Perception and preferences of processed sweet foods: differences between diabetic and nondiabetic consumers

Purpose This paper aims to study how diabetics/pre-diabetics (D) and non-diabetic (regular consumers of sweeteners (C) or not (NC)) perceive and consume sweetened processed food in Brazil. Design/methodology/approach A cross-sectional study (n = 2,204) was carried out to gather information about: consumption of 14 sweetened food/beverage categories, perception of sugar/sweeteners (check-all-that-apply (CATA) test), understanding of sugar claims and socioeconomic/demographic/consume profile. Chi-square test/Fisher exact tests were used to analyze the contingency tables. CATA test results were evaluated using Cochran Q test, RV coefficient and Kruskal-Wallis test. Findings Results revealed that although diabetics/pre-diabetics consumed less sugary products than non-diabetics (p < 0.001), >50.0% of them preferred sugary candies, bakeries, ready-to-drink fruit juice, ice cream, chocolate and ready-to-eat desserts. D, NC and C similarly perceived (RV = 0.99) sugar (sensory desirable, but penalized due to its health impact), naturally extracted sweeteners (opposite description of sugar) and chemically synthesized sweeteners (penalized by sensory and health impacts). Regarding the claims, those that mean the absence of sugar were correctly understood for = 90.0% participants, while incorrect interpretations were observed for “containing sugars from own ingredients” (42.7%) and “light on sugar” (21.0%), without differences between consumer groups (p = 0.93). Research limitations/implications This study was carried out with a convenience sample. Practical implications Results can be applied to support food policies and educational campaigns (improving consumer information on processed sweetened foods) and to guide product development in the food industry. Originality/value This is the first study to evaluate the Brazilians’ behavior regarding the perception of sugar and sweeteners, the choice of different sweetened processed food, and understanding of sugar claims.

Gut Microbiota Functional Dysbiosis Relates to Individual Diet in Subclinical Carotid Atherosclerosis

Gut Microbiota (GM) dysbiosis associates with Atherosclerotic Cardiovascular Diseases (ACVD), but whether this also holds true in subjects without clinically manifest ACVD represents a challenge of personalized prevention. We connected exposure to diet (self-reported by food diaries) and markers of Subclinical Carotid Atherosclerosis (SCA) with individual taxonomic and functional GM profiles (from fecal metagenomic DNA) of 345 subjects without previous clinically manifest ACVD. Subjects without SCA reported consuming higher amounts of cereals, starchy vegetables, milky products, yoghurts and bakery products versus those with SCA (who reported to consume more mechanically separated meats). The variety of dietary sources significantly overlapped with the separations in GM composition between subjects without SCA and those with SCA (RV coefficient between nutrients quantities and microbial relative abundances at genus level = 0.65, p-value = 0.047). Additionally, specific bacterial species (Faecalibacterium prausnitzii in the absence of SCA and Escherichia coli in the presence of SCA) are directly related to over-representation of metagenomic pathways linked to different dietary sources (sulfur oxidation and starch degradation in absence of SCA, and metabolism of amino acids, syntheses of palmitate, choline, carnitines and Trimethylamine n-oxide in presence of SCA). These findings might contribute to hypothesize future strategies of personalized dietary intervention for primary CVD prevention setting.

A general index for linear and nonlinear correlations for high dimensional genomic data

Background With the advance of high throughput sequencing, high-dimensional data are generated. Detecting dependence/correlation between these datasets is becoming one of most important issues in multi-dimensional data integration and co-expression network construction. RNA-sequencing data is widely used to construct gene regulatory networks. Such networks could be more accurate when methylation data, copy number aberration data and other types of data are introduced. Consequently, a general index for detecting relationships between high-dimensional data is indispensable. Results We proposed a Kernel-Based RV-coefficient, named KBRV, for testing both linear and nonlinear correlation between two matrices by introducing kernel functions into RV2 (the modified RV-coefficient). Permutation test and other validation methods were used on simulated data to test the significance and rationality of KBRV. In order to demonstrate the advantages of KBRV in constructing gene regulatory networks, we applied this index on real datasets (ovarian cancer datasets and exon-level RNA-Seq data in human myeloid differentiation) to illustrate its superiority over vector correlation. Conclusions We concluded that KBRV is an efficient index for detecting both linear and nonlinear relationships in high dimensional data. The correlation method for high dimensional data has possible applications in the construction of gene regulatory network.

