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Keywords:

  • CIRRHOSIS;
  • FRACTURE;
  • HIV;
  • HEPATITIS C;
  • OSTEOPOROSIS

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

Osteoporosis is increasingly reported in the aging HIV-positive population, and co-infection with hepatitis C virus (HCV) may further increase the risk of osteoporosis. However, it remains unclear whether HCV-related increased fracture risk is a function of the severity of liver disease. We calculated the time-updated alanine aminotransferase to platelet ratio index (APRI) score (an indirect marker of hepatic fibrosis) in all HIV-infected patients enrolled in the Veterans Affairs' Clinical Case Registry between 1984 and 2009. The association between HCV co-infection and incident osteoporotic fracture (defined as closed wrist, vertebral, or hip fracture) was assessed in univariate and multivariate Cox survival models adjusting for traditional risk factors for osteoporosis and APRI score or the presence of cirrhosis. A total of 772 osteoporotic fractures were identified among 56,660 HIV-infected patients (98.1% male; 31.3% HCV co-infected; median age 44.0 years) contributing 305,237 patient-years of follow-up. Fracture rates were significantly higher among HIV/HCV patients than HIV-only patients (2.57 versus 2.07/1000 patient-years, relative risk = 1.24, p < 0.0001). In a Cox multivariable model including age, race, smoking, drug use, body mass index, and antiretroviral therapy, HCV co-infection remained an independent predictor of osteoporotic fractures after controlling for presence of cirrhosis (hazard ratio [HR] = 1.32; p < 0.001) or APRI score (HR = 1.30; p = 0.003). Among HIV/HCV co-infected patients, cirrhosis strongly predicted osteoporotic fractures (HR = 1.65; 95% confidence interval [CI] 1.11–2.44; p = 0.012), but APRI score was a weaker predictor (HR = 1.008; 95% CI 1.002–1.014; p = 0.015). In conclusion, among HIV-infected patients, severity of liver disease partly explains the HCV-associated increased risk of osteoporotic fractures. Other determinants of this increased risk remain to be defined. © 2013 American Society for Bone and Mineral Research.


Introduction

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

Osteoporosis and osteoporotic fractures have emerged as common complications encountered in the aging HIV-infected population.[1-4] HIV infection itself and its treatment with highly active antiretroviral therapy (HAART) are associated with accelerated bone loss and higher fracture risk.[5-8] Furthermore, an estimated 15% to 30% of HIV-infected patients are co-infected with hepatitis C (HCV), which may further increase the risk of osteoporosis and osteoporotic fractures.[7, 9, 10]

The importance of osteoporosis as a complication of cirrhosis or advanced liver disease is well known.[11] However, it is likely that significant osteopenia or osteoporosis occurs also in earlier stages of chronic hepatitis C infection before the development of cirrhosis.[12] Furthermore, it has been suggested that HIV viral suppression with HAART use may be a stronger predictor of low bone density among HIV/HCV patients than the severity of liver disease.[13]

There is limited information on the impact of HCV co-infection on the fracture risk among HIV-infected patients while controlling for cumulative exposure to any antiretroviral therapy (ART) or HAART. Furthermore, the impact of liver disease severity on the risk of osteoporotic fractures among HIV mono-infected and HIV/HCV co-infected patients has not been previously evaluated. Potential additive or synergistic interactions between the skeletal effects of HIV, HAART use, and HCV in the HIV mono-infected and HIV/HCV co-infected population remain poorly understood.

In this study, we assessed the impact of HCV co-infection on fracture risk in HIV-infected individuals using a large national database. We further evaluated the impact on fracture risk of liver cirrhosis and of the severity of liver fibrosis assessed by a noninvasive marker, the alanine aminotransferase to platelet ratio index (APRI) score.[14]

Materials and Methods

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

Data source

Our data source was the Veterans Health Administration (VHA)'s Clinical Case Registry (CCR), which aggregates demographic, diagnostic, pharmacy, and health care utilization data on all HIV-infected patients from all VHA facilities to the unique patient level since 1984. In analyses for the current study, follow-up time for each subject was defined as time from inclusion into the CCR database to the first occurrence of one of the following: 1) first osteoporotic fracture; 2) last recorded patient encounter; 3) December 31, 2009 (date of censoring).

