Development and Validation of a Simple Hip Fracture Risk Prediction Tool for Type 2 Diabetes: The Fremantle Diabetes Study Phase I

Bone metabolism in diabetes: a clinician's guide to understanding the bone-glucose interplay

Skeletal fragility is an increasingly recognised, but poorly understood, complication of both type 1 and type 2 diabetes. Fracture risk varies according to skeletal site and diabetes-related characteristics. Post-fracture outcomes, including mortality risk, are worse in those with diabetes, placing these people at significant risk. Each fracture therefore represents a sentinel event that warrants targeted management. However, diabetes is a very heterogeneous condition with complex interactions between multiple co-existing, and highly correlated, factors that preclude a clear assessment of the independent clinical markers and pathophysiological drivers for diabetic osteopathy. Additionally, fracture risk calculators and routinely used clinical bone measurements generally underestimate fracture risk in people with diabetes. In the absence of dedicated prospective studies including detailed bone and metabolic characteristics, optimal management centres around selecting treatments that minimise skeletal and metabolic harm. This review summarises the clinical landscape of diabetic osteopathy and outlines the interplay between metabolic and skeletal health. The underlying pathophysiology of skeletal fragility in diabetes and a rationale for considering a diabetes-based paradigm in assessing and managing diabetic bone disease will be discussed.

AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review

Osteoporotic fractures impose a substantial burden on patients with diabetes due to their unique characteristics in bone metabolism, limiting the efficacy of conventional fracture prediction tools. Artificial intelligence (AI) algorithms have shown great promise in predicting osteoporotic fractures. This review aims to evaluate the application of traditional fracture prediction tools (FRAX, QFracture, and Garvan FRC) in patients with diabetes and osteoporosis, review AI-based fracture prediction achievements, and assess the potential efficiency of AI algorithms in this population. This comprehensive literature search was conducted in Pubmed and Web of Science. We found that conventional prediction tools exhibit limited accuracy in predicting fractures in patients with diabetes and osteoporosis due to their distinct bone metabolism characteristics. Conversely, AI algorithms show remarkable potential in enhancing predictive precision and improving patient outcomes. However, the utilization of AI algorithms for predicting osteoporotic fractures in diabetic patients is still in its nascent phase, further research is required to validate their efficacy and assess the potential advantages of their application in clinical practice.

A simple fragility fracture risk score for type 2 diabetes patients: a derivation, validation, comparison, and risk stratification study

OBJECTIVES

The aims of this study were to develop and validate 2 simple scores for stratification of the risks of (1) any fragility (AF) and (2) major osteoporotic fracture (MOF) in type 2 diabetes (T2D) patients; we also compared the performance of these scores with that of the Fracture Risk Assessment Tool (FRAX) and its adjustments.

DESIGN AND METHODS

In this longitudinal cohort study, 1855 patients with T2D were enrolled from January 2015 to August 2019. Cox proportional hazard regression was used to model the 5-year risk of AF and MOF. These scores were internally validated using a bootstrap resampling method of 1000.

RESULTS

During a median follow-up of 5 years, 119 (6.42%) cases of AF and 92 (4.96%) cases of MOFs were identified. Both the concordance index (C-index) and calibration plots indicated improved identification performance using the newly established scores. Furthermore, these scores also showed improved outcomes regarding the decision curve analysis (DCA) and area under the curve (AUC) compared to the widely used FRAX and its derivatives. More importantly, these scores successfully separated T2D patients into risk groups according to significant differences in fracture incidence.

CONCLUSIONS

These novel scores enable simple and reliable fracture risk stratification in T2D patients. Future work is needed to validate these findings in external cohort(s).

Contributors to impaired bone health in type 2 diabetes

Type 2 diabetes (T2D) is associated with numerous complications, including increased risk of fragility fractures, despite seemingly protective factors [e.g., normal bone mineral density and increased body mass index(BMI)]. However, fracture risk in T2D is underestimated by current fracture risk calculators. Importantly, post-fracture mortality is worse in T2D following any fracture, highlighting the importance of identifying high-risk patients that may benefit from targeted management. Several diabetes-related factors are associated with increased fracture risk, including exogenous insulin therapy, vascular complications, and poor glycaemic control, although detailed comprehensive studies to identify the independent contributions of these factors are lacking. The underlying pathophysiological mechanisms are complex and multifactorial, with different factors contributing during the course of T2D disease. These include obesity, hyperinsulinaemia, hyperglycaemia, accumulation of advanced glycation end products, and vascular supply affecting bone-cell function and survival and bone-matrix composition. This review summarises the current understanding of the contributors to impaired bone health in T2D, and proposes an updated approach to managing these patients.

