4 research outputs found
Untargeted Metabolomics as a Screening Tool for Estimating Compliance to a Dietary Pattern
There is a growing interest in studying
the nutritional effects
of complex diets. For such studies, measurement of dietary compliance
is a challenge because the currently available compliance markers
cover only limited aspects of a diet. In the present study, an untargeted
metabolomics approach was used to develop a compliance measure in
urine to distinguish between two dietary patterns. A parallel intervention
study was carried out in which 181 participants were randomized to
follow either a New Nordic Diet (NND) or an Average Danish Diet (ADD)
for 6 months. Dietary intakes were closely monitored over the whole
study period, and 24 h urine samples as well as weighed dietary records
were collected several times during the study. The urine samples were
analyzed by UPLC-qTOF-MS, and a partial least-squares discriminant
analysis with feature selection was applied to develop a compliance
model based on data from 214 urine samples. The optimized model included
52 metabolites and had a misclassification rate of 19% in a validation
set containing 139 samples. The metabolites identified in the model
were markers of individual foods such as citrus, cocoa-containing
products, and fish as well as more general dietary traits such as
high fruit and vegetable intake or high intake of heat-treated foods.
It was easier to classify the ADD diet than the NND diet probably
due to seasonal variation in the food composition of NND and indications
of lower compliance among the NND subjects. In conclusion, untargeted
metabolomics is a promising approach to develop compliance measures
that cover the most important discriminant metabolites of complex
diets
The relationship between clinical parameters and weight regain.
<p>The association between BMI (A, B), fasting insulin (C, D), HOMA-IR (E, F), and fasting glucose (G, H) and relative weight regain were assessed by Spearmanās Ļ correlation analysis. A, C, E, and G illustrate the relationship between baseline CID1 parameters and weight regain, while B, D, F, and H illustrate the relationship between differential parameters (i.e. ĪCID2-CID1) and weight regain. Relative weight regain ā=ā1 represents a 100% regain in body weight. <sup>a</sup>p<0.05 was considered statistically significant.</p
Flowchart for subject selection from the DiOGenes cohort.
<p>Flowchart for subject selection from the DiOGenes cohort.</p
Differences in anthropometric and clinical parameters between women and men.
<p>Box plots depicting LCD induced changes (i.e. ĪCID2-CID1) in anthropometric and clinical parameters for females (white) and males (grey). <sup>a</sup>p<0.05 between male and female values when conducting Studentās t-tests was considered statistically significant.</p