Lipidomic Biomarkers Research Articles

B-280 Nmr-based biomaker measurement determines high-dose methotrexate elimination rates in lymphoma and leukemia treatment

Abstract Background The cytotoxic methotrexate (MTX) is used in high doses in lymphoma and acute lymphocytic leukemia (ALL) treatment. A therapeutic plasma dose range is crucial to minimize MTX accumulation and prolonged exposure, which can lead to myelosuppression and other adverse effects. Determining MTX elimination prior to drug administration is difficult, given its non-linear clearance. Here we present a model to predict MTX elimination rates based on metabolite measurement in patients receiving high-dose methotrexate, using nuclear-magnetic resonance (NMR) spectroscopy. Methods A series of 26 residual sera from patients receiving MTX infusions as per treatment protocols for CNS lymphoma (MATRIX protocol, 3.5 g/m2 over four hours) and ALL (GMALL protocol, 1.5 g/m2 over 24 hours) were collected from patients at the University Hospital Regensburg at four timepoints (t0, t24, t42, and t48). Serum was NMR-measured in five replicates. NMR spectra for each sample were characterized and relevant parameters extracted. First order elimination constants were modeled for two MTX dosage regimes (<4 g, ≥4 g) using characterized NMR features from samples measured at t0. Results Metabolomic and lipidomic biomarkers measured at baseline (t0) were able to accurately predict MTX elimination rate constant k, determined by fitting MTX levels at a t24, t42, and t48, using first order c(t)∼c0*e(-kt) kinetic function (>4 g: R2 = 0.475, p-value < 0.05; ≤4 g: R2 = 0.918, p-value < 0.01) (Fig. A). Results were cross-validated with five-time repeated three-fold cross-validation. Performance values are given as means of the folds. Conclusions NMR-measured biomarkers can predict dose-dependent MTX elimination rates in patients receiving high-dose MTX, presenting a novel method to predict MTX clearance, allowing for personalized dose-monitoring and minimization of adverse events. Validation with a larger cohort is required.

In-depth cerebrovascular lipidomics profiling for discovering novel biomarkers and mechanisms in moyamoya and intracranial atherosclerotic disease.

Despite considerable research efforts, the precise etiology and underlying pathways contributing to Moyamoya Disease (MMD) remain poorly understood. Moreover, the overlapping vascular pathologies shared between MMD and Intracranial Atherosclerotic Disease (ICAD) pose challenges in clinical differentiation, even with gold-standard cerebral angiography. An in-depth exploration of lipidomic alterations in cerebral intracranial MMD vessels could offer valuable insights into the pathogenesis of MMD-related mechanisms, encompassing MMD and ICAD, and unveil novel biomarkers and potential therapeutic targets. However, to date, comprehensive lipidomic profiling has been lacking. To discover novel biomarkers and unravel the pathophysiological mechanisms underlying MMD, we conducted a lipidomics analysis to characterize various lipid species in matched human extracranial and intracranial artery tissues from patients diagnosed with MMD (n=99) and ICAD (n=12). Our analysis identified 569 lipid species and delineated a robust panel of lipidomic biomarkers capable of effectively distinguishing MMD from ICAD (area under curve=0.98), as determined by receiver operating characteristic curve analysis. Notably, we observed a significantly more pronounced positive correlation of diacylglycerols and a negative association of triglycerides in intracranial artery tissues of MMD patients compared to those with ICAD, suggesting a potential role of dysregulated diacylglycerol-induced signaling in MMD pathogenesis. Furthermore, our investigation into the correlations of critical differential intracranial artery vessel lipid species between MMD and ICAD and clinical parameters revealed negative associations with plasma iron levels, implying a potential link between plasma iron metabolism and artery lipid homeostasis during MMD pathogenesis. These findings offer promising prospects for advancing clinical diagnosis for enhanced differentiation between the two disease conditions. Additionally, they shed light on the fundamental mechanisms implicated in MMD pathogenesis and suggest potential therapeutic avenues through targeting artery vessel lipids or plasma iron levels.

