Investigation of Variations in the Human Urine Metabolome amongst European Populations: An Exploratory Search for Biomarkers of People at Risk‐of‐Poverty

Trimigno, Alessia; Khakimov, Bekzod; Savorani, Francesco; Tenori, Leonardo; Hendrixson, Vaiva; Čivilis, Alminas; Glibetić, Marija; Gurinović, Mirjana; Pentikäinen, Saara; Sallinen, Janne; Diaz, Sara Garduno; Pasqui, Francesca; Khokhar, S.; Luchinat, Claudio; Bordoni, Alessandra; Capozzi, Francesco; Engelsen, Søren Balling

doi:10.1002/mnfr.201800216

Cited by 15 publications

(8 citation statements)

References 54 publications

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“…Both methods are based on principal component analysis (PCA), they can handle multiple collinear responses and can be used on any data types. ASCA has gained popularity in metabolomics research [32][33][34]. The output of ASCA are scores and loadings related to the experimental factors, which can be visualised in the same way as for PCA to better understand covariance patterns within the data.…”

Section: Abundance-based Methodsmentioning

confidence: 99%

Analysing microbiome intervention studies: Does choice of a statistical method affect biological interpretation?

Khomich¹,

Måge²,

Berget³

et al. 2020

Preprint

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Background The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. Results We compared the outcomes of a wide range of ANOVA-like statistical methods to explore to what extent the choice of method affects the biological inferences made. The comparison is based on five published dietary intervention studies representing different subjects and study designs. We found that the methods producing outputs at the community level were in agreement regarding both effect size and statistical significance. However, the methods that provided ranking of operational taxonomic units (OTUs) gave incongruent results, implying that the choice of method is likely to influence the biological interpretations. Conclusions We concluded that inferences at the OTU level should be made with caution, and that a combination of several statistical methods allows to interpret the outcomes with higher confidence and simultaneously account for the limitations of each method.

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Section: Abundance-based Methodsmentioning

confidence: 99%

Analysing microbiome intervention studies: Does choice of a statistical method affect biological interpretation?

Khomich¹,

Måge²,

Berget³

et al. 2020

Preprint

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show abstract

“…The contribution of each OTU can be quantified by the loadings or by partial least squares discriminant analysis (PLS-DA) for pairwise comparisons. ASCA has recently gained popularity in metabolomics [37][38][39], and both ASCA and FFMANOVA have successfully been applied to microbiome data [40][41][42][43][44].…”

Section: Plos Onementioning

confidence: 99%

Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods

et al. 2021

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The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing the differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. We compared the outcomes of generic multivariate ANOVA (ASCA and FFMANOVA) against statistical methods commonly used for community analyses (PERMANOVA and SIMPER) and methods designed for analysis of count data from high-throughput sequencing experiments (ALDEx2, ANCOM and DESeq2). The comparison is based on both simulated data and five published dietary intervention trials representing different subjects and study designs. We found that the methods testing differences at the community level were in agreement regarding both effect size and statistical significance. However, the methods that provided ranking and identification of differentially abundant operational taxonomic units (OTUs) gave incongruent results, implying that the choice of method is likely to influence the biological interpretations. The generic multivariate ANOVA tools have the flexibility needed for analysing multifactorial experiments and provide outputs at both the community and OTU levels; good performance in the simulation studies suggests that these statistical tools are also suitable for microbiome data sets.

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“…Nutrimetabolomics was also applied to predict health risks in povertystricken areas. An investigation of urine metabolic changes among people at risk-of-poverty in European populations provided biomarkers of undernutrition [88].…”

Section: Dietary Patternsmentioning

confidence: 99%

A Scoping Review of the Application of Metabolomics in Nutrition Research: The Literature Survey 2000–2019

Shibutami

Takebayashi

2021

Nutrients

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Nutrimetabolomics is an emerging field in nutrition research, and it is expected to play a significant role in deciphering the interaction between diet and health. Through the development of omics technology over the last two decades, the definition of food and nutrition has changed from sources of energy and major/micro-nutrients to an essential exposure factor that determines health risks. Furthermore, this new approach has enabled nutrition research to identify dietary biomarkers and to deepen the understanding of metabolic dynamics and the impacts on health risks. However, so far, candidate markers identified by metabolomics have not been clinically applied and more efforts should be made to validate those. To help nutrition researchers better understand the potential of its application, this scoping review outlined the historical transition, recent focuses, and future prospects of the new realm, based on trends in the number of human research articles from the early stage of 2000 to the present of 2019 by searching the Medical Literature Analysis and Retrieval System Online (MEDLINE). Among them, objective dietary assessment, metabolic profiling, and health risk prediction were positioned as three of the principal applications. The continued growth will enable nutrimetabolomics research to contribute to personalized nutrition in the future.

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Investigation of Variations in the Human Urine Metabolome amongst European Populations: An Exploratory Search for Biomarkers of People at Risk‐of‐Poverty

Cited by 15 publications

References 54 publications

Analysing microbiome intervention studies: Does choice of a statistical method affect biological interpretation?

Analysing microbiome intervention studies: Does choice of a statistical method affect biological interpretation?

Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods

A Scoping Review of the Application of Metabolomics in Nutrition Research: The Literature Survey 2000–2019

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