Ranges of phenotypic flexibility in healthy subjects.

BACKGROUND: A key feature of metabolic health is the ability to adapt upon dietary perturbations. A systemic review defined an optimal nutritional challenge test, the "PhenFlex test" (PFT). Recently, it has been shown that the PFT enables the quantification of all relevant metabolic processes involved in maintaining or regaining homeostasis of metabolic health. Furthermore, it was demonstrated that quantification of PFT response was more sensitive as compared to fasting markers in demonstrating reduced phenotypic flexibility in metabolically impaired type 2 diabetes subjects. METHODS: This study aims to demonstrate that quantification of PFT response can discriminate between different states of health within the healthy range of the population. Therefore, 100 healthy subjects were enrolled (50 males, 50 females) ranging in age (young, middle, old) and body fat percentage (low, medium, high), assuming variation in phenotypic flexibility. Biomarkers were selected to quantify main processes which characterize phenotypic flexibility in response to PFT: flexibility in glucose, lipid, amino acid and vitamin metabolism, and metabolic stress. Individual phenotypic flexibility was visualized using the "health space" by representing the four processes on the health space axes. By quantifying and presenting the study subjects in this space, individual phenotypic flexibility was visualized. RESULTS: Using the "health space" visualization, differences between groups as well as within groups from the healthy range of the population can be easily and intuitively assessed. The health space showed a different adaptation to the metabolic PhenFlex test in the extremes of the recruited population; persons of young age with low to normal fat percentage had a markedly different position in the health space as compared to persons from old age with normal to high fat percentage. CONCLUSION: The results of the metabolic PhenFlex test in conjunction with the health space reliably assessed health on an individual basis. This quantification can be used in the future for personalized health quantification and advice.

Objective eliciting doses of peanut-allergic adults and children can be combined for risk assessment purposes.

BACKGROUND: To improve food labelling strategies, information regarding eliciting doses (EDs) and the effect of patient characteristics on these EDs is necessary. OBJECTIVE: To establish EDs for objective and subjective symptoms and analyse the effect of sensitization levels and other patient characteristics on threshold distribution curves (TDCs). METHODS: Threshold data from 100 adults and 262 children with a positive food challenge were analysed with interval-censoring survival analysis (ICSA) and fitted to a TDC from which EDs could be extracted. Possible influencing factors were analysed as covariates by ICSA. A hazard ratio (HR) was calculated in case of a significant effect. RESULTS: TDCs for both objective and subjective symptoms were significantly different between adults and children (P < 0.001). Objective ED05 values, however, were comparable (2.86 mg peanut protein in adults and 6.38 mg in children). Higher levels of sIgE to Ara h 2 and peanut extract were associated with a larger proportion of patient groups reacting to a dose increase with objective symptoms (adults and children) or subjective symptoms (adults, in children a trend). Age had a similar effect in children (HR 1.05 for objective symptoms and 1.09 for subjective symptoms). Gender had no effect on TDCs. CONCLUSION AND CLINICAL RELEVANCE: Subjective and objective TDCs were different between adults and children, but objective ED05 values were comparable, meaning that threshold data from children and adults can be combined for elaboration of reference doses for risk assessment. Higher sIgE levels to Ara h 2 and peanut extract were associated with a larger proportion of both patient groups to react to a certain dose increase.

Dynamic metabolomic data analysis: a tutorial review.

In metabolomics, time-resolved, dynamic or temporal data is more and more collected. The number of methods to analyze such data, however, is very limited and in most cases the dynamic nature of the data is not even taken into account. This paper reviews current methods in use for analyzing dynamic metabolomic data. Moreover, some methods from other fields of science that may be of use to analyze such dynamic metabolomics data are described in some detail. The methods are put in a general framework after providing a formal definition on what constitutes a 'dynamic' method. Some of the methods are illustrated with real-life metabolomics examples.

Matrix correlations for high-dimensional data: the modified RV-coefficient.

MOTIVATION: Modern functional genomics generates high-dimensional datasets. It is often convenient to have a single simple number characterizing the relationship between pairs of such high-dimensional datasets in a comprehensive way. Matrix correlations are such numbers and are appealing since they can be interpreted in the same way as Pearson's correlations familiar to biologists. The high-dimensionality of functional genomics data is, however, problematic for existing matrix correlations. The motivation of this article is 2-fold: (i) we introduce the idea of matrix correlations to the bioinformatics community and (ii) we give an improvement of the most promising matrix correlation coefficient (the RV-coefficient) circumventing the problems of high-dimensional data. RESULTS: The modified RV-coefficient can be used in high-dimensional data analysis studies as an easy measure of common information of two datasets. This is shown by theoretical arguments, simulations and applications to two real-life examples from functional genomics, i.e. a transcriptomics and metabolomics example. AVAILABILITY: The Matlab m-files of the methods presented can be downloaded from http://www.bdagroup.nl.

Validation and sensitivity analysis of probabilistic models of dietary exposure to micronutrients: an example based on vitamin B6.

Probabilistic modelling can be used to get an insight into the variability and uncertainty of the nutrient intake in a population. When a probabilistic model is used, it is important that it is validated. Furthermore, a sensitivity analysis of the model output can give an insight into the most important input variables of the model and can be used as an aid to describe the reliability of the model. In this study, four models to estimate vitamin B(6) intake among males and females were validated using the method of Kaaks et al. This method compares the relationship between three different kind of measurements with the unknown 'true' intake. In each of these four models, only one input variable (concentration or bioavailability) was changed compared with a reference model. A sensitivity analysis was also performed. The results of the validation showed that for males, a model using a fixed bioavailability factor at the food group level was valid, while for females a model using either a fixed value or a distribution for the bioavailability factor was valid. Use of a distribution for the concentration of vitamin B(6) in supplements was not valid. The results of the sensitivity analysis showed that the concentration of vitamin B(6) in food and supplements was the key contributor to variability and uncertainty in the model estimates of vitamin B(6) intake, in both males and females. All results indicated that when taking variability and uncertainty into account by using probabilistic modelling, the effect on the nutrient intake for nutrients that are present in many common eaten foods, is small. For these broadly available nutrients, fixed concentrations and bioavailability factors give a good estimate of the nutrient intake in a population. When using probabilistic modelling, it is very important to collect more actual information about the concentration.