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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgments
  9. REFERENCES
  10. Supporting Information

Objective

We tested the hypothesis that initiation of tumor necrosis factor α (TNFα) antagonists reduced the risk of fractures compared to nonbiologic comparators in patients with autoimmune diseases.

Methods

Using 4 large administrative databases, we assembled retrospective cohorts of patients with autoimmune diseases who initiated either a TNFα antagonist or a nonbiologic medication. We identified 3 mutually exclusive disease groups: rheumatoid arthritis (RA), inflammatory bowel disease (IBD), and a combined group: psoriasis (PsO), psoriatic arthritis (PsA), or ankylosing spondylitis (AS). We used baseline covariate data to calculate propensity scores (PS) for each disease group and used Cox regression to calculate hazard ratios (HRs) and 95% confidence intervals (95% CIs). We compared the risk of combined hip, radius/ulna, humerus, or pelvic fractures between PS-matched cohorts of new users of TNFα antagonists and nonbiologic comparators.

Results

We identified 9,020, 2,014, and 2,663 new PS-matched episodes of TNFα antagonist and nonbiologic comparator use in RA, IBD, and PsO-PsA-AS cohorts, respectively. The risk of combined fractures was similar between new users of TNFα antagonists and nonbiologic comparators for each disease (HR 1.17, 95% CI 0.91–1.51; HR 1.49, 95% CI 0.72–3.11; and HR 0.92, 95% CI 0.47–1.82 for RA, IBD, and PsO-PsA-AS, respectively). In RA, the risk of combined fractures was associated with an average daily dosage of prednisone equivalents >10 mg/day at baseline compared with no glucocorticoid (HR 1.54, 95% CI 1.03–2.30).

Conclusion

The risk of fractures did not differ between initiators of a biologic agent and a nonbiologic comparator for any disease studied. Among RA patients, use of >10 mg/day of prednisone equivalents at baseline increased the fracture risk.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgments
  9. REFERENCES
  10. Supporting Information

Patients with autoimmune disease, particularly rheumatoid arthritis (RA), have a higher risk of fractures compared to the general population ([1-4]) due to enhanced local and generalized bone loss ([3, 5]). In RA, osteoporosis and increased risk of fractures are associated not only with conventional risk factors such as aging, menopause, and low body mass index ([6]), but also with disease-related factors such as prolonged use of glucocorticoids, ambulatory dysfunction, and inflammation ([1, 6, 7]).

Human and animal studies have shown that inflammatory cytokines have a major effect on bone metabolism ([8, 9]). Tumor necrosis factor α (TNFα) induces osteoclast formation and activity ([10, 11]), and down-regulates osteoblast activity ([12]), thereby increasing bone loss in primary osteoporosis ([13]). Accordingly, inhibition of TNFα might be expected to reduce the risk of fractures by preventing bone loss in patients with inflammatory disease. Information about the effect of TNFα antagonists on bone physiology in patients with autoimmune diseases is restricted to measures of markers of bone turnover and bone mass ([14, 15]). Treatment with TNFα antagonists has been reported to decrease markers of bone resorption and to increase markers of bone formation ([16-18]). Furthermore, use of TNFα antagonists was associated with an increase in bone mass and/or attenuation of expected bone loss ([17, 19-21]). In addition, improved disease control with TNFα antagonists could decrease the use of glucocorticoids, a well-known risk factor for osteoporotic fractures ([22]). Despite these favorable effects of TNFα antagonists on bone turnover and bone density, the effect on fracture risk is unclear. If indeed TNFα antagonists protect against fractures compared to nonbiologic comparators, they could be prescribed preferentially to patients at high risk of fractures with autoimmune diseases (if there is no contraindication) to avoid future fracture burden. Thus, as part of a US multi-institutional initiative, SABER (Safety Assessment of Biological Therapeutics Collaboration), we assembled a large retrospective cohort of patients with autoimmune diseases to assess the hypothesis that TNFα antagonists would reduce the risk of fractures compared to nonbiologic therapy in patients with autoimmune diseases.

