A novel minimally invasive metabolomic strategy for differential diagnosis of spinal infections: blood-derived metabolic signatures as candidate biomarkers distinguishing tuberculous spondylitis from brucellar spondylitis
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background Tuberculous spondylitis (TS) and brucellar spondylitis (BS) can present with overlapping manifestations. This study assessed plasma metabolic differences between the two diagnostic groups. Methods We conducted a retrospective case-control study of 53 patients (36 with TS and 17 with BS) enrolled between January 2021 and December 2024. Diagnoses were established using a composite reference standard integrating compatible clinical and radiological findings with microbiological and/or histopathological evidence. Untargeted ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), multivariate analyses and receiver operating characteristic (ROC) analyses were used. Candidate selection and ROC assessment used the same participants. Results The processed dataset contained 1060 features, of which 190 met the nominal differential-screening criteria (56 with higher and 134 with lower relative peak areas in TS). Lipids and lipid-like molecules accounted for 390 of 1058 classified features (36.86%); 2 features were unclassified. No pathway reached nominal P < 0.05 among 27 tested pathways; linoleic acid metabolism had the lowest P value (0.0767). The highest individual apparent area under the ROC curve (AUC) was 0.922 for the feature annotated as 12-hydroxyjasmonic acid. An unweighted sum of the top nine relative peak areas had an apparent AUC of 0.904 (95% CI 0.825-0.983), apparent sensitivity 0.806 and apparent specificity 0.882. Conclusion The results identify candidate features for distinguishing the sampled TS and BS groups. They do not establish independent associations, specificity against other pathogens, validated diagnostic accuracy or clinical utility.
Source: BMC Musculoskeletal Disorders
Authors: Parhat Yasin, Hongwei Ye, Xinghua Song, Jie Sheng
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,090
OpenAlex ID: W7225095161
