1The COVID-19 pandemic is spreading globally with high disparity in the 2 susceptibility of the disease severity. Identification of the key underlying factors for 3 this disparity is highly warranted. Here we describe constructing a proteomic risk 4 score based on 20 blood proteomic biomarkers which predict the progression to 5 severe COVID-19. We demonstrate that in our own cohort of 990 individuals without 6 infection, this proteomic risk score is positively associated with proinflammatory 7 cytokines mainly among older, but not younger, individuals. We further discovered 8 that a core set of gut microbiota could accurately predict the above proteomic 9 biomarkers among 301 individuals using a machine learning model, and that these gut 10 microbiota features are highly correlated with proinflammatory cytokines in another 11 set of 366 individuals. Fecal metabolomic analysis suggested potential amino 12 acid-related pathways linking gut microbiota to inflammation. This study suggests 13 that gut microbiota may underlie the predisposition of normal individuals to severe : medRxiv preprint ( Figure S1). Gut microbiota data were collected and measured during a follow-up 107 visit of the cohort participants, with a cross-sectional subset of the individuals (n=132) 108 having blood proteomic data at the same time point as the stool collection and another 109 independent prospective subset of the individuals (n=169) having proteomic data at a 110 next follow-up visit ~3 years later than the stool collection. 111 112 Among the cross-sectional subset, using a machine learning-based method: 113 LightGBM and a very conservative and strict tenfold cross-validation strategy, we 114 identified 20 top predictive operational taxonomic units (OTUs), and this subset of 115 core OTUs explained an average 21.5% of the PRS variation (mean out-of-sample 116 R 2 =0.215 across ten cross-validations). The list of these core OTUs along with their 117 taxonomic classification is provided inTable S3. These OTUs were mainly assigned 118 to Bacteroides genus, Streptococcus genus, Lactobacillus genus, Ruminococcaceae 119 family, Lachnospiraceae family and Clostridiales order.120 121To test the verification of the core OTUs, the Pearson correlation analysis showed the 122 coefficient between the core OTUs-predicted PRS and actual PRS reached 0.59 123 (p<0.001), substantially outperforming the predictive capacity of other demographic 124 characteristics and laboratory tests including age, BMI, sex, blood pressure and blood 125 lipids (Pearson's r =0.154, p=0.087) ( Figure 3A). Additionally, we used co-inertia 126 analysis (CIA) to further test co-variance between the 20 identified core OTUs and 20 127 predictive proteomic biomarkers of severe COVID-19, outputting a RV coefficient 128 (ranged from 0 to 1) to quantify the closeness. The results indicated a close 129 association of these OTUs with the proteomic biomarkers (RV=0.12, p<0.05) (Figure 130 S3A). When replicating this analysis stratified by age, significant association was 131 observed...

Background Several recent observational studies have reported that gut microbiota composition is associated with preeclampsia. However, the causal effect of gut microbiota on preeclampsia-eclampsia is unknown. Methods A two-sample Mendelian randomization study was performed using the summary statistics of gut microbiota from the largest available genome-wide association study meta-analysis (n=13,266) conducted by the MiBioGen consortium. The summary statistics of preeclampsia-eclampsia were obtained from the FinnGen consortium R7 release data (5731 cases and 160,670 controls). Inverse variance weighted, maximum likelihood, MR-Egger, weighted median, weighted model, MR-PRESSO, and cML-MA were used to examine the causal association between gut microbiota and preeclampsia-eclampsia. Reverse Mendelian randomization analysis was performed on the bacteria that were found to be causally associated with preeclampsia-eclampsia in forward Mendelian randomization analysis. Cochran’s Q statistics were used to quantify the heterogeneity of instrumental variables. Results Inverse variance weighted estimates suggested that Bifidobacterium had a protective effect on preeclampsia-eclampsia (odds ratio = 0.76, 95% confidence interval: 0.64–0.89, P = 8.03 × 10−4). In addition, Collinsella (odds ratio = 0.77, 95% confidence interval: 0.60–0.98, P = 0.03), Enterorhabdus (odds ratio = 0.76, 95% confidence interval: 0.62–0.93, P = 8.76 × 10−3), Eubacterium (ventriosum group) (odds ratio = 0.76, 95% confidence interval: 0.63–0.91, P = 2.43 × 10−3), Lachnospiraceae (NK4A136 group) (odds ratio = 0.77, 95% confidence interval: 0.65–0.92, P = 3.77 × 10−3), and Tyzzerella 3 (odds ratio = 0.85, 95% confidence interval: 0.74–0.97, P = 0.01) presented a suggestive association with preeclampsia-eclampsia. According to the results of reverse MR analysis, no significant causal effect of preeclampsia-eclampsia was found on gut microbiota. No significant heterogeneity of instrumental variables or horizontal pleiotropy was found. Conclusions This two-sample Mendelian randomization study found that Bifidobacterium was causally associated with preeclampsia-eclampsia. Further randomized controlled trials are needed to clarify the protective effect of probiotics on preeclampsia-eclampsia and their specific protective mechanisms.

Context Several small studies have suggested that the gut microbiome might influence osteoporosis, but there is little evidence from human metabolomics studies to explain this association. Objective This study examined the association of gut microbiome dysbiosis with osteoporosis and explored the potential pathways through which this association occurs using faecal and serum metabolomics. Methods We analysed the composition of the gut microbiota by 16S rRNA profiling and bone mineral density (BMD) using dual-energy X-ray absorptiometry in 1776 community-based adults. Targeted metabolomics in faeces (15 categories) and serum (12 categories) were further analysed in 971 participants using ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). Results This study showed that osteoporosis was related to the beta diversity, taxonomy and functional composition of the gut microbiota. The relative abundance of Actinobacillus, Blautia, Oscillospira, Bacteroides and Phascolarctobacterium was positively associated with osteoporosis. However, Veillonellaceae other, Collinsella and Ruminococcaceae other were inversely associated with the presence of osteoporosis. The association between microbiota biomarkers and osteoporosis was related to levels of peptidases and transcription machinery in microbial function. Faecal and serum metabolomics analyses suggested that tyrosine and tryptophan metabolism and valine, leucine and isoleucine degradation were significantly linked to the identified microbiota biomarkers and to osteoporosis, respectively. Conclusion This large population-based study provided robust evidence connecting gut dysbiosis, faecal metabolomics and serum metabolomics with osteoporosis. Our results suggest that gut dysbiosis and amino acid metabolism could be targets for intervention in osteoporosis.