Global and Partial Effect Assessment in Metabolic Syndrome Explored by Metabolomics.

In nutrition and health research, untargeted metabolomics is actually analyzed simultaneously with clinical data to improve prediction and better understand pathological status. This can be modeled using a multiblock supervised model with several input data blocks (metabolomics, clinical data) being potential predictors of the outcome to be explained. Alternatively, this configuration can be represented with a path diagram where the input blocks are each connected by links directed to the outcome-as in multiblock supervised modeling-and are also related to each other, thus allowing one to account for block effects. On the basis of a path model, we show herein how to estimate the effect of an input block, either on its own or conditionally to other(s), on the output response, respectively called "global" and "partial" effects, by percentages of explained variance in dedicated PLS regression models. These effects have been computed in two different path diagrams in a case study relative to metabolic syndrome, involving metabolomics and clinical data from an older men's cohort (NuAge). From the two effects associated with each path, the results highlighted the complementary information provided by metabolomics to clinical data and, reciprocally, in the metabolic syndrome exploration.

Targeting the gut to prevent and counteract metabolic disorders and pathologies during aging.

Impairment of gut function is one of the explanatory mechanisms of health status decline in elderly population. These impairments involve a decline in gut digestive physiology, metabolism and immune status, and associated to that, changes in composition and function of the microbiota it harbors. Continuous deteriorations are generally associated with the development of systemic dysregulations and ultimately pathologies that can worsen the initial health status of individuals. All these alterations observed at the gut level can then constitute a wide range of potential targets for development of nutritional strategies that can impact gut tissue or associated microbiota pattern. This can be key, in a preventive manner, to limit gut functionality decline, or in a curative way to help maintaining optimum nutrients bioavailability in a context on increased requirements, as frequently observed in pathological situations. The aim of this review is to give an overview on the alterations that can occur in the gut during aging and lead to the development of altered function in other tissues and organs, ultimately leading to the development of pathologies. Subsequently is discussed how nutritional strategies that target gut tissue and gut microbiota can help to avoid or delay the occurrence of aging-related pathologies.

PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management.

INTRODUCTION: Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. OBJECTIVES: To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. METHODS: We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. RESULTS: PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. CONCLUSION: PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest .

HDHL-INTIMIC: A European Knowledge Platform on Food, Diet, Intestinal Microbiomics, and Human Health.

Studies indicate that the intestinal microbiota influences general metabolic processes in humans, thereby modulating the risk of chronic diseases such as type 2 diabetes, allergy, cardiovascular disease, and colorectal cancer (CRC). Dietary factors are also directly related to chronic disease risk, and they affect the composition and function of the gut microbiota. Still, detailed knowledge on the relation between diet, the microbiota, and chronic disease risk is limited. The overarching aim of the HDHL-INTIMIC (INtesTInal MICrobiomics) knowledge platform is to foster studies on the microbiota, nutrition, and health by assembling available knowledge of the microbiota and of the other aspects (e.g., food science and metabolomics) that are relevant in the context of microbiome research. The goal is to make this information findable, accessible, interoperable, and reusable (FAIR) to the scientific community, and to share information with the various stakeholders. Through these efforts a network of transnational and multidisciplinary collaboration has emerged, which has contributed to further develop and increase the impact of microbiome research in human health. The roles of microbiota in early infancy, during ageing, and in subclinical and clinically manifested disease are identified as urgent areas of research in this knowledge platform.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

Multiplatform metabolomics for an integrative exploration of metabolic syndrome in older men.

