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Keywords:

  • Osteoporosis;
  • Inflammation;
  • Biomechanics;
  • Modeling and remodeling;
  • Bone densitometry

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References

Recent studies have reported an increased risk of fracture among patients with systemic lupus erythematosus (SLE) in comparison with the general population. The aim of this study was to examine associations between SLE status and bone geometry in white and African-American women. We compared hip BMD and bone geometry parameters among SLE women and control individuals using hip structure analysis (HSA). One-hundred and fifty-three dual-energy X-ray absorptiometry (DXA) scans from the Study of Lupus Vascular and Bone Long Term Endpoints (68.7% white and 31.3% African American) and 4920 scans from the Third National Health and Nutrition Examination Survey (59.3% white and 40.7% African American) were analyzed. Linear regression was used to examine BMD and bone geometry differences by SLE status and by race/ethnicity after adjusting for age and BMI. Significant differences were detected between SLE and control women. Among white women, age-adjusted BMD (g/cm2), section modulus (cm3), and cross-sectional areas (cm2) were lower among SLE women than among control women at the narrow neck (0.88 versus 0.83 g/cm2, 1.31 versus 1.11 cm2, and 2.56 versus 2.40 cm2, p < 0.001, p < 0.01, and p < 0.0001, respectively), whereas buckling ratio was increased (10.0 versus 10.6, p < 0.01). Likewise, BMD, section modulus, and cross-sectional areas were decreased among African-American SLE women at all subregions, whereas buckling ratios were increased. There were significant bone geometry differences between SLE and control women at all hip subregions. Bone geometry profiles among SLE women were suggestive of increased fragility. © 2011 American Society for Bone and Mineral Research


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References

Osteoporotic fracture represents an enormous and growing public health concern.1, 2 In the United States, 1.5 million fractures are diagnosed annually, with an associated medical cost of $14 billion per year.3, 4 Osteoporosis is a particular concern among certain populations, including patients with systemic lupus erythematosus (SLE), an autoimmune disease of unknown cause. SLE has a variety of manifestations affecting multiple organ systems and occurs primarily in women in their childbearing years. Treatment for SLE frequently includes corticosteroid to control symptoms, with antimalarial, immunosuppressive, or immunomodulatory medications often added. Supportive therapies to minimize side effects from disease manifestations or from medications include strategies to maintain a normal blood pressure and lipid profile and calcium, 25-hydroxyvitamin D, and other medications to maximize bone health.5

Data on the prevalence of osteoporotic fracture among patients with SLE is limited, but recent studies demonstrate that SLE patients are at a higher risk for fragility fracture than the general population.6–8 Fractures among patients with SLE can occur at a younger age, including premenopausal females. Furthermore, African-American race/ethnicity appears not to be protective against osteoporosis in SLE.9 There is recent evidence that autoimmunity and its associated inflammation and vitamin D deficiency play key roles in the pathogenesis of adverse skeletal effects in SLE.10 With recent advances in therapy, life expectancy has improved for patients with SLE,11 and osteoporosis will become an increasingly prevalent complication among SLE patients.

The “gold standard” for osteoporosis diagnosis is the performance of dual-energy X-ray absorptiometry (DXA) scans of the hip and spine.12 Reduction in hip BMD by 1 SD below the age-adjusted mean predicts a 2.4-fold increase in fracture risk.13 Similarly, BMD improvements measured by DXA have been associated with fracture risk reduction in osteoporosis therapeutic trials.12 However, many studies have shown that BMD does not fully account for fracture risk,14 and BMD changes with osteoporosis treatment do not fully account for fracture risk reduction,15 suggesting that this clinical assessment does not adequately account for the complex mechanical characteristics of skeletal fragility.

Bones fracture when internal stresses exceed their load-bearing capacity.16, 17 In osteoporosis, fracture occurs with minimal trauma because the load-bearing capacity of the skeleton is compromised. Reduced load-bearing capacity can result from deterioration in the material composition of the skeleton or from changes in the structural geometry of the bones. While there are no reliable noninvasive ways to measure the material strength of the skeleton, we are not aware of any evidence suggesting that this parameter is altered in osteoporosis. Thus changes in internal skeletal stresses are almost entirely a consequence of structural change. Hip structure analysis (HSA) allows for the determination of structural geometry, from which relevant strength information can be derived.17 This essential information is not available from conventional DXA scans.

