Associations of Circulating Estrogens and Estrogen Metabolites with Fecal and Oral Microbiome in Postmenopausal Women in the Ghana Breast Health Study.

The human fecal and oral microbiome may play a role in the etiology of breast cancer through modulation of endogenous estrogen metabolism. This study aimed to investigate associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. A total of 117 women with fecal (N = 110) and oral (N = 114) microbiome data measured by 16S rRNA gene sequencing, and estrogens and estrogen metabolites data measured by liquid chromatography tandem mass spectrometry were included. The outcomes were measures of the microbiome and the independent variables were the estrogens and estrogen metabolites. Estrogens and estrogen metabolites were associated with the fecal microbial Shannon index (global P < 0.01). In particular, higher levels of estrone (ß = 0.36, P = 0.03), 2-hydroxyestradiol (ß = 0.30, P = 0.02), 4-methoxyestrone (ß = 0.51, P = 0.01), and estriol (ß = 0.36, P = 0.04) were associated with higher levels of the Shannon index, while 16alpha-hydroxyestrone (ß = -0.57, P < 0.01) was inversely associated with the Shannon index as indicated by linear regression. Conjugated 2-methoxyestrone was associated with oral microbial unweighted UniFrac as indicated by MiRKAT (P < 0.01) and PERMANOVA, where conjugated 2-methoxyestrone explained 2.67% of the oral microbial variability, but no other estrogens or estrogen metabolites were associated with any other beta diversity measures. The presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, were associated with several estrogens and estrogen metabolites as indicated by zero-inflated negative binomial regression. Overall, we found several associations of specific estrogens and estrogen metabolites and the fecal and oral microbiome. IMPORTANCE Several epidemiologic studies have found associations of urinary estrogens and estrogen metabolites with the fecal microbiome. However, urinary estrogen concentrations are not strongly correlated with serum estrogens, a known risk factor for breast cancer. To better understand whether the human fecal and oral microbiome were associated with breast cancer risk via the regulation of estrogen metabolism, we conducted this study to investigate the associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. We found several associations of parent estrogens and several estrogen metabolites with the microbial communities, and multiple individual associations of estrogens and estrogen metabolites with the presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, which have estrogen metabolizing properties. Future large, longitudinal studies to investigate the dynamic changes of the fecal and oral microbiome and estrogen relationship are needed.

Evaluation of alcohol-free mouthwash for studies of the oral microbiome.

Oral bacteria play important roles in human health and disease. Oral samples collected using ethanol-containing mouthwash are widely used for oral microbiome studies. However, ethanol is flammable and not ideal for transportation/storage in large quantities, and some individuals may avoid ethanol due to the burning sensation or due to various personal, medical, religious, and/or cultural factors. Here, we compared ethanol-free and ethanol-containing mouthwashes using multiple microbiome metrics and assessed the stability of the mouthwash samples stored up to 10 days before processing. Forty volunteers provided oral wash samples collected using ethanol-free and ethanol-containing mouthwashes. From each sample, one aliquot was immediately frozen, one was stored at 4°C for 5 days and frozen, while the third aliquot was stored for 5 days at 4°C and 5 days at ambient temperature to mimic shipping delays and then frozen. DNA was extracted, the 16S rRNA gene V4 region was amplified and sequenced, and bioinformatic processing was performed using QIIME 2. Microbiome metrics measured in the two mouthwash types were very similar, with intraclass correlation coefficients (ICCs) for alpha and beta diversity metrics greater than 0.85. Relative abundances of some taxa were significantly different, but ICCs of the top four most abundant phyla and genera were high (> 0.75) for the comparability of the mouthwashes. Stability during delayed processing was also high for both mouthwashes based on alpha and beta diversity measures and relative abundances of the top four phyla and genera (ICCs ≥ 0.90). These results demonstrate ethanol-free mouthwash performs similarly to ethanol-containing mouthwash for microbial analyses, and both mouthwashes are stable for at least 10 days without freezing prior to laboratory processing. Ethanol-free mouthwash is suitable for collecting and shipping oral wash samples, and these results have important implications for planning future epidemiologic studies of the oral microbiome.

Stability of the Fecal and Oral Microbiome over 2 Years at -80°C for Multiple Collection Methods.

