Abstract
Purpose
Various risk factors are mentioned for osteoporosis, sarcopenia, and osteosarcopenia. Our aim is to assess the impacts of anti-diabetic drugs on these disorders.
Methods
To perform this study, the participants’ data was extracted from the Bushehr Elderly Health (BEH) program in Iran. Afterward, the data were categorized into three subgroups: osteoporosis, sarcopenia, and osteosarcopenia, based on WHO and European Working Group on Sarcopenia in Older People (EWGSOP-2) working group definitions. Demographic characteristics, anthropometric measures, past medical history, and current medications were recorded. Pearson chi-squared and simple/multiple logistic regression using Python (3.11.4) and R (4.3.1) programming software assessed the association between anti-diabetic agents and these bone disorders.
Results
Out of 1995 participants, 820, 848, and 404 had osteoporosis, sarcopenia, or osteosarcopenia, respectively. Among all types of anti-diabetic drugs, a significant protective association between osteoporosis and consumption of second-generation sulfonylureas was found; Adjusted Odd Ratio (AOR) = 0.65 ([95% CI: 0.45–0.94], p-value = 0.023). No associations were found between sarcopenia and consumption of anti-diabetic agents. A significant association was observed between using Meglitinides and the risk of osteosarcopenia; AOR = 4.98 ([95% CI: 1.5-16.55], p-value = 0.009).
Conclusion
In conclusion, a protective association between consumption of second-generation sulfonylureas and osteoporosis was found. Moreover, a positive association was found between the consumption of meglitinides and osteosarcopenia. However, to support these findings, further studies are recommended.
Graphical abstract
Keywords: Anti-diabetic Drugs, Osteoporosis, Sarcopenia, Osteosarcopenia, Elderly
Introduction
The prevalence of chronic diseases has increased due to the global megatrend of the aging population [1]. Musculoskeletal disorders are one of the predominant examples of these diseases, which are defined as determinant factors of morbidity and mortality among older adults. Osteoporosis is the most prevalent bone disorder, rising substantially in many parts of the world. Various studies have reported an osteoporosis prevalence of 18.3%. When measuring the global burden of osteoporosis in terms of Disability Adjusted Life Years (DALYs), it amounts to 5.8 million, constituting approximately 0.83% of the total burden caused by Non-Communicable Diseases (NCDs) worldwide [2–4]. Sarcopenia is another major musculoskeletal disorder. Based on a different study, the global prevalence of sarcopenia influences 10-16% of the elderly globally [5].
Osteoporosis occurs when osteoclast-mediated bone resorption exceeds osteoblast-mediated bone formation [6]. Various risk factors have been mentioned as the risk factors of osteoporosis. For example, aging, low body mass index (BMI), smoking, female sex, excessive alcohol intake, and history of either type 1 or 2 diabetes mellitus (DM) [7]. In addition, the risk of osteoporosis may rise by using some drugs, such as anticonvulsants, glucocorticoids, heparin, and some antidiabetic agents [8, 9]. On the other hand, muscular size, strength, and functional capacity also decline with age. Sarcopenia is the term used to describe these alterations [10]. Male sex, advanced age, malnutrition, a low body Mass Index (BMI), and diabetes are risk factors for sarcopenia [11]. Some medications, including statins, sulfonylureas, and glinides, have also been mentioned as risk factors for sarcopenia. Contrarily, Angiotensin-converting enzyme inhibitors (ACEI), Angiotensin Receptor Blocker (ARB), calcium and vitamin D, biguanides, and thiazolidinediones (TZD) were reported as beneficial factors for sarcopenia [12].
Osteosarcopenia is a new term that defines a combined syndrome of osteoporosis and sarcopenia. This syndrome mainly occurs in people over 60 [13, 14]. Various studies have reported that the risk of falls, fractures, and death is rising due to osteosarcopenia [15]. The global estimated prevalence of osteosarcopenia is approximately 21% [16]. Several causes are mentioned as the risk factors of osteosarcopenia, including female sex, older age, and past medical history of fracture [16]. It is argued whether the diabetes itself or anti-diabetic agents have a role in the occurrence of osteosarcopenia. It is reported that impaired β cell function may result in osteosarcopenia in old patients with diabetes. Because of this, the practical function of β cells may have a protective impact against osteosarcopenia, and preserving β cell function may have a preventive effect on patients with diabetes and osteosarcopenia [17].
Anti-diabetic agents’ impact on the musculoskeletal system is controversial among studies [18, 19]. Various studies were conducted regarding the impacts of anti-diabetics on osteoporosis. For Example, it was reported that metformin, an activator of AMP-activated protein kinase (AMPK), directly affected ontogenesis by stimulating proliferation and differentiation of osteoblast. Metformin can also be utilized as a treatment against osteoporosis [20].
Some other studies aimed at examining the impacts of anti-diabetics on sarcopenia. For instance, it was reported that sulfonylureas and glinides, which stimulate insulin secretion via inhabitations of the ATP-sensitive K+ channels, may enhance signals related to muscular atrophy [21]. This finding shed light on the negative impacts of sulfonylureas and glinides on sarcopenia. Furthermore, some studies mentioned that thiazolidinones, a ligand of peroxisome proliferator-activated receptors (PPARs), may reduce proteolysis and muscle atrophy by enhancing insulin sensitivity in liver tissue [12, 22].
In spite of the wide range of studies that have been carried out to assess the effects of anti-diabetics on osteoporosis and sarcopenia, no studies have been conducted regarding the impacts of this medication on osteosarcopenia.
For the past few decades, the prevalence of DM has increased significantly worldwide. The same trend is also seen in Iran, particularly among elderly population [23]. Due to the impacts of anti-diabetic agents on musculoskeletal disorders, we aimed to assess the effects of anti-diabetics on osteoporosis, sarcopenia, and osteosarcopenia in old patients with diabetes in Iran.
Methods
Study design and population
In this study, the cross-sectional data of 2426 participants from the second stage of the first phase (baseline) of Bushehr’s Elderly Health (BEH) program was investigated. The BEH program is a population-based cohort study aimed to assess NCDs’ prevalence, risk factors, and outcomes in people 60 years or older in Bushehr, Iran. It used multi-stage stratified cluster random sampling for selecting cases. The first stage of the first phase of the BEH program was carried out from March 2013 to October 2014. In this stage, the prevalence of cardiovascular risk factors was assessed among 3000 men and women aged ≥ 60 years (participation rate = 90.2%). The second stage of the first phase was implemented 2.5 years later on 2772 participants from the first stage. However, in this stage, 78 participants were excluded due to mortality, and 268 cases were not included because of the lack of follow-up. The second stage of the first phase of the BEH program was conducted to evaluate the risk factors of musculoskeletal and cognitive diseases and their incidences [1, 24].
Measurements and variables’ definition
In the current study, we gathered part of the data from the original questionnaires and measurements of the second stage of the first phase of the BEH program. For instance, sociodemographic information (sex, age, marital status, and educational status), lifestyle factors (current or past history of smoking, alcohol consumption, history of physical activity), medical history (diabetes, hypertension, history of fracture after age 45, hyperthyroidism, hypothyroidism, history of low back pain), physical examinations (height, weight, waist circumference (WC), walking speed, diastolic, and systolic blood pressure, and muscle strength), and medications (use/non-use of the drugs). For anthropometric measurements, patients took off their shoes and wore thin clothing. Based on standard protocols, a fixed stadiometer and a digital scale were utilized to measure patients’ weight and height. BMI was calculated by recruiting the formula weight (kg)/ [height (m)]2. WC was determined by measuring a point mid-way between the iliac crest and the lowest rib while standing. High WC was defined as waist circumference > 102 cm in males and > 88 cm in females [25]. In addition, a validated self-report questionnaire was used to determine the average physical activity level per day based on Metabolic Equivalent (MET) values, which were categorized in 3 groups, MET < 1: no activity, 1-1.39: sedentary, and active MET > 1.4 [26]. When assessing muscle power, the handgrip with the highest strength was measured three times for each hand with a digital dynamometer. To perform this test, while seated, participants flexed their elbows in 90∘, brought their forearms into the neutral position, and flexed their wrist dorsally [27].
