Abstract Introduction Type 2 diabetes mellitus among older women has been associated with increased bone mineral density, but paradoxically with increased fracture risk. Findings among older men have varied, and potential mechanisms have not been fully elucidated. Methods A retrospective study of male veterans 65 to 99 years of age who received primary care in the Veterans Health Administration from 2000 to 2010, using administrative data from all 146 Veterans Health Administration medical centers linked to Centers for Medicare and Medicaid Services Medicare fee-for-service data. Potential mediating factors of the diabetes-associated risk were evaluated using negative binomial regression models with the outcomes of any clinical fracture and hip fracture. Results Of 2,798,309 Veterans included in the cohort, 900,402 (32.3%) had a diagnosis of diabetes. After adjusting for age, race, ethnicity, body mass index, alcohol and tobacco use, rheumatoid arthritis, and corticosteroid use, the risk of any clinical fracture associated with diabetes was 1.22 (95% confidence interval, 1.21 to 1.23) and that of hip fracture was 1.21 (95% confidence interval, 1.19 to 1.23). Significant mediating factors included peripheral neuropathy, cardiovascular disease, and congestive heart failure, with 45.5% of the diabetes-associated fracture risk explained by these diagnoses. Conclusions Older male Veterans with diabetes have a 22% increased risk of incident clinical fracture compared with those without. A significant portion of this risk is explained by diabetes-related comorbidities, specifically peripheral neuropathy and congestive heart failure. Identification of these mediating factors suggests possible mechanisms, as well as potential interventions. Diabetes mellitus affects >29 million persons in the United States, with the highest rates among adults >65 years of age (1). At an annual cost of $245 million, a significant proportion of the burden results from complications of diabetes, including renal and cardiovascular disease. Among its complications, diabetes mellitus has been associated with a significant increased risk for fracture (2–5). However, type 2 diabetes mellitus among older adults has also been associated with increased bone mineral density (BMD); thus, the increased fracture rate presents a clinical paradox (6–8). The underlying mechanisms for this paradoxical increased fracture risk remain controversial. A number of complex interactions between bone and diabetes have been previously reviewed (9, 10). Although older adults with diabetes have an increased risk of falls (11–13), several studies have shown that the higher fracture risk persists even after adjusting for fall risk (14, 15). Others have suggested that diabetes-related changes in bone quality also contribute to the increased fracture risk (16, 17). These bone changes could be mediated in part by other diabetes-associated conditions such as impaired renal function or cardiovascular disease, both of which have been associated with lower bone quality and increased fracture risk (18, 19). However, others have postulated that diabetes complications are simply a marker of more severe diabetes and do not indicate a mechanism of action. Some studies have suggested a differential effect of diabetes on fracture risk by sex, with the increased fracture risk confined to women, whereas others have shown no interaction. Furthermore, there has been substantial variation in the magnitude of the risk estimates (20–24). Therefore, we conducted this study to confirm and precisely quantify the risk of fracture (both clinical and hip) among older men with diabetes, and then to identify potential mediating factors of the fracture risk. We hypothesized that comorbidities, especially those related to complications of diabetes, mediate the observed increase in fracture risk. Methods Using administrative data from all 146 Veterans Health Administration (VHA) medical centers, we conducted a retrospective study of a cohort of male veterans aged 65 to 99 years who received primary care in the VHA from 2000 to 2010. Receiving primary care was defined as having two or more visits to a primary care provider in 2 consecutive calendar years. Men with a diagnosis of osteoporosis, osteoporosis medication prescription, or any clinical fracture in the 3 years prior to their start date were excluded. The VHA data sources included the inpatient treatment file, outpatient care file, the fee-basis file, and the Pharmacy Benefits Management Services outpatient treatment file. Because some veterans receive urgent/emergent medical care at non–Veterans Affairs facilities, the cohort was limited to those ≥65 years of age, and VHA data were linked to Centers for Medicare and Medicaid Services (CMS) Medicare fee-for-service data for more complete capture of medical care. The