Literature DB >> 22215562

External validation of the KORA S4/F4 prediction models for the risk of developing type 2 diabetes in older adults: the PREVEND study.

Ali Abbasi1, Eva Corpeleijn, Linda M Peelen, Ron T Gansevoort, Paul E de Jong, Rijk O B Gans, Wolfgang Rathmann, Bernd Kowall, Christine Meisinger, Hans L Hillege, Ronald P Stolk, Gerjan Navis, Joline W J Beulens, Stephan J L Bakker.   

Abstract

Recently, prediction models for type 2 diabetes mellitus (T2DM) in older adults (aged ≥55 year) were developed in the KORA S4/F4 study, Augsburg, Germany. We aimed to externally validate the KORA models in a Dutch population. We used data on both older adults (n = 2,050; aged ≥55 year) and total non-diabetic population (n = 6,317; aged 28-75 year) for this validation. We assessed performance of base model (model 1: age, sex, BMI, smoking, parental diabetes and hypertension) and two clinical models: model 1 plus fasting glucose (model 2); and model 2 plus uric acid (model 3). For 7-year risk of T2DM, we calculated C-statistic, Hosmer-Lemeshow χ(2)-statistic, and integrated discrimination improvement (IDI) as measures of discrimination, calibration and reclassification, respectively. After a median follow-up of 7.7 years, 199 (9.7%) and 374 (5.9%) incident cases of T2DM were ascertained in the older and total population, respectively. In the older adults, C-statistic was 0.66 for model 1. This was improved for model 2 and model 3 (C-statistic = 0.81) with significant IDI. In the total population, these respective C-statistics were 0.77, 0.85 and 0.85. All models showed poor calibration (P < 0.001). After adjustment for the intercept and slope of each model, we observed good calibration for most models in both older and total populations. We validated the KORA clinical models for prediction of T2DM in an older Dutch population, with discrimination similar to the development cohort. However, the models need to be corrected for intercept and slope to acquire good calibration for application in a different setting.

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Year:  2012        PMID: 22215562     DOI: 10.1007/s10654-011-9648-4

Source DB:  PubMed          Journal:  Eur J Epidemiol        ISSN: 0393-2990            Impact factor:   8.082


  23 in total

1.  External validity of risk models: Use of benchmark values to disentangle a case-mix effect from incorrect coefficients.

Authors:  Yvonne Vergouwe; Karel G M Moons; Ewout W Steyerberg
Journal:  Am J Epidemiol       Date:  2010-08-31       Impact factor: 4.897

2.  Plasma procalcitonin and risk of type 2 diabetes in the general population.

Authors:  A Abbasi; E Corpeleijn; D Postmus; R T Gansevoort; P E de Jong; R O B Gans; J Struck; H L Hillege; R P Stolk; G Navis; S J L Bakker
Journal:  Diabetologia       Date:  2011-06-15       Impact factor: 10.122

3.  Identifying individuals at high risk for diabetes: The Atherosclerosis Risk in Communities study.

Authors:  Maria Inês Schmidt; Bruce B Duncan; Heejung Bang; James S Pankow; Christie M Ballantyne; Sherita H Golden; Aaron R Folsom; Lloyd E Chambless
Journal:  Diabetes Care       Date:  2005-08       Impact factor: 19.112

4.  The diabetes risk score: a practical tool to predict type 2 diabetes risk.

Authors:  Jaana Lindström; Jaakko Tuomilehto
Journal:  Diabetes Care       Date:  2003-03       Impact factor: 19.112

5.  Assessing the performance of prediction models: a framework for traditional and novel measures.

Authors:  Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan
Journal:  Epidemiology       Date:  2010-01       Impact factor: 4.822

6.  Global prevalence of diabetes: estimates for the year 2000 and projections for 2030.

Authors:  Sarah Wild; Gojka Roglic; Anders Green; Richard Sicree; Hilary King
Journal:  Diabetes Care       Date:  2004-05       Impact factor: 19.112

7.  Albuminuria assessed from first-morning-void urine samples versus 24-hour urine collections as a predictor of cardiovascular morbidity and mortality.

Authors:  Hiddo J Lambers Heerspink; Auke H Brantsma; Dick de Zeeuw; Stephan J L Bakker; Paul E de Jong; Ron T Gansevoort
Journal:  Am J Epidemiol       Date:  2008-09-05       Impact factor: 4.897

Review 8.  Risk assessment tools for identifying individuals at risk of developing type 2 diabetes.

Authors:  Brian Buijsse; Rebecca K Simmons; Simon J Griffin; Matthias B Schulze
Journal:  Epidemiol Rev       Date:  2011-05-27       Impact factor: 6.222

Review 9.  Risk models and scores for type 2 diabetes: systematic review.

