Prevalence of and Risk Factors for Low Bone Mineral Density Assessed by Quantitative Computed Tomography in People Living With HIV and Uninfected Controls : JAIDS Journal of Acquired Immune Deficiency Syndromes

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Prevalence of and Risk Factors for Low Bone Mineral Density Assessed by Quantitative Computed Tomography in People Living With HIV and Uninfected Controls

Thomsen, Magda T. MDa; Wiegandt, Yaffah L. MDb; Gelpi, Marco MDa; Knudsen, Andreas D. MDa,b; Fuchs, Andreas MD, PhDb; Sigvardsen, Per E. MDb; Kühl, Jørgen T. MD, PhD, DMScb; Nordestgaard, Børge MDc,d; Køber, Lars MD, DMScb; Lundgren, Jens MDe; Hansen, Ann-Brit E. MD, PhDf; Kofoed, Klaus F. MD, DMScg; Jensen, Jens-Erik B. MDh,i; Nielsen, Susanne D. MD, DMSca

Author Information

aViro-immonology Research Unit, Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;

bDepartment of Cardiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;

cDepartment of Clinical Biochemistry, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark;

dFaculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;

eDepartment of Infectious Diseases, Rigshospitalet, CHIP, University of Copenhagen, Copenhagen, Denmark;

fDepartment of Infectious Diseases, Hvidovre Hospital, University of Copenhagen, Copenhagen, Denmark;

gDepartment of Cardiology and Radiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;

hDepartment of Endocrinology, Hvidovre Hospital, University of Copenhagen, Copenhagen, Denmark; and

iInstitute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.

Correspondence to: Susanne D. Nielsen, MD, DMSc, Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2200 Copenhagen, Denmark (e-mail: [email protected]).

Supported by Rigshospitalet Research Council, Region Hovedstaden, The Lundbeck Foundation, The Novo Nordisk Foundation, and The Danish National Research Foundation Grant 126. The study was designed, conducted, analyzed, and written by the authors without involvement of any commercial party.

S.D.N.: Unrestricted research grants from Novo Nordisk Foundation, Lundbeck Foundation, Augustinus Foundation, and Rigshospitalet Research Council. Travelling grants from Gilead and GSK/ViiV. Advisory board activity for Gilead and GSK/ViiV. The remaining authors have no conflicts of interest to disclose.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.jaids.com).

JAIDS Journal of Acquired Immune Deficiency Syndromes 83(2):p 165-172, February 1, 2020. | DOI: 10.1097/QAI.0000000000002245

Abstract

INTRODUCTION

Lower bone mineral density (BMD) and higher prevalence of osteopenia, osteoporosis, and fractures have been described in people living with HIV (PLWH) compared with uninfected individuals.1–4 A meta-analysis from 2017 reported a 2 times higher prevalence of osteopenia and osteoporosis in PLWH compared with uninfected controls.5 Low BMD is associated with a higher risk of incident fractures.6 Hip fractures are a significant cause of disability and mortality,7 and osteoporotic fractures account for approximately 1% of the disability-adjusted life years attributable to noncommunicable diseases.8

Several factors have been proposed to contribute to low BMD in PLWH including a higher prevalence of traditional risk factors, such as low body mass index (BMI), smoking, and physical inactivity.1,9,10 Furthermore, HIV-specific factors also seem to contribute to low BMD, and initiation of combination antiretroviral therapy (cART) has in clinical trials and longitudinal studies been shown to be associated with an initial decline in BMD during the first year of treatment after which BMD remains stable.11,12 Tenofovir disoproxil fumarate (TDF) and protease inhibitors (PI) have in some studies been identified as independent risk factors for low BMD, and switching from a cART regimen containing TDF to a non-TDF regimen is associated with improvement in BMD.13,14 The exact pathogenesis behind these findings is unclear, but studies indicate adverse alterations in bone metabolism and bone microstructure.15

Dual-energy X-ray absorptiometry (DXA) is used as gold standard in diagnosing osteoporosis. However, quantitative computed tomography (QCT) is an alternative method to determine BMD and enables 3-dimensional volumetric density measurements and allows for separate measures of trabecular and cortical bone, less influenced by calcifications and degenerative changes in the spine. Thus, use of QCT may provide additional information in PLWH.