Medicare data sources were derived from fee-for-service Medicare, including part A (inpatient, including skilled nursing, home health, and hospice) and part B (outpatient) claims. Data were analyzed on the Veterans Affairs Informatics and Computing Infrastructure. The study was approved by the Institutional Review Board at the Durham Veterans Administration Medical Center. Diabetes was defined as presence of diabetes-related ICD9 codes (250.xx) on two or more inpatient or outpatient visits, or the presence of one code in addition to prescription for diabetes medication. Birman-Deych et al. (25) validated this definition, showing 75% sensitivity and 99% specificity. Diabetes medications included insulin, metformin, thiazolidinediones, and sulfonylureas/secretagogues. It was not possible to accurately distinguish between type 1 and type 2 diabetes in this administrative dataset; however, >98% of those classified with diabetes had at least one code indicating type 2 diabetes, and prior studies of veterans have documented low rates of true type 1 diabetes because this diagnosis precludes military service (26). Clinically apparent fractures were ascertained from ICD9 codes in inpatient and outpatient Veterans Affairs and CMS files, utilizing codes 733.93 to 733.95, 767.3, 800 to 829, and V54.13, as well as CPT codes for fracture treatment. ICD9 codes related to fractures of the fingers, toes, and skull/face were excluded. Demographic variables included age, race, and ethnicity. Physical measurements included body mass index (BMI) at baseline. Comorbidities included a history of cerebrovascular disease, cardiovascular disease, peripheral vascular disease, myocardial infarction, hypertension, chronic obstructive pulmonary disease, chronic liver disease, prostate cancer, mood disorders, seizure disorders, rheumatoid arthritis, and neuropathy. Presence of a comorbid disease was defined as one or more of the respective ICD9 codes in two or more separate inpatient or outpatient visits. For example, peripheral neuropathy was defined as presence of ICD9 codes 356.x, 357.x, 250.6, 353.5, 713.5, and 354.0 to 355.9, and congestive heart failure was defined as ICD9 codes 425.x and 428.x (additional definitions are available from the authors upon request). Chronic kidney disease was defined as the presence of respective ICD9 codes on two or more inpatient or outpatient visits, or creatinine clearance <60 mL/min on at least two laboratory measurements at least 3 months apart. Chronic corticosteroid use was defined as three or more continuous months of use of glucocorticoid medication at an average daily dose ≥10 mg of prednisone equivalents. History of tobacco use was defined as ICD9 codes for tobacco dependence, tobacco use, or presence of a prescription for nicotine replacement. Natural language processing using a validated extraction process from radiology and consult databases in the Veterans Affairs Informatics and Computing Infrastructure platform was used to extract results from BMD assessments by dual x-ray absorptiometry on individual veterans. The databases were queried for any text fields with keywords such as DEXA, DXA, bone density, BMD, T score, and adjacent text searches that extract site-specific BMD results. Descriptive statistics were used to characterize the study population, with means and standard deviation values for continuous variables and with frequencies and percentages for categorical variables. The χ2 test for categorical variables or the Student t test for continuous variables was used to examine the difference in distributions of baseline characteristics between those with and without diabetes. The fracture outcomes of interest were any clinical fracture and hip fracture. Association of diabetes with clinical fracture risk was evaluated using negative binomial regression modeling and with hip fracture using logistic regression. The initial model evaluated association of diabetes with the fracture outcome, controlling for fracture-related comorbidities, including age, race/ethnicity, BMI, rheumatoid arthritis, corticosteroid use, tobacco use, and alcohol use, which were then added to the model. Relative risks (RRs) of clinical fracture or odds ratio (OR) of hip fracture associated with diabetes with 95% confidence intervals (CIs) are presented. To evaluate potential mediating factors, comorbidities associated with both diabetes and metabolic bone disease were added individually to the fracture-related model, specifically cerebrovascular disease, cardiovascular disease, peripheral vascular disease, myocardial infarction, hypertension, chronic obstructive pulmonary disease, chronic liver disease, prostate cancer, mood disorders, seizure disorders, and neuropathy. Mediation factors were identified by comparing CIs for diabetes-associated fracture risk prior to and after