The value of HSA in predicting skeletal strength has been illustrated by some retrospective analyses of data from large fracture trials.16, 18 However, the clinical applicability of HSA is currently limited by the high intolerance of the technique to skeletal positioning.

These observations are particularly pertinent for patients with SLE because the causes of increased bone fragility in patients with SLE are not fully elucidated. Indeed, BMD measurements in patients with SLE who sustain fractures are often higher than those seen in postmenopausal women who sustain fractures.19 In this study, we compared bone geometry parameters in patients with SLE from the Study of Lupus Vascular and Bone Long Term Endpoints (SOLVABLE) study20 with those of white and African-American women from the Third National Health and Nutrition Examination Survey (NHANES III)21 using HSA. We hypothesized that the presence of SLE was associated with bone geometry profiles that predict increased skeletal fragility.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References

Study subjects

This study is a retrospective review of DXA and clinical data previously collected on SOLVABLE and NHANES III white and African-American participants. The SOLVABLE cohort consists of women 18 years and older who met at least four of the 1982 (or updated 1997) American College of Rheumatology (ACR) classification criteria for SLE.22, 23 Designation of patient race/ethnicity was cultural (by self-declaration) rather than biologic. There were three study visits in the protocol for SOLVABLE. The data from the first study visit for SOLVABLE, which included interviews for osteoporotic risk factors (including self-reported steroid use and 25-hydroxyvitamin D levels), physical examination for anthropometric measurements and SLE disease activity and severity, and imaging for BMD of the hip, are included in this report. SOLVABLE patient enrollment occurred between December 2002 and August 2007.

The control population for this study consisted of nonpregnant white and African-American women who participated in the NHANES III study. The NHANES III study was designed to assess the health and nutritional status of the civilian noninstitutionalized white, African-American, and Mexican-American members of the US population and was conducted between 1988 and 1994. Data collection was accomplished via household interviews and direct physical examination at mobile examination centers. Relevant biochemical studies were done, including measurement of serum 25-hydroxyvitamin D levels. Men and nonpregnant women 20 years of age and older in NHANES III who received physical examination and had not fractured both hips were eligible for a DXA scan of the left hip unless there was a history of previous fracture or surgery on the left side (1%), in which case the right side was scanned.

Our study was approved by the institutional review boards of the Medical University of South Carolina and Northwestern University, Feinberg School of Medicine.

Bone densitometry

Acquisition of DXA data at the hip on SOLVABLE subjects was conducted using a Hologic QDR-4500 (Hologic, Bradford, MA, USA) as specified by the manufacturer with appropriate attention to quality control. Specifically, femur phantoms were measured to assess intrainstrument variation. The intrainstrument coefficient of variation was less than 0.46%. For NHANES III subjects, three mobile Hologic QDR1000 DXA scanners (Hologic, Waltham, MA, USA) were used for BMD measurement, with a rigorous quality-control program including the use of anthropometric phantoms and close scrutiny of individual scans at a central site. Scans were rejected if they did not meet quality criteria for conventional DXA processing (n = 321) or if structural analysis was not possible. Specifically, scans were excluded from structural analysis if they had artifacts or were positioned inappropriately, resulting in excessive anteversion or obscured (and cut off) margins. A total of 2916 white and 2904 African-American scans were available for HSA.21

Owing to the time span between the NHANES III and SOLVABLE studies, our study compares DXA scans obtained using two different DXA generations. Hologic QDR1000 uses pencil-beam technology, whereas Hologic QDR4500 uses fan-beam technology. The main consequence of these differences is a shorter scan time with the QDR4500 than with the QDR1000. While these technical differences are a potential limitation of our study, comparative studies have shown a high correlation between the two scanners (r2 = 0.99, 0.95, and 0.96 at the spine, femoral neck, and total-hip BMD, respectively),24 implying that the practical consequences of the technical differences are probably minimal. Nevertheless, cross-calibration would have been desirable but was not practical owing to the retrospective nature of our study. For bone geometry measurement, the two scanners are reasonably consistent except at the proximal shaft (personal communication from HSA developer).