BACKGROUND: In prospective cohorts, biological samples are generally stored over long periods before an adequate number of cases have accrued. We investigated the impact of sample storage at -80°C for 2 years on the stability of the V4 region of the 16S rRNA gene across seven different collection methods (i.e., no additive, 95% ethanol, RNAlater stabilization solution, fecal occult blood test cards, and fecal immunochemical test tubes for feces; OMNIgene ORAL tubes and Scope mouthwash for saliva) among 51 healthy volunteers. METHODS: Intraclass correlation coefficients (ICC) were calculated for the relative abundance of the top three phyla, the 20 most abundant genera, three alpha-diversity metrics, and the first principal coordinates of three beta-diversity matrices. RESULTS: The subject variability was much higher than the variability introduced by the sample collection type, and storage time. For fecal samples, microbial stability over 2 years was high across collection methods (range, ICCs = 0.70-0.99), except for the samples collected with no additive (range, ICCs = 0.23-0.83). For oral samples, most microbiome diversity measures were stable over time with ICCs above 0.74; however, ICCs for the samples collected with Scope mouthwash were lower for two alpha-diversity measures, Faith's phylogenetic diversity (0.23) and the observed number of operational taxonomic units (0.23). CONCLUSIONS: Fecal and oral samples in most used collection methods are stable for microbiome analyses after 2 years at -80°C, except for fecal samples with no additive. IMPACT: This study provides evidence that samples stored for an extended period from prospective studies are useful for microbiome analyses.

Oral microbiome and risk of incident head and neck cancer: A nested case-control study.

OBJECTIVES: This nested case-control study in the NIH-AARP Diet and Health Study was carried out to prospectively investigate the relationship of oral microbiome with head and neck cancer (HNC). MATERIALS AND METHODS: 56 incident HNC cases were identified, and 112 controls were incidence-density matched to cases. DNA extracted from pre-diagnostic oral wash samples was whole-genome shotgun metagenomic sequenced to measure the overall oral microbiome. ITS2 gene qPCR was used to measure the presence of fungi. ITS2 gene sequencing was performed on ITS2 gene qPCR positive samples. We computed taxonomic and functional alpha-diversity and beta-diversity metrics. The presence and relative abundance of groups of red-complex (e.g., Porphyromonas gingivalis) and/or orange-complex (e.g., Fusobacterium nucleatum) periodontal pathogens were compared between cases and controls using conditional logistic regression models and MiRKAT. RESULTS: Participants with higher taxonomic microbial alpha-diversity had a non-statistically significant decreased risk of HNC. No case-control differences were found for beta diversity by MiRKAT model (all p > 0.05). A greater relative abundance of red-complex periodontal pathogens (OR = 0.51, 95 % CI = 0.26-1.00), orange-complex (OR = 0.38, 95 % CI = 0.18-0.83), and both complexes' pathogens (OR = 0.32, 95 % CI = 0.14-0.75), were associated with reduced risk of HNC. The presence of oral fungi was also strongly associated with reduced risk of HNC compared with controls (OR = 0.39, 95 % CI = 0.17-0.92). CONCLUSION: Greater taxonomic alpha-diversity, the presence of oral fungi, and the presence or relative abundance of multiple microbial species, including the red- and orange-complex periodontal pathogens, were associated with reduced risk of HNC. Future studies with larger sample sizes are needed to evaluate these associations.

Prospective and Cross-sectional Associations of the Rectal Tissue Microbiome with Colorectal Adenoma Recurrence.

BACKGROUND: The gut microbiome is plausibly associated with colorectal cancer risk; however, previous studies mostly investigated this association cross-sectionally. We investigated cross-sectional and prospective associations of the rectal tissue microbiome with adenoma recurrence in the Polyp Prevention Trial (PPT). METHODS: PPT is a 4-year randomized clinical trial of the effect of a dietary intervention on adenoma recurrence among community members. We extracted DNA from rectal biopsies at baseline, end of year 1, and end of year 4 among 455 individuals and sequenced the V4 region of the 16S rRNA gene. At each timepoint, we investigated associations of alpha diversity, beta diversity, and presence and relative abundance of select taxa with adenoma recurrence using multivariable logistic regression. RESULTS: Variation in beta diversity was primarily explained by subject and minimally by year of collection or time between biopsy and colonoscopy. Cross-sectionally, year 4 alpha diversity was strongly, inversely associated with adenoma prevalence [ORQ3 vs. Q1 Shannon index = 0.40 (95% confidence interval, CI: 0.21-0.76)]. Prospective alpha diversity associations (i.e., baseline/year 1 alpha diversity with adenoma recurrence 3-4 years later) were weak or null, as were cross-sectional and prospective beta diversity-adenoma associations. Bacteroides abundance was more strongly, positively associated with adenoma prevalence cross-sectionally than prospectively. CONCLUSIONS: Rectal tissue microbiome profiles may be associated with prevalent adenomas, with little evidence supporting prospective associations. IMPACT: Additional prospective studies, with serial fecal and tissue samples, to explore microbiome-colorectal cancer associations are needed. Eventually, it may be possible to use microbiome characteristics as intervenable risk factors or screening tools.

