Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach.

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation is considered the hallmark of NAFLD and is associated with lipotoxicity and inflammation, as well as increased oxidative stress levels. In this study, a lipidomic approach was used to investigate the plasma lipidome of 12 NASH patients, 10 Nonalcoholic Fatty Liver (NAFL) patients, and 15 healthy controls, revealing significant alterations in lipid classes, such as glycerolipids and glycerophospholipids, as well as fatty acid compositions in the context of steatosis and steatohepatitis. A machine learning XGBoost algorithm identified a panel of 15 plasma biomarkers, including HOMA-IR, BMI, platelets count, LDL-c, ferritin, AST, FA 12:0, FA 18:3 ω3, FA 20:4 ω6/FA 20:5 ω3, CAR 4:0, LPC 20:4, LPC O-16:1, LPE 18:0, DG 18:1_18:2, and CE 20:4 for predicting steatohepatitis. This research offers insights into the connection between imbalanced lipid metabolism and the formation and progression of NAFL D, while also supporting previous research findings. Future studies on lipid metabolism could lead to new therapeutic approaches and enhanced risk assessment methods, as the shift from isolated steatosis to NASH is currently poorly understood.

Lipidomic Analysis of Liver and Adipose Tissue in a High-Fat Diet-Induced Non-Alcoholic Fatty Liver Disease Mice Model Reveals Alterations in Lipid Metabolism by Weight Loss and Aerobic Exercise.

Detailed investigation of the lipidome remodeling upon normal weight conditions, obesity, or weight loss, as well as the influence of physical activity, can help to understand the mechanisms underlying dyslipidemia in metabolic conditions correlated to the emergence and progression of non-alcoholic fatty liver disease (NAFLD). C57BL/6 male mice were fed a normal diet (ND) or a high-fat diet (HFD) for 20 weeks. Subgroups within the high-fat diet (HFD) group underwent different interventions: some engaged in exercise (HFDex), others were subjected to weight loss (WL) by changing from the HFD to ND, and some underwent a combination of weight loss and exercise (WLex) during the final 8 weeks of the 20-week feeding period. To support our understanding, not only tissue-specific lipid remodeling mechanisms but also the cross-talk between different tissues and their impact on the systemic regulation of lipid metabolism are essential. Exercise and weight loss-induced specific adaptations in the liver and visceral adipose tissue lipidomes of mice were explored by the UPLC-TOF-MS/MS untargeted lipidomics methodology. Lipidomic signatures of ND and HFD-fed mice undergoing weight loss were compared with animals with and without physical exercise. Several lipid classes were identified as contributing factors in the discrimination of the groups by multivariate analysis models, such as glycerolipids, glycerophospholipids, sphingolipids, and fatty acids, with respect to liver samples, whereas triglycerides were the only lipid class identified in visceral adipose tissue. Lipids found to be dysregulated in HFD animals are related to well-established pathways involved in the biosynthesis of PC, PE, and TG metabolism. These show a reversing trend back to basic levels of ND when animals change to a normal diet after 12 weeks, whereas the impact of exercise, though in some cases it slightly enhances the reversing trend, is not clear.

Plasma Lipids Profile in the Prediction of Non-Alcoholic Steatohepatitis in Adults: A Case-Control Study.

Patients with non-alcoholic steatohepatitis (NASH) show significantly faster progress in the stages of fibrosis compared to those with non-alcoholic fatty liver (NAFL) disease. The non-invasive diagnosis of NASH remains an unmet clinical need. Preliminary data have shown that sphingolipids, especially ceramides, fatty acids, and other lipid classes may be related to the presence of NASH and the histological activity of the disease. The aim of our study was to assess the association of certain plasma lipid classes, such as fatty acids, acylcarnitines, and ceramides, with the histopathological findings in patients with NASH. The study included three groups: patients with NASH (N = 12), NAFL (N = 10), and healthy [non non-alcoholic fatty liver disease (NAFLD)] controls (N = 15). Plasma samples were collected after 12 h of fasting, and targeted analyses for fatty acids, acylcarnitines, and ceramides were performed. Baseline clinical and demographic characteristics were collected. There was no significant difference in baseline characteristics across the three groups or between NAFL and NASH patients. Patients with NASH had increased levels of several fatty acids, including, among others, fatty acid (FA) 14:0, FA 15:0, FA 18:0, FA 18:3n3, as well as Cer(d18:1/16:0), compared to NAFL patients and healthy controls. No significant difference was found between NAFL patients and healthy controls. In conclusion, patients with NASH exhibited a distinctive plasma lipid profile that can differentiate them from NAFL patients and non-NAFLD populations. More data from larger cohorts are needed to validate these findings and examine possible implications for diagnostic and management strategies of the disease.

