majority (64%) were overweight and obese by body mass index. Appendicular skeletal muscle index (ASMI) correlated with BMD at the femoral neck (r = 0.49, p < .01), total hip (r = 0.54, p < .01), and lumbar spine (r = 0.48, p < .05). Low BMD at the femoral neck was present in 39% (26% osteopenia, 13% osteoporosis). ASMI was lower among those with low BMD compared with normal BMD (p = .02). Low muscle mass measured by ASMI is associated with low BMD in clinically stable older PLWH. Detailed body composition assessment may help guide lifestyle recommendations to prevent bone fractures in older PLWH.
Keywords: HIV, osteoporosis, bone mineral density, frailty fracture, sarcopenia
People living with HIV (PLWH) have lower bone mineral density (BMD) and higher age-adjusted risk of osteoporosis and fractures that are associated with considerable morbidity.1 Sarcopenia, age-related low muscle mass and function, is a well-established independent risk factor for fractures in the geriatric population.2 Sarcopenia, based on the established measure of appendicular skeletal muscle index (ASMI), has been studied in older PLWH, but this research focused on its association with frailty or functional impairment.3,4 In the Women's Interagency HIV Study, muscle mass estimated by bioimpedance was associated with BMD.5 Longitudinal data in young adults (median age 38 years) from A5224s showed that the increased muscle mass after starting antiretroviral (ARV) therapy correlated with increased hip BMD regardless of ARV group, which included tenofovir DF and a boosted protease inhibitor, both associated with low BMD in cross-sectional studies.6 Our study objective was to investigate in older PLWH the relationship between BMD and ASMI.
This cross-sectional study included baseline data from 31 participants who were enrolled in a center-based exercise trial of sedentary PLWH ≥50 years of age on ARV. Details on eligibility and methods are available elsewhere (NCT02101060). Participants underwent a dual-energy X-ray absorptiometry (DXA) scan to determine total lean mass, appendicular lean mass (ALM), and BMD. ASMI was calculated as ALM/height.2 In addition, two definitions for clinical sarcopenia were tested: Baumgartner's ASMI cutoffs only7 and with grip strength added as recommended by the European Working Group on Sarcopenia.8 BMD at the femoral neck, total hip, and lumbar spine (L1–4) was classified per World Health Organization criteria as normal, osteopenic (T-score <−1.0 and >−2.5), or osteoporotic (T-score ≤−2.5.). Low BMD was defined as presence of osteopenia or osteoporosis at the femoral neck. The Shapiro–Wilk test was used to test normality of data distribution. Accordingly, Pearson or Spearman correlation was used to test association between continuous measures; Student's' t-test or Mann–Whitney U test were used to test between group differences. For categorical variables, chi-square test or Fisher's exact test was used. Linear regression was performed with the dependent continuous variable femoral neck BMD and the exposure of interest, ASMI, as the independent variable. Adjusted models include one of three established risk factors for low BMD (e.g., race, age, ARV use).2 All analyses were two tailed and performed using STATA (v14.1; College Station, TX).
Participants were older (53% ≥ 60 years) and overweight or obese (64% body mass index (BMI) ≥25.0 kg/m2). Table 1 describes characteristics of participants, using mean (standard deviation) or median (quartile 1, quartile 3) for measures with normal or skewed distributions, respectively. Wasting was considered absent since none of the participants had low BMI (≤18.5 kg/m2). The prevalence of low BMD varied across bone sites and was highest at the femoral neck (39%; 26% osteopenia, 13% osteoporosis). Descriptive statistics for each site are provided in Table 1. ASMI positively correlated with BMD at the femoral neck (r = 0.49, p < .01), total hip (r = 0.54, p < .01), and lumbar spine (r = 0.48, p < .05). ASMI and other measures of lean mass were associated with low BMD at the femoral neck, but BMI was not (Table 1). Linear regression showed that the relationship of ASMI with BMD (β [95% confidence interval]; 0.051 [0.012–0.091], p = .01) was attenuated when adjusted for race (0.032 [−0.003 to 0.067], p = .07). ASMI remained significantly associated with BMD in separate models adjusted for age and each ARV group (all p < .05). Only four (13%) of the participants met the definition for sarcopenia using ASMI cutoff values. None of these cases of sarcopenia had low grip strength, which was present in only two individuals.
Table 1.
