Berry anthocyanin intake and cardiovascular health

Over half of all cardiovascular (CV) events could be prevented by improved diet. This is reflected in government targets for fruit/vegetable intake, yet these are variable across the world (UK: 5-a-day; USA: 9-a-day), do not identify specific fruits/vegetables, and prove hard to achieve. Mounting evidence from prospective studies, supported by recent randomised controlled trials suggest that the benefits of fruits/vegetables may be due to bioactive substances called flavonoids. Specifically one sub-class of flavonoids, the anthocyanins, responsible for the red/blue hue, are receiving growing attention. Although promising data is emerging from cohort studies, and cell/animal studies, proof of efficacy from longer-term randomised controlled trials, and an understanding of the importance of differential metabolism in relation to clinical efficacy are distinctly lacking. Diet related ill-health are among the leading priorities of our time and simple dietary change, including incorporating a few portions of anthocyanin-rich fruit into our diet could have a significant impact at a public health level

Dietary flavonoid intake and weight maintenance: three prospective cohorts of 124,086 US men and women followed for up to 24 years

Objective: To examine whether dietary intake of specific flavonoid sub-classes is associated with weight change over time, including flavonols, flavones, flavanones, flavan-3-ols, anthocyanins, and flavonoid polymers. Design: Three prospective cohort studies. Setting: Health professionals in the United States. Participants: 124,086 men and women participating in the Health Professionals Follow-up Study (HPFS), Nurses’ Health Study (NHS), and Nurses’ Health Study II (NHS II). Main outcome measure: Self-reported change in weight over multiple 4-year time intervals between 1986 and 2011. Results: Increased consumption of most flavonoid sub-classes, including flavonols, flavan-3-ols, anthocyanins, and flavonoid polymers was inversely associated with weight change over 4-year time intervals, after adjustment for simultaneous changes in other lifestyle factors including other aspects of diet, smoking status, and physical activity. In the pooled results, the greatest magnitude of association was observed for anthocyanins (-0.22 lbs, 95% CI -0.30 to -0.15 lbs per additional SD/day, 10 mg), flavonoid polymers (-0.18 lbs, 95% CI -0.28 to -0.08 lbs per additional SD/day, 138 mg), and flavonols (-0.16 lbs, 95% CI -0.26 to -0.06 lbs per additional SD/day, 7 mg). After additional adjustment for fiber intake associations remained significant for anthocyanins, proanthocyanidins, and total flavonoid polymers but were attenuated and no longer statistically significant for other sub-classes. Conclusions: Higher intake of foods rich in flavonols, flavan-3-ols, anthocyanins, and flavonoid polymers, may contribute to weight maintenance in adulthood, and may help to refine dietary recommendations for the prevention of obesity and its potential sequelae

Cross-platform comparison and visualisation of gene expression data using co-inertia analysis

BACKGROUND: Rapid development of DNA microarray technology has resulted in different laboratories adopting numerous different protocols and technological platforms, which has severely impacted on the comparability of array data. Current cross-platform comparison of microarray gene expression data are usually based on cross-referencing the annotation of each gene transcript represented on the arrays, extracting a list of genes common to all arrays and comparing expression data of this gene subset. Unfortunately, filtering of genes to a subset represented across all arrays often excludes many thousands of genes, because different subsets of genes from the genome are represented on different arrays. We wish to describe the application of a powerful yet simple method for cross-platform comparison of gene expression data. Co-inertia analysis (CIA) is a multivariate method that identifies trends or co-relationships in multiple datasets which contain the same samples. CIA simultaneously finds ordinations (dimension reduction diagrams) from the datasets that are most similar. It does this by finding successive axes from the two datasets with maximum covariance. CIA can be applied to datasets where the number of variables (genes) far exceeds the number of samples (arrays) such is the case with microarray analyses. RESULTS: We illustrate the power of CIA for cross-platform analysis of gene expression data by using it to identify the main common relationships in expression profiles on a panel of 60 tumour cell lines from the National Cancer Institute (NCI) which have been subjected to microarray studies using both Affymetrix and spotted cDNA array technology. The co-ordinates of the CIA projections of the cell lines from each dataset are graphed in a bi-plot and are connected by a line, the length of which indicates the divergence between the two datasets. Thus, CIA provides graphical representation of consensus and divergence between the gene expression profiles from different microarray platforms. Secondly, the genes that define the main trends in the analysis can be easily identified. CONCLUSIONS: CIA is a robust, efficient approach to coupling of gene expression datasets. CIA provides simple graphical representations of the results making it a particularly attractive method for the identification of relationships between large datasets

iBBiG: iterative binary bi-clustering of gene sets

Motivation: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods. Results: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set—phenotype association that predicted tumor metastases within tumor subtypes

