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Diabetesrisikoscores

Ein Update 2014

Diabetes risk scores

Update 2014

  • Leitthema
  • Published:
Der Diabetologe Aims and scope

Zusammenfassung

Hintergrund

In den letzten 15 Jahren ist die Entwicklung von Diabetesrisikoscores immer stärker Gegenstand der epidemiologischen Forschung geworden.

Ziel der Arbeit

In der vorliegenden Arbeit werden zeitliche Trends und die weltweite Verteilung der entwickelten Vorhersagemodelle dargestellt sowie nichtinvasive extern validierte Diabetesrisikoscores beschrieben.

Material und Methoden

Auf der Basis eines im Jahr 2011 publizierten Reviews und einer Online-Datenbank mit jährlichem Update zu Veröffentlichungen mit Diabetesrisikoscores werden Informationen zu der Entwicklung und Validierung dieser Scores gesammelt und zusammengefasst dargestellt.

Ergebnisse

Bis Ende 2013 wurden insgesamt 392 einzelne Diabetesprädiktionsmodelle publiziert. Von diesen sind 23 Modelle (5,9 %) nichtinvasiv, d. h., sie sind ohne Einbezug von biochemischen oder genetischen Parametern und damit für ein breites Spektrum an Einsatzmöglichkeiten in der Bevölkerung geeignet, sowie extern validiert worden. Die Mehrheit wurde in Europa entwickelt. So liegt für Deutschland mit dem DIfE – DEUTSCHER-DIABETES-RISIKO-TEST® (DRT) ein mehrfach validiertes Prädiktionsmodell vor.

Diskussion

Es gibt eine wachsende Anzahl an validierten Risikoscores, die für die Vorhersage eines Diabetesrisikos nutzbar sind. Ihre Eignung für die Praxis hängt davon ab, ob sie in der Zielpopulation validiert wurden und Erhebungs- und Berechnungsmöglichkeiten, z. B. in Form von Fragebögen, zur Verfügung stehen.

Abstract

Background

During the past 15 years the development of diabetes risk scores has increasingly become the subject of epidemiologic research.

Objectives

Trends over time and the worldwide distribution of prediction models that have been developed are presented and non-invasive, externally validated diabetes risk scores are described.

Materials and methods

Based on the review published in 2011 and an annually updated online database on published research of diabetes risk scores, information regarding the development and validation of the scores were collected and summarized.

Results

Overall, 392 single diabetes risk scores were calculated through the end of 2013. Out of these, 23 were non-invasive (i.e., without biochemical or genetic parameters and thereby appropriate for use in various settings in the population) and were validated in an external population. The majority was developed in Europe. With the DIfE – DEUTSCHER-DIABETES-RISIKO-TEST® (DRT), a multiply validated prediction model also exists for Germany.

Discussion

There is a growing number of validated diabetes risk scores which are useful for the prediction of the diabetes risk. Their adequacy for application in clinical practice depends, on the one hand, on whether these were validated in the target population and, on the other hand, on the availability of tools for assessment or calculation (e.g., in the form of questionnaires).

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Literatur

  1. Abbasi A, Corpeleijn E, Peelen LM et al (2012) External validation of the KORA S4/F4 prediction models for the risk of developing type 2 diabetes in older adults: the PREVEND study. Eur J Epidemiol 27:47–52

    Article  PubMed  Google Scholar 

  2. Abbasi A, Peelen LM, Corpeleijn E et al (2012) Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ 345:e5900

    Article  PubMed  PubMed Central  Google Scholar 

  3. Abdul-Ghani MA, Lyssenko V, Tuomi T et al (2009) Fasting versus postload plasma glucose concentration and the risk for future type 2 diabetes: results from the Botnia Study. Diabetes Care 32:281–286

    Article  PubMed  PubMed Central  Google Scholar 

  4. Alssema M, Feskens EJ, Bakker SJ et al (2008) Finnish questionnaire reasonably good predictor of the incidence of diabetes in The Netherlands. Ned Tijdschr Geneeskd 152:2418–2424

    PubMed  CAS  Google Scholar 

  5. Alssema M, Vistisen D, Heymans MW et al (2011) The evaluation of screening and early detection strategies for type 2 diabetes and impaired glucose tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes. Diabetologia 54:1004–1012

