Literature DB >> 25292440

Modelling of OGTT curve identifies 1 h plasma glucose level as a strong predictor of incident type 2 diabetes: results from two prospective cohorts.

Akram Alyass1, Peter Almgren, Mikael Akerlund, Jonathan Dushoff, Bo Isomaa, Peter Nilsson, Tiinamaija Tuomi, Valeriya Lyssenko, Leif Groop, David Meyre.   

Abstract

AIMS/HYPOTHESIS: The relevance of the OGTT in predicting type 2 diabetes is unclear. We assessed the performance of 14 OGTT glucose traits in type 2 diabetes prediction.
METHODS: We studied 2,603 and 2,386 Europeans from the Botnia study and Malmö Prevention Project (MPP) cohorts with baseline OGTT data. Over a follow-up period of 4.94 years and 23.5 years, 155 (5.95%) and 467 (19.57%) participants, respectively, developed type 2 diabetes. The main outcome was incident type 2 diabetes.
RESULTS: One-hour plasma glucose (1h-PG) was a fair/good predictor of incident type 2 diabetes in the Botnia study and MPP (AUC for receiver operating characteristic [AUCROC] 0.80 [0.77, 0.84] and 0.70 [0.68, 0.73]). 1h-PG alone outperformed the prediction model of multiple clinical risk factors (age, sex, BMI, family history of type 2 diabetes) in the Botnia study and MPP (AUCROC 0.75 [0.72, 0.79] and 0.67 [0.64, 0.70]). The same clinical risk factors added to 1h-PG modestly increased prediction for incident type 2 diabetes (Botnia, AUCROC 0.83 [0.80, 0.86]; MPP, AUCROC 0.74 [0.72, 0.77]). 1h-PG also outperformed HbA1c in predicting type 2 diabetes in the Botnia cohort. A 1h-PG value of 8.9 mmol/l and 8.4 mmol/l was the optimal cut-point for initial screening and selection of high-risk individuals in the Botnia study and MPP, respectively, and represented 30% and 37% of all participants in these cohorts. High-risk individuals had a substantially increased risk of incident type 2 diabetes (OR 8.0 [5.5, 11.6] and 3.8 [3.1, 4.7]) and captured 75% and 62% of all incident type 2 diabetes in the Botnia study and MPP. CONCLUSIONS/
INTERPRETATION: 1h-PG is a valuable prediction tool for identifying adults at risk for future type 2 diabetes.

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Year:  2014        PMID: 25292440     DOI: 10.1007/s00125-014-3390-x

Source DB:  PubMed          Journal:  Diabetologia        ISSN: 0012-186X            Impact factor:   10.122


  41 in total

1.  The shape of plasma glucose concentration curve during OGTT predicts future risk of type 2 diabetes.

Authors:  Muhammad A Abdul-Ghani; Valeriya Lyssenko; Tiinamaija Tuomi; Ralph A Defronzo; Leif Groop
Journal:  Diabetes Metab Res Rev       Date:  2010-05       Impact factor: 4.876

2.  Beta-cell function and insulin sensitivity contribute to the shape of plasma glucose curve during an oral glucose tolerance test in non-diabetic individuals.

Authors:  M Kanauchi; K Kimura; K Kanauchi; Y Saito
Journal:  Int J Clin Pract       Date:  2005-04       Impact factor: 2.503

3.  Various randomized designs can be used to evaluate medical tests.

Authors:  Jeroen G Lijmer; Patrick M M Bossuyt
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4.  Heritability and familiality of type 2 diabetes and related quantitative traits in the Botnia Study.

Authors:  P Almgren; M Lehtovirta; B Isomaa; L Sarelin; M R Taskinen; V Lyssenko; T Tuomi; L Groop
Journal:  Diabetologia       Date:  2011-08-09       Impact factor: 10.122

5.  One-hour plasma glucose identifies insulin resistance and beta-cell dysfunction in individuals with normal glucose tolerance: cross-sectional data from the Relationship between Insulin Sensitivity and Cardiovascular Risk (RISC) study.

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6.  Accuracy of plasma glucose measurements in the hypoglycemic range.

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9.  Shape information from glucose curves: functional data analysis compared with traditional summary measures.

Authors:  Kathrine Frey Frøslie; Jo Røislien; Elisabeth Qvigstad; Kristin Godang; Jens Bollerslev; Nanna Voldner; Tore Henriksen; Marit B Veierød
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Review 10.  External validation of multivariable prediction models: a systematic review of methodological conduct and reporting.

