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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.

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 its 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 to 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 < 2.1E-6) the three former previously associated to blood lipids, while CI2 was associated with variants on DIPK2B, LIPC, and 10q21.3 rs11251177 (p-value 1.1E-7) the latter 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 relation between genetic variation and dyslipidemia predictors. These results also contribute to consolidating the genetics of blood lipid and lipid indexes.

Introduction: 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. Methods: 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. Results: We identified genetic variants on SLC47A1, SLC28A1, ABCG2, TBC1D4, and ARID5B that can explain up to 55% of the interindividual variability of HbA1c plasma levels in diabetic patients under treatment. Variants on SLC47A1, SLC28A1, and ABCG2 likely impact the pharmacokinetics (PK) of metformin, while the role of the two latter can be related to insulin resistance and regulation of adipogenesis. Conclusions: Our results confirm previous genetic associations and point to previously unassociated gene variants for metformin PK and glucose control.

Angiotensin-converting enzyme 2 (ACE2) is known as the counter-regulator of the renin-angiotensin system, it cleaves angiotensin II to render Ag 1-7, a potent vasodilator with multiple roles in cardiovascular protection. A few studies have pinpointed ACE2 polymorphisms and their relationship with heart function and hypertension in a sex-dependent manner. These observations still lack replication mostly for admixed populations. This study aimed to report minor allele frequencies of four ACE2 intron variants, rs2285666, rs2048683, rs2106809, and rs4240157, derived from previous research using the GSA, v1.0, microarray in 1231 hypertensive and nonhypertensive patients. Logistic and multiple linear regression models were developed to identify potential associations with hypertension status and systolic and diastolic blood pressure (SBP and DBP). Allele frequency differences were identified for ACE2 rs2048683 and rs4240157 in populations with European ancestry and people of the Americas. Regression analyses identified a significant association of ACE2 rs2048683 and rs4240157 with SBP/DBP in males or females. Our observations confirm sex differences in ACE2 genetic associations with SBP and DBP and contribute to the collection of genetic variation in ACE2 for admixed populations.

SummaryThe dissection of factors that contribute to COVID-19 infection and severity has overwhelmed the scientific community for almost 2 years. Current reports highlight the role of in disease incidence, progression, and severity. Here, we aimed to confirm the presence of previously reported genetic variants in an admixed population. Allele frequencies were assessed and compared between the general population (N=3079) for which at least 30% have not been infected with SARS-CoV2 as per July 2021 versus COVID-19 patients (N=106).Genotyping data from the Illumina GSA array was used to impute genetic variation for 14 COVID-relevant genes, using the 1000G phase 3 as reference based on the human genome assembly hg19, following current standard protocols and recommendations for genetic imputation. Bioinformatic and statistical analyses were performed using MACH v1.0, R, and PLINK.A total of 7953 variants were imputed on, ABO, CCR2, CCR9, CXCR6, DPP9, FYCO1, IL10RB/IFNAR2, LZTFL1, OAS1, OAS2, OAS3, SLC6A20, TYK2, and XCR1. Statistically significant allele differences were reported for 10 and 7 previously identified and confirmed variants, ABO rs657152, DPP9 rs2109069, LZTFL1 rs11385942, OAS1 rs10774671, OAS1 rs2660, OAS2 rs1293767, and OAS3 rs1859330 pOur observations confirm the presence of genetic differences in COVID-19 patients in an admixed population and prompts for the investigation of the statistical relevance of additional variants on these and other genes that could identify local and geographical patterns of COVID-19.

Thiopurines are effectively prescribed for immune and oncology diseases but their toxicity leads to severe myelosuppression. Therefore, TPMT genetic variants have been used to adjust dosing for poor and intermediate metabolizers, significantly preventing adverse drug reactions. In 2018, the Clinical Pharmacogenetics Implementation Consortium included NUDT15 rs116855232 to also guide thiopurines dosing. This variant is not present in Caucasians but have been identified in 10% of Asian and Latin American populations. Despite research efforts to portrait the world’s genetic variation, few studies include the investigation of NUDT15 in large samples. Fifteen NUDT15 and TPMT variants were retrieved for 1270 Mestizos and 20 Natives genotyped from previous studies using the GSA-Illumina microarray. After bioinformatic quality controls, genotypes were available for 12 variants, TPMT rs2842949, rs2842950, rs2842934, rs1800460, rs12201199, rs12663332, rs2518463, rs4449636, rs12529220, rs3931660, rs200591577, and NUD15 rs116855232. Allele frequencies and haplotypes were assessed using PLINK, R, and Haploview. Dosing inferences were described according to the Clinical Pharmacogenomics Implementation Consortium. We report relevant populations differences in actionable TPMT*3B and NUDT15 rs116855232 as the allele frequency of the former is higher in Mestizos compared to Caucasians, and for the latter we report twofold and 1.35-fold higher allele frequencies in Natives and Mestizos compared to Mexicans from Los Angeles. TPMT*3B and NUDT15 rs116855232 actionable markers showed population differences that ought to be considered as dosing inferences highlight the relevance of routine genotyping of these variants for the prescription of thiopurines in Mexican populations.

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.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.We identified genetic variants on SLC47A1, SLC28A1, ABCG2, TBC1D4, and ARID5B that can explain up to 55% of the interindividual variability of HbA1c plasma levels in diabetic patients under treatment. Variants on SLC47A1, SLC28A1, and ABCG2 likely impact the pharmacokinetics (PK) of metformin, while the role of the two latter can be related to insulin resistance and regulation of adipogenesis.Our results confirm previous genetic associations and point to previously unassociated gene variants for metformin PK and glucose control.

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.

Angiotensin-converting enzyme 2 (ACE2) is known as the counter-regulator of the renin-angiotensin system, it cleaves angiotensin II to render Ag 1-7, a potent vasodilator with multiple roles in cardiovascular protection. A few studies have pinpointed