<b><i>Introduction:</i></b> Genetic variants could aid in predicting antidiabetic drug response by associating them with markers of glucose control, such as glycated hemoglobin (HbA1c). However, pharmacogenetic implementation for antidiabetics is still under development, as the list of actionable markers is being populated and validated. This study explores potential associations between genetic variants and plasma levels of HbA1c in 100 patients under treatment with metformin. <b><i>Methods:</i></b> HbA1c was measured in a clinical chemistry analyzer (Roche), genotyping was performed in an Illumina-GSA array and data were analyzed using PLINK. Association and prediction models were developed using R and a 10-fold cross-validation approach. <b><i>Results:</i></b> We identified genetic variants on <i>SLC47A1, SLC28A1, ABCG2, TBC1D4,</i> and <i>ARID5B</i> that can explain up to 55% of the interindividual variability of HbA1c plasma levels in diabetic patients under treatment. Variants on <i>SLC47A1</i>, <i>SLC28A1</i>, and <i>ABCG2</i> likely impact the pharmacokinetics (PK) of metformin, while the role of the two latter can be related to insulin resistance and regulation of adipogenesis. <b><i>Conclusions:</i></b> Our results confirm previous genetic associations and point to previously unassociated gene variants for metformin PK and glucose control.
Atherogenesis and dyslipidemia increase the risk of cardiovascular disease, which is the leading cause of death in developed countries. While blood lipid levels have been studied as disease predictors, their accuracy in predicting cardiovascular risk is limited due to their high interindividual and interpopulation variability. The lipid ratios, atherogenic index of plasma (AIP = log TG/HDL-C) and the Castelli risk index 2 (CI2 = LDL-C/HDL-C), have been proposed as better predictors of cardiovascular risk, but the genetic variability associated with these ratios has not been investigated. This study aimed to identify genetic associations with these indexes. The study population (n = 426) included males (40%) and females (60%) aged 18–52 years (mean 39 years); the Infinium GSA array was used for genotyping. Regression models were developed using R and PLINK. AIP was associated with variation on APOC3, KCND3, CYBA, CCDC141/TTN, and ARRB1 (p-value < 2.1 × 10−6). The three former were previously associated with blood lipids, while CI2 was associated with variants on DIPK2B, LIPC, and 10q21.3 rs11251177 (p-value 1.1 × 10−7). The latter was previously linked to coronary atherosclerosis and hypertension. KCND3 rs6703437 was associated with both indexes. This study is the first to characterize the potential link between genetic variation and atherogenic indexes, AIP, and CI2, highlighting the relationship between genetic variation and dyslipidemia predictors. These results also contribute to consolidating the genetics of blood lipid and lipid indexes.
Pharmacogenomics (PGx), one of the several tools of precision medicine, has been slowly implemented in the clinic during the past decades. This process generally starts with direct and indirect genotype-phenotype associations of gene variants and drug efficacy, or adverse drug reactions, followed by replication and validation studies. Institutional efforts led by the PGx Research Network, The PGx Knowledge Base, and The Clinical Pharmacogenetics Implementation Consortium, mine all available data for further validation or research in additional populations. This data mining gives rise to a detailed classification of over 200 druggene pairs which, with enough documentation, may become part of a publishable guideline to aid clinicians in drug selection and dosing using genetics. The US Food and Drug Administration utilizes these guidelines to issue warnings and recommendations for specific drugs and their cautioning serves clinicians and pharmacists worldwide. Here, we aim to discuss the steps of this process and list existing actionable drug-gene pairs. Moreover, we describe the current status of PGx knowledge in populations from Mexico for actionable variants on the 19 genes listed by present PGx guidelines affecting 47 drugs. Our review collects current allele frequency information for these actionable variants, lists gaps of PGx information for relevant markers, and highlights the importance of continuing PGx research in Native and Mestizo populations. (REV INVEST CLIN. [AHEAD OF PRINT]
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