Hip structure analysis (HSA)

The HSA program is based on a principle first described by Martin and Burr, namely, that mineral profiles created during single-photon absorptiometric (SPA) bone densitometry are a projection of the corresponding bone cross section and can be used to define its geometry at that location.25 The program uses the distribution of mineral mass in a line of pixels across the bone axis to measure geometric properties of cross sections in cut planes traversing the bone. Regions of interest include the narrow femoral neck (located at the narrowest point of the femoral neck), the intertrochanteric region (traversing the bisector of the neck and shaft axes), and the shaft (located 1.5 times the neck width distal to the intersection of the neck and shaft axes). These sites are clinically relevant because the majority of hip fractures involve the femoral neck or intertrochanter area.

Five parallel mass profiles that are spaced about 1 mm apart along the bone axis (corresponding to a 5-mm section thickness) are generated and averaged using an algorithm developed by Thomas Beck26 to derive the following parameters: (1) BMD (g/cm2), (2) outer cortical diameters (cm), (3) bone cross-sectional area (cm2), and (4) cross-sectional moment of inertia. The section modulus (cm3) was calculated as cross-sectional moment of inertia divided by the maximum distance from the center of mass to the outer cortical margin. To estimate the average cortical thickness and buckling ratios, cortices of the narrow neck, intertrochanter, and shaft were modeled as circular annuli with 60%, 70%, and 100% of the measured mass in the cortex, respectively. Buckling ratios were then calculated as the maximum distance from the center of mass to the outer diameter divided by the (estimated) mean cortical thickness. The program also measures neck shaft angle (the angle subtended by the neck and shaft axes) and neck length (the distance between the femoral head and the point of intersection between the neck and shaft axes).

The two studies relied on the same HSA operator, which should reduce systematic measurement error between the two studies. The HSA operator did not know that the SLE patients had SLE or that we would be using the NHANES III data for comparison.

Statistical analysis

Descriptive analyses were performed to examine bone density and geometric parameters among white and African-American women from NHANES III (without SLE) and the SOLVABLE (SLE) studies. Linear regression was used to examine differences in bone density and geometry by SLE status and by race/ethnicity after adjusting for age and BMI. In addition, the mean difference and 95% CIs between those with and without SLE were determined by race/ethnicity. Because the NHANES III population was compared with an external clinical population, NHANES III sampling weights and design variables were not used to account for the survey design of the NHANES III population.27 Data were analyzed using the SAS System (Version 9.1; SAS Institute, Cary, NC, USA).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References

A total of 164 white and African-American women from the Chicago Lupus Study were enrolled in SOLVABLE, from whom 153 hip DXA scans (68.7% white and 31.3% African American) were adequate for HSA. Patients with analyzable scans were of similar age and race/ethnicity distribution as those whose scans were not analyzable (n = 11), with disease markers (C3, C4, and double-stranded DNA antibody levels) also being equally distributed. Smoking status and corticosteroids use also were similar between the two groups. From the NHANES III cohort, a total of 4920 DXA scans (59.3% white and 40.7% African American) were available for analysis.

Demographic characteristics of the SLE patients and NHANES III controls are presented in Table 1. Among white women, NHANES III participants were older, had similar BMI values and 25-hydroxyvitamin D levels, and were more likely to currently smoke but less likely to be former smokers than participants with SLE. Among African-American women, NHANES III participants were a similar age and had similar BMI values and 25-hydroxyvitamin D levels but were more likely to currently smoke than women with SLE. Among women with SLE, disease duration was similar in African-Americans and white women, but the percent of African-American women with American College of Rheumatology/Systemic Lupus International Collaborating Clinics Damage Index (ACRI/SLICC-DI) scores greater than 0 was higher in African-American women than in white women (80.2% versus 55.7%), as was the percent with a systemic lupus activity measure (SLAM) greater than 7 (49.0% versus 28.7%). However, there were no significant differences in any of the HSA parameters measured in the hip between SLE patients with (SLAM > 7) and without (SLAM < 7) active disease or with (SLICC > 0) and without (SLICC = 0) disease damage. Additionally, among women with SLE, African Americans were slightly (2 ± SD years) younger with a higher mean BMI and a lower mean 25-hydroxyvitamin D level than whites. African-American women also were more likely to be current smokers and less likely to be former smokers than white women.

