Diabetes mellitus is an incurable progressive disease, characterized by elevated blood glucose levels, which lead to the development of micro- and macrovascular complications. Although the etiopathology of the disease remains unclear, it seems to be multifactorial, with an important interaction between genetics and environmental causes. Currently, the genetics of type 2 diabetes (T2D) is poorly understood. The recent advance of the genetic technologies and with a better understanding of genetics, more than 120 distinct genetic loci, with more than 150 variants, have been identified that may be involved in the pathogenesis of T2D. However, as these variants can account for only approximately 20% of the heritability of T2D, there is an obvious need for additional approaches to identify susceptibility genes or genetic mechanisms involved in the development of this disease. There is a growing number of genes found to be related to T2D; however, their individual impact on the pathogenesis of the disease appears to be low, while silencing of protective genes may also contribute to the development of this disease. The present review attempts to summarize our current knowledge in the field of genetics of T2D, highlighting the possible practical applications for each approach.

Background. The peroxisome proliferatoractivated receptor-γ co-activator 1-α (PPARGC1A), a key transcription factor involved in the control of metabolism and energy homeostasis, is an important biological and positional candidate of the metabolic syndrome. Association studies of its polymorphisms, however, yielded inconsistent sometimes conflicting results, pointing to important ethnic differences, which call for replication in various populations. Objective. In order to study its most common-potentially functional-polymorphism Gly482Ser (rs8192678), we carried out a case-control study in a central Romanian population. Material and methods. Two hundred and ninety six patients affected by the metabolic syndrome diagnosed according to the International Diabetes Federation proposed criteria and 166 middle-aged control subjects have been investigated. Genotyping was done by PCR-RFLP, using the restriction enzyme MspI. Results. While the G(Gly)/A(Ser) allele frequencies (66.89/33.11 vs. 71.68/28.31 %) and GG/GA/AA genotype distribution (45.27-43.24-11.48 vs. 54.21-34.93-10.84 %) differed in the metabolic syndrome and control group, the risk of developing the metabolic syndrome did not reach the limit of statistical significance (OR=1.43; p=0.06, CI 95%: 0.97-2.09). Metabolic parameters in the two study groups did not show significant differences according to the genotype (p>0.05). Conclusion. rs8192678 could be a functional polymorphism contributing to the development of the metabolic syndrome, but probably its effect is minor, and might depend on gene-gene and gene-environment interactions. Clarification of very small effects would require larger sample sizes.

With the pandemic of type 2 diabetes (T2D), there is an ever-increasing need to fully understand the underlying mechanisms of the disease. Type 2 diabetes shows a high heritability risk (25-80%); however, genes account only for 10% of this risk. From all the risk factors for diabetes, epigenetic mechanisms have the highest statistical scoring in explaining the disease. A multitude of organ-specific epigenomic changes have been linked to type 2 diabetes. Nutritional influences, mainly in the early life, physical activity level, environmental toxins act as epigenetic factors and the recognized epigenetic changes can represent a therapeutical target, new drugs being currently in development for this application. Our current review focuses on the most common epigenetic modifications linked to type 2 diabetes or insulin resistance, the potentially emerging epigenetic-related interventions and pharmacoepigenetic knowledge.

Objective: Methylene-tetrahydrofolate reductase (MTHFR) is involved in adapting metabolism to environmental challenges by various mechanisms, including the control of gene expression by epigenetic and post-translational changes of transcription factors. Though a metabolic syndrome candidate gene, association studies of its common polymorphism rs1801133 (MTHFR-Ala222Val) remain inconclusive with important ethnic differences, and the effect on disease progression was not addressed. Methods: 307 middle-aged metabolic syndrome patients in a central Romanian hospital setting were investigated metabolically, and genotyped by PCR-RFLP. Disease progression was assessed by the age of onset of metabolic components, as well as development of non-alcoholic fatty liver disease and atherosclerotic complications. Results: The minor allele frequency of rs1801133 was 30.13%. Metabolic parameters showed no statistically significant differences according to genotype, but variant carriers developed dysglycemia and dyslipidemia earlier (53.28±10.8 vs 59.44±9.31 years, p<0.05 and 58.57±11.31 vs 64.72±10.6 years, p<0.1).While the polymorphism did not influence hepatic complications, an inverse association was found for manifest atherosclerosis (OR=0.49, p=0.006, 95%CI:0.29-0.81), which may be folate-status dependent, and needs further investigations. Simultaneous analysis with transcription factor polymorphisms (rs1801282, rs8192678) showed that the more protective genotypes were present the later metabolic disturbances developed, and in the presence of the other two variants the apparent protective cardiovascular effect disappeared. Conclusions: The common functional polymorphism rs1801133 may influence metabolic syndrome progression, the age of onset of components and development of atherosclerotic complications. Besides simple additive effects, complex mitigating and aggravating variant interactions may exist, and the protective or predisposing outcome may depend on modifiable environmental factors.

