Abstract
Background
Diabetes and osteoporosis are frequent long-term conditions. There is little information on the relationship between diabetes and the risk of refracture in people who have osteoporotic fractures (OPFs), even though both conditions have been individually associated with increased fracture risk.
Methods
We conducted a retrospective cohort study using the Osteoporotic Fracture Registry System of the Affiliated Kunshan Hospital of Jiangsu University. The study included 2,255 patients aged 50 years or older who were admitted with OPFs, comprising 107 with diabetes and 2,148 without. The risk of refracture within 1, 3, and 5 years was evaluated using Cox proportional hazard regression models based on whether or not a diabetes diagnosis was made during the admission assessment. Furthermore, the rates of refracture between individuals with and without diabetes were compared using Kaplan-Meier curves.
Results
In patients with OPFs, diabetes was significantly positively correlated with refracture risk. For the follow-up periods of 1, 3, and 5 years, the hazard ratios (HRs) in the fully adjusted model were 2.83 (95% confidence interval [CI]: 1.09 to 7.39, P-value = 0.033), 2.65 (95% CI: 1.27 to 5.52, P-value = 0.009), and 2.72 (95% CI: 1.39 to 5.32, P-value = 0.004), respectively.
Conclusions
The findings highlight the importance of monitoring bone health and implementing preventative interventions in individuals with diabetes, since they reveal that diabetic patients face a risk of refracture that is more than twice as high as that of non-diabetic individuals.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40520-024-02917-1.
Keywords: Diabetes, Osteoporotic fractures, Refracture risk, Retrospective cohort study
Introduction
Osteoporosis (OP) is a prevalent chronic skeletal disorder characterized by decreased bone mineral density (BMD) and deterioration of bone microarchitecture. These changes increase the possibility of fractures [1]. Refracture refers to any subsequent osteoporotic fracture that occurs following an initial fracture, regardless of whether it affects the same anatomical site or a different one [2]. It is called a secondary fracture at times. Osteoporotic fractures (OPFs) increase the risk, morbidity, and mortality of refractures and significantly burden the healthcare system financially and medically [3]. Among individuals with an incident fracture, the risk of a refracture increases by 30–40% within 3 years [4]. Post-fracture evaluation and pharmacological treatment are uncommon for individuals with fragility fractures [5, 6], even though bone-protective medicines can be utilized to lower refracture risk [7, 8].
Diabetes mellitus (DM) is a chronic metabolic condition characterized by elevated blood sugar levels resulting from inadequate insulin synthesis, insulin resistance, or both [9]. With an estimated 537 million adults affected globally in 2021 and a predicted increase to 783 million by 2045 [10], diabetes was the eighth greatest cause of death and disability combined in the world [11]. Diabetes also places a significant financial strain on healthcare systems [12, 13]. According to estimates from the International Diabetes Federation (IDF), global healthcare spending is expected to increase from $966 billion in 2021 to $1.054 trillion by 2045 [14]. Diabetes is recognized to be linked to several complications, such as nephropathy, neuropathy, retinopathy, and cardiovascular diseases [15, 16].
Several mechanisms may contribute to the increased risk of refracture in patients with diabetes. First, diabetes can impair bone quality due to the accumulation of advanced glycation end products (AGEs) in bone collagen, which reduces bone strength and increases fragility [17]. Second, hyperglycemia disrupts bone metabolism by inhibiting the maturation and functionality of osteoblast and osteoclast [18]. Third, diabetes-related microvascular complications may compromise bone blood supply and impede healing processes [19]. Additionally, diabetic neuropathy can increase fall risk by impairing balance and proprioception [20], while diabetic nephropathy may affect vitamin D metabolism and calcium homeostasis [21]. Collectively, these mechanisms highlight the substantial impact of diabetes on both initial fracture healing and the risk of subsequent refracture.
Diabetes-related fractures present considerable clinical challenges and can significantly reduce quality of life. To manage and avoid recurring fractures, it is critical to understand the link between diabetes and refractures. Nevertheless, evidence is scarce regarding the correlation between diabetes and the likelihood of refracture. A recent study investigated the correlation between diabetes and the probability of fractures in senior men in Germany [22]. Another pertinent study analyzed a population in Denmark [23]. Due to the limited research on osteoporotic fractures in the Chinese population, particularly with a follow-up duration of up to 5 years, our objective is to carry out a study on the correlation between diabetes and the risk of refracture in a representative group of hospitalized patients with OPFs.
Materials and methods
Study design and participants
This retrospective real-world study was conducted at the Affiliated Kunshan Hospital of Jiangsu University (AKHJU) and used an open enrollment design. This study utilized a database collected in advance from the Osteoporotic Fracture Registry System (OPFRS) of Jiangsu University’s Affiliated Kunshan Hospital (AKHJU). From January 1, 2017, to July 27, 2022, electronic patient data was collected from all participants aged 50 or above who were newly admitted with a diagnosis of OPFs. To refine our analysis to encompass adults with confirmed type 2 diabetes, we excluded individuals diagnosed prior to the age of 50 and those who commenced insulin therapy within one year of their diagnosis. Additionally, these patients had not sustained any fractures in the previous five years, indicating that these were their first OPFs. The enrollment process remained open throughout the entire study period. A total of 5747 successive patients with OPFs who underwent orthopedic surgery were included in this study. The following were the inclusion criteria: (1) must be at least 50 years old; (2) must have a fracture diagnosis from radiography or computed tomography, which includes fractures of the wrist, proximal humerus, lumbar vertebra, thoracic vertebra, femoral neck, and femoral trochanteric/subtrochanteric region; (3) must be receiving surgical treatment in a hospital; (4) hospital clinical data must be available; and (5) could be reached by phone. Deceased patients within the initial month of admission or those who were unreachable were excluded from the analysis. The following criteria were then used to eliminate 3492 patients from the analysis: (1) Patients who had received treatment with corticosteroids, calcitonin, estrogens, or fluorides within six months prior to entering the study (1780 cases); (2) patients with significant chronic conditions such as renal failure, malignant tumors, gastrointestinal abnormalities, hyperthyroidism or hypothyroidism, acromegaly, Cushing’s syndrome, or arthritis (1036 cases) [24]; (3) patients who were non-local residents or had incomplete data (573 cases); and (4) patients with a follow-up period of 60 days or less (103 cases). The final analysis included 2255 patients (Fig. 1), and the Declaration of Helsinki was adhered to precisely throughout the investigation. Approval for ethical review has been obtained from the AKHJU with the approved number 2021-06-015-K01. The investigators conducting the analysis were denied access to patient information. Before enrolling, we obtained informed consent and conducted all procedures in accordance with the approved guidelines.