Lipidomic biomarkers in plasma correlate with disease severity in adrenoleukodystrophy

BackgroundX-linked adrenoleukodystrophy (ALD) is a neurometabolic disorder caused by pathogenic variants in ABCD1 resulting very long-chain fatty acids (VLCFA) accumulation in plasma and tissues. Males can present with various clinical manifestations, including adrenal insufficiency, spinal cord disease, and leukodystrophy. Female patients typically develop spinal cord disease and peripheral neuropathy. Predicting the clinical outcome of an individual patient remains impossible due to the lack of genotype-phenotype correlation and predictive biomarkers.MethodsThe availability of a large prospective cohort of well-characterized patients and associated biobank samples allowed us to investigate the relationship between lipidome and disease severity in ALD. We performed a lipidomic analysis of plasma samples from 24 healthy controls, 92 male and 65 female ALD patients.ResultsHere we show that VLCFA are incorporated into different lipid classes, including lysophosphatidylcholines, phosphatidylcholines, triglycerides, and sphingomyelins. Our results show a strong association between higher levels of VLCFA-containing lipids and the presence of leukodystrophy, adrenal insufficiency, and severe spinal cord disease in male ALD patients. In female ALD patients, VLCFA-lipid levels correlate with X-inactivation patterns in blood mononuclear cells, and higher levels are associated with more severe disease manifestations. Finally, hematopoietic stem cell transplantation significantly reduces, but does not normalize, plasma C26:0-lysophosphatidylcholine levels in male ALD patients. Our findings are supported by the concordance of C26:0-lysophosphatidylcholine and total VLCFA analysis with the lipidomics results.ConclusionsThis study reveals the profound impact of ALD on the lipidome and provides potential biomarkers for predicting clinical outcomes in ALD patients.

Novel metabolic and lipidomic biomarkers of sarcopenia.

The pathophysiology of sarcopenia is complex and multifactorial and has not been fully elucidated. The impact of resistance training and nutritional support (RTNS) on metabolomics and lipodomics in older adults with sarcopenia remains uncertain. This study aimed to explore potential biomarkers of sarcopenia and clinical indicators of RTNS in older sarcopenic adults. Older individuals diagnosed with sarcopenia through routine health checkups at a community hospital were recruited for a 12-week randomized controlled trial focusing on RTNS. Plasma metabolomic and lipidomic profiles of 45 patients with sarcopenia and 47 matched controls were analysed using 1H-nuclear magnetic resonance (1H-NMR) and liquid chromatography-mass spectrometer (LC-MS). At baseline, the patient and control groups had similar age, sex, and height distribution. The patient group had significantly lower weight, BMI, grip strength, gait speed, skeletal muscle index, lean mass of both the upper and lower limbs, and lower limb bone mass. There was a significant difference in 12 metabolites between the control and patient groups. They are isoleucine (patient/control fold change [FC]=0.86±0.04, P=0.0005), carnitine (FC=1.05±0.01, P=0.0110), 1-methylhistamine/3-methylhistamine (FC=1.24±0.14, P=0.0039), creatinine (FC=0.71±0.04, P<0.0001), carnosine (FC=0.71±0.04, P=0.0007), ureidopropionic acid (FC=0.61±0.10, P=0.0107), uric acid (FC=0.88±0.03, P=0.0083), PC (18:2/20:0) (FC=0.69±0.03, P=0.0010), PC (20:2/18:0) (FC=0.70±0.06, P=0.0014), PC (18:1/20:1) (FC=0.74±0.05, P=0.0015), PI 32:1 (FC=4.72±0.17, P=0.0006), and PI 34:3 (FC=1.88±0.13, P=0.0003). Among them, carnitine, 1-methylhistamine/3-methylhistamine, creatinine, ureidopropionic acid, uric acid, PI 32:1, and PI 34:3 were first identified. Notably, PI 32:1 had highest diagnostic accuracy (0.938) for sarcopenia. 1-Methylhistamine/3-methylhistamine, carnosine, PC (18:2/20:0), PI 32:1, and PI 34:3 levels were not different from the control group after RTNS. These metabolites are involved in amino acid metabolism, lipid metabolism, and the PI3K-AKT/mTOR signalling pathway through the ingenuity pathway analysis. These findings provide information on metabolic changes, lipid perturbations, and the role of RTNS in patients with sarcopenia. They reveal new insights into its pathological mechanisms and potential therapies.

The Utility of Lipidomic Analysis in Colorectal Cancer Diagnosis and Prognosis-A Systematic Review of Recent Literature.

Colorectal cancer (CRC) is among the most prevalent and lethal malignancies. Lipidomic investigations have revealed numerous disruptions in lipid profiles across various cancers. Studies on CRC exhibit potential for identifying novel diagnostic or prognostic indicators through lipidomic signatures. This review examines recent literature regarding lipidomic markers for CRC. PubMed database was searched for eligible articles concerning lipidomic biomarkers of CRC. After selection, 36 articles were included in the review. Several studies endeavor to establish sets of lipid biomarkers that demonstrate promising potential to diagnose CRC based on blood samples. Phosphatidylcholine, phosphatidylethanolamine, ceramides, and triacylglycerols (TAGs) appear to offer the highest diagnostic accuracy. In tissues, lysophospholipids, ceramides, and TAGs were among the most altered lipids, while unsaturated fatty acids also emerged as potential biomarkers. In-depth analysis requires both cell culture and animal studies. CRC involves multiple lipid metabolism alterations. Although numerous lipid species have been suggested as potential diagnostic markers, the establishment of standardized methods and the conduct of large-scale studies are necessary to facilitate their clinical application.

