The microbiome has been hypothesized to play a role in cancer development. Because of the diversity of published data, an overview of available epidemiologic evidence linking the microbiome with cancer is now needed. We conducted a systematic review using a tailored search strategy in Medline and EMBASE databases to identify and summarize the current epidemiologic literature on the relationship between the microbiome and different cancer outcomes published until December 2019. We identified 124 eligible articles. The large diversity of parameters used to describe microbial composition made it impossible to harmonize the different studies in a way that would allow metaanalysis, therefore only a qualitative description of results could be performed. Fifty studies reported differences in the gut microbiome between patients with colorectal cancer and various control groups. The most consistent findings were for Fusobacterium, Porphyromonas, and Peptostreptococcus being significantly enriched in fecal and mucosal samples from patients with colorectal cancer. For the oral microbiome, significantly increased and decreased abundance was reported for Fusobacterium and Streptococcus, respectively, in patients with oral cancer compared with controls. Overall, although there was a large amount of evidence for some of these alterations, most require validation in high-quality, preferably prospective, epidemiologic studies.
Summary To summarize the microbiome's role in metabolic disorders (insulin resistance, hyperglycemia, type 2 diabetes, obesity, hyperlipidemia, hypertension, nonalcoholic fatty liver disease [NAFLD], and metabolic syndrome), systematic reviews on observational or interventional studies (prebiotics/probiotics/synbiotics/transplant) were searched in MEDLINE and Embase until September 2020. The 87 selected systematic reviews included 57 meta‐analyses. Methodological quality (AMSTAR2) was moderate in 62%, 12% low, and 26% critically low. Observational studies on obesity (10 reviews) reported less gut bacterial diversity with higher Fusobacterium, Lactobacillus reuteri, Bacteroides fragilis, and Staphylococcus aureus, whereas lower Methanobrevibacter, Lactobacillus plantarum, Akkermansia muciniphila, and Bifidobacterium animalis compared with nonobese. For diabetes (n = 1), the same was found for Fusobacterium and A. muciniphila, whereas higher Ruminococcus and lower Faecalibacterium, Roseburia, Bacteroides vulgatus, and several Bifidobacterium spp. For NAFLD (n = 2), lower Firmicutes, Rikenellaceae, Ruminococcaceae, whereas higher Escherichia and Lactobacillus were detected. Discriminating bacteria overlapped between metabolic disorders, those with high abundance being often involved in inflammation, whereas those with low abundance being used as probiotics. Meta‐analyses (n = 54) on interventional studies reported 522 associations: 54% was statistically significant with intermediate effect size and moderate between‐study heterogeneity. Meta‐evidence was highest for probiotics and lowest for fecal transplant. Future avenues include better methodological quality/comparability, testing functional differences, new intervention strategies, and considerating other body habitats and kingdoms.
Aims/hypothesis The gut microbiome is hypothesised to be related to insulin resistance and other metabolic variables. However, data from population-based studies are limited. We investigated associations between serologic measures of metabolic health and the gut microbiome in the Northern Finland Birth Cohort 1966 (NFBC1966) and the TwinsUK cohort. Methods Among 506 individuals from the NFBC1966 with available faecal microbiome (16S rRNA gene sequence) data, we estimated associations between gut microbiome diversity metrics and serologic levels of HOMA for insulin resistance (HOMA-IR), HbA1c and C-reactive protein (CRP) using multivariable linear regression models adjusted for sex, smoking status and BMI. Associations between gut microbiome diversity measures and HOMA-IR and CRP were replicated in 1140 adult participants from TwinsUK, with available faecal microbiome (16S rRNA gene sequence) data. For both cohorts, we used general linear models with a quasi-Poisson distribution and Microbiome Regression-based Kernel Association Test (MiRKAT) to estimate associations of metabolic variables with alpha- and beta diversity metrics, respectively, and generalised additive models for location scale and shape (GAMLSS) fitted with the zero-inflated beta distribution to identify taxa associated with the metabolic markers. Results In NFBC1966, alpha diversity was lower in individuals with higher HOMA-IR with a mean of 74.4 (95% CI 70.7, 78.3) amplicon sequence variants (ASVs) for the first quartile of HOMA-IR and 66.6 (95% CI 62.9, 70.4) for the fourth quartile of HOMA-IR. Alpha