The Effect of Antihyperglycemic Medications on COVID-19: A Meta-analysis and Systematic Review from Observational Studies.

BACKGROUND: Diabetes, a chronic disease worldwide, may be associated with a poorer prognosis in patients with coronavirus disease 2019 (COVID-19). While some antihyperglycemic medications may be beneficial, others may increase the risk of adverse clinical outcomes of COVID-19. We aimed to analyze the effect of antihyperglycemic medications on COVID-19. METHODS: We searched the Web of Science, Cochrane Library, EMBASE, PubMed, and Scopus databases from December 2019 to June 2022 to identify literature related to patients with COVID-19 and type 2 diabetes mellitus (T2DM) treated with antihyperglycemic medications. RESULTS: 56 studies were included in the analysis. Metformin (OR 0.66; 95% CI 0.58-0.74; p < 0.05), Glucagon-like peptide-1 receptor agonist (GLP-1ra) (OR 0.73; 95% CI 0.59-0.91; p < 0.05), and sodium-dependent glucose transporters 2 inhibitor (SGLT 2i) (OR 0.77; 95% CI 0.69-0.87; p < 0.05) were associated with lower mortality risk, while insulin was associated with increased mortality risk (OR 1.40; 95% CI 1.26-1.55; p < 0.05). Meanwhile, metformin (OR 0.65; 95% CI 0.50-0.85; p < 0.05) and GLP-1ra (OR 0.84; 95% CI 0.76-0.94; p < 0.05) were significantly associated with decreased severe manifestation risk. What's more, metformin (OR 0.77; 95% CI 0.62-0.96; p < 0.05), GLP-1ra (OR 0.86; 95% CI 0.81-0.92; p < 0.05), and SGLT 2i (OR 0.87; 95% CI 0.79-0.97; p < 0.05) were also associated with a decreased risk of hospitalization, but insulin were associated with an increased risk of hospitalization (OR 1.31; 95% CI 1.12-1.52; p < 0.05). Nevertheless, the results of the subgroup analyses showed that the effects of different glucose-lowering agents on COVID-19 may be related to in-hospital use or out-hospital use, elderly or non-elderly patients use, and different geography. CONCLUSION: Metformin, GLP-1ra, and SGLT 2i have shown a positive effect on clinical outcomes in COVID-19, particularly in non-elderly individuals. However, insulin use may pose a higher risk, especially in elderly patients, so need with caution. Meanwhile, DPP-4i, TZD, α-GLUi, and sulfonylureas appeared to have a neutral effect. These results need to be validated in future clinical studies.

Causal relationships between plasma lipids and sepsis: A Mendelian randomization study.

Although observational studies have indicated that plasma lipids are associated with an increased risk of sepsis, due to confounders and reverse causality, the causal relationship remains unclear. This study was designed to assess the causal effects of plasma lipid levels on sepsis. We used a 2-sample Mendelian randomization (MR) method to evaluate the causal effect of plasma lipids on sepsis. MR analysis employs methods such as inverse variance weighted, MR-Egger regression, weighted median regression (WME), simple mode and weighted mode. The inverse variance weighted (IVW) method was predominantly utilized to assess causality. Heterogeneity was affirmed by Cochran Q test, while pleiotropy was corroborated by MR-Egger regression analysis. The robustness and reliability of the results were demonstrated through "leave-one-out" sensitivity analysis. Instrumental variables included 226 single-nucleotide polymorphisms (SNPs), comprising of 7 for triglyceride (TG), 169 for high-density lipoprotein cholesterol (HDL-C), and 50 for low-density lipoprotein cholesterol (LDL-C). The risk of sepsis appeared to increase with rising LDL-C levels, as indicated by the inverse variance weighted analysis (OR 1.11, 95% CI from0.99 to1.24, P = 0.068). However, no causality existed between LDL-C, HDL-C, TG and sepsis. Two-sample MR analysis indicated that increased LDL-C level is a risk factor for sepsis, while TG and HDL-C levels have protective effects against sepsis. However, no significant causal relationship was found between TG, HDL-C, and LDL-C levels and sepsis.

Comparative Transcriptome Analysis Revealed the Tissue-Specific Accumulations of Taxanes among Three Experimental Lines of Taxus yunnanensis.

Taxus yunnanensis (Yew) is known for natural anticancer metabolite paclitaxel (Taxol) and its biosynthesis pathway in yew species still needs to be completely elucidated. In the current study, productions of paclitaxel and 10-DAB III from three different tissues (needle, branch, and root) of T. yunnanensis wild type (WT) and two new cultivars Zhongda-1 (Zd1) and Zhongda-2 (Zd2) were determined, and significant tissue differences in contents of the taxanes were observed among the three experimental lines. The much higher 10-DAB III and lower paclitaxel contents in needle of Zd2 when compared with that of Zd1 indicated the low conversion from 10-DAB III to paclitaxel in the needle of Zd2. In order to uncover the mechanisms of the tissue-specific biosynthesis of the taxanes, transcriptome analysis of cultivar Zd2 was conducted, and the previously reported transcriptome data of Zd1 and WT were used to perform a comparison. The enhancement of TDAT and T10ßH side biosynthetic pathway in roots of Zd2 in early taxane synthesis might lead to the biosynthesis of other toxoids, while the preference of T13αH route in the needle and branch of Zd2 was mainly responsible for the tissue-specific reinforced biosynthesis of 10-DAB III and paclitaxel in Zd2. Different from Zd1, the tissue-specific pattern of paclitaxel biosynthesis genes in Zd2 was similar to WT. However, the lower transcript abundance of final steps genes (TBT, DBAT, BAPT, and DBTNBT) of the paclitaxel biosynthesis pathway in Zd2 than in Zd1 might further promote 10-DAB III accumulation in Zd2.