Qatar Metabolomics Study on Diabetes

The Qatar Metabolomics Study on Diabetes (QMDiab) is a type 2 diabetes case-control, which was conducted in 2012 at the Dermatology Department of Hamad Medical Corporation and the Weill Cornell Medical College in Doha, Qatar. The study was approved by the Institutional Review Boards of HMC and WCM-Q under research protocol number 11131/11. All study participants provided written informed consent.<br><br>Untargeted metabolomics measurements (LC/MS+, LC/MS-, and GC/MS) from plasma, urine, and saliva samples of 374 participants, which are aged 17-81 years, were performed by Metabolon Inc.<br><br>The OrigScale dataset comprises median-scaled data for each body fluid. The Preprocessed dataset comprises missing values treated, normalized, transformed, and scaled data. In both datasets, rows correspond to participants (anonymized) and columns correspond to metabolites. <br><br>Phenotype information (type 2 diabetes status, age, gender, BMI, ethnicity) are available as the last six columns of each dataset.<br>Annotations and pathway assigments are also provided for each metabolite

Network-Based Approach for Analyzing Intra- and Interfluid Metabolite Associations in Human Blood, Urine, and Saliva

Most studies investigating human metabolomics measurements are limited to a single biofluid, most often blood or urine. An organism’s biochemical pool, however, comprises complex transboundary relationships, which can only be understood by investigating metabolic interactions and physiological processes spanning multiple parts of the human body. Therefore, we here propose a data-driven network-based approach to generate an integrated picture of metabolomics associations over multiple fluids. We performed an analysis of 2251 metabolites measured in plasma, urine, and saliva, from 374 participants of the Qatar Metabolomics Study on Diabetes (QMDiab). Gaussian graphical models (GGMs) were used to estimate metabolite-metabolite interactions on different subsets of the data set. First, we compared similarities and differences of the metabolome and the association networks between the three fluids. Second, we investigated the cross-talk between the fluids by analyzing correlations occurring between them. Third, we propose a framework for the analysis of medically relevant phenotypes by integrating type 2 diabetes, sex, age, and body mass index into our networks. In conclusion, we present a generic, data-driven network-based approach for structuring and visualizing metabolite correlations within and between multiple body fluids, enabling unbiased interpretation of metabolomics multifluid data

Ethnicity and skin autofluorescence-based risk-engines for cardiovascular disease and diabetes mellitus

<div><p>Skin auto fluorescence (SAF) is used as a proxy for the accumulation of advanced glycation end products (AGEs) and has been proposed to stratify patients into cardiovascular disease (CVD) and diabetes mellitus (DM) risk groups. This study evaluates the effects of seven different ethnicities (Arab, Central-East African, Eastern Mediterranean, European, North African, South Asian and Southeast Asian) and gender on SAF as well as validating SAF assessment as a risk estimation tool for CVD and DM in an Arabian cohort. SAF data from self-reported healthy 2,780 individuals, collated from three independent studies, has been linear modelled using age and gender as a covariate. A cross-study harmonized effect size (Cohens’<i>d</i>) is provided for each ethnicity. Furthermore, new data has been collected from a clinically well-defined patient group of 235 individuals, to evaluate SAF as a clinical tool for DM and CVD-risk estimation in an Arab cohort. In an Arab population, SAF-based CVD and/or DM risk-estimation can be improved by referencing to ethnicity and gender-specific SAF values. Highest SAF values were observed for the North African population, followed by East Mediterranean, Arab, South Asian and European populations. The South Asian population had a slightly steeper slope in SAF values with age compared to other ethnic groups. All ethnic groups except Europeans showed a significant gender effect. When compared with a European group, effect size was highest for Eastern Mediterranean group and lowest for South Asian group. The Central-East African and Southeast Asian ethnicity matched closest to the Arab and Eastern Mediterranean ethnicities, respectively. Ethnic and gender-specific data improves performance in SAF-based CVD and DM risk estimation. The provided harmonized effect size allows a direct comparison of SAF in different ethnicities. For the first time, gender differences in SAF are described for North African and East Mediterranean populations.</p></div

Effect of smoking cessation on skin autofluorescence in former smokers participating in the LifeLines study.

<p>Dots show mean SAF Z-scores (adjusted for age, BMI, creatinine clearance and diabetes status). Whiskers reflect standard error of the mean. SAF, skin autofluorescence</p

SAF model comparison for different ethnic groups.

<p>SAF model comparison for different ethnic groups.</p

Comparison of SAF intensities between healthy individuals and CVD patients with and without diabetes in an Arab cohort.

<p>Insets show the distribution of risk groups as calculated with the established risk engine that is implemented in the AGE-Reader apparatus and the adjusted risk scheme for Middle Eastern populations, both described in Ahmad <i>et al</i>. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185175#pone.0185175.ref013" target="_blank">13</a>].</p

Ethnicities and sample numbers for all study participants (upper panel); geographic stratification for subsequent statistical analysis (lower panel).

<p>Ethnicities and sample numbers for all study participants (upper panel); geographic stratification for subsequent statistical analysis (lower panel).</p

Skin autofluorescence stratified for smoking class (LifeLines Cohort Study).

<p>Bars represent mean SAF Z scores (adjusted for age, creatinine clearance and diabetes), whiskers reflect standard error of the mean. Never smoker (n = 3670), Former smoker (n = 3321), Light smoker (0–10 gram tobacco per day, n = 878), Moderate smoker (10–20 gram tobacco per day, n = 537), heavy smoker (>20 gram tobacco per day, n = 475). SAF, skin autofluorescence; AU, arbitrary units; NS, not significant.</p

Smoking status and cotinine markers related to skin autofluorescence using multivariable analyses stratified by diabetes status (QMDiab Study).

<p>Smoking status and cotinine markers related to skin autofluorescence using multivariable analyses stratified by diabetes status (QMDiab Study).</p

Heritability of height from second trimester of pregnancy until the age of 36 months.

<p>Values reflect heritability estimates (95% confidence interval). Model in the Generation R Study: height (SDS) = β0+β1 * mid-parental height (SDS). Here the slope ‘β1’ is equal to the heritability ‘h²’ <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039901#pone.0039901-Galton1" target="_blank">[7]</a>. Prenatally height is femur length (SDS). Postnatal growth is additionally adjusted for gestational age at birth. Model in the Netherlands Twin Register: Full twin model with additive genetic (A), shared environmental (C) and non-shared environmental (E) factors.</p