Search

Introduction: Discrimination between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) subtypes in non-small cell lung cancer (NSCLC) patients is a significant challenge in oncology. Lipidomics analysis provides a promising approach for this differentiation.Methods: In an accompanying paper, we explored oxPCs levels in a cohort of 200 NSCLC patients. In this research, we utilized liquid chromatography coupled with mass spectrometry (LC-MS) to analyze the lipidomics profile of matching tissue and plasma samples from 25 NSCLC patients, comprising 11 ADC and 14 SCC cases. This study builds upon our previous findings, which highlighted the elevation of oxidised phosphatidylcholines (oxPCs) in NSCLC patients.Results: We identified eight lipid biomarkers that effectively differentiate between ADC and SCC subtypes using an untargeted approach. Notably, we observed a significant increase in plasma LPA 20:4, LPA 18:1, and LPA 18:2 levels in the ADC group compared to the SCC group. Conversely, tumour PC 16:0/18:2, PC 16:0/4:0; CHO, and plasma PC 16:0/18:2; OH, PC 18:0/20:4; OH, PC 16:0/20:4; OOH levels were significantly higher in the ADC group.Discussion: Our study is the first to report that plasma LPA levels can distinguish between ADC and SCC patients in NSCLC, suggesting a potential role for LPAs in NSCLC subtyping. This finding warrants further investigation into the mechanisms underlying these differences and their clinical implications.

Introduction: Recent data suggest a possible role of endocannabinoids in the regulation of brown adipose tissue (BAT) activity. Those findings indicate potential treatment options for obesity. The aim of this study was to evaluate the relationship between plasma endocannabinoids concentrations and the presence of BAT in humans.Methods: The study group consisted of 25 subjects divided into two groups: BAT positive BAT(+), (n = 17, median age = 25 years) and BAT negative BAT(−), (n = 8, median age = 28 years). BAT was estimated using 18F-FDG PET/MR after 2 h of cold exposure. The level of plasma endocannabinoids was assessed at baseline, 60 min and 120 min of cold exposure.Results: In both groups, BAT(+) and BAT(−), during the cooling, we observed a decrease of the same endocannabinoids: arachidonoylethanolamide (AEA), eicosapentaenoyl ethanolamide (EPEA) and oleoyl ethanolamide (OEA) with a much more profound decline in BAT(+) subjects. Statistically significant fall of PEA (palmitoylethanolamide) and SEA (stearoylethanolamide) concentrations after 60 min (FC = 0.7, p = 0.007 and FC = 0.8, p = 0.03, respectively) and 120 min (FC = 0.81, p = 0.004, and FC = 0.9, p = 0.01, respectively) of cooling was observed only in individuals with BAT.Conclusion: We noticed the profound decline of endocannabinoids concentrations in subjects with increased 18F-FDG PET/MR uptake in BAT. Identification of a new molecules related to BAT activity may create a new target for obesity treatment.

Changes in serum or plasma metabolome may reflect gut microbiota dysbiosis, which is also known to occur in patients with prediabetes and type 2 diabetes (T2DM). Thus, developing a robust method for the analysis of microbiota-dependent metabolites (MDMs) is an important issue. Gas chromatography with mass spectrometry (GC–MS) is a powerful approach enabling detection of a wide range of MDMs in biofluid samples with good repeatability and reproducibility, but requires selection of a suitable solvents and conditions. For this reason, we conducted for the first time the study in which, we demonstrated an optimisation of samples preparation steps for the measurement of 75 MDMs in two matrices. Different solvents or mixtures of solvents for MDMs extraction, various concentrations and volumes of derivatizing reagents as well as temperature programs at methoxymation and silylation step, were tested. The stability, repeatability and reproducibility of the 75 MDMs measurement were assessed by determining the relative standard deviation (RSD). Finally, we used the developed method to analyse serum samples from 18 prediabetic (PreDiab group) and 24 T2DM patients (T2DM group) from our 1000PLUS cohort. The study groups were homogeneous and did not differ in age and body mass index. To select statistically significant metabolites, T2DM vs. PreDiab comparison was performed using multivariate statistics. Our experiment revealed changes in 18 MDMs belonging to different classes of compounds, and seven of them, based on the SVM classification model, were selected as a panel of potential biomarkers, able to distinguish between patients with T2DM and prediabetes.

Risk factors for type 2 diabetes mellitus (T2DM) consist of a combination of an unhealthy, imbalanced diet and genetic factors that may interact with each other. Single nucleotide polymorphism (SNP) in the prospero homeobox 1 (PROX1) gene is a strong genetic susceptibility factor for this metabolic disorder and impaired β-cell function. As the role of this gene in T2DM development remains unclear, novel approaches are needed to advance the understanding of the mechanisms of T2DM development. Therefore, in this study, for the first time, postprandial changes in plasma metabolites were analysed by GC–MS in nondiabetic men with different PROX1 genotypes up to 5 years prior to prediabetes appearance. Eighteen contestants (12 with high risk (HR) and 6 with low risk (LR) genotype) participated in high-carbohydrate (HC) and normo-carbohydrate (NC) meal-challenge tests. Our study concluded that both meal-challenge tests provoked changes in 15 plasma metabolites (amino acids, carbohydrates, fatty acids and others) in HR, but not LR genotype carriers. Postprandial changes in the levels of some of the detected metabolites may be a source of potential specific early disturbances possibly associated with the future development of T2DM. Thus, accurate determination of these metabolites can be important for the early diagnosis of this metabolic disease.

Although brown adipose tissue (BAT) is considered to play a protective role against obesity and type 2 diabetes, the mechanisms of its activation and associations with clinical parameters are not well described. Male adults underwent a 2 h cold exposure (CE) to activate BAT and, based on the results of PET/MRI performed after the CE, were divided into BAT(+) and BAT(−) groups. During the CE procedure, blood samples were collected and alterations in plasma metabolome in both groups were investigated using LC-MS. Additionally, associations between clinical factors and BAT were examined. Moreover, levels of glucose, insulin, leptin, TNF-α, FGF21, and FABP4 were assessed in serum samples. In the BAT(+) group, levels of LPC(17:0), LPE(20:4), LPE(22:4), LPE(22:6), DHA, linoleic acid, and oleic acid increased during CE, whereas levels of sphinganine-phosphate and sphingosine-1-phosphate decreased. Levels of LPE(O-18:0), 9-HpODE, and oleic acid were elevated, while the level of LPE(20:5) was reduced in BAT(+) compared to BAT(−) subjects. AUCs of LPC(18:2), LPC(O-18:2)/LPC(P-18:1), and SM(d32:2) negatively correlated with BAT. In the BAT(+) group, the concentration of FABP4 during and after CE was decreased compared to the basal level. No alterations were observed in the BAT(−) group. In conclusion, using untargeted metabolomics, we proved that the plasma metabolome is affected by cold-induced BAT activation.