Outcome and exposure ascertainment

Our primary outcome was incident closed vertebral, hip, and wrist fractures (selected on the basis of their likelihood of being related to osteoporosis), identified by ICD-9 code (Supplemental Table S1) and referred to herein after as “osteoporotic fractures.” A validation of ICD-9 codes for ascertainment of fractures in the VA databases (compared with a review of the radiology reports) was found to have a level of agreement of 0.97.[15]

Patients were considered HCV-positive when they either had a recorded positive HCV antibody or nucleic acid amplification test or a documented diagnostic code for HCV. A validation study has previously shown that the presence of an HCV code was 94% predictive of a positive HCV laboratory test result, whereas the absence of a code was 90% predictive of the absence of a positive test result. Of all patients with HIV infection, 96% were tested for HCV.[16] Analyses were repeated in the subgroup of patients in whom HCV infection was confirmed by detection of HCV by PCR.

Demographic variables extracted from the registry included age, race, and sex. Body mass index (BMI) was calculated annually, while imputing missing data by assuming a linear trend in BMI with respect to time. Patients with diabetes mellitus, tobacco use, cirrhosis, and drug use (defined as drug dependence or nondependent abuse of drugs) were identified by the respective ICD-9 codes (Supplemental Table S1). Estimated glomerular filtration rate (eGFR) was calculated by the modification of diet in renal disease (MDRD) equation. Cumulative exposure to antiretroviral medications was determined from pharmacy records.

Estimates of liver fibrosis

The APRI was calculated as [aspartate aminotransferase (AST) in (U/L)/upper normal × 100/platelet count (109/L)].[14] APRI is a valid marker of liver fibrosis and predictive of HCV-related liver mortality in mono-infected and HIV/HCV co-infected individuals.[17] We used time-updated APRI scores, which were calculated as the maximum APRI score for each patient and year in the cohort. In categorical analyses, APRI scores were defined as low (<0.5), medium (0.5–1.5), or high (≥1.5) as previously described.[17]

Statistical analysis

We summarize continuous variables by mean and standard deviation and categorical variables by frequency and percentage. Two-sample t test and chi-square test are employed to compare between groups. We computed annual and age-group-specific incidence rates for osteoporotic fractures and used univariate Cox survival models to assess the marginal association between fracture and various risk factors, such as age, race, ART, smoking, drug use, BMI, chronic kidney disease (CKD), HCV co-infection, presence of cirrhosis, and APRI score. Maximum annual BMI, eGFR, and APRI measurements were included into the model as time-dependent covariates. APRI scores were explored as a continuous variable and as categories. Covariates with a p < 0.2 in univariate analyses were considered as candidate predictors in the multivariate model. The final multivariable Cox models were determined by combining the statistical automatic procedure (stepwise) with clinical consideration. Model selection was based on the Schwarz Bayesian Information criterion. Statistical significance was declared at p < 0.05. All statistical analyses were performed using SAS 9.2 (SAS Institute, Cary, NC, USA).

Results

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

Study population

We identified 56,660 patients who used VHA services for HIV disease during the study period (1984 to 2009), including 22,005 who had clinic/outpatient or inpatient discharge data within the last 12 months of observation (January 1, 2009 to December 31, 2009). The mean duration of follow-up was 5.4 years (range 0 to 23.8 years). Total follow-up time was 305,237 patient-years, 258,326 of which were during the HAART period (ie, since January 1, 1996). The median age at registry entry was 44.0 years (interquartile range 37.7 to 51.3 years). The proportion of male patients was 98.1%. A total of 17,762 patients (31.3%) were co-infected with hepatitis C (HIV/HCV). The proportion of patients who had had ART exposure for at least 30 days during the follow-up period was 64.2%, and the median ART exposure was 3.0 years (range 0 to 18.7 years; interquartile range 0.7 to 6.9 years).

