Dietary Patterns Are Associated with Serum Metabolite Patterns and Their Association Is Influenced by Gut Bacteria among Older German Adults.

BACKGROUND: Although dietary intakes and dietary intake patterns (DPs) have been associated with single metabolites, it is unclear whether DPs are also reflected in specific metabolite patterns (MPs). Moreover, the influence of groups of gut bacteria on the relationship between DPs and MPs is underexplored. OBJECTIVES: We aimed to investigate the association of DPs and serum MPs and also the modifying effect of the gut bacteria compositional patterns (BCPs). METHODS: This is a cross-sectional investigation among 225 individuals (median age: 63 y; 53% women) from the European Prospective Investigation into Cancer and Nutrition study. Dietary intakes were assessed by three 24-h dietary recalls, gut bacteria composition was quantified by 16S rRNA gene sequencing, and the serum metabolome was profiled by an untargeted approach. We identified DPs and BCPs by the treelet transform analysis. We modeled associations between DPs and 8 previously published MPs and the modifying effect of BCPs by fitting generalized linear models using DataSHIELD R. RESULTS: We identified 5 DPs and 7 BCPs. The "bread, margarine, and processed meat" and "fruiting vegetables and vegetable oils" DPs were positively associated with the "amino acids" (ß = 0.35; 95% CI: 0.02, 0.69; P = 0.03) and "fatty acids" MPs (ß = 0.45; 95% CI: 0.16, 0.74; P = 0.01), respectively. The "tea and miscellaneous" was inversely associated with the "amino acids" (ß = -0.28; 95% CI: -0.52, -0.05; P = 0.02) and "amino acid derivatives" MPs (ß = -0.21; 95% CI: -0.39, -0.02; P = 0.03). One BCP negatively modified the association between the "bread, margarine, and processed meat" DP and the "amino acids" MP (P-interaction = 0.01). CONCLUSIONS: In older German adults, DPs are reflected in MPs, and the gut bacteria attenuate 1 DP-MP association. These MPs should be explored as biomarkers of these jointly consumed foods while taking into account a potentially modifying role of the gut bacteria.

Plasma antibodies to oral bacteria and risk of pancreatic cancer in a large European prospective cohort study.

OBJECTIVE: Examine the relationship between antibodies to 25 oral bacteria and pancreatic cancer risk in a prospective cohort study. DESIGN: We measured antibodies to oral bacteria in prediagnosis blood samples from 405 pancreatic cancer cases and 416 matched controls, nested within the European Prospective Investigation into Cancer and Nutrition study. Analyses were conducted using conditional logistic regression and additionally adjusted for smoking status and body mass index. RESULTS: Individuals with high levels of antibodies against Porphyromonas gingivalis ATTC 53978, a pathogenic periodontal bacteria, had a twofold higher risk of pancreatic cancer than individuals with lower levels of these antibodies (OR 2.14; 95% CI 1.05 to 4.36; >200 ng/ml vs ≤200 ng/ml). To explore the association with commensal (non-pathogenic) oral bacteria, we performed a cluster analysis and identified two groups of individuals, based on their antibody profiles. A cluster with overall higher levels of antibodies had a 45% lower risk of pancreatic cancer than a cluster with overall lower levels of antibodies (OR 0.55; 95% CI 0.36 to 0.83). CONCLUSIONS: Periodontal disease might increase the risk for pancreatic cancer. Moreover, increased levels of antibodies against specific commensal oral bacteria, which can inhibit growth of pathogenic bacteria, might reduce the risk of pancreatic cancer. Studies are needed to determine whether oral bacteria have direct effects on pancreatic cancer pathogenesis or serve as markers of the immune response.

Lifestyle, dietary factors, and antibody levels to oral bacteria in cancer-free participants of a European cohort study.