Characteristics of Study Population with Low Bone Mineral Density (Osteopenia or Osteoporosis) Versus Normal Bone Mineral Density
| Characteristicsa | Total (N = 31) | BMD |
||
|---|---|---|---|---|
| Osteopenia/osteoporosisb(n = 12) | Normal (n = 19) | p | ||
| Age (years) | 62.1 (6.6) | 61.8 (6.7) | 62.2 (6.7) | .88 |
| Race, n (%) | ||||
| African American | 21 (67) | 4 (33) | 17 (89) | .002 |
| Caucasian | 10 (33) | 8 (64) | 2 (11) | |
| Gender (male), n (%) | 29 (93) | 12 (100) | 17 (90) | .51 |
| Smoking (ever), n (%) | 20 (74) | 9 (82) | 11 (69) | .66 |
| Cocaine/heroin use (ever), n (%) | 20 (72) | 5 (45) | 15 (88) | .03 |
| Alcohol ≥14 drinks/week, n (%) | 3 (11) | 1 (9) | 2 (13) | 1.0 |
| Hypertension, n (%) | 15 (48) | 4 (33) | 11 (57) | .27 |
| Diabetes, n (%) | 11 (35) | 3 (25) | 8 (42) | .45 |
| HCV antibody positive, n (%) | 7 (22) | 2 (16) | 5 (26) | .68 |
| Prior AIDS defining illness, n (%) | 9 (29) | 3 (25) | 6 (31) | 1.0 |
| ARV current regimen, n (%) | ||||
| Tenofovir (TDF/TAF) | 17 (54) | 5 (41) | 12 (63) | .24 |
| NNRTI | 10 (32) | 3 (25) | 7 (37) | .69 |
| Protease inhibitor | 4 (13) | 3 (25) | 1 (5) | .27 |
| ISTI | 20 (65) | 9 (75) | 11 (58) | .45 |
| CD4 count (cells/μL) | 683.9 (293.4) | 651.4 (351.2) | 704.5 (258.8) | .63 |
| HIV-1 RNA <20 c/mL, n (%) | 27 (87) | 12 (100) | 15 (78) | .14 |
| Duration of HIV infection (years) | 20.4 (8.3) | 19.7 (9.7) | 21.0 (7.7) | .68 |
| Body composition | ||||
| BMI (kg/m2) | 27.6 (23.4, 31.0) | 24.9 (23.4, 27.7) | 29.4 (23.4, 31.8) | .12 |
| Truncal fat mass (kg) | 13.2 (8.9, 15.4) | 12.0 (9.1, 14.9) | 13.9 (8.9, 16.7) | .39 |
| Fat mass (kg) | 22.6 (17.7–28.9) | 21.5 (15.5, 28.5) | 22.7 (18.1, 29.4) | .52 |
| Percent body fat (%) | 27.1 (6.0) | 27.2 (6.5) | 27.1 (5.8) | .93 |
| Total lean mass (kg) | 58.9 (53.3, 65.5) | 54.8 (52.6, 59.4) | 62.9 (54.5, 68.2) | .05 |
| Appendicular lean mass (kg) | 25.8 (23.3, 28.9) | 24.1 (22.2, 25.9) | 27.4 (24.8, 29.3) | .04 |
| ASMI (kg/m2) | 8.3 (7.6, 9.3) | 7.7 (7.5, 8.2) | 8.9 (8.2, 9.6) | .02 |
| Sarcopenia present, n (%) | 4 (13) | 2 (11) | 2 (17) | .63 |
| Outcome measures | ||||
| Lumbar spine BMD (g/m2) | 1.10 (0.20) | 0.96 (0.15) | 1.19 (0.18) | |
| Total hip BMD (g/m2) | 0.98 (0.16) | 0.84 (0.12) | 1.07 (0.11) | |
| Femoral neck BMD (g/m2) | 0.83 (0.17) | 0.67 (0.12) | 0.93 (0.11) | |
Substance abuse data missing in five individuals.
Continuous data shown as median (quartile 1, quartile 3) tested by Mann–Whitney U and data shown as mean (standard deviation) tested by Student's t-test. For categorical variables, chi-square test or Fisher's exact test was used.
Osteopenia or osteoporosis defined as T-score <−1.0 at the femoral neck.
ARV, antiretroviral therapy; ASMI, appendicular skeletal muscle index, defined as appendicular lean mass/height2; BMI, body mass index; BMD, bone mineral density; HCV, hepatitis C virus; ISTI, integrase strand transfer inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; Sarcopenia, ASMI <7.26 kg/m2 for men and <5.5 kg/m2 for women; TAF, tenofovir alafenamide; TDF, tenofovir disoproxil fumarate.
This study in clinically stable older PLWH demonstrates an important relationship between muscle mass and BMD that provides insight into sarcopenia as a key factor in osteoporosis prevention. The results have two main clinical implications. First, low BMI is insufficient as a screening tool to identify low muscle mass in stable older PLWH. Each DXA scan for BMD is a missed opportunity to also quantify lean mass and assess for sarcopenia (ICD-10 M62.84). Second, the addition of poor function to the definition of sarcopenia improves the predictive capacity for adverse outcomes in the geriatric population, but needs to be further developed in older PLWH. Our 13% prevalence of sarcopenia based on ASMI cutoffs is lower than the 17% reported in older PLWH in Multicenter AIDS Cohort Study,3 likely due to our study's eligibility criteria. Yet, we found a moderate correlation between ASMI and BMD and significantly lower ASMI among those with osteopenia/osteoporosis. Our addition of poor function using grip strength did not identify additional cases of sarcopenia. Further research is needed to support screening for preclinical sarcopenia to identify this modifiable risk factor for bone fracture. Observational data support the value of exercise training to increase BMD in PLWH. In the Stopping Atherosclerosis and Treating Unhealthy Bone With Rosuvastatin in HIV (SATURN-HIV) study, self-reported high-intensity physical activity was independently associated with higher BMD.9 We acknowledge that our small study population may not be representative of all PLWH. Yet, these results highlight the importance of preserving muscle mass in PLWH as a strategy to decrease risk of bone fracture.
Author Disclosure Statement
No competing financial interests exist.
Funding Information
This study was supported by the Department of Veterans Affairs Veterans Health Administration, Rehabilitation Research and Development Service (I01 RX000667) and Senior Research Career Scientist Award (A.S.R.), and the Claude D. Pepper Older Americans Independence Center (P30AG028747).
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