ArticleBMC medical research methodology2023
External validation of the American prediction model for incident type 2 diabetes in the Iranian population.
Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Hypertriglyceridemia is a dose-dependent risk factor for type 2 diabetes mellitus: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Weight Loss Improves Predicted 10-Year MACE and Type 2 Diabetes Risk, but Benefits Partially Rebound at 1 Year.Obesity (Silver Spring, Md.) · 2026Trial
- External validation of a novel nomogram for diagnosis of Protein Energy Wasting in adult hemodialysis patients.Frontiers in nutrition · 2024Article
- Analysis of risk factors and clinical implications for diabetes in first-degree relatives in the northeastern region of China.Frontiers in endocrinology · 2024Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundThe primary aim of the present study was to validate the REasons for Geographic and Racial Differences in Stroke (REGARDS) model for incident Type 2 diabetes (T2DM) in Iran.
methodsPresent study was a prospective cohort study on 1835 population aged ≥ 45 years from Tehran lipids and glucose study (TLGS).The predictors of REGARDS model based on Bayesian hierarchical techniques included age, sex, race, body mass index, systolic and diastolic blood pressures, triglycerides, high-density lipoprotein cholesterol, and fasting plasma glucose. For external validation, the area under the curve (AUC), sensitivity, specificity, Youden's index, and positive and negative predictive values (PPV and NPV) were assessed.
resultsDuring the 10-year follow-up 15.3% experienced T2DM. The model showed acceptable discrimination (AUC (95%CI): 0.79 (0.76-0.82)), and good calibration. Based on the highest Youden's index the suggested cut-point for the REGARDS probability would be ≥ 13% which yielded a sensitivity of 77.2%, specificity 66.8%, NPV 94.2%, and PPV 29.6%.
conclusionsOur findings do support that the REGARDS model is a valid tool for incident T2DM in the Iranian population. Moreover, the probability value higher than the 13% cut-off point is stated to be significant for identifying those with incident T2DM.
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