Table 1. Age-Adjusted Levels (95% CI) of Population Characteristics Stratified by Race and SLE Status
 White womenAfrican-American women
NHANES III (n = 2916)SLE (n = 105)NHANES III (n = 2004)SLE (n = 48)
  • a

    Unadjusted.

  • b

    Limited to those who went through menopause.

  • c

    Menopause is defined by the following algorithm: A women is considered menopausal if no menses in last 12 months (and not currently breast-feeding and no surgery done that could complicate interpretation of menopausal status); total hysterectomy and bilateral salpingo-oophorectomy; if no menses and had a hysterectomy; ovary status unknown or known to have at least one ovary; FSH > 23 IU/L; or if menopausal status unknown and FSH unknown and age ≥ 50 years. Age 50 years is the median age of menopause in NHANES III women who have not had a hysterectomy or oophorectomy.

  • d

    Age of menopause is defined as age at last menses or when a hysterectomy was preformed prior to menopause, age when FSH was measured > 23 IU/L or at 50 years of age.

  • e

    Years since menopause was defined as current age minus age at menopause plus 1, with the 1 added to differentiate between women who went through menopause the same year as their examination and women who were not menopausal.

  • f

    Obesity is defined as BMI ≥ 30 kg/m2.

  • g

    Severely vitamin D deficient is defined as less than 10 ng/mL.

    ACR/SLICC-DI: American College of Rheumatology/Systemic Lupus International Collaborating Clinics Damage Index; SLAM, Systemic Lupus Activity Measure.

Agea (years)53.8 (53.2, 54.5)44.8 (41.3, 48.3)43.4 (42.6, 44.2)42.3 (37.2, 47.4)
Menopausala,c(%)55.1 (53.3, 56.9)40.0 (31.1, 49.6)34.6 (32.5, 36.7)39.6 (26.9, 53.9)
Age at menopauseb,d (years)47.4 (47.1, 47.8)43.1 (41.0, 45.3)46.0 (45.5, 46.6)39.0 (35.8, 42.2)
Years since menopauseb,e (years)21.9 (21.3, 22.5)11.4 (7.8, 14.9)16.8 (15.9, 17.6)11.5 (6.3, 16.7)
BMI (kg/m2)26.3 (26.1, 26.5)26.3 (25.1, 27.5)29.2 (28.9, 29.5)29.4 (27.6, 31.2)
25-Hydroxyvitamin D (ng/mL)29.9 (29.5, 30.3)31.1 (29.1, 33.1)17.7 (17.3, 18.2)16.4 (13.4, 19.4)
Obesef (%)22.8 (21.3, 24.4)20.5 (13.8, 29.4)38.9 (36.8, 41.1)43.9 (30.5, 58.3)
Severe vitamin D deficientg (%)1.1 (0.8, 1.6)2.9 (0.9, 8.5)11.0 (9.6, 12.5)19.7 (10.5, 33.7)
Current smoker (%)23.2 (21.6, 24.8)4.9 (2.2, 10.6)24.4 (22.5, 26.3)14.0 (7.0, 25.8)
Former smoker (%)21.6 (20.1, 23.2)33.6 (25.1, 43.2)12.8 (11.4, 14.4)26.7 (15.9, 41.2)
SLE duration (years)12.0 (10.4, 13.7)13.5 (11.0, 15.9)
ACR/SLICC-DI > 0 (%)55.7 (45.6, 65.3)80.2 (66.6, 89.2)
SLAM > 7 (%)28.7 (20.8, 38.1)49.0 (35.1, 63.0)

After stratifying by SLE status and race/ethnicity, age- and BMI-adjusted means were determined for HSA parameters at the femoral neck, trochanter, and proximal femoral shaft (Table 2). In general, BMD and bone geometry parameters were significantly different among both white and African-American women with SLE compared with NHANES III women at all the areas of the skeleton, with some site-specific differences between the race/ethnic groups (Fig. 1A–C). Specifically, BMD was reduced in both white and African-American women with SLE compared with NHANES III controls women at all areas, although the difference between white women did not attain statistical significance at the femoral shaft region.

