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...

OBJECTIVE To identify the core gut microbial features associated with type 2 diabetes risk and potential demographic, adiposity, and dietary factors associated with these features. RESEARCH DESIGN AND METHODS We used an interpretable machine learning framework to identify the type 2 diabetes–related gut microbiome features in the cross-sectional analyses of three Chinese cohorts: one discovery cohort (n = 1,832, 270 cases of type 2 diabetes) and two validation cohorts (cohort 1: n = 203, 48 cases; cohort 2: n = 7,009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 participants without type 2 diabetes and assessed the correlation between the MRS and host blood metabolites (n = 1,016). We transferred human fecal samples with different MRS levels to germ-free mice to confirm the MRS–type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity, and dietary factors with the MRS (n = 1,832). RESULTS The MRS (including 14 microbial features) consistently associated with type 2 diabetes, with risk ratio for per 1-unit change in MRS 1.28 (95% CI 1.23–1.33), 1.23 (1.13–1.34), and 1.12 (1.06–1.18) across three cohorts. The MRS was positively associated with future glucose increment (P &lt; 0.05) and was correlated with a variety of gut microbiota–derived blood metabolites. Animal study further confirmed the MRS–type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome–type 2 diabetes relationship. CONCLUSIONS Our results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment.

Background Little is known about the inter-relationship among fruit and vegetable intake, gut microbiota and metabolites, and type 2 diabetes (T2D) in human prospective cohort study. The aim of the present study was to investigate the prospective association of fruit and vegetable intake with human gut microbiota and to examine the relationship between fruit and vegetable-related gut microbiota and their related metabolites with type 2 diabetes (T2D) risk. Methods This study included 1879 middle-age elderly Chinese adults from Guangzhou Nutrition and Health Study (GNHS). Baseline dietary information was collected using a validated food frequency questionnaire (2008–2013). Fecal samples were collected at follow-up (2015–2019) and analyzed for 16S rRNA sequencing and targeted fecal metabolomics. Blood samples were collected and analyzed for glucose, insulin, and glycated hemoglobin. We used multivariable linear regression and logistic regression models to investigate the prospective associations of fruit and vegetable intake with gut microbiota and the association of the identified gut microbiota (fruit/vegetable-microbiota index) and their related fecal metabolites with T2D risk, respectively. Replications were performed in an independent cohort involving 6626 participants. Results In the GNHS, dietary fruit intake, but not vegetable, was prospectively associated with gut microbiota diversity and composition. The fruit-microbiota index (FMI, created from 31 identified microbial features) was positively associated with fruit intake (p < 0.001) and inversely associated with T2D risk (odds ratio (OR) 0.83, 95%CI 0.71–0.97). The FMI-fruit association (p = 0.003) and the FMI-T2D association (OR 0.90, 95%CI 0.84–0.97) were both successfully replicated in the independent cohort. The FMI-positive associated metabolite sebacic acid was inversely associated with T2D risk (OR 0.67, 95%CI 0.51–0.86). The FMI-negative associated metabolites cholic acid (OR 1.35, 95%CI 1.13–1.62), 3-dehydrocholic acid (OR 1.30, 95%CI 1.09–1.54), oleylcarnitine (OR 1.77, 95%CI 1.45–2.20), linoleylcarnitine (OR 1.66, 95%CI 1.37–2.05), palmitoylcarnitine (OR 1.62, 95%CI 1.33–2.02), and 2-hydroglutaric acid (OR 1.47, 95%CI 1.25–1.72) were positively associated with T2D risk. Conclusions Higher fruit intake-associated gut microbiota and metabolic alteration were associated with a lower risk of T2D, supporting the public dietary recommendation of adopting high fruit intake for the T2D prevention.

ObjectiveThe human gut fungal community, known as the mycobiome, plays a fundamental role in the gut ecosystem and health. Here we aimed to investigate the determinants and long-term stability of gut mycobiome among middle-aged and elderly adults. We further explored the interplay between gut fungi and bacteria on metabolic health.DesignThe present study included 1244 participants from the Guangzhou Nutrition and Health Study. We characterised the long-term stability and determinants of the human gut mycobiome, especially long-term habitual dietary consumption. The comprehensive multiomics analyses were performed to investigate the ecological links between gut bacteria, fungi and faecal metabolome. Finally, we examined whether the interaction between gut bacteria and fungi could modulate the metabolic risk.ResultsThe gut fungal composition was temporally stable and mainly determined by age, long-term habitual diet and host physiological states. Specifically, compared with middle-aged individuals, Blastobotrys and Agaricomycetes spp were depleted, while Malassezia was enriched in the elderly. Dairy consumption was positively associated with Saccharomyces but inversely associated with Candida. Notably, Saccharomycetales spp interacted with gut bacterial diversity to influence insulin resistance. Bidirectional mediation analyses indicated that bacterial function or faecal histidine might causally mediate an impact of Pichia on blood cholesterol.ConclusionWe depict the sociodemographic and dietary determinants of human gut mycobiome in middle-aged and elderly individuals, and further reveal that the gut mycobiome may be closely associated with the host metabolic health through regulating gut bacterial functions and metabolites.

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.