inclusion of potential factors in the regression model. Nonoverlapping 95% CIs were deemed to be significant factors. A final model was evaluated that included all significant mediating factors into a single model. The percentage of the mediated effect was calculated as the relative change in the parameter estimate after inclusion of the potential mediator to the model. All analyses were done in the SAS System for Windows (version 9.3; SAS Institute, Cary, NC). Results Among the 2,798,309 veterans in the cohort, 900,402 (32.3%) had diabetes. Baseline characteristics of the study population stratified by diabetes status are presented in Table 1. Compared with those without diabetes, men with diabetes were slightly older (71.2 vs 70.8 years), with greater BMI (30.1 vs 28.0 kg/m2), and more likely to report Black race (10.2% vs 7.6%). Older men with diabetes were also more likely to suffer from chronic kidney disease, cardiovascular disease, and neuropathy. Among those with a BMD assessment ordered in routine clinical care during the cohort period (n = 150,026), those with diabetes had a higher femoral neck BMD, compared with those without diabetes (T score −0.51 vs −0.28, P < 0.001). Table 1. Baseline Characteristics by Diabetes Status Without Diabetes Mellitus (N = 1,897,907) With Diabetes Mellitus (N = 900,402) P Age, y (mean ± SD) 71.8 ± 6.6 72.1 + 6.5 <0.001 BMI, kg/m2 (mean ± SD) 28.0 ± 4.7 30.1 ± 5.4 <0.001 Race, % White 71.7 71.2 <0.001 Black 7.6 10.2 Other 3.4 5.0 Unknown 17.3 13.6 Femoral BMD T scorea −0.51 ± 1.44 −0.28 ± 1.45 <0.001 Comorbidities, % Alcohol abuse 18.1 17.5 <0.001 Tobacco use 22.5 20.1 <0.001 Rheumatoid arthritis 0.7 0.5 <0.001 Corticosteroid use 1.7 1.6 0.19 Chronic kidney disease Stage 3 22.7 27.7 <0.001 Stage 4/5 0.9 2.0 Neuropathy 13.6 23.8 <0.001 Cardiovascular disease 8.4 11.2 <0.001 Cerebrovascular disease 0.65 1.00 <0.001 Congestive heart failure 12.6 21.3 <0.001 Chronic lung disease 25.0 24.5 <0.001 Chronic liver disease 7.7 7.9 <0.001 Seizure disorder 3.9 4.8 <0.001 Without Diabetes Mellitus (N = 1,897,907) With Diabetes Mellitus (N = 900,402) P Age, y (mean ± SD) 71.8 ± 6.6 72.1 + 6.5 <0.001 BMI, kg/m2 (mean ± SD) 28.0 ± 4.7 30.1 ± 5.4 <0.001 Race, % White 71.7 71.2 <0.001 Black 7.6 10.2 Other 3.4 5.0 Unknown 17.3 13.6 Femoral BMD T scorea −0.51 ± 1.44 −0.28 ± 1.45 <0.001 Comorbidities, % Alcohol abuse 18.1 17.5 <0.001 Tobacco use 22.5 20.1 <0.001 Rheumatoid arthritis 0.7 0.5 <0.001 Corticosteroid use 1.7 1.6 0.19 Chronic kidney disease Stage 3 22.7 27.7 <0.001 Stage 4/5 0.9 2.0 Neuropathy 13.6 23.8 <0.001 Cardiovascular disease 8.4 11.2 <0.001 Cerebrovascular disease 0.65 1.00 <0.001 Congestive heart failure 12.6 21.3 <0.001 Chronic lung disease 25.0 24.5 <0.001 Chronic liver disease 7.7 7.9 <0.001 Seizure disorder 3.9 4.8 <0.001 Abbreviation: SD, standard deviation. a When available (N = 150,026). View Large During the study period, there were 56,905 clinical fractures among older men without diabetes and 25,840 among those with diabetes. Of those, there were 7575 hip fractures among those without diabetes and 3601 among those with diabetes. The overall fracture rate was 0.117 per 1000 days at risk, with 0.130 per 1000 days among older men with diabetes compared with 0.111 per 1000 days among those without diabetes. Common sites of fracture among older men with diabetes included vertebra, rib, hip, and lower extremity (Table 2). Table 2. Fracture Site According to Diabetes Status Fracture Site, n (%) Without Diabetes Mellitus (N = 1,897,907) With Diabetes Mellitus (N = 900,402) Total 56,905 25,840 Vertebral 15,319 (26.9) 5,745 (22.2) Rib 10,314 (18.1) 4,677 (18.1) Hip 7,575 (13.3) 3,601 (13.9) Other lower limb 6,471 (11.4) 2,983 (11.5) Tibia/fibula 4,779 (8.4) 2,726 (10.6) Radius/ulna 4,250 (7.5) 2,024 (7.8) Humerus 2,421 (4.3) 1,468 (5.7) Scapula/clavicle 1,568 (2.8) 692 (2.7) Pelvis 1,464 (2.6) 649 (2.5) Femur 1,391 (2.4) 619 (2.4) Patella 921 (1.6) 407 (1.6) Sternum 194 (0.3) 90 (0.3) Other upper limb 134 (0.2) 73 (0.3) Other 23 (0.0) 6 (0.0) Fracture Site, n (%) Without Diabetes Mellitus (N = 1,897,907) With Diabetes Mellitus (N = 900,402) Total 56,905 25,840 Vertebral 15,319 (26.9) 5,745 (22.2) Rib 10,314 (18.1) 4,677 (18.1) Hip 7,575 (13.3) 3,601 (13.9) Other lower limb 6,471 (11.4) 2,983 (11.5) Tibia/fibula 4,779 (8.4) 2,726 (10.6) Radius/ulna 4,250 (7.5) 2,024 (7.8) Humerus 2,421 (4.3) 1,468 (5.7) Scapula/clavicle 1,568 (2.8) 692 (2.7) Pelvis 1,464 (2.6) 649 (2.5) Femur 1,391 (2.4) 619 (2.4) Patella 921 (1.6) 407 (1.6) Sternum 194 (0.3) 90 (0.3) Other upper limb 134 (0.2) 73 (0.3) Other 23 (0.0) 6 (0.0) View Large After adjusting for age, race, ethnicity, BMI, alcohol and tobacco use, and corticosteroid use, the risk of any clinical fracture associated with diabetes was 1.22 (95% CI, 1.21 to 1.23). Fracture risk was similar between