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Serum lipidomics reveals distinct metabolic profiles for asymptomatic hyperuricemic and gout patients.
- Source :
- Rheumatology; Jun2022, Vol. 61 Issue 6, p2644-2651, 8p
- Publication Year :
- 2022
-
Abstract
- Objectives This study aimed to characterize the systemic lipid profile of patients with asymptomatic hyperuricemia (HUA) and gout using lipidomics, and to find potential underlying pathological mechanisms therefrom. Methods Sera were collected from Affiliated Hospital of Nanjing University of Chinese Medicine as centre 1 (discovery and internal validation sets) and Suzhou Hospital of Traditional Chinese Medicine as centre 2 (external validation set), including 88 normal subjects, 157 HUA and 183 gout patients. Lipidomics was performed by ultra high performance liquid chromatography plus Q-Exactive mass spectrometry (UHPLC-Q Exactive MS). Differential metabolites were identifed by both variable importance in the projection ≥1 in orthogonal partial least-squares discriminant analysis mode and false discovery rate adjusted P ≤ 0.05. Biomarkers were found by logistic regression and receiver operating characteristic (ROC) analysis. Results In the discovery set, a total of 245 and 150 metabolites, respectively, were found for normal subjects vs HUA and normal subjects vs gout. The disturbed metabolites included diacylglycerol, triacylglycerol (TAG), phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, etc. We also found 116 differential metabolites for HUA vs gout. Among them, the biomarker panel of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 could differentiate well between HUA and gout. The area under the receiver operating characteristic ROC curve was 0.8288, the sensitivity was 82% and the specificity was 78%, at a 95% CI 0.747, 0.9106. In the internal validation set, the predictive accuracy of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 panel for differentiation of HUA and gout reached 74.38%, while it was 84.03% in external validation set. Conclusion We identified serum biomarkers panel that have the potential to predict and diagnose HUA and gout patients. [ABSTRACT FROM AUTHOR]
- Subjects :
- GOUT diagnosis
HYPERURICEMIA
BIOMARKERS
TRIGLYCERIDES
PREDICTIVE tests
ACADEMIC medical centers
HIGH performance liquid chromatography
GLYCERIDES
CONFIDENCE intervals
METABOLOMICS
DIFFERENTIAL diagnosis
BLOOD collection
DISCRIMINANT analysis
COMPARATIVE studies
RISK assessment
DESCRIPTIVE statistics
MASS spectrometry
LOGISTIC regression analysis
RECEIVER operating characteristic curves
PHOSPHOLIPIDS
SENSITIVITY & specificity (Statistics)
LIPIDS
GOUT
METABOLITES
LECITHIN
DISEASE risk factors
Subjects
Details
- Language :
- English
- ISSN :
- 14620324
- Volume :
- 61
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Rheumatology
- Publication Type :
- Academic Journal
- Accession number :
- 157263705
- Full Text :
- https://doi.org/10.1093/rheumatology/keab743