Lipidome- and Genome-Wide Study to Understand Sex Differences in Circulatory Lipids

Background Despite well-recognized differences in the atherosclerotic cardiovascular disease risk between men and women, sex differences in risk factors and sex-specific mechanisms in the pathophysiology of atherosclerotic cardiovascular disease remain poorly understood. Lipid metabolism plays a central role in the development of atherosclerotic cardiovascular disease. Understanding sex differences in lipids and their genetic determinants could provide mechanistic insights into sex differences in atherosclerotic cardiovascular disease and aid in precise risk assessment. Herein, we examined sex differences in plasma lipidome and heterogeneity in genetic influences on lipidome in men and women through sex-stratified genome-wide association analyses. Methods and Results We used data consisting of 179 lipid species measured by shotgun lipidomics in 7266 individuals from the Finnish GeneRISK cohort and sought for replication using independent data from 2045 participants. Significant sex differences in the levels of 141 lipid species were observed (PPeer reviewe

Partially constrained gravity models for predicting spatial interactions with elastic demand

The levels of spatial interactions with certain types of public service, such as libraries and recreation facilities, may vary with the total numbers of opportunities available. Two partially constrained gravity models are developed that provide for this possibility of elastic demand. Circulation data for public libraries in Indianapolis, Indiana, are used to estimate the parameters of these models. The results show the presence of elasticity in the demand for library services.

GC-FID data formats.

<p><b>A.</b> Three hypothetical chromatograms are shown corresponding to samples A, B and C. Integrated peaks (filled areas) are annotated with retention times and peak heights. <b>B.</b> Using proprietary software (see main text), retention times and quantification measures like the peak height can be extracted and written to a peak list that contains sample identifiers (’Sample_A’, ‘Sample_B’ and ‘Sample_C’), variable names (’retention_time’ and ‘peak_height’) and respective values. Computations described in this manuscript use a retention matrix as the working format.</p

Effects of different parameter combinations on alignment error rates for three bumblebee datasets (see main text for details).

<p>Each point shows the alignment error rate for a given combination of max_diff_peak2mean and min_diff_peak2peak.</p

Overview of the three-step alignment algorithm implemented in GCalignR using a hypothetical dataset.

<p><b>A.</b> Linear shifts are implemented to account for systematic drifts in retention times between each sample and the reference (Sample_A). In this hypothetical example, all of the peaks within Sample_B are shifted towards smaller retention times, while the peaks within Sample_C are shifted towards larger retention times. <b>B</b> and <b>C</b> work on retention time matrices, in which rows correspond to putative substances and columns correspond to samples. For illustrative purposes, each cell is colour coded to refer to the putative identity of each substance in the final alignment. <b>B.</b> Consecutive manipulations of the matrices are shown in clockwise order. Here, black rectangles indicate conflicts that are solved by manipulations of the matrices. Zeros indicate absence of peaks and are therefore not considered in computations. Peaks are aligned row by row according to a user-defined criterion, <i>a</i> (see main text for details). <b>C.</b> Rows of similar mean retention time are subsequently merged according to the user-defined criterion, <i>b</i> (see main text for details).</p

A flow chart showing the three sequential steps of the alignment algorithm of the peak alignment method.

<p>A flow chart showing the three sequential steps of the alignment algorithm of the peak alignment method.</p

Diagnostic plots summarising the alignment of the Antarctic fur seal chemical dataset.

<p><b>A</b> shows the number of peaks both prior to and after alignment; <b>B</b> shows a histogram of linear shifts across all samples; <b>C</b> shows the variation across samples in peak retention times; and <b>D</b> shows a frequency distribution of substances shared across samples.</p

Mandatory arguments of the function align_chromatograms.

<p>Mandatory arguments of the function align_chromatograms.</p

Boxplot showing changes in retention time deviation of twenty homologous substances relative to the raw data after having aligned a dataset of 330 European earwigs within GCalignR and ptw respectively (see main text for details).

<p>Boxplot showing changes in retention time deviation of twenty homologous substances relative to the raw data after having aligned a dataset of 330 European earwigs within GCalignR and ptw respectively (see main text for details).</p