In this study, we examined the prevalence of low and very low BMD measured by QCT in a large cohort of well-treated PLWH (n = 718) and in uninfected individuals matched on sex and age (n = 718). We tested the hypothesis that PLWH have a higher prevalence of low BMD, and we assessed whether HIV status was an independent risk factor for reduced BMD and aimed to identify HIV-associated factors.

METHODS

Participants

The Copenhagen Co-Morbidity in HIV infection (COCOMO) study is a longitudinal, observational study initiated in March 2015. The aim of the COCOMO study is to assess the burden of non-AIDS comorbidity in PLWH. Participants eligible for inclusion were HIV-positive outpatients at Department of Infectious Diseases at either University Hospital Copenhagen Rigshospitalet or at Hvidovre University Hospital with an age ≥18 years. A total of 1099 patients were included between March 2015 and December 2016 equivalent to >40% of all PLWH from the greater Copenhagen area. The procedures for recruitment and data collection have been described elsewhere.16 Participants on cART and aged 40 years and older with a CT were eligible for inclusion in this study.

Uninfected controls were recruited from the Copenhagen General Population Study (CGPS), an ongoing population study initiated in 2003 with an eligible study population of >100.000. All residents in the greater Copenhagen area ≥40 years and 25% of 20–40 years old are invited to participate. Response rate is of approximately 42%. From July 2011 to July 2013, 2548 participants aged 40 years and older underwent CT examination and were eligible for inclusion in this study.17

Eligible COCOMO participants and eligible controls from the CGPS were matched by age and sex using a propensity score matching method. As the sex and age distribution in COCOMO and CGPS varies, it was only possible to identify 1 uninfected control per PLWH.

Ethical approval was obtained by the Regional Ethics Committee of Copenhagen (COCOMO: H-15017350; CGPS: H-KF-01-144/01). Written informed consent was obtained from all participants.

Clinical Assessments

The COCOMO and CGPS studies collected all data uniformly. Information regarding demographics, smoking habits, alcohol consumption, physical activity, and milk and cheese intake was obtained from identical structured questionnaires in both COCOMO and CGPS.

HIV-related variables were collected from patient records. All HIV-positive participants and controls had a medical examination performed by trained clinical staff following the same protocol. Height was measured without shoes to 1 decimal place (Stadiometer; Soehnle, Nassau, Germany), and weight was measured with light clothes to 1 decimal place (Scale; Soehnle). Hip circumference and waist circumference were measured to nearest whole number (Measuring tape; SECA, Birmingham, United Kingdom). Waist-hip ratio and BMI were calculated according to World Health Organization guidelines.18,19

Nonfasting blood samples were collected, and plasma-creatinine was measured and used to calculate estimated glomerular filtration rate (eGFR). All blood samples from both COCOMO and CGPS participants were analyzed at Department of Clinical Biochemistry, Herlev University Hospital, Copenhagen.

BMD Measurements by QCT

All CT imaging was performed at Rigshospitalet, Copenhagen, on the same 320 Multidetector Scanner (Aquilion One ViSION Edition; Canon Medical Systems, Otawara, Japan). Scanner settings used were gantry rotation time 350 ms, detector collimation 0.5 × 320, tube voltage ranging from 100 to 135 depending on BMI, and tube current between 280 and 500 mA. Reconstructions at best phase of the R–R interval using an automated raw data motion analysis tool (PhaseXact; Toshiba, Minato, Japan) were performed. Scans were reconstructed with 0.5/0.5-mm thickness/increment for BMD assessment.

A calibration phantom pad, 130 × 41 cm (INTable; Image Analysis, KY), containing cylindrical bone-equivalent calcium hydroxyapatite rods of different densities (0 mg/cm3, 75 mg/cm3, 150 mg/cm3, and 1 fat equivalent) were placed underneath each participant during the scan for calibrated BMD measurements (Fig. 1).

F1
FIGURE 1.:
Identification of region of interest in CT scan images. Screenshots from software N-Vivo. The ROI was placed at the center of 3 consecutive thoracic vertebras. The ROI has been highlighted for the sake of clarity.