Authors:  Gary S Collins; Joris A de Groot; Susan Dutton; Omar Omar; Milensu Shanyinde; Abdelouahid Tajar; Merryn Voysey; Rose Wharton; Ly-Mee Yu; Karel G Moons; Douglas G Altman
Journal:  BMC Med Res Methodol       Date:  2014-03-19       Impact factor: 4.615

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  40 in total

1.  Pitfalls of HbA1c in the Diagnosis of Diabetes.

Authors:  Michael Bergman; Muhammad Abdul-Ghani; João Sérgio Neves; Mariana P Monteiro; Jose Luiz Medina; Brenda Dorcely; Martin Buysschaert
Journal:  J Clin Endocrinol Metab       Date:  2020-08-01       Impact factor: 5.958

2.  One-Hour Plasma Glucose Compared With Two-Hour Plasma Glucose in Relation to Diabetic Retinopathy in American Indians.

Authors:  Ethan Paddock; Helen C Looker; Paolo Piaggi; William C Knowler; Jonathan Krakoff; Douglas C Chang
Journal:  Diabetes Care       Date:  2018-04-05       Impact factor: 19.112

3.  Reducing the prevalence of dysglycemia: is the time ripe to test the effectiveness of intervention in high-risk individuals with elevated 1 h post-load glucose levels?

Authors:  Michael Bergman; Ram Jagannathan; Martin Buysschaert; Jose Luis Medina; Mary Ann Sevick; Karin Katz; Brenda Dorcely; Jesse Roth; Angela Chetrit; Rachel Dankner
Journal:  Endocrine       Date:  2017-01-25       Impact factor: 3.633

4.  Heterogeneity in glucose response curves during an oral glucose tolerance test and associated cardiometabolic risk.

Authors:  Adam Hulman; Rebecca K Simmons; Dorte Vistisen; Adam G Tabák; Jacqueline M Dekker; Marjan Alssema; Femke Rutters; Anitra D M Koopman; Thomas P J Solomon; John P Kirwan; Torben Hansen; Anna Jonsson; Anette Prior Gjesing; Hans Eiberg; Arne Astrup; Oluf Pedersen; Thorkild I A Sørensen; Daniel R Witte; Kristine Færch
Journal:  Endocrine       Date:  2016-10-03       Impact factor: 3.633

5.  One-hour and two-hour postload plasma glucose concentrations are comparable predictors of type 2 diabetes mellitus in Southwestern Native Americans.

Authors:  Ethan Paddock; Maximilian G Hohenadel; Paolo Piaggi; Pavithra Vijayakumar; Robert L Hanson; William C Knowler; Jonathan Krakoff; Douglas C Chang
Journal:  Diabetologia       Date:  2017-06-29       Impact factor: 10.122

6.  Low Levels of High-Density Lipoprotein Cholesterol Do Not Predict the Incidence of Type 2 Diabetes in an Iranian High-Risk Population: The Isfahan Diabetes Prevention Study.

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7.  Clinical utility of 30-min plasma glucose for prediction of type 2 diabetes among people with prediabetes: Ancillary analysis of the diabetes community lifestyle improvement program.

Authors:  Ram Jagannathan; Mary Beth Weber; Ranjit M Anjana; Harish Ranjani; Lisa R Staimez; Mohammed K Ali; Viswanathan Mohan; K M Venkat Narayan
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8.  Glucose patterns during the OGTT and risk of future diabetes in an urban Indian population: The CARRS study.

Authors:  Adam Hulman; Unjali P Gujral; K M Venkat Narayan; Rajendra Pradeepa; Deepa Mohan; Ranjit Mohan Anjana; Viswanathan Mohan; Kristine Færch; Daniel R Witte
Journal:  Diabetes Res Clin Pract       Date:  2017-02-17       Impact factor: 5.602

9.  Delayed timing of post-challenge peak blood glucose predicts declining beta cell function and worsening glucose tolerance over time: insight from the first year postpartum.

Authors:  Caroline K Kramer; Chang Ye; Anthony J G Hanley; Philip W Connelly; Mathew Sermer; Bernard Zinman; Ravi Retnakaran
Journal:  Diabetologia       Date:  2015-03-12       Impact factor: 10.122

10.  Metabolic and Genetic Determinants of Glucose Shape After Oral Challenge in Obese Youths: A Longitudinal Study.

Authors:  Alfonso Galderisi; Domenico Tricò; Chiara Dalla Man; Nicola Santoro; Bridget Pierpont; Leif Groop; Claudio Cobelli; Sonia Caprio
Journal:  J Clin Endocrinol Metab       Date:  2020-02-01       Impact factor: 5.958

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