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

Simone P. Rauh; Martijn W. Heymans; Anitra D. M. Koopman; Giel Nijpels; Coen D. Stehouwer; Barbara Thorand; Wolfgang Rathmann; Christa Meisinger; Annette Peters; Tonia de Las Heras Gala; Charlotte Glümer; Oluf Pedersen; Henna Cederberg; Johanna Kuusisto; Markku Laakso; Ewan R. Pearson; Paul W. Franks; Femke Rutters; Jacqueline M. Dekker

doi:10.1371/journal.pone.0171816

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

Simone P. Rauh (Lead / Corresponding author), Martijn W. Heymans, Anitra D. M. Koopman, Giel Nijpels, Coen D. Stehouwer, Barbara Thorand, Wolfgang Rathmann, Christa Meisinger, Annette Peters, Tonia de Las Heras Gala, Charlotte Glümer, Oluf Pedersen, Henna Cederberg, Johanna Kuusisto, Markku Laakso, Ewan R. Pearson, Paul W. Franks, Femke Rutters, Jacqueline M. Dekker

Molecular and Clinical Medicine

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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 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.

Original language	English
Article number	e0171816
Pages (from-to)	1-13
Number of pages	13
Journal	PLoS ONE
Volume	12
Issue number	2
DOIs	https://doi.org/10.1371/journal.pone.0171816
Publication status	Published - 10 Feb 2017

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Rauh, S. P., Heymans, M. W., Koopman, A. D. M., Nijpels, G., Stehouwer, C. D., Thorand, B., Rathmann, W., Meisinger, C., Peters, A., de Las Heras Gala, T., Glümer, C., Pedersen, O., Cederberg, H., Kuusisto, J., Laakso, M., Pearson, E. R., Franks, P. W., Rutters, F., & Dekker, J. M. (2017). Predicting glycated hemoglobin levels in the non-diabetic general population: Development and validation of the DIRECT-DETECT prediction model - a DIRECT study. PLoS ONE, 12(2), 1-13. [e0171816]. https://doi.org/10.1371/journal.pone.0171816

Rauh, Simone P. ; Heymans, Martijn W. ; Koopman, Anitra D. M. ; Nijpels, Giel ; Stehouwer, Coen D. ; Thorand, Barbara ; Rathmann, Wolfgang ; Meisinger, Christa ; Peters, Annette ; de Las Heras Gala, Tonia ; Glümer, Charlotte ; Pedersen, Oluf ; Cederberg, Henna ; Kuusisto, Johanna ; Laakso, Markku ; Pearson, Ewan R. ; Franks, Paul W. ; Rutters, Femke ; Dekker, Jacqueline M. / Predicting glycated hemoglobin levels in the non-diabetic general population : Development and validation of the DIRECT-DETECT prediction model - a DIRECT study. In: PLoS ONE. 2017 ; Vol. 12, No. 2. pp. 1-13.

@article{a2eccd25bbe74806a4627a176cfd327d,

title = "Predicting glycated hemoglobin levels in the non-diabetic general population: Development and validation of the DIRECT-DETECT prediction model - a DIRECT study",

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 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.",

author = "Rauh, {Simone P.} and Heymans, {Martijn W.} and Koopman, {Anitra D. M.} and Giel Nijpels and Stehouwer, {Coen D.} and Barbara Thorand and Wolfgang Rathmann and Christa Meisinger and Annette Peters and {de Las Heras Gala}, Tonia and Charlotte Gl{\"u}mer and Oluf Pedersen and Henna Cederberg and Johanna Kuusisto and Markku Laakso and Pearson, {Ewan R.} and Franks, {Paul W.} and Femke Rutters and Dekker, {Jacqueline M.}",

note = "The work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007‐2013) and EFPIA companies{\textquoteright} in kind contribution (http://www.direct-diabetes.org/). The Hoorn Study was supported by Vrije Universiteit Amsterdam, the VU University Medical Center, the Dutch Diabetes Research Foundation, the Dutch Organization for Scientific Research, the Netherlands Heart Foundation, and the Health Research and Development Council of the Netherlands. The KORA research platform (KORA, Cooperative Research in the Region of Augsburg) was initiated and financed by the Helmholtz Zentrum M{\"u}nchen—German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research and by the State of Bavaria. The Diabetes Cohort Study was funded by a German Research Foundation project grant to W.R. (RA 459/2-1) from the German Diabetes Center. The German Diabetes Center is funded by the German Federal Ministry of Health and the Ministry of Innovation, Science, Research and Technology of the federal state of North Rhine Westfalia. The Inter99 Study was supported by the Danish Medical Research Council, the Danish Center for Evaluation and Health Technology Assessment, Novo Nordisk, Copenhagen County, the Danish Heart Foundation, the Danish Diabetes Association, the Danish Pharmaceutical Association, the Augustinus Foundation, the Ib Henriksen Foundation, and the Becket Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.",