Metabolic Fingerprinting of Muscat of Alexandria Grape Musts during Industrial Alcoholic Fermentation Using HS-SPME and Liquid Injection with TMS Derivatization GC-MS Methods.

Muscat of Alexandria is one of the most aromatic grape cultivars, with a characteristic floral and fruity aroma, producing popular appellation of origin wines. The winemaking process is a critical factor contributing to the quality of the final product, so the aim of this work was to study metabolomic changes during the fermentation of grape musts at the industrial level from 11 tanks, 2 vintages, and 3 wineries of Limnos Island. A Headspace Solid-Phase Microextraction (HS-SPME) and a liquid injection with Trimethylsilyl (TMS) derivatization Gas Chromatography-Mass Spectrometry (GC-MS) methods were applied for the profiling of the main volatile and non-volatile polar metabolites originating from grapes or produced during winemaking, resulting in the identification of 109 and 69 metabolites, respectively. Multivariate statistical analysis models revealed the differentiation between the four examined time points during fermentation, and the most statistically significant metabolites were investigated by biomarker assessment, while their trends were presented with boxplots. Whilst the majority of compounds (ethyl esters, alcohols, acids, aldehydes, sugar alcohols) showed an upward trend, fermentable sugars, amino acids, and C6-compounds were decreased. Terpenes presented stable behavior, with the exception of terpenols, which were increased at the beginning and were then decreased after the 5th day of fermentation.

Untargeted Metabolomics Pilot Study Using UHPLC-qTOF MS Profile in Sows' Urine Reveals Metabolites of Bladder Inflammation.

Urinary tract infections (UTI) of sows (characterized by ascending infections of the urinary bladder (cyst), ureters, and renal pelvis), are major health issues with a significant economic impact to the swine industry. The current detection of UTI incidents lacks sensitivity; thus, UTIs remain largely under-diagnosed. The value of metabolomics in unraveling the mechanisms of sow UTI has not yet been established. This study aims to investigate the urine metabolome of sows for UTI biomarkers. Urine samples were collected from 58 culled sows from a farrow-to-finish herd in Greece. Urine metabolomic profiles in 31 healthy controls and in 27 inflammatory ones were evaluated. UHPLC-qTOF MS/MS was applied for the analysis with a combination of multivariate and univariate statistical analysis. Eighteen potential markers were found. The changes in several urine metabolites classes (nucleosides, indoles, isoflavones, and dipeptides), as well as amino-acids allowed for an adequate discrimination between the study groups. Identified metabolites were involved in purine metabolism; phenylalanine; tyrosine and tryptophan biosynthesis; and phenylalanine metabolism. Through ROC analysis it was shown that the 18 identified metabolite biomarkers exhibited good predictive accuracy. In summary, our study provided new information on the potential targets for predicting early and accurate diagnosis of UTI. Further, this information also sheds light on how it could be applied in live animals.

Metabolic Phenotyping Study of Mouse Brain Following Microbiome Disruption by C.difficile Colonization.

Clostridioides difficile infection (CDI) is responsible for an increasing number of cases of post-antibiotic diarrhea worldwide, which has high severity and mortality among hospitalized elderly patients. The disruption of gut microbiota due to antibacterial medication facilitates the intestinal colonization of C. difficile. In the present study, a murine model was used to investigate the potential effects of antibiotic administration and subsequent colonization by C. difficile, as well as the effects of three different 10-day treatments (metronidazole, probiotics, and fecal microbiota transplantation), on the brain metabolome for the first time. Four different metabolomic-based methods (targeted HILIC-MS/MS, untargeted RP-LC-HRMS/MS, targeted GC-MS/MS, and untargeted GC-MS) were applied, resulting in the identification of 217 unique metabolites in the brain extracts, mainly glycerophospholipids, glycerolipids, amino acids, carbohydrates, and fatty acids. Univariate and multivariate statistical analysis revealed that CDI, as well as the subsequent treatments, altered significantly several brain metabolites, probably due to gut dysbiosis, and affected the brain through the gut-brain axis. Notably, none of the therapeutic approaches completely restored the brain metabolic profile to the original, healthy, and non-infected phenotype, even after 10 days of treatment.

Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial.

Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691−0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD.

Correlation of Serum Acylcarnitines with Clinical Presentation and Severity of Coronary Artery Disease.