Major osteoporosis fracture prediction in type 2 diabetes: a derivation and comparison study

The widely recommended fracture prediction tool FRAX was developed based on and for the general population. Although several adjusted FRAX methods were suggested for type 2 diabetes (T2DM), they still need to be evaluated in T2DM cohort.

INTRODUCTION

This study was undertaken to develop a prediction model for Chinese diabetes fracture risk (CDFR) and compare its performance with those of FRAX.

METHODS

In this retrospective cohort study, 1730 patients with T2DM were enrolled from 2009.08 to 2013.07. Major osteoporotic fractures (MOFs) during follow-up were collected from Electronic Health Records (EHRs) and telephone interviews. Multivariate Cox regression with backward stepwise selection was used to fit the model. The performances of the CDFR model, FRAX, and adjusted FRAX were compared in the aspects of discrimination and calibration.

RESULTS

6.3% of participants experienced MOF during a median follow-up of 10 years. The final model (CDFR) included 8 predictors: age, gender, previous fracture, insulin use, diabetic peripheral neuropathy (DPN), total cholesterol, triglycerides, and apolipoprotein A. This model had a C statistic of 0.803 (95%CI 0.761-0.844) and calibration χ² of 4.63 (p = 0.86). The unadjusted FRAX underestimated the MOF risk (calibration χ² 134.5, p < 0.001; observed/predicted ratio 2.62, 95%CI 2.17-3.08), and there was still significant underestimation after diabetes adjustments. Comparing FRAX, the CDFR had a higher AUC, lower calibration χ², and better reclassification of MOF.

CONCLUSION

The CDFR model has good performance in 10-year MOF risk prediction in T2DM, especially in patients with insulin use or DPN. Future work is needed to validate our model in external cohort(s).

Fracture prediction tools in diabetes

PURPOSE OF REVIEW

Type 1 (T1D) and 2 diabetes (T2D) are associated with increased risk of fracture independent of bone mineral density (BMD). Fracture risk prediction tools can identify individuals at highest risk, and therefore, most likely to benefit from antifracture therapy. This review summarizes recent advances in fracture prediction tools as applied to individuals with diabetes.

RECENT FINDINGS

The Fracture Risk Assessment (FRAX) tool, Garvan Fracture Risk Calculator (FRC), and QFracture tool are validated tools for fracture risk prediction. FRAX is most widely used internationally, and considers T1D (but not T2D) under secondary osteoporosis disorders. FRAX underestimates fracture risk in both T1D and T2D. Trabecular bone score and other adjustments for T2D-associated risk improve FRAX-based estimations. Similar adjustments for T1D are not identified. Garvan FRC does not incorporate diabetes as an input but does includes falls. Garvan FRC slightly underestimates osteoporotic fracture risk in women with diabetes. QFracture incorporates both T1D and T2D and falls as input variables, but has not been directly validated in individuals with diabetes.

SUMMARY

Further research is needed to validate and compare available fracture prediction tools and their performance in individuals with diabetes.

Assessment and treatment of osteoporosis and fractures in type 2 diabetes

There is substantial, and growing, evidence that type 2 diabetes (T2D) is associated with skeletal fragility, despite often preserved bone mineral density. As post-fracture outcomes, including mortality, are worse in people with T2D, bone management should be carefully considered in this highly vulnerable group. However, current fracture risk calculators inadequately predict fracture risk in T2D, and dedicated randomised controlled trials identifying optimal management in patients with T2D are lacking, raising questions about the ideal assessment and treatment of bone health in these people. We synthesise the current literature on evaluating bone measurements in T2D and summarise the evidence for safety and efficacy of both T2D and anti-osteoporosis medications in relation to bone health in these patients.

