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Osteoporosis is more frequent in postmenopausal female and male patients with MS.
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Disability is a MS specific risk factor for osteoporosis.
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A new risk score allows to estimate the individual probability of osteoporosis.
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This risk score enables individual screening recommendation and prevent osteoporosis-associated morbidity.
Abstract
Background
Due to the demographic development and improved treatment options, the role of comorbidities is of increasing importance in the medical care of people with MS (pwMS). A higher risk of osteoporosis is well known in chronic autoimmune diseases, and is also described in MS. While there are several screening guidelines in the elderly or in patients with rheumatoid arthritis, there are no generally accepted recommendations when to perform bone mineral testing in pwMS under the age of 65 years. We aimed to determine risk factors of osteoporosis in pwMS and to develop a risk score which can be applied in daily clinical routine.
Methods
Densitometry (hip and lumbar spine) was performed in 159 pwMS aged ≤65 years and in 81 age- and sex-matched healthy controls (HC). Osteoporosis was defined according to WHO criteria as a bone density 2.5 standard deviation or more below the mean of young adults. Risk factors were identified by logistic regression analysis.
Results
Osteoporosis occurred more frequently in postmenopausal pwMS and male pwMS as compared to HC. Besides age, sex, menopausal status in females, body-mass-index and smoking, a higher degree of disability - as assessed by the Expanded Disability Status Scale - was identified as MS specific risk factor for osteoporosis, whereas the cumulative glucocorticoid dose was not associated with osteoporosis risk. Based on these risk factors, we developed an MS-specific risk score which allows to estimate the individual probability of osteoporosis.
Conclusion
This risk score enables individual screening recommendation for pwMS and, subsequently, early prevention of osteoporosis which probably should result in reduction of fractures and morbidity.
). In people with MS (pwMS), a higher incidence of osteoporosis is a consistent finding, and a recent systematic review revealed a pooled osteoporosis prevalence of 17% in pwMS (
). Several studies reported that disability, i.e. Expanded Disability Status Scale (EDSS), - besides age and low body mass index (BMI) - significantly correlates with low bone mass density (BMD) and, respectively, osteoporosis in pwMS (
). Although long-term glucocorticoid (GC) treatment is a well-known risk factor for osteoporosis, the role of short-term GC treatment as used for relapse treatment in MS is under debate. Despite evidence of an association of a high cumulative GC dose (>15 gr) with low BMD in pwMS (
). Furthermore, reduced bone mass (osteopenia and osteoporosis) was also observed in half of patients with clinically isolated syndrome and early MS with a low cumulative GC dose and no or only minimal disability (
) suggesting that there might be a shared pathogenetic mechanism.
Even in the general population, there is a “treatment gap” of osteoporosis in Europe that is associated with a high socioeconomic burden by fragility fractures (
Osteoporosis in the European Union: medical management, epidemiology and economic burden. A report prepared in collaboration with the International Osteoporosis Foundation (IOF) and the European Federation of Pharmaceutical Industry Associations (EFPIA).
). Based on a literature review, in 2010 an osteoporosis screening algorithm was proposed for pwMS fulfilling either of the following criteria: i) postmenopausal women; ii) EDSS score ≥6; iii) EDSS score <6, but fractures; or iv) >3 month GC therapy or antiepileptic therapy (
). There is still a lack of evidence regarding the degree of disability where osteoporosis risk is markedly increased and limited data are available for male individuals with MS. Recently, the development of more proactive and easily applicable screening guideline was recommended (
), we aimed to determine risk factors of osteoporosis in pwMS under the age of 65 years and to develop a risk score which can be applied in daily clinical routine for these patients.