Characteristics of patients with and without HCV co-infection are presented in Table 1. HCV co-infected patients were slightly older (median age 44.9 versus 43.5 years, p < 0.0001) and more likely to carry diagnoses of diabetes, nicotine or drug use, and more likely to have a BMI <20 kg/m2 than HIV mono-infected patients (p < 0.0001 for each). The time-updated median APRI scores were higher in HIV/HCV than in HIV patients (1.57 versus 0.71; p < 0.0001).

Table 1. Comparison of CCR Cohort According to HCV Status
 HIV/HCV co-infected (n = 17,762)HIV mono-infected (n = 38,898)p Value
  1. CCR = Clinical Case Registry; IQR = interquartile range; BMI = body mass index; APRI = alanine aminotransferase to platelet ratio index.

Age (years), median (IQR)44.9 (39.7–50.4)43.5 (36.7–51.8)<0.0001
Male (%)98.298.00.02
White (%)34.849.3<0.0001
Tobacco (%)48.225.8<0.0001
Diabetes (%)21.712.3<0.0001
BMI <20 kg/m2 (%)14.714.1<0.0001
Drug use (%)56.824.3<0.0001
APRI   
Median (IQR)1.57 (0.78–3.59)0.71 (0.42–1.48)<0.0001
<0.5 (%)11.633.1<0.0001
0.5–1.49 (%)36.942.2<0.0001
≥1.5 (%)51.524.7<0.0001

Osteoporotic fracture rates and patient characteristics

A total of 772 individual patients sustained at least one osteoporotic fracture during the study period (120 vertebral, 318 wrist, and 334 hip). Rates of osteoporotic fractures increased with age (Fig. 1), and were significantly higher in HCV/HIV co-infected patients than in HIV mono-infected patients (2.57 versus 2.07/1000 patient-years, relative risk = 1.24, p < 0.0001).

image

Figure 1. Incidence of osteoporotic fractures in HIV-infected patients based on age and HCV status.

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Compared with patients without fractures, patients who sustained an osteoporotic fracture were more likely to be white (56.3% versus 43.3%), to carry a diagnosis of nicotine use (55.8% versus 32.5%), drug use (56.9% versus 34.2%), or diabetes (24.9% versus 15.1%), to have a BMI below 20 (18.6% versus 14.2%), or to have HCV co-infection (50.0% versus 31.1%) (p < 0.0001 for all comparisons) (Table 2). Median APRI scores were significantly higher in patients with fractures than in those without fractures (1.31 versus 0.91, p < 0.0001).

Table 2. Characteristics of Patients With or Without Osteoporotic Fractures
 TotalWith fractureWithout fracturep Valuea
(n = 56,660)(n = 772)(n = 55,882)
  • IQR = interquartile range; BMI = body mass index; APRI = alanine aminotransferase to platelet ratio index.

  • a

    p value is for comparison of patients with versus without fracture.

Age (years), median (IQR)45.0 (37.7–51.3)46.3 (40.3–52.8)44.0 (37.7–51.2)<0.0001
Male (%)98.198.498.00.91
White (%)43.556.343.3<0.0001
Smoker (%)32.955.832.5<0.0001
Diabetes (%)15.324.915.1<0.0001
BMI <20 kg/m2 (%)14.318.614.2<0.0001
HCV co-infected (%)31.450.031.1<0.0001
Drug use (%)34.556.934.2<0.0001
APRI    
Median (IQR)0.92 (0.49–2.17)1.31 (0.65–3.10)0.91 (0.49–2.15)<0.0001
<0.5 (%)25.415.925.6<0.0001
0.5–1.49 (%)40.338.840.3<0.0001
≥1.5 (%)34.345.334.1<0.0001

