Prevalence and risk factors for bone loss in Southern Chinese with rheumatic diseases

doi:10.21203/rs.2.24765/v1

Objectives: This study was to explore the prevalence of different severities of bone loss and the potential risk factors in rheumatic patients. Method: An analytical cross-sectional study design was performed. For the present study, 1,398 rheumatic patients and 302 healthy subjects were recruited and further accepted for data collection and blood and bone mineral density tests. Risk factors for bone loss in rheumatic patients were further analyzed by using multivariate logistic regression analysis. Results: (1) Rheumatic patients in this study are consisted of 40.0% rheumatoid arthritis, 14.7% systemic lupus erythematosus, 14.2% osteoarthritis, 9.2% ankylosing spondylosis, 7.9% gout, 7.0% primary Sjogren syndrome, 3.8% systemic sclerosis, and 3.2% mixed connective tissue disease. (2) In male patients aged under 50 and premenopausal female patients, the bone mineral density score of patients is lower than the healthy persons with the same gender and age (34.3% vs 18.2%， P =0.045). (3) Osteopenia and osteoporosis are more prevailing in male patients aged or older than 50 and postmenopausal female patients than healthy counterparts (92.7% vs 87.2%, P =0.017). (4) Regression analysis indicated that compared with a healthy population, those with systemic lupus erythematosus, rheumatoid arthritis and ankylosing spondylosis gained the highest odd ratio of ‘score below the expected range for age’, osteopenia and osteoporosis, respectively. (5) Hypovitaminosis D, ‘CRP elevated’ was commonly-associated with impaired bone mineral density in all rheumatic patients. Conclusion: Bone loss commonly occurs at any age in the setting of rheumatic diseases. Hypovitaminosis D, elevated CRP was probably commonly-associated with impaired BMD in all age groups of rheumatic patients.

Orthopedics

rheumatic patients

bone mineral density

osteopenia

osteoporosis

Osteoporosis (OP) is a skeletal disease that refers to the reduction of bone mass and the deterioration of microstructure of bone tissue and leads to an increased risk of bone fragility and fracture and consequently, disability and mortality. Older age, low body mass index(BMI, kg/m²), female and post-menopause, smoking, vitamin D deficiency^1 − 3 have been proved to be generally and strongly related to OP and osteoporotic fracture. Rheumatic diseases (RD), including arthritis, diffuse connective tissue diseases, spondyloarthropathies, etc., are proved to be relevant to bone loss ⁴⁻¹³. Disease-specific causes of secondary OP are well-established and shared in RD, like inflammation-associated osteoclast activation^{14 15},routine glucocorticoid (GC) treatment^{16 − 19}, and reduced physical activity, which in turn leads altered bone metabolism (favoring bone resorption)^{20 21} due to musculoskeletal pain and weakness. Also, disease activities would inhibit intestinal calcium and vitamin D absorption. In fact, chronic, systemic or local inflammation and/or exposure to GC treatment cause an imbalance between bone formation and bone resorption²² and which are both important determinants of bone loss in RD. Hence, rheumatic patients are more likely to suffer from osteoporosis. Nevertheless, more factors need to be included to explore the association. Levels of inflammatory markers, alcohol intaking, and medical history (e.g. diabetes, hypertension, dyslipidemia, and hyperuricemia) may play a role in the decreased bone mineral density (BMD).

Many studies have reported on the prevalence of different severities of bone loss in rheumatic patients^{5 10 19 23 24}. However, the results are often presented in the form of one specific disease type comparing with RA and healthy subjects, such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and systemic sclerosis (SSc), instead of as a general rheumatic population. In 2016, a cross-sectional study in the South Korean reported the frequency of OP in the RA population was 46.8%²⁵. In 2017, a Canada retrospective study revealed the occurrence of ‘score lower than expected range for age’, osteopenia and OP among 286 patients with SLE was 17.3%, 12.3% and 43.2%, respectively²³. A French comparative study enrolled 71 patients with SSc and 139 patients with RA showed a high prevalence of OP (30%), was increased compared to healthy controls and similar to RA group (32%) ⁶.

There is still insufficient data on the general prevalence of combining with bone loss in diverse rheumatic diseases with a large sample size in China. Almost studies published already were about a single disease. For increasing physician’s awareness of bone loss in rheumatic patients so as to improve early diagnosis in order to ease the social economic burden, we primarily sought to determine the prevalence of the impaired bone mass in patients with rheumatism and further investigate the potential risk factors by conducting a cross-sectional survey in four hospitals in different districts in Southern China: Third Affiliated Hospital of Sun Yat-sen University, Ganzhou Municipal Hospital, Fujian General Hospital, and the Shantou Second General Hospital. The principal center was the Third Affiliated Hospital, Sun Yat-sen University.