Fig. 1.
Study flow chart
Exposure and outcome variables
Diabetes status was assessed based on patients’ self-reported history at baseline. The variable of outcome was refracture. The study participants were identified through the Regional Health Registration Platform (RHRP) of Kunshan City and the Population Death Registration System (PDRS) of Jiangsu Province, China. Connection of the RHRP and PDRS to the AKHJU Registration System using patient identifiers, hospital records, and relevant dates, allowed the collection of comprehensive follow-up data. Initial fractures were defined as fractures of the wrist, proximal humerus, hip, or intravertebral area, diagnosed using the International Statistical Classification of Diseases and Related Health Problems (ICD-10 nomenclature, 10th edition) with codes starting with S22, S32, S42, S52, or S72. Refracture was defined as any subsequent osteoporotic fracture occurring after an initial fracture, regardless of anatomical site. Data on refractures, including admission time, diagnosis, and relevant clinical information, were obtained from the RHRP of Kunshan City. The initial osteoporotic fracture-related hospital admission date served as the starting point for each patient. The end point was refracture. The follow-up duration was determined by analyzing the occurrence of refracture as the primary outcome event. It was defined as the time interval between the date of discharge after the initial fracture and the date of the second fracture, the patient’s transfer out of the area, or the end date of the study (July 27, 2022).
Covariate analyses
Trained clinical researchers gathered baseline patient data. Body mass index (BMI), sex, age, smoking, drinking, the American Society of Anesthesiologists (ASA) score, hypertension, phosphorus, magnesium, sodium, hemoglobin, lymphocyte, monocyte, neutrophil, and platelet counts, albumin, calcium, aspartate aminotransferase (AST), alanine transaminase (ALT), serum uric acid (SUA), blood urea nitrogen (BUN), creatinine (Cr), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and fracture classification were the candidate covariates used in the analysis. The formula for calculating BMI was weight (kg) divided by height (m) squared. Anyone who has smoked during the last 12 months, whether current or past, was defined as smoking. Drinking was defined as consuming alcohol at least once per week for the previous 12 months [25]. The ASA score was utilized to assess the patients’ physical status [26]. The chief anesthesiologist of the case calculated and documented the ASA scores [27]. Blood samples were collected from the participants after an overnight fast of at least 8 h. The hematological analyzer utilized to assess the blood samples was the Sysmex XN-10 (B4). The Beckman Coulter AU5800 automated biochemical analyzer was utilized for the purpose of biochemical testing detection. The index fracture site, which comprises fractures of the wrist, proximal humerus, lumbar vertebra, thoracic vertebra, femoral neck, and femoral trochanteric/subtrochanteric region, is referred to as the fracture classification.
Statistical analyses
All analyses were conducted using EmpowerStats (http://www.empowerstats.com) and R packages (http://www.R-project.org), with a significance limit of a two-sided P-value ≤ 0.05. The frequency (%) is used for categorical data. The examination of categorical data involved the use of either Pearson’s chi-square test or Fisher’s exact test for univariate analysis. The independent samples t-test was used for continuous data that followed a normal distribution, whereas the Mann-Whitney U test was applied for data that did not follow a normal distribution. Furthermore, univariate analyses were performed to investigate the relationships between refracture-related patient features and OPFs. To assess the independent association between diabetes and refracture status in patients with OPFs, Cox proportional hazard regression models were used, taking into account the effects of covariance. We presented the results from three different analyses: the unadjusted analysis, the minimum adjusted analysis, and the completely adjusted analysis, following the recommendation of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. A collinearity diagnosis was first performed using the Variance Inflation Factor (VIF). Covariate adjustments were determined based on two criteria: Criterion 1 required the inclusion or exclusion of covariates in the model, which initially included only diabetes status and refracture without covariates. The full model incorporated all potential covariates, including age, sex, BMI, smoking, drinking, magnesium, sodium, phosphorus, platelet count, hemoglobin, albumin, calcium, neutrophil count, lymphocyte count, monocyte count, ALT, AST, Cr, BUN, SUA, TC, TG, HDL, LDL, hypertension, ASA classification, and fracture classification. A covariate was considered necessary for adjustment if its inclusion or exclusion resulted in a change of ≥ 10% in the odds ratio (OR). Criterion 2 required that a covariate either met Criterion 1 or had a P-value < 0.1 in the univariate model. Finally, the following three models were determined: Model 2 (minimally adjusted model) was adjusted for age and BMI; Model 3 (completely adjusted model) was adjusted to account for fracture classification, platelet count, and hypertension in addition to age and BMI. Model 1 was not adjusted. We conducted a survival analysis by employing the log-rank test and examining 1-, 3-, and 5-year Kaplan-Meier (K-M) curves to determine the potential association between the existence of diabetes and the occurrence of refractures. We performed multiple subgroup analyses to evaluate the stability and potential variability of the association between diabetes and refracture risk across various covariates. Continuous variables including age, BMI, and platelet count, were divided into tertiles based on their distribution within the study population to ensure balanced sample sizes. The likelihood ratio test (LRT) was employed to analyze subgroup interactions and modifications. The P-value for interaction was calculated to assess whether the association between diabetes and refracture risk significantly differed across subgroup levels, with a P-value < 0.05 indicating a significant interaction effect.
Results
Patient characteristics
Table 1 displays the characteristics of individuals categorized based on their diabetes status. The study registered 2255 persons in total; 107 were in the group of patients with diabetes, 2148 in the group without diabetes. The patients’ mean age was 72.01 having a standard deviation of 10.53. The mean age of the two groups was somewhat greater in the diabetic patients group (73.11 years) than in the non-diabetic patients group (71.96 years) (standardized difference = 0.12, 95% confidence interval [CI]: -0.08 to 0.31, P-value = 0.269). The measurement of BMI yielded an overall mean BMI of 22.72 kg/m2 with a standard deviation of 3.33. The mean BMI of the diabetic patient group was 23.24 kg/m2, which was marginally higher than the non-diabetic patient group’s 22.70 kg/m2 (standardized difference = 0.17, 95% CI: -0.03 to 0.36, P-value = 0.098). Other measured variables, including magnesium, sodium, phosphorus, BUN, SUA, TC, TG, HDL and LDL, neutrophil, lymphocyte, monocyte, and platelet counts, hemoglobin, albumin, calcium, ALT, AST, and Cr, did not significantly differ between the two groups.