2. Methods
2.1 Study population
For this study, premenopausal women, postmenopausal women, and men with MS aged between 18 and 65 years, as well as age-, sex- and in case of women menopausal status-matched healthy controls (HC) (2:1) were prospectively included at the Department of Neurology of the Medical University Innsbruck between March 2020 and October 2021. All pwMS were diagnosed according to 2017 McDonald criteria (
Disability was determined by EDSS, pyramidal functional system score (FSS) and ambulation score at inclusion by the treating neurologist. In addition, the treating neurologist documented disease course, comorbidities, concomitant medication, disease duration in years and past and/ or current disease-modifying treatment (DMT). DMT was categorized in moderately effective treatment (glatiramer acetate, interferon-beta, teriflunomide, dimethyl fumarate), highly active treatment (alemtuzumab, cladribine, fingolimod, natalizumab, ocrelizumab, rituximab), depleting DMTs (alemtuzumab, cladribine, ocrelizumab, rituximab) or no treatment. Furthermore, demographic data, weight, height, BMI, abdominal circumference, smoking status, alcohol consumption, date of menopause, family status, occupational status, education, parental hip fracture, and familiar osteoporosis were collected.
Postmenopausal status was defined as a 12-month primary amenorrhea and confirmed by hormonal status. To assess activity the Baecke questionnaire was used (
). Exposure to GC was assessed as cumulative lifetime methylprednisolone dose. Exposure of GC and number of relapses were collected by retrospectively reviewing the prospectively collected Innsbruck MS data base by the study investigators and medical files of the patients (AZ, FDP).
Exclusion criteria comprised a history of osteoporosis, an ongoing pregnancy as a contraindication for densitometry, and conditions that confound dual-energy X ray absorptiometry (DXA) such as a lumbar spinal fusion surgery, and lack of documented history of cumulative GC dose.
2.2 Densitometry
Every study participant underwent BMD measurement of lumbar spine (L1-L4) and right hip (femoral neck, Ward`s triangle, trochanteric, intertrochanteric, and total hip) by DXA (Hologic Discovery™ A) at the Department of Nuclear Medicine, Medical University Innsbruck according to standard protocol. Analysis of results was performed by a blinded rater of the Department of Nuclear Medicine (ED and AK). Osteoporosis was defined according to the WHO criteria (
) in study participants with T score ≤ −2.5 in at least one skeletal site.
2.3 X-Ray
To detect vertebral osteoporotic fractures a thoracic and lumbar spine X-Ray was performed in all study participants.
2.4 Laboratory analysis
Laboratory analysis included hemoglobin A1c (HbA1c), total cholesterol, high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol, calcium, phosphate, alkaline phosphatase, C-reactive protein (CRP), interleukin-6 (IL-6), thyroid-stimulating hormone (TSH), parathyroid hormone (PTH), 25-hydroxyvitamin D (measured by high performance liquid chromatography), osteopontin, osteoprotegerin, aminoterminal propeptide of type I collagen (P1NP), Beta-Crosslaps (CTX), luteinizing hormone (LH), follicle-stimulating hormone (FSH), 7-beta estradiol, progesterone, prolactin, androstenedione, dehydroepiandrosterone, testosterone, sex-hormone-binding globulin, free androgen index, estrone, inhibin B, anti-Mullerian hormone. All analysis except osteopontin were performed by the Central Institute of Medical and Chemical Laboratory Diagnostics (ZIMCL), University Hospital of Innsbruck, according to routine laboratory standards (eTable 1). Osteopontin was measured with the commercially available human ProcartaPlex simplex kit (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's instructions. Measurements were performed on a MagPix (Luminex Corporation; Software: xPonent 4.2) reader.
2.5 Statistical analysis
Statistical analysis was performed using the statistical software R (
). Distribution of data was assessed graphically and by the Kolmogorov-Smirnov test and data were displayed as mean ± standard deviation, or as median and interquartile range or range, as appropriate. For group comparisons, Mann-Whitney-U test or χ2 test was applied.
To identify predictors of osteoporosis, logistic regression was employed with osteoporosis as the dependent variable and current smoking status, BMI, parental hip fractures, cumulative GC dose, disease duration, EDSS score, spasmolytic treatment, DMT, Vitamin D intake and patient group (premenopausal women, postmenopausal women, men ≤ 45 and men > 45 years) as independent variables. In addition, logistic regression was employed with FSS as well as ambulation score instead of EDSS score. McFadden's R square, accuracy, sensitivity and specificity were the model quality criteria.
