Comprehensive plasma metabolomics and lipidomics of benign and malignant solitary pulmonary nodules.

INTRODUCTION: Solitary pulmonary nodules (SPNs) are commonly found in imaging technologies, but are plagued by high false-positive rates. OBJECTIVE: We aimed to identify metabolic alterations in SPN etiology and diagnosis using less invasive plasma metabolomics and lipidomics. METHODS: In total, 1160 plasma samples were obtained from healthy volunteers (n = 280), benign SPNs (n = 157) and malignant SPNs (stage I, n = 723) patients enrolled from 5 independent centers. Gas chromatography-triple quadrupole mass spectrometry (GC‒MS) and liquid chromatography-Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometry (LC‒MS) were used to analyze the samples for untargeted metabolomics and lipidomics. RESULTS AND CONCLUSION: GC‒MS-based metabolomics revealed 1336 metabolic features, while LC‒MS-based lipidomics revealed 6088 and 2542 lipid features in the positive and negative ion modes, respectively. The metabolic and lipidic characteristics of healthy vs. benign or malignant SPNs exhibited substantial pattern differences. Of note, benign and malignant SPNs had no significant variations in circulating metabolic and lipidic markers and were validated in four other centers. This study demonstrates evidence of early metabolic alterations that can possibly distinguish SPNs from healthy controls, but not between benign and malignant SPNs.

Baicalin attenuate diet-induced metabolic syndrome by improving abnormal metabolism and gut microbiota.

In this work, we examined whether baicalin (BC), a bioactive flavonoid in Scutellaria baicalensis Georgi, can reduce high-fat diet (HFD)-induced metabolic syndrome (MetS) in mice. The UPLC-QTOF/MS was used for metabolome profiles analysis, and an analysis of bacterial 16S rDNA in feces was used to examine the effects of BC on gut microbiota composition. Our results showed that BC (400 mg/kg) could reduce the body weight gain, decrease hepatic fat accumulation and abnormal blood lipids, and increase insulin sensitivity after 8 weeks of treatment. BC could reverse the alteration of 7 metabolites induced by HFD and the metabolic pathways responsive to BC intervention including citrate cycle, alanine, aspartate and glutamate metabolism, glycerophospholipid metabolism, and aminoacyl-tRNA biosynthesis. 16S rDNA analysis demonstrated that BC altered the composition and function of gut microbiota in MetS mice. Notably, we found that the change in succinic acid was negatively associated with the changes in Bacteroides and Sutterella, and positively associated with the change in Mucispirillum. Moreover, we confirmed that succinic acid displayed a metabolic protective effect on MetS mice. The antibiotic treatment verified that BC exerts metabolic protection through gut microbiota. Our findings suggested BC may be a potential therapeutic drug to ameliorate diet induced MetS and gut microbiome may be a novel mechanistic target of BC for treatment of MetS.

Serum lipidomics reveals distinct metabolic profiles for asymptomatic hyperuricemic and gout patients.

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.

Serum-Urine Matched Metabolomics for Predicting Progression of Henoch-Schonlein Purpura Nephritis.

Henoch-Schonlein purpura nephritis (HSPN) is a common glomerulonephritis secondary to Henoch-Schonlein purpura (HSP) that affects systemic metabolism. Currently, there is a rarity of biomarkers to predict the progression of HSPN. This work sought to screen metabolic markers to predict the progression of HSPN via serum-urine matched metabolomics. A total of 90 HSPN patients were enrolled, including 46 HSPN (+) patients with severe kidney damage (persistent proteinuria >0.3 g/day) and 44 HSPN (-) patients without obvious symptoms (proteinuria < 0.3 g/day). Untargeted metabolomics was determined by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-Q/TOF-MS). A total of 38 and 50 differential metabolites were, respectively, identified in serum and urine from the comparison between HSPN (+) and HSPN (-) patients. Altered metabolic pathways in HSPN (+) mainly included glycerophospholipid metabolism, pyruvate metabolism, and citrate cycle. A panel of choline and cis-vaccenic acid gave areas under the curve of 92.69% in serum and 72.43% in urine for differential diagnosis between HSPN (+) and HSPN (-). In addition, the two metabolites showed a significant association with clinical indices of HSPN. These results suggest that serum-urine matched metabolomics comprehensively characterized the metabolic differences between HSPN (+) and HSPN (-), and choline and cis-vaccenic acid could serve as biomarkers to predict HSPN progression.

Serum-plasma matched metabolomics for comprehensive characterization of benign thyroid nodule and papillary thyroid carcinoma.

Metabolomics offers a noninvasive methodology to identify metabolic markers for pathogenesis and diagnosis of diseases. This work aimed to characterize circulating metabolic signatures of benign thyroid nodule (BTN) and papillary thyroid carcinoma (PTC) via serum-plasma matched metabolomics. A cohort of 1,540 serum-plasma matched samples and 114 tissues were obtained from healthy volunteers, BTN and PTC patients enrolled from 6 independent centers. Untargeted metabolomics was determined by liquid chromatography-quadrupole time-of-flight mass spectrometric and multivariate statistical analyses. The use of serum-plasma matched samples afforded a broad-scope detection of 1,570 metabolic features. Metabolic phenotypes revealed significant pattern differences for healthy versus BTN and healthy versus PTC. Perturbed metabolic pathways related mainly to amino acid and lipid metabolism. It is worth noting that, BTN and PTC showed no significant differences but rather overlap in circulating metabolic signatures, and this observation was replicated in all study centers. For differential diagnosis of healthy versus thyroid nodules (BTN + PTC), a panel of 6 metabolic markers, namely myo-inositol, α-N-phenylacetyl-L-glutamine, proline betaine, L-glutamic acid, LysoPC(18:0) and LysoPC(18:1) provided area under the curve of 97.68% in the discovery phase and predictive accuracies of 84.78-98.18% in the 4 validation centers. Taken together, serum-plasma matched metabolomics showed significant differences in circulating metabolites for healthy versus nodules but not for BTN versus PTC. Our results highlight the true metabolic nature of thyroid nodules, and potentially decrease overtreatment that exposes patients to unnecessary risks.