BMD scores were calculated using a dedicated external postprocessing workstation and commercially available semiautomated software (N-Vivo; Image Analysis). The method for BMD measurements has been previously described,17 but in short, volumetric trabecular BMD was measured in 3 consecutive thoracic vertebras, starting with the vertebra in level with left main coronary artery. The center of each vertebra was manually marked with a horizontal and vertical line, and the region of interest (ROI) was placed automatically with a 2- to 3-mm distance to cortical bone by the software (Fig. 1). In cases with degenerative changes that could influence the BMD measurements, the ROI was placed manually using free tracing. The software program automatically identified the calcium hydroxyapatite rods on all slices and computed automated calibrations for the bone densitometry. This minimized the influence of inaccuracies in large patients with significant and/or inhomogeneous overlying soft tissue. The program reported BMD values for each analyzed vertebra as well as mean BMD and T-scores.

In case of fractures, large osteophytes, bone islands, diffuse density changes, and other degenerative changes in the spine that prevented BMD measurements, the vertebra concerned was excluded, and mean BMD consisted of measurements of the 2 remaining vertebras. In cases where 2 vertebras were excluded, the entire scan was excluded. Scans with inadequate image quality due to heavy noise or motion artifacts and scans where the calibration phantom was not sufficiently visible in the scan field were also excluded.

All analyses were performed by 2 trained observers. Interobserver and intraobserver variability was tested on 50 and 29 scans, respectively. Intraobserver and interobserver variability showed good correlation (intraobserver: Pearson correlation coefficient: 0.999, coefficient of variance: 0.35%. interobserver: Pearson correlation coefficient: 0.992, coefficient of variance: 0.29%).

Definition of Outcome

T-scores were computed by the software N-Vivo. BMD categories were defined by mean T-score values: > −1, normal BMD; between −1 and −2.5, low BMD; and ≤ −2.5, very low BMD.

In logistic regression analyses, very low BMD was defined as a T-score ≤ −2.5.

Statistics

Continuous variables were reported as median and interquartile range (IQR) and categorical variables as percentage and frequency. The Student t test and Wilcoxon rank-sum test were used for comparison of continuous variables with normal and non-normal distribution, respectively, and χ2 test and Fisher exact test for comparison of categorical variables.

The associations between very low BMD (T-score ≤ −2.5) and HIV status and osteoporosis risk factors were tested using univariable and multivariable logistic regression analyses. The association between BMD values and HIV status and osteoporosis risk factors was tested using univariable and multivariable linear regression analyses. Multivariable analyses were adjusted a priori for a base model including known risk factors for low BMD: age in decades, sex, smoking in pack-years, BMI, alcohol per 10 units/week, ethnicity (Scandinavian, other European, Middle East and Indian subcontinent, and other), and physical activity (inactive, slightly active, moderately active, and very active).

Separate models were conducted to assess HIV-specific predictors of low BMD. In multivariable linear and logistic regression analyses, HIV-specific variables (CD4 nadir <200 cells/mL, years since HIV infection, previous AIDS-defining condition, detectable hepatitis C virus, current or previous TDF, or PI use) were added to the base model one at a time. Results from logistic regression analyses are reported as odds ratios (ORs) and 95% confidence intervals (CI) and results from linear regression analysis as β-coefficients and 95% confidence limits.

A P value <0.05 was considered statistically significant. All analyses were generated using SAS Enterprise Guide, version 7.11 (SAS Institute Inc, Cary, NC).

RESULTS

Study Population

Of 1099 PLWH included in the COCOMO study, a total of 296 did not meet inclusion criteria (n = 173 did not have a CT scan, n = 15 were not currently on cART, and n = 108 were younger than 40 years) and 85 PLWH were excluded because of invalid BMD measurements. This resulted in 718 PLWH included in the current study. PLWH were matched 1:1 with uninfected controls from CGPS. Clinical characteristics of COCOMO and CGPS participants are shown in Table 1. Median age was 52.2 and 53.8 years in PLWH and uninfected controls, respectively, and 86% of the participants were men. A greater proportion of PLWH were non-Scandinavians and current smokers compared with controls. Furthermore, PLWH had slightly lower BMI and lower eGFR compared with controls (Table 1).

T1
TABLE 1.:
Demographic and Clinical Characteristics of the Study Population

For PLWH, median time living with HIV was 15.8 (IQR: 8.7–23.1) years, and 96.1% had undetectable viral replication (Table 1).