Table 1.
Patient characteristics based on different diabetes status groups
| Variables | Total | Mean ± SDa / N (%)b | Standardize Differencec | P-value | P-value* | |
|---|---|---|---|---|---|---|
| Non-diabetic patients | Diabetic patients | |||||
| N | 2255 | 2148 | 107 | |||
| Age, years | 72.01 ± 10.53 | 71.96 ± 10.61 | 73.11 ± 8.71 | 0.12 (-0.08, 0.31) | 0.269 | 0.234 |
| BMI, kg/m2 | 22.72 ± 3.33 | 22.70 ± 3.34 | 23.24 ± 3.12 | 0.17 (-0.03, 0.36) | 0.098 | 0.046 |
| Magnesium, mmol/L | 0.89 ± 0.10 | 0.89 ± 0.10 | 0.89 ± 0.09 | 0.01 (-0.18, 0.21) | 0.903 | 0.990 |
| Sodium, mmol/L | 140.75 ± 2.92 | 140.73 ± 2.94 | 140.97 ± 2.68 | 0.08 (-0.11, 0.28) | 0.412 | 0.238 |
| Phosphorus, mmol/L | 1.07 ± 0.22 | 1.07 ± 0.22 | 1.04 ± 0.20 | 0.16 (-0.04, 0.36) | 0.127 | 0.194 |
| Platelet count, ×109/L | 176.47 ± 61.61 | 176.58 ± 61.60 | 174.42 ± 62.07 | 0.03 (-0.16, 0.23) | 0.724 | 0.520 |
| Hemoglobin, g/L | 125.67 ± 18.33 | 125.59 ± 18.45 | 127.33 ± 15.85 | 0.10 (-0.09, 0.30) | 0.340 | 0.545 |
| Albumin, g/L | 39.98 ± 4.24 | 39.99 ± 4.24 | 39.71 ± 4.35 | 0.07 (-0.13, 0.26) | 0.508 | 0.567 |
| Calcium, mmol/L | 2.21 ± 0.13 | 2.21 ± 0.13 | 2.20 ± 0.11 | 0.07 (-0.12, 0.27) | 0.504 | 0.484 |
| Neutrophil count, ×109/L | 6.56 ± 3.13 | 6.54 ± 3.11 | 6.85 ± 3.50 | 0.09 (-0.10, 0.29) | 0.316 | 0.656 |
| Lymphocyte count, ×109/L | 1.24 ± 0.54 | 1.24 ± 0.54 | 1.25 ± 0.48 | 0.02 (-0.18, 0.21) | 0.861 | 0.611 |
| Monocyte count, ×109/L | 0.51 ± 0.26 | 0.51 ± 0.26 | 0.49 ± 0.22 | 0.08 (-0.11, 0.28) | 0.423 | 0.627 |
| ALT, U/L | 23.49 ± 20.92 | 23.39 ± 20.10 | 25.32 ± 33.48 | 0.07 (-0.13, 0.26) | 0.355 | 0.635 |
| AST, U/L | 26.48 ± 24.48 | 26.37 ± 23.88 | 28.71 ± 34.47 | 0.08 (-0.12, 0.27) | 0.338 | 0.414 |
| Cr, µmol/L | 65.92 ± 29.72 | 66.10 ± 30.21 | 62.34 ± 16.91 | 0.15 (-0.04, 0.35) | 0.204 | 0.338 |
| BUN, mmol/L | 6.03 ± 2.43 | 6.03 ± 2.44 | 5.86 ± 2.16 | 0.08 (-0.12, 0.27) | 0.464 | 0.428 |
| SUA, µmol/L | 283.23 ± 91.66 | 283.91 ± 91.80 | 269.56 ± 88.11 | 0.16 (-0.04, 0.35) | 0.116 | 0.093 |
| TC, mmol/L | 4.26 ± 0.93 | 4.26 ± 0.94 | 4.30 ± 0.76 | 0.05 (-0.20, 0.30) | 0.741 | 0.445 |
| TG, mmol/L | 1.24 ± 0.98 | 1.24 ± 0.98 | 1.29 ± 1.05 | 0.05 (-0.20, 0.30) | 0.680 | 0.887 |
| HDL, mmol/L | 1.36 ± 0.31 | 1.36 ± 0.31 | 1.37 ± 0.28 | 0.04 (-0.21, 0.29) | 0.752 | 0.688 |
| LDL, mmol/L | 2.55 ± 0.76 | 2.55 ± 0.77 | 2.54 ± 0.64 | 0.01 (-0.25, 0.26) | 0.970 | 0.943 |
| Sex | 0.06 (-0.14, 0.25) | 0.582 | - | |||
| Female | 1612 (71.49%) | 1533 (71.37%) | 79 (73.83%) | |||
| Male | 643 (28.51%) | 615 (28.63%) | 28 (26.17%) | |||
| Smoking | 0.03 (-0.16, 0.22) | 0.756 | - | |||
| No | 2123 (94.15%) | 2023 (94.18%) | 100 (93.46%) | |||
| Yes | 132 (5.85%) | 125 (5.82%) | 7 (6.54%) | |||
| Drinking | 0.07 (-0.12, 0.27) | 0.506 | - | |||
| No | 2189 (97.07%) | 2084 (97.02%) | 105 (98.13%) | |||
| Yes | 66 (2.93%) | 64 (2.98%) | 2 (1.87%) | |||
| Fracture classification | 0.58 (0.38, 0.77) | < 0.001 | - | |||
| Thoracic vertebra | 360 (15.96%) | 350 (16.29%) | 10 (9.35%) | |||
| Lumbar vertebra | 631 (27.98%) | 605 (28.17%) | 26 (24.30%) | |||
| Wrist | 101 (4.48%) | 97 (4.52%) | 4 (3.74%) | |||
| Proximal humerus | 260 (11.53%) | 258 (12.01%) | 2 (1.87%) | |||
| Femoral neck | 578 (25.63%) | 543 (25.28%) | 35 (32.71%) | |||
| Femoral trochanteric/subtrochanteric | 325 (14.41%) | 295 (13.73%) | 30 (28.04%) | |||
| ASA recodedd | 0.32 (0.12, 0.51) | 0.014 | - | |||
| 1 | 155 (6.87%) | 153 (7.12%) | 2 (1.87%) | |||
| 2 | 1462 (64.83%) | 1398 (65.08%) | 64 (59.81%) | |||
| ≥ 3 | 638 (28.29%) | 597 (27.79%) | 41 (38.32%) | |||
| Hypertension | 1.39 (1.19, 1.59) | < 0.001 | - | |||
| No | 1849 (82.00%) | 1819 (84.68%) | 30 (28.04%) | |||
| Yes | 406 (18.00%) | 329 (15.32%) | 77 (71.96%) | |||
aContinuous variables
bCategorical variables
cStandardized differences of < 0.10 for a given covariate indicate a relatively small imbalance
dHigher scores indicate more severe physical status
P-value*: Kruskal−Wallis Rank Test for continuous variables, Fisher Exact for categorical variables with Expects < 10
Abbreviations: SD, standard deviation; BMI, body mass index; ALT, alanine aminotransferase; AST, aspartate aminotransferase; Cr, creatinine; BUN, blood urea nitrogen; SUA, serum uric acid; TC, total cholesterol; TG, triglycerides; HDL, High-density lipoprotein; LDL, Low-density lipoprotein; ASA, American Society of Anesthesiologists
Sex did not significantly differ across the groups in terms of categorical factors (standardized difference = 0.06, 95% CI: -0.14 to 0.25, P-value = 0.582). The female gender constituted the majority of patients in both the non-diabetic patient group (71.49%) and the diabetes patient group (73.83%). Similarly, there were no significant differences in drinking or smoking between the two groups. The groups differed significantly in terms of fracture classification, which was classified as thoracic vertebra, lumbar vertebra, wrist, proximal humerus, femoral neck, and femoral trochanteric/subtrochanteric (standardized difference = 0.58, 95% CI: 0.38 to 0.77, P-value < 0.001). A significant difference between the groups was also evident in the ASA score (standardized difference = 0.32, 95% CI: 0.12 to 0.51; P-value = 0.014). There was a significant difference in the occurrence of hypertension across the groups, with a higher rate in the group of individuals with diabetes (standardized difference = 1.39, 95% CI: 1.19 to 1.59, P-value < 0.001).