To provide a practical risk score, we dropped the insignificant variables, categorized BMI (as <20, 20–30 and >30) and EDSS score (as <4.5/ ≥4.5) and re-computed the logistic regression. The corresponding odds ratios were used to compute the risk score. Various ranges of the risk score with its associated probability for osteoporosis were summarized for practical use.
A-priori power analysis with a significance level of 5%, a power of 80%, and 8 independent variables was computed. According to previous findings in the literature (
). The analysis revealed a necessary sample size of 109 patients for the logistic regression with all independent variables.
2.6 Ethics and consent
The study was approved by the ethics committee of the Medical University of Innsbruck (approval number: 1261/2018). Written informed consent was obtained from all study participants.
2.7 Data availability statement
Data supporting the findings of this study are available from the corresponding author upon reasonable request by a qualified researcher and upon approval by the ethics committee of the Medical University of Innsbruck.
3. Results
A total of 159 pwMS and 81 age- and sex-matched controls with a median age of 48 years and a female predominance of 68% were included in the study. Detailed demographic and clinical characteristics of pwMS and controls are displayed in Table 1. PwMS had higher abdominal circumference, lower grade of activity and had more often a history of smoking compared to controls. There was also a trend for a higher BMI and a higher frequency of hyperlipidemia and arterial hypertension in pwMS. PwMS suffered more frequently from depression compared to controls, were more often retired or unemployed and had a lower educational level.
Table 1Characteristics of pwMS and controls at study inclusion.
PwMS
Controls
P value
No. of patients
159
81
Demographics
Age, years
48 (37–55)
48 (35–57)
0.686
Sex (females), n (%)
107 (67.3)
55 (67.9)
0.925
Clinical characteristics
BMI
25 (21–29)
23 (21–27)
0.069
Abdominal circumference (cm)
91 (80–102)
83 (76–93)
0.006
Alcohol consumption (glasses per week)
0 (0–2)
2 (0–4)
<0.001
Current smoking, n (%)
42 (26.4)
18 (22.2)
0.478
Ever smoking, n (%)
108 (67.9)
38 (46.9)
0.002
Socioeconomic status
Occupational status, n (%) working retired unemployed
103 (64.8) 42 (26.4) 14 (8.8)
77 (95.1) 3 (3.7) 1 (1.2)
<0.001
Family status, n (%) single divorced widowed relationship married
43 (27.2) 18 (11.4) 3 (1.9) 16 (10.1) 78 (49.4)
24 (30.8) 7 (9.0) 0 (0) 16 (20.5) 31 (39.7)
0.126
Educational grade, n (%) ≤9 years of schooling secondary schooling apprenticeship tertiary degree others
25 (15.7) 43 (27.0) 48 (30.2) 37 (23.3) 6 (3.8)
4 (4.9) 21 (25.9) 14 (17.3) 42 (51.9) 0 (0)
<0.001
Baecke questionnaire total Work activity Sport activity Nonsport leisure activity
DMT, n (%) No DMT Moderately effective Highly effective
55 (34.6) 39 (24.5) 65 (40.9)
NA
NA
Depleting DMT, n (%)
38 (23.9)
NA
NA
Legend:.
Data are given as median and interquartile range unless specified otherwise. For group comparisons, Mann-Whitney-U test or χ2 test was applied as appropriate.
Abbreviations: BMI, body-mass index; DMT, disease-modifying treatment; EDSS, Expanded Disability Status Scale; FSS, Functional System Score; GC, glucocorticoid; MS, multiple sclerosis; n, number; NA, not appropriate; PwMS, people with multiple sclerosis.
Overall, 115 (72%) of 159 pwMS had a relapsing disease course, while 44 (28%) showed a progressive disease course. The median disease duration was 10 (range 0–37) years and the median EDSS score was 2.5 (range 0–8). There was a high correlation between pyramidal FSS and EDSS score (Spearman r = 0.75), as well as between ambulation score and EDSS score (Spearman r = 0.88). We provide a mosaic plot that shows these correlations (eFigure 1). Fifty-five (34.6%) patients had no DMT, whereas 39 (24.5%) and 65 (40.9%) patients were treated with a moderately and highly effective DMT, respectively. Depleting agents were used in 38 (23.9%) patients. The median cumulative lifetime GC dose was 12 g (maximum 56 g). No study participant used low dose long-term GC treatment.