Predictors of osteoporotic fractures

In univariate analyses, white race, older age, diabetes mellitus, HCV co-infection, low BMI (<20 kg/m2), low eGFR (<60 mL/min/1.73 m2), tobacco use, and cumulative ART use were all associated with a greater likelihood of osteoporotic fracture. After adjusting for race, age, low BMI, low eGFR, diabetes, ART, and tobacco use, HCV co-infection was associated with a significantly higher risk of fracture events among HIV-infected patients (hazard ratio [HR] = 1.27; 95% confidence interval [CI] 1.08—1.50; p < 0.005). In sensitivity analysis, HCV co-infection confirmed by PCR detection of viral RNA was similarly associated with a significantly higher risk of osteoporotic fracture in multivariable analysis (HR = 1.24; 95% CI 1.03–1.48; p < 0.02). In another sensitivity analysis, virologically demonstrated chronic HCV co-infection (defined as detection of HCV RNA by PCR more than 1 year after initial HCV-positive antibody) was similarly associated with significantly higher risk of fracture (HR = 1.54; 95% CI 1.19–2.00; p = 0.001). Other factors associated with osteoporotic fractures in the multivariable model included white race (HR = 1.91, 95% CI 1.59–2.29; p < 0.0001); older age (HR = 1.58 for each 10-year increment; 95% CI 1.45–1.71; p < 0.0001), tobacco use (HR = 1.36; 95% CI 1.15–1.60; p = 0.0003); and low BMI (HR = 1.64; 95% CI 1.35–1.99; p < 0.0001), whereas cumulative ART use was no longer associated with risk of osteoporotic fracture (HR = 1.001; 95% CI 0.998–1.003; p = 0.68).

Severity of liver disease as a predictor of osteoporotic fractures

In the univariate model, high APRI scores (≥1.50) were associated with a significantly higher risk of osteoporotic fracture in the entire cohort when compared with low (<0.50) APRI scores (HR = 1.65; 95% CI 1.34–2.04; p < 0.0001) and intermediate (0.5–1.49) APRI scores (HR = 1.43; 95% CI 1.15–1.79; p = 0.002) (Table 3). The risk of osteoporotic fracture was not significantly different in intermediate versus low APRI scores (Table 3).

Table 3. Impact of Hepatitis C and Severity of Liver Disease on Fracture Risk
 HR for fracture in entire cohort (95% CI; p value)HR for fracture in HCV/HIV co-infected (95% CI; p value)HR for fracture in HIV mono-infected (95% CI; p value)
  • HR = hazard ratio; CI = confidence interval.

  • a

    Multivariable model included age, race, low BMI, smoking, drug use, and cumulative ART use in addition to listed variables for each model.

Univariate models
HCV + ve1.26 (1.21–1.69; p = 0.002)N/AN/A
Cirrhosis2.27 (1.64–3.14; p < 0.0001)1.89 (1.29–2.76; p = 0.001)3.18 (1.70–5.97; p = 0.0003)
APRI score
Per unit change1.005 (1.000–1.010; p = 0.045)1.007 (1.000–1.013; p = 0.039)1.002 (0.982–1.023; p = 0.847)
0.5–1.49 versus <0.501.15 (0.98–1.36; p = 0.085)0.96 (0.76–1.21; p = 0.70)1.24 (0.97–1.59; p = 0.08)
≥1.50 versus <0.501.65 (1.34–2.04; p < 0.0001)1.30 (0.99–1.71; p = 0.06)1.98 (1.36–2.88; p = 0.0004)
≥1.50 versus 0.5–1.491.43 (1.15–1.79, p = 0.002)1.36 (1.04–1.78, p = 0.025)1.59 (1.06–2.38, p = 0.024)
Multivariate modelsa
Controlling for cirrhosis
HCV + ve1.32 (1.12–1.57; p = 0.001)N/ANA
Cirrhosis1.74 (1.23–2.47; p = 0.002)1.65 (1.11–2.44; p = 0.012)2.28 (0.98–4.40; p = 0.057)
Controlling for APRI score
HCV + ve1.30 (1.09–1.54; p = 0.003)N/AN/A
APRI score
Per unit change1.006 (1.001–1.011; p = 0.013)1.008 (1.002–1.014; p = 0.015)1.005 (0.99–1.02; p = 0.5)
0.5–1.49 versus <0.501.08 (0.90–1.30; p = 0.41)0.91 (0.71–1.17; p = 0.46)1.26 (0.97–1.65; p = 0.086)
≥1.50 versus <0.501.61 (1.26–2.06; p = 0.0002)1.31 (0.97–1.76; p = 0.074)2.40 (1.56–3.69; p < 0.0001)
≥1.50 versus 0.5–1.491.49 (1.16–1.90, p = 0.002)1.44 (1.07–1.94; p = 0.02)1.90 (1.19–3.01; p = 0.007)