Prevalence of Low BMD and Associations Between HIV Status and BMD

The distribution of BMD categories was different in PLWH and controls (χ,2P = 0.003), with higher prevalence of very low BMD in PLWH (17.2% vs. 11.0%) (Table 2). In unadjusted analysis, HIV status was associated with very low BMD (OR 1.68 [95% CI: 1.24–2.27], P = 0.001), but this association was not significant in a model adjusted for sex, age, pack-years, BMI, alcohol, ethnicity, and physical activity (adjusted odds ratio 1.26 [95% CI: 0.88–1.83], P = 0.210, Table 3).

T2
TABLE 2.:
BMD and T-Scores in PLWH and Controls
T3
TABLE 3.:
Unadjusted and Adjusted Logistic Regression for Very Low BMD and Unadjusted and Adjusted Linear Regression Estimates for BMD

Median BMD was 144.2 mg/cm3 in PLWH vs. 146.6 mg/cm3 in controls (P = 0.580) (Table 2). HIV status was not associated with continuous BMD values in univariable or multivariable analyses.

Traditional Risk Factors Associated With BMD

Unadjusted and adjusted logistic and linear regression analyses for very low BMD and continuous BMD values to traditional risk factors, that is, age, sex, BMI, pack-years, and physical activity, were examined for the whole study group and in a model only including PLWH. In both models, very low BMD was found to be associated with age, BMI and pack-years. Increasing age, male sex, and pack-years were negatively associated with continuous BMD values, and higher BMI and physical activity were positively associated with BMD values (Table 3, see Table 1 Supplemental Digital Content, https://links.lww.com/QAI/B406).

HIV-Specific Variables Associated With Low BMD

A history of previous AIDS-defining condition was associated with 11.06 mg/cm3 lower BMD in linear multivariable analyses (Fig. 2). After adjusting for confounders, no significant associations were found between BMD and CD4 nadir <200, time since HIV infection, detectable hepatitis C virus RNA, and previous or current exposure to PI or TDF (Fig. 2).

F2
FIGURE 2.:
Unadjusted and adjusted logistic regression for very low BMD and unadjusted and adjusted linear regression estimates for BMD in relation to HIV-specific variables. Multivariable models have been adjusted for age, sex, pack-years, BMI, alcohol, ethnicity, and physical activity. aOR, adjusted odds ratio.

DISCUSSION

We assessed volumetric BMD in the thoracic spine of PLWH and uninfected controls, matched by age and sex and recruited from the same geographical area. There was no difference in median BMD between PLWH and uninfected controls; however, PLWH had a higher prevalence of very low BMD than uninfected controls. After adjusting for traditional risk factors, HIV status was not associated with low BMD. Regardless of HIV status, we found that traditional risk factors for low BMD, that is, higher age, lower BMI, physical inactivity, and pack-years, were associated with low BMD. Furthermore, previous diagnosis with AIDS-defining condition was associated with a lower BMD, but we found no other significant associations to HIV-specific variables.

Volumetric BMD in the spine assessed by QCT has been examined in a few previous studies of PLWH,20–22 but to the best of our knowledge, this is the first study to compare volumetric trabecular BMD in both PLWH and uninfected controls. DXA is the most commonly used bone density assessment method and estimates fracture risk based on areal BMD expressed in g/cm.2 Areal BMD is influenced by vertebral size, and measurements are based on both trabecular and cortical bone. A limitation of DXA, especially in the older population, is an overestimation of BMD in the spine because of artifacts such as aortic calcifications, osteophytes, and other degenerative changes in the spine.23–25 Unlike DXA, QCT allows for separate measurements of cortical and trabecular bone and provides volumetric density measurements in mg/cm.3 As trabecular bone is more metabolically active than cortical bone, QCT has been demonstrated to be more sensitive to detect age-related changes in BMD compared with DXA.24,26 Previous studies comparing DXA and QCT have indicated that QCT may be more sensitive in detecting osteoporosis in postmenopausal women and more sensitive to discriminate between subjects with and without vertebral fractures.27 Although most studies assess BMD on lumbar spine, measurements at the thoracic level have been validated as well.28–30