Plasma lipidomics profiling in predicting the chemo-immunotherapy response in advanced non-small cell lung cancer.

Advanced non-small cell lung cancer (NSCLC) presents significant treatment challenges, with chemo-immunotherapy emerging as a promising approach. This study explores the potential of lipidomic biomarkers to predict responses to chemo-immunotherapy in advanced non-small cell lung cancer (NSCLC) patients. A prospective analysis was conducted on 68 NSCLC patients undergoing chemo-immunotherapy, divided into disease control (DC) and progressive disease (PD) groups based on treatment response. Pre-treatment serum samples were subjected to lipidomic profiling using liquid chromatography-mass spectrometry (LC-MS). Key predictive lipids (biomarkers) were identified through projection to latent structures discriminant analysis. A biomarker combined model and a clinical combined model were developed to enhance the prediction accuracy. The predictive performances of the clinical combined model in different histological subtypes were also performed. Six lipids were identified as the key lipids. The expression levels of PC(16:0/18:2), PC(16:0/18:1), PC(16:0/18:0), CE(20:1), and PC(14:0/18:1) were significantly up-regulated. While the expression level of TAG56:7-FA18:2 was significantly down-regulated. The biomarker combined model demonstrated a receiver operating characteristic (ROC) curve of 0.85 (95% CI: 0.75-0.95) in differentiating the PD from the DC. The clinical combined model exhibited an AUC of 0.87 (95% CI: 0.79-0.96) in differentiating the PD from the DC. The clinical combined model demonstrated good discriminability in DC and PD patients in different histological subtypes with the AUC of 0.78 (95% CI: 0.62-0.96), 0.79 (95% CI: 0.64-0.94), and 0.86 (95% CI: 0.52-1.00) in squamous cell carcinoma, large cell carcinoma, and adenocarcinoma subtype, respectively. Pathway analysis revealed the metabolisms of linoleic acid, alpha-linolenic acid, glycerolipid, arachidonic acid, glycerophospholipid, and steroid were implicated in the chemo-immunotherapy response in advanced NSCLC. Lipidomic profiling presents a highly accurate method for predicting responses to chemo-immunotherapy in patients with advanced NSCLC, offering a potential avenue for personalized treatment strategies.

The Non-Targeted Lipidomic-Based Classifier Reveals Two Candidate Biomarkers for Ischemic Stroke in Hypertensive Individuals.

Traditional clinical risk factors are insufficient to estimate the residual risk of large-vessel ischemic stroke. Non-targeted lipidomic techniques provide an opportunity to evaluate these risks. Plasma samples were collected from 113 hypertensive individuals, including 55 individuals at high risk of ischemic stroke and 58 matched individuals, in a prospective nested case-control cohort. To identify dysregulated lipid metabolites, we conducted multivariate and univariate analyses. A classifier based on a cross-validated procedure was employed to select the optimal combination of lipid species and their ratios. We identified 23 dysregulated lipid species in patients with and without ischemic stroke, including 16 (69.6%) up-regulated and 7 (30.4%) down-regulated lipid species. Through internal cross-validation, the optimal combination of two lipid features (phosphatidylcholine 34:2 and triglyceride 18:1/18:1/22:1 / phosphatidylcholine 34:2, referred to as ischemic stroke-related 2 lipid features - IS2LP) was selected, leading to a more precise prediction probability for ischemic stroke within 3.9 years. In the comparison of different risk factors, the traditional risk score, the IS2LP risk score, and the combination of the traditional risk score with IS2LP yield AUC values of 0.613(95% CI:0.509-0.717), 0.833(95% CI:0.755-0.911), and 0.843(95% CI:0.777-0.916), respectively. The combination of the traditional risk score and IS2LP exhibited significantly improved discriminative performance, with an integrated discrimination improvement (IDI) of 0.31 (p<0.001) and a continuous net reclassification improvement (NRI) of 1.06 (p < 0.001) compared to the traditional risk score. We identified new lipidomic biomarkers associated with the futural event of large-vessel ischemic stroke. These lipid species could serve as potential blood biomarkers for assessing the residual risk of ischemic stroke in hypertensive individuals.