Table 2. Age- and BMI-Adjusted Mean (95% CI) BMD and Bone Structure Levels Stratified by Race and SLE Status
 WhitesAfrican Americans
NHANES III (n = 2916)SLE (n = 105)NHANES III (n = 2004)SLE (n = 48)
  • p Values are for comparisons within racial group between NHANES III participants and individuals with SLE:

  • *

    p < 0.01;

  • p < 0.001;

  • p < 0.0001.

Narrow neck region
 BMD (g/cm2)0.88 (0.88, 0.89)0.83 (0.80, 0.86)0.96 (0.95, 0.96)0.83 (0.79, 0.87)
 Section modulus (cm3)1.31 (1.30, 1.32)1.11 (1.06, 1.17)*1.38 (1.37, 1.40)1.01 (0.93, 1.09)
 Cross-sectional area (cm2)2.56 (2.55, 2.58)2.40 (2.32, 2.47)2.74 (2.72, 2.76)2.32 (2.20, 2.44)
 Width (cm)3.07 (3.06, 3.08)3.04 (3.00, 3.09)3.03 (3.02, 3.04)2.94 (2.87, 3.01)
 Buckling ratio10.0 (9.94, 10.1)10.6 (10.2, 11.0)*9.13 (9.03, 9.23)10.3 (9.67, 10.9)
Intertrochanter region
 BMD (g/cm2)0.86 (0.86, 0.87)0.82 (0.79, 0.85)*0.94 (0.93, 0.94)0.80 (0.75, 0.84)
 Section modulus (cm3)3.76 (3.74, 3.79)3.54 (3.40, 3.68)*3.81 (3.78, 3.85)3.03 (2.81, 3.24)
 Cross-sectional area (cm2)4.35 (4.32, 4.37)4.05 (3.91, 4.20)4.61 (4.58, 4.65)3.76 (3.55, 3.97)
 Width (cm)5.31 (5.30, 5.32)5.19 (5.13, 5.26)5.20 (5.18, 5.22)4.98 (4.89, 5.08)
 Buckling ratio9.25 (9.17, 9.32)9.27 (8.88, 9.66)8.58, 8.49, 8.67)9.49 (8.90, 10.1)*
Femur shaft region
 BMD (g/cm2)1.29 (1.29, 1.30)1.28 (1.24, 1.31)1.34 (1.33, 1.35)1.19 (1.14, 1.25)
 Section modulus (cm3)2.26 (2.25, 2.28)1.93 (1.85, 2.00)2.32 (2.30, 2.34)1.67 (1.55, 1.79)
 Cross-sectional area (cm2)3.78 (3.76, 3.80)3.59 (3.48, 3.69)3.91 (3.89, 3.94)3.23 (3.06, 3.39)
 Width (cm)3.09 (3.08, 3.10)2.95 (2.90, 3.00)3.08 (3.07, 3.09)2.86 (2.79, 2.94)
 Buckling ratio3.62 (3.59, 3.70)3.39 (3.21, 3.57)3.46 (3.42, 3.51)3.57 (3.29, 3.86)
thumbnail image

Figure 1. (A) Comparisons (SLE versus NHANES III) for BMD and bone geometry parameters (SM = section modulus; CSA = cross-sectional area; BR = buckling ratio) were made for white (open bars) and African-American (gray bars) subjects. Units depend on variable. (B) Comparisons (SLE versus NHANES III) for BMD and bone geometry parameters were made for white (open bars) and African-American (gray bars) subjects. Units depend on variable. (C) Comparisons (SLE versus NHANES III) for BMD and bone geometry parameters were made for white (open bars) and African-American (gray bars) subjects. Units depend on variable.