Study design, sample size and population

The analytical cross-sectional study design was carried out, and rheumatic in-patients were consecutively recruited considering individual classification criteria from the rheumatism departments in four hospitals from May 2017 to August 2018. We also contemporarily recruited healthy subjects who were free from rheumatic diseases and selected randomly from applicants for health checks in the same hospital. The ethical approval was obtained from the Ethics Committee of the Third Affiliated Hospital, Sun Yat-sen University, and all participants provided informed consent for publication of their clinical details. Patients who were diagnosed with (1) rheumatoid arthritis (RA); (2) osteoarthritis (OA); (3) systemic lupus erythematosus (SLE); (4) systemic sclerosis (SSc); (5) ankylosing spondylosis (AS); (6) primary Sjogren syndrome (pSS); (7) gout; (8) mixed connective tissue disease (MCTD) ; we also excluded (1) pregnant; (3) with malignant tumor and/or receiving chemotherapy; (4) aged under 18; (5) refusing to write informed consent. A systematic sampling design was used to select the participants. The sample sizes were estimated by PASS 15 software (https://www.ncss.com), with the statistical power (1-β) set 0.90, type I error (α) set 0.05 and assuming that the prevalence of complicating with OP was 35% among rheumatic patients and 20%²⁶ among healthy controls. The software calculated that a total sample size of at least 1653 would suffice. To ensure adequate events of each group, we finally recruited 1398 patients and 302 healthy controls (HC), totally 1700 participants for this study.

Data collection, procedures, and tools

A standardized five-part questionnaire was designed to collect data. The first part of this questionnaire contained demographic information such as age, gender, height, weight, menopausal status, etc. The second part focused on medical history, diabetes mellitus (type 2), hypertension (primary or secondary), dyslipidemia and hyperuricemia. Part three consisted of the patient’s lifestyle habits including drinking and smoking and medication history. All variables in part two were dichotomous except conventional disease-modifying antirheumatic drugs (cDMARDs), which was an ordinal one; and part four of the questionnaire consisted of biochemical examinations. Detailed results of BMD test were recorded in the last part of the questionnaire.

The procedures of collection were in two steps. Participants filled in the first part of the questionnaire after admission. The other parts were completed by the trained physician according to the patients’ medical records or the HC reports after the patient had finished the blood test and BMD test at the same hospital.

Blood samples and DXA tests

Blood samples were analyzed by standard laboratory techniques at the participating hospitals. Fresh blood samples were collected from each patient after the patient had been admitted, included detailed concentrations of blood calcium, serum phosphate, serum 25(OH)D3, serum creatine (sCr) and serum uric acid (sUA), c-reactive protein level (CRP), erythrocyte sedimentation rate (ESR) and plasm complement component 4 (C4) . Blood lipid examination was also performed with no detail showing in our study but finally diagnosis.

Statistics After the blood samples had been taken, the patients were taken to the nuclear medicine department for bone mass density then assessed by dual-energy X-ray absorptiometry (DXA; Hologic Discovery A densitometer, Badford, MA, USA) at the lumbar spine L2~ L4(anterior-posterior view), femoral neck and total hip.

Definitions

Body mass index (BMI) was calculated by dividing body weight by the square of height in meters (kg/m²). According to the definition of by WHO, BMI was categorized as underweight, normal, overweight and obese in the Chinese population when the individual had a BMI of <18.5, ≥18.5 – <24, and ≥24 – <28, ≥28 respectively^{27 28}. Cigarette and alcohol consumption was further described as former/current smokers and non-smokers; regular or never/seldom drinking.

Meditation history of participants defined as follow: (1) those who have consecutively taken orally or took GC ≥3 months¹⁸ in the last one year before the day of BMD examination were ‘former or current chronic therapy of oral GC’; (2) those who had a history of consecutively taking cDMARDs ≥1 months, or used biological DMARDs (bDAMRDs) in the last one year were cDMARDs and/or bDMARDs users; (3) those has regularly taken NSAIDs ≥1 month was ‘NSAIDs user’.

BMD was expressed in standard deviation (SD) from the mean of healthy age- and sex-matched people (the Z-score) and as the number of SD from the mean of healthy, young sex-matched people (the T-score). All procedures were performed in accordance with the manufacturer’s standardized analysis software for hip and spine BMD measurements. T-score is recommended for males ≥50-year-old and postmenopausal women, but Z-score is preferable for males < 50-year-old and premenopausal women. Corresponding T-score or Z-score of each detective site was evaluated separately, but the lowest value of BMD in these measured sites was used. Final results met the WHO classification²⁹ and the 2005 International Society for Clinical Densitometry (ISCD) ³⁰ official positions.

Data processing and statistical analysis

Data were entered into Microsoft Office Excel (version 2016), and then two of the physicians rechecked and transferred this data to the R software (version 3.6.1) for analysis. Descriptive statistics for continuous variables included means and standard deviation (with normal distribution) and medians and interquartile ranges (with non-normal distribution), while categorical variables are presented as frequency and percentage. Group comparisons between the rheumatic patients and the healthy subjects were performed by Student’s two-tailed t-test for normally distributed continuous variables and Kruskal-Wallis H test for non-normally distributed ones. Pearson’s chi-square test or Fisher’s exact test was performed for categorical variables and Cochran-Armitage trend test for ordinal variables as appropriate. To determine the association between impaired BMD and rheumatic diseases and potential risk factors, we conducted logistic regression analyses to calculate the odds ratios (OR) and corresponding 95% confidence intervals (95% CI). A P-value < 0.05 was considered statistically significant. No imputations of missing values were performed. Comparison analyses were carried out by using R-3.6.1 for windows, package ‘compareGroups’ version 4.1³¹.

