ArticleBMC ophthalmology2026
Untargeted serum metabolomic profiling in patients with generalized and ocular myasthenia gravis.
Article in BMC ophthalmology, 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
6 authors.
Funding
Abstract
backgroundMyasthenia Gravis (MG) is divided into ocular (OMG) and generalized (GMG) subtypes. While clinical diagnosis is well-established, understanding the underlying biochemical mechanisms and metabolic shifts during disease progression remains challenging; untargeted metabolomics offers a novel perspective to explore these systemic alterations.
objectiveTo characterize the serum metabolic landscape of MG patients and identify potential metabolic signatures associated with disease subtypes (OMG and GMG) via untargeted metabolomics.
methods91 participants (41 GMG, 22 OMG, 28 healthy controls [HC]) were enrolled. Fasting serum samples were analyzed by LC-MS/MS. Multivariate analyses (PCA, PLS-DA/OPLS-DA), differential metabolite screening (VIP > 1.0, p < 0.05), and KEGG pathway enrichment were performed.
resultsHC and MG groups showed distinct metabolic profiles. MG had 515 (175 up, 340 down) and 368 (146 up, 222 down) differential metabolites in positive/negative ion modes, respectively. Key perturbed pathways included glycerophospholipid, sphingolipid metabolism, and unsaturated fatty acid biosynthesis. Ten representative metabolites (e.g., ubiquinone, cortisol) differed significantly among groups; clustering analysis revealed distinct metabolite abundance trajectories across HC, OMG, and GMG.
conclusionMG is associated with notable systemic metabolic dysregulation, particularly in lipid-related pathways. Rather than serving as immediate diagnostic tools, these integrative metabolic signatures provide a crucial biochemical framework for understanding disease pathogenesis and offer valuable clues for future hypothesis-driven research and prospective validation.
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.