BACKGROUND: Increasing evidence suggests that oral microbiota play a pivotal role in chronic diseases, in addition to the well-established role in periodontal disease. Moreover, recent studies suggest that oral bacteria may also be involved in carcinogenesis; periodontal disease has been linked to several cancers. In this study, we examined whether lifestyle factors have an impact on antibody levels to oral bacteria. METHODS: Data on demographic characteristics, lifestyle factors, and medical conditions were obtained at the time of blood sample collection. For the current analysis, we measured antibody levels to 25 oral bacteria in 395 cancer-free individuals using an immunoblot array. Combined total immunoglobin G (IgG) levels were obtained by summing concentrations for all oral bacteria measured. RESULTS: IgG antibody levels were substantially lower among current and former smokers (1,697 and 1,677 ng/mL, respectively) than never smokers (1,960 ng/mL; p trend = 0.01), but did not vary by other factors, including body mass index, diabetes, physical activity, or by dietary factors, after adjusting for age, sex, education, country, and smoking status. The highest levels of total IgG were found among individuals with low education (2,419 ng/mL). CONCLUSIONS: Our findings on smoking are consistent with previous studies and support the notion that smokers have a compromised humoral immune response. Moreover, other major factors known to be associated with inflammatory markers, including obesity, were not associated with antibody levels to a large number of oral bacteria.

Serum Lipid and Serum Metabolite Components in relation to anthropometric parameters in EPIC-Potsdam participants.

BACKGROUND/AIM: Lipidomic and metabolomic techniques become more and more important in human health research. Recent developments in analytical techniques enable the investigation of high amounts of substances. The high numbers of metabolites and lipids that are detected with among others mass spectrometric techniques challenge in most cases the statistical processes to bring out stable and interpretable results. This study targets to use the novel non-established statistical method treelet transform (TT) to investigate high numbers of metabolites and lipids and to compare the results with the established method principal component analysis (PCA). Serum lipid and metabolite profiles are investigated regarding their association to anthropometric parameters associated to obesity. METHODS: From 226 participants of the EPIC (European Prospective Investigation into Cancer and Nutrition)-Potsdam study blood samples were investigated with an untargeted metabolomics approach regarding serum metabolites and lipids. Additionally, participants were surveyed anthropometrically to assess parameters of obesity, such as body mass index (BMI), waist-to-hip-ratio (WHR) and body fat mass. TT and PCA are used to generate treelet components (TCs) and factors summarizing serum metabolites and lipids in new, latent variables without too much loss of information. With partial correlations TCs and factors were associated to anthropometry under the control for relevant parameters, such as sex and age. RESULTS: TT with metabolite variables (p=121) resulted in 5 stable and interpretable TCs explaining 18.9% of the variance within the data. PCA on the same variables generated 4 quite complex, less easily interpretable factors explaining 37.5% of the variance. TT on lipidomic data (p=353) produced 3 TCs as well as PCA on the same data resulted in 3 factors; the proportion of explained variance was 17.8% for TT and 39.8% for PCA. In both investigations TT ended up with stable components that are easier to interpret than the factors from the PCA. In general, the generated TCs and factors were similar in their structure when the factors are considered regarding the original variables loading high on them. Both TCs and factors showed associations to anthropometric measures. CONCLUSIONS: TT is a suitable statistical method to generate summarizing, latent variables in data sets with more variables than observations. In the present investigation it resulted in similar latent variables compared to the established method of PCA. Whereby less variance is explained by the summarizing constructs of TT compared to the factors of PCA, TCs are easier to interpret. Additionally the resulting TCs are quite stable in bootstrap samples.

The influence of whole grain products and red meat on intestinal microbiota composition in normal weight adults: a randomized crossover intervention trial.

Intestinal microbiota is related to obesity and serum lipid levels, both risk factors for chronic diseases constituting a challenge for public health. We investigated how a diet rich in whole grain (WG) products and red meat (RM) influences microbiota. During a 10-week crossover intervention study, 20 healthy adults consumed two isocaloric diets, one rich in WG products and one high in RM. Repeatedly data on microbiota were assessed by 16S rRNA based denaturing gradient gel electrophoresis (DGGE). A blood sample and anthropometric data were collected. Mixed models and logistic regression were used to investigate effects. Microbiota showed interindividual variability. However, dietary interventions modified microbiota appearance: 8 bands changed in at least 4 participants during the interventions. One of the bands appearing after WG and one increasing after RM remained significant in regression models and were identified as Collinsella aerofaciens and Clostridium sp. The WG intervention lowered obesity parameters, while the RM diet increased serum levels of uric acid and creatinine. The study showed that diet is a component of major relevance regarding its influence on intestinal microbiota and that WG has an important role for health. The results could guide investigations of diet and microbiota in observational prospective cohort studies. Trial registration: ClinicalTrials.gov NCT01449383.