Evidence map›Paper›PMID 42608554›Full record

ReviewNature protocols2026

Using MetaboAnalyst 6.0 for exposomics data analysis-from LC-MS2 spectra processing to dose-response modeling and causal inference.

Zhiqiang Pang, Yao Lu, Guangyan Zhou, Huiting Ou, Charles Viau, Fumihiko Matsuda, Niladri Basu, Jianguo Xia

Abstract readReview
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In one paragraph

Review in Nature protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zhiqiang PangDepartment of Microbiology and Immunology, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada.
Yao LuDepartment of Microbiology and Immunology, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada.
Guangyan ZhouDepartment of Microbiology and Immunology, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada.
Huiting OuDepartment of Human Genetics, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0002-6039-4895
Charles ViauDepartment of Microbiology and Immunology, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0003-4350-4308
Fumihiko MatsudaCenter for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Niladri BasuFaculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, Quebec, Canada.
Jianguo XiaDepartment of Microbiology and Immunology, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada. jeff.xia@mcgill.ca.ORCID http://orcid.org/0000-0003-2040-2624

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Exposomics is an emerging field of research that aims to comprehensively investigate individuals' environmental exposures and how these exposures relate to health outcomes. Liquid chromatography-tandem mass spectrometry is widely used in exposomics studies. MetaboAnalyst ( https://www.metaboanalyst.ca/ ) is a widely used platform for statistical and functional analysis of metabolomics data. The current MetaboAnalyst 6.0 release incorporates updates to meet the needs of exposomics studies, including improved support for tandem mass spectrometry compound identification, exposome annotation, dose-response analysis and linking to genetics and functions. Here we extend our 2022 Nature Protocol by providing step-by-step instructions on how to use MetaboAnalyst 6.0 for exposomics data analysis, including: liquid chromatography-tandem mass spectrometry spectra processing and compound identification (Stage 1), exposomics data processing and exploratory analysis (Stage 2), dose-response modeling to study metabolic responses to exposure levels (Stage 3) and leveraging known genetic associations for causal inference (Stage 4). We demonstrate Stages 1-3 using data from a recent blood exposomics study concerning electronic waste exposure. Stage 4 is illustrated through an investigation of the potential causal link between ʟ-isoleucine and type 2 diabetes. Stage 1 may take ~2 h to complete depending on server load, and the remaining stages may be executed in a total of ~90 min.

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.