Box 1. Significance & Innovations

  • No association between risk of fractures and initiation of biologic therapy.
  • Risk of fractures associated with >10 mg/day of prednisone equivalents in rheumatoid arthritis.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgments
  9. REFERENCES
  10. Supporting Information

Data source and study population

We studied a retrospective cohort of patients ages ≥18 years with study-defined autoimmune diseases using Tennessee Medicaid (TennCare, 1998–2005), Kaiser Permanente Northern California (1998–2007), New Jersey's Pharmaceutical Assistance to the Aged and Disabled and Pennsylvania Pharmaceutical Assistance Contract for the Elderly (1998–2006), and the National Medicare and Medicaid (2000–2006, excluding Tennessee) databases. This cohort was categorized into 3 mutually exclusive groups using the earliest International Classification of Diseases, Ninth Revision (ICD-9)–coded health care encounters within the year before cohort entry (baseline). The 3 groups are as follows: 1) RA (ICD-9-Clinical Modification [CM] codes 714.**, except 714.3*), 2) inflammatory bowel disease (IBD; ICD-9-CM codes 556.* and 555.*), and 3) psoriasis (PsO), psoriatic arthritis (PsA), or ankylosing spondylitis (AS); ICD-9-CM codes 696.0, 696.1, and 720.0). Patients were required to have 365 days of baseline information to ascertain other study selection criteria and covariates. Records missing sex information and patients with other autoimmune diseases that might warrant biologic and nonbiologic treatment (e.g., juvenile RA, systemic lupus erythematosus) or with other serious diseases (Paget's disease, organ transplant, human immunodeficiency virus/acquired immunodeficiency syndrome, dialysis, cancer, and liver or lung failure) were excluded. Patients entered the cohort on the date (t0) they filled the first prescription for the specific study regimen if they met the following criteria: continuous enrollment for at least 1 year before entering the cohort (gaps of ≤30 days were allowed), and no prescription for the study regimen filled during this period.

Episodes of exposure and exposure groups

To compare the risk of fractures between TNFα antagonists and nonbiologic comparator regimens, we first defined episodes of exposure. A new episode started on the date a first prescription for any of the study regimens was filled (t0), with no prescription filled for the same regimen during the year previous, and ended with the earliest of the following events: loss of enrollment, development of a serious disease (defined previously as exclusion criteria), death, end of the study, study outcome (first fracture), change in the medication regimen (e.g., filling a TNFα antagonist for nonbiologic agent users, or filling a different TNFα antagonist for biologic agent users, when comparing different TNFα antagonists), or 365 days without regimen medication available. We first identified episodes of TNFα antagonist use and did not allow overlapping of person-time with other episodes. Within each episode of new use, we identified current use of each regimen, which extended from the start of the episode through the 30th day without medication available or the end of the episode, whichever occurred first. Only current use of study medication was included in the analyses.

We performed the following comparisons in each disease group: 1) for patients with RA, in separate analyses, we compared the risk of fractures between new TNFα antagonist users (etanercept, infliximab [INF], or adalimumab [ADA]) versus new users of hydroxychloroquine (HCQ) and/or sulfasalazine (SSZ) and/or leflunomide (LEF) identified among patients who had used methotrexate during baseline and new users of specific TNFα antagonists (INF versus etanercept, INF versus ADA, and ADA versus etanercept); 2) for patients with IBD we compared new users of INF or ADA versus new users of azathioprine (AZA) or 6-mercaptopurine (6-MP); and 3) for patients with PsO-PsA-AS we compared new users of TNFα antagonists versus new users of any nonbiologic comparator regimen (i.e., methotrexate and/or SSZ and/or HCQ and/or LEF).

Outcomes measure

The primary outcomes were a combined end point that included first hip, humerus, radius/ulna, or pelvic fracture, which are osteoporotic nonvertebral fractures that are accurately assessed in automated databases ([23]), and hip fracture alone. Due to a small number of events, the hip fracture end point was only evaluated among patients with RA. The secondary outcome for the RA cohort was the first clinical vertebral fracture. Fracture identification was performed using validated outcome algorithms for nonvertebral ([23]) and clinical vertebral fractures ([24]). We excluded fractures caused by major trauma and other conditions (see Supplementary Appendix A and Supplementary Table 1, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.21937/abstract). For clinical vertebral fractures, we excluded patients with a diagnosis/procedure compatible with vertebral fracture during baseline.