year = "2017",

month = feb,

day = "10",

doi = "10.1371/journal.pone.0171816",

language = "English",

volume = "12",

pages = "1--13",

journal = "PLoS ONE",

issn = "1932-6203",

publisher = "Public Library of Science",

number = "2",

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Rauh, SP, Heymans, MW, Koopman, ADM, Nijpels, G, Stehouwer, CD, Thorand, B, Rathmann, W, Meisinger, C, Peters, A, de Las Heras Gala, T, Glümer, C, Pedersen, O, Cederberg, H, Kuusisto, J, Laakso, M, Pearson, ER, Franks, PW, Rutters, F & Dekker, JM 2017, 'Predicting glycated hemoglobin levels in the non-diabetic general population: Development and validation of the DIRECT-DETECT prediction model - a DIRECT study', PLoS ONE, vol. 12, no. 2, e0171816, pp. 1-13. https://doi.org/10.1371/journal.pone.0171816

Predicting glycated hemoglobin levels in the non-diabetic general population : Development and validation of the DIRECT-DETECT prediction model - a DIRECT study. / Rauh, Simone P. (Lead / Corresponding author); Heymans, Martijn W.; Koopman, Anitra D. M.; Nijpels, Giel; Stehouwer, Coen D.; Thorand, Barbara; Rathmann, Wolfgang; Meisinger, Christa; Peters, Annette; de Las Heras Gala, Tonia; Glümer, Charlotte; Pedersen, Oluf; Cederberg, Henna; Kuusisto, Johanna; Laakso, Markku; Pearson, Ewan R.; Franks, Paul W.; Rutters, Femke; Dekker, Jacqueline M.

In: PLoS ONE, Vol. 12, No. 2, e0171816, 10.02.2017, p. 1-13.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Predicting glycated hemoglobin levels in the non-diabetic general population

T2 - Development and validation of the DIRECT-DETECT prediction model - a DIRECT study

AU - Rauh, Simone P.

AU - Heymans, Martijn W.

AU - Koopman, Anitra D. M.

AU - Nijpels, Giel

AU - Stehouwer, Coen D.

AU - Thorand, Barbara

AU - Rathmann, Wolfgang

AU - Meisinger, Christa

AU - Peters, Annette

AU - de Las Heras Gala, Tonia

AU - Glümer, Charlotte

AU - Pedersen, Oluf

AU - Cederberg, Henna

AU - Kuusisto, Johanna

AU - Laakso, Markku

AU - Pearson, Ewan R.

AU - Franks, Paul W.

AU - Rutters, Femke

AU - Dekker, Jacqueline M.

N1 - The work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007‐2013) and EFPIA companies’ in kind contribution (http://www.direct-diabetes.org/). The Hoorn Study was supported by Vrije Universiteit Amsterdam, the VU University Medical Center, the Dutch Diabetes Research Foundation, the Dutch Organization for Scientific Research, the Netherlands Heart Foundation, and the Health Research and Development Council of the Netherlands. The KORA research platform (KORA, Cooperative Research in the Region of Augsburg) was initiated and financed by the Helmholtz Zentrum München—German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research and by the State of Bavaria. The Diabetes Cohort Study was funded by a German Research Foundation project grant to W.R. (RA 459/2-1) from the German Diabetes Center. The German Diabetes Center is funded by the German Federal Ministry of Health and the Ministry of Innovation, Science, Research and Technology of the federal state of North Rhine Westfalia. The Inter99 Study was supported by the Danish Medical Research Council, the Danish Center for Evaluation and Health Technology Assessment, Novo Nordisk, Copenhagen County, the Danish Heart Foundation, the Danish Diabetes Association, the Danish Pharmaceutical Association, the Augustinus Foundation, the Ib Henriksen Foundation, and the Becket Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

PY - 2017/2/10

Y1 - 2017/2/10

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pone.0171816

DO - 10.1371/journal.pone.0171816

M3 - Article

C2 - 28187151

VL - 12

SP - 1

EP - 13

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 2

M1 - e0171816

ER -