those with diabetes treated without antihyperglycemic agents [RR, 1.17 (1.16 to 1.18)] and those prescribed oral agents only [RR, 1.18 (1.16 to 1.19)]. The most common oral agent prescriptions were sulfonylureas (73.6%), metformin (54.5%), and thiazolidinediones (10.4%). However, fracture risk was significantly higher for those prescribed both insulin and an oral agent [RR, 1.40 (1.37 to 1.43)] and those prescribed insulin only [RR, 1.54 (1.51-1.58)]. Significant mediating factors for diabetes-associated fracture risk included comorbidities associated with diabetes complications (Table 3): peripheral neuropathy explained 21.1% of the fracture risk, congestive heart failure explained 16.6%, and cardiovascular disease explained 6.9%. However, chronic kidney disease and cerebrovascular disease did not significantly mediate the fracture risk. As expected, comorbidities that are not associated with diabetic complications (e.g., chronic liver disease, chronic pulmonary disease) were not significant mediation factors and were not included in the final model. In the final model, inclusion of peripheral neuropathy, cardiovascular disease, and congestive heart failure resulted in mediation of 45.5% of the total fracture risk. Table 3. RR of Any Clinical Fracture Associated With Diabetes and Mediating Effect of Comorbidities on Diabetes-Associated Risk RR (95% CI) % of Diabetes Effect Mediated Diabetesa 1.22 (1.21–1.23) — + Chronic kidney disease 1.21 (1.20–1.22) NS + Neuropathy 1.17 (1.16–1.18) 21.1 + Cardiovascular disease 1.19 (1.18–1.20) 6.9 + Cerebrovascular disease 1.21 (1.20–1.22) NS + Congestive heart failure 1.18 (1.17–1.19) 16.6 + Chronic obstructive lung disease 1.22 (1.21–1.23) NS + Chronic liver disease 1.22 (1.21–1.23) NS RR (95% CI) % of Diabetes Effect Mediated Diabetesa 1.22 (1.21–1.23) — + Chronic kidney disease 1.21 (1.20–1.22) NS + Neuropathy 1.17 (1.16–1.18) 21.1 + Cardiovascular disease 1.19 (1.18–1.20) 6.9 + Cerebrovascular disease 1.21 (1.20–1.22) NS + Congestive heart failure 1.18 (1.17–1.19) 16.6 + Chronic obstructive lung disease 1.22 (1.21–1.23) NS + Chronic liver disease 1.22 (1.21–1.23) NS Abbreviation: NS, not significant. a All models were adjusted for age, race/ethnicity, tobacco use, alcohol use, glucocorticoid use, rheumatoid arthritis, and BMI. View Large For hip fracture, the OR associated with diabetes, after adjusting for demographic factors and fracture-related comorbidities, was 1.21 (95% CI, 1.19 to 1.23). As observed with risk of any clinical fracture, peripheral neuropathy was a significant mediator of the diabetes-associated hip fracture risk (20% of fracture risk explained) (Table 4). Both cardiovascular disease and congestive heart failure trended toward a significant mediation effect; however, the CIs were overlapping with the baseline model. Forty-one percent of the hip fracture risk associated with diabetes was explained with inclusion of peripheral neuropathy, cardiovascular disease, and congestive heart failure. Table 4. OR for Hip Fracture Associated With Diabetes and Mediating Effect of Comorbidities on Diabetes-Associated Risk OR (95% CI) % of Diabetes Effect Mediated Diabetesa 1.21 (1.19–1.23) — + Chronic kidney disease 1.20 (1.18–1.21) NS + Neuropathy 1.16 (1.15–1.18) 20.0 + Cardiovascular disease 1.18 (1.17–1.20) NS + Cerebrovascular disease 1.21 (1.19–1.22) NS + Congestive heart failure 1.18 (1.16–1.19) NS + Chronic obstructive lung disease 1.21 (1.19–1.23) NS + Chronic liver disease 1.21 (1.19–1.22) NS OR (95% CI) % of Diabetes Effect Mediated Diabetesa 1.21 (1.19–1.23) — + Chronic kidney disease 1.20 (1.18–1.21) NS + Neuropathy 1.16 (1.15–1.18) 20.0 + Cardiovascular disease 1.18 (1.17–1.20) NS + Cerebrovascular disease 1.21 (1.19–1.22) NS + Congestive heart failure 1.18 (1.16–1.19) NS + Chronic obstructive lung disease 1.21 (1.19–1.23) NS + Chronic liver disease 1.21 (1.19–1.22) NS Abbreviation: NS, not significant. a All models were adjusted for age, race/ethnicity, tobacco use, alcohol use, glucocorticoid use, rheumatoid arthritis, and BMI. View Large Discussion In this large retrospective cohort study of men >65 years of age, after controlling for multiple fracture-related risk factors, diabetes mellitus remained independently associated with a significant 20% increase in any clinical fracture and hip fracture. Our estimate of the magnitude of the fracture risk associated with diabetes in men is largely consistent with, but more precise than, prior prospective studies. The Osteoporotic Fractures in Men (MrOS) cohort had a 22% increased risk associated with diabetes; however, there was not a significant difference in rate of hip fracture associated with diabetes mellitus in the MrOS study [3.3 per 1000-person-years (nondiabetes) vs 3.1 per 1000-person-years (diabetes)] (24). In the Health ABC study, the unadjusted RR was 1.61; the