MiRKAT: kernel machine regression-based global association tests for the microbiome

Summary Distance-based tests of microbiome beta diversity are an integral part of many microbiome analyses. MiRKAT enables distance-based association testing with a wide variety of outcome types, including continuous, binary, censored time-to-event, multivariate, correlated and high-dimensional outcomes. Omnibus tests allow simultaneous consideration of multiple distance and dissimilarity measures, providing higher power across a range of simulation scenarios. Two measures of effect size, a modified R-squared coefficient and a kernel RV coefficient, are incorporated to allow comparison of effect sizes across multiple kernels. Availability and implementation MiRKAT is available on CRAN as an R package. Supplementary information Supplementary data are available at Bioinformatics online.

TOPOLOGICAL STRUCTURAL ANALYSIS OF CHINA'S NEW ENERGY STOCK MARKET: A MULTI-DIMENSIONAL DATA NETWORK PERSPECTIVE

In this paper, we apply an RV coefficient network to investigate the topological structure of China’s new energy stock market via daily prices of 60 component stocks of CSI (China Stock Index) New Energy Index spanning the period January 4, 2012 to March 29, 2019. Compared with the Pearson correlation coefficient, RV coefficient can better reflect the similarity between stocks from the perspective of multi-dimensional data. The empirical result indicates that (1) the scale-free characteristics of China’s new energy stock market are not significant; (2) the new energy storage is the leading sub-sector of the new energy sector and the new energy interactive equipment plays a connecting role between renewable energy production and new energy storage; (3) the most influential stock in the network is Group DMEGC Magnetics Co., Ltd., Xiamen Tungsten Co., Ltd. and GEM Co., Ltd. play an important role in the network connection. These findings are of great significance to understand the interaction between Chinese new energy stocks and the pricing mechanism of stocks. The authority should pay more attention to the new energy storage industry. Investor’s portfolios can be optimized according to the influence assessment of stocks and sub-sectors.

Dependence and dependence structures: estimation and visualization using the unifying concept of distance multivariance

Distance multivariance is a multivariate dependence measure, which can detect dependencies between an arbitrary number of random vectors each of which can have a distinct dimension. Here we discuss several new aspects, present a concise overview and use it as the basis for several new results and concepts: in particular, we show that distance multivariance unifies (and extends) distance covariance and the Hilbert-Schmidt independence criterion HSIC, moreover also the classical linear dependence measures: covariance, Pearson’s correlation and the RV coefficient appear as limiting cases. Based on distance multivariance several new measures are defined: a multicorrelation which satisfies a natural set of multivariate dependence measure axioms and m-multivariance which is a dependence measure yielding tests for pairwise independence and independence of higher order. These tests are computationally feasible and under very mild moment conditions they are consistent against all alternatives. Moreover, a general visualization scheme for higher order dependencies is proposed, including consistent estimators (based on distance multivariance) for the dependence structure.Many illustrative examples are provided. All functions for the use of distance multivariance in applications are published in the R-package multivariance.

Another look at matrix correlations

Motivation High throughput technologies are widely employed in modern biomedical research. They yield measurements of a large number of biomolecules in a single experiment. The number of experiments usually is much smaller than the number of measurements in each experiment. The simultaneous measurements of biomolecules provide a basis for a comprehensive, systems view for describing relevant biological processes. Often it is necessary to determine correlations between the data matrices under different conditions or pathways. However, the techniques for analyzing the data with a low number of samples for possible correlations within or between conditions are still in development. Earlier developed correlative measures, such as the RV coefficient, use the trace of the product of data matrices as the most relevant characteristic. However, a recent study has shown that the RV coefficient consistently overestimates the correlations in the case of low sample numbers. To correct for this bias, it was suggested to discard the diagonal elements of the outer products of each data matrix. In this work, a principled approach based on the matrix decomposition generates three trace-independent parts for every matrix. These components are unique, and they are used to determine different aspects of correlations between the original datasets. Results Simulations show that the decomposition results in the removal of high correlation bias and the dependence on the sample number intrinsic to the RV coefficient. We then use the correlations to analyze a real proteomics dataset. Availability and implementation The python code can be downloaded from http://dynamic-proteome.utmb.edu/MatrixCorrelations.aspx. Supplementary information Supplementary data are available at Bioinformatics online.