[Is PON1 a factor of cardiovascular risk in type 2 diabetes?]

Using a Targeted Proteomics Chip to Explore Pathophysiological Pathways for Incident Diabetes- The Malmö Preventive Project

Multiplex proteomic platforms provide excellent tools for investigating associations between multiple proteins and disease (e.g., diabetes) with possible prognostic, diagnostic, and therapeutic implications. In this study our aim was to explore novel pathophysiological pathways by examining 92 proteins and their association with incident diabetes in a population-based cohort (146 cases of diabetes versus 880 controls) followed over 8 years. After adjusting for traditional risk factors, we identified seven proteins associated with incident diabetes. Four proteins (Scavenger receptor cysteine rich type 1 protein M130, Fatty acid binding protein 4, Plasminogen activator inhibitor 1 and Insulin-like growth factor-binding protein 2) with a previously established association with incident diabetes and 3 proteins (Cathepsin D, Galectin-4, Paraoxonase type 3) with a novel association with incident diabetes. Galectin-4, with an increased risk of diabetes, and Paraoxonase type 3, with a decreased risk of diabetes, remained significantly associated with incident diabetes after adjusting for plasma glucose, implying a glucose independent association with diabetes.

Association of PON2 Gene Polymorphisms (Ser311Cys and Ala148Gly) With the Risk of Developing Type 2 Diabetes Mellitus in the Chinese Population

Background: The association between paraoxonase 2 (PON2) gene polymorphisms and type 2 diabetes mellitus (T2DM) has been extensively investigated in the Chinese population with conflicting results. In this study, we systematically evaluated the association between PON2 Ser311Cys and Ala148Gly polymorphisms and T2DM risk by pooling all relevant studies. Methods: We searched PubMed, Embase, CNKI, and Wanfang databases for the studies. The strength of association was determined by the allelic, homozygous, heterozygous, recessive, and dominant genetic models and measured as odds ratio (OR) and 95% confidence interval (CI), under fixed- or random-effect models. Results: There was no significant association between PON2 Ser311Cys polymorphism and T2DM under any of the genetic models: allelic (OR = 1.06, 95% CI = 0.77-1.45; P = 0.721), heterozygous (OR = 1.13, 95% CI = 0.87-1.45; P = 0.362), dominant (OR = 1.10, 95% CI = 0.80-1.51; P = 0.562), recessive (OR = 0.87, 95% CI = 0.48-1.58; P = 0.648), homozygous (OR = 0.94, 95% CI = 0.47-1.89; P = 0.865). Similarly, no significant association was found in PON2 Arg148Gly polymorphism under any of the models: allelic (OR = 1.17, 95% CI = 0.91-1.50; P = 0.218), heterozygous (OR = 1.28, 95% CI = 0.94-1.74; P = 0.117), dominant (OR = 1.25, 95% CI = 0.93-1.67; P = 0.142), recessive (OR = 0.99, 95% CI = 0.52-1.88; P = 0.973), homozygous (OR = 1.08, 95% CI = 0.57-2.07; P = 0.808). Conclusions: The PON2 Ser311Cys and Ala148Gly polymorphisms were not associated with the risk of developing T2DM in the Chinese population.