In this study, we also included our main outcomes of interest, osteoporosis, sarcopenia, and osteosarcopenia [1, 21]. Bone Mineral Density (BMD), fat, and muscle mass were evaluated using Dual x-ray absorptiometry (DXA, Discovery W1, Hologic, Bedford, Virginia, USA). The patients had to be in the correct position while assessing BMD. BMD was measured at three levels, the lumbar spine, the total hip, and Femoral Neck. The lesser trochanter was visible in the BMD measurement at femoral neck. BMD assessment at lumbar region (L1-L4) included the ileum and T12 vertebrae and 12th rib while the spinous process was centered on the straight midline [28]. Walking speed (m/s) was measured manually by using a stopwatch. Low performance in both genders was defined as speed < 0.8 m/s [28]. Based on standard criteria of WHO and by considering young white women as reference group, osteoporosis was defined when the T Score was − 2.5 or below in DXA of BMD at least in one of these regions: lumbar spine, femoral neck, total hip, with/without history of fracture [29]. Appendicular skeletal Muscle Mass (ASM) was calculated to define as the sum of the muscle mass of the 4 limbs. Skeletal Muscle mass index was calculated by adjusting with height [28]. According to the European Working Group on Sarcopenia in Older People (EWGSOP-2), sarcopenia occurs when patients has both low muscle strength and muscle mass. The cutoff point of low muscle mass in Iranian population is Skeletal Muscle Mass Index SMI < 7Kg/m2 in men and less than 5.4 kg/m2 in women, while the cutoff value of muscle strength is below 26 kg in male and 18 kg for female, whereas low physical performance < 0.8 m/s in both gender [30, 31].
All participants with a fasting blood sugar (FBS) level > 126 mg/dl or HbA1C level > 6.5% defined as diabetics. Moreover, participants who were under-treatment of anti-diabetic were also classified as diabetics and included to current study [1]. For measuring FBS, enzyme (Glucose oxidase) colorimetric method was recruited by utilizing commercial kit (Pars Azmun). To evaluate HbA1C, Boranate affinity method was also recruited using CERA-STAT system (CERAGEM MEDISYS chungcheongnam-do, Korea). Since we aimed to assess the impacts of anti-diabetics on osteoporosis, sarcopenia, and osteosarcopenia, different types of anti-diabetic agents were included; insulins, alpha glucosidase inhibitors (AGI), biguanides, 2nd Generation sulfonylureas, TZD, meglitinides, and DDP-4In.
The proposal of current study reviewed and approved by the Research Ethics Committee of Endocrinology and Metabolism Research Institute (EMRI), Tehran University of Medical Sciences (TUMS), Tehran, Iran (IR. TUMS. EMRI. REC. 1401. 118). With regards to research ethics, all participants signed an informed consent.
Statistical analysis
Categorical variables were presented using frequency and percentage, while continuous variables were summarized using mean and standard deviation. To compare the general characteristics of participants between those with and without osteoporosis, sarcopenia, and osteosarcopenia, Pearson chi-squared test was used for categorical variables, and the two-sample independent t-test was used for continuous variables. It is important to note that for drug-related variables, disease (presence/absence) and drug administration (receiving/not receiving) were considered for the Pearson chi-squared test. Logistic regression was employed to evaluate the association of independent variables with diseases. Firstly, simple logistic regression was conducted to obtain the crude Odds Ratio (OR) for each independent variable. Independent variables with a potentially confounding effect based on the literature that have a p-value > 0.2 in the simple logistic regression analysis were not included to the multiple logistic regression analysis. Additionally, among collinear variables, the one that was clinically more relevant was chosen for inclusion in the multiple logistic regression analysis. Then the forward stepwise selection approach was used to determine the final multiple logistic regression model, representing the best-adjusted model with the least Akaike Information Criterion (AIC). A significance level of p-value < 0.05 was considered for statistical significance. Prevalent Odds Ratio were estimated with 95% confidence intervals (95% CI). All analyses were conducted using Python programming software version 3.11.4 with Statmodels library (https://www.python.org/), and R programming software version 4.3.1 (https://cran.r-project.org/) was employed for performing forward stepwise selection.
Results
Characteristics of participants
Among 2426 participants, 431 were excluded due to lack of information. On account of this, 1995 participants were included in the analysis. 1091 (54.69%) participants were female. The average age [mean ± Standard Deviation (SD)] of our patients was 69.41 ± 6.34, ranged 60–94 years old. The analysis was conducted on three subgroups based on the presence or absence of osteoporosis, sarcopenia, and osteosarcopenia. Participants with missing values regarding osteoporosis, sarcopenia, or osteosarcopenia were excluded from the analysis. As a result, 1984, 1942, and 1932 participants were assessed for osteoporosis, sarcopenia, and osteosarcopenia, respectively. Among participants, we found 820 individuals (41.33% (95% CI:39.16-43.5)) suffered from osteoporosis, 848 subjects (43.67% (95% CI:41.46-45.87)) had sarcopenia, and 404 individuals (20.91% (95% CI:19.1-22.72)) had osteosarcopenia.
Osteoporosis
The average age of patients with osteoporosis was 70.75 ± 6.86. We also found that osteoporosis was more prevalent among participants aged > 80 years old. Osteoporosis was more prevalent among female participants in comparison with male participants with (57.84% [ 95%CI:54,91%-60.77%]) and (21.16%[95%CI:18.49-23.84%]), respectively. It was also found that a significant number of patients with past medical history of fracture also suffered from osteoporosis with (55.03%[95%CI:50.52-59.54%]). The average BMI in patients with osteoporosis was 26.92 ± 5.29 kg/m2, and the average WC was 96.66 ± 13.00 cm. Moreover, osteoporosis was more prevalent among patients who had no physical activity with: (66.91%[95%CI:59.0-74.82%]). Only a third of patients with DM also had osteoporosis (33.66% [95%CI: 30.42-36.9%]). Among all types of anti-diabetics, Biguanides were the most prevalent medication consumed by our patients with osteoporosis: (19.88% [95% CI: 17.15-22.61%]), while, the less prevalent anti-diabetics were DPP-4I (0.61% [95%CI: 0.08-1.14%]), Table 1.
Table 1.
General characteristics of the osteoporotic patients
| Osteoporosis (Number, %, CI95%) | ||||
|---|---|---|---|---|