In this study, we found no difference in median BMD between PLWH and controls, but we found a higher prevalence of very low BMD in PLWH indicating more outliers among the PLWH. However, HIV status was not an independent risk factor for low BMD. Thus, HIV status was significantly associated with low BMD in univariable analyses, but after adjusting for traditional risk factors for osteoporosis, the association was no longer significant suggesting that although HIV may have an influence on BMD, traditional risk factors have a greater impact on BMD in PLWH. Our results are in line with previous studies describing no association between HIV status and BMD after adjusting for traditional risk factors.31,32 Similar results were reported by a meta-analysis from 2007 and a review from 2015, stating that lower BMD in PLWH reported in several cross-sectional studies using DXA was primarily due to differences in body weight.33,34 Our results are, however, in contrast with the notion that PLWH have excess risk of lower BMD compared with controls due to HIV infection per se.5,35 The use of different BMD measurement methods, protocols and generally smaller study populations, may partly explain these contrasting results. Furthermore, compared with our well-treated PLWH population, many former studies have included untreated PLWH and PLWH with a higher prevalence of lifestyle risk factors such as hepatitis coinfection and substance abuse.3,35–37

In this study, previous AIDS was associated with lower BMD in both unadjusted and adjusted analyses. Since most COCOMO study participants are well-treated (96.1% with suppressed viral replication), we did not have enough power to investigate whether low current viral load or low current CD4 was associated with lower BMD, but we did not find CD4 nadir to be associated with lower BMD in multivariable analyses. This may indicate that bone loss during a period with AIDS may persist even after treatment initiation and stabilization of the infection. We found no statistically significant association between time since HIV infection, detectable hepatitis C virus RNA, PI exposure, and BMD in adjusted models, which is consistent with other studies of well-treated PLWH.31,32 Although possible associations between these HIV-specific factors and low BMD have been previously reported,38–40 our findings may partly be explained by the well-treated HIV-positive population included in this study. TDF has been associated with damaged trabecular bone microarchitecture in spine and peripheral bone,11,41 and replacing TDF with a non-TDF cART regimen has been associated with improvement in BMD assessed by DXA in clinical trials.13,14 We did not find associations between current or former TDF exposure and continuous BMD values or very low BMD, but we did find an association between current or former exposure vs. no exposure to TDF and a T-score < −1 in adjusted analyses (data not shown). Our finding may be due to confounding by indication since patients with known osteoporosis or risk factors for osteoporosis may have received non-TDF containing cART.

As the World Health Organization recommends the use of DXA for diagnosing osteoporosis,42 we cannot comment conclusively on either the risk of or the prevalence of osteopenia and osteoporosis. Previous studies comparing bone density in PLWH and controls have assessed BMD by DXA, and although both DXA and QCT are valid methods, they cannot be directly compared, as BMD by DXA is based on vertebral areal and involves measurement of both cortical and trabecular bone while BMD by QCT is based on volumetric measurements of trabecular bone alone. Whether the higher prevalence of very low BMD in PLWH described in this study is of clinical significance is unknown. However, a population-based cohort study in Denmark found that PLWH exposed to cART had an increased risk of low-energy fractures compared with controls.2

Our study had several limitations. Since we had relatively few women in our cohort, it was not possible to examine men and women separately. We had no data regarding fractures or markers of bone metabolism; thus, it was not possible to evaluate a possible clinical impact of lower BMD. The strengths of our study include a large study population that includes matched uninfected controls, the use of QCT to quantify BMD, and the opportunity to investigate the effect of HIV status on BMD by adjusting for traditional risk factors to minimize the effect of possible confounders.

In conclusion, we found no difference in BMD assessed by QCT between a large cohort of well-treated PLWH and uninfected controls. PLWH had a higher prevalence of very low BMD than controls. However, HIV status was not independently associated with very low BMD indicating that traditional risk factors for osteoporosis contribute to the difference. Thus, continued focus on effects of lifestyle factors on BMD and efforts to reinforce healthy ways of living are indicated to prevent low BMD in PLWH especially in those with previous AIDS.

ACKNOWLEDGMENTS

The authors thank all the study subjects for their participation. The authors thank the staff at the Department of Infectious Diseases at Rigshospitalet and at Hvidovre Hospital for their dedicated participation.

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Keywords:

HIV; bone mineral density; quantitative computed tomography; comorbidity; cross-sectional studies

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