The Food Exposome

Scalbert, Augustin; Huybrechts, Inge; Gunter, Marc J.

doi:10.1007/978-3-319-89321-1_8

Cited by 8 publications

(10 citation statements)

References 101 publications

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“…2 except for Swedish participants (n=14, heparin plasma). 3 Helmhotz Zentrum München. 4 Imperial College London Supplementary Figure 1.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…In sensitivity analyses, ICC values were estimated using only duplicate samples originating from distinct blood matrices (serum and citrate plasma). For each metabolite, we estimated its ICC using a linear mixed effects model of the form [16] mik = gi + xik (3) where mik is the k-th replicate measurement of subject i, k=1, 2, gi ~ N(µ, sg 2 ) is a subject-specific random effect (with µ corresponding to the general mean of mik), xik ~ N(0, sx 2 ) is the residual random error for replicate k of subject i, and Cov(xik, gi)=0. Under this model, ICC = Var(gI)/Var(gI +xik), so the ICC estimate was defined as the ratio of the estimated between-subject variance to the estimated total variance (between-and within-subject).…”

Section: Computation Of the Intra-class Correlation Coefficient Using Duplicated Samplesmentioning

confidence: 99%

“…2 except for Swedish participants (n=14, heparin plasma). 3 Helmhotz Zentrum München. 4 Imperial College London The y-axis represents values computed with normalized measurements produced by our approach, while the x-axis represents values computed with imputed (non-normalized) measurements (left), and normalized measurements produced by the simpler normalization approach (right), which corrected for study and batch effects without specifically attempting to preserve variation due to study center, BMI, gender and alcohol intake.…”

Section: Computation Of the Intra-class Correlation Coefficient Using Duplicated Samplesmentioning

confidence: 99%

“…Metabolomics is a powerful tool for investigating candidate etiological pathways for chronic diseases [1][2][3][4] . Using either untargeted or targeted (via sets of pre-defined annotated metabolites) approaches, prior metabolomics studies have identified metabolites associated with the risk of several chronic conditions, including type-2 diabetes (T2D) [5] , cardiovascular diseases (CVD) [6] , and cancer [7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

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A new pipeline for the normalization and pooling of metabolomics data

Viallon

His

Rinaldi

et al. 2021

Preprint

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Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through PC-PR2 analysis; (iii) application of linear mixed models to remove unwanted variability, including samples originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

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“…2 except for Swedish participants (n=14, heparin plasma). 3 Helmhotz Zentrum München. 4 Imperial College London Supplementary Figure 1.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Section: Computation Of the Intra-class Correlation Coefficient Using Duplicated Samplesmentioning

confidence: 99%

Section: Computation Of the Intra-class Correlation Coefficient Using Duplicated Samplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new pipeline for the normalization and pooling of metabolomics data

Viallon

His

Rinaldi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Both short-term human adaptation (epigenetic change) and long-term evolutionary change can occur due to variations in exposure to key vitamins. 22,23 The food exposome represents the totality of dietary exposures, 24 and is a substantial challenge to examine due to the wide range of foods consumed and the variability in the amount and frequency of intake, which are modulated by food preference, season, and other factors. 24 Fortunately, two vitamins have such profound biological effects that their dietary abundance, and physicochemical loss (folate) or biosynthesis (vitamin D) through secondary environmental exposure (UV exposome) have shaped the human phenome from both evolutionary and health perspectives.…”

Section: A Novel Example Of a Relatively Delimited Systemmentioning

confidence: 99%