4. Osteoporosis is increased in PWMS
Osteoporosis was detected in 47 (30%) of 159 pwMS, while in 7 (9%) of 81 controls (Fig. 1A). In the group of pwMS, several characteristics differed between patients with and without osteoporosis (Table 2). Patients with osteoporosis were older, had lower BMI and lower abdominal circumference, showed longer disease duration, higher EDSS score, higher pyramidal FSS, higher ambulation score, more frequently a progressive disease course and received less frequently DMT but more frequently antispasmodic treatment. In female patients, postmenopausal status was associated with the occurrence of osteoporosis.
Fig. 1Osteoporosis frequency in pwMS and controls. Legend: y-axis shows percentage of osteoporosis frequency.
Osteoporotic fractures in patients with osteoporosis detected by X-Ray, n (%)
11 (23.4)
NA
NA
MS characteristics
Disease duration, years
15 (10–22)
8 (3–14)
<0.001
EDSS score
3.5 (2–6)
2 (1–4)
<0.001
Pyramidal FSS
2 (0–3)
1 (0–1.5)
<0.001
Ambulation Score
1 (0–7)
0 (0–1)
<0.001
Relapses since diagnosis
3 (2–8)
3 (1–6)
0.463
Cumulative lifetime GC dose in g
14 (7–21)
11 (4–19)
0.179
Number of different DMT
2 (1–2)
1 (1–2)
0.863
DMT, n (%) No DMT Moderately effective Highly effective
22 (46.8) 17 (36.2) 8 (17.0)
28 (25.0) 33 (29.5) 51 (45.5)
<0.001
Depleting DMT, n (%)
14 (29.8)
24 (21.4)
0.259
Legend:.
Data are given as median and interquartile range unless specified otherwise. For group comparisons, Mann-Whitney-U test or χ2 test was applied as appropriate.
Abbreviations: BMI, body-mass index; DMT, disease-modifying treatment; EDSS, Expanded Disability Status Scale; FSS, Functional System Score; GC, glucocorticoid; MS, multiple sclerosis; n, number; NA, not appropriate; PwMS, people with multiple sclerosis.
In subgroup analysis, osteoporosis was detected in 6/57 (10.5%) of premenopausal pwMS and in 1/28 (3.6%) of premenopausal HC; in 25/50 (50%) of postmenopausal pwMS, but only in 4/27 (14.8%) of postmenopausal controls. In male pwMS aged ≤45 years osteoporosis was detected in 7/28 (25.0%), while in none of the controls ≤45 years, but in 9/24 (37.5%) pwMS >45 years and 2/11 (18.2%) controls >45 years (Fig. 1B-E). For further details please refer to eTable 2.
Logistic regression analysis including current smoking status, BMI, parental hip fractures, cumulative GC dose, disease duration, EDSS score, antispasmodic treatment, DMT, vitamin D intake and patient group (premenopausal women, postmenopausal women, men ≤45 and men >45 years) as independent variables revealed that BMI, smoking status, EDSS score and allocation into one of the four patient groups were statistically significant predictors of osteoporosis (eTable 3). Similar to the EDSS score, the pyramidal FSS (eTable 4 A) as well as the ambulation score (eTable 4 B) predicted osteoporosis.
To simplify the risk assessment of osteoporosis, we dropped statistically non-significant variables (Table 3) and used categorized variables of BMI and EDSS score (eTable 5). The cut-point of 4.5 for the EDSS score was derived from the estimated osteoporosis probability dependent on the EDSS score (Fig. 2). The quality of the model with the variables on their original scale and the one of the model with the two categorized variables and without statistically insignificant variables (simplified model) were quite similar (eTable 6). With the simplified model (eTable 5), the calculation steps to obtain patients’ risk probability are: i) the total risk score of a patient is calculated as the product of the odds ratios of applicable risk factors multiplied with the normalization factor (Fig. 3A) and ii) the risk score gives the corresponding risk probability (Fig. 3B). Three computational examples are provided.
