k-means clustering and correlograms were performed using R software (https://www.datanovia.com/en/lessons/k-means-clustering-in-r-algorith-and-practical-examples/). Obesity clusters (k = 2; obesity prone, OP and obesity resistant, OR) were generated using fat pad weight, body mass index, and total weight gain as the 3 variables. Palatable food consumption-based clusters (k = 3; low palatable consumers, LPC, high sucrose consumers, HSC and high fat consumers, HFC) were generated using total palatable food, total sucrose, and total fat consumption as the 3 variables. Because the data contained more than 2 variables, scatter plot visualization was performed by applying a dimensionality reduction algorithm (PCA) that outputs two new variables from the three original variables. All qPCR data were analyzed throughout obesity hallmarks and palatable food intake-based subgroups, with the main limitation being a low number of animals in some subgroups. Only statistically significant results are reported.

All data were tested for normality and homoscedasticity before applying parametric statistical tests. Statistical analysis for body weight gain, food consumption, qPCR, and mass spectrometry results were performed using Graphpad Prism 6.0 software. Differences between control and fcHFHS groups were assessed using unpaired two-tailed t-tests, and by one-way ANOVA with Tukey’s post hoc test for multiple comparisons to assess differences between more than two groups (cluster comparisons). Repeated measures two-way ANOVA and multiple comparison tests including Sidak correction were used to assess differences in food intake and body weight changes over time between groups. Correlation and linear regressions were performed to compare sucrose/fat intake with time, CB2, and GPR55 relative expression between them and in relation to sucrose consumption.

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