Lipidomic and Fatty Acid Biomarkers in Whole Blood Can Predict the Dietary Intake of Eicosapentaenoic and Docosahexaenoic Acids in a Danish Population

BackgroundThe intake of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have been associated with health benefits. Blood levels of these fatty acids, measured by gas chromatography (GC), are associated with their dietary intake, but the relationships with lipidomic measurements are not well defined. ObjectivesThis study aimed to determine the lipidomic biomarkers in whole blood that predict intakes of EPA + DHA and examine the relationship between lipidomic and GC-based n–3 polyunsaturated fatty acid (n–3 PUFA) biomarkers. MethodsLipidomic and fatty acid analyses were completed on 120 whole blood samples collected from Danish participants. Dietary intakes were completed using a web-based 7-d food diary. Stepwise multiple linear regression was used to identify the fatty acid and lipidomic variables that predict intakes of EPA + DHA and to determine lipidomic species that predict commonly used fatty acid biomarkers. ResultsStepwise regression selected lipidomic variables with an R2 = 0.52 for predicting EPA + DHA intake compared to R2 = 0.40 for the selected fatty acid GC-based variables. More predictive models were generated when the lipidomic variables were selected for females only (R2 = 0.62, n = 68) and males only (R2 = 0.72, n = 52). Phosphatidylethanolamine plasmalogen species containing EPA or DHA tended to be the most predictive lipidomic variables. Stepwise regression also indicated that selected lipidomic variables can predict commonly used fatty acid GC-based n–3 PUFA biomarkers as the R2 values ranged from 0.84 to 0.91. ConclusionsBoth fatty acid and lipidomic data can be used to predict EPA + DHA intakes, and fatty acid GC-based biomarkers can be emulated by lipidomic species. Lipidomic-based biomarkers appear to be influenced by sex differences, probably in n–3 PUFA and lipoprotein metabolism. These results improve our ability to understand the relationship between novel lipidomic data and GC fatty acid data and will increase our ability to apply lipidomic methods to fatty acid and lipid nutritional research.

Review and targeted lipidomic biomarkers in atherosclerosis

Ischemic cardiovascular events (ischemic stroke, myocardial infarction) are the most common complications of cardiovascular diseases. One of the leading mechanisms of these complications is atherosclerosis. Lipids play an important role in plaque development. Recently, the lipidome has been of greatest interest, since it may have a prognostic value in atherosclerosis development. With an increase in the circulation of proatherogenic lipidomic biomarkers, the risk of atheroscle­rosis destabilization and ischemic complications increases.The conducted studies made it possible to create additional risk stratification scales, for example, Cardiovascular Event Risk Test (CERT) 1 and 2. They make it possible to estimate the residual risk in patients taking statins. The lipidome examination in extracranial artery atherosclerosis can identify those plaques that have signs of instability, and therefore are dangerous for ischemic stroke development.The review describes the features of plasma lipidome in various cardiovascular diseases associated with atherosclerosis.

Lipidomic Biomarkers in Diabetic Distal Symmetrical Polyneuropathy with Conduction Slowing (P8-11.015)

Lipidomic Biomarkers in Diabetic Distal Symmetrical Polyneuropathy with Conduction Slowing (P8-11.015)

Appraising associations between signature lipidomic biomarkers and digestive system cancer risk: novel evidences from a prospective cohort study of UK Biobank and Mendelian randomization analyses

