MEC-15-690-Dryad

This zip file extracts data used in Roy et al MEC-15-0690. The ".txt" file is the raw microsatellite scores in number of repeats formatted for the Convert.exe program. The different forlders contain the data and results (Lositan and Bayescan) for various tests and analyses performed in the MS cited above. ".in" files are for structurama 2.0 input (Mac format), ".u" files are for IMa2 input (also accompanied by ".priors" files), while structure inputs list only "Project_data" files (and should be renamed as such prior to use)

Schematic state-transition model representation.

<p>Schematic state-transition model representation.</p

Savings with vs. without probiotics according to two scenarios.

<p>Savings with vs. without probiotics according to two scenarios.</p

Summary of model inputs–Epidemiological parameters, base case Canada.

<p>Summary of model inputs–Epidemiological parameters, base case Canada.</p

Prevented RTI-related events with vs. without probiotics according to two scenarios.

<p>Prevented RTI-related events with vs. without probiotics according to two scenarios.</p

Summary of model inputs–Resource utilization and costs parameters, base case Canada (2015 costs).

<p>Summary of model inputs–Resource utilization and costs parameters, base case Canada (2015 costs).</p

AFLP data

The file contains the internal code followed by the site abbreviation following table 1. All 481 AFLP loci are given

aflp.txt

The file contains the internal code followed by the site abbreviation following table 1. All 481 AFLP loci are given

Swiss Stn381 survey

The file contains the internal sample code, the population site abbreviated as in table 1 and the STN381 loci for each individual

Data_Sheet_1_A discriminant analysis of plasma metabolomics for the assessment of metabolic responsiveness to red raspberry consumption.docx

BackgroundMany studies show that the intake of raspberries is beneficial to immune-metabolic health, but the responses of individuals are heterogeneous and not fully understood.MethodsIn a two-arm parallel-group, randomized, controlled trial, immune-metabolic outcomes and plasma metabolite levels were analyzed before and after an 8-week red raspberry consumption. Based on partial least squares discriminant analysis (PLS-DA) on plasma xenobiotic levels, adherence to the intervention was first evaluated. A second PLS-DA followed by hierarchical clustering was used to classify individuals into response subgroups. Clinical immune and metabolic outcomes, including insulin resistance (HOMA-IR) and sensitivity (Matsuda, QUICKI) indices, during the intervention were assessed and compared between response subgroups.ResultsTwo subgroups of participants, type 1 responders (n = 17) and type 2 responders (n = 5), were identified based on plasma metabolite levels measured during the intervention. Type 1 responders showed neutral to negative effects on immune-metabolic clinical parameters after raspberry consumption, and type 2 responders showed positive effects on the same parameters. Changes in waist circumference, waist-to-hip ratio, fasting plasma apolipoprotein B, C-reactive protein and insulin levels as well as Matsuda, HOMA-IR and QUICKI were significantly different between the two response subgroups. A deleterious effect of two carotenoid metabolites was also observed in type 1 responders but these variables were significantly associated with beneficial changes in the QUICKI index and in fasting insulin levels in type 2 responders. Increased 3-ureidopropionate levels were associated with a decrease in the Matsuda index in type 2 responders, suggesting that this metabolite is associated with a decrease in insulin sensitivity for those subjects, whereas the opposite was observed for type 1 responders.ConclusionThe beneficial effects associated with red raspberry consumption are subject to inter-individual variability. Metabolomics-based clustering appears to be an effective way to assess adherence to a nutritional intervention and to classify individuals according to their immune-metabolic responsiveness to the intervention. This approach may be replicated in future studies to provide a better understanding of how interindividual variability impacts the effects of nutritional interventions on immune-metabolic health.</p