Reusing a prepaid health plan's fecal immunochemical tests for microbiome associations with colorectal adenoma.

An altered colonic microbiota probably increases colorectal adenoma (CRA) and cancer (CRC) risk, but large, unbiased fecal collections are needed to examine the relationship of gut microbiota diversity and composition to colorectal carcinogenesis. This study assessed whether fecal immunochemical tests (FITs) from CRA/CRC screening may fulfill this requirement. Using FIT, self-collected by members of Kaiser Permanente Hawaii (KPH), as well as interspersed quality control (QC) specimens, DNA was extracted and amplified to generate 16S rRNA microbiome profiles rarified at 10,000 reads. CRA/CRC were diagnosed by colonoscopy and histopathology. Covariates were from electronic KPH records. Of 921 participants' FIT devices, 538 (58%) yielded at least 10,000 rRNA reads and 1016 species-level variants mapped to 46 genera. Of the 538 evaluable participants, 63 (11.7%) were FIT-negative per protocol, and they were considered negative for CRA/CRC. Of the 475 FIT + participants, colonoscopy and pathologic review revealed that 8 (1.7%) had CRC, 71 (14.9%) had high-risk CRA, 107 (22.5%) had low-risk CRA, and 289 (60.8%) did not have CRA/CRC. Men were 2.27-fold [95% confidence interval (CI) 1.32-3.91] more likely than women to be FIT+ . Men also had 1.96-fold (CI 1.24-3.07) higher odds of low-risk CRA, with similar trends for high-risk CRA and CRC. CRA/CRC were not associated with overweight, obesity, diabetes, or antibiotic prescriptions in this study. QC analysis across 24 batches of FIT devices revealed QC outliers in four batches. With or without exclusion of the four QC-outlier batches, as well as lenient (1000-read) rarefaction, CRA/CRC had no consistent, statistically significant associations with fecal microbiome alpha diversity, beta diversity or genera relative abundance. CRA/CRC had expected associations with male sex but not with microbiome metrics. Fecal microbiome profiling using DNA extracted from at-home collected, re-used FIT devices is feasible, albeit with substantial challenges. Using FITs for prospective microbiome studies of CRA/CRC risk should consider the impact of the current findings on statistical power and requisite sample sizes.

The Oral Microbiome and Lung Cancer Risk: An Analysis of 3 Prospective Cohort Studies.

BACKGROUND: Previous studies suggested associations between the oral microbiome and lung cancer, but studies were predominantly cross-sectional and underpowered. METHODS: Using a case-cohort design, 1306 incident lung cancer cases were identified in the Agricultural Health Study; National Institutes of Health-AARP Diet and Health Study; and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Referent subcohorts were randomly selected by strata of age, sex, and smoking history. DNA was extracted from oral wash specimens using the DSP DNA Virus Pathogen kit, the 16S rRNA gene V4 region was amplified and sequenced, and bioinformatics were conducted using QIIME 2. Hazard ratios and 95% confidence intervals were calculated using weighted Cox proportional hazards models. RESULTS: Higher alpha diversity was associated with lower lung cancer risk (Shannon index hazard ratio = 0.90, 95% confidence interval = 0.84 to 0.96). Specific principal component vectors of the microbial communities were also statistically significantly associated with lung cancer risk. After multiple testing adjustment, greater relative abundance of 3 genera and presence of 1 genus were associated with greater lung cancer risk, whereas presence of 3 genera were associated with lower risk. For example, every SD increase in Streptococcus abundance was associated with 1.14 times the risk of lung cancer (95% confidence interval = 1.06 to 1.22). Associations were strongest among squamous cell carcinoma cases and former smokers. CONCLUSIONS: Multiple oral microbial measures were prospectively associated with lung cancer risk in 3 US cohort studies, with associations varying by smoking history and histologic subtype. The oral microbiome may offer new opportunities for lung cancer prevention.

The oral microbiome and breast cancer and nonmalignant breast disease, and its relationship with the fecal microbiome in the Ghana Breast Health Study.