Table 3Logistic regression analysis with the significant risk factors of osteoporosis in MS.
Fig. 2Probability of osteoporosis depends on disability. Legend: Probability estimated by logistic regression (Table 3) is provided for patients with differing EDSS scores. For group comparisons, Mann-Whitney-U test or χ2 test was applied as appropriate.
Fig. 3Calculating osteoporosis risk. Legend: Reference categories are1 non-smokers,2 BMI of 20–30,3 premenopausal women, and 4 EDSS score ≤4.5,5 If all risk factors are set to the reference category, the risk score equals the normalization factor of 0.04 (as shown in Example 1). BMI, body-mass index; EDSS, Expanded Disability Status Scale.
Laboratory analysis revealed higher LDL levels, slightly lower HDL and phosphate levels in pwMS compared to HC. In addition, 25-hydroxyvitamin D levels were significantly decreased in patients with MS (eTable 7). In pwMS/controls with osteoporosis compared to pwMS/controls without osteoporosis, total cholesterol, alkaline phosphatase, P1NP, CTX, LH and FSH were significantly increased, whereas, 17-beta estradiol, progresterone, prolactin, dehydroepiandrosterone, androstenedione, estrone, inhibin B and anti-Mullerian hormone were significantly decreased. There was a trend to higher osteopontin levels in pwMS/controls with osteoporosis compared to pwMS/controls without osteoporosis (eTable 8). In eTable 9, laboratory analysis in controls without or with osteoporosis and in MS patients without or with osteoporosis were compared.
6. Discussion
Osteoporosis is an underestimated comorbidity in MS associated with an increased morbidity and mortality. Early identification of patients at risk is crucial as an effective prophylactic treatment is available. In this study, we determined risk factors for osteoporosis in pwMS and propose a prospectively developed and highly accurate risk score as an easy screening tool in pwMS under the age of 65 years. Osteoporosis detected by DXA was found in approximately 30% of pwMS patients under 65 years. In a recent meta-analysis, a pooled prevalence of 17% was described (
), which may be explained by the different study population regarding age, disability, and BMI. Furthermore, most studies included a relatively small number of patients. In addition, not all studies screened patients with DXA. The largest study included in the meta-analysis detected low bone mass only by a voluntary self-report registry (reporting 15.4%) (
Bone mineral density in patients with multiple sclerosis, hereditary ataxia or hereditary spastic paraplegia after at least 10 years of disease - a case control study.
Several factors were suggested to be associated with osteoporosis, low BMD or pronounced bone loss in pwMS. In our study, risk factors for osteoporosis were age, sex, postmenopausal status in females, BMI and smoking corroborating well to known risk factors in the general population (
). In male pwMS, the frequency of osteoporosis is less clear. In our study, risk was already increased in young male pwMS compared to premenopausal women and male HC and further increased with age. This is consistent with a high prevalence of osteoporosis in male individuals with MS (37.5%) disproportionate to their age (
In addition, a higher degree of physical disability - as assessed by the EDSS - was identified as an independent MS specific risk factor. A cut-off of a clinically relevant EDSS score >4.5 was determined. Greater disability was also associated with osteoporosis in a recent population-based study, however, this study considered use of home care services as a proxy for disability and did not determine EDSS score (
). Other studies showed that ambulatory status, higher disability or severe MS course were associated with bone loss, low BMD and osteopenia or osteoporosis (
). Even though one study in male patients reported that osteoporosis is more likely diagnosed with an EDSS score ≥5.5, bone loss was detected in up to 60% of patients with a lower EDSS score (
), but manifest osteoporosis as measured by DXA was low in these studies. Altogether, these results are consistent with ours showing osteoporosis risk increased by 34% per increase of one point in EDSS score.
In contrast to disability, lifetime cumulative GC dose was not associated with an increased osteoporosis risk. Evidence of the influence of short-term GC administration on osteoporosis development in pwMS is inconsistent and a cumulative dose of > 15 gr was suggested to negatively influence bone mass (
). Our study includes patients with a median cumulative lifetime GC dose of 12 gr and a maximum of 56 gr; therefore, it can be regarded as representative.