When controlling for age, race, smoking, drug use, body mass index, antiretroviral use, and cirrhosis in the multivariable model, HCV co-infection (HR = 1.32; 95% CI 1.12–1.57; p = 0.001) and cirrhosis (HR = 1.74; 95% CI 1.23–2.47; p = 0.002) independently predicted osteoporotic fractures. When we controlled for time-updated APRI scores in lieu of cirrhosis, HCV status remained independently predictive (HR = 1.30; 95% CI 1.09–1.54; p = 0.003), but APRI score was a much weaker predictor (HR = 1.006; p = 0.013) (Table 3). Higher APRI scores were associated with a greater risk of osteoporotic fracture compared with low and intermediate APRI score categories in the entire cohort (Table 3). Similar associations were seen when analyses were restricted to the subgroup of HIV/HCV co-infected patients or of the HIV mono-infected patients. There was no significant interaction between HCV and APRI (p = 0.58) or cirrhosis (p = 0.90) for fracture risk.

Discussion

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

The goal of this study was to evaluate the impact of hepatitis C and liver fibrosis and cirrhosis on osteoporotic fractures in HIV-infected patients. Using a cohort of 56,660 HIV-infected patients followed for a median of 5.4 patient-years, we identified HCV co-infection as a significant independent risk factor for osteoporotic fractures along with other traditional osteoporotic risk factors. Our results are in agreement with the previously reported higher incidence of fractures among HIV/HCV co-infected patients compared with HIV mono-infected patients.[9, 10, 18] Our data also show that the clinical diagnosis of cirrhosis and APRI fibrosis scores in the highest category (≥1.5) are strong predictors of osteoporotic fractures in both groups of patients. For the first time, we show that although the impact of HCV on fracture risk was in part explained by severity of liver disease (cirrhosis, high APRI score), the fracture risk conferred by HCV status persisted after adjusting for the presence of cirrhosis or the degree of fibrosis (Table 3). This corroborates findings that, although osteoporosis is an important complication of cirrhosis or advanced liver disease,[11] significant osteopenia or osteoporosis occurs also in earlier stages of chronic hepatitis C infection, even without cirrhosis.[12]

The pathophysiologic mechanisms underlying the effects of hepatitis C and HIV on bone health are not entirely understood. One possible explanation for the association of HIV/HCV co-infection with bone disease is an imbalance in pro- and anti-inflammatory mediators, favoring the former.[19] HCV infection is associated with higher levels of inflammation (TNF-α, IL-8) in the general (non-HIV infected) population[20] and among HIV-infected patients.[21] These inflammatory cytokines could in turn enhance osteoclastogenesis, leading to excessive bone resorption and osteoporosis.[22, 23] Another potential explanation is that hepatitis C in the setting of HIV may be acting through worsening liver fibrosis. Several studies have demonstrated that the rate of fibrosis progression is higher in HCV/HIV co-infected patients than in HCV mono-infected patients.[24, 25] Finally, HCV might be associated with other risk factors that might directly impact bone health and that we were unable to control for.

The APRI score is a noninvasive surrogate of liver fibrosis that has been validated against findings from liver biopsy in HCV mono-infected patients[14] and in HIV/HCV co-infected patients.[26] Nevertheless, there are limitations in using the calculated APRI score. These include potential problems with measurement of AST levels or platelet count, changes in serum AST and platelet count that may not be related to liver disease itself, and the fact that APRI is not perfect at predicting biopsy-proven liver fibrosis. Although HCV co-infection was expectedly associated with significantly higher levels of markers of liver fibrosis, our data also show that a significant proportion of HIV mono-infected patients exhibit an elevated APRI score (Table 1). Higher APRI scores in HIV mono-infected individuals have been previously reported in smaller cohorts.[27, 28] They could be a reflection of alcoholic and nonalcoholic fatty liver disease (NAFLD), which are increasingly reported in this patient population and can progress to nonalcoholic steatohepatitis (NASH) and cirrhosis.[29, 30] However, the usefulness of APRI score in this population has been debated because low platelet counts and higher serum AST may be directly related to sequelae of HIV infection rather than liver fibrosis.[31, 32] In our cohort, APRI score was only a weak predictor of fracture risk (HR = 1.006; p = 0.013), although scores in the highest category (≥1.5) were significantly associated with higher fracture risk in HIV mono-infected individuals (Table 3).