Univariate analysis of factors associated with refracture status
The results of the univariate analysis examining variables associated with refracture status are presented in Table S1. Significantly, the analysis highlights that diabetes mellitus is associated with an increased risk of refracture [hazard ratio (HR) = 2.02, 95% CI: 1.05 to 3.86, P-value = 0.034]. Other factors such as age and platelet count also showed statistically significant associations with refracture risk, while variables like BMI, magnesium, and lipid components did not show significant correlations. The table also includes categorical assessments of sex, smoking, drinking, and fracture classification, with specific HRs indicating the relative risk of refracture among these different categories.
Evaluation of the relationship between diabetes and refracture status
The examination of the association between diabetes and refracture status during three different follow-up periods—1, 3, and 5 years—is shown in Table 2. The table includes three models: the non-adjusted model, the minimally adjusted model, and the fully adjusted model. Diabetes was consistently associated with a considerably elevated risk of refracture across all models and throughout all follow-up periods. In the fully adjusted model, this link remained significant even after controlling for possible confounders including age, sex, BMI, hypertension, platelet count, and fracture classification. For the follow-up periods of 1, 3, and 5 years, the HRs in the fully adjusted model are 2.83 (95% CI: 1.09 to 7.39, P-value = 0.033), 2.65 (95% CI: 1.27 to 5.52, P-value = 0.009), and 2.72 (95% CI: 1.39 to 5.32, P-value = 0.004), in that order. These findings indicate that individuals with diabetes are at a significantly higher risk of experiencing refracture, with the risk being more than double compared to those without diabetes. The relationship between diabetes status and cumulative refracture rates was evaluated using Kaplan–Meier curves (Fig. 2). There is a significant difference in the rates of refracture between the groups with and without diabetes. The diabetic group has a significantly elevated risk of refracture in comparison to the non-diabetic group. The Kaplan-Meier curves for the 1-year, 3-year, and 5-year follow-up periods display the same trend.
Table 2.
Association between diabetes and refracture status in different models
| Follow-up period | Diabetes | No. of Events | Refracture Rate (%) | HR (95% CI) P-value | ||
|---|---|---|---|---|---|---|
| Non-Adjusted Model | Minimally-Adjusted Model | Fully-Adjusted Model | ||||
| 1 year | No | 50 | 2.3 | Reference | Reference | Reference |
| Yes | 6 | 5.6 | 2.50 (1.07, 5.84) 0.0336 | 2.50 (1.07, 5.86) 0.0344 | 2.83 (1.09, 7.39) 0.0332 | |
| 3 years | No | 104 | 4.8 | Reference | Reference | Reference |
| Yes | 10 | 9.3 | 2.02 (1.05, 3.86) 0.0342 | 1.92 (1.00, 3.68) 0.0492 | 2.65 (1.27, 5.52) 0.0091 | |
| 5 years | No | 128 | 6.0 | Reference | Reference | Reference |
| Yes | 12 | 11.2 | 1.94 (1.07, 3.50) 0.0285 | 1.86 (1.03, 3.36) 0.0403 | 2.72 (1.39, 5.32) 0.0035 | |
Outcome variable: Refracture status, Exposed variables: Diabetes status
Minimally adjusted model adjusted for age, sex, and BMI
Fully adjusted model adjusted for age, sex, BMI, hypertension, platelet count, and fracture classification
Abbreviations: HR, Hazard Ratio; BMI, body mass index
Fig. 2.
Cumulative hazard of refracture for OPFs patients with diabetes (red line) compared with OPFs patients without diabetes (blue line) at 1 year (A), 3 years (B), and 5 years (C) follow-up. Adjusted covariates included age, sex, BMI, hypertension, platelet count, and fracture classification. OPFs, osteoporotic fracture; BMI, body mass index
Subgroup analysis
This study stratified all subgroups by age, sex, BMI, fracture classification, hypertension and platelet count to further verify the reliability of the outcome results in the fully adjusted model when potential confounding variables were represented. All analyses, except for the subgroup variable, were adjusted for these covariates. Table 3 demonstrates an extremely uniform pattern. The age group 77–105 showed a trend toward higher risk in the age group subgroup analysis (HR = 3.67, 95% CI: 1.14 to 11.78, P-value = 0.029). Within the subgroup analysis based on sex, diabetes and refracture were found to be significantly associated in females (HR = 2.79, 95% CI: 1.28 to 6.06, P-value = 0.010), but not in men (HR = 1.87, 95% CI: 0.20 to 17.07, P-value = 0.579). The study revealed a strong association between diabetes and refracture in the subgroup analysis of individuals with a BMI between 21.24 and 24.15 (HR = 4.83, 95% CI: 1.63 to 14.33, P-value = 0.005). However, this correlation was not observed in the other BMI groups. No significant correlations were observed between diabetes and refracture in subgroup analyses that considered fracture classification, hypertension status, and platelet count.
