SynthesisPeerJ2026
Metabolomic datasets in COVID-19 research: a systematic literature review of availability, characteristics, and methodologies.
Synthesis in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has accelerated the integration of metabolomics and Machine Learning in biomedical research, resulting in the creation of numerous datasets with high potential for reuse. However, information regarding their accessibility, quality, and usability remains scattered and inconsistent. This systematic review aims to identify and evaluate publicly available human metabolomic datasets related to COVID-19, providing detailed information on their main characteristics and how to access them, with the goal to inform their potential for reuse in future research. Following PRISMA guidelines and the Kitchenham methodology, we conducted a comprehensive search of the scientific literature and specialized metabolomics repositories, identifying 110 unique datasets. Each dataset was assessed based on 15 variables related to data availability, accessibility, collection methodologies, sample sizes, and the extent of participant metadata provided. These datasets offer significant value for secondary analyses and ML applications, contributing to insights into disease mechanisms, early diagnosis, and patient stratification. By offering a structured overview of dataset characteristics, this review aims to support researchers in identifying suitable resources, encourages data reuse, and promotes best practices for data sharing and standardization in the context of COVID-19 and metabolomics. Nonetheless, our findings reveal critical limitations, including the underuse of dedicated repositories, frequent unavailability of raw data, lack of standardization in processed data, and insufficient metadata-particularly regarding participant demographics and clinical information. Inconsistencies in data formats and reporting standards further hinder dataset findability, interoperability, and reuse. To enhance the value and impact of future metabolomic research, we recommend adopting standardized reporting guidelines, improving metadata completeness, ensuring the availability of raw data, and promoting the use of interoperable repositories to facilitate reproducibility, integration, and broader application of shared datasets.
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Identifiers
What Socratic holds
Registered trials
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.