BackgroundThe roles of serum lipids on digestive system cancer (DSC) risk were still inconclusive. In this study, we systematically assessed indicative effects of signature lipidomic biomarkers (high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG)) on DSC (oesophagus, stomach, colorectal, liver, gallbladder, and pancreas cancers) risk.MethodsHDL-C, LDL-C, and TG concentration measurements were respectively analyzed with enzyme immunoinhibition, enzymatic selective protection, and GPO-POD methods in AU5800 supplied from Beckman Coulter. The diagnoses of DSCs were coded using International Classification of Diseases, Tenth Revision (ICD-10) codes updated until October 2022 in the UK Biobank (UKB). In this study, we assessed phenotypic association patterns between signature lipidomic biomarkers and DSC risk using restricted cubic splines (RCSs) in multivariable-adjusted Cox proportional hazards regression models. Moreover, linear and nonlinear causal association patterns of signature lipidomic biomarkers with DSC risk were determined by linear and nonlinear Mendelian randomization (MR) analyses.ResultsA median follow-up time of 11.8 years was recorded for 319,568 participants including 6916 DSC cases. A suggestive independent nonlinear phenotypic association was observed between LDL-C concentration and stomach cancer risk (Pnonlinearity < 0.05, Poverall < 0.05). Meanwhile, a remarkable independent linear negative phenotypic association was demonstrated between HDL-C concentration and stomach cancer risk (Pnonlinearity > 0.05, Poverall < 0.008 (0.05/6 outcomes, Bonferroni-adjusted P)), and suggestive independent linear positive associations were observed between HDL-C concentration and colorectal cancer risk, and between TG concentration and gallbladder cancer risk (Pnonlinearity > 0.05, Poverall < 0.05). Furthermore, based on nonlinear and linear MR-based evidences, we observed an suggestive independent negative causal association (hazard ratio (HR) per 1 mmol/L increase: 0.340 (0.137-0.843), P = 0.020) between LDL-C and stomach cancer risk without a nonlinear pattern (Quadratic P = 0.901, Cochran Q P = 0.434). Meanwhile, subgroup and stratified MR analyses both supported the category of LDL-C ≥ 4.1 mmol/L was suggestively protective against stomach cancer risk, especially among female participants (HR: 0.789 (0.637-0.977), P = 0.030) and participants aged 60 years or older (HR: 0.786 (0.638-0.969), P = 0.024), and the category of TG ≥ 2.2 mmol/L concluded to be a suggestive risk factor for gallbladder cancer risk in male participants (HR: 1.447 (1.020-2.052), P = 0.038) and participants aged 60 years or older (HR: 1.264 (1.003-1.593), P = 0.047).ConclusionsOur findings confirmed indicative roles of signature lipidomic biomarkers on DSC risk, notably detecting suggestive evidences for a protective effect of high LDL-C concentration on stomach cancer risk, and a detrimental effect of high TG concentration on gallbladder cancer risk among given participants.

Abstract A017: Lipidomic biomarkers of usual alcohol use and their association with pancreatic ductal adenocarcinoma risk

Abstract There is suggestive epidemiologic evidence for an association between alcohol use and pancreatic ductal adenocarcinoma (PDAC). Previous studies reported alcohol use to be associated with high-density lipoprotein and fatty acyl ethyl esters. Lipidomic metabolites may improve assessment of self-reported alcohol use and provide mechanistic insights for the alcohol-PDAC association. We aimed to identify lipidomic signatures of self-reported alcohol use and evaluate their associations with PDAC risk using comprehensive lipidomic measures from nested case-control studies within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial cohorts (716 case-control sets). Controls were matched to cases (1:1) by age, sex, race, date of blood draw and cohort. Serum samples and self-reported alcohol use were collected at baseline up to 24 years before PDAC diagnosis. Absolute concentrations of 611 lipid species across 15 classes were measured on the Complex Lipid Panel (Lipidizer Sciex SelexIon-5500 QTRAP platform) and grouped into 161 class-specific fatty acids (FA) and 28 total FAs. We conducted cross-sectional analyses using linear regression to identify associations between log-transformed alcohol and 1-standard deviation (SD) increase in log-lipid concentrations among controls within each cohort. Conditional logistic regression and fixed-effects meta-analysis were used to calculate PDAC odds ratios (OR) per 1-SD increase in the log-concentration identified alcohol-lipidomic metabolites in common in both cohorts. All models were adjusted for age, smoking, BMI, height, diabetes, education, and diet quality. Alcohol was associated with 36 species, 13 class-specific FAs and 3 total FAs at Bonferroni thresholds &amp;lt;8.18 × 10−5, &amp;lt;3.11 × 10−4, and &amp;lt;1.79 × 10−3, respectively. These belonged to ceramides, cholesterylesters, diacylglycerols, hexosylceramides, lactosylceramides, lysophosphatidylcholines, phosphatidylcholines, phospatidylethanolamines, phosphatidylinositols, and triacylglycerols lipid classes, and mostly holding FA15:0 (pentadecanoic acid), FA17:0 (margaric acid) and FA18:2 (linoleic acid). Nine triacylglycerols (TAG(49:3-FA18:2), TAG(51:3-FA18:2), TAG(49:2-FA18:2), TAG(49:3-FA15:0), TAG(51:3-FA15:0), TAG(51:4-FA18:3), TAG(51:2-FA18:2), TAG(51:5-FA18:3), TAG(51:2-FA15:0)) and one phosphatidylcholine (PC(15:0/18:2)) lipid species and one total FA (FA15:0) showed significant inverse associations with PDAC with ORs ranging from 0.82 to 0.86 (FDR&amp;lt;0.10). There was no association between class-specific FAs and PDAC. Our results support the hypothesis that alcohol use is associated with serum lipid species from 10 lipid classes, mostly with FA15:0, FA17:0 and FA18:2. Nine triacylglycerols, one phosphatidylcholine and total FA15:0 showed inverse prospective associations with PDAC, the latter having been shown to decrease inflammation and regulate glucose metabolism in previous studies. These findings may help understand the inconsistent association between alcohol use and PDAC. Citation Format: Sabine Naudin, Paloma Mitra, Ting Zhang, Demetrius Albanes, Steven C. Moore, Rachael Stolzenberg-Solomon. Lipidomic biomarkers of usual alcohol use and their association with pancreatic ductal adenocarcinoma risk [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A017.