Table 3.
Subgroup analyses exploring the association between diabetes and refracture status
| Subgroup | N | No. of Events | Refracture Rate (%) | HR (95% CI) P-value | P-value for interaction |
|---|---|---|---|---|---|
| AGE group | |||||
| 50–66 | 715 | 26 | 3.64 | 1.21 (0.15, 9.94) 0.8608 | 0.6213 |
| 67–76 | 743 | 38 | 5.11 | 2.94 (0.96, 9.00) 0.0597 | |
| 77–105 | 797 | 50 | 6.27 | 3.67 (1.14, 11.78) 0.0289 | |
| Sex | |||||
| Female | 1612 | 95 | 5.89 | 2.79 (1.28, 6.06) 0.0098 | 0.7406 |
| Male | 643 | 19 | 2.95 | 1.87 (0.20, 17.07) 0.5794 | |
| BMI group | |||||
| 13.52–21.23 | 752 | 38 | 5.05 | 1.16 (0.14, 9.66) 0.8889 | 0.3393 |
| 21.24–24.15 | 751 | 39 | 5.19 | 4.83 (1.63, 14.33) 0.0046 | |
| 24.16–40 | 752 | 37 | 4.92 | 1.79 (0.57, 5.65) 0.3193 | |
| Fracture classification | |||||
| Thoracic vertebra | 360 | 23 | 6.39 | 2.50 (0.30, 20.87) 0.3986 | 0.7818 |
| Lumbar vertebra | 631 | 36 | 5.71 | 3.77 (0.94, 15.12) 0.0611 | |
| Wrist | 101 | 3 | 2.97 | 0.00 (0.00, Inf) 0.9995 | |
| Proximal humerus | 260 | 10 | 3.85 | 0.00 (0.00, Inf) 0.9997 | |
| Femoral neck | 578 | 28 | 4.84 | 3.01 (1.00, 9.10) 0.0505 | |
| Femoral trochanteric/subtrochanteric | 325 | 14 | 4.31 | 1.13 (0.13, 9.52) 0.9099 | |
| Hypertension group | |||||
| No | 1849 | 94 | 5.08 | 3.53 (1.27, 9.78) 0.0154 | 0.4966 |
| Yes | 406 | 20 | 4.93 | 2.12 (0.78, 5.78) 0.1408 | |
| Platelet group | |||||
| 10–146 | 731 | 30 | 4.1 | 3.52 (0.87, 14.31) 0.0787 | 0.7726 |
| 147–194.6 | 736 | 42 | 5.71 | 1.87 (0.61, 5.75) 0.2776 | |
| 195–751 | 745 | 38 | 5.1 | 2.55 (0.69, 9.47) 0.1608 |
Adjusted for age, sex, BMI, fracture classification, hypertension, and platelet count, except the subgroup variable
Abbreviations: HR, hazard ratio; BMI, body mass index
Discussion
This 5-year retrospective cohort study examined the occurrence of refracture in 2255 hospitalized patients with OPFs between a group of patients with diabetes and a group without diabetes. Several subgroup analyses were conducted to assess the relationship between patient characteristics, refracture, and diabetes status. The findings indicated a significant and evident direct relationship between diabetes status and refracture in patients with OPFs, considering the inclusion of additional variables in the fully adjusted model. According to the findings, those with diabetes are over twice as likely to experience a refracture than people without the disease.
Despite some data indicating that diabetes decreases bone density [28–30], the risk of fracture and its clinical significance in individuals with diabetes remain controversial [31]. Diabetes and the risk of fracture have been established correlated by numerous major prospective studies and meta-analyses [32–36]. However, the information regarding the incidence of fractures in individuals with diabetes is still conflicting in a small number of other research; some findings suggest a lower risk or no association [37, 38]. Patients with diabetes who had recently received their diagnosis had a lower fracture risk in a subgroup analysis of a large Rotterdam research. However, it was discovered that the risk rose in patients with late-stage diabetes who were getting treatment [39]. Interestingly, a prospective cohort study conducted in Spain found that among 202 overweight white women with type 2 diabetes over 65, the risk of fractures did not increase [40]. Additionally, a different Swedish study found that older diabetic women who did not have severe renal impairment also had lower bone turnover and higher bone mass. Low bone turnover and high bone mass, however, are unlikely to have a major effect on fracture susceptibility [41]. The reported differences in outcomes from Western studies are believed to be influenced by variations in race and geographic region, while the precise causes remain unidentified. The study specifically examined Asian individuals with OPFs and discovered a significant and independent association between the presence of diabetes and the probability of experiencing refracture.
While numerous studies have focused on the initial fracture risk in diabetes, research specifically examining the refracture risk in diabetic patients with previous osteoporotic fractures is relatively limited. Our findings demonstrated that diabetes significantly increases the risk of subsequent fractures in patients who have already experienced an initial osteoporotic fracture. This higher refracture risk in diabetic patients may be attributed to several mechanisms that particularly affect bone healing and recovery after the initial fracture.
The mechanism behind the increased risk of refracture in patients with diabetes differs from that of typical osteoporosis and may be particularly relevant after an initial fracture. Patients with diabetes face unique challenges in bone healing and recovery due to several factors. These include decreased cortical density, altered bone geometry, faster bone loss, microdamage accumulation at low bone turnover regions, and AGEs accumulation [42]. Specifically, people with diabetes frequently lose bone at a faster pace than people without the disease [43]. This is coupled with a decline in cortical density, which further reduces the bone strength [44]. Changes in the structure of the bone may potentially increase the risk of fracture because they can affect the way stresses are distributed throughout the bone [45]. Furthermore, in areas with low bone turnover, the accumulation of microdamage can erode the structural integrity of the bone, raising the risk of fractures [41]. The accumulation of AGEs is another crucial element. AGEs, which are proteins or lipids that have undergone glycation as a result of being exposed to sugars, may significantly change the properties of collagen. Tensile strength in bone is provided by the essential protein collagen. The bone becomes more brittle as AGEs change its characteristics. The paradoxical rise in fracture risk that is seen in diabetics is further exacerbated by this process [46]. An increased chance of falls or other nonskeletal risk factors associated with diabetes may be another mechanism causing an increased risk [47]. Hypoglycemia, a side effect of some diabetic medications, can lead to disorientation and dizziness, which raises the risk of falls [48]. All these factors contribute significantly to the higher risk of subsequent fractures that diabetics experience. Our findings along with those of others, demonstrate that factors other than BMD affect subsequent fracture risk, particularly in individuals with diabetes.