risk was lower in our study compared with that observed in the Health ABC study, but study participants in Health ABC were slightly older than those in MrOS and in the present study (8). A prior population-based study in Ontario, Canada, showed a significant increase in hip fracture risk associated with diabetes, after adjusting for multiple confounders, including age, medical comorbidities, and medication use (hazard ratio, 1.18; 95% CI, 1.12 to 1.24) (5). Interestingly, there was a statistically significant sex interaction, with men having lower risk than women. Compared with prior studies among older women, our study suggests that the magnitude of both any clinical fracture and hip fracture risk, associated with diabetes, among older men is comparable to that reported in older women. Nearly half of the increased risk in clinical fracture was mediated by diabetes-related comorbidities, including neuropathy and cardiovascular disease, but not by unrelated comorbidities such as chronic liver or pulmonary disease. There was no mediation observed by chronic kidney disease or cerebrovascular disease. In contrast to our results, diabetes-related comorbidities were not associated with increased fracture risk in the Health ABC study; however, those authors acknowledge that they may not have had sufficient power to observe such an effect. Although some have hypothesized that diabetes is simply a marker of poor health status, our study had ample power to suggest a direct effect of diabetes on incident fracture risk by specific mechanisms. Peripheral neuropathy was the most important mediator of fracture risk observed in our cohort. Prior studies have shown a significant risk of falls associated with both peripheral neuropathy and type 2 diabetes, as well as a contribution of such falls with regard to fracture risk (11, 12, 27, 28). For example, in the Women’s Health Initiative–Observational Study, Bonds et al. (14) found that older women with diabetes reported more falls at baseline and during follow-up, compared with those without diabetes (44% vs 32%). Thus, our data reinforce the importance of fall risk reduction interventions for both female and male patients with diabetes, particularly those with peripheral neuropathy. We also found significant mediation by congestive heart failure, suggesting a direct effect of diabetes on bone and calcium metabolism. Congestive heart failure has been associated with lower serum 25-hydroxyvitamin D levels, increased parathyroid hormone levels, increased magnesium and calcium excretion, and fracture risk (29–32). In animal studies, increased aldosterone (as found in heart failure) stimulated increased calcium excretion and increased parathyroid hormone levels (33, 34). In a case-control study of male veterans, Carbone et al. (35) showed fewer fractures in patients prescribed spironolactone, an aldosterone antagonist. Also, studies have shown increased levels of inflammatory cytokines (e.g., interleukin-6 and tumor necrosis factor-α) associated with congestive heart failure (36, 37), which have also been associated with increased fracture risk, as well as frailty and physical performance (38–41). Although this suggests that congestive heart failure may also lead to increased falls risk, the mediation seen in the present study appears to be independent of that seen with peripheral neuropathy. Our findings suggest that bone density screening and fracture prevention strategies should be emphasized for patients with comorbid diabetes and heart failure. Our results should be interpreted in light of the study strengths and weaknesses. To our knowledge, this is the largest cohort study among older men of diabetes and fracture risk, allowing for precise estimates of fracture risk within this population. Because of the linkage with the CMS database, there was high ascertainment for clinical fracture, as prior studies have demonstrated good sensitivity and specificity for ICD9 fracture codes (42–44). However, potential weaknesses should be acknowledged. In contrast to prior prospective studies, covariates in this study were limited to those that were available in administrative datasets, and some laboratory (e.g., 25-hydroxyvitamin D, parathyroid hormone levels) and fracture-related risk factors (e.g., falls not resulting in health care utilization, parental hip fracture history) were not measured. We were unable to quantify duration of diabetes, or accurately assess diabetes control in our dataset. Finally, prior studies have shown that veterans have higher risk of fracture compared with nonveterans (45–48). Therefore, the generalizability of the results in this study may be limited. However, the comparison group in this study was also veterans, thus reducing variability between groups and selection bias. In conclusion, this large cohort study confirmed a 