Evidence map›Paper›PMID 42023302›Full record

ArticleFood science & nutrition2026

Candidate Obesity Biomarkers Identified Through Multi-Omics Analysis, Mendelian Randomization, and Mediation Analysis.

Mahan Si, Yongsheng Zhao, Yingting Wu, Wenli Zhao, Xuan Liu, Mengxi Jiang

Abstract read
In one paragraph

Article in Food science & nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

6 authors.

Mahan SiDepartment of Pharmacology, School of Basic Medical Sciences Capital Medical University Beijing China.ORCID https://orcid.org/0000-0002-4326-0430
Yongsheng ZhaoDepartment of Pharmacology, School of Basic Medical Sciences Capital Medical University Beijing China.
Yingting WuSchool of Pharmacy Capital Medical University Beijing China.
Wenli ZhaoDepartment of Pharmacology, School of Basic Medical Sciences Capital Medical University Beijing China.
Xuan LiuSchool of Traditional Chinese Medicine Capital Medical University Beijing China.
Mengxi JiangDepartment of Pharmacology, School of Basic Medical Sciences Capital Medical University Beijing China.ORCID https://orcid.org/0000-0003-4446-984X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Obesity has become a major public health problem worldwide and a key driver of metabolic disorders. Metabolomics and lipidomics have enabled the discovery of circulating biomarkers, yet their causal relevance remains unclear. In this study, we profiled fasting serum samples from non-obese and obese participants using untargeted ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) metabolomics and liquid chromatography-tandem mass spectrometry (LC-MS/MS) lipidomics to identify obesity-associated metabolites and lipids, and prioritized candidates by integrating two-sample bidirectional Mendelian randomization (MR) and MR-based mediation analyses with immune cell traits based on publicly available genome-wide association studies (GWAS) summary statistics. Receiver operating characteristic (ROC) analyses and regression-based association analyses were further performed to evaluate discriminatory performance and relationships with clinical parameters. We identified hexanoylglycine and lysophosphatidylcholine (LPC (16:0)) as significantly altered in obesity. Both biomarkers showed consistent MR evidence suggesting potential causal relevance to obesity and exhibited associations with obesity status and body mass index (BMI). Two-step MR further suggested that immune cell traits might partially mediate these relationships. In addition, these biomarkers were associated with blood pressure measures, a key indicator of cardiometabolic risk. Collectively, our findings highlight hexanoylglycine and LPC (16:0) as candidate serum biomarkers linked to obesity.

Indexed as

Mendelian randomizationobesityomics

Identifiers

PMID42023302
PMCPMC13096563

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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 OpenQuestion graph.