Additionally, due to the extended duration of our follow-up period, our study included individuals with long-standing prevalent diabetes. Because of this, our study participants were more likely to have diabetes complications and to be using antidiabetic drugs, which may have had an impact on the observed incidence of bone fractures. Individuals with diabetes have an increased risk of developing cardiovascular, renal, and neuropathy disorders [49]. Diabetes-related complications may impair the healing process following an initial fracture, thereby increasing the risk of subsequent fractures [50, 51]. Furthermore, the healing process in diabetic patients may be further hindered by the effects of medication. Although insulin obviously has an anabolic action in bone, which helps some of the beneficial effects on BMD, the skeletal benefits of insulin treatment are still controversial [52]. Furthermore, thiazolidinediones (TZDs) such as pioglitazone and rosiglitazone, are among the various groups of oral antidiabetic medications that still have the risk of adversely affecting bone health. TZDs function as agonists for the nuclear receptor known as peroxisome proliferator-activated receptor gamma (PPARγ), which is necessary for adipogenesis and insulin sensitivity. While PPARγ activation can improve insulin resistance, it also promotes the differentiation of mesenchymal stem cells into adipocytes rather than osteoblasts. This alteration in cellular specialization reduces the production of new bone and bone cells [53, 54]. This could be particularly detrimental for patients recovering from an initial fracture, and may contribute to an increased risk of refracture.
The development of secondary prevention measures is essential because individuals with diabetes have a risk of refracture that is more than double that of those without the disease. From this point, the clinical implications of our findings emphasize the need for enhanced secondary fracture prevention strategies in patients with diabetes who have experienced an initial osteoporotic fracture. These patients require particularly careful monitoring and management to prevent subsequent fractures. Regular monitoring of bone health and periodic evaluations of BMD and other indicators of bone turnover should form part of patients with diabetes’s normal treatment. Additionally, very important for secondary prevention efforts are lifestyle changes that improve general bone health. These may include the promotion of weight-bearing exercises, ensuring an adequate intake of calcium and vitamin D, maintaining a healthy weight, and advocating smoking cessation and moderate alcohol consumption. Examining each patient’s medication plan in relation to their individual fracture risk profile is also very important. If a patient has a high risk of fractures or has already had one, healthcare professionals may think about changing their diabetes prescription to those less likely to affect bone health or include osteoporosis drugs into their treatment plan. Understanding the increased fracture risk in people with diabetes and using proactive steps to prevent it allows us to apply comprehensive multifactorial approaches tailored depending on patient profiles. This strategic approach might considerably raise patient outcomes.
This study has several limitations. Firstly, due to the retrospective nature of our study, we were unable to gather data on key factors such as the length of diabetes, occurrences of hypoglycemia, instances of falls, specific types of antidiabetic medications, comorbidities, and complications related to diabetes. These variables may be essential for comprehending the correlation between diabetes and the risk of refracture. Additionally, our study solely focused on Chinese individuals, which restricts the applicability of our findings to other demographics and ethnicities. Furthermore, a notable limitation of our study is the relatively small number of diabetic patients (n = 107) compared to non-diabetic patients (n = 2148), which reduces the statistical power and highlights the need for future studies with larger, multicenter cohorts to enhance the model. Additionally, since the study period (January 2017 to July 2022) slightly exceeded five years, only a subset of participants had the potential for a full 5-year follow-up, potentially affecting the robustness of our long-term findings. Future study should focus on collecting extensive data in a systematic manner, incorporating a wider range of patients, and performing analyses that take into account the presence of various comorbid conditions. This will lead to a more detailed and nuanced understanding of the connection between diabetes and the risk of fractures.
Nevertheless, our study contained some significant advantages. The study sample comprised Chinese individuals aged 50 years or older residing in metropolitan areas. The study was conducted as a retrospective analysis of real-world data, with follow-up assessments at 1, 3, and 5 years. Regarding the study results, three independent models were employed to completely examine the relationship between diabetes and refracture status. These models were adjusted for several potential confounding variables such as age, sex, BMI, hypertension, platelet count, and fracture classification. Additionally, we conducted subgroup analyses and found that the results were similar across several models and subgroups, indicating the stability and reliability of our findings.
Conclusions
This study offers valuable insights into the correlation between diabetes and the risk of refracture in individuals with OPFs. The results demonstrate a significant and positive correlation between the risk of refracture and diabetes, suggesting that individuals with diabetes are more than twice as likely to experience a refracture compared to those without diabetes. The results indicate the need for comprehensive preventive measures, including lifestyle changes that enhance overall bone health, a thorough evaluation of medication schedules based on each patient’s specific fracture risk profile, and potentially the inclusion of osteoporosis medications in the treatment plans of individuals at high risk. Additional study is necessary to validate these findings in different populations and to investigate the underlying mechanisms more thoroughly.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
Not applicable.
Author contributions
Study design: KL. Study conduct: CL and KL. Data collection: SHG, JX, MZX, KL and YQG, Data analysis: SHG and JX. Data interpretation: SHG and KL. Drafting manuscript: SHG. Revising manuscript content: JX, MZX and KL. Approving the final version of the manuscript: CL, YQG and KL. SHG and KL take responsibility for the integrity of the data analysis. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.
Funding
The study was supported by Suzhou City Major Disease Multicenter Clinical Research Project (CN) (DZXYJ202312), Elderly Health Research Project of Jiangsu Province (CN) (LKZ2022020), Special Funding for Jiangsu Province Science and Technology Plan (Key Research and Development Program for Social Development) (CN) (BE2023738), Suzhou Collaborative Innovation Research Project of Medical and Industrial Integration (CN) (SLJ2022023), Gusu Health Talent Plan Scientific Research Project (CN) (GSWS2022105), and Postgraduate Research & Practice Innovation Program of Jiangsu Province (CN) (SJCX24_2446).
Competing interests
All authors state they have no competing interests.
