|Year : 2021 | Volume
| Issue : 4 | Page : 395-401
Prevalence, risk assessment, and predictors of osteoporosis among chronic obstructive pulmonary disease patients
Ahmad Naoras Bitar1, Syed Azhar Syed Sulaiman2, Irfhan Ali Bin Hyder Ali3, Amer Hayat Khan1
1 Department of Clinical Pharmacy, Universiti Sains Malaysia, Gelugore, Malaysia
2 Department of Clinical Pharmacy, Universiti Sains Malaysia, Gelugore; Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Jln Tun Hamdan Sheikh Tahir, Kepala Batas, Pulau Pinang, Malaysia
3 Department of Pulmonary, Penang General Hospital, Georgetown, Malaysia
|Date of Submission||19-Apr-2021|
|Date of Decision||27-Jul-2021|
|Date of Acceptance||31-Jul-2021|
|Date of Web Publication||20-Oct-2021|
Dr. Ahmad Naoras Bitar
Department of Clinical Pharmacy, Universiti Sains Malaysia, Gelugor 11800, Penang
Dr. Amer Hayat Khan
Department of Clinical Pharmacy, Universiti Sains Malaysia, Gelugor 11800, Penang
Source of Support: None, Conflict of Interest: None
The link between chronic obstructive pulmonary disease (COPD) and osteoporosis is unclear and yet to be understood. The study goals were to detect the prevalence of osteoporosis and investigate its predictors among COPD patients. This is a longitudinal study conducted in a tertiary care setting. During the study, patients' bone mineral density was checked, pulmonary parameters were recorded, and a risk assessment tool was validated. Based on T-score, more than 50% of subjects were osteoporotic. Spirometric parameters were significantly lower among osteoporotic patients. For the risk assessment tool, a cutoff point of 34 made the optimum balance between sensitivity and specificity (0.867 and 0.087, respectively) with a generated area under the curve of 0.934. Severe COPD patients were four times at higher risk of getting osteoporosis, forced expiratory volume (FEV) % predicted, and FEV/forced vital capacity was inversely related to the risk of osteoporosis. Patients with severe dyspnea had twice the risk of getting osteoporosis. Osteoporosis was prevalent among COPD patients, and severe COPD patients were at higher risk of getting osteoporosis.
Keywords: Chronic obstructive pulmonary disease, osteoporosis, predictors, prevalence, risk assessment
|How to cite this article:|
Bitar AN, Sulaiman SA, Ali IA, Khan AH. Prevalence, risk assessment, and predictors of osteoporosis among chronic obstructive pulmonary disease patients. J Adv Pharm Technol Res 2021;12:395-401
|How to cite this URL:|
Bitar AN, Sulaiman SA, Ali IA, Khan AH. Prevalence, risk assessment, and predictors of osteoporosis among chronic obstructive pulmonary disease patients. J Adv Pharm Technol Res [serial online] 2021 [cited 2022 Aug 13];12:395-401. Available from: https://www.japtr.org/text.asp?2021/12/4/395/328639
| Introduction|| |
Chronic obstructive pulmonary disease (COPD) is among the leading causes of death worldwide. It is a serious lung disease known for causing irreversible and progressive airway obstruction and severe breathing limitation that can lead to emergency intervention and hospital admission. COPD is often underdiagnosed. It is estimated that more than 300 million patients are suffering from COPD worldwide, and it is considered among the most common respiratory conditions in the world. It can be associated with an exaggerated chronic inflammatory response in the airways after contact with smoke, air pollution, noxious fumes or gases, and cigarette smoking.,
Osteoporosis is one of the comorbidities that can be associated with COPD. The national osteoporosis foundation in the United States of America has described osteoporosis as “a bone disease that occurs when the body loses too much bone, makes too little bone, or both.” The link between these two diseases remains unclear; a recent meta-analysis indicated that osteoporosis is more prevalent among COPD patients than anticipated. Osteoporosis can be asymptomatic; the low bone mineral density (BMD) among osteoporotic patients increases the risk of fractures, most common of which are the wrist, hip, and spinal. The impaired capacity to move due to osteoporotic fractures was linked to a faster decline in COPD patients' pulmonary function.