Covariates

Baseline covariates included demographic factors (age, sex, race [white/African American/other], residence [urban/rural], nursing home [yes/no], and calendar year), generic markers of comorbidity (number of hospitalizations, outpatient and emergency room visits, use of selected medications, chronic obstructive pulmonary disease, diabetes mellitus, and modified Charlson-Deyo score [an adapted comorbidity index that uses administrative data instead of clinical variables] [25]), surrogate markers of disease severity (extraarticular disease manifestations, number of intraarticular or orthopedic procedures, number of tests for inflammatory markers, and use of nonbiologic disease-modifying antirheumatic drugs [DMARDs]), and other known risk factors for fractures (previous fractures, diagnosis of osteoporosis, use of oral glucocorticoids [average daily dose in prednisone equivalents], nonsteroidal antiinflammatory drugs, narcotics, sedative hypnotics, muscle relaxants, antidepressants, antipsychotic agents, and use of drugs that affect bone metabolism [e.g., bisphosphonates, estrogens, thiazides]). Baseline glucocorticoid exposure was calculated as the average of the daily dosage of prednisone equivalents in the 6 months preceding t0, and categorized in 4 dosage levels: none, <5 mg/day, 5– 10 mg/day, and >10 mg/day.

Statistical analysis

In each data set and disease group, a propensity score (PS) that included all baseline covariates above except oral glucocorticoids was calculated for each exposure episode using logistic regression models. This allowed control of potential confounders through PS greedy matching and assured that shared data were not individually identifiable. The variables and methodology for the creation of the PS have been described previously ([26]). The risk of fractures between disease-specific PS-matched exposed groups was compared using Cox proportional hazards regression models and adjusted by baseline glucocorticoid use. Because patients could contribute ≥1 episodes of medication use, the Huber-White sandwich variance estimator was used to calculate robust SEs for all hazard ratios (HRs) and 95% confidence intervals (95% CIs). Planned subgroup analyses included stratification by age, previous fractures, and baseline glucocorticoid use. The Tennessee Bureau of TennCare and the Institutional Review Boards of Vanderbilt, University of Alabama at Birmingham, Partners Healthcare, and the Kaiser Foundation Research Institution approved the study protocol and waived consent requirements.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgments
  9. REFERENCES
  10. Supporting Information

As previously reported ([27]), the SABER cohort consisted of a total of 407,319 patients with an autoimmune disease who filled a study DMARD prescription and had complete baseline information. After excluding 170,788 patients (42%) with disease that did not meet study criteria or those with more than 1 study disease, we identified 139,611 patients with RA, 45,188 with IBD, and 51,732 with PsO-PsA-AS. After selection criteria were applied we had 9,020, 2,014, and 2,663 PS-matched episodes of TNFα antagonists and comparator nonbiologic medications use in the RA, IBD, and PsO-PsA-AS cohorts, respectively (Figure 1).

image

Figure 1. Assembly of the retrospective cohort of patients with autoimmune disease. Episode is the observation time from the initiation to the end of followup. MAX/MED = National Medicare and Medicaid database; TennCare = Tennessee Medicaid database; PAAD/PACE = New Jersey Pharmaceutical Assistance to the Aged and Disabled and Pennsylvania Pharmaceutical Assistance Contract for the Elderly database; KPNC = Kaiser Permanente Northern California database; RA = rheumatoid arthritis; IBD = inflammatory bowel disease; PsO = psoriasis; PsA = psoriatic arthritis; AS = ankylosing spondylitis; TNFα = tumor necrosis factor α; HCQ = hydroxychloroquine; SSZ = sulfasalazine; LEF = leflunomide; INF = infliximab; ADA = adalimumab; AZA = azathioprine; 6MP = 6-mercaptopurine; PS = propensity score.

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RA

Baseline characteristics for both exposure groups were similar after PS matching (Table 1). Patients with RA that started a new regimen were mainly white females with a median age of 58 years. The median followup period per episode was approximately 2 person-years. Baseline prednisone equivalents were not included in the PS matching, but were controlled for separately in our analyses. The use of >10 mg/day of prednisone equivalents at baseline was modestly higher in the TNFα antagonist users than the comparator group after PS matching. Of those using >10 mg/day of prednisone equivalents, 37% and 38% of nonbiologic agent and biologic agent users, respectively, were using >15 mg of prednisone equivalents daily, which is considered “high dose.” Although, by definition, all patients were prescribed methotrexate in the baseline year, approximately 51% of the patients in the HCQ/SSZ/LEF group and 38% in the TNFα antagonists group were current users of methotrexate at t0. We found no differences in the incidence of hip fractures between exposure groups (Table 2 and Figure 2). The survival curves for combined fractures, hip, and clinical vertebral fractures were similar between both exposure groups (Figure 2).

