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

  1. Rauh, Simone P.
  2. Heymans, Martijn W.
  3. Koopman, Anitra D. M.
  4. Nijpels, Giel
  5. Stehouwer, Coen D.
  6. Thorand, Barbara
  7. Rathmann, Wolfgang
  8. Meisinger, Christa
  9. Peters, Annette ORCID logo
  10. Heras Gala, Tonia de las
  11. Glümer, Charlotte
  12. Pedersen, Oluf
  13. Cederberg, Henna
  14. Kuusisto, Johanna
  15. Laakso, Markku
  16. Pearson, Ewan R.
  17. Franks, Paul W.
  18. Rutters, Femke
  19. Dekker, Jacqueline M.

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1000 Titel
  • Predicting glycated hemoglobin levels in the non-diabetic general population: Development and validation of the DIRECT-DETECT prediction model - a DIRECT study
1000 Autor/in
  1. Rauh, Simone P. |
  2. Heymans, Martijn W. |
  3. Koopman, Anitra D. M. |
  4. Nijpels, Giel |
  5. Stehouwer, Coen D. |
  6. Thorand, Barbara |
  7. Rathmann, Wolfgang |
  8. Meisinger, Christa |
  9. Peters, Annette |
  10. Heras Gala, Tonia de las |
  11. Glümer, Charlotte |
  12. Pedersen, Oluf |
  13. Cederberg, Henna |
  14. Kuusisto, Johanna |
  15. Laakso, Markku |
  16. Pearson, Ewan R. |
  17. Franks, Paul W. |
  18. Rutters, Femke |
  19. Dekker, Jacqueline M. |
1000 Erscheinungsjahr 2017
1000 LeibnizOpen
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2017-02-10
1000 Erschienen in
1000 Quellenangabe
  • 12(2):e0171816
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2017
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pone.0171816 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5302787/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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/INTEPRETATION: 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.
1000 Sacherschließung
lokal Type 2 diabetes
lokal Diabetes diagnosis and management
lokal Diabetes mellitus
lokal Cohort studies
lokal Antihypertensive drugs
lokal Graphs
lokal Forecasting
lokal HbA1c
1000 Fachgruppe
  1. Medizin |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/creator/UmF1aCwgU2ltb25lIFAu|https://frl.publisso.de/adhoc/creator/SGV5bWFucywgTWFydGlqbiBXLg==|https://frl.publisso.de/adhoc/creator/S29vcG1hbiwgQW5pdHJhIEQuIE0u|https://frl.publisso.de/adhoc/creator/TmlqcGVscywgR2llbA==|https://frl.publisso.de/adhoc/creator/U3RlaG91d2VyLCBDb2VuIEQu|https://frl.publisso.de/adhoc/creator/VGhvcmFuZCwgQmFyYmFyYQ==|http://d-nb.info/gnd/112013597|https://frl.publisso.de/adhoc/creator/TWVpc2luZ2VyLCBDaHJpc3Rh|http://orcid.org/0000-0001-6645-0985|https://frl.publisso.de/adhoc/creator/SGVyYXMgR2FsYSwgVG9uaWEgZGUgbGFzIA==|https://frl.publisso.de/adhoc/creator/R2zDvG1lciwgQ2hhcmxvdHRl|https://frl.publisso.de/adhoc/creator/UGVkZXJzZW4sIE9sdWY=|https://frl.publisso.de/adhoc/creator/Q2VkZXJiZXJnLCBIZW5uYQ==|https://frl.publisso.de/adhoc/creator/S3V1c2lzdG8sIEpvaGFubmE=|https://frl.publisso.de/adhoc/creator/TGFha3NvLCBNYXJra3U=|https://frl.publisso.de/adhoc/creator/UGVhcnNvbiwgRXdhbiBSLg==|https://frl.publisso.de/adhoc/creator/RnJhbmtzLCBQYXVsIFcu|https://frl.publisso.de/adhoc/creator/UnV0dGVycywgRmVta2U=|https://frl.publisso.de/adhoc/creator/RGVra2VyLCBKYWNxdWVsaW5lIE0u
1000 Label
1000 Förderer
  1. Innovative Medicines Initiative Joint Undertaking
  2. European Union
  3. EFPIA companies
  4. Vrije Universiteit Amsterdam
  5. VU University Medical Center
  6. Dutch Diabetes Research Foundation
  7. Dutch Organization for Scientific Research
  8. Netherlands Heart Foundation
  9. Health Research and Development Council of the Netherlands
  10. Helmholtz Zentrum München—German Research Center for Environmental Health
  11. German Federal Ministry of Education and Research
  12. State of Bavaria
  13. German Research Foundation
  14. German Federal Ministry of Health
  15. Ministry of Innovation, Science, Research and Technology of the federal state of North Rhine Westfalia
  16. Danish Medical Research Council
  17. Danish Center for Evaluation and Health Technology Assessment
  18. Novo Nordisk
  19. Copenhagen County
  20. Danish Heart Foundation
  21. Danish Diabetes Association
  22. Danish Pharmaceutical Association
1000 Fördernummer
  1. 115317
  2. FP7/2007‐2013
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  13. RA 459/2-1
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1000 Förderprogramm
  1. DIRECT
  2. Seventh Framework Programme
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