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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.

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.