Personalized Nutrition for APOE4 carriers: mechanisms and biomarkers that can inform future trials

AbstractCurrent studies suggest that the beneficial effects of increased polyunsaturated fat consumption (Mediterranean diet, fish consumption or long‐term DHA supplementation) on cognition are stronger in younger and middle aged APOE4 carriers. The efficacy of these nutritional interventions in APOE4 appears to wane with advancing age and cognitive disease. In addition, a recent systematic review revealed that ketogenic diets do not appear to have a beneficial effect on cognitive performance in APOE4 carriers compared to non‐carriers.In this talk, we focus on leading mechanisms of cerebral bioenergetic breakdown in the aging APOE4 brain that implicate neuroinflammation in several brain cell types including those of the blood‐brain barrier (BBB). We then address consequences of neuroinflammation and disturbed bioenergetics at the BBB on the delivery and brain metabolism of nutrients such as omega‐3s, nutrients and ketones. We provide examples of novel lipidomic biomarkers that can capture neuroinflammation in the Religious Order Study, DHA Brain Pilot Trial, and the ADCS DHA trial. Advances in brain imaging of polyunsaturated fatty acid brain uptake by PET are provided to obtain insights into APOE4 brain fatty acid consumption changes during the lifespan. Finally, we highlight how the ongoing PreventE4 trial is integrating measures of the gut microbiome to understand the effects of omega‐3 supplementation on the brain in individuals carrying the APOE4 allele and at risk for dementia.

Features of Metabolite Changes in Disease Evolution in Cholecystolithiasis

BackgroundCholecystolithiasis is defined as a disease caused by complex and changeable factors. Advanced age, female sex, and a hypercaloric diet rich in carbohydrates and poor in fiber, together with obesity and genetic factors, are the main factors that may predispose people to choledocholithiasis. However, serum biomarkers for the rapid diagnosis of choledocholithiasis remain unclear.AimsThis study was designed to explore the pathogenesis of cholecystolithiasis and identify the possible metabolic and lipidomic biomarkers for the diagnosis of the disease.MethodsUsing UHPLC-MS/MS and GC–MS, we detected the serum of 28 cholecystolithiasis patients and 19 controls. Statistical analysis of multiple variables included Principal Component Analysis (PCA). Visualization of differential metabolites was performed using volcano plots. The screened differential metabolites were further analyzed using clustering heatmaps. The quality of the model was assessed using random forests.ResultsIn this study, dramatically altered lipid homeostasis was detected in cholecystolithiasis group. In addition, the levels of short-chain fatty acids and amino acids were noticeably changed in patients with cholecystolithiasis. They detected higher levels of FFA.18.1, FFA.20.1, LPC16.0, and LPC20.1, but lower levels of 1-Methyl-l-histidine and 4-Hydroxyproline. In addition, glycine and l-Tyrosine were higher in choledocholithiasis group. Analyses of metabolic serum in affected patients have the potential to develop an integrated metabolite-based biomarker model that can facilitate the early diagnosis and treatment of the disease.ConclusionOur results highlight the value of integrating lipid, amino acid, and short-chain fatty acid to explore the pathophysiology of cholecystolithiasis disease, and consequently, improve clinical decision-making.Graphical Using UHPLC-MS/MS and GC–MS, the serum of 28 cholecystolithiasis patients and 19 controls were analyzed. Through differential metabolite analysis, we found that the cholecystolithiasis group was detected with dramatically altered lipid homeostasis compared with healthy controls. In addition, the levels of short-chain fatty acids and amino acids were noticeably changed in patients with cholecystolithiasis. They detected higher levels of FFA.18.1, FFA.20.1, LPC16.0, and LPC20.1, but lower levels of 1-Methyl-l-histidine and 4-Hydroxyproline. Moreover, glycine and l-Tyrosine were higher in choledocholithiasis group. The results not only identify new targets for the clinical diagnosis and treatment of cholecystolithiasis but may also provide valuable insights for the research of the disease.

