Search

BackgroundAtrial fibrillation (AF) is an important heart rhythm disorder in aging populations. The gut microbiome composition has been previously related to cardiovascular disease risk factors. Whether the gut microbial profile is also associated with the risk of AF remains unknown.MethodsWe examined the associations of prevalent and incident AF with gut microbiota in the FINRISK 2002 study, a random population sample of 6763 individuals. We replicated our findings in an independent case-control cohort of 138 individuals in Hamburg, Germany.FindingsMultivariable-adjusted regression models revealed that prevalent AF (N=116) was associated with nine microbial genera. Incident AF (N=539) over a median follow-up of 15 years was associated with eight microbial genera with false discovery rate (FDR)-corrected P&nbsp

Microbial communities contain a broad phylogenetic diversity of organisms;however, the majority of methods center on describing bacteria and archaea. Fungi are important symbionts in many ecosystemsand are potentially important members of the human microbiome, beyond those that can cause disease. To expand our analysis of microbial communities to include data from thefungal internal transcribed spacer (ITS) region, five candidate DNA extraction kits were compared against our standardized protocol for describing bacteria and archaea using 16S rRNA gene amplicon- and shotgun metagenomics sequencing. The results are presented considering a diverse panel of host-associated and environmental sample typesand comparing the cost, processing time, well-to-well contamination, DNA yield, limit of detectionand microbial community composition among protocols. Across all criteria, the MagMAX Microbiome kit was foundto perform best. The PowerSoil Pro kit performed comparablybut with increased cost per sample and overall processing time. The Zymo MagBead, NucleoMag Foodand Norgen Stool kits were included.

The collection of fecal material and developments in sequencing technologies have enabled standardised and non-invasive gut microbiome profiling. Microbiome composition from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterised due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a well-phenotyped and representative population cohort from Finland (n=7211). We report robust taxonomic and functional microbiome signatures related to the Enterobacteriaceae family that are associated with mortality risk during a 15-year follow-up. Our results extend previous cross-sectional studies, and help to establish the basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status. Gut microbiome composition has a role in health and disease. Here the authors show that microbiome signatures related to the Enterobacteriaceae family are associated with cause-specific mortality risk in a well phenotyped Finish population over a 15-year follow-up.

Additional file 7: Figure S7. Bacterial (16S rRNA gene amplicon sequencing read count) and human biomass (RNAse P Ct) is higher in floor samples than other surface sample types.

Additional file 6: Figure S6. Floor sample SARS-CoV-2 status is associated with higher biomass and with significantly different bacterial community composition. Two independent metrics were used to assess biomass; 16S rRNA gene amplicon sequencing read count, which because of our equal volume sequencing library pooling approach correlates with total bacterial load [27, 74], and the Ct value from the CDC’s human RNAse P RT-qPCR target, which correlates with human biomass. (A) Abundance of 16S rRNA gene amplicon sequencing read count in SARS-CoV-2 positive floor samples showing no correlation with SARS-CoV-2 viral load. (B) Ct value of human RNAse P in SARS-CoV-2 positive floor samples showing significant correlation with SARS-CoV-2 viral load. Statistical analysis of scatter plots represents Pearson correlation, and box plots represents independent t-tests; *p < 0.05, **p < 0.01, ***p < 0.001. The legend in panel B applies to panel A as well. (C) Effect size of significant, non-redundant variables identified from Redundancy Analysis on unweighted UniFrac PCoA of floor samples.

Additional file 10: Table S1. Hospital surface materials and cleaning practices.

Additional file 1: Figure S1. Patient (n = 16) demographics (A), antibiotics intake (B), comorbidities (C).

Additional file 2: Figure S2. Ili’ spatial mapping of standard hospital (non-ICU) room and intensive care unit (ICU) room. Heatmap depicts the percent of samples collected at each site that were positive for SARS-CoV-2.

Additional file 3: Figure S3. Snapshot of variability in longitudinal sample collection and SARS-CoV-2 viral RNA load per swab between patients and their hospital rooms, starting at patient admission time. For samples where SARS-CoV-2 was detected (+), a darker color indicates a higher viral load. White boxes represent samples with no detectable virus (-). Patient A was admitted 12 days after symptom onset and was moved to a general surgery unit room after 6 days in the ICU. Patient B was admitted 8 days after symptom onset and moved from general surgery to the ICU, where they were intubated. Patient C was admitted to the ICU 9 days after symptom onset, and despite having symptoms consistent with COVID-19 repeatedly tested negative by clinical nasopharyngeal swab; their only clinical positive came from a tracheal aspirate sample mid-way through their stay in the ICU.

Additional file 9: Figure S9. Unweighted UniFrac distance between forehead and nares samples from the same host. ‘Shedder’ (n = 12) is a patient who had detectable virus on the surface in their room and ‘non-shedder’ (n = 4) did not. Bootstrapped Kruskal-Wallis p-value is 0.003.

Additional file 8: Figure S8. Random Forest classifier performance with 100-fold cross validation in the outside floor (n = 108; 81 not detected vs. 27 positives) and bed rail samples (n = 46; 38 not detected vs. 8 positives).

Additional file 5: Figure S5. Beta diversity has a statistically significant but weak correlation with viral load. PCoA of unweighted UniFrac distances between samples, with SARS-CoV-2 positive samples colored by viral load across the whole dataset (A) and subset by each patient with at least one surface positive (B). Statistical analysis performed with Adonis (PERMANOVA) found a small (R2

Additional file 4: Figure S4. Source tracker on meta-analysis data. Floor samples formed a distinct cluster in this dataset; source tracking [31] with floor samples (n = 215) as the sink and meta-analysis samples (n = 1,990) as the source reveals that these floor samples match other built environment samples. The other built environment samples included in this meta-analysis were mostly floor (27.7%), faucet handles (19.6%), and gloves (15%).

The collection of fecal material and developments in sequencing technologies have enabled standardised and non-invasive gut microbiome profiling. Microbiome composition from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterised due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a well-phenotyped and representative population cohort from Finland (n = 7211). We report robust taxonomic and functional microbiome signatures related to the Enterobacteriaceae family that are associated with mortality risk during a 15-year follow-up. Our results extend previous cross-sectional studies, and help to establish the basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status.

Funder: Academy of Finland (Suomen Akatemia); doi: https://doi.org/10.13039/501100002341, Funder: Suomen Lääketieteen Säätiö (Finnish Medical Foundation); doi: https://doi.org/10.13039/100008723, Funder: Paavo Nurmen Säätiö (Paavo Nurmi Foundation); doi: https://doi.org/10.13039/501100008484, The collection of fecal material and developments in sequencing technologies have enabled standardised and non-invasive gut microbiome profiling. Microbiome composition from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterised due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a well-phenotyped and representative population cohort from Finland (n = 7211). We report robust taxonomic and functional microbiome signatures related to the Enterobacteriaceae family that are associated with mortality risk during a 15-year follow-up. Our results extend previous cross-sectional studies, and help to establish the basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status.