BACKGROUND: Metabolic syndrome (MetS), a cluster of factors associated with risks of developing cardiovascular diseases, is a public health concern because of its growing prevalence. Considering the combination of concomitant components, their development and severity, MetS phenotypes are largely heterogeneous, inducing disparity in diagnosis. METHODS: A case/control study was designed within the NuAge longitudinal cohort on aging. From a 3-year follow-up of 123 stable individuals, we present a deep phenotyping approach based on a multiplatform metabolomics and lipidomics untargeted strategy to better characterize metabolic perturbations in MetS and define a comprehensive MetS signature stable over time in older men. FINDINGS: We characterize significant changes associated with MetS, involving modulations of 476 metabolites and lipids, and representing 16% of the detected serum metabolome/lipidome. These results revealed a systemic alteration of metabolism, involving various metabolic pathways (urea cycle, amino-acid, sphingo- and glycerophospholipid, and sugar metabolisms…) not only intrinsically interrelated, but also reflecting environmental factors (nutrition, microbiota, physical activity…). INTERPRETATION: These findings allowed identifying a comprehensive MetS signature, reduced to 26 metabolites for future translation into clinical applications for better diagnosing MetS. FUNDING: The NuAge Study was supported by a research grant from the Canadian Institutes of Health Research (CIHR; MOP-62842). The actual NuAge Database and Biobank, containing data and biologic samples of 1,753 NuAge participants (from the initial 1,793 participants), are supported by the Fonds de recherche du Québec (FRQ; 2020-VICO-279753), the Quebec Network for Research on Aging, a thematic network funded by the Fonds de Recherche du Québec - Santé (FRQS) and by the Merck-Frost Chair funded by La Fondation de l'Université de Sherbrooke. All metabolomics and lipidomics analyses were funded and performed within the metaboHUB French infrastructure (ANR-INBS-0010). All authors had full access to the full data in the study and accept responsibility to submit for publication.

An Early Stage Researcher's Primer on Systems Medicine Terminology.

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Elevated gut microbiome abundance of Christensenellaceae, Porphyromonadaceae and Rikenellaceae is associated with reduced visceral adipose tissue and healthier metabolic profile in Italian elderly.

Aging is accompanied by physiological changes affecting body composition and functionality, including accumulation of fat mass at the expense of muscle mass, with effects upon morbidity and quality of life. The gut microbiome has recently emerged as a key environmental modifier of human health that can modulate healthy aging and possibly longevity. However, its associations with adiposity in old age are still poorly understood. Here we profiled the gut microbiota in a well-characterized cohort of 201 Italian elderly subjects from the NU-AGE study, by 16S rRNA amplicon sequencing. We then tested for association with body composition from dual-energy X-ray absorptiometry (DXA), with a focus on visceral and subcutaneous adipose tissue. Dietary patterns, serum metabolome and other health-related parameters were also assessed. This study identified distinct compositional structures of the elderly gut microbiota associated with DXA parameters, diet, metabolic profiles and cardio-metabolic risk factors.

Non-targeted metabolomics analyses by mass spectrometry to explore metabolic stress after six training weeks in high level swimmers.

The objective was to compare the metabolic responses of high-level national swimmers to threshold or polarised training. 22 swimmers (n = 12 males and 10 females) participated in a 28-week cross-over intervention study consisting of 2 × 6 period weeks of training. Swimmers were assigned randomly to either training group for the first period: polarised (POL) (81% in energetic zone 1: blood lactate [La]b ≤ 2 mmol.L-1; 4% in zone 2: 2 mmol.L-1 <[La]b ≤ 4 mmol.L-1; 15% in zone 3: [La]b > 4 mmol.L-1) or threshold (THR) (65%/25%/10%). Before and after each training period, urine samples were collected for non-targeted metabolomics analysis. Mixed model analysis was performed on metabolomics data including fatigue class factors and/or training and/or interaction. Ion intensities of 6-keto-decanoylcarnitine (+31%), pregnanediol-3-glucuronide (+81%), P-cresol sulphate (+18%) were higher in the threshold group (P < 0.05) indicating higher glycogenic depletion and inflammation without alteration of the neuroendocrine stress axis. 4-phenylbutanic acid sulphate was 200% higher in less fatigued swimmers (P < 0.01) linking the anti-inflammatory activity at the cell membrane level to the subjective perception of fatigue. This research suggests the importance of replenishing glycogen stores and reducing inflammation during high thresholds training loads.