The link between COPD and osteoporosis is unclear and yet to be understood. In this study, we tried to detect the prevalence of osteoporosis among COPD patients, and we clinically evaluated the cases. We also tested and validated a novel osteoporosis risk assessment tool designed to identify patients at high risk of osteoporosis.
| Methodology|| |
Study design and setting
This study is a longitudinal study conducted in a tertiary care setting in Malaysia. Participants were divided into groups with osteoporosis and without osteoporosis. Ethical approval for the study was obtained from the Medical Research and Ethics Committee, Ministry of Health Malaysia, with the following number: KKM/NIHEC/P19-528(11).
Cases that met the inclusion criteria were coded and shuffled by an independent staff, then samples were selected randomly, and then subjects were invited to participate in the study. A competent pulmonologist and an investigator clinically examined all recruited subjects. In the prerecruitment interview, written informed consent was obtained from all participants.
Tests and measurements
Data collection tool was used to collect patients' information, including demographics, socioeconomic data, medical history, and clinical test results: the modified Medical Research Council (mMRC) dyspnea scores, COPD assessment test scores, spirometry results [Supplementary File 1]. A well-trained nurse conducted the spirometry based on the American Thoracic Society guidelines; all recruited subjects were professionally diagnosed with COPD according to the latest GOLD guidelines. The patients' BMD was tested every visit using quantitative ultrasonography (QUS) at the calcaneus area (SONOST 3000, by OsteoSys Co., Ltd. Guro-Dong 152-848, Seoul, South Korea).
Clinical evaluation tool for bone health
A closed-ended risk assessment tool was used; then, patients' risk for osteoporosis was estimated based on a simple additive scoring system in the tool. This tool was divided into two sections; the first one consisted of 18 questions related to bone health, each of which carries specific points; from 1 to 3 (1: No, 2: I do not know/Not sure, 3: Yes), [Supplementary File 2]. The lowest possible is 18, while the highest possible score is 54. Based on the obtained scores in the first section, the cases were divided into two categories: 1 – Nonosteoporotic: (18–34) points, 2 – Osteoporotic: (35–54) points.
Validation of the designed tool
The designed tool's components and items were examined and evaluated by a panel of experts. Receiver operator characteristic analysis was conducted to determine the constructed diagnostic tool's sensitivity, specificity, and precision. Results were analyzed to determine the best cutoff point of the obtained scores using (SPSS 24, Inc., Chicago, IL, USA). The generated area under the curve (AUC) was used to evaluate the tool's overall performance compared to QUS results; AUC should not be ≤0.500.
Inclusion and exclusion criteria
All male COPD patients above 40 years old who visited the respiratory clinic or the ward were included in the study. Female patients were excluded because of the drastic postmenopausal hormonal effect on osteoporosis development and prognosis. Patients with other severe conditions that might significantly impact bone health were excluded (cancerous diseases, hepatic malfunction, kidney disease, Paget's disease, mastocytosis, osteogenesis imperfecta, and severe malabsorption), Patients with severe endocrinal disorders such as Addison's disease, Cushing's syndrome, and Graves' disease were also excluded. Patients who were already diagnosed with a bone disease or who were on bone treatment were excluded.
The statistical analysis was conducted using the latest version of the Statistical Package for the Social Sciences (SPSS) (Version 27.0; IBM corp.). Descriptive statistics were done. Chi-square test was performed for categorical variables and t-test to compare the means in continuous variables. Linear regression was conducted to examine the relationship between the common extensor tendon and T-score during the study. Logistic regression was performed to calculate the risk of having osteoporosis among COPD subjects. The adopted statistical significance cut point was at P < 0.05.