Table 1. Baseline characteristics for patients with rheumatoid arthritis, SABER 1998–2007*
CharacteristicsBefore PS matchingAfter PS matching
HCQ/SSZ/LEFTNFα antagonistsHCQ/SSZ/LEFTNFα antagonists
  1. Values are the percentage unless indicated otherwise. SABER = Safety Assessment of Biological Therapeutics; PS = propensity score; HCQ = hydroxychloroquine; SSZ = sulfasalazine; LEF = leflunomide; TNFα = tumor necrosis factor α; COPD = chronic obstructive pulmonary disease; DMARD = disease-modifying antirheumatic drug; NA = not applicable.

  2. a

    Continuous variables were expressed as median (interquartile range).

  3. b

    Includes International Classification of Diseases, Ninth Revision, codes 710.2 (Sicca syndrome), 714.1 (Felty's syndrome), 782.2 (localized superficial swelling mass or lump), 714.81 (rheumatoid lung), 793.1 (nonspecific [abnormal] findings on radiologic or other examinations of body structure lung field), and 518.89 (other diseases of lung, not elsewhere classified).

  4. c

    t0: time of initiation of any study drug.

  5. d

    Any diagnosis of a fracture during the year before cohort entry.

  6. e

    Includes use of raloxifene, teriparatide, calcitonin, and estrogens.

Episodes, no.10,37021,7439,0209,020
Followup (person-years/episode)a2.2 (0.9–3.9)1.9 (0.9–3.3)2.0 (0.9–3.6)2.2 (1.0–3.9)
Age, yearsa58 (48–69)58 (47–69)58 (48–69)58 (48–69)
Female86.085.586.286.3
Race    
White61.362.360.860.4
African American15.916.316.216.4
Other22.921.523.023.1
Rural residence23.823.623.623.7
Region    
Midwest13.714.615.014.0
Northeast13.317.214.114.1
South32.534.732.533.4
West40.533.538.438.5
Nursing home resident3.93.74.13.5
Ambulatory outpatient visits99.999.999.8100.0
Hospitalizations24.827.025.625.5
Charlson comorbidity scorea1 (1–2)1 (1–2)1 (1–2)1 (1–2)
COPD11.712.211.711.3
Diabetes mellitus18.418.619.018.2
Kidney disease0.50.50.40.4
Cerebrovascular disease3.43.73.43.1
At least 1 inflammatory marker tested37.436.837.037.3
Any orthopedic surgery5.17.05.45.1
Any intraarticular injection29.234.830.131.9
Extraarticular manifestationsb4.55.14.64.8
Daily dosage of prednisone equivalents at baseline    
None43.840.343.640.7
<5 mg/day30.531.031.329.7
5–10 mg/day17.318.817.119.4
>10 mg/day8.49.98.110.2
DMARD initiated at t0c    
EtanerceptNA42.3NA43.5
InfliximabNA33.8NA37.0
AdalimumabNA23.9NA19.4
LEF37.2NA37.7NA
HCQ49.8NA49.4NA
SSZ13.0NA12.9NA
Diagnosis of osteoporosis10.612.611.011.0
Previous fractured5.56.45.75.5
Use of bisphosphonates20.426.522.322.9
Use of other osteoporosis medicatione20.819.319.920.6
Table 2. Initiation of TNFα antagonists and the risk of fractures in patients with autoimmune diseases after propensity score matching*
DiseaseFracturesExposureNo. eventsNo. person-yearsRatea95% CIAdjusted for baseline glucocorticoid use
HR95% CI
  1. TNFα = tumor necrosis factor α; 95% CI = 95% confidence interval; HR = hazard ratio; RA = rheumatoid arthritis; HCQ = hydroxychloroquine; SSZ = sulfasalazine; LEF = leflunomide; Ref. = reference; IBD = inflammatory bowel disease; INF = infliximab; ADA = adalimumab; AZA = azathioprine; 6-MP = 6-mercaptopurine; PsO = psoriasis; PsA = psoriatic arthritis; AS = ankylosing spondylitis.