| Variables | No (n = 1164) | Yes (n = 820) | Total (n = 1984) | p-value |
| Age Group | ||||
|
60–64 65–69 70–74 75–79 =>80 |
334 (69.73%) (65.61–73.84) 466 (60.52%) (57.07–63.97) 185 (56.4%) (51.04–61.77) 111 (46.84%) (40.48–53.19) 68 (40.0%) (32.64–47.36) |
145 (30.27%) (26.16–34.39) 304 (39.48%) (36.03–42.93) 143 (43.6%) (38.23–48.96) 126 (53.16%) (46.81–59.52) 102 (60.0%) (52.64–67.36) |
479 (24.14%) (22.26–26.03) 770 (38.81%) (36.67–40.95) 328 (16.53%) (14.9-18.17) 237 (11.95%) (10.52–13.37) 170 (8.57%) (7.34–9.8) |
< 0.001* |
| Gender, Female | 460 (42.16%) (39.23–45.09) | 631 (57.84%) (54.91–60.77) | 1091 (54.99%) (52.8-57.18) | < 0.001* |
| Marital Status, Married | 997 (66.29%) (63.9-68.68) | 507 (33.71%) (31.32–36.1) | 1504 (75.81%) (73.92–77.69) | < 0.001* |
| Alcohol | ||||
|
No Yes |
1146 (58.5%) (56.32–60.68) 16 (76.19%) (57.97–94.41) |
813 (41.5%) (39.32–43.68) 5 (23.81%) (5.59–42.03) |
1959 (98.94%) (98.49–99.39) 21 (1.06%) (0.61–1.51) |
0.157 |
| Hypothyroidism | 113 (55.94%) (49.09–62.79) | 89 (44.06%) (37.21–50.91) | 202 (10.19%) (8.86–11.52) | 0.43 |
| Educational Status | ||||
|
Illiterate Primary School Diploma Academic |
290 (43.67%) (39.9-47.45) 414 (57.5%) (53.89–61.11) 342 (75.66%) (71.71–79.62) 118 (80.82%) (74.44–87.21) |
374 (56.33%) (52.55–60.1) 306 (42.5%) (38.89–46.11) 110 (24.34%) (20.38–28.29) 28 (19.18%) (12.79–25.56) |
664 (33.5%) (31.42–35.58) 720 (36.33%) (34.21–38.44) 452 (22.81%) (20.96–24.65) 146 (7.37%) (6.22–8.52) |
< 0.001* |
| Smoking | ||||
|
Never Past Current |
559 (62.74%) (59.56–65.91) 398 (56.53%) (52.87–60.2) 207 (53.49%) (48.52–58.46) |
332 (37.26%) (34.09–40.44) 306 (43.47%) (39.8-47.13) 180 (46.51%) (41.54–51.48) |
891 (44.95%) (42.76–47.14) 704 (35.52%) (33.41–37.63) 387 (19.53%) (17.78–21.27) |
0.003* |
| Diabetes Mellitus | 491 (64.1%) (60.7–67.5) | 275 (35.9%) (32.5–39.3) | 766 (38.67%) (36.52–40.81) | < 0.001* |
| Hypertension | 920 (59.43%) (56.99–61.88) | 628 (40.57%) (38.12–43.01) | 1548 (78.1%) (76.28–79.92) | 0.252 |
| Physical Activity (MET) | ||||
|
NO activity Sedentary Active |
45 (33.09%) (25.18-41.0) 850 (59.65%) (57.1–62.2) 269 (63.59%) (59.01–68.18) |
91 (66.91%) (59.0-74.82) 575 (40.35%) (37.8–42.9) 154 (36.41%) (31.82–40.99) |
136 (6.85%) (5.74–7.97) 1425 (71.82%) (69.85–73.8) 423 (21.32%) (19.52–23.12) |
< 0.001* |
| High WC | 879 (65.11%) (62.57–67.65) | 471 (34.89%) (32.35–37.43) | 1350 (68.11%) (66.06–70.16) | < 0.001* |
| Anti-diabetic agents | ||||
| AGI | 32.0 (64.0%) (50.7–77.3) | 18.0 (36.0%) (22.7–49.3) | 50.0 (2.52%) (1.83–3.21) | 0.5288 |
| Second Generation Sulfonylureas | 229.0 (67.55%) (62.57–72.54) | 110.0 (32.45%) (27.46–37.43) | 339.0 (17.09%) (15.43–18.74) | 0.0003* |
| Insulin | 51.0 (66.23%) (55.67–76.8) | 26.0 (33.77%) (23.2-44.33) | 77.0 (3.88%) (3.03–4.73) | 0.2088 |
| Biguanides | 306.0 (65.25%) (60.94–69.55) | 163.0 (34.75%) (30.45–39.06) | 469.0 (23.64%) (21.77–25.51) | 0.0011* |
| Thiazolidinediones | 30.0 (60.0%) (46.42–73.58) | 20.0 (40.0%) (26.42–53.58) | 50.0 (2.52%) (1.83–3.21) | 0.9616 |
| Meglitinides | 10.0 (58.82%) (35.43–82.22) | 7.0 (41.18%) (17.78–64.57) | 17.0 (0.86%) (0.45–1.26) | 1 |
| DDP-4In | 7.0 (58.33%) (30.44–86.23) | 5.0 (41.67%) (13.77–69.56) | 12.0 (0.6%) (0.26–0.95) | 1 |
| Other medications | ||||
| Calcium-Vitamin D | 129.0 (47.6%) (41.66–53.55) | 142.0 (52.4%) (46.45–58.34) | 271.0 (13.66%) (12.15–15.17) | 0.0001* |
| Supplements-Vitamins | 59.0 (54.63%) (45.24–64.02) | 49.0 (45.37%) (35.98–54.76) | 108.0 (5.44%) (4.45–6.44) | 0.4376 |
| Bisphosphonate | 14.0 (29.17%) (16.31–42.03) | 34.0 (70.83%) (57.97–83.69) | 48.0 (2.42%) (1.74–3.1) | 0.0001* |
| Statins | 466.0 (60.13%) (56.68–63.58) | 309.0 (39.87%) (36.42–43.32) | 775.0 (39.06%) (36.92–41.21) | 0.3123 |
| Salicylates | 580.0 (59.24%) (56.17–62.32) | 399.0 (40.76%) (37.68–43.83) | 979.0 (49.34%) (47.14–51.54) | 0.6401 |
| Fibric Acid | 62.0 (74.7%) (65.35–84.05) | 21.0 (25.3%) (15.95–34.65) | 83.0 (4.18%) (3.3–5.06) | 0.0035* |
| PPI | 74.0 (48.37%) (40.45–56.28) | 79.0 (51.63%) (43.72–59.55) | 153.0 (7.71%) (6.54–8.89) | 0.0091* |
| Glucocorticoids | 15.0 (37.5%) (22.5–52.5) | 25.0 (62.5%) (47.5–77.5) | 40.0 (2.02%) (1.4–2.63) | 0.0098* |
| Antiepileptic | 94.0 (49.74%) (42.61–56.86) | 95.0 (50.26%) (43.14–57.39) | 189.0 (9.53%) (8.23–10.82) | 0.0109* |
| Levothyroxine | 109.0 (55.61%) (48.66–62.57) | 87.0 (44.39%) (37.43–51.34) | 196.0 (9.88%) (8.57–11.19) | 0.4014 |
| Loop Diuretics | 31.0 (53.45%) (40.61–66.29) | 27.0 (46.55%) (33.71–59.39) | 58.0 (2.92%) (2.18–3.66) | 0.4938 |
| Alpha 5 Reductase Inhibitors | 57.0 (73.08%) (63.23–82.92) | 21.0 (26.92%) (17.08–36.77) | 78.0 (3.93%) (3.08–4.79) | 0.0118* |
| Beta Blockers | 522.0 (59.52%) (56.27–62.77) | 355.0 (40.48%) (37.23–43.73) | 877.0 (44.2%) (42.02–46.39) | 0.5223 |
| Alpha Blockers | 88.0 (72.73%) (64.79–80.66) | 33.0 (27.27%) (19.34–35.21) | 121.0 (6.1%) (5.05–7.15) | 0.0017* |
| Nitrates | 163.0 (54.88%) (49.22–60.54) | 134.0 (45.12%) (39.46–50.78) | 297.0 (14.97%) (13.4-16.54) | 0.1696 |
| NSAIDS | 134.0 (50.19%) (44.19–56.18) | 133.0 (49.81%) (43.82–55.81) | 267.0 (13.46%) (11.96–14.96) | 0.0031* |
| COX2 | 30.0 (48.39%) (35.95–60.83) | 32.0 (51.61%) (39.17–64.05) | 62.0 (3.12%) (2.36–3.89) | 0.1237 |
| ACEI | 167.0 (63.02%) (57.21–68.83) | 98.0 (36.98%) (31.17–42.79) | 265.0 (13.36%) (11.86–14.85) | 0.1395 |
| CCB | 232.0 (59.03%) (54.17–63.9) | 161.0 (40.97%) (36.1-45.83) | 393.0 (19.81%) (18.05–21.56) | 0.9153 |
| ARB | 335.0 (60.8%) (56.72–64.87) | 216.0 (39.2%) (35.13–43.28) | 551.0 (27.77%) (25.8-29.74) | 0.2529 |
WC Waist Circumference, AGI Alpha Glucosidase Inhibitors, DDP-4In Dipeptidyl Peptidase 4 Inhibitors, PPI Proton Pomp Inhibitors, NSAIDs Non-Steroidal-Anti-Inflammatory Drugs, COX2 Cyclooxygenase Inhibitors 2, ACEI Angiotensin Converting Enzyme Inhibitors, CCB Calcium Channel Blockers, ARB Angiotensin Receptor Blocker
* P significant in Pearson chi-squared test
Sarcopenia
The average age of patients with sarcopenia was 71.35 ± 6.86. Sarcopenia was more prevalent among participants over 80 years old (70.3% [95%CI: 63.3-77.3%]). Sarcopenia was less prevalent among women in comparison with men; (36.21% [95%CI:33.33-39.1%]), and (52.74%[95%CI:49.43-56.05%]), respectively. We also found that sarcopenia was more prevalent among current smoker with: (50.53% [95%CI: 45.5-55.55%]). The average of BMI and WC were 26.92 ± 5.29 kg/m2, and 93.47 ± 10.58 cm, respectively. Sarcopenia was more prevalent in patients without physical activity (59.06% [95%CI: 50.5-67.61%]). We also found that (40.89% [95% CI:37.35-44.43%]) of our patients with diabetes also had sarcopenia. It was also found that the majority of patients who utilized Meglitinides also had sarcopenia (58.82%[95%CI:35.43-82.22%]), Table 2.
Table 2.