| Results|| |
The total number of invited subjects was 469. Ninety-three were reluctant, and we lost contact with 61 subjects, so 65 more were enrolled to compensate, and the total number was 380 participants [Figure 1]. Based on QUS T-score results, osteoporotic subjects accounted for 51.6% of patients. The overall mean ± standard deviation (SD) for patients' age was 65.4 ± 10.04. The overall mean ± SD for patients' body mass index (BMI) was 23.32 ± 5.43 [Table 1].
|Figure 1: Flow chart of search, screening, and recruitment of subjects in the study|
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Chronic obstructive pulmonary disease among osteoporotic and nonosteoporotic patients
The overall mean SD of forced expiratory volume (FEV) 1% predicted and FEV1/forced vital capacity (FVC) ratios showed no significant difference between the baseline and final follow-up. However, during the study, both were significantly lower in osteoporotic subjects compared to the nonosteoporotic ones (FEV1% pred [baseline]: 51.45 ± 14.8 vs. 61.85 ± 16.1, FEV1% pred [final]: 51.13 ± 14.6 vs. 61.79 ± 15.7, FEV1/FVC [baseline]: 62.9 ± 16.2 vs. 68.32 ± 15.6, and FEV1/FVC [final]: 62 ± 16 vs. 67.87 ± 15.1) [Table 2]. Patients' T-score was significantly lower in the last visit of the study, while the risk assessment tool showed higher scores among osteoporotic subjects.
|Table 2: Chronic obstructive pulmonary disease among osteoporotic and nonosteoporotic patients|
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The risk assessment tool
Most of the risk assessment tool components showed a significant association with osteoporosis [Figure 2]. The best cutoff point to optimize the tool's sensitivity and precision was between 34 and 35 points (34.5) of the score obtained. Where 86.7% of positive outcomes are correctly predicted or classified by the tool, while the 1-specificity at this point was 0.087, which means that around 9% of negative outcomes are incorrectly classified or identified as positive at this point [Figure 3]a. For the exact cutoff point, the precision was 0.914, and the recall was also 0.867 [Figure 3]b. The AUC for the conducted test was 93.4%, and the overall model quality was above 0.5 (0.91) [Figure 3]c.
|Figure 2: Osteoporosis risk factors. OST: Osteoporotic group, Non-OST: Non-osteoporotic group, *: P < 0.05, **: P < 0.01, ***: P < 0.001|
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|Figure 3: Risk assessment tool's validation (the clinical evaluation tool). (a) Receiver operator characteristic (sensitivity-specificity curve). (b) Precision-recall curve. (c) Overall model quality|
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Relationship between the risk assessment tool and quantitative ultrasonography's T-score
In [Figure 4]a, the data showed positive linearity and homoscedasticity. As the tool's scores go up, the T-score goes down with a statistically significant correlation (P < 0.01). The linear regression correlation was statistically significant between the tool's overall score and the overall T-score, r (378) = 0.832, P < 0.001. The bootstrapped 95 confidence interval (CI) for the slop to predict T-score from the evaluation score ranged from 0.1 to 0.12; thus, for each point increased score of the tool, the patients' T-score score increased by about 0.10–0.12 points. The Durbin–Watson statistics were 1.4, which meets the assumption of dependence; the normal p-p plot of the standardized residual of the performed regression showed that the dots generally line up around the slop, so we have normality of residuals [Figure 4]b.
|Figure 4: The relationship between the clinical evaluation tool and T-score. (a) Scatter plot of T-score by clinical evaluation plan score with total regression fit line. (b) Normal P-P plot of regression standardized residual|
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Predictors for osteoporosis among chronic obstructive pulmonary disease patients
Patients with severe COPD were four times at higher risk of getting osteoporosis (odds ratio [OR]: 3.917, 95 CI: [2.430–6.314], P < 0.001) [Table 3]. FEV1% predicted and FEV1/FVC ratio results were inversely statistically significant; the lower the spirometric values, the higher the risk of osteoporosis to predict osteoporosis (OR: 0.970, 95 CI: [0.954–0.986], P = 0.001 and OR: 0.984, 95 CI: [0.970–0.999], P = 0.035, respectively). Those who had more severe dyspnea were at higher risk of osteoporosis; the mMRC dyspnea scale demonstrated a reasonable predictability power (OR: 2.046, 95 CI: [1.122–3.733], P = 0.02).