Ethics approval
This study complies with the Declaration of Helsinki and has been approved by the Ethics Committee of the First People’s Hospital of Kunshan (no. 2021-06-015-K01).
Data availability
Data is provided within the supplementary information files.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.FORCIEA M A, MCLEAN R M QASEEMA (2017) Treatment of low bone density or osteoporosis to prevent fractures in men and women [J]. Ann Intern Med 167(12):904 [DOI] [PubMed] [Google Scholar]
- 2.ROUX C, THOMAS T, PACCOU J et al (2021) Refracture and mortality following hospitalization for severe osteoporotic fractures: the Fractos study [J]. JBMR Plus 5(7):e10507 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.BACHOUR F, RIZKALLAH M (2017) Fracture liaison service: report on the first successful experience from the Middle East [J]. Arch Osteoporos 12(1):79 [DOI] [PubMed] [Google Scholar]
- 4.HO-PHAM L T, NGUYEN TV (2017) The Vietnam osteoporosis study: Rationale and design [J]. Osteoporos Sarcopenia 3(2):90–97 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.GIANGREGORIO L, PAPAIOANNOU A, CRANNEY A et al (2006) Fragility fractures and the osteoporosis care gap: an international phenomenon [J]. Semin Arthritis Rheum 35(5):293–305 [DOI] [PubMed] [Google Scholar]
- 6.ELLIOT-GIBSON V, BOGOCH E R, JAMAL S A et al (2004) Practice patterns in the diagnosis and treatment of osteoporosis after a fragility fracture: a systematic review [J]. Osteoporos Int 15(10):767–778 [DOI] [PubMed] [Google Scholar]
- 7.PAPAIOANNOU A, MORIN S, CHEUNG A M et al (2010) 2010 clinical practice guidelines for the diagnosis and management of osteoporosis in Canada: summary [J]. CMAJ 182(17):1864–1873 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.GOEREE HOPKINSRB (2011) The relative efficacy of nine osteoporosis medications for reducing the rate of fractures in post-menopausal women [J]. BMC Musculoskelet Disord 12:209 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.LI YY, WANG H, YANG X X et al (2017) Small Ubiquitin-Like modifier 4 (SUMO4) gene M55V polymorphism and type 2 diabetes Mellitus: a Meta-analysis including 6,823 subjects [J]. Front Endocrinol (Lausanne) 8:303 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.SUN H, SAEEDI P et al (2022) KARURANGA S,. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045 [J]. Diabetes research and clinical practice, 183: 109119 [DOI] [PMC free article] [PubMed]
- 11.Global burden (2020) Of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of Disease Study 2019 [J]. Lancet 396(10258):1204–1222 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.CHAN J C N, LIM L L, WAREHAM N J et al (2021) The Lancet Commission on diabetes: using data to transform diabetes care and patient lives [J]. Lancet 396(10267):2019–2082 [DOI] [PubMed] [Google Scholar]
- 13.AFROZ A, ALRAMADAN M J, HOSSAIN M N et al (2018) Cost-of-illness of type 2 diabetes mellitus in low and lower-middle income countries: a systematic review [J]. BMC Health Serv Res 18(1):972 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.MAGLIANO, D J (2022) BOYKO E J. IDF diabetes atlas [J]
- 15.BLAIR M. Diabetes Mellitus Review [J]. Urol Nurs, (2016) 36(1): 27–36 [PubMed]
- 16.DEFRONZO R A, FERRANNINI E (2015) Type 2 diabetes mellitus [J]. Nat Rev Dis Primers 1:15019 [DOI] [PubMed] [Google Scholar]
- 17.YAMAMOTO M (2016) Advanced Glycation End products, Diabetes, and bone strength [J]. Curr Osteoporos Rep 14(6):320–326 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.DIENELT A, ZUR NIEDEN N (2011) I. Hyperglycemia impairs skeletogenesis from embryonic stem cells by affecting osteoblast and osteoclast differentiation [J]. Stem Cells Dev 20(3):465–474 [DOI] [PubMed] [Google Scholar]
- 19.MELLADO-VALERO A, FERRER GARCíA JC, HERRERA BALLESTER A et al (2007) Effects of diabetes on the osseointegration of dental implants [J]. Med Oral Patol Oral Cir Bucal 12(1):E38–43 [PubMed] [Google Scholar]
- 20.D’SILVA L J, LIN J, STAECKER H et al (2016) Impact of Diabetic complications on Balance and Falls: contribution of the vestibular system [J]. Phys Ther 96(3):400–409 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.GALUŠKA D, PáCAL L, KAŇKOVá K (2021) Pathophysiological Implication of Vitamin D in Diabetic kidney disease [J]. Kidney Blood Press Res 46(2):152–161 [DOI] [PubMed] [Google Scholar]
- 22.WöLFEL EM, BARTSCH B, KOLDEHOFF J et al (2023) When cortical bone Matrix Properties are Indiscernible between Elderly men with and without type 2 diabetes, fracture resistance follows suit [J]. JBMR Plus 7(12):e10839 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.KVIST A V, NASSER M I, VESTERGAARD P et al (2023) Site-specific fracture incidence rates among patients with type 1 diabetes, type 2 diabetes, or without diabetes in Denmark (1997–2017) [J]. Diabetes Care 46(3):633–642 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.GALLAGHER JC (1990) Treatment of postmenopausal osteoporosis with high doses of synthetic calcitriol. A randomized controlled study [J]. Ann Intern Med 113(9):649–655 [DOI] [PubMed] [Google Scholar]
- 25.LU K, WU Y M, SHI Q et al (2023) The impact of acute-phase reaction on mortality and re-fracture after zoledronic acid in hospitalized elderly osteoporotic fracture patients [J]. Osteoporos Int 34(9):1613–1623 [DOI] [PubMed] [Google Scholar]
- 26.CHARLSON ME, POMPEI P, ALES K L et al (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation [J]. J Chronic Dis 40(5):373–383 [DOI] [PubMed] [Google Scholar]