Metaproteomics Approach and Pathway Modulation in Obesity and Diabetes: A Narrative Review.

Low-grade inflammatory diseases revealed metabolic perturbations that have been linked to various phenotypes, including gut microbiota dysbiosis. In the last decade, metaproteomics has been used to investigate protein composition profiles at specific steps and in specific healthy/pathologic conditions. We applied a rigorous protocol that relied on PRISMA guidelines and filtering criteria to obtain an exhaustive study selection that finally resulted in a group of 10 studies, based on metaproteomics and that aim at investigating obesity and diabetes. This batch of studies was used to discuss specific microbial and human metaproteome alterations and metabolic patterns in subjects affected by diabetes (T1D and T2D) and obesity. We provided the main up- and down-regulated protein patterns in the inspected pathologies. Despite the available results, the evident paucity of metaproteomic data is to be considered as a limiting factor in drawing objective considerations. To date, ad hoc prepared metaproteomic databases collecting pathologic data and related metadata, together with standardized analysis protocols, are required to increase our knowledge on these widespread pathologies.

Deep learning in systems medicine.

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.

Heme-Iron-Induced Production of 4-Hydroxynonenal in Intestinal Lumen May Have Extra-Intestinal Consequences through Protein-Adduct Formation.

Some epidemiological studies show that heme iron consumption, in red meat, is associated to the development of several chronic diseases, including cancers and cardio-metabolic diseases. As heme iron intestinal absorption is finely regulated, we hypothesized that heme iron may act indirectly, through the peroxidation of dietary lipids, in food or in the intestinal lumen during digestion. This heme-iron-induced lipid peroxidation provokes the generation of toxic lipid oxidation products that could be absorbed, such as 4-hydroxynonenal (HNE). In a first experiment, heme iron given to rats by oral gavage together with the linoleic-acid-rich safflower oil induced the formation of HNE in the intestinal lumen. The HNE major urinary metabolite was elevated in the urine of the treated rats, indicating that this compound has been absorbed. In a second experiment, we showed that stable isotope-labeled HNE given orally to rats was able to reach non-intestinal tissues as a bioactive form and to make protein-adducts in heart, liver and skeletal muscle tissues. The presence of HNE-protein adducts in those tissues suggests a putative biological role of diet-originating HNE in extra-intestinal organs. This finding could have major consequences on the onset/development of chronic diseases associated with red meat over-consumption, and more largely to peroxidation-prone food consumption.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine.

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Mediterranean diet intervention alters the gut microbiome in older people reducing frailty and improving health status: the NU-AGE 1-year dietary intervention across five European countries.

OBJECTIVE: Ageing is accompanied by deterioration of multiple bodily functions and inflammation, which collectively contribute to frailty. We and others have shown that frailty co-varies with alterations in the gut microbiota in a manner accelerated by consumption of a restricted diversity diet. The Mediterranean diet (MedDiet) is associated with health. In the NU-AGE project, we investigated if a 1-year MedDiet intervention could alter the gut microbiota and reduce frailty. DESIGN: We profiled the gut microbiota in 612 non-frail or pre-frail subjects across five European countries (UK, France, Netherlands, Italy and Poland) before and after the administration of a 12-month long MedDiet intervention tailored to elderly subjects (NU-AGE diet). RESULTS: Adherence to the diet was associated with specific microbiome alterations. Taxa enriched by adherence to the diet were positively associated with several markers of lower frailty and improved cognitive function, and negatively associated with inflammatory markers including C-reactive protein and interleukin-17. Analysis of the inferred microbial metabolite profiles indicated that the diet-modulated microbiome change was associated with an increase in short/branch chained fatty acid production and lower production of secondary bile acids, p-cresols, ethanol and carbon dioxide. Microbiome ecosystem network analysis showed that the bacterial taxa that responded positively to the MedDiet intervention occupy keystone interaction positions, whereas frailty-associated taxa are peripheral in the networks. CONCLUSION: Collectively, our findings support the feasibility of improving the habitual diet to modulate the gut microbiota which in turn has the potential to promote healthier ageing.

