Chromatography Uncovers Potential Obesity Biomarkers

Obesity has become a major global health issue and a leading cause of metabolic diseases. Although advanced tools have found substances in the blood linked to it, it is still unclear whether they play a direct role.This lack of clarity has inspired a study where researchers 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. A paper based on their efforts was published in Food Science & Nutrition.¹

What Have Metabolomics and Lipidomics Studies Revealed About Obesity?

Metabolomics and lipidomics have been found to enable systematic biomarker discovery; both have revealed distinct metabolic and lipidomic profiles in obesity.^2-4 Branched-chain and aromatic amino acids, glutamine, and fatty acids for example, all have been reported to be altered in obese individuals.^5-7 While recent large-scale lipidomics studies further suggest characteristic obesity-related lipid signatures, such as altered ceramides and lysophospholipids,^8-10 findings across studies remain inconsistent, and most evidence is observational, which, in the words of the researchers, “limits causal interpretation and mechanistic understanding.”¹

What Did the Research Reveal?

The researchers 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.¹

“These findings,” write the authors of the paper,¹ “provide novel insights into potential pathways underlying obesity and highlight hexanoylglycine and LPC (16:0) as candidate biomarkers for clinical prediction and future mechanistic studies.”

The researchers recommend that future work should prioritize replication in larger multi-ethnic cohorts with detailed phenotyping and ancestry-matched genetic instruments, prospective validation for incident obesity and cardiometabolic outcomes, and targeted LC–MS/MS assays enabling absolute quantification and standardized sample handling. Mechanistic studies are also needed to interrogate the immune-related pathways suggested by the genetic and multi-omics analyses.¹

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References

1. Si, M.; Zhao, Y.; Wu, Y. et al. Candidate Obesity Biomarkers Identified Through Multi-Omics Analysis, Mendelian Randomization, and Mediation Analysis. Food Sci Nutr. 2026, 14 (4), e71803. DOI: 10.1002/fsn3.71803
2. Ammar, N. M,; Farag, M. A.; Kholeif, T. E. et al. Serum Metabolomics Reveals the Mechanistic Role of Functional Foods and Exercise for Obesity Management in Rats. J Pharm Biomed Anal. 2017, 142, 91-101. DOI: 10.1016/j.jpba.2017.05.001
3. Guleken, Z.; Ceylan, Z.; Çeçen, S. et al. Chemical Changes in Childhood Obesity Blood as a Marker of the Disease. A Raman-Based Machine Learning Study. J Pharm Biomed Anal. 2023, 233, 115445. DOI: 10.1016/j.jpba.2023.115445
4. Men, L.; Pi, Z.; Zhou, Y. et al. Urine Metabolomics of High-Fat Diet Induced Obesity Using UHPLC-Q-TOF-MS. J Pharm Biomed Anal. 2017, 132, 258-266. DOI: 10.1016/j.jpba.2016.10.012
5. Boden, G. Obesity and Free Fatty Acids. Endocrinol Metab Clin North Am. 2008, 37 (3), 635-646, viii-ix. DOI: 10.1016/j.ecl.2008.06.007
6. Jennings, A.; MacGregor, A.; Pallister, T. et al. Associations Between Branched Chain Amino Acid Intake and Biomarkers of Adiposity and Cardiometabolic Health Independent of Genetic Factors: A Twin Study. Int J Cardiol. 2016, 223, 992-998. DOI: 10.1016/j.ijcard.2016.08.307
7. Park, J. S.; Ahmed, K.; Yim, J. E. Serum Branch Chain Amino Acids and Aromatic Amino Acids Ratio and Metabolic Risks in Koreans with Normal-Weight or Obesity: A Cross-Sectional Study. Korean J Community Nutr. 2024, 29 (3), 212-221. DOI: 10.5720/kjcn.2024.29.3.212
8. Chew, W. S.; Torta, F.; Ji, S. et al. Large-Scale Lipidomics Identifies Associations Between Plasma Sphingolipids and T2DM Incidence. JCI Insight 2019, 5 (13), e126925. DOI: 10.1172/jci.insight.126925
9. Huang, Y.; Sulek, K.; Stinson, S. E. et al. Lipid Profiling Identifies Modifiable Signatures of Cardiometabolic Risk in Children and Adolescents with Obesity. Nat Med. 2025, 31 (1), 294-305. DOI: 10.1038/s41591-024-03279-x
10. Rauschert, S.; Uhl, O.; Koletzko, B. et al. Lipidomics Reveals Associations of Phospholipids With Obesity and Insulin Resistance in Young Adults. J Clin Endocrinol Metab. 2016, 101 (3), 871-879. DOI: 10.1210/jc.2015-3525