A study of Integrated multi-organ, multi-omic, and gut microbiome signatures of fat and sucrose dietary oversupply in cardiometabolic disease. Liu et al.

Description

Cardiometabolic disease is the greatest challenge facing global health. Increasingly, evidence suggests that Western diet comprising an over-supply of energy from fat and sucrose leads to obesity, insulin resistance, hypertension, and cardiovascular disease. Traditional preclinical animal studies of cardiometabolic disease often adopt a reductionist approach, focusing on individual components. To overcome this, we comprehensively assessed cardiometabolic phenotypes - anthropometric, physiological, and metabolic – along with the molecular changes consequent upon fat or sucrose dietary over-supply, or both in male C57BL/6J mice. Molecular assessment included measurement of the gut microbiome and several metabolite pools including plasma, heart, liver, and gut contents (cecal and fecal). In these mice, we identified key changes across phenotypes, metabolites, microbiota, and their inter-relationship, and synthesized all the data into 4 distinct phenogroups that explain the variance across cardiometabolic parameters. The molecular components of each phenogroup reveal new insight into inter-organ regulation of Western diet-dependent cardiometabolic phenotypes and highlight important avenues for further study. Mice were allocated to four different diets consisting of a control diet (CHOW), a high-sucrose diet (HSD), a high-fat diet (HFD), and a high-fat high-sucrose (western) diet (HFSD). Physiological, metabolic and echocardiographic measurements were performed on these mice during a 30-week diet protocol and metabolomics and microbiome data was collected after euthanization. Data were analyzed in a two-by-two factorial framework, testing for main effects of high-fat, and high-sugar as well as a high-fat-high-sugar interaction, treating the chow-fed group as the control. Where we have repeated measures over time (e.g., body mass, food intake etc), outcomes were analyzed using a generalized additive mixed model (GAMM), treating animal ID and cage as random effects, time as a non-parametric smooth term, and dietary fat and sugar as parametric fixed effects with an interaction. For outcomes measured at a single time-point (i.e., without repeated measures), data were analyzed using linear-mixed effects models (LMMs) with cage as a random effect and dietary fat and sugar as fixed effects with an interaction. Data were analyzed and plots made in the statistical programming environment R, with GAMMs implemented using the ‘gam’ function in mgcv, and LMMs implemented using the ‘lmer’ function in lme4 58-60. To ascertain the statistical significance of main and interactive effects of the dietary exposures ANOVA-tables were created for models using the ‘anova’ function (lmerTest and mgcv packages). Data were visualized using ggplot2. Principle component analysis (PCA) was implemented using the ‘princomp’ function in R.

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