  2. a

    Rate expressed as per 100 person-years.

  3. b

    Includes first hip, pelvis, humerus, and/or radius/ulna fracture.

  4. c

    Clinical vertebral fractures exclude episodes with previous vertebral fractures during baseline period (365 days before t0 [time of initiation of any study drug]).

RACombinedbTNFα antagonists17211,652.021.481.27–1.711.170.91–1.51
  HCQ/SSZ/LEF977,969.481.221.00–1.49Ref.
 HipTNFα antagonists6611,802.300.560.44–0.710.870.60–1.27
  HCQ/SSZ/LEF508,020.580.620.47–0.82Ref.
 Clinical vertebralcTNFα antagonists3111,815.000.260.18–0.370.710.43–1.19
  HCQ/SSZ/LEF297,984.470.360.25–0.52Ref.
IBDCombinedbINF/ADA191,620.791.170.75–1.841.490.72–3.11
  AZA/6-MP111,419.190.760.43–1.40Ref.
PsO/PsA/ASCombinedbTNFα antagonists222,813.720.780.51–1.190.920.47–1.82
  Nonbiologic therapy151,651.790.910.55–1.51Ref.
image

Figure 2. Time to event for fractures in patients with rheumatoid arthritis in propensity score–matched cohorts, SABER (Safety Assessment of Biological Therapeutics) 1998–2007. Combined fracture includes hip, pelvis, ulna/radius, and humerus; vertebral fractures are limited to clinical vertebral fractures. TNF-α = tumor necrosis factor α; HCQ = hydroxychloroquine; SSZ = sulfasalazine; LEF = leflunomide.

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We found no differences in the incidence of combined fractures when we compared different TNFα antagonists: INF versus etanercept (HR 1.13, 95% CI 0.85–1.49), ADA versus INF (HR 0.92, 95% CI 0.63–1.35), and ADA versus etanercept (HR 0.89, 95% CI 0.63–1.26).

We observed a trend for increased risk of fractures with baseline glucocorticoid use in all the fracture groups studied. The association reached statistical significance for the combined fractures at >10 mg/day of prednisone equivalents compared to no use of glucocorticoids at baseline (HR 1.54, 95% CI 1.03–2.30) (Table 3).

Table 3. Daily dosage of prednisone equivalents at baseline and risk of fractures in patients with autoimmune disease*
DiseaseOutcome (fractures)Daily dosage prednisone equivalents at baseline, mg/dayHR95% CI
  1. HR = hazard ratio; 95% CI = 95% confidence interval; RA = rheumatoid arthritis; IBD = inflammatory bowel disease; PsO = psoriasis; PsA = psoriatic arthritis; AS = ankylosing spondylitis.

  2. a

    Includes first hip, pelvis, humerus, and/or radius/ulna fracture.

  3. b

    Clinical vertebral fractures exclude episodes with previous vertebral fractures during baseline period (365 days before t0 [time of initiation of any study drug]).

RACombinedaNone1Reference
  0–50.950.71–1.28
  5–101.160.83–1.61
  >101.541.03–2.30
 HipNone1Reference
  0–50.900.57–1.42
  5–101.370.85–2.22
  >101.420.76–2.66
 Clinical vertebralbNone1Reference
  0–51.580.86–2.93
  5–101.380.66–2.89
  >102.060.85–4.99
IBDCombinedaNone1Reference
  0–51.120.35–3.64
  5–102.570.99–6.64
  >102.150.87–5.29
PsO-PsA-ASCombinedaNone1Reference
  0–51.300.57–2.98
  5–100.750.10–5.56
  >103.020.93–9.82

In subgroup analyses, the incidence of combined fractures was similar between exposure groups when stratified by age, previous fractures, and baseline glucocorticoid use: age ≤65 years (HR 1.39, 95% CI 0.91–2.11) and age >65 years (HR 1.06, 95% CI 0.79–1.42), no previous fracture in the baseline period (HR 1.20, 95% CI 0.93–1.54), history of previous fracture in the baseline period (HR 0.90, 95% CI 0.25–3.23), use of ≤5 mg/day of prednisone equivalents at baseline (HR 1.16, 95% CI 0.86–1.57), and use of >5 mg/day of prednisone equivalents at baseline (HR 1.09, 95% CI 0.71–1.69).