The oral microbiome, like the fecal microbiome, may be related to breast cancer risk. Therefore, we investigated whether the oral microbiome was associated with breast cancer and nonmalignant breast disease, and its relationship with the fecal microbiome in a case-control study in Ghana. A total of 881 women were included (369 breast cancers, 93 nonmalignant cases and 419 population-based controls). The V4 region of the 16S rRNA gene was sequenced from oral and fecal samples. Alpha-diversity (observed amplicon sequence variants [ASVs], Shannon index and Faith's Phylogenetic Diversity) and beta-diversity (Bray-Curtis, Jaccard and weighted and unweighted UniFrac) metrics were computed. MiRKAT and logistic regression models were used to investigate the case-control associations. Oral sample alpha-diversity was inversely associated with breast cancer and nonmalignant breast disease with odds ratios (95% CIs) per every 10 observed ASVs of 0.86 (0.83-0.89) and 0.79 (0.73-0.85), respectively, compared to controls. Beta-diversity was also associated with breast cancer and nonmalignant breast disease compared to controls (P ≤ .001). The relative abundances of Porphyromonas and Fusobacterium were lower for breast cancer cases compared to controls. Alpha-diversity and presence/relative abundance of specific genera from the oral and fecal microbiome were strongly correlated among breast cancer cases, but weakly correlated among controls. Particularly, the relative abundance of oral Porphyromonas was strongly, inversely correlated with fecal Bacteroides among breast cancer cases (r = -.37, P ≤ .001). Many oral microbial metrics were strongly associated with breast cancer and nonmalignant breast disease, and strongly correlated with fecal microbiome among breast cancer cases, but not controls.

Comparison of fecal and oral collection methods for studies of the human microbiota in two Iranian cohorts.

BACKGROUND: To initiate fecal and oral collections in prospective cohort studies for microbial analyses, it is essential to understand how field conditions and geographic differences may impact microbial communities. This study aimed to investigate the impact of fecal and oral sample collection methods and room temperature storage on collection samples for studies of the human microbiota. RESULTS: We collected fecal and oral samples from participants in two Iranian cohorts located in rural Yazd (n = 46) and urban Gonbad (n = 38) and investigated room temperature stability over 4 days of fecal (RNAlater and fecal occult blood test [FOBT] cards) and comparability of fecal and oral (OMNIgene ORAL kits and Scope mouthwash) collection methods. We calculated interclass correlation coefficients (ICCs) based on 3 alpha and 4 beta diversity metrics and the relative abundance of 3 phyla. After 4 days at room temperature, fecal stability ICCs and ICCs for Scope mouthwash were generally high for all microbial metrics. Similarly, the fecal comparability ICCs for RNAlater and FOBT cards were high, ranging from 0.63 (95% CI: 0.46, 0.75) for the relative abundance of Firmicutes to 0.93 (95% CI: 0.89, 0.96) for unweighted Unifrac. Comparability ICCs for OMNIgene ORAL and Scope mouthwash were lower than fecal ICCs, ranging from 0.55 (95% CI: 0.36, 0.70) for the Shannon index to 0.79 (95% CI: 0.69, 0.86) for Bray-Curtis. Overall, RNAlater, FOBT cards and Scope mouthwash were stable up to 4 days at room temperature. Samples collected using FOBT cards were generally comparable to RNAlater while the OMNIgene ORAL were less similar to Scope mouthwash. CONCLUSIONS: As microbiome measures for feces samples collected using RNAlater, FOBT cards and oral samples collected using Scope mouthwash were stable over four days at room temperature, these would be most appropriate for microbial analyses in these populations. However, one collection method should be consistently since each method may induce some differences.

Cigarette Smoking and Opium Use in Relation to the Oral Microbiota in Iran.

Cigarettes and opium contain chemicals and particulate matter that may modify the oral microbiota. This study aimed to investigate the association between cigarette and opium use with the oral microbiota. A total of 558 participants were recruited from Iran between 2011 and 2015. Individuals were categorized as never cigarette nor opium users, ever cigarette-only smokers, ever opium-only users, and ever both cigarette and opium users. Participants provided saliva samples for 16S rRNA gene sequencing. Logistic regression, microbiome regression-based kernel association test (MiRKAT), and zero-inflated beta regression models were calculated. For every increase in 10 observed amplicon sequence variants (ASVs), the odds for being a cigarette-only smoker, opium-only user, and both user compared to never users decreased by 9% (odds ratio [OR] = 0.91; 95% confidence interval [95% CI] = 0.86 to 0.97), 13% (OR = 0.87; 95% CI = 0.75 to 1.01), and 12% (OR = 0.88; 95% CI = 0.80 to 0.96), respectively. The microbial communities differed by cigarette and opium use as indicated by MiRKAT models testing the three beta-diversity matrices (P < 0.05 for all). Three genera were less likely and one genus was more likely to be detected in cigarette-only smokers or opium-only users than in never users. The relative abundance of the phylum Actinobacteria (never, 14.78%; both, 21.20%) was higher and the phyla Bacteroidetes (never, 17.63%; both, 11.62%) and Proteobacteria (never, 9.06%; both, 3.70%) were lower in users of both cigarettes and opium, while the phylum Firmicutes (never, 54.29%; opium, 65.49%) was higher in opium-only users. Cigarette and opium use was associated with lower alpha-diversity, overall oral microbiota community composition, and both the presence and relative abundance of multiple taxa. IMPORTANCE Cigarette smoking and opium use are associated with periodontal disease caused by specific bacteria such as Porphyromonas gingivalis, which suggests a link between cigarette smoking and opium use and the oral microbiota. Alterations of the oral microbiota in cigarette smokers compared to nonsmokers have been reported, but this has not been studied across diverse populations. Additionally, the association of opium use with the oral microbiota has not been investigated to date. We conducted this study to investigate differences in the oral microbiota between ever users of cigarettes only, opium only, and both cigarettes and opium and never users of cigarettes and opium in Iran. Lower alpha-diversity, distinct overall oral microbial communities, and the presence and relative abundance of multiple taxa have been found for users of cigarettes and/or opium.