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We examined our data for differences in skeletal outer diameter to determine if skeletal redistribution was associated with the BMD reduction seen among women with SLE. In both whites and African Americans, the outer diameter was reduced at all areas (SLE patients versus control individuals), although the difference was not significant at the narrow neck among whites. Since a narrower bone with equivalent bone quantity would have a higher BMD, these results suggest that reductions in the quantity of bone, rather than expanded diameter, are responsible for the low BMD seen among SLE patients. In support of this theory, low BMD was associated with reductions in bone tissue cross-sectional area among African-American and white patients with SLE at all areas analyzed (Fig. 1A–C).

We then examined associations between SLE status and skeletal resistance to bending forces and to local buckling by looking at associations between disease status and section modulus and buckling ratio at common fracture sites (ie, femoral neck and trochanter). Section modulus was reduced in both race/ethnic groups, suggesting reductions in bending resistance among women with SLE. This change was associated with reductions in outer diameter, as noted earlier. At the same time, buckling ratio was increased in both race/ethnic groups (nonsignificant difference among whites at the intertrochanter), suggesting an increased tendency for buckling. This finding suggests that cortical thinning proceeds faster among SLE patients than among control individuals given that SLE patients have a narrower outer skeletal diameter.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References

Our study presents the first data available comparing bone geometry parameters between women with SLE and control women. Using HSA, we detected the presence of significant bone geometry differences between patients and control individuals, a finding that has major implications in the management of patients with SLE, considering the impact of bone geometry on strength. Specifically, the presence of reduced section moduli and cross-sectional areas, along with increased buckling ratios at common fracture sites among SLE patients, implies that SLE patients are more likely to suffer a hip fracture than the general population in the event of a fall.

This clinically important information, which is not available from conventional DXA, is consistent with epidemiologic data and highlights the need for better ways for fracture prediction among patients with SLE. Our findings set the stage for studies to look for factors that underlie the differences in bone geometry and strength, a process that could pave the way for the development of therapeutic agents that specifically target these factors.

It is well documented that SLE is associated with reduced BMD at the hip and spine,28, 29 but the mechanistic explanation for this difference has hitherto been lacking. BMD reduction can result from changes in skeletal mineralization, its distribution, or its quantity.30 While we did not look at mineralization, the finding of reduced outer skeletal diameters at the proximal hip among women with SLE suggests that reduced bone quantity rather than skeletal redistribution by radial expansion is the mechanism responsible for reduced BMD at this skeletal site in SLE. This position is supported additionally by the presence of reduced proximal hip cross-sectional areas among the women with SLE.

The dimensional differences seen in our study suggest that the adaptive changes that normally preserve bone strength in the face of aging-associated bone loss may be altered in SLE. Specifically, as BMD declines with age, section modulus (a measure of bending resistance) is maintained by subperiosteal apposition, a process that results in radial expansion of the skeleton while there is ongoing endosteal bone loss, as was documented by Beck and colleagues.21 Section modulus scales exponentially with changes in bone diameter, making subperiosteal apposition a highly efficient mechanism to preserve bone strength because only a fraction of bone lost on the endocortical surface needs to be deposited on the subperiosteal surface for biomechanical equivalence. In our study, BMD reduction was accompanied by significant reductions in section modulus among SLE patients, implying failure of subperiosteal apposition.

There are several reasons to account for alterations in bone geometry among patients with SLE, including the effects of inflammation, corticosteroids, vitamin D deficiency, and altered skeletal loading from arthritis, myositis, and/or osteonecrosis.

