ArticleBMC geriatrics2023
Development and validation of a risk prediction model for osteoporosis in elderly patients with type 2 diabetes mellitus: a retrospective and multicenter study.
Article in BMC geriatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 10 citations in OpenAlex.
- An explainable machine learning model for predicting one-year osteoporosis risk: development and validation in a prospective cohort.BMC musculoskeletal disorders · 2026Article
- Regional Differences in Knee Osteoporosis Based on Coronal Alignment Phenotype in Patients Undergoing Preoperative CT Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- Serum CTRP3 and CysC correlate with glucose, lipid, and bone metabolism in elderly patients with type 2 diabetes mellitus complicated with osteoporosis.Journal of diabetes investigation · 2026Article
- Development and validation of a nomogram incorporating triglyceride-glucose index and aggregate index of systemic inflammation for assessing osteoporosis risk in type 2 diabetes mellitus: a dual-center retrospective study.Frontiers in endocrinology · 2026Observational
- Development and validation of a multimodal interpretable machine learning model for the identification of osteoporosis in patients with type 2 diabetes mellitus: a multicenter retrospective study.Frontiers in endocrinology · 2026Article
- Development and validation of an interpretable machine learning model for osteoporosis prediction using routine blood tests: a retrospective cohort study.BMC medical informatics and decision making · 2025Article
- Prediction models for encephalitis and in-hospital death associated with severe fever with thrombocytopenia syndrome.BMC infectious diseases · 2025Article
- Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus.Frontiers in endocrinology · 2025Article
- Multi-feature integrated machine learning prediction model for early nephropathy in elderly living with type 2 diabetes mellitus.Frontiers in endocrinology · 2025Article
- Risk factors for osteoporosis in elderly patients with type 2 diabetes: A protocol for systematic review and meta-analysis.PloS one · 2025Article
- Establishment and validation of a nomogram clinical prediction model for osteoporosis in senile patients with type 2 diabetes mellitus.Scientific reports · 2024Article
- Antidiabetic drug administration prevents bone mineral density loss: Evidence from a two-sample Mendelian randomization study.PloS one · 2024Article
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Authors and funding
12 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to construct a risk prediction model to estimate the odds of osteoporosis (OP) in elderly patients with type 2 diabetes mellitus (T2DM) and evaluate its prediction efficiency.
methodsThis study included 21,070 elderly patients with T2DM who were hospitalized at six tertiary hospitals in Southwest China between 2012 and 2022. Univariate logistic regression analysis was used to screen for potential influencing factors of OP and least absolute shrinkage. Further, selection operator regression (LASSO) and multivariate logistic regression analyses were performed to select variables for developing a novel predictive model. The area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the performance and clinical utility of the model.
resultsThe incidence of OP in elderly patients with T2DM was 7.01% (1,476/21,070). Age, sex, hypertension, coronary heart disease, cerebral infarction, hyperlipidemia, and surgical history were the influencing factors. The seven-variable model displayed an AUROC of 0.713 (95% confidence interval [CI]:0.697-0.730) in the training set, 0.716 (95% CI: 0.691-0.740) in the internal validation set, and 0.694 (95% CI: 0.653-0.735) in the external validation set. The optimal decision probability cut-off value was 0.075. The calibration curve (bootstrap = 1,000) showed good calibration. In addition, the DCA and CIC demonstrated good clinical practicality. An operating interface on a webpage ( https://juntaotan.shinyapps.io/osteoporosis/ ) was developed to provide convenient access for users.
conclusionsThis study constructed a highly accurate model to predict OP in elderly patients with T2DM. This model incorporates demographic characteristics and clinical risk factors and may be easily used to facilitate individualized prediction.
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