scholarly journals Risk-prediction models for incident type 2 diabetes in Chinese people with impaired glucose tolerance: a systematic literature review and external validation study

The Lancet ◽  
2020 ◽  
Vol 396 ◽  
pp. S7
Author(s):  
Shishi Xu ◽  
Qin Wan ◽  
Ruth Coleman ◽  
Jaakko Tuomilehto ◽  
Nanwei Tong ◽  
...  
Diabetes Care ◽  
2015 ◽  
pp. dc150509 ◽  
Author(s):  
Mary E. Lacy ◽  
Gregory A. Wellenius ◽  
Mercedes R. Carnethon ◽  
Eric B. Loucks ◽  
April P. Carson ◽  
...  

Diabetes Care ◽  
2013 ◽  
Vol 37 (2) ◽  
pp. 537-545 ◽  
Author(s):  
C. A. Bannister ◽  
C. D. Poole ◽  
S. Jenkins-Jones ◽  
C. L. Morgan ◽  
G. Elwyn ◽  
...  

Author(s):  
Mojtaba Lotfaliany ◽  
Farzad Hadaegh ◽  
Mohammad Ali Mansournia ◽  
Fereidoun Azizi ◽  
Brian Oldenburg ◽  
...  

Background: Recent evidence recommended stepwise screening methods for identifying individuals at high risk of type 2 diabetes to be recruited in the lifestyle intervention programs for the prevention of the disease. This study aims to assess the performance of different stepwise screening methods that combine non-invasive measurements with lab-based measurements for identifying those with 5-years incident type 2 diabetes. Methods: 3037 participants aged ≥30 years without diabetes at baseline in the Tehran Lipid and Glucose Study (TLGS) were followed. Thirty-two stepwise screening methods were developed by combining a non-invasive measurement (an anthropometric measurement (waist-to-height ratio, WtHR) or a score based on a non-invasive risk score [Australian Type 2 Diabetes Risk Assessment Tool, AUSDRISK]) with a lab-based measurement (different cut-offs of fasting plasma glucose [FPG] or predicted risk based on three lab-based prediction models [Saint Antonio, SA; Framingham Offspring Study, FOS; and the Atherosclerosis Risk in Communities, ARIC]). The validation, calibration, and usefulness of lab-based prediction models were assessed before developing the stepwise screening methods. Cut-offs were derived either based on previous studies or decision-curve analyses. Results: 203 participants developed diabetes in 5 years. Lab-based risk prediction models had good discrimination power (area under the curves [AUCs]: 0.80-0.83), achieved acceptable calibration and net benefits after recalibration for population’s characteristics and were useful in a wide range of risk thresholds (5%-21%). Different stepwise methods had sensitivity ranged 20%-68%, specificity 70%-98%, and positive predictive value (PPV) 14%-46%; they identified 3%-33% of the screened population eligible for preventive interventions. Conclusion: Stepwise methods have acceptable performance in identifying those at high risk of incident type 2 diabetes.


BMJ ◽  
2012 ◽  
Vol 345 (sep18 2) ◽  
pp. e5900-e5900 ◽  
Author(s):  
A. Abbasi ◽  
L. M. Peelen ◽  
E. Corpeleijn ◽  
Y. T. van der Schouw ◽  
R. P. Stolk ◽  
...  

2019 ◽  
Author(s):  
Jose L Flores-Guerrero ◽  
Margery A Connelly ◽  
Dion Groothof ◽  
Eke G Gruppen ◽  
Stephan JL Bakker ◽  
...  

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