The finding of low BMD among steroid-naive SLE patients has led to the hypothesis that inflammation by itself can lead to bone loss and fracture,31 although the mechanisms by which this comes about are yet to be elucidated. In one study, markers of inflammation were correlated with markers of bone turnover, and bone loss was more severe among patients with higher levels of proinflammatory cytokines, highlighting the potential role of inflammation in the acceleration of bone turnover.32 Recent studies using animal models of inflammatory arthritis have shown that inflammation can lead not only to low BMD but also to unfavorable bone geometry parameters, raising the possibility that both skeletal remodeling and skeletal modeling can be affected by inflammation,33, 34 ultimately leading to fragility. While we did not examine inflammation directly in our study, the presence of low BMD and abnormal bone geometry supports the concept that skeletal modeling and remodeling can be affected simultaneously by SLE.

Corticosteroids represent a potent therapy for inflammation and are used commonly in SLE. Corticosteroids have many adverse skeletal effects that can lead to bone loss and ultimately fracture.35 It has been reported that up to 50% of patients on long-term corticosteroids or patients with Cushing syndrome develop fragility fracture.36–39 However, a recent meta-analysis looking at fracture risk among corticosteroid-treated patients showed that steroid-associated BMD change did not fully account for the increased fracture risk among steroid-treated patients.40 Moreover, corticosteroid-treated patients tended to fracture at higher BMD values than control individuals,40 suggesting that other skeletal parameters are involved in the fragility seen among steroid-treated patients. Kaji and colleagues examined the effect of corticosteroids on bone geometry among middle-aged and elderly women.41 Using quantitative computed tomography (QCT) scans of the distal radius, they demonstrated that a variety of bone geometry parameters including total skeletal area, periosteal circumference, cortical area, and polar strength-strain index were significantly lower among steroid-treated premenopausal women compared with control women, findings that were similar to the effects of endogenous cortisol hypersecretion seen in Cushing syndrome patients.41 Moreover, corticosteroid-treated patients with vertebral fractures had lower total area, cortical area, periosteal circumference, and polar strength-strain index compared with steroid-treated patients without vertebral fracture.41 Collectively, these findings suggest that hypercortisolism can bring about changes in bone geometry that resemble the changes we detected among SLE patients.

Vitamin D is known to protect against fracture, but its mechanisms of action have not been fully elucidated. A recent meta-analysis showed that supplementation with 700 to 800 IU of vitamin D significantly reduced the risk of hip and nonvertebral fracture (relative risk reductions of 26% and 23%, respectively).42 While these fracture benefits are partly a consequence of improved muscle function with reduced fall risk, it is widely believed that vitamin D also improves bone strength, although mechanical studies in support of this assertion have not been available. In a 2-year randomized, controlled trial that examined the consequences of vitamin D (and calcium) supplementation versus placebo in middle-aged to elderly men using QCT, Daly and colleagues showed that there was expansion of midfemur medullary area among the placebo subjects, with significant reductions in cross-sectional area, whereas these parameters were preserved among subjects taking vitamin D and calcium.43 Preservation of cross-sectional area has a direct consequence on compressional resistance, with potential implications for buckling susceptibility as well. In addition, there was relative preservation of torsional resistance among the vitamin D and calcium recipients. Increased midfemur medullary area seen among placebo subjects probably resulted from increased endocortical resorption, highlighting the skeletal impact of vitamin D in high-turnover situations. Several cross-sectional studies examining skeletal health in SLE have reported low levels of vitamin D,44 raising the possibility that reduced bone strength owing to abnormal bone geometry may be a factor in skeletal fragility.

Another potential explanation for the observed differences in section modulus lies in the possibility of differential skeletal loading between SLE patients and control individuals. Mechanical loading of the skeleton is believed to be one of the most potent stimuli for skeletal adaptation during postnatal life,45 with the greatest effect occurring during growth. According to Frost, muscle forces dominate a bone's postnatal structural adaptations to mechanical usage, modified somewhat by body weight and one's voluntary physical activities.46 This assertion suggests that without exposure to optimal levels of muscle-generated bone strains, one can fail to realize one's full (genetically determined) mechanical potential. In SLE, there are a number of factors that can compromise skeletal loading, thereby preventing full expression of the genetically specified blueprint of modeling and remodeling, potentially resulting in reduced skeletal strength. These include inflammation, pain, fatigue, and steroid-induced myopathy.

