Evidence map›Paper›PMID 42707101›Full record

Observational studyFrontiers in endocrinology2026

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.

Yuan Luo, Hongyan Wei, Li Chen, Zhongbao Tang, Liyue Zhang

Abstract readObservational StudyMulticenter StudyValidation Study
In one paragraph

Observational study in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Yuan Luo *Rehabilitation Medicine Department, The First People's Hospital of Neijiang, Sichuan, China.
Hongyan Wei *Health Management Center of Southwest Medical University Affiliated Hospital, Luzhou, Sichuan, China.
Li ChenRehabilitation Medicine Department, The First People's Hospital of Neijiang, Sichuan, China.
Zhongbao TangShuangcai Central Health Center, Dongxing District, Neijiang, Sichuan, China.
Liyue ZhangRehabilitation Medicine Department, The First People's Hospital of Neijiang, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: In order to identify the factors related to osteoporosis in patients with type 2 diabetes, it is necessary to establish a visual risk classification model to evaluate the current risk and verify its effectiveness. Methods: This retrospective observational study included 2,265 patients with type 2 diabetes who were admitted to the First People's Hospital of Neijiang City between January 2021 and December 2025. All these patients underwent dual-energy X-ray absorptiometry (DXA) examinations during the same admission or physical examination visit. They were divided into the training group and the validation group in a ratio of 7:3. A predictive model was established using multivariate Logistic regression analysis and the Least Absolute Shrinkage and Selection Operator (Lasso) regression. A nomogram for clinical application was also established and its discrimination ability, calibration, and clinical practicability were verified. Results: Multivariate Logistic regression analysis revealed that age, gender, BMI, Triglyceride, Monocyte count, TyG and AISI were independent risk factors for osteoporosis in patients with type 2 diabetes. The nomogram model showed a reliable predictive effect, with an area under the curve (AUC) of 0.847 (95% CI, 0.821-0.874) in the training cohort and 0.833 (95% CI, 0.791-0.875) in the validation cohort. Decision curve analysis (DCA) confirmed that the nomogram model had strong clinical practical value. Conclusion: The risk factor assessment chart for osteoporosis in patients with type 2 diabetes established in this study has good discriminative ability and provides an objective tool for clinical personnel to conduct early assessment.

Indexed as

Blood GlucoseDiabetes Mellitus, Type 2InflammationNomogramsOsteoporosisTriglyceridesAbsorptiometry, PhotonAgedFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsBlood GlucoseTriglycerideslogisticnomogramosteoporosisprediction modeltype 2 diabetes

Identifiers

PMID42707101
PMCPMC13547037

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.