Baseline characteristics of patients

A total of 1398 patients and 302 healthy subjects participated in this study. RA group took up the largest proportion of patients (40.0%), followed by SLE (14.7%) and OA (14.2%), details are shown in Figure 1A. The basic demographic characteristics of the participants stratified by diagnosis are presented in Table 1. Age and gender compositions in some groups of patients differed from HC. The other general characteristics are shown in Table 2. Smoking and drinking were not frequent in our cohort (8.9% and 6.3%, respectively). Hypertension was the most complication (24.9%), followed by hyperuricemia (23.2%). Hypovitaminosis D was common in our cohort (68.6%).

Prevalence of BMD

As shown in Figure 1B, compared with healthy counterparts, either young rheumatic patients (those with Z-score, 34.3% vs 18.2%，P=0.045) or the elder (those with T-score, 92.7% vs 87.2%, P=0.017) had a statistical significance of higher prevalence of bone loss.

The detailed prevalence of ‘score below than expected range of age’ is shown in Figure 1C. Patients with AS (53.9%, P<0.001) and SLE (39.6%, P=0.034) have a significant higher occurrence of bone loss, compared with HC (18.2%).

Prevalence of varying degrees of bone loss among men aged ≥ 50 and postmenopausal women is shown in Figure 1D. It was obviously higher in patients with RA (P for trend <0.001), OA (P for trend =0.02) and SLE (P for trend =0.011), but lower in gout (P for trend = 0.001) compared with healthy peers.

The odds ratio for bone loss in rheumatic patients

Figure 2-4 shows the relationships among the impaired BMD and variables in rheumatic patients compared with the healthy group, using an age-, gender- and BMI-adjusted logistic regression model. Results showed young patients with SLE gained the highest risk, reached about 6.5-fold and followed by AS (5.6-fold). In patients classified by T-score, namely men aged 50 or over and postmenopausal women, RA (4.5-fold) and SLE (2.8-fold) patients had both greater risk of osteopenia and osteoporosis. Patients with AS and SSc obtained the highest risk of osteoporosis, similarly 5 times higher. Notwithstanding, in patients with OA, pSS, gout, and MCTD, no significant risk of any sort of bone loss was found.

We next aimed to explore risk factors that account for bone loss among rheumatic patients, stratified by age groups (i.e. scoring methods). What shown in Table 3 is that underweight, hypovitaminosis D and elevated CRP had negative effects on bone mass in young rheumatic patients; however, increased age, female, regular drinking were protective factors for bone loss.

In those scored with T-score, longer disease duration, obese, GC therapeutic history, hypovitaminosis D and elevated CRP were both associated with osteopenia and osteoporosis (shown in Table 4). Apart from the above-mentioned, increased age, low BMI, female, hyperuricemia and elevated ESR were significantly associated with osteoporosis, and dyslipidemia was further found relative to osteopenia.

Prevalence of bone loss and odd ratios

In this multi-central, cross-sectional study with age-stratification, we compared the frequency and odd ratios of reduced BMD in all rheumatic patients with healthy counterparts, and examined risk factors for bone loss in patients, aimed to help prevent and efficiently treat less-heeded bone loss. No contradiction was found in our study on the prevalence of bone loss with previous literature for some rheumatic diseases: for RA patients, the prevalence of ‘score below the expected range for age’ was reported 7.8%~18% ^{5 32}, and osteopenia and osteoporosis was 46.8%~55.7%^{25 33}; a retrospective study in Spain in 2010 showed a high prevalence of osteopenia (average 36.9%) among 105 female patients with SLE³⁴. Bone loss was found in 5%~44% patients with AS¹¹. The risk of OP in SSc was reported closely analogous to RA²⁴, and the occurrence of OP was 51.1%³⁵.

For increasing the comparability, multivariate logistic regression analyses found the adjusted odds ratios of ‘score below the expected range for age’ in patients with SLE and AS gained the highest, 6.5 times and 5.6 times higher risk, respectively. Patients with RA and SLE achieved a higher risk of osteopenia, achieving 4.5-fold and 2.8-fold respectively. Moreover, the strongest association with osteoporosis was found in AS, reaching 5.8 times higher.

OA, pSS, gout, and MCTD were not discovered related to higher risk of bone loss in our study. Except for the small sample size, a plausible scenario could be in the follows. OA is an age strongly-related degenerative disease, and BMI has opposing effect on OA and OP; also, local inflammation caused by mechanical injury, rather than systemic one caused by autoimmunology, is its salient feature³⁶. Whether pSS would gain higher prevalence of OP or osteopenia is still uncertain¹²，and in present study were mostly in early-onset and untreated. That might be another reason why risk of bone loss in these patients didn’t increase. A protective effect of uric acid (UA) on lumbar spine BMD has been reported in male patients³⁷ and hypothesized its potent antioxidant effect or via its interaction with the vitamin D/parathyroid hormone pathway³⁸, but high levels of serum UA (sUA) could cause oxidative stress and microinflammation as a pro-oxidant³⁹; the role that high sUA/gout plays in OPF is also paradoxical ^40-42. In our study, likewise, hyperuricemia showed a positive effect on OP.