Characteristics of the participants based on their sarcopenia status
| Sarcopenia (Number, %, CI95%) | ||||
|---|---|---|---|---|
| Variables | No (n = 1094) | Yes (n = 848) | Total (n = 1942) | p-value |
| Age Group | ||||
|
60–64 65–69 70–74 75–79 =>80 |
330 (70.66%) (66.53–74.79) 476 (63.38%) (59.94–66.83) 152 (47.2%) (41.75–52.66) 87 (36.71%) (30.57–42.85) 49 (29.7%) (22.73–36.67) |
137 (29.34%) (25.21–33.47) 275 (36.62%) (33.17–40.06) 170 (52.8%) (47.34–58.25) 150 (63.29%) (57.15–69.43) 116 (70.3%) (63.33–77.27) |
467 (24.05%) (22.15–25.95) 751 (38.67%) (36.51–40.84) 322 (16.58%) (14.93–18.23) 237 (12.2%) (10.75–13.66) 165 (8.5%) (7.26–9.74) |
< 0.001* |
| Gender, Female | 680 (63.79%) (60.9-66.67) | 386 (36.21%) (33.33–39.1) | 1066 (54.89%) (52.68–57.1) | < 0.001* |
| Marital Status, Married | 827 (56.18%) (53.65–58.72) | 645 (43.82%) (41.28–46.35) | 1472 (75.8%) (73.89–77.7) | 0.853 |
| Alcohol | ||||
|
No Yes |
836 (56.68%) (54.15–59.21) 251 (54.68%) (50.13–59.24) |
639 (43.32%) (40.79–45.85) 208 (45.32%) (40.76–49.87) |
1475 (76.27%) (74.37–78.16) 459 (23.73%) (21.84–25.63) |
0.485 |
| Hypothyroidism | 142 (72.45%) (66.19–78.7) | 54 (27.55%) (21.3-33.81) | 196 (10.09%) (8.75–11.43) | < 0.001* |
| Educational Status | ||||
|
Illiterate Primary School Diploma Academic |
343 (52.37%) (48.54–56.19) 401 (57.04%) (53.38–60.7) 267 (60.82%) (56.25–65.39) 83 (57.24%) (49.19–65.29) |
312 (47.63%) (43.81–51.46) 302 (42.96%) (39.3-46.62) 172 (39.18%) (34.61–43.75) 62 (42.76%) (34.71–50.81) |
655 (33.73%) (31.63–35.83) 703 (36.2%) (34.06–38.34) 439 (22.61%) (20.75–24.47) 145 (7.47%) (6.3–8.64) |
0.047* |
| Smoking | ||||
|
Never Past Current |
513 (58.7%) (55.43–61.96) 393 (57.12%) (53.42–60.82) 188 (49.47%) (44.45–54.5) |
361 (41.3%) (38.04–44.57) 295 (42.88%) (39.18–46.58) 192 (50.53%) (45.5-55.55) |
874 (45.01%) (42.79–47.22) 688 (35.43%) (33.3-37.55) 380 (19.57%) (17.8-21.33) |
0.009* |
| Diabetes Mellitus | 438 (59.11%) (55.57–62.65) | 303 (40.89%) (37.35–44.43) | 741 (38.24%) (36.07–40.4) | 0.065 |
| Hypertension | 868 (57.41%) (54.91–59.9) | 644 (42.59%) (40.1-45.09) | 1512 (77.86%) (76.01–79.7) | 0.083 |
| Physical Activity (MET) | ||||
|
NO activity Sedentary Active |
52 (40.94%) (32.39–49.5) 773 (55.41%) (52.8-58.02) 269 (64.05%) (59.46–68.64) |
75 (59.06%) (50.5-67.61) 622 (44.59%) (41.98–47.2) 151 (35.95%) (31.36–40.54) |
127 (6.54%) (5.44–7.64) 1395 (71.83%) (69.83–73.83) 420 (21.63%) (19.8-23.46) |
< 0.001* |
| High WC | 901 (68.41%) (65.9-70.92) | 416 (31.59%) (29.08–34.1) | 1317 (67.82%) (65.74–69.89) | < 0.001* |
| Anti-diabetic agents | ||||
| AGI | 27 (56.25%) (42.22–70.28) | 21 (43.75%) (29.72–57.78) | 48 (2.47%) (1.78–3.16) | 1 |
| 2nd Generation Sulfonylureas | 189 (56.93%) (51.6-62.25) | 143 (43.07%) (37.75–48.4) | 332 (17.1%) (15.42–18.77) | 0.858 |
| Insulin | 43 (59.72%) (48.39–71.05) | 29 (40.28%) (28.95–51.61) | 72 (3.71%) (2.87–4.55) | 0.639 |
| Biguanides | 275 (60.71%) (56.21–65.2) | 178 (39.29%) (34.8-43.79) | 453 (23.33%) (21.45–25.21) | 0.037* |
| Thiazolidinediones | 29 (59.18%) (45.42–72.95) | 20 (40.82%) (27.05–54.58) | 49 (2.52%) (1.83–3.22) | 0.794 |
| Meglitinides | 7 (41.18%) (17.78–64.57) | 10 (58.82%) (35.43–82.22) | 17 (0.88%) (0.46–1.29) | 0.308 |
| DPP-4I | 6 (54.55%) (25.12–83.97) | 5 (45.45%) (16.03–74.88) | 11 (0.57%) (0.23–0.9) | 1 |
| Other medications | ||||
| Calcium-Vitamin D | 165 (60.89%) (55.08–66.7) | 106 (39.11%) (33.3-44.92) | 271 (13.95%) (12.41–15.5) | 0.118 |
| Supplements-Vitamins | 55 (50.93%) (41.5-60.35) | 53 (49.07%) (39.65–58.5) | 108 (5.56%) (4.54–6.58) | 0.286 |
| Bisphosphonate | 28 (58.33%) (44.39–72.28) | 20 (41.67%) (27.72–55.61) | 48 (2.47%) (1.78–3.16) | 0.892 |
| Statins | 473 (62.48%) (59.03–65.93) | 284 (37.52%) (34.07–40.97) | 757 (38.98%) (36.81–41.15) | < 0.001* |
| Salicylates | 551 (57.28%) (54.15–60.4) | 411 (42.72%) (39.6-45.85) | 962 (49.54%) (47.31–51.76) | 0.433 |
| Fibric Acid | 57 (70.37%) (60.43–80.31) | 24 (29.63%) (19.69–39.57) | 81 (4.17%) (3.28–5.06) | 0.013* |
| PPI | 79 (51.97%) (44.03–59.92) | 73 (48.03%) (40.08–55.97) | 152 (7.83%) (6.63–9.02) | 0.297 |
| Glucocorticoids | 21 (50.0%) (34.88–65.12) | 21 (50.0%) (34.88–65.12) | 42 (2.16%) (1.52–2.81) | 0.497 |
| Antiepileptic | 99 (53.23%) (46.06–60.4) | 87 (46.77%) (39.6-53.94) | 186 (9.58%) (8.27–10.89) | 0.412 |
| Levothyroxine | 134 (70.53%) (64.04–77.01) | 56 (29.47%) (22.99–35.96) | 190 (9.78%) (8.46–11.11) | < 0.001* |
| Loop Diuretics | 29 (50.0%) (37.13–62.87) | 29 (50.0%) (37.13–62.87) | 58 (2.99%) (2.23–3.74) | 0.394 |
| Alpha 5 Reductase Inhibitors | 35 (46.67%) (35.38–57.96) | 40 (53.33%) (42.04–64.62) | 75 (3.86%) (3.01–4.72) | 0.109 |
| Beta Blockers | 506 (59.04%) (55.75–62.34) | 351 (40.96%) (37.66–44.25) | 857 (44.13%) (41.92–46.34) | 0.036* |
| Alpha Blockers | 56 (47.86%) (38.81–56.91) | 61 (52.14%) (43.09–61.19) | 117 (6.02%) (4.97–7.08) | 0.07 |
| Nitrates | 160 (55.94%) (50.19–61.7) | 126 (44.06%) (38.3-49.81) | 286 (14.73%) (13.15–16.3) | 0.937 |
| NSAIDs | 150 (57.25%) (51.26–63.24) | 112 (42.75%) (36.76–48.74) | 262 (13.49%) (11.97–15.01) | 0.799 |
| COX2 | 34 (55.74%) (43.27–68.2) | 27 (44.26%) (31.8-56.73) | 61 (3.14%) (2.37–3.92) | 1 |
| ACEI | 147 (55.89%) (49.89–61.89) | 116 (44.11%) (38.11–50.11) | 263 (13.54%) (12.02–15.06) | 0.93 |
| CCB | 224 (58.49%) (53.55–63.42) | 159 (41.51%) (36.58–46.45) | 383 (19.72%) (17.95–21.49) | 0.373 |
| ARB | 319 (58.96%) (54.82–63.11) | 222 (41.04%) (36.89–45.18) | 541 (27.86%) (25.86–29.85) | 0.161 |
WC Waist Circumference, AGI Alpha Glucosidase Inhibitors, DDP-4In Dipeptidyl Peptidase 4 Inhibitors, PPI Proton Pomp Inhibitors, NSAIDs Non-Steroidal-Anti-Inflammatory Drugs, COX2 Cyclooxygenase Inhibitors 2, ACEI Angiotensin Converting Enzyme Inhibitors, CCB Calcium Channel Blockers, ARB Angiotensin Receptor Blocker
* P significant in Pearson chi-squared test
Osteosarcopenia
The average age of participants with osteosarcopenia was 72.26 ± 7.15 years old. We found that osteosarcopenia was more prevalent among participants over 80 years old with (42.94% [95%CI: 35.35-50.54%]). Osteosarcopenia was significantly more prevalent among women in comparison with men with (25.9% [95% CI: 23.26-28.54%], and (14.87% [95%CI: 12.52-17.23%], sequentially. It was also found that osteosarcopenia were more prevalent among current smoker in comparison with non-smoker with (27.63% [95%CI:23.14-32.13%]) and (17.28% [95%CI: 14.77-19.8%]). Surprisingly, we found that osteosarcopenia was less prevalent among patients who had DM with: (16.64%[95%CI:13.96-19.33%]). Furthermore, among all patients who were assessed for osteosarcopenia, we found that the most prevalent anti-diabetics was Biguanides. Among all types of anti-diabetics, osteosarcopenia were more prevalent in patients who consumed meglitinides, Table 3.
