Enteroviruses (EVs) have been connected to type 1 diabetes in various studies. The current study evaluates the association between specific EV subtypes and type 1 diabetes by measuring typespecific antibodies against the group B coxsackieviruses (CVBs), which have been linked to diabetes in previous surveys. Altogether, 249 children with newly diagnosed type 1 diabetes and 249 control children matched according to sampling time, sex, age, and country were recruited in Finland, Sweden, England, France, and Greece between 2001 and 2005 (mean age 9 years; 55% male). Antibodies against CVB1 were more frequent among diabetic children than among control children (odds ratio 1.7 [95% CI 1.0-2.9]), whereas other CVB types did not differ between the groups. CVB1-associated risk was not related to HLA genotype, age, or sex. Finnish children had a lower frequency of CVB antibodies than children in other countries. The results support previous studies that suggested an association between CVBs and type 1 diabetes, highlighting the possible role of CVB1 as a diabetogenic virus type. A connection between enterovirus (EV) infections and human type 1 diabetes has been documented in a variety of studies (1-3). Meta-analyses of studies on direct detection of EVs in blood or tissues have indicated a clear risk effect (odds ratios [ORs] 5.5-17.4) (4), whereas serological studies have shown inconsistent results (5). Accordingly, invasive infection, as reflected by the presence of EV in blood or tissues, rather than superficial

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SMARTDIAB is a platform designed to support the monitoring, management, and treatment of patients with type 1 diabetes mellitus (T1DM), by combining state-of-the-art approaches in the fields of database (DB) technologies, communications, simulation algorithms, and data mining. SMARTDIAB consists mainly of two units: 1) the patient unit (PU); and 2) the patient management unit (PMU), which communicate with each other for data exchange. The PMU can be accessed by the PU through the internet using devices, such as PCs/laptops with direct internet access or mobile phones via a Wi-Fi/General Packet Radio Service access network. The PU consists of an insulin pump for subcutaneous insulin infusion to the patient and a continuous glucose measurement system. The aforementioned devices running a user-friendly application gather patient's related information and transmit it to the PMU. The PMU consists of a diabetes data management system (DDMS), a decision support system (DSS) that provides risk assessment for long-term diabetes complications, and an insulin infusion advisory system (IIAS), which reside on a Web server. The DDMS can be accessed from both medical personnel and patients, with appropriate security access rights and front-end interfaces. The DDMS, apart from being used for data storage/retrieval, provides also advanced tools for the intelligent processing of the patient's data, supporting the physician in decision making, regarding the patient's treatment. The IIAS is used to close the loop between the insulin pump and the continuous glucose monitoring system, by providing the pump with the appropriate insulin infusion rate in order to keep the patient's glucose levels within predefined limits. The pilot version of the SMARTDIAB has already been implemented, while the platform's evaluation in clinical environment is being in progress.

The most important gene loci defining risk of type 1 diabetes mellitus (T1DM) are located within the HLA gene region. HLA-DQ molecules are of primary importance but HLA-DR gene products modify the risk conferred by HLA-DQ. The risk associated with an HLA genotype is defined by the particular combination of susceptible and protective alleles. The highest risk is associated with a combination of two different risk haplotypes (7% risk to develop T1DM in Finland) whereas protective genotypes covering 69% of population have a risk of less than 0.2%). The complicated analysis of HLA genotypes is simplified by strong linkage disequilibrium between HLA-DRB1, -DQA1 and -DQB1 loci. In many cases one can deduce the alleles of other loci based on determination of the alleles in one locus. Differences between various populations in the frequency of marker alleles and in the linkages between them has to be taken into account. We have developed PCR based typing methods that utilize blood spot samples, microtiter plate format and lanthanide labeled oligonucleotide probes to define HLA-DQ and -DR alleles relevant for T1DM risk. Typing is run stepwise so that after initial HLA-DQB1 typing only those samples will be further analyzed in which -DQA1 or -DRB1 typing is informative and expected to contribute to the risk estimation. This method has been used to screen more than 50,000 newborn infants in Finland over a time period of 6 years, and it has been able to identify most children who have developed T1D during the follow-up period. The efficiency of the procedure has also been tested in Finnish and Greek populations.