22% increased risk of incident clinical fracture associated with diabetes among older men. This risk was independent of other known risk factors for fracture. A significant portion of this risk was explained by diabetes-related comorbidities, specifically peripheral neuropathy and congestive heart failure. Identification of these mediating factors suggests possible mechanisms for the diabetes effect, as well as potential interventions. Abbreviations: BMD bone mineral density BMI body mass index CI confidence interval CMS Centers for Medicare and Medicaid Services MrOS Osteoporotic Fractures in Men OR odds ratio RR relative risk VHA Veterans Health Administration. Acknowledgments Results from this study were presented in part at the American Diabetes Association annual meeting in New Orleans, Louisiana (2016) and the American Society of Bone and Mineral Research annual meeting in Atlanta, Georgia (2016). Financial Support: This work was supported by Department of Defense Grant W81XWH-12-2-0093. R.H.L. acknowledges support from National Institutes of Health/National Institute on Aging Grant R03 AG048119 and American Diabetes Association Grant 7-14-ASP-01. R.H.L., C.F.P., and C.C.E. acknowledge support from the Duke Claude A. Pepper Center/National Institute on Aging Grant P30 AG028716. C.C.E. acknowledges support from National Institutes of Health/National Institute on Aging Grant K24 AG049077-02. Disclosure Summary: The authors have nothing to disclose. References 1. Centers for Disease Control and Prevention. National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States, 2014 . Atlanta, GA: U.S. Department of Health and Human Services; 2014. 2. Vestergaard P. Discrepancies in bone mineral density and fracture risk in patients with type 1 and type 2 diabetes—a meta-analysis. Osteoporos Int . 2007; 18( 4): 427– 444. Google Scholar CrossRef Search ADS PubMed 3. Leslie WD, Lix LM, Prior HJ, Derksen S, Metge C, O’Neil J. Biphasic fracture risk in diabetes: a population-based study. Bone . 2007; 40( 6): 1595– 1601. Google Scholar CrossRef Search ADS PubMed 4. Melton III LJ, Leibson CL, Achenbach SJ, Therneau TM, Khosla S. Fracture risk in type 2 diabetes: update of a population-based study. J Bone Miner Res . 2008; 23( 8): 1334– 1342. Google Scholar CrossRef Search ADS PubMed 5. Lipscombe LL, Jamal SA, Booth GL, Hawker GA. The risk of hip fractures in older individuals with diabetes: a population-based study. Diabetes Care . 2007; 30( 4): 835– 841. Google Scholar CrossRef Search ADS PubMed 6. Schwartz AV, Vittinghoff E, Bauer DC, Hillier TA, Strotmeyer ES, Ensrud KE, Donaldson MG, Cauley JA, Harris TB, Koster A, Womack CR, Palermo L, Black DM; Study of Osteoporotic Fractures (SOF) Research GroupOsteoporotic Fractures in Men (MrOS) Research GroupHealth, Aging, and Body Composition (Health ABC) Research Group. Association of BMD and FRAX score with risk of fracture in older adults with type 2 diabetes. JAMA . 2011; 305( 21): 2184– 2192. Google Scholar CrossRef Search ADS PubMed 7. de Liefde II, van der Klift M, de Laet CE, van Daele PL, Hofman A, Pols HA; de L. Bone mineral density and fracture risk in type-2 diabetes mellitus: the Rotterdam Study. Osteoporos Int . 2005; 16( 12): 1713– 1720. Google Scholar CrossRef Search ADS PubMed 8. Janghorbani M, Van Dam RM, Willett WC, Hu FB. Systematic review of type 1 and type 2 diabetes mellitus and risk of fracture. Am J Epidemiol . 2007; 166( 5): 495– 505. Google Scholar CrossRef Search ADS PubMed 9. Napoli N, Chandran M, Pierroz DD, Abrahamsen B, Schwartz AV, Ferrari SL; IOF Bone and Diabetes Working Group. Mechanisms of diabetes mellitus-induced bone fragility. Nat Rev Endocrinol . 2017; 13( 4): 208– 219. Google Scholar CrossRef Search ADS PubMed 10. Shanbhogue VV, Mitchell DM, Rosen CJ, Bouxsein ML. Type 2 diabetes and the skeleton: new insights into sweet bones. Lancet Diabetes Endocrinol . 2016; 4( 2): 159– 173. Google Scholar CrossRef Search ADS PubMed 11. Hanlon JT, Landerman LR, Fillenbaum GG, Studenski S. Falls in African American and white community-dwelling elderly residents. J Gerontol A Biol Sci Med Sci . 2002; 57( 7): M473– M478. Google Scholar CrossRef Search ADS PubMed 12. Schwartz AV, Hillier TA, Sellmeyer DE, Resnick HE, Gregg E, Ensrud KE, Schreiner PJ, Margolis KL, Cauley JA, Nevitt MC, Black DM, Cummings SR. Older women with diabetes have a higher risk of falls: a prospective study. Diabetes Care . 2002; 25( 10): 1749– 1754. Google Scholar CrossRef Search ADS PubMed 13. Schwartz AV, Vittinghoff E, Sellmeyer DE, Feingold KR, de Rekeneire N, Strotmeyer ES, Shorr RI, Vinik AI, Odden MC, Park SW, Faulkner KA, Harris TB; Health, Aging, and Body Composition Study. Diabetes-related complications, glycemic control, and falls in older adults. Diabetes Care . 