| Discussion|| |
Osteoporosis was highly prevalent among COPD patients. According to a recent meta-analysis, the pooled prevalence of osteoporosis among COPD patient was 37.62%; however, looking at the included studies, we found that the range was wide from 14% up to 66%. Liao et al. found that osteoporosis was underdiagnosed among COPD patients. The FEV1% predicted and FEV1/FVC ratios were significantly lower among osteoporotic patients than the nonosteoporotic ones, similar to a recent Taiwanese study.
A significant association with osteoporosis has been observed in most of the risk factors. In Shanghai, they found that being underweight or malnourished and low-level activities were significantly associated with osteoporosis, which matches our findings. A recent study from Sweden indicated that smoking was associated with an increased risk of osteoporosis and fractures; however, in a regression meta-analysis, the researchers have reached inconclusive results regarding the effect of smoking. The significant risk factors for osteoporosis among COPD patients included low BMI, frequent exacerbations, the use of steroids, systemic inflammation, low vitamin D, lack of physical activities, and hyperthyroidism. Similar to our findings, among male subjects in Taiwan, a higher prevalence of osteoporosis was observed among COPD patients, and lower BMI was associated with osteoporosis; after binary regression, low BMI was an insignificant risk factor, which is in contrast with Chuealee et al. findings.
A few risk assessment tools for osteoporosis were developed in the past. Recent research has shown that most of these tools were lacking precision (ranging from 0.04 to 0.12), and the simple calculated osteoporosis risk estimation (SCORE) had the best balance between recall and precision among the tested tools, and the AUC was the highest (0.072–0.161). A study has tested FRAX without BMD, and they found its sensitivity to be 33.3% with a specificity of 86.4% and an AUC of 60% at a threshold of ≥9.3%, while the AUC in our findings was around 90%. Ettinger et al. found that FRAX was the predictability of fractures varied a lot. Although the addiction of BMD test results to the tool's calculations improved the FRAX risk estimate, it did little to improve its predictive performance.
Our results have shown that severe COPD patients were at higher risk of osteoporosis. Similarly, Graumam et al. found that patients with more advanced COPD stages were at higher risk of osteoporosis. Furthermore, in a longitudinal study, it has been noticed that the increased exacerbation rates were independently associated with the progression of osteoporosis among COPD patients. This was attributed to the exaggerated inflammatory response among COPD patients and increased hypoxia and oxidative stress, besides the imbalanced protease/antiprotease system.
| Conclusions|| |
Osteoporosis was prevalent among COPD patients. The risk assessment tool was sensitive, and severe COPD patients were at higher of getting osteoporosis.
The authors would like to thank the Director-General of Health Malaysia for permission to publish this paper. The authors acknowledge that this project was partially sponsored by Bridging/ Bridging-Incentive Grants by Universiti Sians Malaysia (Grant Number: 304.PFARMASI.6316508).
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Leap J, Arshad O, Cheema T, Balaan M. Pathophysiology of COPD. Crit Care Nurs Q 2021;44:2-8.
Bitar AN, Ghoto MA, Dayo A, Arain MI, Parveen R. Pathophysiological Correlation between Diabetes Mellitus Type-II & Chronic Obstructive Pulmonary Diseases. J Liaquat Uni Med Health Sci. 2017;16(01):41-8. doi: 10.22442/jlumhs.171610504.
Learn What Osteoporosis Is and What It's Caused by. In: National Osteoporosis Foundation. Akkawi I, Zmerly H. Osteoporosis: Current Concepts. Joints. 2018;6:122-7. doi: 10.1055/s-0038-1660790. PMID: 30051110; PMCID: PMC6059859. Available from: https://www.nof.org/patients/what-is-osteoporosis/
. [Last accessed on 2021 Mar 04].
