Sources of Variability in Metabolite Measurements from Urinary Samples

<div><p>Background</p><p>The application of metabolomics in epidemiological studies would potentially allow researchers to identify biomarkers associated with exposures and diseases. However, within-individual variability of metabolite levels caused by temporal variation of metabolites, together with technical variability introduced by laboratory procedures, may reduce the study power to detect such associations. We assessed the sources of variability of metabolites from urine samples and the implications for designing epidemiologic studies.</p><p>Methods</p><p>We measured 539 metabolites in urine samples from the Navy Colon Adenoma Study using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectroscopy (GC-MS). The study collected 2–3 samples per person from 17 male subjects (age 38–70) over 2–10 days. We estimated between-individual, within-individual, and technical variability and calculated expected study power with a specific focus on large case-control and nested case-control studies.</p><p>Results</p><p>Overall technical reliability was high (median intraclass correlation = 0.92), and for 72% of the metabolites, the majority of total variance can be attributed to between-individual variability. Age, gender and body mass index explained only a small proportion of the total metabolite variability. For a relative risk (comparing upper and lower quartiles of “usual” levels) of 1.5, we estimated that a study with 500, 1,000, and 5,000 individuals could detect 1.0%, 4.5% and 75% of the metabolite associations.</p><p>Conclusions</p><p>The use of metabolomics in urine samples from epidemiological studies would require large sample sizes to detect associations with moderate effect sizes.</p></div

Profiling the Serum Albumin Cys34 Adductome of Solid Fuel Users in Xuanwei and Fuyuan, China

Xuanwei and Fuyuan counties in China have the highest lung cancer rates in the world due to household air pollution from combustion of smoky coal for cooking and heating. To discover potential biomarkers of indoor combustion products, we profiled adducts at the Cys34 locus of human serum albumin (HSA) in 29 nonsmoking Xuanwei and Fuyuan females who used smoky coal, smokeless coal, or wood and 10 local controls who used electricity or gas fuel. Our untargeted “adductomics” method detected 50 tryptic peptides of HSA, containing Cys34 and prominent post-translational modifications. Putative adducts included Cys34 oxidation products, mixed disulfides, rearrangements, and truncations. The most significant differences in adduct levels across fuel types were observed for <i>S</i>-glutathione (<i>S</i>-GSH) and <i>S</i>-γ-glutamylcysteine (<i>S</i>-γ-GluCys), both of which were present at lower levels in subjects exposed to combustion products than in controls. After adjustment for age and personal measurements of airborne benzo­(<i>a</i>)­pyrene, the largest reductions in levels of <i>S</i>-GSH and <i>S</i>-γ-GluCys relative to controls were observed for users of smoky coal, compared to users of smokeless coal and wood. These results point to possible depletion of GSH, an essential antioxidant, and its precursor γ-GluCys in nonsmoking females exposed to indoor-combustion products in Xuanwei and Fuyuan, China

The curves show the proportion of metabolites expected to be detected in a case control study as a function of effect size.

<p>Effect size is defined by the relative risk (RR, on the x-axis) of disease comparing individuals in the top and bottom quartiles of the “usual” metabolite level. The top axis shows the naïve relative risk that would be observed in the study without adjusting for measurement error. Each figure varies one parameter: sample size, α-level, or the number of samples/individual. (A) presents power curves according to different sample size (n of 500, 1,000 and 5,000) under a Bonferroni-adjusted α-levels (0.05/539); (B) presents power curves with different α-levels in a case-control study of 1,000 individuals; (C) presents power curves in a case-control study of 1,000 individuals, with different number of distinct urinary samples (1, 3, and 10, α-level = 0.05/539).</p

The plots illustrate the distribution of technical ICCs (A) and (B) of overnight urinary samples in the Navy Colon Adenoma Study.

<p>The ICC is a measure of laboratory variability. The is a measure of between-individual variance. The curves illustrate the ICC and for the specified metabolite quantile ranking. Median ICC: 0.92. Median : 0.62.</p

The distribution of (A), (B), and (C).

<p>The x-axis represents the metabolite quantile ranking, and the y-axis represents . The black areas under the curve illustrate the for the specified metabolite quantile ranking, which shows the variance explained by these three covariates.</p

The plot illustrates the distribution of , an estimate of a measure of autocorrelation over time, for all metabolites.

<p>The curve illustrates that the majority of ρ are likely above 0 and that measurements collected on consecutive days are likely more similar than those collected one week apart. The x-axis indicates the quantile ranking and y-axis indicates for a metabolite at that ranking. For example, the median level, that of the metabolite ranked 284, is 0.49.</p

Percentage of metabolites exceeding parameter thresholds^a in the Navy Colon Adenoma Study.

a<p>Each row list the percentage of metabolites with an estimated parameter (ICC, and ) exceeding the threshold of 0.2, 0.5 and 0.8.</p>b<p>ICC represents the proportion of total variation attributable to biological variance.</p>c<p> represents the proportion of biological variability attributable to between-individual variance.</p>d<p> represents the proportion of total variation attributable to between-individual variance.</p

Selected characteristics of cases and controls, by race, in the Kidney Cancer Study, 2002–2007.

<p>Abbreviation: H.S. = high school.</p

Association between lipid peroxidation and risk of type 2 diabetes in women

In-vitro and animal studies demonstrate that lipid peroxidation plays an important role in the pathogenesis of type 2 diabetes (T2D). However, human data from prospective studies are limited and contradictory. We used data originally collected in two nested case-control studies of cancer to prospectively evaluate whether systemic levels of lipid peroxidation were associated with incidence of T2D in 1917 women who were 40–70 years old and diabetes-free at baseline. Lipid peroxidation was measured by urinary F2-isoprostanes (F2-IsoPs) and its major metabolite 2,3-dinor-5,6-dihydro-15-F2t-IsoP (F2-IsoP-M) with GC/NICI-MS assays. The Cox regression model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident T2D. After a median follow-up of 10.1 years, 187 women were diagnosed with T2D. Urinary concentrations of both F2-IsoPs and F2-IsoP-M were significantly higher in T2D cases than in non-cases. Both biomarkers were positively associated with subsequent risk of T2D in multivariable-adjusted Cox models. When further adjusted for body mass index (BMI), the positive association with F2-IsoP-M was attenuated and no longer statistically significant, whereas the association with F2-IsoPs remained (P for overall significance 2-IsoPs. Moreover, this association appeared more pronounced among women with higher BMI. In summary, our study suggests that F2-IsoPs could be of significance in T2D risk prediction among middle-aged and elderly women.</p

Overlapping sets of genes determined by two linear models.

<p>Two linear mixed models were used, a published model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091828#pone.0091828-McHale2" target="_blank">[13]</a> (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091828#pone.0091828.e019" target="_blank">Equation (2</a>)) and a modified version including counts of different blood cell types as potential confounders of gene expression (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091828#pone.0091828.e012" target="_blank">Equation (1</a>)). Differential expression was determined based on altered fold changes in at least one of the four previously chosen dose ranges of benzene exposure, with an FDR-adjusted <i>p</i>-value<0.05.</p