Effects of processed meat and drinking water nitrate on oral and fecal microbial populations in a controlled feeding study.

BACKGROUND: One mechanism that can explain the link between processed meat consumption and colorectal cancer (CRC) is the production of carcinogenic N-nitroso compounds (NOCs) in the gastrointestinal tract. Oral and gut microbes metabolize ingested proteins (a source of secondary and tertiary amines and amides) and can reduce nitrate to nitrite, generating potentially carcinogenic NOCs. OBJECTIVE: We evaluated whether nitrate/nitrite in processed meat or water influences the fecal or salivary microbiota. DESIGN: In this dietary intervention study, 63 volunteers consumed diets high in conventional processed meats for two weeks, switched to diets high in poultry for two weeks, and then consumed phytochemical-enriched conventional processed or low-nitrite processed meat diets for two weeks. During the intervention, they drank water with low nitrate concentrations and consumed a healthy diet with low antioxidants. Then the volunteers drank nitrate-enriched water for 1 week, in combination with one of the four different diets. We measured creatinine-adjusted urinary nitrate levels and characterized the oral and fecal microbiota using 16S rRNA amplicon sequencing. RESULTS: Using linear mixed models, we found that, compared to baseline, urinary nitrate levels were reduced during the phytochemical-enriched low-nitrite meat diet (p-value = 0.009) and modestly during the poultry diet (p-value = 0.048). In contrast, urinary nitrate increased after 1-week of drinking nitrate-enriched water (p-value<10-5). Nitrate-enriched water, but not processed meats with or without phytochemicals, altered the saliva microbial population (p-value ≤0.001), and significantly increased abundance of 8 bacterial taxa, especially genus Neisseria and other nitrate-reducing taxa. Meats, phytochemicals and nitrate-enriched water had no significant effects on saliva alpha diversity or any diversity parameter measured for the fecal microbiota. CONCLUSION: These findings support the hypothesis that drinking high nitrate water increases oral nitrate-reducing bacteria, which likely results in increased NOC. However, meat nitrate/nitrite at the levels tested had no effect on either the gut or oral bacteria. CLINICALTRIALS. GOV IDENTIFIER: NCT04138654.

Associations of fecal microbial profiles with breast cancer and nonmalignant breast disease in the Ghana Breast Health Study.

The gut microbiota may play a role in breast cancer etiology by regulating hormonal, metabolic and immunologic pathways. We investigated associations of fecal bacteria with breast cancer and nonmalignant breast disease in a case-control study conducted in Ghana, a country with rising breast cancer incidence and mortality. To do this, we sequenced the V4 region of the 16S rRNA gene to characterize bacteria in fecal samples collected at the time of breast biopsy (N = 379 breast cancer cases, N = 102 nonmalignant breast disease cases, N = 414 population-based controls). We estimated associations of alpha diversity (observed amplicon sequence variants [ASVs], Shannon index, and Faith's phylogenetic diversity), beta diversity (Bray-Curtis and unweighted/weighted UniFrac distance), and the presence and relative abundance of select taxa with breast cancer and nonmalignant breast disease using multivariable unconditional polytomous logistic regression. All alpha diversity metrics were strongly, inversely associated with odds of breast cancer and for those in the highest relative to lowest tertile of observed ASVs, the odds ratio (95% confidence interval) was 0.21 (0.13-0.36; Ptrend < .001). Alpha diversity associations were similar for nonmalignant breast disease and breast cancer grade/molecular subtype. All beta diversity distance matrices and multiple taxa with possible estrogen-conjugating and immune-related functions were strongly associated with breast cancer (all Ps < .001). There were no statistically significant differences between breast cancer and nonmalignant breast disease cases in any microbiota metric. In conclusion, fecal bacterial characteristics were strongly and similarly associated with breast cancer and nonmalignant breast disease. Our findings provide novel insight into potential microbially-mediated mechanisms of breast disease.

