ObjectivePre-eclampsia (PE) is one of the malignant metabolic diseases that complicate pregnancy. Gut dysbiosis has been identified for causing metabolic diseases, but the role of gut microbiome in the pathogenesis of PE remains unknown.DesignWe performed a case–control study to compare the faecal microbiome of PE and normotensive pregnant women by 16S ribosomal RNA (rRNA) sequencing. To address the causative relationship between gut dysbiosis and PE, we used faecal microbiota transplantation (FMT) in an antibiotic-treated mouse model. Finally, we determined the microbiome translocation and immune responses in human and mouse placental samples by 16S rRNA sequencing, quantitative PCR and in situ hybridisation.ResultsPatients with PE showed reduced bacterial diversity with obvious dysbiosis. Opportunistic pathogens, particularly Fusobacterium and Veillonella, were enriched, whereas beneficial bacteria, including Faecalibacterium and Akkermansia, were markedly depleted in the PE group. The abundances of these discriminative bacteria were correlated with blood pressure (BP), proteinuria, aminotransferase and creatinine levels. On successful colonisation, the gut microbiome from patients with PE triggered a dramatic, increased pregestational BP of recipient mice, which further increased after gestation. In addition, the PE-transplanted group showed increased proteinuria, embryonic resorption and lower fetal and placental weights. Their T regulatory/helper-17 balance in the small intestine and spleen was disturbed with more severe intestinal leakage. In the placenta of both patients with PE and PE-FMT mice, the total bacteria, Fusobacterium, and inflammatory cytokine levels were significantly increased.ConclusionsThis study suggests that the gut microbiome of patients with PE is dysbiotic and contributes to disease pathogenesis.
Hemorrhagic fever with renal syndrome (HFRS) is one of the most common infectious diseases globally. With the most reported cases in the world, the epidemic characteristics are still remained unclear in China. This paper utilized the seasonal-trend decomposition (STL) method to analyze the periodicity and seasonality of the HFRS data, and used the exponential smoothing model (ETS) model to predict incidence cases from July to December 2016 by using the data from January 2006 to June 2016. Analytic results demonstrated a favorable trend of HFRS in China, and with obvious periodicity and seasonality, the peak of the annual reported cases in winter concentrated on November to January of the following year, and reported in May and June also constituted another peak in summer. Eventually, the ETS (M, N and A) model was adopted for fitting and forecasting, and the fitting results indicated high accuracy (Mean absolute percentage error (MAPE) = 13.12%). The forecasting results also demonstrated a gradual decreasing trend from July to December 2016, suggesting that control measures for hemorrhagic fever were effective in China. The STL model could be well performed in the seasonal analysis of HFRS in China, and ETS could be effectively used in the time series analysis of HFRS in China.
Background: To evaluate whether metformin use assuredly alters overall all-cause death in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). Methods: Pubmed, Web of Science, Embase, and Cochrane Central Register of Controlled Trials were systematically searched from inception to Feb. 29, 2020 with no language restriction. All related articles comparing all-cause death of T2DM and CKD patients after metformin use (monotherapy or combination) versus non-metformin treatment were identified. Pooled risk ratios (RR) and 95% confidence intervals (CI) were computed using random-effects models regardless of the heterogeneity quantified by Cochrane c 2 and I 2 statistics. Results: Totally 13 studies (9 cohort studies [CSs], 3 subanalyses or post-hoc analyses of randomized controlled trials [RCTs], and 1 nested case-control article) involving 303,540 patients were included. Metformin-based treatments relative to any other measure displayed significantly lower risks of all-cause mortality (Pooled RRs 0.71, 95%CI 0.61 to 0.84; I 2 = 79.0%) and cardiovascular events (Pooled RRs 0.76, 95%CI 0.60 to 0.97; I 2 = 87.0%) in CKD patients at stage G1-3, with substantial heterogeneity. Metformin use was not significantly related with these end points in advanced CKD patients. Conclusions: Metformin use is connected with significantly less risks of all-cause mortality and cardiovascular events in patients with T2DM and mild/moderate CKD. However, RCTs with large sample sizes are warranted in the future to assess whether these key benefits extend to later stages of CKD by dose adjustment.
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