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Brandt IAG, Starup-Linde J, Andersen SS, Viggers R. Diagnosing Osteoporosis in Diabetes-A Systematic Review on BMD and Fractures. Curr Osteoporos Rep 2024; 22:223-244. [PMID: 38509440 DOI: 10.1007/s11914-024-00867-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/08/2024] [Indexed: 03/22/2024]
Abstract
PURPOSE OF REVIEW Recently, the American Diabetes Association updated the 2024 guidelines for Standards of Care in Diabetes and recommend that a T-score of - 2.0 in patients with diabetes should be interpreted as equivalent to - 2.5 in people without diabetes. We aimed to evaluate the most recent findings concerning the bone mineral density (BMD)-derived T-score and risk of fractures related to osteoporosis in subjects with diabetes. RECENT FINDINGS The dual-energy X-ray absorptiometry (DXA) scan is the golden standard for evaluating BMD. The BMD-derived T-score is central to fracture prediction and signifies both diagnosis and treatment for osteoporosis. However, the increased fracture risk in diabetes is not sufficiently explained by the T-score, complicating the identification and management of fracture risk in these patients. Recent findings agree that subjects with type 2 diabetes (T2D) have a higher T-score and higher fracture risk compared with subjects without diabetes. However, the actual number of studies evaluating the direct association of higher fracture risk at higher T-score levels is scant. Some studies support the adjustment based on the 0.5 BMD T-score difference between subjects with T2D and subjects without diabetes. However, further data from longitudinal studies is warranted to validate if the T-score treatment threshold necessitates modification to prevent fractures in subjects with diabetes.
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Affiliation(s)
- Inge Agnete Gerlach Brandt
- Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark.
- Department of Endocrinology, Aalborg University Hospital, Aalborg, Denmark.
| | - Jakob Starup-Linde
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Sally Søgaard Andersen
- Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark
- Department of Endocrinology, Aalborg University Hospital, Aalborg, Denmark
| | - Rikke Viggers
- Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark
- Department of Endocrinology, Aalborg University Hospital, Aalborg, Denmark
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Di Monaco M, Castiglioni C, Bardesono F, Freiburger M, Milano E, Massazza G. Femoral bone mineral density at the time of hip fracture is higher in women with versus without type 2 diabetes mellitus: a cross-sectional study. J Endocrinol Invest 2024; 47:59-66. [PMID: 37296371 DOI: 10.1007/s40618-023-02122-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE To compare femoral bone mineral density (BMD) levels in hip-fracture women with versus without type 2 diabetes mellitus (T2DM). We hypothesized that BMD levels could be higher in the women with T2DM than in controls and we aimed to quantify the BMD discrepancy associated with the presence of T2DM. METHODS At a median of 20 days after the occurrence of an original hip fracture due to fragility we measured BMD by dual-energy x-ray absorptiometry at the non-fractured femur. RESULTS We studied 751 women with subacute hip fracture. Femoral BMD was significantly higher in the 111 women with T2DM than in the 640 without diabetes: mean T-score between-group difference was 0.50, (95% CI from 0.30 to 0.69, P < 0.001). The association between the presence of T2DM and femoral BMD persisted after adjustment for age, body mass index, hip-fracture type, neurologic diseases, parathyroid hormone, 25-hydroxyvitamin D and estimated glomerular filtration rate (P < 0.001). For a woman without versus with T2DM, the adjusted odds ratio to have a femoral BMD T-score below the threshold of - 2.5 was 2.13 (95% CI from 1.33 to 3.42, P = 0.002). CONCLUSIONS Fragility fractures of the hip occurred in women with T2DM at a femoral BMD level higher than in control women. In the clinical assessment of fracture risk, we support the adjustment based on the 0.5 BMD T-score difference between women with and without T2DM, although further data from robust longitudinal studies is needed to validate the BMD-based adjustment of fracture risk estimation.