Power of Microbiome Beta-Diversity Analyses Based on Standard Reference Samples.

A simple method to analyze microbiome beta-diversity computes mean beta-diversity distances from a test sample to standard reference samples. We used reference stool and nasal samples from the Human Microbiome Project and regressed an outcome on mean distances (2 degrees-of-freedom (df) test) or additionally on squares and cross-product of mean distances (5-df test). We compared the power of 2-df and 5-df tests with the microbiome regression-based kernel association test (MiRKAT). In simulations, MiRKAT had moderately greater power than the 2-df test for discriminating skin versus saliva and skin versus nasal samples, but differences were negligible for skin versus stool and stool versus nasal samples. The 2-df test had slightly greater power than MiRKAT for Dirichlet multinomial samples. In associating body mass index with beta-diversity in stool samples from the American Gut Project, the 5-df test yielded smaller P values than MiRKAT for most taxonomic levels and beta-diversity measures. Unlike procedures like MiRKAT that are based on the beta-diversity matrix, mean distances to reference samples can be analyzed with standard statistical tools and shared or meta-analyzed without sharing primary DNA data. Our data indicate that standard reference tests have power comparable to MiRKAT's (and to permutational multivariate analysis of variance), but more simulations and applications are needed to confirm this.

Variation in oral microbiome is associated with future risk of lung cancer among never-smokers.

OBJECTIVE: To prospectively investigate whether diversity in oral microbiota is associated with risk of lung cancer among never-smokers. DESIGN AND SETTING: A nested case-control study within two prospective cohort studies, the Shanghai Women's Health Study (n=74 941) and the Shanghai Men's Health Study (n=61 480). PARTICIPANTS: Lifetime never-smokers who had no cancer at baseline. Cases were subjects who were diagnosed with incident lung cancer (n=114) and were matched 1:1 with controls on sex, age (≤2 years), date (≤30 days) and time (morning/afternoon) of sample collection, antibiotic use during the week before sample collection (yes/no) and menopausal status (for women). MAIN OUTCOMES AND MEASURES: Metagenomic shotgun sequencing was used to measure the community structure and abundance of the oral microbiome in pre-diagnostic oral rinse samples of each case and control. Multivariable logistic regression models were used to estimate the association of lung cancer risk with alpha diversity metrics and relative abundance of taxa. The Microbiome Regression-Based Kernel Association Test (MiRKAT) evaluated the association between risk and the microbiome beta diversity. RESULTS: Subjects with lower microbiota alpha diversity had an increased risk of lung cancer compared with those with higher microbial alpha diversity (Shannon: ptrend=0.05; Simpson: ptrend=0.04; Observed Species: ptrend=0.64). No case-control differences were apparent for beta diversity (pMiRKAT=0.30). After accounting for multiple comparisons, a greater abundance of Spirochaetia (ORlow 1.00 (reference), ORmedium 0.61 (95% CI 0.32 to 1.18), ORhigh 0.42 (95% CI 0.21 to 0.85)) and Bacteroidetes (ORlow 1.00 (reference), ORmedium 0.66 (95% CI 0.35 to 1.25), ORhigh 0.31 (95% CI 0.15 to 0.64)) was associated with a decreased risk of lung cancer, while a greater abundance of the Bacilli class (ORlow 1.00 (reference), ORmedium 1.49 (95% CI 0.73 to 3.08), ORhigh 2.40 (95% CI 1.18 to 4.87)) and Lactobacillales order (ORlow 1.00 (reference), ORmedium 2.15 (95% CI 1.03 to 4.47), ORhigh 3.26 (95% CI 1.58 to 6.70)) was associated with an increased risk of lung cancer. CONCLUSIONS: Our prospective study of never-smokers suggests that lower alpha diversity was associated with a greater risk of lung cancer and the abundance of certain specific taxa was associated with altered risk, providing further insight into the aetiology of lung cancer in the absence of active tobacco smoking.

Association of Body Mass Index with Fecal Microbial Diversity and Metabolites in the Northern Finland Birth Cohort.

