Open Access Research

Predictive models for type 2 diabetes onset in middle-aged subjects with the metabolic syndrome

Michal Ozery-Flato1*, Naama Parush1, Tal El-Hay1, Žydrūnė Visockienė24, Ligita Ryliškytė34, Jolita Badarienė34, Svetlana Solovjova34, Milda Kovaitė34, Rokas Navickas34 and Aleksandras Laucevičius34

Author Affiliations

1 Machine Learning and Data Mining group, IBM Research - Haifa, Mount Carmel, Haifa 3498825, Israel

2 Centre of Endocrinology, Vilnius University Hospital Santariškių Klinikos, Santariskiu g. 2, Vilnius LT-08661, Lithuania

3 Centre of Cardiology and Angiology, Vilnius University Hospital Santariškių Klinikos, Santariskiu g. 2, Vilnius LT-08661, Lithuania

4 Vilnius University, Medical Faculty, M. K. Ciurlionio g. 21, Vilnius LT-03101, Lithuania

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Diabetology & Metabolic Syndrome 2013, 5:36  doi:10.1186/1758-5996-5-36

Published: 15 July 2013

Abstract

Objective

To investigate the predictive value of different biomarkers for the incidence of type 2 diabetes mellitus (T2DM) in subjects with metabolic syndrome.

Methods

A prospective study of 525 non-diabetic, middle-aged Lithuanian men and women with metabolic syndrome but without overt atherosclerotic diseases during a follow-up period of two to four years. We used logistic regression to develop predictive models for incident cases and to investigate the association between various markers and the onset of T2DM.

Results

Fasting plasma glucose (FPG), body mass index (BMI), and glycosylated haemoglobin can be used to predict diabetes onset with a high level of accuracy and each was shown to have a cumulative predictive value. The estimated area under the receiver-operating characteristic curve (AUC) for this combination was 0.92. The oral glucose tolerance test (OGTT) did not show cumulative predictive value. Additionally, progression to diabetes was associated with high values of aortic pulse-wave velocity (aPWV).

Conclusion

T2DM onset in middle-aged metabolic syndrome subjects can be predicted with remarkable accuracy using the combination of FPG, BMI, and HbA1c, and is related to elevated aPWV measurements.

Keywords:
Metabolic syndrome; Type 2 diabetes mellitus; Risk assessment; Biomarkers; Arterial markers; Predictive models