diversity was also lower with higher HbA1c (number of ASVs and Shannon’s diversity, p < 0.001 and p = 0.003, respectively) and higher CRP (number of ASVs, p = 0.025), even after adjustment for BMI and other potential confounders. In TwinsUK, alpha diversity measures were also lower among participants with higher measures of HOMA-IR and CRP. When considering beta diversity measures, we found that microbial community profiles were associated with HOMA-IR in NFBC1966 and TwinsUK, using multivariate MiRKAT models, with binomial deviance dissimilarity p values of <0.001. In GAMLSS models, the relative abundances of individual genera Prevotella and Blautia were associated with HOMA-IR in both cohorts. Conclusions/interpretation Overall, higher levels of HOMA-IR, CRP and HbA1c were associated with lower microbiome diversity in both the NFBC1966 and TwinsUK cohorts, even after adjustment for BMI and other variables. These results from two distinct population-based cohorts provide evidence for an association between metabolic variables and gut microbial diversity. Further experimental and mechanistic insights are now needed to provide understanding of the potential causal mechanisms that may link the gut microbiota with metabolic health. Graphical abstract
Experimental evidence has implicated genotoxic Escherichia coli ( E. coli ) and enterotoxigenic Bacteroides fragilis (ETBF) in the development of colorectal cancer (CRC). However, evidence from epidemiological studies is sparse. We therefore assessed the association of serological markers of E. coli and ETBF exposure with odds of developing CRC in the European Prospective Investigation into Nutrition and Cancer (EPIC) study. Serum samples of incident CRC cases and matched controls (n = 442 pairs) were analyzed for immunoglobulin (Ig) A and G antibody responses to seven E. coli proteins and two isoforms of the ETBF toxin via multiplex serology. Multivariable-adjusted conditional logistic regression analyses were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association of sero-positivity to E. coli and ETBF with CRC. The IgA-positivity of any of the tested E. coli antigens was associated with higher odds of developing CRC (OR: 1.42; 95% CI: 1.05–1.91). Dual-positivity for both IgA and IgG to E. coli and ETBF was associated with >1.7-fold higher odds of developing CRC, with a significant association only for IgG (OR: 1.75; 95% CI: 1.04, 2.94). This association was more pronounced when restricted to the proximal colon cancers (OR: 2.62; 95% CI: 1.09, 6.29) compared to those of the distal colon (OR: 1.24; 95% CI: 0.51, 3.00) ( p heterogeneity = 0.095). Sero-positivity to E. coli and ETBF was associated with CRC development, suggesting that co-infection of these bacterial species may contribute to colorectal carcinogenesis. These findings warrant further exploration in larger prospective studies and within different population groups.
Background: Colorectal cancer screening programs with fecal sample collection may provide a platform for population-based gut microbiome disease research. We investigated sample collection and storage method impact on the accuracy and stability of the V3-V4 region of the 16S rRNA genes and bacterial quantity across seven different collection methods [i.e., no solution, two specimen collection cards, and four types of fecal immunochemical test (FIT) used in four countries] among 19 healthy volunteers. Methods: Intraclass correlation coefficients (ICC) were calculated for the relative abundance of the top three phyla, the most abundant genera, alpha diversity metrics, and the first principal coordinates of the beta diversity matrices to estimate the stability of microbial profiles after storage for 7 days at room temperature, 4°C or 30°C, and after screening for the presence of occult blood in the stool. In addition, accuracy was estimated for samples frozen immediately compared to samples with no solution (i.e., the putative gold standard). Results: When compared with the putative gold standard, we observed significant variation for all collection methods. However, interindividual variability was much higher than the variability introduced by the collection method. Stability ICCs were high (≥0.75) for FIT tubes that underwent colorectal cancer screening procedures. The relative abundance of Actinobacteria (0.65) was an exception and was lower for different FIT tubes stored at 30°C (range, 0.41–0.90) and room temperature (range, 0.06–0.94). Conclusions: Paper-based collection cards and different types of FIT are acceptable tools for microbiome measurements. Impact: Our findings inform on the utility of commonly used fecal sample collection methods for developing microbiome-focused cohorts nested within screening programs.
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