Table 3.
Characteristics of the participants based on their osteosarcopenia status
| Osteosarcopenia (Number, %, CI95%) |
||||
|---|---|---|---|---|
| Variables | No (n = 1528) | Yes (n = 404) | Total (n = 1932) | p-value |
| Age Group | ||||
|
60–64 65–69 70–74 75–79 =>80 |
410 (88.36%) (85.44–91.28) 631 (84.13%) (81.52–86.75) 237 (74.06%) (69.26–78.86) 157 (66.81%) (60.79–72.83) 93 (57.06%) (49.46–64.65) |
54 (11.64%) (8.72–14.56) 119 (15.87%) (13.25–18.48) 83 (25.94%) (21.14–30.74) 78 (33.19%) (27.17–39.21) 70 (42.94%) (35.35–50.54) |
464 (24.02%) (22.11–25.92) 750 (38.82%) (36.65–40.99) 320 (16.56%) (14.91–18.22) 235 (12.16%) (10.71–13.62) 163 (8.44%) (7.2–9.68) |
< 0.001* |
| Gender, Female | 784 (74.1%) (71.46–76.74) | 274 (25.9%) (23.26–28.54) | 1058 (54.76%) (52.54–56.98) | < 0.001* |
| Marital Status, Married | 1202 (82.05%) (80.08–84.01) | 263 (17.95%) (15.99–19.92) | 1465 (75.83%) (73.92–77.74) | < 0.001* |
| Alcohol | ||||
|
No Yes |
1189 (81.05%) (79.04–83.06) 332 (72.65%) (68.56–76.73) |
278 (18.95%) (16.94–20.96) 125 (27.35%) (23.27–31.44) |
1467 (76.25%) (74.35–78.15) 457 (23.75%) (21.85–25.65) |
< 0.001* |
| Hypothyroidism | 164 (84.1%) (78.97–89.23) | 31 (15.9%) (10.77–21.03) | 195 (10.09%) (8.75–11.44) | 0.085 |
| Educational Status | ||||
|
Illiterate Primary School Diploma Academic |
453 (70.12%) (66.59–73.65) 557 (79.23%) (76.23–82.23) 388 (88.38%) (85.39–91.38) 130 (90.28%) (85.44–95.12) |
193 (29.88%) (26.35–33.41) 146 (20.77%) (17.77–23.77) 51 (11.62%) (8.62–14.61) 14 (9.72%) (4.88–14.56) |
646 (33.44%) (31.33–35.54) 703 (36.39%) (34.24–38.53) 439 (22.72%) (20.85–24.59) 144 (7.45%) (6.28–8.62) |
< 0.001* |
| Smoking | ||||
|
Never Past Current |
718 (82.72%) (80.2-85.23) 535 (78.22%) (75.12–81.31) 275 (72.37%) (67.87–76.86) |
150 (17.28%) (14.77–19.8) 149 (21.78%) (18.69–24.88) 105 (27.63%) (23.14–32.13) |
868 (44.93%) (42.71–47.15) 684 (35.4%) (33.27–37.54) 380 (19.67%) (17.9-21.44) |
< 0.001* |
| Diabetes Mellitus | 616 (83.36%) (80.67–86.04) | 123 (16.64%) (13.96–19.33) | 739 (38.31%) (36.14–40.48) | < 0.001* |
| Hypertension | 1196 (79.57%) (77.54–81.61) | 307 (20.43%) (18.39–22.46) | 1503 (77.8%) (75.94–79.65) | 0.361 |
| Physical Activity (MET) | ||||
|
NO activity Sedentary Active |
75 (59.52%) (50.95–68.09) 1103 (79.52%) (77.4-81.65) 350 (83.53%) (79.98–87.08) |
51 (40.48%) (31.91–49.05) 284 (20.48%) (18.35–22.6) 69 (16.47%) (12.92–20.02) |
126 (6.52%) (5.42–7.62) 1387 (71.79%) (69.78–73.8) 419 (21.69%) (19.85–23.53) |
< 0.001* |
| High WC | 1150 (87.72%) (85.94–89.5) | 161 (12.28%) (10.5-14.06) | 1311 (67.86%) (65.77–69.94) | < 0.001* |
| Anti-diabetic agents | ||||
| AGI | 39 (81.25%) (70.21–92.29) | 9 (18.75%) (7.71–29.79) | 48 (2.48%) (1.79–3.18) | 0.847 |
| Second Generation Sulfonylureas | 279 (84.29%) (80.37–88.21) | 52 (15.71%) (11.79–19.63) | 331 (17.13%) (15.45–18.81) | 0.013 |
| Insulin | 63 (87.5%) (79.86–95.14) | 9 (12.5%) (4.86–20.14) | 72 (3.73%) (2.88–4.57) | 0.101 |
| Biguanides | 381 (84.29%) (80.94–87.65) | 71 (15.71%) (12.35–19.06) | 452 (23.4%) (21.51–25.28) | 0.002* |
| Thiazolidinediones | 41 (83.67%) (73.32–94.02) | 8 (16.33%) (5.98–26.68) | 49 (2.54%) (1.84–3.24) | 0.534 |
| Meglitinides | 11 (64.71%) (41.99–87.42) | 6 (35.29%) (12.58–58.01) | 17 (0.88%) (0.46–1.3) | 0.244 |
| DPP-4I | 9 (81.82%) (59.03–100.0) | 2 (18.18%) (0.0-40.97) | 11 (0.57%) (0.23–0.9) | 1 |
| Other medications | ||||
| Calcium-Vitamin D | 200 (74.35%) (69.13–79.57) | 69 (25.65%) (20.43–30.87) | 269 (13.92%) (12.38–15.47) | 0.048* |
| Supplements-Vitamins | 78 (72.22%) (63.77–80.67) | 30 (27.78%) (19.33–36.23) | 108 (5.59%) (4.57–6.61) | 0.092 |
| Bisphosphonate | 30 (62.5%) (48.8–76.2) | 18 (37.5%) (23.8–51.2) | 48 (2.48%) (1.79–3.18) | 0.007* |
| Statins | 617 (81.83%) (79.08–84.58) | 137 (18.17%) (15.42–20.92) | 754 (39.03%) (36.85–41.2) | 0.021* |
| Salicylates | 760 (79.33%) (76.77–81.9) | 198 (20.67%) (18.1-23.23) | 958 (49.59%) (47.36–51.82) | 0.838 |
| Fibric Acid | 71 (87.65%) (80.49–94.82) | 10 (12.35%) (5.18–19.51) | 81 (4.19%) (3.3–5.09) | 0.072 |
| PPI | 110 (73.33%) (66.26–80.41) | 40 (26.67%) (19.59–33.74) | 150 (7.76%) (6.57–8.96) | 0.089 |
| Glucocorticoids | 26 (65.0%) (50.22–79.78) | 14 (35.0%) (20.22–49.78) | 40 (2.07%) (1.44–2.71) | 0.044* |
| Antiepileptic | 138 (74.59%) (68.32–80.87) | 47 (25.41%) (19.13–31.68) | 185 (9.58%) (8.26–10.89) | 0.137 |
| Levothyroxine | 156 (82.54%) (77.13–87.95) | 33 (17.46%) (12.05–22.87) | 189 (9.78%) (8.46–11.11) | 0.257 |
| Loop Diuretics | 45 (77.59%) (66.85–88.32) | 13 (22.41%) (11.68–33.15) | 58 (3.0%) (2.24–3.76) | 0.903 |
| Alpha 5 Reductase Inhibitors | 59 (78.67%) (69.4-87.94) | 16 (21.33%) (12.06–30.6) | 75 (3.88%) (3.02–4.74) | 1 |
| Beta Blockers | 689 (80.68%) (78.03–83.33) | 165 (19.32%) (16.67–21.97) | 854 (44.2%) (41.99–46.42) | 0.141 |
| Alpha Blockers | 97 (82.91%) (76.08–89.73) | 20 (17.09%) (10.27–23.92) | 117 (6.06%) (4.99–7.12) | 0.352 |
| Nitrates | 217 (76.41%) (71.47–81.35) | 67 (23.59%) (18.65–28.53) | 284 (14.7%) (13.12–16.28) | 0.261 |
| NSAIDs | 203 (77.78%) (72.73–82.82) | 58 (22.22%) (17.18–27.27) | 261 (13.51%) (11.99–15.03) | 0.632 |
| COX2 | 46 (75.41%) (64.6-86.22) | 15 (24.59%) (13.78–35.4) | 61 (3.16%) (2.38–3.94) | 0.577 |
| ACEI | 216 (82.44%) (77.84–87.05) | 46 (17.56%) (12.95–22.16) | 262 (13.56%) (12.03–15.09) | 0.176 |
| CCB | 304 (79.79%) (75.76–83.82) | 77 (20.21%) (16.18–24.24) | 381 (19.72%) (17.95–21.49) | 0.76 |
| ARB | 438 (81.72%) (78.44–84.99) | 98 (18.28%) (15.01–21.56) | 536 (27.74%) (25.75–29.74) | 0.09 |
WC Waist Circumference, AGI Alpha Glucosidase Inhibitors, DDP-4In Dipeptidyl Peptidase 4 Inhibitors, PPI Proton Pomp Inhibitors, NSAIDs Non-Steroidal-Anti-Inflammatory Drugs, COX2 Cyclooxygenase Inhibitors 2, ACEI Angiotensin Converting Enzyme Inhibitors, CCB Calcium Channel Blockers, ARB Angiotensin Receptor Blocker
* P significant in Pearson chi-squared test
Among all potentially confounding variables, those with p-value less than 0.2 in the simple logistic regression models were selected to enter into the multiple logistic regression models, and the forward stepwise method was used in the models fitting. Thus, the variables that were selected for inclusion in the multiple logistic regression model for osteoporosis, were age groups, educational status, smoking, physical activity, marriage, DM, sex, BMI, and consumption of second-generation sulfonylureas. For the multiple logistic regression of sarcopenia, age groups, physical activity, sex, and BMI were selected. Age groups, educational status, smoking, physical activity, sex, BMI, DM, and meglitinides consumption were considered for the osteosarcopenia model.