Given the aging of the HIV population and the increasing importance of non-AIDS complications, a better understanding the impact of HCV infection on bone health is needed. Although exposure to some anti-HIV medications might lead to a decline in bone mineral density[5] and increased fracture risk,[7] limited data suggest that HCV therapy improves bone disease and/or markers of inflammation: In a small cohort of 30 HCV-infected patients, anti-HCV therapy led to on-treatment increases of lumbar spine and hip bone mineral density.[33] Successful HCV clearance was also shown to reduce the risk of fracture in a small study of postmenopausal women with osteoporosis and chronic HCV-induced liver disease.[34] Until recently, a major limitation of HCV therapy was the low rate of HCV suppression defined by sustained viral response (SVR). New directly acting antiviral agents induce SVR in a large proportion of HCV-infected patients[35, 36] and are rapidly emerging as the preeminent therapy in clinical practice. Although these newer therapies improve HCV clearance, their impact on skeletal health remains to be defined.

Limitations of our study include the fact that exposure and outcome variables were passively abstracted from the Veterans Health Administration Clinical Case Registry and hence depend on completeness of records. Nevertheless, missing data on either exposures or outcomes would likely only lead to an underestimation of the incidence of osteoporotic fractures and the strength of the association between HCV and osteoporotic fracture. Our cohort overwhelmingly consisted of male patients, and the results might not be generalizable to HIV-infected women. Another limitation is that the diagnosis of hepatitis C was based on the presence of hepatitis C antibodies and was not confirmed by assessment of HCV viral load in all patients. However, our sensitivity analyses confirm our findings in the subset of patients in whom HCV viral load was measured. The use of BMI in patients with cirrhosis (and particularly with ascites) may impact the association between BMI and fracture risk in the models used. However, prior studies have suggested that BMI is still a reliable parameter to detect malnutrition in cirrhotic patients, although different cut-offs have been suggested depending on the presence and severity of ascites.[37] Because of the nature of the database used, we were unable to determine the mechanism of injury leading to the fracture and thus to differentiate between low-energy (osteoporotic) and high-energy fractures. A previously published study did suggest that HIV/HCV co-infection was associated with a higher risk for both high-trauma and low-trauma fractures.[9] Finally, our data did not adjust for some known risk factors for osteoporotic fractures such as calcium and vitamin D intake, alcohol abuse, exposure to glucocorticoids, and family history of the osteoporosis. The strengths of our study include the number of events and patient-years of observation, uniform data collection across the VA system, and the inclusion of BMI, APRI scores, and ART use as time-dependent covariates.

In conclusion, our results suggest that hepatitis C co-infection independently increases the risk of osteoporotic fractures among HIV-infected patients. The impact of HCV on fracture risk is only in part explained by the severity of liver disease. Additional studies are needed to better define the determinants of this increased risk and to assess whether it will be mitigated by anti-HCV therapy.

Disclosures

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

The authors report no use of materials including experimental animals in the study. The authors also report no restrictions on access to raw data or statistical analyses. Roger Bedimo has received research grants from Merck & Co. and Janssen Therapeutics, and has served as constant for AIDS Arms. All other authors state that they have no conflicts of interest.

Acknowledgments

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

The study was funded by Veterans Administration Merit Grant I01 CX000418–01A1.

Authors' roles: Study design: NM, RB, and HD. Study conduct: NM, GB, RB, and HD. Data collection: NM, GB, RB, and HD. Data analysis: HD and SZ. Data interpretation: NM, SZ, HD, GB, PT, and RB. Drafting manuscript: NM, RB, HD, and PT. Revising manuscript content: NM and RB. Approving final version of manuscript: NM, SZ, HD, GB, PT, and RB. NM takes responsibility for the integrity of the data analysis.

References

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgments
  9. References
  10. Supporting Information

Additional Supporting Information may be found in the online version of this article.

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jbmr1988-sm-0001-SuppTab-S1.doc50KSupplemental Table S1.

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