Risk factors for bone loss in different age groups of rheumatic patients

The well-known association^1-3 of elder age, female and underweight (BMI <18.5 Kg/㎡) were also found associated with OP in our study, similar to the reported^{19 25 43}. But the contrast was found in ‘score below the expected range for age’. It might be attributed to more than half of AS patients were young male (68.8%) in our study, who were strongly related to impaired BMD, and in female patients, estrogen has direct effects on osteocytes, osteoclasts, and osteoblasts, leading to inhibition of bone resorption and maintenance of bone formation^{3 44}. Obesity was found even as a protective factor for BMD. Dyslipidemia was found a protective factor for osteopenia, probably it is one of the results of obesity; the association between lipid profiles and osteoporosis is still uncertain⁴⁵. Post-menopause is well-documented risk factor for OP, owing to low level of estrogen， and 2.1 times higher risk than male peers were found in our study.

Disease duration is collinear with age and partially reflecting the therapeutic period of GC. Long-term GC therapy and high cumulative dose have been proved to be strongly related to OP and fragile fracture^{16 46}. In a previous South Korean study⁵ showed that evaluated cumulative GC dose did not correlate with reduced BMD in different detective sites but those who had a history of taking GCs. Likewise, our results showed a higher risk of osteopenia and OP upon chronic GC therapeutic history.

In addition, we found regular alcohol intake had a significant protective effect upon ‘score below the expected range for age’, after gender-adjustment, but no such effect found in cigarette consumption due to insufficient samples who exposed to cigarettes and undetailed daily and period of consumption in our study. A British study⁴⁷ on 651 young males showed that moderate alcohol intake perhaps benefited to BMD, but smoking was detrimental, even short duration of smoking.

Bone loss commonly occurs at any age in the setting of rheumatic diseases. Hypovitaminosis D, elevated CRP was probably commonly-associated with impaired BMD in all age groups of rheumatic patients.

OP: osteoporosis

BMI: body mass index

RD: rheumatic diseases

GC: glucocorticoid

BMD: bone mineral density

RA: rheumatoid arthritis

SLE: systemic lupus erythematosus

SSc: systemic sclerosis

OA: osteoarthritis

AS: ankylosing spondylosis

pSS: primary Sjogren syndrome

MCTD: mixed connective tissue disease

HC: healthy controls

cDMARDs: conventional disease-modifying antirheumatic drugs

sCr: serum creatine

sUA: serum uric acid

CRP: c-reactive protein level

ESR: erythrocyte sedimentation rate

C4: plasm complement 4

WHO: World health Organisation

ISCD: International Society for Clinical Densitometry

OR: odd ratio

95%CI: 95% confidence intervals

Compliance with ethics approval and consent to participate

The ethical approval was obtained from the Ethic Committee of the Third Affiliated Hospital of Sun Yat-sen University (Guangzhou, China). Informed consent was obtained from all individuals participating in this study.

Availability of data and materials

The data that support the findings of this study are available from hospital informational system of 1) Third Affiliated Hospital of Sun Yat-sen University, 2) Ganzhou Municipal Hospital, Ganzhou, Jiangxi Province, China, 3) Fujian Provincial Hospital, Fuzhou, Fujian Province, China, 4) Second Affiliated Hospital of Shantou University Medical College, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permissions.

Competing interests

The authors declare that they have no competing interests.

Funding

Special thanks to the supports from Guangdong Natural Science Funds for Distinguished Young Scholar (Grant No.2014A030306039), High-level personnel of special support program for Technology Innovative Talents and the Top Young of Guangdong Province (Grant No.2015TQ01R516), Distinguished Young Scholar Candidates Programme for The Third Affiliated Hospital of Sun Yat-Sen University and Pearl River Nova Program of Guangzhou (Grant No. 201610010005).

Authors’ contributions

ZRH: collected parts of data, analysing and manuscript. SMX: collected and processed data, result interpretation. Contributed equally to this study. HL, NWF, QYY and KQD: collected and processed parts of data, critical revision. ZML, JQ, and JRG: study conception, design, and project management. All authors read and approved the final manuscript.

Acknowledgement

We thank all patients involved in the study.

Authors' information

¹Division of Rheumatology, Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou 510630, People Republic of China.²Division of Rheumatology, Ganzhou Municipal Hospital, Ganzhou 341000, People Republic of China.³ Division of Rheumatology, Fujian Provincial Hospital, Fuzhou 350000, People Republic of China. ⁴Division of Rheumatology, Second Affiliated Hospital of Shantou University Medical College, Shantou 515000, People Republic of China.

