Lack of Association of Type 2 Diabetes Susceptibility Genotypes and Body Weight on the Development of Islet Autoimmunity and Type 1 Diabetes

AIM: To investigate whether type 2 diabetes susceptibility genes and body weight influence the development of islet autoantibodies and the rate of progression to type 1 diabetes. METHODS: Genotyping for single nucleotide polymorphisms (SNP) of the type 2 diabetes susceptibility genes CDKAL1, CDKN2A/2B, FTO, HHEX-IDE, HMGA2, IGF2BP2, KCNJ11, KCNQ1, MTNR1B, PPARG, SLC30A8 and TCF7L2 was obtained in 1350 children from parents with type 1 diabetes participating in the BABYDIAB study. Children were prospectively followed from birth for islet autoantibodies and type 1 diabetes. Data on weight and height were obtained at 9 months, 2, 5, 8, 11, and 14 years of age. RESULTS: None of type 2 diabetes risk alleles at the CDKAL1, CDKN2A/2B, FTO, HHEX-IDE, HMGA2, IGF2BP2, KCNJ11, KCNQ1, MTNR1B, PPARG and SLC30A8 loci were associated with the development of islet autoantibodies or diabetes. The type 2 diabetes susceptible genotype of TCF7L2 was associated with a lower risk of islet autoantibodies (7% vs. 12% by age of 10 years, P = 0.015, P(corrected) = 0.18). Overweight children at seroconversion did not progress to diabetes faster than non-overweight children (HR: 1.08; 95% CI: 0.48-2.45, P>0.05). CONCLUSIONS: These findings do not support an association of type 2 diabetes risk factors with islet autoimmunity or acceleration of diabetes in children with a family history of type 1 diabetes

Investigating the Role of T-Cell Avidity and Killing Efficacy in Relation to Type 1 Diabetes Prediction

During the progression of the clinical onset of Type 1 Diabetes (T1D), high-risk individuals exhibit multiple islet autoantibodies and high-avidity T cells which progressively destroy beta cells causing overt T1D. In particular, novel autoantibodies, such as those against IA-2 epitopes (aa1-577), had a predictive rate of 100% in a 10-year follow up (rapid progressors), unlike conventional autoantibodies that required 15 years of follow up for a 74% predictive rate (slow progressors). The discrepancy between these two groups is thought to be associated with T-cell avidity, including CD8 and/or CD4 T cells. For this purpose, we build a series of mathematical models incorporating first one clone then multiple clones of islet-specific and pathogenic CD8 and/or CD4 T cells, together with B lymphocytes, to investigate the interaction of T-cell avidity with autoantibodies in predicting disease onset. These models are instrumental in examining several experimental observations associated with T-cell avidity, including the phenomenon of avidity maturation (increased average T-cell avidity over time), based on intra- and cross-clonal competition between T cells in high-risk human subjects. The model shows that the level and persistence of autoantibodies depends not only on the avidity of T cells, but also on the killing efficacy of these cells. Quantification and modeling of autoreactive T-cell avidities can thus determine the level of risk associated with each type of autoantibodies and the timing of T1D disease onset in individuals that have been tested positive for these autoantibodies. Such studies may lead to early diagnosis of the disease in high-risk individuals and thus potentially serve as a means of staging patients for clinical trials of preventive or interventional therapies far before disease onset

Etiopathogenesis of type 1 diabetes mellitus: prognostic factors for the evolution of residual β cell function

Type 1A diabetes mellitus (T1ADM) is a progressive autoimmune disease mediated by T lymphocytes with destruction of beta cells. Up to now, we do not have precise methods to assess the beta cell mass, "in vivo" or "ex-vivo". The studies about its genetic susceptibility show strong association with class II antigens of the HLA system (particularly DQ). Others genetics associations are weaker and depend on the population studied. A combination of precipitating events may occur at the beginning of the disease. There is a silent loss of immune-mediated beta cells mass which velocity has an inverse relation with the age, but it is influenced by genetic and metabolic factors. We can predict the development of the disease primarily through the determination of four biochemically islet auto antibodies against antigens like insulin, GAD65, IA2 and Znt8. Beta cell destruction is chronically progressive but at clinical diagnosis of the disease a reserve of these cells still functioning. The goal of secondary disease prevention is halt the autoimmune attack on beta cells by redirecting or dampening the immune system. It is remains one of the foremost therapeutic goals in the T1ADM. Glycemic intensive control and immunotherapeutic agents may preserve beta-cell function in newly diagnosed patients with T1ADM. It may be assessed through C-peptide values, which are important for glycemic stability and for the prevention of chronic complications of this disease. This article will summarize the etiopathogenesis mechanisms of this disease and the factors can influence on residual C-peptide and the strategies to it preservation

Capture-recapture and multiple-record systems estimation II: Applications in human diseases

This article evaluates the potential epidemiologic uses of capture- recapture, which include the primary area of determining disease frequency. Capture-recapture may be a means to effectively 'count' new cases (incidence) or count existing cases (prevalence). Specific applications of capture- recapture in epidemiology are presented, one of which is its use in estimating death rates in a region close to Calcutta, India. The method also has considerable potential to assess suicides, and it may be the only technique to assess disease frequency in developing countries. In addition to generating an estimate of population size, another application of capture- recapture is to assess the costs of ascertainment relative to the degree of accuracy. This approach provides a formal means for assessing the cost- benefits of lists for the identification of cases. The authors believe that with careful and appropriate use, capture-recapture methods will provide a new approach that can considerably improve our ability to monitor disease.link_to_subscribed_fulltex