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      Azoxymethane Alters the Plasma Metabolome to a Greater Extent in Mice Fed a High-Fat Diet Compared to an AIN-93 Diet

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          Abstract

          Consumption of a high-fat diet (HFD) links obesity to colon cancer in humans. Our data show that a HFD (45% energy fat versus 16% energy fat in an AIN-93 diet (AIN)) promotes azoxymethane (AOM)-induced colonic aberrant crypt foci (ACF) formation in a mouse cancer model. However, the underlying metabolic basis remains to be determined. In the present study, we hypothesize that AOM treatment results in different plasma metabolomic responses in diet-induced obese mice. An untargeted metabolomic analysis was performed on the plasma samples by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). We found that 53 of 144 identified metabolites were different between the 4 groups of mice (AIN, AIN + AOM, HFD, HFD + AOM), and sparse partial least-squares discriminant analysis showed a separation between the HFD and HFD + AOM groups but not the AIN and AIN + AOM groups. Moreover, the concentrations of dihydrocholesterol and cholesterol were inversely associated with AOM-induced colonic ACF formation. Functional pathway analyses indicated that diets and AOM-induced colonic ACF modulated five metabolic pathways. Collectively, in addition to differential plasma metabolomic responses, AOM treatment decreases dihydrocholesterol and cholesterol levels and alters the composition of plasma metabolome to a greater extent in mice fed a HFD compared to the AIN.

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          Colorectal cancer statistics, 2017.

          Colorectal cancer (CRC) is one of the most common malignancies in the United States. Every 3 years, the American Cancer Society provides an update of CRC incidence, survival, and mortality rates and trends. Incidence data through 2013 were provided by the Surveillance, Epidemiology, and End Results program, the National Program of Cancer Registries, and the North American Association of Central Cancer Registries. Mortality data through 2014 were provided by the National Center for Health Statistics. CRC incidence rates are highest in Alaska Natives and blacks and lowest in Asian/Pacific Islanders, and they are 30% to 40% higher in men than in women. Recent temporal patterns are generally similar by race and sex, but differ by age. Between 2000 and 2013, incidence rates in adults aged ≥50 years declined by 32%, with the drop largest for distal tumors in people aged ≥65 years (incidence rate ratio [IRR], 0.50; 95% confidence interval [95% CI], 0.48-0.52) and smallest for rectal tumors in ages 50 to 64 years (male IRR, 0.91; 95% CI, 0.85-0.96; female IRR, 1.00; 95% CI, 0.93-1.08). Overall CRC incidence in individuals ages ≥50 years declined from 2009 to 2013 in every state except Arkansas, with the decrease exceeding 5% annually in 7 states; however, rectal tumor incidence in those ages 50 to 64 years was stable in most states. Among adults aged <50 years, CRC incidence rates increased by 22% from 2000 to 2013, driven solely by tumors in the distal colon (IRR, 1.24; 95% CI, 1.13-1.35) and rectum (IRR, 1.22; 95% CI, 1.13-1.31). Similar to incidence patterns, CRC death rates decreased by 34% among individuals aged ≥50 years during 2000 through 2014, but increased by 13% in those aged <50 years. Progress against CRC can be accelerated by increasing initiation of screening at age 50 years (average risk) or earlier (eg, family history of CRC/advanced adenomas) and eliminating disparities in high-quality treatment. In addition, research is needed to elucidate causes for increasing CRC in young adults. CA Cancer J Clin 2017. © 2017 American Cancer Society. CA Cancer J Clin 2017;67:177-193. © 2017 American Cancer Society.
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            Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

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              Centering, scaling, and transformations: improving the biological information content of metabolomics data

              Background Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability. Results Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis. Conclusion Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis). In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Metabolites
                Metabolites
                metabolites
                Metabolites
                MDPI
                2218-1989
                09 July 2021
                July 2021
                : 11
                : 7
                : 448
                Affiliations
                [1 ]Grand Forks Human Nutrition Research Center, Agricultural Research Service, United States Department of Agriculture, Grand Forks, ND 58203, USA; michael.bukowski@ 123456usda.gov
                [2 ]Department of Surgery and University of Kansas Cancer Center, Kansas City, KS 66160, USA; sumar@ 123456kumc.edu
                [3 ]School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003, USA; zliu@ 123456nutrition.umass.edu
                Author notes
                [* ]Correspondence: huawei.zeng@ 123456usda.gov ; Tel.: +1-701-795-8465
                Author information
                https://orcid.org/0000-0002-2621-5531
                Article
                metabolites-11-00448
                10.3390/metabo11070448
                8307161
                f559e077-777c-491c-a5cd-5867a97f3d0f
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 07 May 2021
                : 02 July 2021
                Categories
                Article

                aberrant crypt foci,azoxymethane,colon cancer,high-fat diet,inflammation,metabolome,obesity

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