Lane NE. Epidemiology, etiology, and diagnosis of osteoporosis. American journal of obstetrics and gynecology 2006;194(2 Suppl):S3-11. doi: 10.1016/j.ajog.2005.08.047 [published Online First: 2006/02/02]
Hossein-Nezhad A, Holick MF. Vitamin D for health: A global perspective. Mayo Clinic Proceedings 2013;88(7):720-55. doi: 10.1016/j.mayocp.2013.05.011
Black DM, Rosen CJ. Postmenopausal Osteoporosis. New England Journal of Medicine 2016;374(3):254-62. doi: 10.1056/NEJMcp1513724
Sapir-Koren R, Livshits G. Postmenopausal osteoporosis in rheumatoid arthritis: The estrogen deficiency-immune mechanisms link. Bone 2017;103:102-15. doi: 10.1016/j.bone.2017.06.020
Lee SG, Park YE, Park SH, et al. Increased frequency of osteoporosis and BMD below the expected range for age among South Korean women with rheumatoid arthritis. International Journal of Rheumatic Diseases 2012;15(3):289-96. doi: 10.1111/j.1756-185X.2012.01729.x
Avouac J, Koumakis E, Toth E, et al. Increased risk of osteoporosis and fracture in women with systemic sclerosis: A comparative study with rheumatoid arthritis. Arthritis Care and Research 2012;64(12):1871-187Avouac, J., Koumakis, E., Toth, E., Meunie. doi: 10.1002/acr.21761
Bultink IEM, Lems WF. Lupus and fractures. Current opinion in rheumatology 2016;28(4):426-32. doi: 10.1097/BOR.0000000000000290
Carli L, Tani C, Spera V, et al. Risk factors for osteoporosis and fragility fractures in patients with systemic lupus erythematosus. Lupus science & medicine 2016;3(1):e000098. doi: 10.1136/lupus-2015-000098 [published Online First: 2016/02/06]
Chen ZY, Kok VC, Hung GD, et al. Gout as a risk factor for osteoporosis: epidemiologic evidence from a population-based longitudinal study involving 108,060 individuals. Osteoporosis International 2018;29(4):973-85. doi: 10.1007/s00198-018-4375-2
Kim YH, Lee JS, Park JH. Association between bone mineral density and knee osteoarthritis in Koreans: the Fourth and Fifth Korea National Health and Nutrition Examination Surveys. Osteoarthritis and cartilage 2018;26(11):1511-17. doi: 10.1016/j.joca.2018.07.008 [published Online First: 2018/07/30]
Klingberg E, Lorentzon M, Mellström D, et al. Osteoporosis in ankylosing spondylitis - prevalence, risk factors and methods of assessment. Arthritis Research and Therapy 2012;14(3):R108-R08. doi: 10.1186/ar3833
Gravani F, Papadaki I, Antypa E, et al. Subclinical atherosclerosis and impaired bone health in patients with primary Sjogren's syndrome: Prevalence, clinical and laboratory associations. Arthritis Research and Therapy 2015;17(1):1-13. doi: 10.1186/s13075-015-0613-6
Bodolay E, Bettembuk P, Balogh A, et al. Osteoporosis in mixed connective tissue disease. Clin Rheumatol 2003;22(3):213-7. doi: 10.1007/s10067-003-0701-x [published Online First: 2003/09/25]
Gough AK, Lilley J, Eyre S, et al. Generalised bone loss in patients with early rheumatoid arthritis. Lancet 1994;344(8914):23-7. doi: 10.1016/s0140-6736(94)91049-9 [published Online First: 1994/07/02]
Gough A, Sambrook P, Devlin J, et al. Osteoclastic activation is the principal mechanism leading to secondary osteoporosis in rheumatoid arthritis. J Rheumatol 1998;25(7):1282-9. [published Online First: 1998/07/24]
Buckley L, Guyatt G, Fink HA, et al. 2017 American College of Rheumatology Guideline for the Prevention and Treatment of Glucocorticoid-Induced Osteoporosis. Arthritis & rheumatology (Hoboken, NJ) 2017;69(8):1521-37. doi: 10.1002/art.40137 [published Online First: 2017/06/07]
Axelsson KF, Nilsson AG, Lorentzon M. Alendronate and Hip Fracture in Patients Using Glucocorticoids—Reply. JAMA 2017;318(17):1712-12. doi: 10.1001/jama.2017.14295
Rentero ML, Amigo E, Chozas N, et al. Prevalence of fractures in women with rheumatoid arthritis and/or systemic lupus erythematosus on chronic glucocorticoid therapy Epidemiology of musculoskeletal disorders. BMC Musculoskeletal Disorders 2015;16(1):4-13. doi: 10.1186/s12891-015-0733-9
Wang DM, Zeng QY, Chen SB, et al. Prevalence and risk factors of osteoporosis in patients with ankylosing spondylitis: a 5-year follow-up study of 504 cases. Clinical and experimental rheumatology 2015;33(4):465-70. [published Online First: 2015/05/13]
Papageorgiou M, Dolan E, Elliott-Sale KJ, et al. Reduced energy availability: implications for bone health in physically active populations. European journal of nutrition 2018;57(3):847-59. doi: 10.1007/s00394-017-1498-8 [published Online First: 2017/07/20]
Kerr C, Bottomley C, Shingler S, et al. The importance of physical function to people with osteoporosis. Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA 2017;28(5):1597-607. doi: 10.1007/s00198-017-3911-9 [published Online First: 2017/03/08]
Compston J. Glucocorticoid-induced osteoporosis: an update. Endocrine 2018;61(1):7-16. doi: 10.1007/s12020-018-1588-2 [published Online First: 2018/04/25]
Cramarossa G, Urowitz MB, Su J, et al. Prevalence and associated factors of low bone mass in adults with systemic lupus erythematosus. Lupus 2017;26(4):365-72. doi: 10.1177/0961203316664597 [published Online First: 2016/08/16]
Kilic G, Kilic E, Akgul O, et al. Increased risk for bone loss in women with systemic sclerosis: a comparative study with rheumatoid arthritis. International journal of rheumatic diseases 2016;19(4):405-11. doi: 10.1111/1756-185X.12242
Lee JH, Sung YK, Choi CB, et al. The frequency of and risk factors for osteoporosis in Korean patients with rheumatoid arthritis. BMC Musculoskeletal Disorders 2016 doi: 10.1186/s12891-016-0952-8
Chen P, Li Z, Hu Y. Prevalence of osteoporosis in China: a meta-analysis and systematic review. BMC public health 2016;16(1):1039-39. doi: 10.1186/s12889-016-3712-7
Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363(9403):157-63. doi: 10.1016/s0140-6736(03)15268-3 [published Online First: 2004/01/17]
Wildman RP, Gu D, Reynolds K, et al. Appropriate body mass index and waist circumference cutoffs for categorization of overweight and central adiposity among Chinese adults. The American journal of clinical nutrition 2004;80(5):1129-36. doi: 10.1093/ajcn/80.5.1129 [published Online First: 2004/11/09]
Prevention and management of osteoporosis. World Health Organization technical report series 2003;921:1-164, back cover. [published Online First: 2004/08/06]
Hans D, Downs RW, Jr., Duboeuf F, et al. Skeletal sites for osteoporosis diagnosis: the 2005 ISCD Official Positions. Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry 2006;9(1):15-21. doi: 10.1016/j.jocd.2006.05.003 [published Online First: 2006/05/30]
Subirana I, Sanz H, Vila J. Building Bivariate Tables: The compareGroups Package for R. 2014 2014;57(12):16. doi: 10.18637/jss.v057.i12 [published Online First: 2014-03-13]
Oelzner P, Schwabe A, Lehmann G, et al. Significance of risk factors for osteoporosis is dependent on gender and menopause in rheumatoid arthritis. Rheumatology international 2008;28(11):1143-50. doi: 10.1007/s00296-008-0576-x
Oelzner P, Schwabe A, Lehmann G, et al. Significance of risk factors for osteoporosis is dependent on gender and menopause in rheumatoid arthritis. Rheumatology international 2008;28(11):1143-50. doi: 10.1007/s00296-008-0576-x [published Online First: 2008/05/01]
Monte TS, Ruiz JP, Torrente-Segarra V, et al. Prevalence of osteopenia and osteoporosis in women with systemic lupus erythematosus. Annals of the Rheumatic Diseases 2013;72(Suppl 3):A920-A21.
Souza RB, Borges CT, Takayama L, et al. Systemic sclerosis and bone loss: the role of the disease and body composition. Scandinavian journal of rheumatology 2006;35(5):384-7. doi: 10.1080/03009740600704296 [published Online First: 2006/10/26]
Geusens PP, van den Bergh JP. Osteoporosis and osteoarthritis: shared mechanisms and epidemiology. Current opinion in rheumatology 2016;28(2):97-103. doi: 10.1097/BOR.0000000000000256
Xiao J, Chen W, Feng X, et al. Serum uric acid is associated with lumbar spine bone mineral density in healthy Chinese males older than 50 years. Clinical interventions in aging 2017;12:445-52. doi: 10.2147/cia.S130690 [published Online First: 2017/03/11]
Chen W, Roncal-Jimenez C, Lanaspa M, et al. Uric acid suppresses 1 alpha hydroxylase in vitro and in vivo. Metabolism: clinical and experimental 2014;63(1):150-60. doi: 10.1016/j.metabol.2013.09.018 [published Online First: 2013/11/26]
Kang DH, Ha SK. Uric Acid Puzzle: Dual Role as Anti-oxidantand Pro-oxidant. Electrolyte & blood pressure : E & BP 2014;12(1):1-6. doi: 10.5049/ebp.2014.12.1.1 [published Online First: 2014/07/26]
Wang Y, Zhou R, Zhong W, et al. Association of gout with osteoporotic fractures. International Orthopaedics 2018;42(9):2041-47. doi: 10.1007/s00264-018-4033-5
Kim BJ, Baek S, Ahn SH, et al. Higher serum uric acid as a protective factor against incident osteoporotic fractures in Korean men: a longitudinal study using the National Claim Registry. Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA 2014;25(7):1837-44. doi: 10.1007/s00198-014-2697-2 [published Online First: 2014/03/29]
Kim SC, Paik JM, Liu J, et al. Gout and the Risk of Non-vertebral Fracture. Journal of Bone and Mineral Research 2017;32(2):230-36. doi: 10.1002/jbmr.2978
Omair MA, Pagnoux C, McDonald-Blumer H, et al. Low bone density in systemic sclerosis. A systematic review. J Rheumatol 2013;40(11):1881-90. doi: 10.3899/jrheum.130032 [published Online First: 2013/09/17]
Henes M, Froeschlin J, Taran FA, et al. Ovarian reserve alterations in premenopausal women with chronic inflammatory rheumatic diseases: impact of rheumatoid arthritis, Behcet's disease and spondyloarthritis on anti-Mullerian hormone levels. Rheumatology (Oxford) 2015;54(9):1709-12. doi: 10.1093/rheumatology/kev124 [published Online First: 2015/05/10]
Chen YY, Wang WW, Yang L, et al. Association between lipid profiles and osteoporosis in postmenopausal women: a meta-analysis. European review for medical and pharmacological sciences 2018;22(1):1-9. doi: 10.26355/eurrev_201801_14093 [published Online First: 2018/01/25]
Hartmann K, Koenen M, Schauer S, et al. Molecular Actions of Glucocorticoids in Cartilage and Bone During Health, Disease, and Steroid Therapy. Physiological reviews 2016;96(2):409-47. doi: 10.1152/physrev.00011.2015 [published Online First: 2016/02/05]
Eleftheriou KI, Rawal JS, James LE, et al. Bone structure and geometry in young men: the influence of smoking, alcohol intake and physical activity. Bone 2013;52(1):17-26. doi: 10.1016/j.bone.2012.09.003 [published Online First: 2012/09/19]

