Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS).

The Tsuruoka Metabolomics Cohort Study (TMCS) is an ongoing population-based cohort study being conducted in the rural area of Yamagata Prefecture, Japan. This study aimed to enhance the precision prevention of multi-factorial, complex diseases, including non-communicable and aging-associated diseases, by improving risk stratification and prediction measures. At baseline, 11,002 participants aged 35-74 years were recruited in Tsuruoka City, Yamagata Prefecture, Japan, between 2012 and 2015, with an ongoing follow-up survey. Participants underwent various measurements, examinations, tests, and questionnaires on their health, lifestyle, and social factors. This study uses an integrative approach with deep molecular profiling to identify potential biomarkers linked to phenotypes that underpin disease pathophysiology and provide better mechanistic insights into social health determinants. The TMCS incorporates multi-omics data, including genetic and metabolomic analyses of 10,933 participants, and comprehensive data collection ranging from physical, psychological, behavioral, and social to biological data. The metabolome is used as a phenotypic probe because it is sensitive to changes in physiological and external conditions. The TMCS focuses on collecting outcomes for cardiovascular disease, cancer incidence and mortality, disability and functional decline due to aging and disease sequelae, and the variation in health status within the body represented by omics analysis that lies between exposure and disease. It contains several sub-studies on aging, heated tobacco products, and women's health. This study is notable for its robust design, high participation rate (89%), and long-term repeated surveys. Moreover, it contributes to precision prevention in Japan and East Asia as a well-established multi-omics platform.

A population-based urinary and plasma metabolomics study of environmental exposure to cadmium.

BACKGROUND: The application of metabolomics-based profiles in environmental epidemiological studies is a promising approach to refine the process of health risk assessment. We aimed to identify potential metabolomics-based profiles in urine and plasma for the detection of relatively low-level cadmium (Cd) exposure in large population-based studies. METHOD: We analyzed 123 urinary metabolites and 94 plasma metabolites detected in fasting urine and plasma samples collected from 1,412 men and 2,022 women involved in the Tsuruoka Metabolomics Cohort Study. Regression analysis was performed for urinary N-acetyl-beta-D-glucosaminidase (NAG), plasma, and urinary metabolites as dependent variables, and urinary Cd (U-Cd, quartile) as an independent variable. The multivariable regression model included age, gender, systolic blood pressure, smoking, rice intake, BMI, glycated hemoglobin, low-density lipoprotein cholesterol, alcohol consumption, physical activity, educational history, dietary energy intake, urinary Na/K ratio, and uric acid. Pathway-network analysis was carried out to visualize the metabolite networks linked to Cd exposure. RESULT: Urinary NAG was positively associated with U-Cd, but not at lower concentrations (Q2). Among urinary metabolites in the total population, 45 metabolites showed associations with U-Cd in the unadjusted and adjusted models after adjusting for the multiplicity of comparison with FDR. There were 12 urinary metabolites which showed consistent associations between Cd exposure from Q2 to Q4. Among plasma metabolites, six cations and one anion were positively associated with U-Cd, whereas alanine, creatinine, and isoleucine were negatively associated with U-Cd. Our results were robust by statistical adjustment of various confounders. Pathway-network analysis revealed metabolites and upstream regulator changes associated with mitochondria (ACACB, UCP2, and metabolites related to the TCA cycle). CONCLUSION: These results suggested that U-Cd was associated with metabolites related to upstream mitochondrial dysfunction in a dose-dependent manner. Our data will help develop environmental Cd exposure profiles for human populations.

Optimal index for detecting splenic involvement on 18F-fluorodeoxyglucose positron emission tomography/computed tomography imaging in diffuse large B-cell lymphoma.

Accurate clinical staging is important in diffuse large B-cell lymphoma (DLBCL) to adapt to optimal therapy. Splenic involvement of DLBCL has been recently more detectable with the advancement of a diagnostic scan by 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT). Our clinical question is whether splenic involvement was adequately diagnosed by FDG-PET/CT imaging. This retrospective study aimed to determine the optimal index for evaluating splenic involvement in patients with DLBCL. Patients with newly diagnosed DLBCL who were examined with FDG-PET/CT at diagnosis and the end of induction chemotherapy (EOI) was enrolled. The splenic involvement with the splenic FDG uptake value higher than that of the liver at diagnosis or with the decrease of splenic uptake at EOI by visual evaluation was evaluated as positive. The calculative evaluation of splenic involvement, based on the data of standardized uptake value (SUV) of the spleen, used maximum SUV (SUVmax), mean SUV (SUVmean), spleen total lesion glycolysis (spleen TLG), and spleen length. A change in each index following induction chemotherapy was expressed as an index. Receiver operating characteristic analysis was used to set the cutoff value for each index. This study included 52 patients. Spleen TLG (0.904) showed the best accuracy, followed by SUVmax (0.885) and SUVmean (0.885), among the 5 indexes for splenic involvement at diagnosis. Splenic involvement was predicted with a higher accuracy level (0.923) when selecting the cases with values higher than the cutoff level on both spleen TLG and SUVmax. The decision at EOI was more suitable by selecting both positive cases of ∆ TLG and ∆ SUVmax. Obtaining both the positive spleen TLG and SUVmax is recommended at diagnosis to predict splenic involvement. The assessment by ∆ spleen TLG and ∆ SUVmax seems to be optimal.