Bone Turnover Markers Do Not Predict Fracture Risk in Type 2 Diabetes

Type 2 diabetes (T2D) is characterized by increased fracture risk despite higher BMD and reduced bone turnover. BMD underestimates fracture risk in T2D, but the predictive role of bone turnover markers (BTMs) on fracture risk in T2D has not been explored. Thus, we sought to determine whether BTMs predict incident fractures in subjects with T2D. For this case-cohort study, we used data from the Health, Aging, and Body Composition (Health ABC) Study of well-functioning older adults, aged 70 to 79 years at baseline (April 1997-June 1998). The case-cohort sample consisted of (i) the cases, composed of all 223 participants who experienced incident fractures of the hip, clinical spine, or distal forearm within the first 9 years of study follow-up; and (ii) the subcohort of 508 randomly sampled participants from three strata at baseline (T2D, prediabetes, and normoglycemia) from the entire Health ABC cohort. A total of 690 subjects (223 cases, of whom 41 were in the subcohort) were included in analyses. BTMs (C-terminal telopeptide of type I collagen [CTX], osteocalcin [OC], and procollagen type 1 N-terminal propeptide [P1NP]) were measured in archived baseline serum. Cox regression with robust variance estimation was used to estimate the adjusted hazard ratio (HR) for fracture per 20% increase in BTMs. In nondiabetes (prediabetes plus normoglycemia), fracture risk was increased with higher CTX (HR 1.10; 95% confidence interval [CI], 1.01 to 1.20 for each 20% increase in CTX). Risk was not increased in T2D (HR 0.92; 95% CI, 0.81 to 1.04; p for interaction .045). Similarly, both OC and P1NP were associated with higher risk of fracture in nondiabetes, but not in T2D, with p for interaction of .078 and .109, respectively. In conclusion, BTMs did not predict incident fracture risk in T2D but were modestly associated with fracture risk in nondiabetes. © 2020 American Society for Bone and Mineral Research.

Comorbid Conditions and GFR Predict Nonvertebral Fractures in Patients With Diabetes in an Ethnic-Specific Manner

CONTEXT

Diabetes mellitus (DM) is associated with an increased risk of fracture, but it is not clear which diabetes and nondiabetes risk factors may be most important.

OBJECTIVE

The aim of the study was to evaluate risk factors for incident major osteoporotic fractures (MOFs) of the hip, wrist, and humerus in African American (AA), Hispanic (HIS), and Caucasian (CA) subjects with DM.

METHODS

This was a retrospective cohort study of 18 210 subjects with DM (7298 CA, 7009 AA and 3903 HIS) at least 40 years of age, being followed at a large healthcare system in Philadelphia, Pennsylvania.

RESULTS

In a global model in CA with DM, MOF were associated with dementia (HR 4.16; 95% CI, 2.13-8.12), OSA (HR 3.35; 95% CI, 1.78-6.29), COPD (HR 2.43; 95% CI, 1.51-3.92), and diabetic neuropathy (HR 2.52; 95% CI, 1.41-4.50). In AA, MOF were associated with prior MOF (HR 13.67; 95% CI, 5.48-34.1), dementia (HR 3.10; 95% CI, 1.07-8.98), glomerular filtration rate (GFR) less than 45 (HR 2.05; 95% CI, 1.11-3.79), thiazide use (HR 0.54; 95% CI, 0.31-0.93), metformin use (HR 0.59; 95% CI, 0.36-0.97), and chronic steroid use (HR 5.03; 95% CI, 1.51-16.7). In HIS, liver disease (HR 3.06; 95% CI, 1.38-6.79) and insulin use (HR 2.93; 95% CI, 1.76-4.87) were associated with MOF.

CONCLUSION

In patients with diabetes, the risk of fracture is related to both diabetes-specific variables and comorbid conditions, but these relationships vary by race/ethnicity.

Diabetes and bone

Bone disease is a serious complication to diabetes. Patients with type 1 diabetes (T1D) and type 2 diabetes (T2D) suffer from an increased risk of fracture, most notably at the hip, compared with patients without diabetes. Confounders such as patient sex, age, body mass index, blood glucose status, fall risk, and diabetes medications may influence the fracture risk. Different underlying mechanisms contribute to bone disease in patients with diabetes. Bone quality is affected by low bone turnover in T1D and T2D, and furthermore, incorporation of advanced glycation end-products, changes in the incretin hormone response, and microvascular complications contribute to impaired bone quality and increased fracture risk. Diagnosis of bone disease in patients with diabetes is a challenge as current methods for fracture prediction such as bone mineral density T-score and fracture risk assessment tools underestimate fracture risk for patients with T1D and T2D. This review focuses on bone disease and fracture risk in patients with diabetes regarding epidemiology, underlying disease mechanisms, and diagnostic methods, and we also provide considerations regarding the management of diabetes patients with bone disease in terms of an intervention threshold and different treatments.