- 27.VARADY N H, GILLINOV S M, YEUNG C M et al (2021) The Charlson and Elixhauser scores Outperform the American Society of Anesthesiologists Score in assessing 1-year Mortality Risk after hip fracture surgery [J]. Clin Orthop Relat Res 479(9):1970–1979 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.DU Y J, LIU N N, ZHONG X et al (2022) Risk factors for nonalcoholic fatty liver disease in Postmenopausal Women with type 2 diabetes Mellitus and the correlation with bone Mineral density at different locations [J]. Diabetes Metab Syndr Obes 15:1925–1934 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.YUAN J, JIA P, ZHOU JB (2022) Comparison of bone Mineral Density in US adults with diabetes, prediabetes and Normoglycemia from 2005 to 2018 [J]. Front Endocrinol (Lausanne) 13:890053 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.SAMELSON E J, DEMISSIE S, CUPPLES L A et al (2018) Diabetes and deficits in cortical bone density, microarchitecture, and bone size: Framingham HR-pQCT study [J]. J Bone Min Res 33(1):54–62 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.VESTERGAARD P (2007) Discrepancies in bone mineral density and fracture risk in patients with type 1 and type 2 diabetes–a meta-analysis [J]. Osteoporos Int 18(4):427–444 [DOI] [PubMed] [Google Scholar]
- 32.OEI L, ZILLIKENS M C, DEHGHAN A et al (2013) High bone mineral density and fracture risk in type 2 diabetes as skeletal complications of inadequate glucose control: the Rotterdam study [J]. Diabetes Care 36(6):1619–1628 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.NAPOLI N, STROTMEYER E S, ENSRUD K E et al (2014) Fracture risk in diabetic elderly men: the MrOS study [J]. Diabetologia 57(10):2057–2065 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.JANGHORBANI M, VAN DAM R M, WILLETT W C et al (2007) Systematic review of type 1 and type 2 diabetes mellitus and risk of fracture [J]. Am J Epidemiol 166(5):495–505 [DOI] [PubMed] [Google Scholar]
- 35.BAI J, GAO Q, WANG C et al (2020) Diabetes mellitus and risk of low-energy fracture: a meta-analysis [J]. Aging Clin Exp Res 32(11):2173–2186 [DOI] [PubMed] [Google Scholar]
- 36.JIA P, BAO L, CHEN H et al (2017) Risk of low-energy fracture in type 2 diabetes patients: a meta-analysis of observational studies [J]. Osteoporos Int 28(11):3113–3121 [DOI] [PubMed] [Google Scholar]
- 37.DYTFELD J (2017) Type 2 diabetes and risk of low-energy fractures in postmenopausal women: meta-analysis of observational studies [J]. Aging Clin Exp Res 29(2):301–309 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.TANG Y, ZHANG L, YE D et al (2023) Causal relationship between type 1 diabetes and osteoporosis and fracture occurrence: a two-sample mendelian randomization analysis [J]. Osteoporos Int 34(6):1111–1117 [DOI] [PubMed] [Google Scholar]
- 39.VAN DAELE P L, STOLK R P, BURGER H et al (1995) Bone density in non-insulin-dependent diabetes mellitus. The Rotterdam study [J]. Ann Intern Med 122(6):409–414 [DOI] [PubMed] [Google Scholar]
- 40.SOSA M, SAAVEDRA P, JóDAR E et al (2009) Bone mineral density and risk of fractures in aging, obese post-menopausal women with type 2 diabetes. The GIUMO study [J]. Aging Clin Exp Res 21(1):27–32 [DOI] [PubMed] [Google Scholar]
- 41.GERDHEM P, ISAKSSON A, AKESSON K et al (2005) Increased bone density and decreased bone turnover, but no evident alteration of fracture susceptibility in elderly women with diabetes mellitus [J]. Osteoporos Int 16(12):1506–1512 [DOI] [PubMed] [Google Scholar]
- 42.MERLOTTI D, GENNARI L, DOTTA F et al (2010) Mechanisms of impaired bone strength in type 1 and 2 diabetes [J]. Nutr Metab Cardiovasc Dis 20(9):683–690 [DOI] [PubMed] [Google Scholar]
- 43.GREGORIO F, CRISTALLINI S, SANTEUSANIO F et al (1994) Osteopenia associated with non-insulin-dependent diabetes mellitus: what are the causes? [J]. Diabetes Res Clin Pract 23(1):43–54 [DOI] [PubMed] [Google Scholar]
- 44.FARR J N KHOSLAS (2016) Determinants of bone strength and quality in diabetes mellitus in humans [J]. Bone 82:28–34 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.PATSCH JM, BURGHARDT A J, YAP S P et al (2013) Increased cortical porosity in type 2 diabetic postmenopausal women with fragility fractures [J]. J Bone Min Res 28(2):313–324 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.SAITO M (2010) Collagen cross-links as a determinant of bone quality: a possible explanation for bone fragility in aging, osteoporosis, and diabetes mellitus [J]. Osteoporos Int 21(2):195–214 [DOI] [PubMed] [Google Scholar]
- 47.LEIDIG-BRUCKNER G, ZIEGLER R (2001) Diabetes mellitus a risk for osteoporosis? [J]. Exp Clin Endocrinol Diabetes 109(Suppl 2):S493–514 [DOI] [PubMed] [Google Scholar]
- 48.SCHWARTZ A V VITTINGHOFFE, SELLMEYER D E et al (2008) Diabetes-related complications, glycemic control, and falls in older adults [J]. Diabetes Care 31(3):391–396 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.MARTINEZ-LAGUNA D, TEBE C, JAVAID M K et al (2015) Incident type 2 diabetes and hip fracture risk: a population-based matched cohort study [J]. Osteoporos Int 26(2):827–833 [DOI] [PubMed] [Google Scholar]
- 50.SENNERBY U, MELHUS H, GEDEBORG R et al (2009) Cardiovascular diseases and risk of hip fracture [J]. JAMA 302(15):1666–1673 [DOI] [PubMed] [Google Scholar]
- 51.MIAO J, BRISMAR K, NYRéN O et al (2005) Elevated hip fracture risk in type 1 diabetic patients: a population-based cohort study in Sweden [J]. Diabetes Care 28(12):2850–2855 [DOI] [PubMed] [Google Scholar]
- 52.STATHI D, FOUNTOULAKIS N (2023) Impact of treatment with active vitamin D calcitriol on bone turnover markers in people with type 2 diabetes and stage 3 chronic kidney disease [J]. Bone 166:116581 [DOI] [PubMed] [Google Scholar]
- 53.RZONCA S O, SUVA L J, GADDY D et al (2004) Bone is a target for the antidiabetic compound rosiglitazone [J]. Endocrinology 145(1):401–406 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.SCHWARTZ AV (2008) TZDs and Bone: A Review of the Recent Clinical Evidence [J]. PPAR Res, 2008: 297893 [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data is provided within the supplementary information files.