A multivariate approach to the integration of multi-omics datasets

Background: To leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets. Based on a covariance optimization criterion, MCIA simultaneously projects several datasets into the same dimensional space, transforming diverse sets of features onto the same scale, to extract the most variant from each dataset and facilitate biological interpretation and pathway analysis. Results: We demonstrate integration of multiple layers of information using MCIA, applied to two typical “omics” research scenarios. The integration of transcriptome and proteome profiles of cells in the NCI-60 cancer cell line panel revealed distinct, complementary features, which together increased the coverage and power of pathway analysis. Our analysis highlighted the importance of the leukemia extravasation signaling pathway in leukemia that was not highly ranked in the analysis of any individual dataset. Secondly, we compared transcriptome profiles of high grade serous ovarian tumors that were obtained, on two different microarray platforms and next generation RNA-sequencing, to identify the most informative platform and extract robust biomarkers of molecular subtypes. We discovered that the variance of RNA-sequencing data processed using RPKM had greater variance than that with MapSplice and RSEM. We provided novel markers highly associated to tumor molecular subtype combined from four data platforms. MCIA is implemented and available in the R/Bioconductor “omicade4” package. Conclusion: We believe MCIA is an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals. The method is not dependent on feature annotation, and thus it can extract important features even when there are not present across all datasets. MCIA provides simple graphical representations for the identification of relationships between large datasets

Measuring Shapes of Galaxy Images I: Ellipticity and Orientation

We suggest a set of morphological measures that we believe can help in quantifying the shapes of two-dimensional cosmological images such as galaxies, clusters, and superclusters of galaxies. The method employs non-parametric morphological descriptors known as the Minkowski functionals in combination with geometric moments widely used in the image analysis. For the purpose of visualization of the morphological properties of image contour lines we introduce three auxiliary ellipses representing the vector and tensor Minkowski functionals. We study the discreteness, seeing, and noise effects on elliptic contours as well as their morphological characteristics such as the ellipticity and orientation. In order to reduce the effect of noise we employ a technique of contour smoothing. We test the method by studying simulated elliptic profiles of toy spheroidal galaxies ranging in ellipticity from E0 to E7. We then apply the method to real galaxies, including eight spheroidals, three disk spirals and one peculiar galaxy, as imaged in the near-infrared $K_s$-band (2.2 microns) with the Two Micron All Sky Survey (2MASS). The method is numerically very efficient and can be used in the study of hundreds of thousands images obtained in modern surveys.Comment: Accepted for publication in MNRAS. Revised version contains 20 pages, 17 PostScript figures. Results unchanged; high-resolution figures # 1,6,7,11,13,16 can be obtained from author

The potential for dietary factors to prevent or treat osteoarthritis

Osteoarthritis (OA) is a degenerative joint disease for which there are no disease-modifying drugs. It is a leading cause of disability in the UK. Increasing age and obesity are both major risk factors for OA and the health and economic burden of this disease will increase in the future. Focusing on compounds from the habitual diet that may prevent the onset or slow the progression of OA is a strategy that has been under-investigated to date. An approach that relies on dietary modification is clearly attractive in terms of risk/benefit and more likely to be implementable at the population level. However, before undertaking a full clinical trial to examine potential efficacy, detailed molecular studies are required in order to optimise the design. This review focuses on potential dietary factors that may reduce the risk or progression of OA, including micronutrients, fatty acids, flavonoids and other phytochemicals. It therefore ignores data coming from classical inflammatory arthritides and nutraceuticals such as glucosamine and chondroitin. In conclusion, diet offers a route by which the health of the joint can be protected and OA incidence or progression decreased. In a chronic disease, with risk factors increasing in the population and with no pharmaceutical cure, an understanding of this will be crucial

The impact of rainfall and school break time policies on physical activity in 9-10 year old British children: a repeated measures study.

BACKGROUND: The weather may be a driver of seasonal patterns in children's physical activity (PA). A better understanding of the relationships between weather and PA may help increase children's PA. This study aims to examine the association between PA and rainfall in 9-10 year old children, and how it may be modified by school policies. METHODS: 1794 participants in the SPEEDY study in Norfolk, UK recorded PA using ActiGraph accelerometers over up to six days in the summer term of 2007. Multilevel regression models were used to determine the day-by-day association between rainfall and minutes spent sedentary, in moderate-to-vigorous PA (MVPA), and average counts per minute (cpm) over the whole day (07:00-21:00) and the lunchtime period (12:00-14:00). School policies for break times in bad weather were fitted as interaction terms with rainfall. RESULTS: Relative to days with no rain, children spent 9.4 minutes (95%CI 7.0 to 11.9) fewer in MVPA, were sedentary for 13.6 minutes (8.8 to 18.4) more, and accumulated 85.9 cpm (66.2 to 105.5) fewer over the whole day on the wettest days. Children allowed to play outside in wet weather showed the lowest lunchtime PA levels on the wettest days, undertaking 9.8 minutes (6.2 to 13.5) fewer MVPA, 16.1 minutes (10.3 to 21.9) more sedentary, and accumulating 408.0 cpm (250.9 to 565.1) fewer than those allowed to be active indoors. CONCLUSIONS: Rainfall is negatively associated with PA in primary school children, but providing indoor physical activities in wet weather may help children maintain physical activity levels irrespective of rainfall.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are