Non-target ROIMCR LC–MS analysis of the disruptive effects of TBT over time on the lipidomics of Daphnia magna

IntroductionThis study has investigated the temporal disruptive effects of tributyltin (TBT) on lipid homeostasis in Daphnia magna. To achieve this, the study used Liquid Chromatography–Mass Spectrometry (LC–MS) analysis to analyze biological samples of Daphnia magna treated with TBT over time. The resulting data sets were multivariate and three-way, and were modeled using bilinear and trilinear non-negative factor decomposition chemometric methods. These methods allowed for the identification of specific patterns in the data and provided insight into the effects of TBT on lipid homeostasis in Daphnia magna.ObjectivesInvestigation of how are the changes in the lipid concentrations of Daphnia magna pools when they were exposed with TBT and over time using non-targeted LC–MS and advanced chemometric analysis.MethodsThe simultaneous analysis of LC–MS data sets of Daphnia magna samples under different experimental conditions (TBT dose and time) were analyzed using the ROIMCR method, which allows the resolution of the elution and mass spectra profiles of a large number of endogenous lipids. Changes obtained in the peak areas of the elution profiles of these lipids caused by the dose of TBT treatment and the time after its exposure are analyzed by principal component analysis, multivariate curve resolution-alternative least square, two-way ANOVA and ANOVA-simultaneous component analysis.Results87 lipids were identified. Some of these lipids are proposed as Daphnia magna lipidomic biomarkers of the effects produced by the two considered factors (time and dose) and by their interaction. A reproducible multiplicative effect between these two factors is confirmed and the optimal approach to model this dataset resulted to be the application of the trilinear factor decomposition model.ConclusionThe proposed non-targeted LC–MS lipidomics approach resulted to be a powerful tool to investigate the effects of the two factors on the Daphnia magna lipidome using chemometric methods based on bilinear and trilinear factor decomposition models, according to the type of interaction between the design factors.

Anti-Obesity Effect of Chlorin e6-Mediated Photodynamic Therapy on Mice with High-Fat-Diet-Induced Obesity.

This study aimed to evaluate the efficacy of Chlorin e6 (Ce6)-based photodynamic therapy (PDT) for anti-obesity activities in high-fat-diet (HFD)-induced obesity mouse models. We induced obesity in C57BL/6 mice by HFD and administered Ce6 (2.5 or 5 mg/kg) orally with 3 h of incubation. The mice were then exposed to light of high fluence rate (4.96 mW/cm2) or low fluence rate (2.56 mW/cm2) in the designed LED mouse chamber 2-3 days a week for up to 8 weeks. The study also analyzed the pharmacokinetics and optimization of the drug by evaluating the absorption, distribution, metabolism, and excretion (ADME) of Ce6 in the rat models. Both low doses (2.5 mg/kg) and high doses (5 mg/kg) of Ce6 with high irradiation dose showed better anti-obesity effects than other groups with decreased body weight. The lipid accumulation in the liver and adipocyte size in epididymal adipose tissues were found to be decreased by Ce6-PDT in comparison to vehicle-treated HFD groups. We also observed increased levels of the lipidomic biomarkers, such as leptin and LDL cholesterol, while observing decreasing levels of total cholesterol and adiponectin in the Ce6-PDT-treated mice. These findings may provide valuable insight into Ce6-PDT as an alternative and non-invasive therapeutic methodology for obesity and obesity-related diseases.

Causal effect of early life adiposity on gestational diabetes mellitus and mediating roles of lipidomic biomarkers.

The causal relationship between early life adiposity and gestational diabetes mellitus (GDM) and the underlying mechanisms remains unclear. This study aimed to investigate the independent causal association between early life adiposity and GDM and identify potential metabolic mediators and their mediating effects on this relationship. Using genome-wide association study (GWAS) summary statistics from the publicly available database of early life adiposity (5,530 cases and 8,318 controls) and GDM (11,279 cases and 179,600 controls), a two-step, two-sample Mendelian randomization (MR) was conducted to estimate the causal mediation effects of lipidomic biomarkers including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride, apolipoprotein A-Ι, and apolipoprotein B on the relationship between early life adiposity and GDM. Genetically predicted childhood adiposity was positively associated with risk of GDM (OR: 1.21, 95%CI: 1.09-1.34, p = 4.58 × 10-4). This causal relationship remained after accounting for adult adiposity traits in the multivariable MR analyses. Two-step MR identified three candidate mediators that partially mediated the effect of early life adiposity on GDM, including HDL-C (5.81, 95%CI: 3.05-8.57%), apolipoprotein A-Ι (4.16, 95%CI: 1.64-6.69%), and triglyceride (2.20, 95%CI: 0.48-3.92%). This MR study demonstrated that the causal effect of childhood obesity on future GDM risk was independent of adult adiposity. We identified three mediators, including HDL-C, apolipoprotein A-Ι, and triglyceride, in this association pathway. Our results provide insights into the pathogenesis of GDM and suggest additional prevention and treatment targets for GDM related to early life adiposity.