Bitar AN, Syed Sulaiman SA, Ali IAH, Khan I, Khan AH. Osteoporosis among patients with chronic obstructive pulmonary disease: Systematic review and meta-analysis of prevalence, severity, and therapeutic outcomes. J Pharm Bioallied Sci 2019;11:310-20.
Ravn Jakobsen P, Hermann AP, Søendergaard J, Kock Wiil U, Myhre Jensen C, Clemensen J. The gap between women's needs when diagnosed with asymptomatic osteoporosis and what is provided by the healthcare system: A qualitative study. Chronic Illn 2021;17:3-16.
Kakoullis L, Sampsonas F, Karamouzos V, Kyriakou G, Parperis K, Papachristodoulou E, et al
. The impact of osteoporosis and vertebral compression fractures on mortality and association with pulmonary function in COPD: A meta-analysis. Joint Bone Spine. 2021;89:105249. doi: 10.1016/j.jbspin.2021.105249. Epub ahead of print. PMID: 34265476.
Liao KM, Chiu KL, Chen CY. Prescription patterns in patients with chronic obstructive pulmonary disease and osteoporosis. Int J Chron Obstruct Pulmon Dis 2021;16:761-9.
Graham BL, Steenbruggen I, Miller MR, Barjaktarevic IZ, Cooper BG, Hall GL, et al
. Standardization of spirometry 2019 update. An official american thoracic society and European respiratory society technical statement. Am J Respir Crit Care Med 2019;200:e70-88.
Fawcett T. An introduction to ROC analysis. Pattern Recogniti Lett 2006;27:861-74.
Lin CH, Chen KH, Chen CM, Chang CH, Huang TJ, Lin CH. Risk factors for osteoporosis in male patients with chronic obstructive pulmonary disease in Taiwan. PeerJ 2018;6:e4232.
Zhang Q, Cai W, Wang G, Shen X. Prevalence and contributing factors of osteoporosis in the elderly over 70 years old: An epidemiological study of several community health centers in Shanghai. Ann Palliat Med 2020;9:231-8.
Li H, Wallin M, Barregard L, Sallsten G, Lundh T, Ohlsson C, et al
. Smoking-induced risk of osteoporosis is partly mediated by cadmium from tobacco smoke: The MrOS Sweden study. J Bon Miner Res 2020;35:1424-9.
Chen YW, Ramsook AH, Coxson HO, Bon J, Reid WD. Prevalence and risk factors for osteoporosis in individuals with COPD: A systematic review and meta-analysis. Chest 2019;156:1092-110.
Chuealee W, Foocharoen C, Mahakkanukrauh A, Suwannaroj S, Pongchaiyakul C, Nanagara R. Prevalence and predictive factors of osteoporosis in Thai systemic sclerosis. Sci Rep 2021;11:9424.
Toh LS, Lai PS, Wu DB, Bell BG, Dang CP, Low BY, et al
. A comparison of 6 osteoporosis risk assessment tools among postmenopausal women in Kuala Lumpur, Malaysia. Osteoporos Sarcopenia 2019;5:87-93.
Crandall CJ. Risk assessment tools for osteoporosis screening in postmenopausal women: A systematic review. Curr Osteoporos Rep 2015;13:287-301.
Ettinger B, Ensrud KE, Blackwell T, Curtis JR, Lapidus JA, Orwoll ES, et al
. Performance of FRAX in a cohort of community-dwelling, ambulatory older men: The osteoporotic fractures in men (MrOS) study. Osteoporos Int 2013;24:1185-93.
Graumam RQ, Pinheiro MM, Nery LE, Castro CH. Increased rate of osteoporosis, low lean mass, and fragility fractures in COPD patients: Association with disease severity. Osteoporos Int 2018;29:1457-68.
Kiyokawa H, Muro S, Oguma T, Sato S, Tanabe N, Takahashi T, et al
. Impact of COPD exacerbations on osteoporosis assessed by chest CT scan. COPD 2012;9:235-42.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3]