It has been suggested that DMT may have a beneficial effect on bone density in pwMS patients (
). Accordingly, an association of no DMT with higher risk of osteoporosis was observed. However, in multivariate analysis including confounding factors such as age and EDSS score, use of DMT was no longer associated with reduced osteoporosis risk. This reflects that patients with more progressed disease are less likely to receive DMT.
Laboratory analysis revealed lower 25-hydroxyvitamin D levels in pwMS compared to HC. However, there was no association of low 25-hydroxyvitamin D levels and osteoporosis in our study. Whereas in some studies lower 25-hydroxyvitamin D levels were associated with low BMD and a faster bone loss (
In participants with osteoporosis, known bone turn over markers such as alkaline phosphatase, P1NP, and CTX were increased and typical postmenopausal, respectively age-related, hormonal changes (increase of LH and FSH, decrease of 17-beta estradiol, progresterone, prolactin, dehydroepiandrosterone, androstenedione, estrone, inhibin B and anti-Mullerian hormone) were observed compared to participant without osteoporosis. However, no additional effect in pwMS with osteoporosis was detected regarding bone metabolism markers. This is in line with previous results that showed that low testosterone levels alone did not explain the bone loss in male pwMS (
). However, this study detected a correlation of Omentin-1 with BMD in pwMS and osteopontin levels suggesting a cross-talk between adipose tissue and bone in MS. Further studies are needed to elucidate a possible shared pathophysiological mechanism between osteoporosis and MS.
Based on a literature review, in 2010 an osteoporosis screening algorithm was proposed for pwMS with an EDSS score ≥6; postmenopausal women; an EDSS score <6 with a fracture; >3 month GC therapy or on antiepileptic therapy (
). However, in our study, neither fracture during the last 12 month nor ever fracture, antiepileptic or antispasmodic treatment was significant in the multivariate analysis. Therefore, in contrast to this review-based recommendation, we developed a MS-specific osteoporosis risk score based on a prospective study, which allows to determine the individual probability of osteoporosis in patients under 65 years. Importantly, EDSS score of 4.5 was identified as a relevant cut-point, which corresponds to an impaired ambulation status. The risk-score is an easily applicable tool as it only requires sex, age, smoking status, BMI, (post)menopausal status, and EDSS score as necessary parameters. With this newly developed score, the probability of osteoporosis can be predicted with an accuracy of 84%, a sensitivity of 62% and a specificity of 94% in pwMS under 65 years.
A limitation of this study is that alcohol consumption and long-term low dose GC treatment could not be considered, due to the low alcohol consumption rate in pwMS and higher alcohol consumption in postmenopausal healthy women and male HC as well as the lack of participants using low dose long-term GC treatment. In addition, history of falls was not assessed, however, disability measured by EDSS considers gait disturbance and may be indirect evidence for the risk of falls. Data whether regular physiotherapy was done was not assessed in this study. This is probably another risk factor and should be considered by further research. Recent fractures were identified as relevant risk factor for osteoporosis prediction in MS (
). The number of fractures in the last year was very low and was not included in the proposed risk score. A possible explanation may be that we excluded patients with known osteoporosis and in most patients with fragility fracture osteoporosis screening is performed, this may be a bias underestimating the relevance of fragility fractures as relevant risk factor. However, as in the case of a fragility fracture DXA is recommended, it is not a relevant limitation for the application of the risk score.
In conclusion, this study identified risk factors of osteoporosis and provides for an easily applicable risk score that allows the identification of even young pwMS with a high probability of osteoporosis and provides a rationale for osteoporosis screening, e.g., by DXA.