Additional stratification by baseline glucocorticoid use indicated that >10 mg/day of prednisone equivalents was associated with an increased risk of combined fractures among patients ages ≤65 years (HR 1.92, 95% CI 1.08–3.39), in those without previous fractures during the baseline period (HR 1.57, 95% CI 1.05–2.34), and when compared to 5–10 mg/day of prednisone equivalents at baseline (HR 1.57, 95% CI 1.01–2.42) (Table 4). The risk of fractures was higher with higher doses of prednisone equivalents for the other subgroups studied (age >65 years, history of previous fractures) and when compared to those using <5 mg/day of daily dosage of prednisone equivalents at baseline, but these associations did not reach significance, likely due to the small number of episodes in these subgroups (Table 4).

Table 4. Risk of combined fractures and daily dose of prednisone equivalents (mg/day) at baseline in patients with RA in different subgroups
Subgroup analysisDaily dosage prednisone equivalents at baseline, mg/dayHR95% CI
  1. * RA = rheumatoid arthritis; HR = hazard ratio; 95% CI = 95% confidence interval.

Age ≤65 yearsNoneReference
 0–51.010.62–1.64
 5–100.950.54–1.69
 >101.921.08–3.39
Age >65 yearsNoneReference
 0–50.880.63–1.24
 5–101.180.80–1.74
 >101.681.00–2.81
No previous fractures (baseline)NoneReference
 0–50.980.73–1.32
 5–101.190.86–1.66
 >101.571.05–2.34
Previous fractures (baseline)NoneReference
 0–50.700.13–3.66
 5–101.160.19–6.93
 >101.590.24–10.33
≤5 mg/day of prednisone equivalents at baselineNoneReference
 0–51.030.77–1.38
>5 mg/day of prednisone equivalents at baseline5–10Reference
 >101.571.01–2.42

IBD

Baseline characteristics before and after matching by PS are described in Supplementary Table 2 (available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.21937/abstract). Among patients with IBD, the incidence of combined fractures among new INF/ADA users was not significantly higher than new AZA/6-MP users (HR 1.49, 95% CI 0.72–3.11) after adjusting for baseline glucocorticoid use (Table 2). In this disease group, daily dose of prednisone equivalents at baseline was not associated with a statistically significant increased risk of combined fractures when compared to no use (Table 3).

PsO-PsA-AS

Baseline characteristics before and after matching by PS are described in Supplementary Table 3 (available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.21937/abstract). Among patients with PsO-PsA-AS, there were no differences in the risk of combined fractures between new TNFα antagonist users and new users of a nonbiologic comparator (HR 0.92, 95% CI 0.47–1.82) after adjusting for baseline glucocorticoid use (Table 2). Similar to IBD, >10 mg/day of prednisone equivalents at baseline did not significantly increase the risk of combined fractures when compared to no use (Table 3).

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgments
  9. REFERENCES
  10. Supporting Information

Our main finding is that fracture risk did not differ between initiators of a TNFα antagonist compared to a nonbiologic comparator regimen for any study disease (RA, IBD, and PsO-PsA-AS). Notably, the risk of combined fractures was associated with higher daily doses of prednisone equivalents at baseline in RA patients. Although some evidence indicates that treatment with TNFα antagonists may have positive effects on bone health in RA patients ([16, 19, 21]), we found no association between the initiation of TNFα antagonists and the occurrence of fractures. We also found that the risk of combined fractures was similar among the 3 different TNFα antagonists that we studied.