    Article  PubMed  CAS  Google Scholar 

  6. Balkau B, Lange C, Fezeu L et al (2008) Predicting diabetes: clinical, biological, and genetic approaches: data from the epidemiological study on the insulin resistance syndrome (DESIR). Diabetes Care 31:2056–2061

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  7. Buijsse B, Simmons RK, Griffin SJ et al (2011) Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev 33:46–62

    Article  PubMed  PubMed Central  Google Scholar 

  8. Cameron AJ, Magliano DJ, Zimmet PZ et al (2008) The metabolic syndrome as a tool for predicting future diabetes: the AusDiab study. J Intern Med 264:177–186

    Article  PubMed  CAS  Google Scholar 

  9. Chen L, Magliano DJ, Balkau B et al (2010) AUSDRISK: an australian type 2 diabetes risk assessment tool based on demographic, lifestyle and simple anthropometric measures. Med J Aust 192:197–202

    PubMed  Google Scholar 

  10. Chien K, Cai T, Hsu H et al (2009) A prediction model for type 2 diabetes risk among Chinese people. Diabetologia 52:443–450

    Article  PubMed  CAS  Google Scholar 

  11. Collins GS, Altman DG (2011) External validation of QDSCORE® for predicting the 10-year risk of developing Type 2 diabetes. Diabet Med 28(5):599−607

    Article  PubMed  CAS  Google Scholar 

  12. Griffin SJ, Little PS, Hales CN et al (2000) Diabetes risk score: towards earlier detection of type 2 diabetes in general practice. Diabetes Metab Res Rev 16:164–171

    Article  PubMed  CAS  Google Scholar 

  13. Guasch-Ferre M, Bullo M, Costa B et al (2012) A risk score to predict type 2 diabetes mellitus in an elderly Spanish Mediterranean population at high cardiovascular risk. PLoS One 7:e33437

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  14. Hartwig S, Kuss O, Tiller D et al (2013) Validation of the German Diabetes Risk Score within a population-based representative cohort. Diabet Med 30:1047–1053

    Article  PubMed  CAS  Google Scholar 

  15. Hippisley-Cox J, Coupland C, Robson J et al (2009) Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ 338:b880

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kahn HS, Cheng YJ, Thompson TJ et al (2009) Two risk-scoring systems for predicting incident diabetes mellitus in U.S. adults age 45 to 64 years. Ann Intern Med 150:741–751

    Article  PubMed  Google Scholar 

  17. Kengne AP, Beulens JW, Peelen LM et al (2014) Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. Lancet Diabetes Endocrinol 2:19–29

    Article  PubMed  Google Scholar 

  18. Kowall B, Rathmann W, Bongaerts B et al (2013) Are diabetes risk scores useful for the prediction of cardiovascular diseases? Assessment of seven diabetes risk scores in the KORA S4/F4 cohort study. J Diabetes Complications 27:340−345

    Article  PubMed  Google Scholar 

  19. Lindström J, Tuomilehto J (2003) The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26:725–731

    Article  PubMed  Google Scholar 

  20. Lyssenko V, Jonsson A, Almgren P et al (2008) Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med 359:2220–2232

    Article  PubMed  CAS  Google Scholar 

  21. Mohan V, Deepa R, Deepa M et al (2005) A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects. J Assoc Physicians India 53:759–763

    PubMed  CAS  Google Scholar 

  22. Mühlenbruch K (2013) Prädiktionsmodelle in der epidemiologischen Ernährungsforschung: Beispiel Deutscher Diabetes-Risiko-Test® (DRT). Ernährungsumschau 132–139

  23. Mühlenbruch K, Joost H-G, Boeing H et al (2014) Risk prediction for type 2 diabetes in the German population with the updated German Diabetes Risk Score (GDRS). Ernährungsumschau 61:90–93

    Google Scholar 

  24. Mühlenbruch K, Ludwig T, Jeppesen C et al (2014) Update of the German Diabetes Risk Score and external validation in the German MONICA/KORA study. Diabetes Res Clin Pract 104:459–466

    Article  PubMed  Google Scholar 

  25. Nichols GA, Brown JB (2008) Validating the Framingham Offspring Study equations for predicting incident diabetes mellitus. Am J Manag Care 14:574–580