Recent studies support that acylcarnitines exert a significant role in cardiovascular disease development and progression. The aim of this metabolomics-based study was to investigate the association of serum acylcarnitine levels with coronary artery disease (CAD) severity, as assessed via SYNTAX Score. Within the context of the prospective CorLipid trial (NCT04580173), the levels of 13 circulating acylcarnitines were accurately determined through a newly developed HILIC-MS/MS method in 958 patients undergoing coronary angiography in the AHEPA University Hospital of Thessaloniki, Greece. Patients presenting with acute coronary syndrome had significantly lower median acylcarnitine C8, C10, C16, C18:1 and C18:2 values, compared to patients with chronic coronary syndrome (p = 0.012, 0.007, 0.018, 0.011 and <0.001, respectively). Among CAD subgroups, median C5 levels were significantly decreased in unstable angina compared to STEMI (p = 0.026), while median C10, C16, C18:1 and C18:2 levels were higher in stable angina compared to STEMI (p = 0.019 p = 0.012, p = 0.013 and p < 0.001, respectively). Moreover, median C2, C3, C4 and C8 levels were significantly elevated in patients with diabetes mellitus (p < 0.001, <0.001, 0.029 and 0.011, respectively). Moreover, short-chain acylcarnitine C2, C4, C5 and C6 levels were elevated in patients with heavier calcification and lower left ventricular ejection fraction (LVEF) % (all p-values less than 0.05). With regard to CAD severity, median C4 and C5 levels were elevated and C16 and C18:2 levels were reduced in the high CAD complexity group with SYNTAX Score > 22 (p = 0.002, 0.024, 0.044 and 0.012, respectively), indicating a potential prognostic capability of those metabolites and of the ratio C4/C18:2 for the prediction of CAD severity. In conclusion, serum acylcarnitines could serve as clinically useful biomarkers leading to a more individualized management of patients with CAD, once further clinically oriented metabolomics-based studies provide similar evidence.

Plasma Lipidomic and Metabolomic Profiling after Birth in Neonates Born to SARS-CoV-19 Infected and Non-Infected Mothers at Delivery: Preliminary Results.

Pregnant women are among the high-risk populations for COVID-19, whereas the risk of vertical transmission to the fetus is very low. Nevertheless, metabolic alternations described in COVID-19 patients may also occur in pregnant women and their offspring. We prospectively evaluated the plasma lipidomic and metabolomic profiles, soon after birth, in neonates born to infected mothers (cases, n = 10) and in the offspring of uninfected ones at delivery (controls, n = 10). All cases had two negative tests for SARS-CoV-2 (nasopharyngeal swabs) performed 72 h apart. Blood samples were obtained within the first hours after birth. Liquid chromatography-high resolution mass spectrometry (UHPLC-TOF/MS) and gas chromatography-mass spectrometry (GC-MS) were applied for the analyses. Multivariate statistical analysis was performed for data evaluation. Changes in several plasma lipid species-classes (long-chain fatty acids phosphatidylcholines, triglycerides), and amino-acids were identified that allowed for clear discrimination between the study groups. The results of this preliminary investigation suggest that neonates born to Sars-Cov-19 positive mothers, without evidence of viral infection at birth, have a distinct plasma lipidomic and metabolomic profile compared to those of uninfected mothers. Whether these findings are reflective of maternal metabolic alternations due to the virus or a metabolic response following an unidentified neonatal infection warrants further investigation.

Liquid chromatography tandem mass spectrometry for the determination of nine insecticides and fungicides in human postmortem blood and urine.

Pesticide poisoning is a common occurrence due to their widespread use, easy access and high toxicity even in small concentrations. The most common poisoning fatalities have been observed due to exposure to organophosphates, carbamates and neonicotinoids, thus development of a method for the rapid determination of these compounds in blood and urine is of great importance for clinical and toxicology laboratories. A simple, fast and reliable method was developed for the determination of 9 pesticides in blood and urine using HPLC-MS/MS instrumentation. In order to find the most suitable sample pretreatment technique, three different sample preparation procedures: SPE, protein precipitation and QuEChERS were compared. The final optimized analytical method was fully validated with the values of parameters such as calibration linearity, accuracy, precision, recovery, matrix effect and stability being acceptable. The method proved reliable, accurate, robust and sensitive and was successfully applied for the quantitation of pesticides in three postmortem cases of pesticides poisoning.

A Study of Blood Fatty Acids Profile in Hyperlipidemic and Normolipidemic Subjects in Association with Common PNPLA3 and ABCB1 Polymorphisms.