After adjusting for potential confounders, among all types of anti-diabetics, there was only a negative association between osteoporosis and consumption of 2nd generation sulfonylureas with Adjusted Odd Ratio (AOR) = 0.67 [CI 95%: 0.47–0.95], p-value < 0.001 (Table 4). None of the anti-diabetics had association with sarcopenia (Table 4). It was also found that among all types of anti-diabetics, there was solely an association between using meglitinides as anti-diabetics and increased risk of osteosarcopenia with AOR = 5.83 [CI 95%: 1.79–18.99], p-value = 0.003 (Table 4).
Table 4.
Association between anti-diabetic agents and osteoporosis, sarcopenia and osteosarcopenia based on logistic regression analysis
| Osteoporosis | Sarcopenia | Osteosarcopenia | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age Group | crude OR (CI95%) | p-value | AOR (CI95%) | p-value | crude OR (CI95%) | p-value | AOR(CI95%) | p-value | crude OR (CI95%) | p-value | AOR (CI95%) | p-value |
| 60–64 | Reference | |||||||||||
| 65–69 | 1.5 (1.18–1.92) | 0.001 | 1.39 (1.05–1.84) | 0.02* | 1.39 (1.09–1.78) | 0.009 | 1.41 (1.05–1.9) | 0.022* | 1.43 (1.01–2.02) | 0.041 | 1.26(0.85–1.88) | 0.255 |
| 70–74 | 1.78 (1.33–2.39) | < 0.001 | 1.57 (1.11–2.23) | 0.011* | 2.69 (2.0-3.62) | < 0.001 | 2.5 (1.76–3.56) | < 0.001* | 2.66 (1.82–3.88) | < 0.001 | 2.3(1.47–3.6) | < 0.001* |
| 75–79 | 2.61 (1.9–3.6) | < 0.001 | 2.13 (1.44–3.17) | < 0.001* | 4.15 (2.98–5.78) | < 0.001 | 3.41 (2.27–5.13) | < 0.001* | 3.77 (2.55–5.59) | < 0.001 | 2.54(1.56–4.15) | < 0.001* |
| =>80 | 3.46 (2.4–4.97) | < 0.001 | 1.99 (1.25–3.19) | 0.004* | 5.7 (3.87–8.41) | < 0.001 | 3.96 (2.43–6.46) | < 0.001* | 5.71 (3.75–8.7) | < 0.001 | 2.71(1.57–4.69) | < 0.001* |
| Gender | 0.2 (0.16–0.24) | < 0.001 | 0.14 (0.11–0.19) | < 0.001* | 1.97 (1.64–2.36) | < 0.001 | - | - | 0.5 (0.4–0.63) | < 0.001 | 0.23(0.16–0.32) | < 0.001* |
| Marital Status | 0.27 (0.22–0.34) | < 0.001 | 0.68 (0.52–0.89) | 0.005* | 1.03 (0.83–1.26) | 0.812 | - | - | 0.51 (0.4–0.64) | < 0.001 | - | - |
| Alcohol | 0.44 (0.16–1.21) | 0.111 | - | - | 0.69 (0.28–1.74) | 0.435 | - | - | 0.42 (0.1–1.8) | 0.242 | - | - |
| Hypothyroidism | 1.14 (0.85–1.52) | 0.396 | - | - | 0.46 (0.33–0.63) | < 0.001 | 0.73 (0.49–1.1) | 0.134 | 0.69 (0.46–1.03) | 0.071 | - | - |
| Educational Status | illiterate Reference | |||||||||||
| Primary School | 0.57 (0.46–0.71) | < 0.001 | 0.94 (0.73–1.21) | 0.625 | 0.83 (0.67–1.03) | 0.084 | - | - | 0.62 (0.48–0.79) | < 0.001 | 0.98(0.71–1.34) | 0.877 |
| Diploma | 0.25 (0.19–0.32) | < 0.001 | 0.61 (0.44–0.84) | 0.003* | 0.71 (0.55–0.91) | 0.006 | - | - | 0.31 (0.22–0.43) | < 0.001 | 0.6(0.39–0.93) | 0.021* |
| Academic | 0.18 (0.12–0.29) | < 0.001 | 0.62 (0.37–1.03) | 0.067 | 0.82 (0.57–1.18) | 0.288 | - | - | 0.25 (0.14–0.45) | < 0.001 | 0.62(0.3–1.26) | 0.184 |
| Smoking | Never Reference | |||||||||||
| Past | 1.29 (1.06–1.58) | 0.012 | 1.29 (1.01–1.65) | 0.038* | 1.07 (0.87–1.31) | 0.532 | - | - | 1.33 (1.04–1.72) | 0.026 | 1.35(0.98–1.85) | 0.063 |
| Current | 1.46 (1.15–1.86) | 0.002 | 1.4 (1.05–1.88) | 0.022* | 1.45 (1.14–1.85) | 0.003 | - | - | 1.83 (1.37–2.43) | < 0.001 | 1.53(1.07–2.18) | 0.02 |
| Diabetes Mellitus | 0.7 (0.58–0.84) | < 0.001 | 0.81 (0.57–1.14) | 0.223 | 0.84 (0.69–1.01) | 0.058 | 1.02 (0.71–1.45) | 0.928 | 0.65 (0.52–0.83) | < 0.001 | 1.02(0.63–1.58) | 0.944 |
| Hypertension | 0.88 (0.71–1.09) | 0.23 | - | - | 0.82 (0.66–1.02) | 0.074 | - | - | 0.88 (0.68–1.14) | 0.327 | - | - |
| Physical Activity | No activity reference | |||||||||||
| Sedentary | 0.33 (0.23–0.49) | < 0.001 | 0.44 (0.27–0.69) | < 0.001* | 0.56 (0.39–0.81) | 0.002 | 0.9 (0.55–1.47) | 0.667 | 0.38 (0.26–0.55) | < 0.001 | 0.62(0.37–1.03) | 0.066 |
| Active | 0.28 (0.19–0.43) | < 0.001 | 0.34 (0.2–0.57) | < 0.001* | 0.39 (0.26–0.58) | < 0.001 | 0.62 (0.36–1.06) | 0.083 | 0.29 (0.19–0.45) | < 0.001 | 0.45(0.25–0.82) | 0.009* |
| High WC | 0.44 (0.36–0.53) | < 0.001 | - | - | 0.21 (0.17–0.25) | < 0.001 | - | - | 0.22 (0.17–0.27) | < 0.001 | - | - |
| Anti-diabetics | ||||||||||||
| AGI | 0.79 (0.44–1.42) | 0.439 | 1.27 (0.63–2.57) | 0.507 | 1.0 (0.56–1.79) | 0.991 | 1.13 (0.55–2.32) | 0.734 | 0.87 (0.42–1.81) | 0.71 | 1.64(0.66–4.06) | 0.286 |
| 2nd Generation sulfonylureas | 0.63 (0.49–0.81) | < 0.001 | 0.65 (0.45–0.94) | 0.023* | 0.97 (0.77–1.23) | 0.811 | 1.01 (0.69–1.47) | 0.962 | 0.66 (0.48–0.91) | 0.011 | 0.66(0.41–1.09) | 0.102 |
| Insulin | 0.71 (0.44–1.16) | 0.171 | 0.78 (0.42–1.43) | 0.416 | 0.87 (0.54–1.4) | 0.555 | 1.41 (0.74–2.66) | 0.292 | 0.53 (0.26–1.07) | 0.078 | 0.56(0.23–1.37) | 0.204 |
| Biguanides | 0.7 (0.56–0.86) | < 0.001 | 0.92 (0.64–1.32) | 0.649 | 0.79 (0.64–0.98) | 0.032 | 0.97 (0.67–1.4) | 0.861 | 0.64 (0.48–0.85) | 0.002 | 0.85(0.53–1.35) | 0.486 |
| Thiazolidinediones | 0.95 (0.53–1.68) | 0.847 | 1.71 (0.85–3.44) | 0.135 | 0.89 (0.5–1.58) | 0.684 | 1.8 (0.85–3.82) | 0.125 | 0.73 (0.34–1.58) | 0.426 | 1.43(0.54–3.77) | 0.467 |
| Meglitinides | 0.99 (0.38–2.62) | 0.99 | 2.07 (0.66–6.54) | 0.214 | 1.85 (0.7–4.89) | 0.213 | 2.22 (0.76–6.49) | 0.146 | 2.08 (0.76–5.66) | 0.152 | 4.98(1.5-16.55) | 0.009* |
| DPP-4I | 1.01 (0.32–3.21) | 0.981 | 1.07 (0.28–4.07) | 0.917 | 1.08 (0.33–3.54) | 0.905 | 1.15 (0.25–5.36) | 0.856 | 0.84 (0.18–3.9) | 0.824 | 0.97(0.17–5.57) | 0.971 |
| Other medications | ||||||||||||
| Calcium-Vitamin D | 1.68 (1.3–2.17) | < 0.001 | - | - | 0.8 (0.62–1.05) | 0.104 | - | - | 1.37 (1.01–1.84) | 0.04 | - | - |
| Supplements-Vitamins | 1.19 (0.81–1.76) | 0.381 | - | - | 1.26 (0.85–1.86) | 0.244 | - | - | 1.49 (0.96–2.31) | 0.073 | - | - |
| Bisphosphonate | 3.55 (1.89–6.66) | < 0.001 | 2.21 (1.09–4.5) | 0.028* | 0.92 (0.51–1.64) | 0.777 | - | - | 2.33 (1.28–4.22) | 0.005 | 2.9(1.31–6.43) | 0.009* |
| Statins | 0.91 (0.75–1.09) | 0.291 | - | - | 0.66 (0.55–0.8) | < 0.001 | - | - | 0.76 (0.6–0.95) | 0.018 | - | - |