Metabolomic and Lipidomic Signatures of Metabolic Syndrome and its Physiological Components in Adults: A Systematic Review.

The aim of this work was to conduct a systematic review of human studies on metabolite/lipid biomarkers of metabolic syndrome (MetS) and its components, and provide recommendations for future studies. The search was performed in MEDLINE, EMBASE, EMB Review, CINHAL Complete, PubMed, and on grey literature, for population studies identifying MetS biomarkers from metabolomics/lipidomics. Extracted data included population, design, number of subjects, sex/gender, clinical characteristics and main outcome. Data were collected regarding biological samples, analytical methods, and statistics. Metabolites were compiled by biochemical families including listings of their significant modulations. Finally, results from the different studies were compared. The search yielded 31 eligible studies (2005-2019). A first category of articles identified prevalent and incident MetS biomarkers using mainly targeted metabolomics. Even though the population characteristics were quite homogeneous, results were difficult to compare in terms of modulated metabolites because of the lack of methodological standardization. A second category, focusing on MetS components, allowed comparing more than 300 metabolites, mainly associated with the glycemic component. Finally, this review included also publications studying type 2 diabetes as a whole set of metabolic risks, raising the interest of reporting metabolomics/lipidomics signatures to reflect the metabolic phenotypic spectrum in systems approaches.

Untargeted plasma metabolomic profiles associated with overall diet in women from the SU.VI.MAX cohort.

PURPOSE: Dietary intakes are reflected in plasma by the presence of hundreds of exogenous metabolites and variations in endogenous metabolites. The exploration of diet-related plasma metabolic profiles could help to better understand the impact of overall diet on health. Our aim was to identify metabolomic signatures reflecting overall diet in women from the French general population. METHODS: This cross-sectional study included 160 women in the SU.VI.MAX cohort with detailed dietary data (≥ 10 24-h dietary records) selected according to their level of adherence to the French dietary recommendations, represented by the validated score mPNNS-GS; 80 women from the 10th decile of the score were matched with 80 women from the 1st decile. Plasma metabolomic profiles were acquired using untargeted UPLC-QToF mass spectrometry analysis. The associations between metabolomic profiles and the mPNNG-GS, its components and Principal Component Analyses-derived dietary patterns were investigated using multivariable conditional logistic regression models and partial correlations. RESULTS: Adherence to the dietary recommendations was positively associated with 3-indolepropionic acid and pipecolic acid (also positively associated with fruit and vegetable intake and a healthy diet)-2 metabolites linked to microbiota and inversely associated with lysophosphatidylcholine (LysoPC(17:1)), acylcarnitine C9:1 (also inversely associated with a healthy diet), acylcarnitine C11:1 and 2-deoxy-D-glucose. Increased plasma levels of piperine and Dihydro4mercapto-3(2H) furanone were observed in women who consumed a Western diet and a healthy diet, respectively. Ethyl-ß-D-glucopyranoside was positively associated with alcohol intake. Plasma levels of LysoPC(17:1), cholic acid, phenylalanine-phenylalanine and phenylalanine and carnitine C9:1 decreased with the consumption of vegetable added fat, sweetened food, milk and dairy products and fruit and vegetable intakes, respectively. CONCLUSION: This study highlighted several metabolites from both host and microbial metabolism reflecting the long-term impact of the overall diet. TRIAL REGISTRATION: SU.VI.MAX, clinicaltrials.gov NCT00272428. Registered 3 January 2006, https://clinicaltrials.gov/show/NCT00272428.

Diet-Related Metabolomic Signature of Long-Term Breast Cancer Risk Using Penalized Regression: An Exploratory Study in the SU.VI.MAX Cohort.