2008; 31( 3): 391– 396. Google Scholar CrossRef Search ADS PubMed 14. Bonds DE, Larson JC, Schwartz AV, Strotmeyer ES, Robbins J, Rodriguez BL, Johnson KC, Margolis KL. Risk of fracture in women with type 2 diabetes: the Women’s Health Initiative Observational Study. J Clin Endocrinol Metab . 2006; 91( 9): 3404– 3410. Google Scholar CrossRef Search ADS PubMed 15. Lee RH, Pieper CF, Colón-Emeric C. Functional impairments mediate association between clinical fracture risk and type 2 diabetes mellitus in older women. J Am Geriatr Soc . 2015; 63( 8): 1546– 1551. Google Scholar CrossRef Search ADS PubMed 16. Schwartz AV, Garnero P, Hillier TA, Sellmeyer DE, Strotmeyer ES, Feingold KR, Resnick HE, Tylavsky FA, Black DM, Cummings SR, Harris TB, Bauer DC; Health, Aging, and Body Composition Study. Pentosidine and increased fracture risk in older adults with type 2 diabetes. J Clin Endocrinol Metab . 2009; 94( 7): 2380– 2386. Google Scholar CrossRef Search ADS PubMed 17. Patsch JM, Burghardt AJ, Yap SP, Baum T, Schwartz AV, Joseph GB, Link TM. Increased cortical porosity in type 2 diabetic postmenopausal women with fragility fractures. J Bone Miner Res . 2013; 28( 2): 313– 324. Google Scholar CrossRef Search ADS PubMed 18. Vestergaard P, Rejnmark L, Mosekilde L. Diabetes and its complications and their relationship with risk of fractures in type 1 and 2 diabetes. Calcif Tissue Int . 2009; 84( 1): 45– 55. Google Scholar CrossRef Search ADS PubMed 19. Viégas M, Costa C, Lopes A, Griz L, Medeiro MA, Bandeira F. Prevalence of osteoporosis and vertebral fractures in postmenopausal women with type 2 diabetes mellitus and their relationship with duration of the disease and chronic complications. J Diabetes Complications . 2011; 25( 4): 216– 221. Google Scholar CrossRef Search ADS PubMed 20. Chen HF, Ho CA, Li CY. Increased risks of hip fracture in diabetic patients of Taiwan: a population-based study. Diabetes Care . 2008; 31( 1): 75– 80. Google Scholar CrossRef Search ADS PubMed 21. Giangregorio LM, Leslie WD, Lix LM, Johansson H, Oden A, McCloskey E, Kanis JA. FRAX underestimates fracture risk in patients with diabetes. J Bone Miner Res . 2012; 27( 2): 301– 308. Google Scholar CrossRef Search ADS PubMed 22. Oei L, Zillikens MC, Dehghan A, Buitendijk GH, Castaño-Betancourt MC, Estrada K, Stolk L, Oei EH, van Meurs JB, Janssen JA, Hofman A, van Leeuwen JP, Witteman JC, Pols HA, Uitterlinden AG, Klaver CC, Franco OH, Rivadeneira F. High bone mineral density and fracture risk in type 2 diabetes as skeletal complications of inadequate glucose control: the Rotterdam Study. Diabetes Care . 2013; 36( 6): 1619– 1628. Google Scholar CrossRef Search ADS PubMed 23. Strotmeyer ES, Cauley JA, Schwartz AV, Nevitt MC, Resnick HE, Bauer DC, Tylavsky FA, de Rekeneire N, Harris TB, Newman AB. Nontraumatic fracture risk with diabetes mellitus and impaired fasting glucose in older white and black adults: the health, aging, and body composition study. Arch Intern Med . 2005; 165( 14): 1612– 1617. Google Scholar CrossRef Search ADS PubMed 24. Napoli N, Strotmeyer ES, Ensrud KE, Sellmeyer DE, Bauer DC, Hoffman AR, Dam TT, Barrett-Connor E, Palermo L, Orwoll ES, Cummings SR, Black DM, Schwartz AV. Fracture risk in diabetic elderly men: the MrOS study. Diabetologia . 2014; 57( 10): 2057– 2065. Google Scholar CrossRef Search ADS PubMed 25. Birman-Deych E, Waterman AD, Yan Y, Nilasena DS, Radford MJ, Gage BF. Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Med Care . 2005; 43( 5): 480– 485. Google Scholar CrossRef Search ADS PubMed 26. VA/DoD Clinical Practice Guideline for the Management of Type 2 Diabetes Mellitus in Primary Care, version 5.0 . Washington, DC: Departments of Veterans Affairs and Defense, 2017. 27. Brown SJ, Handsaker JC, Bowling FL, Boulton AJ, Reeves ND. Diabetic peripheral neuropathy compromises balance during daily activities. Diabetes Care . 2015; 38( 6): 1116– 1122. Google Scholar CrossRef Search ADS PubMed 28. MacGilchrist C, Paul L, Ellis BM, Howe TE, Kennon B, Godwin J. Lower-limb risk factors for falls in people with diabetes mellitus. Diabet Med . 2010; 27( 2): 162– 168. Google Scholar CrossRef Search ADS PubMed 29. Laguardia SP, Dockery BK, Bhattacharya SK, Nelson MD, Carbone LD, Weber KT. Secondary hyperparathyroidism and hypovitaminosis D in African-Americans with decompensated heart failure. Am J Med Sci . 2006; 332( 3): 112– 118. Google Scholar CrossRef Search ADS PubMed 30. Shane E, Mancini D, Aaronson K, Silverberg SJ, Seibel MJ, Addesso V, McMahon DJ. Bone mass, vitamin D deficiency, and hyperparathyroidism in congestive heart failure. Am J Med . 1997; 103( 3): 197– 207. Google Scholar CrossRef Search ADS PubMed 31. Terrovitis J, Zotos P, Kaldara E, Diakos N, Tseliou E, Vakrou S, Kapelios C, Chalazonitis A, Nanas S, Toumanidis S, Kontoyannis D, Karga E, Nanas J. Bone mass loss in chronic heart failure is associated with secondary hyperparathyroidism and has prognostic significance. Eur J Heart Fail . 