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Predicting glycated hemoglobin levels in the non-diabetic general population : Development and validation of the DIRECTDETECT prediction model-a DIRECT study

Rauh, Simone P.; Heymans, Martijn W.; Koopman, Anitra D M; Nijpels, Giel; Stehouwer, Coen D A; Thorand, Barbara; Rathmann, Wolfgang; Meisinger, Christa; Peters, Annette and De Las Heras Gala, Tonia, et al. (2017) In PLoS ONE 12(2).
Abstract

Aims/hypothesis To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. Methods Data 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... (More)

Aims/hypothesis To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. Methods Data 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. Results At 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/interpretation In 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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PLoS ONE
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2
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Public Library of Science
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  • scopus:85012240340
  • wos:000394244300046
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1932-6203
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10.1371/journal.pone.0171816
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English
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27ca5c80-c322-4ebc-bc9b-63932d38c35a
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2017-02-28 08:33:47
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@article{27ca5c80-c322-4ebc-bc9b-63932d38c35a,
  abstract     = {<p>Aims/hypothesis To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. Methods Data 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 R<sup>2</sup>, 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. Results At 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/interpretation In 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.</p>},
  articleno    = {e0171816},
  author       = {Rauh, Simone P. and Heymans, Martijn W. and Koopman, Anitra D M and Nijpels, Giel and Stehouwer, Coen D A and Thorand, Barbara and Rathmann, Wolfgang and Meisinger, Christa and Peters, Annette and De Las Heras Gala, Tonia and Glümer, Charlotte and Pedersen, Oluf and Cederberg, Henna and Kuusisto, Johanna and Laakso, Markku and Pearson, Ewan R and Franks, Paul W. and Rutters, Femke and Dekker, Jacqueline M},
  issn         = {1932-6203},
  language     = {eng},
  month        = {02},
  number       = {2},
  publisher    = {Public Library of Science},
  series       = {PLoS ONE},
  title        = {Predicting glycated hemoglobin levels in the non-diabetic general population : Development and validation of the DIRECTDETECT prediction model-a DIRECT study},
  url          = {http://dx.doi.org/10.1371/journal.pone.0171816},
  volume       = {12},
  year         = {2017},
}