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Affiliation(s)
- M Di Monaco
- Division of Physical and Rehabilitation Medicine, Osteoporosis Research Center, Presidio Sanitario San Camillo, Fondazione Opera San Camillo, Strada Santa Margherita 136, 10131, Turin, Italy.
| | - C Castiglioni
- Division of Physical and Rehabilitation Medicine, Osteoporosis Research Center, Presidio Sanitario San Camillo, Fondazione Opera San Camillo, Strada Santa Margherita 136, 10131, Turin, Italy
| | - F Bardesono
- Division of Physical and Rehabilitation Medicine, Osteoporosis Research Center, Presidio Sanitario San Camillo, Fondazione Opera San Camillo, Strada Santa Margherita 136, 10131, Turin, Italy
| | - M Freiburger
- Division of Physical and Rehabilitation Medicine, Department of Surgical Sciences, University of Turin, Turin, Italy
| | - E Milano
- Division of Physical and Rehabilitation Medicine, Osteoporosis Research Center, Presidio Sanitario San Camillo, Fondazione Opera San Camillo, Strada Santa Margherita 136, 10131, Turin, Italy
| | - G Massazza
- Division of Physical and Rehabilitation Medicine, Department of Surgical Sciences, University of Turin, Turin, Italy
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Li Z, Zhao W, Lin X, Li F. AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review. J Orthop Surg Res 2023; 18:956. [PMID: 38087332 PMCID: PMC10714483 DOI: 10.1186/s13018-023-04446-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
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.
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Affiliation(s)
- Zeting Li
- Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Wen Zhao
- The Reproductive Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiahong Lin
- Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
| | - Fangping Li
- Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
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Chu S, Jiang A, Chen L, Zhang X, Shen X, Zhou W, Ye S, Chen C, Zhang S, Zhang L, Chen Y, Miao Y, Wang W. Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China. Heliyon 2023; 9:e18186. [PMID: 37501989 PMCID: PMC10368844 DOI: 10.1016/j.heliyon.2023.e18186] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 07/29/2023] Open
Abstract
Background Patients with diabetes are more likely to be predisposed to fractures compared to those without diabetes. In clinical practice, predicting fracture risk in diabetics is still difficult because of the limited availability and accessibility of existing fracture prediction tools in the diabetic population. The purpose of this study was to develop and validate models using machine learning (ML) algorithms to achieve high predictive power for fracture in patients with diabetes in China. Methods In this study, the clinical data of 775 hospitalized patients with diabetes was analyzed by using Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost) and Probabilistic Classification Vector Machines (PCVM) algorithms to construct risk prediction models for fractures. Moreover, the risk factors for diabetes-related fracture were identified by the feature selection algorithms. Results The ML algorithms extracted 17 most relevant factors from raw clinical data to maximize the accuracy of the prediction results, including bone mineral density, age, sex, weight, high-density lipoprotein cholesterol, height, duration of diabetes, total cholesterol, osteocalcin, N-terminal propeptide of type I, diastolic blood pressure, and body mass index. The 7 ML models including LR, SVM, RF, DT, GBDT, XGBoost, and PCVM had f1 scores of 0.75, 0.83, 0.84, 0.85, 0.87, 0.88, and 0.97, respectively. Conclusions This study identified 17 most relevant risk factors for diabetes-related fracture using ML algorithms. And the PCVM model proved to perform best in predicting the fracture risk in the diabetic population. This work proposes a cheap, safe, and extensible ML algorithm for the precise assessment of risk factors for diabetes-related fracture.
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Affiliation(s)
- Sijia Chu
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Graduate School, Wannan Medical College, Wuhu, China
| | - Aijun Jiang
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Lyuzhou Chen
- School of Data Science, University of Science and Technology of China, Hefei, China
| | - Xi Zhang
- Department of Endocrinology, The People's Hospital of Chizhou, Chizhou, China
| | | | - Wan Zhou
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Shandong Ye
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Chao Chen
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Shilu Zhang
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Graduate School, Wannan Medical College, Wuhu, China
| | - Li Zhang
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Graduate School, Wannan Medical College, Wuhu, China
| | - Yang Chen
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Graduate School, Anhui Medical University, Hefei, China
| | - Ya Miao
- Institution of Advanced Technology, University of Science and Technology of China, Hefei, China
| | - Wei Wang
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
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Abstract
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.
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Affiliation(s)
- Arnav Agarwal
- Division of General Internal Medicine, Department of Medicine, McMaster University, Hamilton, Ontario
| | - William D Leslie
- Department of Medicine (C5121), University of Manitoba, Winnipeg, Manitoba, Canada
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