ArticleThe Journal of nutrition2025
Amino Acid Associations in Healthy and Unhealthy Obesity.
Article in The Journal of nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Article
- Dietary Patterns, Eating Disorder Risk, Emotional Distress, and Amino Acid Intake in Adults Seeking Weight Loss.Nutrients · 2026Article
- Branched-Chain and Aromatic Amino Acids Mark Early Metabolic Shifts in Adults with Varying Adiposity.International journal of molecular sciences · 2026Article
- Systemic Inflammation and Myocardial Repolarization Heterogeneity in Heart Failure and Obstructive Sleep Apnea: Impact on Arrhythmic Risk.Medicina (Kaunas, Lithuania) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundPrior research shows that amino acid metabolism plays a role in the biological processes distinguishing metabolically healthy nonobese (MHN), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) phenotypes. Metabolic network analysis tools are available to test for differences in amino acid associations but have not been applied to MHN, MHO, and MUO.
objectivesWe aimed to characterize amino acid metabolic networks and identify key metabolic shifts distinguishing MHN, MHO, and MUO phenotypes through metabolomics and network analysis.
methodsThe plasma metabolome was analyzed by liquid chromatography with high-resolution mass spectrometry in a cross-sectional study of 213 middle-aged adults classified as MHN, MHO, or MUO based on body mass index, blood pressure, blood lipids, and blood glucose. Differential metabolic network and pathway enrichment analyses were used to measure the centrality of amino acid metabolism and associated metabolic pathways.
resultsPartial least squares-discriminant analysis showed that amino acids were among the top discriminatory metabolites (variable importance in projection >2) for MHN, MHO, and MUO and included tryptophan (Trp), phenylalanine (Phe), tyrosine, cystine (disulfide of cysteine), alanine, glutamate, valine, and leucine/isoleucine. Network analyses with these discriminatory amino acids showed that Trp had a high network centrality in all groups, with the highest value in MHN and MHO. Phe gained centrality in obese phenotypes and became the central amino acid in MUO. Pathway enrichment analyses revealed that Phe-centered metabolic communities in obese groups (MHO and MUO) were enriched fatty acid oxidation pathways. Network and pathway analyses using all amino acids showed comparable results.
conclusionsAmino acid networks differ in healthy and unhealthy obesity phenotypes, with the most central amino acid Trp in MHN and MHO shifting to Phe in MUO. Mechanistic studies are needed to determine whether increased Phe centrality is a cause or effect of metabolic dysfunctions in obesity.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.