| Fibric Acid | 0.47 (0.28–0.77) | 0.003 | 0.43 (0.25–0.77) | 0.004* | 0.53 (0.33–0.86) | 0.01 | - | - | 0.52 (0.27–1.02) | 0.057 | - | - |
| PPI | 1.57 (1.13–2.19) | 0.007 | - | - | 1.21 (0.87–1.69) | 0.26 | - | - | 1.42 (0.97–2.07) | 0.072 | - | - |
| Glucocorticoids | 2.41 (1.26–4.6) | 0.008 | 1.8 (0.85–3.82) | 0.124 | 1.3 (0.7–2.39) | 0.404 | - | - | 2.07 (1.07–4.01) | 0.03 | - | - |
| Antiepileptic | 1.49 (1.1–2.01) | 0.009 | - | - | 1.15 (0.85–1.56) | 0.369 | - | - | 1.33 (0.93–1.88) | 0.115 | - | - |
| Levothyroxine | 1.15 (0.85–1.55) | 0.36 | - | - | 0.51 (0.37–0.7) | < 0.001 | - | - | 0.78 (0.53–1.16) | 0.22 | - | - |
| Alpha 5 Reductase Inhibitors | 0.51 (0.31–0.85) | 0.01 | - | - | 1.5 (0.94–2.38) | 0.087 | - | - | 1.03 (0.58–1.8) | 0.927 | - | - |
| Beta Blockers | 0.94 (0.78–1.12) | 0.493 | - | - | 0.82 (0.68–0.98) | 0.032 | - | - | 0.84 (0.67–1.05) | 0.126 | - | - |
| Alpha Blockers | 0.51 (0.34–0.77) | 0.001 | - | - | 1.44 (0.99–2.09) | 0.058 | - | - | 0.77 (0.47–1.26) | 0.296 | - | - |
| Nitrates | 1.2 (0.94–1.54) | 0.151 | - | - | 1.02 (0.79–1.31) | 0.886 | - | - | 1.2 (0.89–1.62) | 0.23 | - | - |
| NSAIDS | 1.49 (1.15–1.93) | 0.003 | - | - | 0.96 (0.74–1.25) | 0.747 | - | - | 1.09 (0.8–1.5) | 0.576 | - | - |
| COX2 | 1.54 (0.93–2.55) | 0.097 | - | - | 1.03 (0.61–1.71) | 0.924 | - | - | 1.24 (0.69–2.25) | 0.474 | - | - |
| ACEI | 0.81 (0.62–1.06) | 0.123 | - | - | 1.02 (0.79–1.33) | 0.877 | - | - | 0.78 (0.56–1.1) | 0.152 | - | - |
| ARB | 0.88 (0.72–1.08) | 0.233 | - | - | 0.86 (0.7–1.05) | 0.146 | - | - | 0.8 (0.62–1.03) | 0.079 | - | - |
WC Waist Circumference, AGI Alpha Glucosidase Inhibitors, DDP-4In Dipeptidyl Peptidase 4 Inhibitors, PPI Proton Pomp Inhibitors, NSAIDs Non-Steroidal-Anti-Inflammatory Drugs, COX2 Cyclooxygenase Inhibitors 2, ACEI Angiotensin Converting Enzyme Inhibitors, CCB Calcium Channel Blockers, ARB Angiotensin Receptor Blocker
* P significant in Multiple Logistic Regression analysis
Discussion
In the current cross-sectional study, we aimed to evaluate the association of anti-diabetic agents and osteoporosis, sarcopenia, and osteosarcopenia. Among all types of anti-diabetic agents, we found a significant protective association between consumption of second-generation sulfonylureas and osteoporosis. None of the anti-diabetics have an association with sarcopenia. Finally, it was found that using meglitinides as an anti-diabetic agent is positively associated with osteosarcopenia.
The impacts of anti-diabetics on musculoskeletal disorders (osteoporosis, sarcopenia) are a controversial subject among different studies. For instance, some studies reported that anti-diabetics, such as biguanides, TZD, and DDP–4In have a positive effect on BMD, while the reverse was mentioned in other studies [19, 32–35].
In a cohort study by Rajpathak et al. [36], 13,195 patients aged ≥ 65 years who consumed sulfonylureas were compared with non-consumers. Over 4 years of follow-up, they found that sulfonylureas consumption was associated with increased risk of hip fracture. Although we did not follow up our patients, our finding regarding the negative association between using of sulfonylureas and osteoporosis was consistent with Rajpathak findings. In the study by Tsang et al. in Taiwan, 14,611 patients with type 2 diabetes mellitus (T2DM) were assessed from 2006 to 2011. They found that the incidence rate of osteoporosis was lower in patients who consumed metformin in comparison with those who had never used this anti-diabetic agent. Furthermore, based on their sensitivity analysis, it was reported that metformin consumption reduced the risk of osteoporosis by 30-40% [37]. In contrast, the present study did not reveal any association between the use of metformin and osteoporosis. This lack of association might be attributed to the relatively smaller sample size in our study compared to Tsang’s. Furthermore, unlike the Tsang study, we did not conduct a follow-up with our patients to track the incidence of osteoporosis. In a case control study by Wang et al., in China, 120 male patients with T2DM were assessed, over 12 weeks. Half of the patients were in the case group and received high dose of metformin (0.5 g four times a day). The remain participants received low dose of metformin (0.5 g two times a day). They found that BMD was improved significantly in both groups. Moreover, they found that BMD was higher in case group comparing with control group [38]. This can be interpreted that metformin may have protective impact against osteoporosis. However, no association was found between consumption of metformin and osteoporosis in our study. This may happen due to the fact that the current study is a retrospective cross-sectional study, and we were not able to include control group as well as following up the patients in our study. Since, our participants were elderly and it maybe they have degrees of cognition impairment, their response to the used dosage of the metformin and other medications will not be reliable.
In a cohort study by Yang et al., 514,510 South Korean patients aged 50–99, who used one of these anti-diabetics, sulfonylureas, DDP-4In, and TZD, were reported a significant association between consumption of DDP-4In and decreased risk of osteoporosis. Moreover, no significant differences were found among participants who consumed DDP-4In and those who used sulfonylureas regarding the relative risk of osteoporosis. Although the types of sulfonylureas were not mentioned in Yang’s study, the impact of 2nd generation sulfonylureas on osteoporosis was also confirmed in our study. Yang et al., also found a weak association between TZD consumption and increased risk of osteoporosis [39]. In contrast, neither TZD nor DDP-4In had an association with osteoporosis in our study. This may occur because the current study was carried out on Iranian population, while Yang study was performed on a different ethnicity (South Korean).
