Data analysis
T2D
REGRESSION, -20
Baseline characteristics of participants are shown as means and standard deviations (SD), medians and interquartile ranges or frequency and percentages. Group differences were assessed by t-tests, Wilcoxon-signed rank tests and chi-square tests, respectively. All participants underwent the three test days, and data from all participants were included in the analysis. Two observations from one participant were missing (GLP1 at -20 and 0 minutes). Linear mixed effects models were used, which can handle missing time points. None of the co-variates was missing. The entero-endocrine response was characterized both as the total response by area under the curve (AUC) analyses, and by the response at different sampling time points. The AUC was calculated with the trapezoidal rule. First, we assessed effect modification by sex and T2D status. We stratified the analyses by T2D status, because we identified effect modification by T2D status for all metabolites (e.g. for insulin and GIP after the swallowed pre-load; p<0.001). We did not identify effect modification by sex (p>0.05), and did thus not stratify by sex. Next, sampling time points were tested as being independent to identify the time points at which the entero-endocrine response differed most. To investigate the association of nutrient sensing with endocrine responses, a mixed model for repeated measures with pre-treatment as exposure and the endocrine response as outcome was used, participants were included as a random effect.Finally, associations between gut and oral microbiome and the entero-endocrine response (as continuous variable) were assessed after the swallowed pre-load, thus considering both oral and gut sensing of the pre-load. Microbiome data was rescaled and transformed using Wisconsin double and square root transformations. Composition for all samples was plotted using stacked bar charts in order to assess their composition in case a sample appeared anomalous. First, we assessed with multiple linear regression whether alpha diversity was associated with the entero-endocrine response, using the Inverse Simpson and Shannon diversity indices. Second, Canonical Correspondence Analysis (CCA) was applied to determine whether there was an association between the oral and fecal microbiome composition and the metabolic response. Participants with missing microbiome data (n = 6, anterior tongue; n = 1 posterior tongue) were omitted from these analyses as tongue swab samples were not missing at random. All microbiome analyses were conducted both unadjusted and adjusted for age, sex and T2D status.All analyses were conducted in R studio version 4.0.3. The ‘lme4’ package was used for the generalized linear mixed models. We used the packages ‘vegan’ for nonmetric multidimensional scaling and PERMANOVA, ‘phyloseq’ for alpha-diversity, ‘CCA’ for RCCA and ‘ggplot2’ for visualization. A two-tailed p-value <0.05 was considered as statistically significant. We did not adjust for multiple-testing as this is a proof-of-concept study.