BACKGROUND: Diet has been recognized as a modifiable risk factor for breast cancer. Highlighting predictive diet-related biomarkers would be of great public health relevance to identify at-risk subjects. The aim of this exploratory study was to select diet-related metabolites discriminating women at higher risk of breast cancer using untargeted metabolomics. METHODS: Baseline plasma samples of 200 incident breast cancer cases and matched controls, from a nested case-control study within the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, were analyzed by untargeted LC-MS. Diet-related metabolites were identified by partial correlation with dietary exposures, and best predictors of breast cancer risk were then selected by Elastic Net penalized regression. The selection stability was assessed using bootstrap resampling. RESULTS: 595 ions were selected as candidate diet-related metabolites. Fourteen of them were selected by Elastic Net regression as breast cancer risk discriminant ions. A lower level of piperine (a compound from pepper) and higher levels of acetyltributylcitrate (an alternative plasticizer to phthalates), pregnene-triol sulfate (a steroid sulfate), and 2-amino-4-cyano butanoic acid (a metabolite linked to microbiota metabolism) were observed in plasma from women who subsequently developed breast cancer. This metabolomic signature was related to several dietary exposures such as a "Western" dietary pattern and higher alcohol and coffee intakes. CONCLUSIONS: Our study suggested a diet-related plasma metabolic signature involving exogenous, steroid metabolites, and microbiota-related compounds associated with long-term breast cancer risk that should be confirmed in large-scale independent studies. IMPACT: These results could help to identify healthy women at higher risk of breast cancer and improve the understanding of nutrition and health relationship.

Analytic Correlation Filtration: A New Tool to Reduce Analytical Complexity of Metabolomic Datasets.

Metabolomics generates massive and complex data. Redundant different analytical species and the high degree of correlation in datasets is a constraint for the use of data mining/statistical methods and interpretation. In this context, we developed a new tool to detect analytical correlation into datasets without confounding them with biological correlations. Based on several parameters, such as a similarity measure, retention time, and mass information from known isotopes, adducts, or fragments, the algorithm principle is used to group features coming from the same analyte, and to propose one single representative per group. To illustrate the functionalities and added-value of this tool, it was applied to published datasets and compared to one of the most commonly used free packages proposing a grouping method for metabolomics data: 'CAMERA'. This tool was developed to be included in Galaxy and will be available in Workflow4Metabolomics (http://workflow4metabolomics.org). Source code is freely available for download under CeCILL 2.1 license at https://services.pfem.clermont.inra.fr/gitlab/grandpa /tool-acf and implement in Perl.

Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data.

INTRODUCTION: Metabolomics is a powerful phenotyping tool in nutrition and health research, generating complex data that need dedicated treatments to enrich knowledge of biological systems. In particular, to investigate relations between environmental factors, phenotypes and metabolism, discriminant statistical analyses are generally performed separately on metabolomic datasets, complemented by associations with metadata. Another relevant strategy is to simultaneously analyse thematic data blocks by a multi-block partial least squares discriminant analysis (MBPLSDA) allowing determining the importance of variables and blocks in discriminating groups of subjects, taking into account data structure. OBJECTIVE: The present objective was to develop a full open-source standalone tool, allowing all steps of MBPLSDA for the joint analysis of metabolomic and epidemiological data. METHODS: This tool was based on the mbpls function of the ade4 R package, enriched with functionalities, including some dedicated to discriminant analysis. Provided indicators help to determine the optimal number of components, to check the MBPLSDA model validity, and to evaluate the variability of its parameters and predictions. RESULTS: To illustrate the potential of this tool, MBPLSDA was applied to a real case study involving metabolomics, nutritional and clinical data from a human cohort. The availability of different functionalities in a single R package allowed optimizing parameters for an efficient joint analysis of metabolomics and epidemiological data to obtain new insights into multidimensional phenotypes. CONCLUSION: In particular, we highlighted the impact of filtering the metabolomic variables beforehand, and the relevance of a MBPLSDA approach in comparison to a standard PLS discriminant analysis method.

WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data.

Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.