Adiponutrin (patatin-like phospholipase domain-containing 3; PNPLA3), encoded in humans by the PNPLA3 gene, is a protein associated with lipid droplet and endoplasmic reticulum membranes, where it is apparently involved in fatty acid redistribution between triglycerides and phospholipids. A common polymorphism of PNPLA3 (I148M, rs738409), linked to increased PNPLA3 presence on lipid droplets, is a strong genetic determinant of non-alcoholic fatty liver disease (NAFLD) and of its progression. P-glycoprotein (Pgp, MDR1, ABCB1), encoded by the ABCB1 gene, is another membrane protein implicated in lipid homeostasis and steatosis. In the past, common ABCB1 polymorphisms have been associated with the distribution of serum lipids but not with fatty acids (FA) profiles. Similarly, data on the effect of PNPLA3 I148M polymorphism on blood FAs are scarce. In this study, a gas chromatography-flame ionization detection (GC-FID) method was optimized, allowing us to analyze twenty FAs (C14: 0, C15: 0, C15: 1, C16: 0, C16: 1, C17: 0, C17: 1, C18: 0, C18: 1cis, C18: 2cis, C20: 0, C20: 1n9, C20: 2, C20: 3n6, C20: 4n6, C20: 5, C23: 0, C24: 0, C24: 1 and C22: 6) in whole blood, based on the indirect determination of the fatty acids methyl esters (FAMES), in 62 hyperlipidemic patients and 42 normolipidemic controls. FA concentrations were then compared between the different genotypes of the rs738409 and rs2032582 (ABCB1 G2677T) polymorphisms, within and between the hyperlipidemic and normolipidemic groups. The rs738409 polymorphism appears to exert a significant effect on the distribution of blood fatty acids, in a lipidemic and fatty acid saturation state-depending manner. The effect of rs2032582 was less pronounced, but the polymorphism did appear to affect the relative distribution of blood fatty acids between hyperlipidemic patients and normolipidemic controls.

GC-MS analysis of underivatised new psychoactive substances in whole blood and urine.

Herein a method was develop and validated for the detection and quantification of five new psychoactive substances (NPS) belonging to three categories: synthetic cathinones (mephedrone, 3,4-MDPV), opioids (AH-7921) and cannabinoids (JWH-018, AM-2201) by EI GC-MS. Target analytes were quantified in whole blood; in urine the same compounds plus methylone were detected. Liquid-liquid extraction by MTBE - butyl acetate (1:1, v/v) in blood and butyl acetate in urine was applied for the recovery of analytes, while no derivatization was necessary for their sensitive detection and quantification. The method showed good linearity for all analytes within a concentration range from 0.25 to 2 µg/mL for mephedrone, from 0.02 to 0.16 µg/mL for 3,4-MDPV and AH-7921 and from 0.005 to 0.04 µg/mL for JWH-018 and AM-2201. LOD ranged from 0.002 µg/mL (JWH-018 and AM-2201 in blood and urine), to 0.08 µg/mL (mephedrone in urine). LOQ in blood ranged from 0.005 µg/mL for JWH-018 and AM-2201 to 0.25 µg/mL for mephedrone. Accuracy was within acceptable limits with % bias ranging from +20% to -17.98% for intra-assay study and from +18.87% to -11.16% for inter-assay study. Precision was found to be between 2.60% and 17.17% (CV%) for intra-assay study and from 6.03% to 13.72% (CV%) for inter-assay study. An intra laboratory comparison provided proof of the method robustness. The developed method can be used for the reliable and fast quantification of five NPS in blood and the detection of six NPS in urine within the practice of a clinical or forensic toxicology laboratory.

Α Simple Method for the Determination of Lacosamide in Blood by GC-MS.

Lacosamide is a functionalized amino acid with antiepileptic function. Therapeutic drug monitoring (TDM) in patients for lacosamide is critical as it allows clinicians to control epileptic seizures. A single liquid-liquid extraction step was applied for the extraction of lacosamide from whole blood samples which were thereafter analyzed by GC-MS. Optimum extraction conditions were selected on the basis of experiments with various solvents at different pHs, indicating ethyl acetate at pH 12 as the most efficient parameters for the extraction of lacosamide. Method exhibited linearity from 2 to 100 µg/mL with R2  = 0.998. Accuracy and precision were evaluated at three concentrations and found to be within acceptable limits. LOD and LOQ were determined at 0.1 and 0.5 µg/mL, respectively. Lacosamide was found to be stable at storage conditions. The developed method was applied successfully in clinical samples and postmortem blood sample from an overdose case.