Grey et al., assessed the impacts of Rosiglitazone (a Peroxisome Proliferator-Activated Receptor- Υ Agonist) in BMD of postmenopausal women in randomized controlled trials. Among 50 participants, 25 received rosiglitazone, while the remaining received a placebo for 14 weeks. They found that BMD in the lumbar spine and total hip decreased significantly in patients who received rosiglitazone. They concluded that short-term therapy with rosiglitazone have detrimental effects on bone formation [40]. Likewise, it could potentially elevate the risk of osteoporosis. Additionally, in a study conducted by De Martinis et al., they observed that 94.5% of osteoporosis patients were women. They also found that women have lower BMD in comparison with men by (T score values: -2.33 ± 1.14 vs. -1.31 ± 1.55; p < 0.001) [40].
In the current study, we did not analyze patients who received anti-diabetics on the basis of their gender. However, we found that nearly 55% of our patients were women. Moreover, 57.84% of women who were assessed for osteoporosis, dealt with this disease. Since all of our patients were women, aged older than 60 years old and were in the postmenopausal ages. According to the above-mentioned, to some extent, our finding may be consistent with Grey et al. study [41].
In a study by Majumdar et al. in Canada, the impacts of sitagliptin consumption were assessed in 72,738 patients with DM type 2. Within 90-day window of follow-up, a certain group of patients was being exposed to sitagliptin, while the control group did not use this anti-diabetic. No association was found between consumption of sitagliptin and osteoporosis. Similarly, in the present study, no association was found between the consumption of sitagliptin and osteoporosis [42].
The evidence regarding the effects of sulfonylureas on BMD was limited. However, in a study by Vestergaaard et al. it was reported that consumption of sulfonylureas decreased risk of fractures [43]. They mentioned that reduction in the serum level of C-terminal telopeptide of type 1 collagen (CTX) and osteoclastin as an underlying factor of decreased risk of osteoporotic fracture [44]. Although the type of sulfonylureas was not mentioned in Vestergaaard study, their finding was consistent with what had been found in present study that mention the association between consumption of 2nd generation sulfonylureas and decreased risk of osteoporosis. In a study by Chen et al., it was reported that the risk of osteoporosis was increased while patients used TZDs or Repaglinide (from group of Meglitinides) [45]. To some extent, their findings tie with our study that mentions meglitinides consumption increased the risk of osteosarcopenia.
In a study by Losada-Grande et al., in Spain, 53,853 new cases of type 2 DM were assessed Among this population, 3227 patients had consumed insulin as an anti-diabetic for at least 2 months. Within the 5 years of follow up, the incidence risk of osteoporotic fracture was higher in patients who used insulin therapy in comparison with those who did not use this anti-diabetic. They concluded that the risk of osteoporotic fracture is increasing in patients using insulin as anti-diabetic by 38% [46]. In contrast, no association was found related to impacts of insulin on osteoporosis. This may happen due to the fact that, in our study, we did not follow up our patients for a period of time. Moreover, in current study, we do not exactly know when the patients started to use insulin.
Furthermore, some studies assessed the effects of anti-diabetic agents on sarcopenia. In study by Chen et al., which was performed on 1427 patient aged > 60, in China, the protective impact of metformin, solely or align with other anti-diabetics, on sarcopenia was reported [47]. In comparison, in our study no associations were found among consuming metformin and decreased risk of sarcopenia. This may occur due to the fact that only a few participants consumed Biguanides as anti-diabetic agents in Chen study. On the other hand, in this study, a considerable number of our patients consumed Biguanides (the most used anti-diabetic in Iran).
In other study by Rizzo et al., in Italy, it was reported that DDP-4In can preserve lean body mass, meaning that DDP-4In consumption may mitigate the occurrence of sarcopenia. Rizzo interpreted that this finding might be happened because of the fact that DDP-4In drugs improve glycemic controls and plasma level of inflammatory parameters [48]. It was also mentioned that Rizzo’s patients consumed anti-diabetic drugs for at least 2 years. Nevertheless, in current study, no associations were found regarding DDP-4In consumption and sarcopenia. This may occur due to the fact that a negligible number of our patients used DDP-4In as anti-diabetic agents. (9 users out of 1995 participants).
It has been posited that both diabetes itself and anti-diabetic medications could potentially affect the development of osteosarcopenia. A study conducted by Pechman et al. reported a higher prevalence of osteosarcopenia among individuals with Type 2 DM compared to the control group. Furthermore, they noted a significant association between osteosarcopenia and diabetes’ complications [49]. In contrast, we found no significant association between osteosarcopenia and DM, in our study.
Our study has numerous strengths and limitations. It should be noted that there is not any study regarding to impacts of anti-diabetics on osteosarcopenia. This may be caused that osteosarcopenia is new medical term. For this reason, our finding about the impact of meglitinides on osteosarcopenia is the novelty of current study. Through our research, we have identified that our study is the first of its kind. Not only did we examine the association between anti-diabetic agents and osteoporosis, sarcopenia, and osteosarcopenia, but we also rigorously controlled for potential confounding factors. This was achieved through the use of multiple logistic regression analysis, which can be considered a primary strength of our study.
The main limitation of our study is its cross-sectional nature. So, we were unable to claim the causal relationship between anti-diabetics’ consumption and osteoporosis, sarcopenia, or osteosarcopenia. In this study we aimed to controlling the effect of most confounders, such as having diabetes. However, due to cross-sectional nature of study and lack of enough data in certain cases, some confounders were not accounted in current study. So, generalizing the result of current study to other population, might be challenging. In addition, by the time of the conduction of this study, the diversity of anti-diabetic agents in Iran, was low. Due to this, some types of anti-diabetics, such as, sodium-glucose cotransporter 2 inhibitors (SGLT-2I) were not used by our participants. Moreover, the dose and duration of the used drugs were not taken into account.
Conclusion
To conclude, our findings are a snapshot of the association between anti-diabetic agents and osteoporosis, sarcopenia, and osteosarcopenia among Iranian older adults. We found that after adjusting for potential confounders, there was a protective association between using 2nd generation sulfonylureas and osteoporosis. Furthermore, we found a positive association between meglitinides consumption and osteosarcopenia. It means, focusing on anti-diabetic agents could not only prevent osteoporosis, sarcopenia, and osteosarcopenia, but also influence on their quality of life. However, to select and manage anti-diabetic agents in real-world clinical practice is required to conduct further more well-designed cohort studies with sufficient follow-up duration, more sample size, adjusted findings with confounders, and wide diversity of the anti-diabetic agents.
Acknowledgements
We greatly appreciate of all staff and participants of Bushehr Elderly Health (BEH) Program.
Abbreviations
- ACEI
Angiotensin converting enzyme inhibitors
- AGI
Alpha glucosidase inhibitors
- AIC
Akaike Information Criterion
- AOR
Adjusted Odd Ratio
- ARB
Angiotensin Receptor Blocker
- ASM
Appendicular skeletal Muscle Mass
- BEH
Bushehr Elderly Health program
- BMD
Bone mineral density
- BMI
Body Mass Index
- 95% CI
95% confidence intervals
- CTX
C-terminal telopeptide of type 1 collagen (CTX)
- DALYs
Disability Adjusted Life Years
- DDP-4In
Dipeptidyl Peptidase 4 Inhibitors
- DM
Diabetes mellitus
- EMRI
Endocrinology and Metabolism Research Institute
- EWGSOP-2
European Working Group on Sarcopenia in Older People-2
- FBS
Fating blood sugar
- NCDs
Non-communicable diseases
- OR
Odds Ratio
- SD
Standard deviation
- SGLT-2I
Sodium-glucose cotransporter 2 inhibitors
- TUMS
Tehran University of Medical Sciences
- T2DM
Type 2 diabetes mellitus
- TZD
Thiazolidiniones
- WC
Waist circumference
Authors’ contributions
OTM conceived and coordinated the study. OTM and KK participated in design of the study. AG and KK analyzed the data. YHS wrote draft the manuscript. AG, GP, OTM, KK, NF, AO, MS, IN and BL helped to edit of the manuscript draft. All authors read and approved the final manuscript.
Funding
This work was financially supported by grants from the Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences (grant number 1401-4-221-63644).
Data availability
All data produced in the present study are available to the corresponding authors upon reasonable request.
Declarations
Ethics approval and consent to participate
Propose of the study approved by the Research Ethics Committee of Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
Consent for publication
Not applicable.
Competing interests
Yasmin Heydarzadeh Sohi, Ali Golestani, Ghodratollah Panahi, Ozra Tabatabaei-Malazy, Kazem Khalagi, Noushin Fahimfar, Afshin Ostovar, Mahnaz Sanjari, Bagher Larijani, Iraj Nabipour declare that they have no conflict of interest.
Footnotes
Yasmin Heydarzadeh Sohi and Ali Golestani are equally first authors. Ozra Tabatabaei-Malazy and Kazem Khalagi are equally corresponding authors.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Ozra Tabatabaei-Malazy, Email: tabatabaeiml@sina.tums.ac.ir.
Kazem Khalagi, Email: kkhalagi@yahoo.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data produced in the present study are available to the corresponding authors upon reasonable request.