Authors:  Douglas Noble; Rohini Mathur; Tom Dent; Catherine Meads; Trisha Greenhalgh
Journal:  BMJ       Date:  2011-11-28

10.  Population ageing research: a family of disciplines.

Authors:  Ronald P Stolk; Inge Hutter; Rafael P M Wittek
Journal:  Eur J Epidemiol       Date:  2009-10-28       Impact factor: 8.082

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  11 in total

Review 1.  The potential of novel biomarkers to improve risk prediction of type 2 diabetes.

Authors:  Christian Herder; Bernd Kowall; Adam G Tabak; Wolfgang Rathmann
Journal:  Diabetologia       Date:  2014-01       Impact factor: 10.122

2.  The Rotterdam Study: 2014 objectives and design update.

Authors:  Albert Hofman; Sarwa Darwish Murad; Cornelia M van Duijn; Oscar H Franco; André Goedegebure; M Arfan Ikram; Caroline C W Klaver; Tamar E C Nijsten; Robin P Peeters; Bruno H Ch Stricker; Henning W Tiemeier; André G Uitterlinden; Meike W Vernooij
Journal:  Eur J Epidemiol       Date:  2013-11-21       Impact factor: 8.082

3.  The Generation R Study: Biobank update 2015.

Authors:  Claudia J Kruithof; Marjolein N Kooijman; Cornelia M van Duijn; Oscar H Franco; Johan C de Jongste; Caroline C W Klaver; Johan P Mackenbach; Henriëtte A Moll; Hein Raat; Edmond H H M Rings; Fernando Rivadeneira; Eric A P Steegers; Henning Tiemeier; Andre G Uitterlinden; Frank C Verhulst; Eppo B Wolvius; Albert Hofman; Vincent W V Jaddoe
Journal:  Eur J Epidemiol       Date:  2014-12-21       Impact factor: 8.082

4.  Handgrip strength improves prediction of type 2 diabetes: a prospective cohort study.

Authors:  Setor K Kunutsor; Ari Voutilainen; Jari A Laukkanen
Journal:  Ann Med       Date:  2020-09-03       Impact factor: 4.709

5.  Improved Functional Causal Likelihood-Based Causal Discovery Method for Diabetes Risk Factors.

Authors:  Xiue Gao; Wenxue Xie; Zumin Wang; Bo Chen; Shengbin Zhou
Journal:  Comput Math Methods Med       Date:  2021-05-14       Impact factor: 2.238

6.  BiobankConnect: software to rapidly connect data elements for pooled analysis across biobanks using ontological and lexical indexing.

Authors:  Chao Pang; Dennis Hendriksen; Martijn Dijkstra; K Joeri van der Velde; Joel Kuiper; Hans L Hillege; Morris A Swertz
Journal:  J Am Med Inform Assoc       Date:  2014-10-31       Impact factor: 4.497

7.  Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study.

Authors:  Sophie Molnos; Simone Wahl; Mark Haid; E Marelise W Eekhoff; René Pool; Anna Floegel; Joris Deelen; Daniela Much; Cornelia Prehn; Michaela Breier; Harmen H Draisma; Nienke van Leeuwen; Annemarie M C Simonis-Bik; Anna Jonsson; Gonneke Willemsen; Wolfgang Bernigau; Rui Wang-Sattler; Karsten Suhre; Annette Peters; Barbara Thorand; Christian Herder; Wolfgang Rathmann; Michael Roden; Christian Gieger; Mark H H Kramer; Diana van Heemst; Helle K Pedersen; Valborg Gudmundsdottir; Matthias B Schulze; Tobias Pischon; Eco J C de Geus; Heiner Boeing; Dorret I Boomsma; Anette G Ziegler; P Eline Slagboom; Sandra Hummel; Marian Beekman; Harald Grallert; Søren Brunak; Mark I McCarthy; Ramneek Gupta; Ewan R Pearson; Jerzy Adamski; Leen M 't Hart
Journal:  Diabetologia       Date:  2017-10-25       Impact factor: 10.122

8.  Liver function tests and risk prediction of incident type 2 diabetes: evaluation in two independent cohorts.

Authors:  Ali Abbasi; Stephan J L Bakker; Eva Corpeleijn; Daphne L van der A; Ron T Gansevoort; Rijk O B Gans; Linda M Peelen; Yvonne T van der Schouw; Ronald P Stolk; Gerjan Navis; Annemieke M W Spijkerman; Joline W J Beulens
Journal:  PLoS One       Date:  2012-12-17       Impact factor: 3.240

9.  Adapting and validating diabetes simulation models across settings: accounting for mortality differences using administrative data.

Authors:  Alison J Hayes; Wendy A Davis; Timothy M Davis; Philip M Clarke
Journal:  J Diabetes Complications       Date:  2013-06-13       Impact factor: 3.219

10.  Validation of the German Diabetes Risk Score among the general adult population: findings from the German Health Interview and Examination Surveys.

Authors:  Rebecca Paprott; Kristin Mühlenbruch; Gert B M Mensink; Silke Thiele; Matthias B Schulze; Christa Scheidt-Nave; Christin Heidemann
Journal:  BMJ Open Diabetes Res Care       Date:  2016-11-21
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