Table 1. Demographic characteristics of participants
	HC	RA	P	OA	P	SLE	P	SSc	P
	N=302	N=559		N=198		N=206		N=53
Age, years, median[IQR]	63.0 [53.2;72.8]	58.0 [50.0;66.0]	<0.001	61.0 [53.0;69.8]	0.188	45.0 [35.0;54.0]	<0.001	54.0 [45.0;59.0]	<0.001
Disease duration†, years, median[IQR]	NA	5.6 [2.0;12.0]	NA	4.0 [1.5;10.0]	NA	3.0 [1.0;7.0]	NA	3.5 [2.0;7.0]	NA
Age groups, years, n(%)			<0.001		0.008		<0.001		0.019
<30	19 (6.3)	10 (1.8)		2 (1.0)		37 (18.0)		2 (3.8)
31-39	9 (3.0)	24 (4.2)		4 (2.0)		38 (18.4)		5 (9.4)
40-49	25 (8.3)	105 (18.8)		29 (14.6)		50 (24.3)		10 (18.9)
≥50	249 (82.5)	420 (75.1)		163 (82.3)		81 (39.3)		36 (67.9)
BMI, Kg/㎡, mean(SD)	22.5 (3.7)	21.9 (3.5)	0.321	24.1 (4.0)	<0.001	22.0 (3.6)	0.085	21.8 (3.0)	0.935
Gender, female, n(%)	229 (75.8)	450 (80.5)	0.173	165 (83.3)	0.087	187 (90.8)	<0.001	38 (71.7)	0.676
Menopause status of female, n(%)			0.01		0.277		<0.001		0.067
Post-menopause	202 (88.2)	359 (79.8)		139 (84.2)		91 (48.7)		29 (76.3)
Early menopause, age≤45	31 (15.3)	48 (13.4)		18 (12.9)		18 (19.8)		4 (13.8)
	HC	AS	P	pSS	P	Gout	P	MCTD	P
	N=302	N=128		N=98		N=111		N=45
Age, years, median[IQR]	63.0 [53.2;72.8]	36.5 [27.0;45.2]	<0.001	54.5 [46.0;60.0]	<0.001	61.0 [48.0;72.0]	0.269	56.0 [46.0;62.0]	<0.001
Disease duration†, years, median[IQR]	NA	6.5 [3.0;12.0]	NA	1.2[0.6;2.3]	NA	6.0 [3.0;11.5]	NA	1.3 [0.5;4.5]	NA
Age groups, years, n(%)			<0.001		0.003		0.006		0.003
<30	19 (6.3)	42 (32.8)		5 (5.1)		5 (4.5)		2 (4.4%)
31-39	9 (3.0)	30 (23.4)		10 (10.2)		7 (6.31)		2 (4.4)
40-49	25 (8.3)	36 (28.1)		17 (17.3)		22 (19.8)		13 (28.9)
≥50	249 (82.5)	20 (15.6)		66 (67.3)		77 (69.4)		28 (62.2)
BMI, Kg/㎡, mean(SD)	22.5 (3.7)	22.0 (4.2)	0.865	21.3 (2.9)	0.094	24.1 (3.3)	0.003	21.6 (3.0)	0.79
Gender, female, n(%)	229 (75.8)	40 (31.2)	<0.001	91 (92.9)	0.001	18 (16.2)	<0.001	39 (86.7)	0.197
Menopause status of female, n(%)			<0.001		0.001		0.734		0.001
Post-menopause	202 (88.2)	9 (22.5)		66 (72.5)		17 (94.4)		26 (66.7)
Early menopause, age≤45	31 (15.3)	3(33.3)		5 (7.6)		5 (29.4)		7 (26.9)
*: Compared with healthy controls. †:correlated with age HC: healthy controls; RA: rheumatoid arthritis; OA: osteoarthritis; SLE: systemic lupus erythematosus; SSc: systemic scleroderma; AS: ankylosing spondylitis; pSS: primary Sjogren syndrome; MCTD: mixed connective tissue disease.