Most prior studies described the beneficial effects of TNFα antagonists on bone mineral density (BMD) and/or markers of bone turnover ([16, 17, 21, 28, 29]), but few tested this hypothesis in randomized placebo-controlled trials ([20, 30, 31]). Overall, the main finding of those studies was that treatment with TNFα antagonists resulted in arrest of bone loss at the hip and spine compared to baseline ([19, 21, 28]) or to other nonbiologic comparator regimens ([16, 17]), with less effects on bone loss in the metacarpals ([20, 29, 30]). However, in the Dutch Treatment Strategies for Rheumatoid Arthritis trial (Behandelstrategieën voor Reumatoide Artritis), generalized BMD loss in the spine, hip, and hands was similar among different treatment groups (including combination therapy with INF) after adjustment for baseline differences, and was associated with erosion progression ([32]). In the PREMIER trial, hand BMD loss was reduced in patients receiving the combination of ADA plus methotrexate compared to methotrexate alone only in patients with high disease activity, but not in patients with low disease activity or in remission. Hand bone loss in the combination group was similar in patients with different disease activity, and levels of loss were similar to those in patients in remission on methotrexate ([31]).

One study in female patients enrolled in the CORRONA (Consortium of Rheumatology Researchers of North America) registry examined the fracture risk among different treatment regimens ([33]). In this study, monotherapy with a TNFα antagonist was associated with a decreased risk of overall fractures when compared with methotrexate monotherapy, but the expected differences in BMD were not observed. Instead, only the combination of methotrexate and other nonbiologic medication had a positive correlation with hip T score when compared with methotrexate monotherapy. Although this study included a large number of patients seen in clinical practice and used clinical covariates in the analysis, medication (exposure) groups were not clearly defined ([33]). Concordant with our findings, a recent study using data from a US commercially insured population and a Canadian province found that the risk of fractures in RA patients did not differ among initiators of different regimens (including TNFα antagonists, methotrexate, and other nonbiologic regimens) after adjusting by multiple clinical covariates ([34]).

Many studies have reported an increased risk of fractures at different anatomic sites with the use of glucocorticoids ([22, 33, 35]). We previously reported in this same cohort that the initiation of both TNFα antagonists and other nonbiologic regimens was followed by small reductions in the use of glucocorticoids ([36]). The percentage of patients taking glucocorticoids decreased less than 10% for new users of anti-TNF drugs and nonbiologic DMARDs 1 year after initiation of therapy (60.9% to 51.8% for anti-TNF users, 63.9% to 58.2% for LEF users, and 59.6% to 50.0% for HCQ/SSZ users). In the current study, we found an association between the use of >10 mg/day of prednisone equivalents at baseline and the risk of combined fractures, and that TNFα antagonists and other nonbiologic regimens had a similar risk of fractures at specific anatomic sites.

Our subgroup analyses suggest that >10 mg/day of prednisone equivalents at baseline was associated with an increased risk of combined fractures in patients ages ≤65 years, and in those without previous fractures, compared to those with no use of baseline glucocorticoids. In the subgroup of patients ages >65 years, and in those with history of previous fractures, the risk of fractures was also higher in those using >10 mg/day of prednisone equivalents at baseline, but did not reach statistical significance, probably due to the small sample size in each subgroup.

In the IBD and PsO-PsA-AS cohorts, we found that initiation of TNFα antagonists was not associated with a decrease in the risk of combined fractures. A few studies with a small number of patients ([37-39]) and without a control group ([38, 40]) have assessed the effect of INF treatment on BMD in patients with spondyloarthropathies. In general, these studies reported that treatment with INF increased the spine and hip BMD ([37-41]). A post hoc and exploratory analysis of a randomized placebo-controlled trial in patients with AS reported that the median percent increase in spine and hip BMD score was higher in the INF-treated group compared to placebo 24 weeks after treatment initiation (2.5% versus 0.5% in the spine; P < 0.001 and 0.5% versus 0.2% at the hip; P < 0.033) ([18]).

