ACE2 and TMPRSS2 SNPs as Determinants of Susceptibility to, and Severity of, a COVID-19 Infection.

Background: Genetic risk factors may be related to the infectivity and severity of SARS-CoV-2 infection. Angiotensin-converting enzyme 2 (ACE2) and host transmembrane serine protease (TMPRSS2) have key role in viral cell entrance and priming. Methods: This case-control study on 147 healthy controls and 299 COVID-19 patients identified potential determinants and risk factors, including gene polymorphism involved in the severity (mild, moderate, severe) of COVID-19 disease defined by CORAD radiological criteria. Results: The ACE2 s2285666 and TMPRSS2 rs12329760 SNPs were significantly linked with COVID-19 disease severity, as were certain co-morbidities (hypertension, heart disease) and laboratory parameters. Both SNPs were amongst the highest predictors of disease severity: TMPRSS2 rs12329760 CT + TT [odds ratio (95% CI) 17.6 (5.1-61.10), ACE2 rs2285666 CT + TT 9.9 (3.2-30.9), both p < 0.001]. There was an increase in the expression of genotype frequencies of ACE2 rs2285666 and TMPRSS2 rs1232976 (TT), (CT + TT), and (T) allele in severe COVID-19 group compared to control and mild groups. Disease severity was also linked to elevated CRP, ferritin and D-dimer, and lower lymphocytes and platelet count (all p < 0.001). Conclusion: ACE2 rs2285666 and TMPRSS2 rs12329760 SNPs, in addition to lymphocyte count, CRP, D-dimers, ferritin, and hypertension, are predictors of COVID-19 disease severity.

Targeted metabolomics as a tool for the diagnosis of kidney disease in Type II diabetes mellitus.

Background: Diabetic kidney disease (DKD) is an increasing health problem and an extra burden to health services. The study of characteristic metabolic alterations of DKD is crucial for a better understanding of pathogenesis to identify new potential biomarkers and drug targets. We hypothesized that metabolic profiling of amino acids, acylcarnitines, and organic acids are useful new biomarkers for the diagnosis of the early stages of DKDMethods: The hypothesis was testing in a case-control study of 232 patients with type 2 diabetes mellitus and 150 healthy controls. Patients were classified according to urinary albumin and estimated glomerular filtration rate (eGFR) into 100 with normoalbuminuria and 132 with microalbuminuria group. Eighteen AcylCNs and 17 amino acids were measured in the blood by tandem mass spectrometry while 17 urinary organic acids were quantitatively measured by gas chromatography - mass spectrometry.Results: Regression analysis found that dodecanoylcarnitines C12 (effect size 2.03 [95%CI 1.73-2.32]), triglylcarnitine C5:1 (2.01 [1.70-2.30]), and isovalerylcarnitine C5 (1.78 [1.48-2.07]) were stronger predictors of albumin/creatinine ratio than HbA1c (1.50 [1.20-1.78]) and hence they could serve as potential biomarkers for the diagnosis of the early stages of DKD.Conclusions: Targeted metabolic profiling offers a new, non-invasive approach for detecting biomarkers for the early diagnosis of DKD suggesting new pathogenetic phases that might be new targets for treatment.