Type 2 Diabetes Susceptibility in the Greek-Cypriot Population: Replication of Associations with TCF7L2, FTO, HHEX, SLC30A8 and IGF2BP2 Polymorphisms.

Type 2 diabetes (T2D) has been the subject of numerous genetic studies in recent years which revealed associations of the disease with a large number of susceptibility loci. We hereby initiate the evaluation of T2D susceptibility loci in the Greek-Cypriot population by performing a replication case-control study. One thousand and eighteen individuals (528 T2D patients, 490 controls) were genotyped at 21 T2D susceptibility loci, using the allelic discrimination method. Statistically significant associations of T2D with five of the tested single nucleotide polymorphisms (SNPs) (TCF7L2 rs7901695, FTO rs8050136, HHEX rs5015480, SLC30A8 rs13266634 and IGF2BP2 rs4402960) were observed in this study population. Furthermore, 14 of the tested SNPs had odds ratios (ORs) in the same direction as the previously published studies, suggesting that these variants can potentially be used in the Greek-Cypriot population for predictive testing of T2D. In conclusion, our findings expand the genetic assessment of T2D susceptibility loci and reconfirm five of the worldwide established loci in a distinct, relatively small, newly investigated population.

Dietary patterns analysis using data mining method. An application to data from the CYKIDS study.

Data mining is a computational method that permits the extraction of patterns from large databases. We applied the data mining approach in data from 1140 children (9-13 years), in order to derive dietary habits related to children's obesity status. Rules emerged via data mining approach revealed the detrimental influence of the increased consumption of soft dinks, delicatessen meat, sweets, fried and junk food. For example, frequent (3-5 times/week) consumption of all these foods increases the risk for being obese by 75%, whereas in children who have a similar dietary pattern, but eat >2 times/week fish and seafood the risk for obesity is reduced by 33%. In conclusion patterns revealed from data mining technique refer to specific groups of children and demonstrate the effect on the risk associated with obesity status when a single dietary habit might be modified. Thus, a more individualized approach when translating public health messages could be achieved.

Assessment of the risk factors of coronary heart events based on data mining with decision trees.

Coronary heart disease (CHD) is one of the major causes of disability in adults as well as one of the main causes of death in the developed countries. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop a data-mining system for the assessment of heart event-related risk factors targeting in the reduction of CHD events. The risk factors investigated were: 1) before the event: a) nonmodifiable-age, sex, and family history for premature CHD, b) modifiable-smoking before the event, history of hypertension, and history of diabetes; and 2) after the event: modifiable-smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 528 cases were collected from the Paphos district in Cyprus, most of them with more than one event. Data-mining analysis was carried out using the C4.5 decision tree algorithm for the aforementioned three events using five different splitting criteria. The most important risk factors, as extracted from the classification rules analysis were: 1) for MI, age, smoking, and history of hypertension; 2) for PCI, family history, history of hypertension, and history of diabetes; and 3) for CABG, age, history of hypertension, and smoking. Most of these risk factors were also extracted by other investigators. The highest percentages of correct classifications achieved were 66%, 75%, and 75% for the MI, PCI, and CABG models, respectively. It is anticipated that data mining could help in the identification of high and low risk subgroups of subjects, a decisive factor for the selection of therapy, i.e., medical or surgical. However, further investigation with larger datasets is still needed.