Anne Zinganell, designed study, data acquisition, writing - review & editing
Harald Hegen, designed study, data acquisition, formal analysis, drafted paper, writing - review & editing
Angelika Bauer, data acquisition, writing - review & editing
Klaus Berek, data acquisition, writing - review & editing
Robert Barket, data acquisition, writing - review & editing
Michael Auer, data acquisition, writing - review & editing
Gabriel Bsteh, designed study, writing - review & editing
Evelin Donnemiller, data acquisition, writing - review & editing
Alexander Egger, data acquisition, writing - review & editing
Astrid Grams, data acquisition, writing - review & editing
Andrea Griesmacher, data acquisition, writing - review & editing
Alexander Stephan Kroiss, data acquisition, writing - review & editing
Florian Rettenwander, data acquisition, writing - review & editing
Maximillian Tschallener, data acquisition, writing - review & editing
Alexander Tschoner, data acquisition, writing - review & editing
Thomas Berger, designed study, writing - review & editing
Florian Deisenhammer, designed study, data acquisition, writing - review & editing
Franziska Di Pauli, designed study, data acquisition, analysed data, drafted paper, writing - review & editing
Disclosures
AZ has participated in meetings sponsored by, received speaking honoraria or travel funding from Biogen, Merck, Sanofi-Genzyme and Teva; HH has participated in meetings sponsored by, received speaker honoraria or travel funding from Bayer, Biogen, Celgene, Merck, Novartis, Sanofi-Genzyme, Siemens, Teva, and received honoraria for acting as consultant for Biogen, Celgene, Novartis and Teva: JW has nothing to disclose; MA received speaker honoraria and/or travel grants from Biogen, Merck, Novartis and Sanofi; RB has participated in meetings sponsored by or received travel grants from Biogen, Novartis and Sanofi; AB has participated in meetings sponsored by or received travel funding from Novartis, Sanofi-Genzyme, Merck, Almirall and Biogen; KB has participated in meetings sponsored by and received travel funding from Roche and Biogen; GB has participated in meetings sponsored by, received speaker honoraria or travel funding from Biogen, Celgene/BMS, Lilly, Merck, Novartis, Roche, Sanofi-Genzyme and Teva, and received honoraria for consulting Biogen, Celgene/BMS, Novartis, Roche, Sanofi-Genzyme and Teva. He has received unrestricted research grants from Celgene/BMS and Novartis; ED has nothing to disclose; AE has nothing to disclose; AGra has nothing to disclose; AGri has nothing to disclose; ASK has nothing to disclose; FR has nothing to disclose; MT has nothing to disclose; AT has nothing to disclose; TB has participated in meetings sponsored by and received honoraria (lectures, advisory boards, consultations) from pharmaceutical companies marketing treatments for MS: Allergan, Bayer, Biogen, Bionorica, BMS/Celgene, Genesis, GSK, GW/Jazz Pharma, Horizon, Janssen-Cilag, MedDay, Merck, Novartis, Octapharma, Roche, Sandoz, Sanofi-Genzyme, Teva and UCB. His-institution has received financial support in the past 12 months by unrestricted research grants (Biogen, Bayer, BMS/Celgene, Merck, Novartis, Roche, Sanofi-Genzyme, Teva and for participation in clinical trials in multiple sclerosis sponsored by Alexion, Bayer, Biogen, Merck, Novartis, Octapharma, Roche, Sanofi-Genzyme, Teva; FD has participated in meetings sponsored by or received honoraria for acting as an advisor/speaker for Almirall, Alexion, Biogen, Celgene, Genzyme-Sanofi, Janssen, Merck, Novartis Pharma, Roche, and TEVA ratiopharm. His-institution has received research grants from Biogen and Genzyme Sanofi. He is section editor of the MSARD Journal (Multiple Sclerosis and Related Disorders); FDP has participated in meetings sponsored by, received honoraria (lectures, advisory boards, consultations) or travel funding from Bayer, Biogen, Janssen-Cilag, Merck, Novartis, Sanofi-Genzyme, Teva, Celgene and Roche. Her institution has received research grants from Roche.
Acknowledgement
We thank Markus Reindl for his advice and guidance in study design.
Osteoporosis in the European Union: medical management, epidemiology and economic burden. A report prepared in collaboration with the International Osteoporosis Foundation (IOF) and the European Federation of Pharmaceutical Industry Associations (EFPIA).
Bone mineral density in patients with multiple sclerosis, hereditary ataxia or hereditary spastic paraplegia after at least 10 years of disease - a case control study.
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