These results suggest that the FABP2 T54 allele may have a minor contribution to the metabolic syndrome in our region.

Background: Hereditary angioedema due to C1 inhibitor deficiency (C1-INH-HAE) caused by SERPING1 mutations is a rare monogenic disorder characterized by a high frequency of de novo mutations, allelic heterogeneity and populational differences. Geno- and phenotype correlation data are limited. Addressing the pathogenic complexity, we proposed to analyze the clinical and genetic characteristics in a set of Romanian patients. Material and Methods: 49 patients from 22 unrelated families with C1-INH-HAE were investigated, by calculating clinical severity score (CSS), C1-INH and C4 level assessment by nephelometric assays, C1-INH function study by functional enzyme-linked immunosorbent assay, and mutation analysis by sequencing and MLPA. Clinical manifestations by missense vs other mutation mechanisms were compared. Results: The mean age at diagnosis and onset was 28.8±14.7 and 15.1±15.2 years, while the diagnostic delay 13.1±10.1 years. CSS ranged from 2 to 9, with a mean of 5.4±1.8. The frequency of missense and nonsense mutations, splice defects, frameshift mutations and large gene rearrangements was 61.22, 6.12, 22.4, 6.12 and 4.08%; in the regulatory sequence no mutation was described. In type II, only missense mutations were noted. Lower levels of C1-INH characterized index cases caused by mechanisms other than missense mutation, with more severe consequences on protein synthesis (p=0.017). 53% of the cases were identified by familial screening. Conclusion: A later onset of disease manifestations and a higher frequency of missense mutations characterize HAE in Romanian patients with SERPING1 mutation. Genetic analysis improves the management of affected families, and may inform about disease severity.

Objective: Insulin resistance has been shown to be a risk factor for type 2 diabetes and cardiovascular disease. The assessment of insulin sensitivity in the clinical practice, however, faces several difficulties. The study proposes to analyze surrogate measures of insulin resistance based on fasting insulin levels in central Romania, and check whether the diagnosis of the metabolic syndrome is an adequate strategy to identify middle-aged persons with reduced insulin sensitivity. Methods: Anthropometric measurements, metabolic profile, and surrogates measures of insulin sensitivity (GIR, HOMA, QUICKI, FIRI, Belfiore, Bennett, Raynaud, McAuley index) based on fasting insulin levels were assessed in 233 non-diabetic middle aged subjects. Results: Cutoff values, determined as the lowest quartile of insulin sensitivity for fasting insulin, HOMA, IRI (1/QUICKI), FIRI and Belfiore's, Bennett's, Raynaud's and McAuley's insulin sensitivity indices were 10.49 mU/L, 2.1, 3.01, 2.32, and 0.03, 1.34, 3.81, 6.29, 5.82. Components of the metabolic syndrome showed moderate but significant correlations with the surrogate measures of insulin resistance (r = 0.22-0.56, p <0.05). HOMA-IR and McAuley indices were the best predictors of clustered cardiometabolic risk factors (AUC - 0.83, 0.81 and 0.82). The metabolic syndrome diagnosis performed well in identifying patients with reduced insulin sensitivity (McAuley 2: sensitivity - 0.78, specificity - 0.84). Conclusion: Fasting insulin derived insulin sensitivity indices may help the recognittion of insulin resistant states predicting cardiometabolic disorders. Actively looking for insulin resistance by these simple indices, or by diagnosing the metabolic syndrome, those at increased risk can be recognized

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