Despite the advantage of studying a large cohort of patients from real practice that allowed PS matching for multiple covariates, our study has several limitations: 1) we assumed that prescription fills represent medication use, but the actual use of study medications is unknown, 2) we relied on coded information to identify fractures, which could have resulted in some misclassification, although we minimized outcome misclassification by using previously validated definitions, 3) clinical information is limited in these databases, so we relied on surrogate covariates to asses disease severity, 4) the study was not randomized and, while we controlled for measured confounders through a PS-matching method, major changes in time-dependent covariates could have unbalanced the study groups; notably, this would be more likely to occur with outcomes that require long-term exposures, 5) information related to lifetime cumulative doses of glucocorticoid, a risk factor for fractures ([42]), was not obtained; instead, we used average daily dosage of prednisone equivalents in the 6 months prior to study entry in the analysis, since some evidence suggested that average daily dosage over a treatment period was more closely related to the risk of nonvertebral fractures than cumulative doses ([35]), 6) presence of unmeasured confounders (e.g., BMD, physical activity, and use of over-the-counter vitamin D and calcium supplements) that are balanced through randomization but not necessarily by PS matching could have biased our results, and 7) we had relatively short periods of followup that resulted in a relatively small number of events and limited our ability to detect small differences. Ideally, PsO, PsA, and AS would be analyzed separately since the clinical profiles of these diseases are different. However, we aggregated data from these 3 seronegative spondyloarthropathies due to the small number of end points in each disease category. It is possible that TNFα antagonist users have less bone loss compared to nonbiologic therapy users, but longer followup time may be required to ascertain long-term effects of specific therapies on fracture risk. For example, studies that examined the effects of antiresorptive drugs on the risk of osteoporotic fractures found evidence of protection in patients with preexisting fractures and/or low BMD early in treatment ([43-46]). However, there was little affect among those without preexisting fractures and preserved BMD even after long followup periods when compared to placebo ([47, 48]). Thus, the effects of anti-TNF therapy may be most evident in patient populations with low BMD or after extended followup in an unselected population.

On the other hand, inflammation generates an array of inflammatory mediators that can activate osteoclasts through pathways that are independent of TNFα leading to bone resorption ([49, 50]). Thus, it is possible that inhibition of TNFα alone may not prevent increased bone loss if there is ongoing inflammation.

In conclusion, we observed that the risk of fractures in patients with autoimmune diseases did not differ between new users of TNFα antagonists and a nonbiologic comparator medication, but was associated with >10 mg/day of prednisone equivalents among RA patients. The role of TNFα antagonists in bone health is still poorly defined. Studies of larger cohorts of patients with longer followup are needed to determine if changes in bone density translate into changes in fracture risk. Currently, there is no clear evidence that suggests that TNFα antagonists are strongly associated with the risk of fractures.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgments
  9. REFERENCES
  10. Supporting Information

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be submitted for publication. Dr. Griffin had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Grijalva, Curtis, Solomon, Delzell, Chen, Ouellet-Hellstrom, Herrinton, Liu, Mitchell, Stein, Griffin.

Acquisition of data. Curtis, Solomon, Herrinton, Liu, Mitchell, Griffin.

Analysis and interpretation of data. Kawai, Grijalva, Curtis, Delzell, Chen, Ouellet-Hellstrom, Herrinton, Liu, Mitchell, Stein, Griffin.

Acknowledgments

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgments
  9. REFERENCES
  10. Supporting Information

Dr. Arbogast's death in August 2012 represents a major loss for his friends and coworkers. We are grateful for his contribution to this study as an outstanding statistician and collaborator. We dedicate this manuscript to his memory.

We are indebted to the Tennessee Bureau of TennCare of the Department of Finance and Administration, which provided the data on TennCare recipients, and to the SABER Collaboration: Parivash Nourjah (Agency for Healthcare Research and Quality); Robert Glynn, Mary Kowal, Joyce Lii, Jeremy Rassen, Sebastian Schneeweiss (Brigham and Women's Hospital); Leslie Harrold (Fallon Medical Center and University of Massachusetts); David Graham, Carolyn McCloskey, Kristin Phucas (Food and Drug Administration); Marcia Raebel (Kaiser Permanente Northern California, Kaiser Permanente Colorado); Nivedita Patkar, Kenneth Saag, Fenglong Xie (University of Alabama at Birmingham); Kevin Haynes, James Lewis (University of Pennsylvania).

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  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  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. AUTHOR CONTRIBUTIONS
  8. Acknowledgments
  9. REFERENCES
  10. Supporting Information

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

FilenameFormatSizeDescription
ACR_21937_sm_SupplTable1.doc29KSupplementary Table 1
ACR_21937_sm_SupplTable2.doc82KSupplementary Table 2
ACR_21937_sm_SupplTable3.doc86KSupplementary Table 3
ACR_21937_sm_SupplApp.doc28KSupplementary Data

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