Table 2. General characteristics of participants
Characteristic	Results
Former or current smoking, n(%)	151 (8.9)
Always drinking, n(%)	107 (6.3)
Medical history, n(%)
Diabetes Mellitus	203 (11.9)
Hypertension	424 (24.9)
Dyslipidemia	284 (16.7)
Hyperuricemia	395 (23.2)
Femoral head necrosis	28 (1.6)
Osteoporotic fracture	75 (4.4)
Medication history, yes, n(%)
Former or current chronic oral Glucocorticoid therapy	713 (41.9)
NSAIDs	799 (47.0)
cDMARDs
1 Type	261 (15.5)
2 Types	336 (20.0)
3 Types	124 (7.4)
bDMARDs	112 (6.6)
Blood calcium level, mean (SD)	2.6 (7.8)
Serum phosphate level, mean (SD)	1.3(2.6)
Serum creatinine level, median [IQR]	62.0 [53.0, 75.3]
Serum Uric acid level, n(%)
<360	1068 (63.6)
360-419	249 (14.8)
420~539	243 (14.5)
≥540	118 (7.0)
CRP, median [IQR]	7.1 [1.5, 30.9]
ESR, median [IQR]	34.5 [15.0, 66.0]
Serum 25(OH)D3 level, mean (SD)	64.0 (26.3)
Hypovitaminosis D, n(%)	1167 (68.6)
Elevated inflammatory markers, n(%)
ESR	972 (63.8)
CRP	729 (47.8)
NSAIDs: non-steroidal anti-inflammatory drugs; cDMARDs: conventional disease-modifying anti-rheumatic drugs; bDMARDs: biological disease-modifying anti-rheumatic drugs; ESR: erythrocyte sedimentation rate; CRP: c-reactive protein

