A candidate marker, not a finished test
Researchers report that the blood metabolite C17-sphinganine may help distinguish kidney-liver deficiency syndrome from heat-dampness syndrome among people with rheumatoid arthritis. The paper presents it as a candidate biomarker, not a finished clinical test. The primary comparison involved 40 patients, with 20 in each syndrome group. Because this was a cross-sectional observational study, the findings show a pattern of association or group discrimination within the study, not that C17-sphinganine causes either syndrome or changes rheumatoid arthritis.
Researchers also examined sphingosine-1-phosphate, or S1P, and a ratio used as an indirect index of sphingosine kinase 1, or SPHK1, activity. The ratio was calculated as S1P divided by C17-sphinganine, rather than by measuring the enzyme directly. The analysis used LASSO logistic regression, receiver operating characteristic analysis, the DeLong test to compare area under the curve values, and decision-curve analysis to estimate net benefit. Those tools assess how well measurements separate the two groups in the available data; they do not establish that the same performance will hold in a new population.
What the blood analysis found
To look for candidate signals, the team used untargeted serum metabolomics on 5 millilitres of fasting blood. The samples were analyzed by UPLC-QTOF in both positive and negative ion modes, and the data were processed with MSDIAL version 4.24. Initial screening required a variable-importance-in-projection score of at least 1 and a P value below 0.05.
The two syndrome groups were broadly comparable on demographics, inflammatory markers and medication use. Cholinesterase, uric acid and monocyte percentage did differ between them. When the researchers checked 40 clinical indicators against the metabolites, no pair remained significant after false-discovery-rate correction at q below 0.05. The finding means the analysis did not identify a metabolite-clinical-indicator pair that survived correction; it does not show that the metabolite is independent of every possible clinical factor.
The gene-expression picture differed by tissue
The serum analysis was paired with secondary analyses of three public GEO transcriptomic datasets, covering synovium, whole blood and peripheral blood mononuclear cells. These datasets came from separate sources and were not matched sample by sample to the clinical cohort. The authors used GEO2R to screen for differential expression, with an absolute log2 fold-change threshold above 0.5 and P below 0.05, and defined 29 core sphingolipid-metabolism genes from KEGG. The comparisons included rheumatoid arthritis, osteoarthritis and normal synovium in one dataset, and rheumatoid arthritis against healthy-control blood or peripheral blood mononuclear cells in the others.
Immune-cell patterns were inferred from gene expression rather than measured directly. For each immune signature, the study averaged expression across a curated marker-gene set. When multiple probes mapped to the same gene symbol, it retained the probe with the highest expression value. The resulting scores were used to examine correlations between immune signatures and sphingolipid-related genes. They are computational estimates, so they cannot by themselves establish immune-cell abundance or direct recruitment.
Why the result remains preliminary
The integrated results led the authors to interpret C17-sphinganine as a core candidate for distinguishing the two syndrome classifications and to propose that sphingolipid regulation may differ by tissue. They also set out a possible connection between glutathione and sphingolipid metabolism. That crosstalk model remains a mechanistic hypothesis: the study did not test it experimentally, directly assay SPHK1 activity, or validate the pattern at the protein level. The immune associations likewise came from transcriptomic scoring, not laboratory counts of immune cells.
The practical test is whether the signal survives in a larger and more varied group of patients. The primary sample was small and drawn from one center, and the diagnostic threshold was not validated in an independent multicenter cohort. The study therefore does not demonstrate treatment benefit, prognosis, or a causal role for sphingolipid metabolites or genes in rheumatoid arthritis or syndrome classification. For now, the work offers a preliminary candidate marker and a set of associations that require independent and functional validation.
Paper data and sources
Original title: Integrative transcriptomics–metabolomics reveals sphingolipid metabolic reprogramming in traditional Chinese medicine syndrome differentiation of rheumatoid arthritis
Authors: Li Y, Wu Y, Zeng J et al.
Journal/Repository: Frontiers in immunology
Status: Peer-reviewed
First online: 2026-10-09
DOI: Not available
Original paper · Full text