2012; 14( 3): 326– 332. Google Scholar CrossRef Search ADS PubMed 32. Khouzam RN, Dishmon DA, Farah V, Flax SD, Carbone LD, Weber KT. Secondary hyperparathyroidism in patients with untreated and treated congestive heart failure. Am J Med Sci . 2006; 331( 1): 30– 34. Google Scholar CrossRef Search ADS PubMed 33. Chhokar VS, Sun Y, Bhattacharya SK, Ahokas RA, Myers LK, Xing Z, Smith RA, Gerling IC, Weber KT. Loss of bone minerals and strength in rats with aldosteronism. Am J Physiol Heart Circ Physiol . 2004; 287( 5): H2023– H2026. Google Scholar CrossRef Search ADS PubMed 34. Runyan AL, Chhokar VS, Sun Y, Bhattacharya SK, Runyan JW, Weber KT. Bone loss in rats with aldosteronism. Am J Med Sci . 2005; 330( 1): 1– 7. Google Scholar CrossRef Search ADS PubMed 35. Carbone LD, Cross JD, Raza SH, Bush AJ, Sepanski RJ, Dhawan S, Khan BQ, Gupta M, Ahmad K, Khouzam RN, Dishmon DA, Nesheiwat JP, Hajjar MA, Chishti WA, Nasser W, Khan M, Womack CR, Cho T, Haskin AR, Weber KT. Fracture risk in men with congestive heart failure risk reduction with spironolactone. J Am Coll Cardiol . 2008; 52( 2): 135– 138. Google Scholar CrossRef Search ADS PubMed 36. Briasoulis A, Androulakis E, Christophides T, Tousoulis D. The role of inflammation and cell death in the pathogenesis, progression and treatment of heart failure. Heart Fail Rev . 2016; 21( 2): 169– 176. Google Scholar CrossRef Search ADS PubMed 37. Hamid T, Gu Y, Ortines RV, Bhattacharya C, Wang G, Xuan YT, Prabhu SD. Divergent tumor necrosis factor receptor-related remodeling responses in heart failure: role of nuclear factor-κB and inflammatory activation. Circulation . 2009; 119( 10): 1386– 1397. Google Scholar CrossRef Search ADS PubMed 38. Sun M, Yang J, Wang J, Hao T, Jiang D, Bao G, Liu G. TNF-α is upregulated in T2DM patients with fracture and promotes the apoptosis of osteoblast cells in vitro in the presence of high glucose. Cytokine . 2016; 80: 35– 42. Google Scholar CrossRef Search ADS PubMed 39. Brinkley TE, Leng X, Miller ME, Kitzman DW, Pahor M, Berry MJ, Marsh AP, Kritchevsky SB, Nicklas BJ. Chronic inflammation is associated with low physical function in older adults across multiple comorbidities. J Gerontol A Biol Sci Med Sci . 2009; 64( 4): 455– 461. Google Scholar CrossRef Search ADS PubMed 40. Peterson MJ, Thompson DK, Pieper CF, Morey MC, Kraus VB, Kraus WE, Sullivan P, Fillenbaum G, Cohen HJ. A novel analytic technique to measure associations between circulating biomarkers and physical performance across the adult life span. J Gerontol A Biol Sci Med Sci . 2016; 71( 2): 196– 202. Google Scholar CrossRef Search ADS PubMed 41. Barbour KE, Lui LY, Ensrud KE, Hillier TA, LeBlanc ES, Ing SW, Hochberg MC, Cauley JA; Study of Osteoporotic Fractures (SOF) Research Group. Inflammatory markers and risk of hip fracture in older white women: the study of osteoporotic fractures. J Bone Miner Res . 2014; 29( 9): 2057– 2064. Google Scholar CrossRef Search ADS PubMed 42. Lix LM, Azimaee M, Osman BA, Caetano P, Morin S, Metge C, Goltzman D, Kreiger N, Prior J, Leslie WD. Osteoporosis-related fracture case definitions for population-based administrative data. BMC Public Health . 2012; 12( 1): 301. Google Scholar CrossRef Search ADS PubMed 43. Ray WA, Griffin MR, Fought RL, Adams ML. Identification of fractures from computerized Medicare files. J Clin Epidemiol . 1992; 45( 7): 703– 714. Google Scholar CrossRef Search ADS PubMed 44. Tamblyn R, Reid T, Mayo N, McLeod P, Churchill-Smith M. Using medical services claims to assess injuries in the elderly: sensitivity of diagnostic and procedure codes for injury ascertainment. J Clin Epidemiol . 2000; 53( 2): 183– 194. Google Scholar CrossRef Search ADS PubMed 45. Bass E, Pracht E, Foulis P. Bone mineral density scans in veterans. Clin Interv Aging . 2007; 2( 2): 255– 261. Google Scholar PubMed 46. LaFleur J, Rillamas-Sun E, Colón-Emeric CS, Knippenberg KA, Ensrud KE, Gray SL, Cauley JA, LaCroix AZ. Fracture rates and bone density among postmenopausal veteran and non-veteran women from the Women’s Health Initiative. Gerontologist . 2016; 56( Suppl 1): S78– S90. Google Scholar CrossRef Search ADS PubMed 47. Nelson RE, Nebeker JR, Sauer BC, LaFleur J. Factors associated with screening or treatment initiation among male United States veterans at risk for osteoporosis fracture. Bone . 2012; 50( 4): 983– 988. Google Scholar CrossRef Search ADS PubMed 48. Swislocki A, Green JA, Heinrich G, Barnett CA, Meadows ID, Harmon EB, Rank MF, Noth RH. Prevalence of osteoporosis in men in a VA rehabilitation center. Am J Manag Care . 2010; 16( 6): 427– 433. Google Scholar PubMed Copyright © 2018 Endocrine Society
Journal of Clinical Endocrinology and Metabolism – Oxford University Press
Published: Jan 1, 2018
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