Overall, skeletal loading can be expected to have a more localized skeletal effect, whereas the nonmechanical factors can be expected to have a more global scope of action. As a result, it is very likely that the bone geometry differences seen at the proximal hip also exist at other skeletal sites, implying a global fracture risk in SLE. Furthermore, it has been suggested that the nonmechanical factors act by either altering the mechanical regulation of bone cells (by altering the mechanical set point of bone) or by a direct effect on bone cells themselves, independent of mechanical stimuli.47 Accordingly, the nonmechanical factors have the potential to affect both the structural and material properties of the skeleton, whereas skeletal loading primarily would alter the structural properties of bone.47

While bone geometry trends among SLE patients were in the direction consistent with increased fragility, proximal shaft buckling ratios trended in the opposite direction in our study. The reasons for these unexpected findings are not immediately clear to us, although there was a slight difference in the location of this region of interest between the two studies. Additional studies are needed to clarify the causes and consequences of the unexpected proximal shaft buckling ratios.

This study has some limitations that may have affected the results. First, we used a 2D technique to determine cross-sectional diameters and assess bending properties in the image plane.48 It is very likely that there are significant differences between patients and control individuals in other planes as well, which we are unable to capture. Second, the cortical thickness component of this study is modeled on the assumption that the cortex occupies a fixed percentage of the wall and that bone loss in SLE affects the cortical and trabecular components to the same degree as occurs with aging. Accordingly, our study would overestimate cortical thickness (and underestimate buckling ratios) if the percent involvement of the cortex is greater than in the general population, and vice versa. Third, BMD differences at the femur also could result from differences in fat distribution around the femur between patients and control individuals,49 but this is made less likely by the lack of a significant difference in BMI after controlling for race/ethnicity. Moreover, we were unable to perform scanner cross-calibration by scanning recommended hip phantoms at each of the DXA machines, as has been recommended. Furthermore, slight incompatibilities exist between software versions used in NHANES III and our study that could have affected geometry estimates.

Another area of potential concern pertains to the use of medications among the SOLVABLE population. Use of bisphosphonates in this young population was rare out of concerns for pregnancy. Steroids were used in less than half the patients, and the average dose was low. Thus, although steroids can affect bone health, the low dose and reduced use in this population should minimize this concern.

In conclusion, our findings show that SLE is associated with changes in bone geometry that are suggestive of increased fracture risk. These changes are not captured by conventional DXA and highlight the importance of including bone geometry on fracture prediction models for patients with SLE. Our study focuses on the structural composition of the skeleton. Other aspects of bone quality, including bone microarchitecture, bone turnover, and material composition that are known to influence bone strength, were not studied. Considering that SLE patients often have multiple osteoporosis risk factors that potentially could alter these parameters, it will be worth examining these parameters in future studies to determine their impact on fracture risk in SLE.

Disclosures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References

JDA has received Speaker's Bureau honoraria from Amgen and Novartis. All the other authors state that they have no conflicts of interest.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References

We thank Dr Steward Spies (Department of Radiology, Northwestern University) for graciously providing access to the SOLVABLE DXA scans, Dr Thomas Beck (vice president and chief technology officer, Quantum Medical Metrics, LLC) for his generous assistance with hip structure analysis, and Dr Craig Langman (Department of Pediatric Nephrology, Northwestern University and Children's Memorial Hospital) for laboratory assistance in completing the 25-hydroxyvitamin D assays.

Funding for this work was provided by MCRC Pilot Grant NIH NIAMS P60 AR049459 (to JDA) and NIH Grants K23 AR 052364 (to DLK), R01-MD004251 (to KJH), and K24 AR 002138, P60 2 AR30692, and UL1RR025741 (to RRG).

Authors' roles: All the authors made substantial contributions to study conception and design; the acquisition, analysis, and interpretation of data; writing and revision of manuscript for important intellectual content; and its final approval for publication. We take full responsibility for the methods, data collection, data analysis, and writing of this article. All the authors had full access to the data in the study and take full responsibility for the integrity of the data and accuracy of data analysis.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Disclosures
  8. Acknowledgements
  9. References