Table 3. Odd ratios of variables in rheumatic patients with ‘score below the expected range for age’
Variables	‘Score below the expected range for age’
Variables	OR (95% CIs)	P
Age	0.95 [0.93;0.97]	<0.001
Disease duration†	1.02 [0.99;1.06]	0.943
BMI, Kg/㎡
<18.5 (underweight)	3.98 [2.35;6.86]	<0.001
18.5-23.9 (normal)	Ref.	Ref.
24-27.9 (overweight)	0.91 [0.53;1.52]	0.719
≥28（obese)	0.69 [0.28;1.52]	0.374
Gender, compared with male
Female	0.41 [0.27;0.61]	<0.001
Medical history*
Diabetes Mellitus	0.67 [0.21;1.79]	0.453
Hypertension	1.60 [0.71;3.48]	0.243
Dyslipidemia	0.87 [0.45;1.61]	0.664
Hyperuricemia	1.33 [0.82;2.14]	0.239
Former or current smokers**	0.69 [0.41;1.13]	0.142
Regular drinking**	0.40 [0.19;0.75]	0.008
Former or current chronic therapy of oral GC	0.86 [0.58;1.26]	0.054
Hypovitaminosis D	1.57 [1.04;2.42]	0.033
CRP elevated	1.68 [1.15;2.48]	0.008
ESR elevated	1.37 [0.93;2.05]	0.113

Table 4. Odd ratios of variables in rheumatic patients with osteopenia and osteoporosis
Variables	Osteopenia			Osteoporosis
	OR (95% CIs)	P	OR (95% CIs)			P
Age	1.02 [1.00;1.04]	0.116	1.08 [1.05;1.10]		<0.001
Disease duration†	1.05 [1.00;1.09]	0.032	1.04 [1.00;1.09]		0.031
BMI, Kg/㎡
<18.5 (underweight)	1.10 [0.39;3.98]	0.871	3.00 [1.18;10.3]		0.019
18.5-23.9 (normal)	Ref.	Ref.	Ref.		Ref.
24-27.9 (overweight)	0.62 [0.38;1.01]	0.053	0.33 [0.20;0.53]		<0.001
≥28（obese)	0.45 [0.23;0.89]	0.024	0.23 [0.12;0.46]		<0.001
Gender, compared with male
Female	1.36 [0.84;2.15]	0.204	2.13 [1.34;3.33]		0.002
Menopause status
Post-menopause	Ref.	Ref.	Ref.		Ref.
Early menopause, age ≤45	2.42 [0.93;8.46]	0.073	3.01 [0.86;20.6]		0.091
Medical history*
Diabetes Mellitus	0.62 [0.35;1.13]	0.11	0.65 [0.38;1.17]		0.135
Hypertension	0.96 [0.58;1.59]	0.858	0.69 [0.42;1.13]		0.13
Dyslipidemia	0.57 [0.34;0.97]	0.033	0.62 [0.38;1.04]		0.065
Hyperuricemia	0.71 [0.41;1.24]	0.216	0.41 [0.24;0.72]		0.002
Former or current smokers**	0.94 [0.55;1.64]	0.82	1.02 [0.60;1.78]		0.936
Regular drinking**	0.87 [0.49;1.59]	0.629	0.94 [0.54;1.71]		0.836
Former or current chronic therapy of oral GC	1.87 [1.17;3.05]	0.008	1.95 [1.24;3.14]		0.003
Hypovitaminosis D	1.41 [0.91;2.19]	0.125	2.00 [1.30;3.06]		0.002
CRP elevation	1.61 [1.01;2.61]	0.045	1.85 [1.18;2.95]		0.007
ESR elevation	1.39 [0.88;2.21]	0.159	2.21 [1.41;3.47]		0.001

Prevalence and risk factors for bone loss in Southern Chinese with rheumatic diseases

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Methods

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Tables

Status:

Journal Publication

Version 1