Exploring the Pathophysiology of ATP-Dependent Potassium Channels in Insulin Resistance.

Ionic channels are present in eucaryotic plasma and intracellular membranes. They coordinate and control several functions. Potassium channels belong to the most diverse family of ionic channels that includes ATP-dependent potassium (KATP) channels in the potassium rectifier channel subfamily. These channels were initially described in heart muscle and then in other tissues such as pancreatic, skeletal muscle, brain, and vascular and non-vascular smooth muscle tissues. In pancreatic beta cells, KATP channels are primarily responsible for maintaining the membrane potential and for depolarization-mediated insulin release, and their decreased density and activity may be related to insulin resistance. KATP channels' relationship with insulin resistance is beginning to be explored in extra-pancreatic beta tissues like the skeletal muscle, where KATP channels are involved in insulin-dependent glucose recapture and their activation may lead to insulin resistance. In adipose tissues, KATP channels containing Kir6.2 protein subunits could be related to the increase in free fatty acids and insulin resistance; therefore, pathological processes that promote prolonged adipocyte KATP channel inhibition might lead to obesity due to insulin resistance. In the central nervous system, KATP channel activation can regulate peripheric glycemia and lead to brain insulin resistance, an early peripheral alteration that can lead to the development of pathologies such as obesity and Type 2 Diabetes Mellitus (T2DM). In this review, we aim to discuss the characteristics of KATP channels, their relationship with clinical disorders, and their mechanisms and potential associations with peripheral and central insulin resistance.

Pharmacogenetics of Metformin Transporters Suggests No Association with Therapeutic Inefficacy among Diabetes Type 2 Mexican Patients.

Mexico has been under official epidemiological alert due to diabetes since 2016. This study presents new information on the frequency and variants of metformin transporters OCT1, OCT2, OCT3, ABCB1, and CYP2C9 variants as well. It also reports the association with HbA1c control on 103 DMT2 patients. They were genotyped through real-time PCR (TaqMan assays) and grouped according to treatment: metformin and metformin + glibenclamide. Metformin plasmatic levels were determined through mass spectrometry. The analysis of HbA1c showed statistical significance across genotypes in polymorphisms rs72552763 (p = 0.022), rs622342 (p = 0.009), rs1128503 (p = 0.021), and rs2032582 (p = 0.009) within the monotherapy group. Bivariate analysis found no association between any polymorphism and HbA1c control. Two logistic regression models accounted for two diplotypes in OCT1 and ABCB1, including statistically significant covariates. The first model yielded significance in age (p = 0.026), treatment period [p = 0.001], BMI ≥ 25 kg/m2 (p = 0.043), and combined therapy (p < 0.001). There was no association with GAT/GAT of rs72552763 or A/A rs622342 in OCT1. The second model yielded significance in age (p = 0.017), treatment period (p = 0.001), BMI ≥ 25 kg/m2 (p = 0.042), and combined therapy (p < 0.001), finding no association with C/C of rs1128503 or G/G of rs2032582 in ABCB1. Our multinomial logistic regression results may benefit future predictive analyses in diabetic populations.

Identification of genetic variants in pharmacogenetic genes associated with type 2 diabetes in a Mexican-Mestizo population.

Type 2 diabetes mellitus (T2DM) is one of the most prevalent chronic pathologies in the world. In developing countries, such as Mexico, its prevalence represents an important public health and research issue. Determining factors triggering T2DM are environmental and genetic. While diet, exercise and proper weight control are the first measures recommended to improve the quality of life and life expectancy of patients, pharmacological treatment is usually the next step. Within every population there are variations in interindividual drug response, which may be due to genetic background. Some of the most frequent first line T2DM treatments in developing countries are sulfonylureas (SU), whose targets are ATP-sensitive potassium channels (KATP). Single nucleotide polymorphisms (SNPs) of the KATP coding genes, potassium voltage-gated channel subfamily J member 11 (KCNJ11) and ATP binding cassette subfamily C member 8 (ABCC8) have been associated with SU response variability. To date, there is little information regarding the mechanism by which these SNPs work within Mexican populations. The present study describes the distribution of three SNPs [KCNJ11 rs5219 (E23K), ABCC8 rs757110 (S1369A) and rs1799854 (-3C/T)] among Mestizo Mexican (MM) T2DM patients, and compares it with published data on various healthy subjects and T2DM populations. Through this comparison, no difference in the KCNJ11 rs5219 and ABCC8 rs757110 allelic and genotypic frequencies in MM were observed compared with the majority of the reported populations of healthy and diabetic individuals among other ethnic groups; except for African and Colombian individuals. By contrast, ABCC8 rs1799854 genomic and allelic frequencies among MM were observed to be significantly different from those reported by the 1000 Genomes Project, and from diabetic patients within other populations reported in the literature, such as the European, Asian and Latin-American individuals [T=0.704, G=0.296; CC=0.506, CT=0.397, TT=0.097; 95% confidence interval (CI); P≤0.05]; except for South Asian and Iberian populations, which may reflect the admixture origins of the present Mexican population. This genetic similarity has not been observed in the other Latin-American groups. To the best of our knowledge, this is the first study of ABCC8 rs757110 and rs1799854 SNP frequencies in any Mexican population and, specifically with diabetic Mexicans. Knowledge of the genetic structure of different populations is key to understanding the interindividual responses to drugs, such as SU and whether genotypic differences affect clinical outcome.