ArticlePloS one2017
Predicting glycated hemoglobin levels in the non-diabetic general population: Development and validation of the DIRECT-DETECT prediction model - a DIRECT study.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia.The Cochrane database of systematic reviews · 2018Pooled it
- Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records.JMIR medical informatics · 2021Article
- Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative.Frontiers in medicine · 2021Review
- Next-generation epidemiology: the role of high-resolution molecular phenotyping in diabetes research.Diabetologia · 2020Review
- The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study.Diabetologia · 2020Article
- Article
- Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record.JMIR medical informatics · 2018Article
- The Association between Social Jetlag, the Metabolic Syndrome, and Type 2 Diabetes Mellitus in the General Population: The New Hoorn Study.Journal of biological rhythms · 2017Article
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Authors and funding
19 authors at 9 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aims/hypothesisTo develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors.
methodsData from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, Inter99, KORA S4/F4) were combined to predict HbA1c levels at six year follow-up. Using backward selection, age, BMI, waist circumference, use of anti-hypertensive medication, current smoking and parental history of diabetes remained in sex-specific linear regression models. To minimize overfitting of coefficients, we performed internal validation using bootstrapping techniques. Explained variance, discrimination and calibration were assessed using R2, classification tables (comparing highest/lowest 50% HbA1c levels) and calibration graphs. The model was externally validated in 2,765 non-diabetic subjects of the population-based cohort METSIM.
resultsAt baseline, mean HbA1c level was 5.6% (38 mmol/mol). After a mean follow-up of six years, mean HbA1c level was 5.7% (39 mmol/mol). Calibration graphs showed that predicted HbA1c levels were somewhat underestimated in the Inter99 cohort and overestimated in the Hoorn and KORA cohorts, indicating that the model's intercept should be adjusted for each cohort to improve predictions. Sensitivity and specificity (95% CI) were 55.7% (53.9, 57.5) and 56.9% (55.1, 58.7) respectively, for women, and 54.6% (52.7, 56.5) and 54.3% (52.4, 56.2) for men. External validation showed similar performance in the METSIM cohort. CONCLUSIONS/
interpretationIn the non-diabetic population, our DIRECT-DETECT prediction model, including readily available predictors, has a relatively low explained variance and moderate discriminative performance, but can help to distinguish between future highest and lowest HbA1c levels. Absolute HbA1c values are cohort-dependent.
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