    PubMed  Google Scholar 

  26. Noble D, Mathur R, Dent T et al (2011) Risk models and scores for type 2 diabetes: systematic review. BMJ 343:d7163

    Article  PubMed  PubMed Central  Google Scholar 

  27. Noto D, Cefalu AB, Barbagallo CM et al (2012) Prediction of incident type 2 diabetes mellitus based on a twenty-year follow-up of the Ventimiglia heart study. Acta Diabetol 49:145–151

    Article  PubMed  CAS  Google Scholar 

  28. Rahman M, Simmons RK, Harding AH et al (2008) A simple risk score identifies individuals at high risk of developing Type 2 diabetes: a prospective cohort study. Fam Pract 25:191–196

    Article  PubMed  Google Scholar 

  29. Rathmann W, Kowall B, Heier M et al (2010) Prediction models for incident type 2 diabetes mellitusin the older population: KORA S4/F4 cohort study. Diabet Med 27:1116–1123

    Article  PubMed  CAS  Google Scholar 

  30. Rosella LC, Manuel DG, Burchill C et al (2011) A population-based risk algorithm for the development of diabetes: development and validation of the Diabetes Population Risk Tool (DPoRT). J Epidemiol Community Health 65:613–620

    Article  PubMed  PubMed Central  Google Scholar 

  31. Schmid R, Vollenweider P, Bastardot F et al (2012) Current genetic data do not improve the prediction of type 2 diabetes mellitus: the CoLaus study. J Clin Endocrinol Metab 97:E1338–E1341

    Article  PubMed  CAS  Google Scholar 

  32. Schmid R, Vollenweider P, Bastardot F et al (2012) Validation of 7 type 2 diabetes mellitus risk scores in a population-based cohort: CoLaus study. Arch Intern Med 172:188–189

    Article  PubMed  CAS  Google Scholar 

  33. Schmidt MI, Duncan BB, Bang H et al (2005) Identifying individuals at high risk for diabetes: the Atherosclerosis Risk in Communities study. Diabetes Care 28:2013–2018

    Article  PubMed  Google Scholar 

  34. Schulze M, Hu F (2014) Epidemiology of diabetes. In: Ahrens W, Pigeot I (Hrsg) Handbook of epidemiology. Springer, New York, S 2429–2467

  35. Schulze MB, Hoffmann K, Boeing H et al (2007) An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care 30:510–515

    Article  PubMed  Google Scholar 

  36. Simmons RK, Harding AH, Wareham NJ et al (2007) Do simple questions about diet and physical activity help to identify those at risk of Type 2 diabetes? Diabet Med 24:830–835

    Article  PubMed  CAS  Google Scholar 

  37. Stern M, Williams K, Eddy D et al (2008) Validation of prediction of diabetes by the Archimedes model and comparison with other predicting models. Diabetes Care 31:1670–1671

    Article  PubMed  PubMed Central  Google Scholar 

  38. Stern MP, Williams K, Haffner SM (2002) Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med 136:575–581

    Article  PubMed  Google Scholar 

  39. Talmud PJ, Hingorani AD, Cooper JA et al (2010) Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ 340:b4838

    Article  PubMed  PubMed Central  Google Scholar 

  40. Von Eckardstein A, Schulte H, Assmann G (2000) Risk for diabetes mellitus in middle-aged Caucasian male participants of the PROCAM study: implications for the definition of impaired fasting glucose by the American Diabetes Association. Prospective Cardiovascular Munster. J Clin Endocrinol Metab 85:3101–3108

    Article  Google Scholar 

  41. Wannamethee SG, Papacosta O, Whincup PH et al (2011) The potential for a two-stage diabetes risk algorithm combining non-laboratory-based scores with subsequent routine non-fasting blood tests: results from prospective studies in older men and women. Diabet Med 28:23–30

    Article  PubMed  CAS  Google Scholar 

  42. Wilson PW, Meigs JB, Sullivan L et al (2007) Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med 167:1068–1074

    Article  PubMed  Google Scholar 

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Einhaltung ethischer Richtlinien

Interessenkonflikt. K. Mühlenbruch und M. B. Schulze geben an, dass kein Interessenkonflikt besteht. Dieser Beitrag beinhaltet keine Studien an Menschen oder Tieren.

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Correspondence to K. Mühlenbruch.

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Mühlenbruch, K., Schulze, M. Diabetesrisikoscores. Diabetologe 10, 554–565 (2014). https://doi.org/10.1007/s11428-014-1209-5

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