Genetic mutation in Escherichia coli genome during adaptation to the murine intestine is optimized for the host diet.

Mammalian gut microbes colonize the intestinal tract of their host and adapt to establish a microbial ecosystem. The host diet changes the nutrient profile of the intestine and has a high impact on microbiota composition. Genetic mutations in Escherichia coli, a prevalent species in the human gut, allow for adaptation to the mammalian intestine, as reported in previous studies. However, the extent of colonization fitness in the intestine elevated by genetic mutation and the effects of diet change on these mutations in E. coli are still poorly known. Here, we show that notable mutations in sugar metabolism-related genes (gatC, araC, and malI) were detected in the E. coli K-12 genome just 2 weeks after colonization in the germ-free mouse intestine. In addition to elevated fitness by deletion of gatC, as previously reported, deletion of araC and malI also elevated E. coli fitness in the murine intestine in a host diet-dependent manner. In vitro cultures of medium containing nutrients abundant in the intestine (e.g., galactose, N-acetylglucosamine, and asparagine) also showed increased E. coli fitness after deletion of the genes-of-interest associated with their metabolism. Furthermore, the host diet was found to influence the developmental trajectory of gene mutations in E. coli. Taken together, we suggest that genetic mutations in E. coli are selected in response to the intestinal environment, which facilitates efficient utilization of nutrients abundant in the intestine under laboratory conditions. Our study offers some insight into the possible adaptation mechanisms of gut microbes.IMPORTANCEThe gut microbiota is closely associated with human health and is greatly impacted by the host diet. Bacteria such as Escherichia coli live in the gut all throughout the life of a human host and adapt to the intestinal environment. Adaptive mutations in E. coli are reported to enhance fitness in the mammalian intestine, but to what extent is still poorly known. It is also unknown whether the host diet affects what genes are mutated and to what extent fitness is affected. This study suggests that genetic mutations in the E. coli K-12 strain are selected in response to the intestinal environment and facilitate efficient utilization of abundant nutrients in the germ-free mouse intestine. Our study provides a better understanding of these intestinal adaptation mechanisms of gut microbes.

Reliability of Time-Series Plasma Metabolome Data over 6 Years in a Large-Scale Cohort Study.

Studies examining long-term longitudinal metabolomic data and their reliability in large-scale populations are limited. Therefore, we aimed to evaluate the reliability of repeated measurements of plasma metabolites in a prospective cohort setting and to explore intra-individual concentration changes at three time points over a 6-year period. The study participants included 2999 individuals (1317 men and 1682 women) from the Tsuruoka Metabolomics Cohort Study, who participated in all three surveys-at baseline, 3 years, and 6 years. In each survey, 94 plasma metabolites were quantified for each individual and quality control (QC) sample. The coefficients of variation of QC, intraclass correlation coefficients, and change rates of QC were calculated for each metabolite, and their reliability was classified into three categories: excellent, fair to good, and poor. Seventy-six percent (71/94) of metabolites were classified as fair to good or better. Of the 39 metabolites grouped as excellent, 29 (74%) in men and 26 (67%) in women showed significant intra-individual changes over 6 years. Overall, our study demonstrated a high degree of reliability for repeated metabolome measurements. Many highly reliable metabolites showed significant changes over the 6-year period, suggesting that repeated longitudinal metabolome measurements are useful for epidemiological studies.

Association of Nonalcoholic Fatty Liver Disease with Arterial Stiffness and its Metabolomic Profiling in Japanese Community-Dwellers.

AIMS: Nonalcoholic fatty liver disease (NAFLD) is known to be associated with atherosclerosis. This study focused on upstream changes in the process by which NAFLD leads to atherosclerosis. The study aimed to confirm the association between NAFLD and the cardio-ankle vascular index (CAVI), an indicator of subclinical atherosclerosis, and explore metabolites involved in both by assessing 94 plasma polar metabolites. METHODS: A total of 928 Japanese community-dwellers (306 men and 622 women) were included in this study. The association between NAFLD and CAVI was examined using a multivariable regression model adjusted for confounders. Metabolites commonly associated with NAFLD and CAVI were investigated using linear mixed-effects models in which batch numbers of metabolite measurements were used as a random-effects variable, and false discovery rate-adjusted p-values were calculated. To determine the extent to which these metabolites mediated the association between NAFLD and CAVI, mediation analysis was conducted. RESULTS: NAFLD was positively associated with CAVI (coefficients [95% Confidence intervals (CI)]=0.23 [0.09-0.37]; p=0.001). A total of 10 metabolites were involved in NAFLD and CAVI, namely, branched-chain amino acids (BCAAs; valine, leucine, and isoleucine), aromatic amino acids (AAAs; tyrosine and tryptophan), alanine, proline, glutamic acid, glycerophosphorylcholine, and 4-methyl-2-oxopentanoate. Mediation analysis showed that BCAAs mediated more than 20% of the total effect in the association between NAFLD and CAVI. CONCLUSIONS: NAFLD was associated with a marker of atherosclerosis, and several metabolites related to insulin resistance, including BCAAs and AAAs, could be involved in the process by which NAFLD leads to atherosclerosis.