BACKGROUND: Obesity is an established risk factor for multiple cancer types. Lower microbial richness has been linked to obesity, but human studies are inconsistent, and associations of early-life body mass index (BMI) with the fecal microbiome and metabolome are unknown. METHODS: We characterized the fecal microbiome (n = 563) and metabolome (n = 340) in the Northern Finland Birth Cohort 1966 using 16S rRNA gene sequencing and untargeted metabolomics. We estimated associations of adult BMI and BMI history with microbial features and metabolites using linear regression and Spearman correlations (rs ) and computed correlations between bacterial sequence variants and metabolites overall and by BMI category. RESULTS: Microbial richness, including the number of sequence variants (rs = -0.21, P < 0.0001), decreased with increasing adult BMI but was not independently associated with BMI history. Adult BMI was associated with 56 metabolites but no bacterial genera. Significant correlations were observed between microbes in 5 bacterial phyla, including 18 bacterial genera, and metabolites in 49 of the 62 metabolic pathways evaluated. The genera with the strongest correlations with relative metabolite levels (positively and negatively) were Blautia, Oscillospira, and Ruminococcus in the Firmicutes phylum, but associations varied by adult BMI category. CONCLUSIONS: BMI is strongly related to fecal metabolite levels, and numerous associations between fecal microbial features and metabolite levels underscore the dynamic role of the gut microbiota in metabolism. IMPACT: Characterizing the associations between the fecal microbiome, the fecal metabolome, and BMI, both recent and early-life exposures, provides critical background information for future research on cancer prevention and etiology.

Comparison of Oral Microbiota Collected Using Multiple Methods and Recommendations for New Epidemiologic Studies.

Epidemiologic studies use various biosample collection methods to study associations between human oral microbiota and health outcomes. However, the agreement between the different methods is unclear. We compared a commercially available OMNIgene ORAL kit to three alternative collection methods: Saccomanno's fixative, Scope mouthwash, and nonethanol mouthwash. Oral samples were collected from 40 individuals over 4 visits. Two samples were collected from each subject per visit: one with OMNIgene and one with an alternative method. DNA was extracted using the DSP DNA Virus Pathogen kit, and the V4 region of the 16S rRNA gene was PCR amplified and sequenced using MiSeq. Oral collection methods were compared based on alpha and beta diversity metrics and phylum- and genus-level relative abundances. All alpha diversity metrics were significantly lower for Saccomanno's fixative than for OMNIgene (P < 0.001), whereas the two mouthwashes were more similar to OMNIgene. Principal-coordinate analysis (PCoA) using the Bray-Curtis and weighted UniFrac beta diversity matrices showed large differences in the microbial compositions of samples collected with Saccomanno's compared to those with OMNIgene and the mouthwashes. Clustering by collection method was not observed in unweighted UniFrac PCoA plots, suggesting differences in relative abundances but not specific taxa detected by the collection methods. Relative abundances of most taxa were significantly different between OMNIgene and the other methods at each taxonomic level, with Saccomanno's showing the least agreement with OMNIgene. There were clear differences in oral microbial communities between the four oral collection methods, particularly for Saccomanno's fixative.IMPORTANCE We compared four different oral collection methods for studying the human oral microbiome: an OMNIgene ORAL kit, Scope mouthwash, nonethanol mouthwash, and Saccomanno's fixative. Our study shows that the type of the collection method can have a large impact on the results of an oral microbiome analysis. We recommend that one consistent oral collection method should be used for all oral microbiome comparisons. While Scope and nonethanol mouthwashes are less expensive and provide results similar to those with OMNIgene, Saccomanno's fixative may be unfavorable due to the microbial differences detected in this study. Our results will help guide the design of future oral microbiome studies.

Oral microbial community composition is associated with pancreatic cancer: A case-control study in Iran.

BACKGROUND: Oral microbiota may be related to pancreatic cancer risk because periodontal disease, a condition linked to multiple specific microbes, has been associated with increased risk of pancreatic cancer. We evaluated the association between oral microbiota and pancreatic cancer in Iran. METHODS: A total of 273 pancreatic adenocarcinoma cases and 285 controls recruited from tertiary hospitals and a specialty clinic in Tehran, Iran provided saliva samples and filled out a questionnaire regarding demographics and lifestyle characteristics. DNA was extracted from saliva and the V4 region of the 16S rRNA gene was PCR amplified and sequenced on the MiSeq. The sequencing data were processed using the DADA2 plugin in QIIME 2 and taxonomy was assigned against the Human Oral Microbiome Database. Logistic regression and MiRKAT models were calculated with adjustment for potential confounders. RESULTS: No association was observed for alpha diversity with an average of 91.11 (standard deviation [SD] 2.59) sequence variants for cases and 89.42 (SD 2.58) for controls. However, there was evidence for an association between beta diversity and case status. The association between the Bray-Curtis dissimilarity and pancreatic cancer was particularly strong with a MiRKAT P-value of .000142 and specific principal coordinate vectors had strong associations with cancer risk. Several specific taxa were also associated with case status after adjustment for multiple comparisons. CONCLUSION: The overall microbial community appeared to differ between pancreatic cancer cases and controls. Whether these reflect differences evident before development of pancreatic cancer will need to be evaluated in prospective studies.

The respiratory tract microbiome and its relationship to lung cancer and environmental exposures found in rural china.

We previously reported that bacterial diversity in sputum samples from never-smoking women in rural China varied by lung cancer status and household air pollution (HAP) exposure type. Here, we expand on our associations between environmental exposures and respiratory tract microbiota with an additional 90 never-smoking women from Xuanwei, China. DNA from sputum samples of cases (n = 45) and controls (n = 45) was extracted using a multistep enzymatic and physical lysis, followed by a standardized clean up. V1-V2 regions of 16S rRNA genes were Polymerase chain reaction (PCR) amplified. Purified amplicons were sequenced by 454 FLX Titanium pyrosequencing and high-quality sequences were evaluated for diversity and taxonomic membership. In our population of never-smokers, increased risk of lung cancer was associated with lower alpha diversity compared to higher alpha diversity (Shannon: ORhigh = 1.00 [reference], ORmedium = 3.84 [1.02-14.48], ORlow = 3.78 [1.03-13.82]; observed species: ORhigh = 1.00 [reference], ORmedium = 2.37 [0.67-8.48], ORlow = 2.01 [0.58-6.97]; Phylogenetic Diversity (PD) whole tree: ORhigh = 1.00 [reference], ORmedium = 3.04 [0.85-10.92], ORlow = 2.53 [0.72-8.96]), as well as a decreased relative abundance of Fusobacteria (ORhigh = 1.00 [reference], ORmedium = 1.24 [0.42-3.66], ORlow = 2.01 [0.63-6.44], ptrend = 0.03). Increasing alpha diversity was associated with smoky coal use compared to clean fuel use among all subjects (observed species, P = 0.001; PD whole tree, P = 0.006; Shannon, P = 0.0002), as well as cases (observed species, P = 0.02; PD whole tree, P = 0.03; Shannon, P = 0.03) and controls (observed species, P = 0.01; PD whole tree, P = 0.05; Shannon, P = 0.002). Increased diversity was also associated with presence of livestock (observed species, P = 0.02; PD whole tree, P = 0.02; Shannon, P = 0.03) in the home for cases. Our study is the first to report that decreased microbial diversity is associated with risk of lung cancer. Larger studies are necessary to elucidate the direct and indirect effects attributed to the disease-specific, HAP-specific, and animal-specific associations. Environ. Mol. Mutagen. 2019. © 2019 Wiley Periodicals, Inc.

Using standard microbiome reference groups to simplify beta-diversity analyses and facilitate independent validation.

Motivation: Comparisons of microbiome communities across populations are often based on pairwise distance measures (beta-diversity). Standard analyses (principal coordinate plots, permutation tests, kernel methods) require access to primary data if another investigator wants to add or compare independent data. We propose using standard reference measurements to simplify microbiome beta-diversity analyses, to make them more transparent, and to facilitate independent validation and comparisons across studies. Results: Using stool and nasal reference sets from the Human Microbiome Project (HMP), we computed mean distances (actually Bray-Curtis or Pearson correlation dissimilarities) to each reference set for each new sample. Thus, each new sample has two mean distances that can be plotted and analyzed with classical statistical methods. To test the approach, we studied independent (not reference) HMP subjects. Simple Hotelling tests demonstrated statistically significant differences in mean distances to reference sets between all pairs of body sites (stool, skin, nasal, saliva and vagina) at the phylum, class, order, family and genus levels. Using the distance to a single reference set was usually sufficient, but using both reference sets always worked well. The use of reference sets simplifies standard analyses of beta-diversity and facilitates the independent validation and combining of such data because others can compute distances to the same reference sets. Moreover, standard statistical methods for survival analysis, logistic regression and other procedures can be applied to vectors of mean distances to reference sets, thereby greatly expanding the potential uses of beta-diversity information. More work is needed to identify the best reference sets for particular applications. Availability and implementation: https://github.com/NCI-biostats/microbiome-fixed-reference. Supplementary information: Supplementary data are available at Bioinformatics online.