Lipidomics Profiling and Risk of Coronary Artery Disease in the BioHEART-CT Discovery Cohort

The current coronary artery disease (CAD) risk scores for predicting future cardiovascular events rely on well-recognized traditional cardiovascular risk factors derived from a population level but often fail individuals, with up to 25% of first-time heart attack patients having no risk factors. Non-invasive imaging technology can directly measure coronary artery plaque burden. With an advanced lipidomic measurement methodology, for the first time, we aim to identify lipidomic biomarkers to enable intervention before cardiovascular events. With 994 participants from BioHEART-CT Discovery Cohort, we collected clinical data and performed high-performance liquid chromatography with mass spectrometry to determine concentrations of 683 plasma lipid species. Statin-naive participants were selected based on subclinical CAD (sCAD) categories as the analytical cohort (n = 580), with sCAD+ (n = 243) compared to sCAD− (n = 337). Through a machine learning approach, we built a lipid risk score (LRS) and compared the performance of the existing Framingham Risk Score (FRS) in predicting sCAD+. We obtained individual classifiability scores and determined Body Mass Index (BMI) as the modifying variable. FRS and LRS models achieved similar areas under the receiver operating characteristic curve (AUC) in predicting the validation cohort. LRS enhanced the prediction of sCAD+ in the healthy-weight group (BMI &lt; 25 kg/m2), where FRS performed poorly and identified individuals at risk that FRS missed. Lipid features have strong potential as biomarkers to predict CAD plaque burden and can identify residual risk not captured by traditional risk factors/scores. LRS compliments FRS in prediction and has the most significant benefit in healthy-weight individuals.

New insight into the toxic effects of lithium in the ragworm Perinereis cultrifera as revealed by lipidomic biomarkers, redox status, and histopathological features.

Lithium (Li) is a toxic monovalent alkaline metal used in household items common to industrial applications. The present work was aimed at investigating the potential toxic effects of LiCl on the redox status, fatty acid composition, and histological aspects of the marine ragworm Perinereis cultrifera. Sea worms were exposed to LiCl graded doses (20, 40, and 80mg/L) for 48h. Compared with the control group, the saturated fatty acids (SFA) decreased while monounsaturated (MUFA) and polyunsaturated fatty acids (PUFA) increased upon exposure to LiCl. The increase in PUFA n-3 and PUFA n-6 was concomitant to an increase in docosahexaenoic (DHA: C22:6n-3), eicosapentaenoic (EPA: C20:5n-3), and docosapentaenoic acid (C22:5n-6) fatty acids. Results showed that LiCl-treated specimens accumulate lithium with increasing exposure gradient. Indeed, the exposure to LiCl doses promoted oxidative stress with an increase of the ferric reducing antioxidant power (FRAP), malondialdehyde (MDA), hydrogen peroxide (H2O2), advanced oxidation protein product (AOPP), and protein carbonyl (PCO) as well as the enzymatic and non-enzymatic antioxidants (non-protein thiols (NPSH), catalase (CAT), superoxide dismutase (SOD), reduced glutathione (GSH), glutathione peroxidase (GPx), glutathione S-transferase (GST), and metallothionein (MT)) levels in all treated groups. Our biochemical findings have been affirmed by the histopathological observations showing hyperplasia and loss of the intestine structure in treated specimens. Overall, our findings give new insights on the toxic effect of LiCl on the redox status of P. cultrifera body tissue and highlighted the usefulness of the FA composition as an early sensitive bioindicators to better understand LiCl mechanism of toxicity in marine polychaetes.

Lipid Profiling Reveals Lipidomic Signatures of Weight Loss Interventions.

Obesity is an epidemic all around the world. Weight loss interventions that are effective differ from each other with regard to various lipidomic responses. Here, we aimed to find lipidomic biomarkers that are related to beneficial changes in weight loss. We adopted an untargeted liquid chromatography with tandem mass spectrometry (LC-MS/MS) method to measure 953 lipid species for Exercise (exercise intervention cohort, N = 25), 1388 lipid species for LSG (laparoscopic sleeve gastrectomy cohort, N = 36), and 886 lipid species for Cushing (surgical removal of the ACTH-secreting pituitary adenomas cohort, N = 25). Overall, the total diacylglycerol (DG), triacylglycerol (TG), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylserine (PS), and sphingomyelin (SM) levels were associated with changes in BMI, glycated hemoglobin (HbA1c), triglyceride, and total cholesterol according to weight loss interventions. We found that 73 lipid species changed among the three weight loss interventions. We screened 13 lipid species with better predictive accuracy in diagnosing weight loss situations in either Exercise, LSG, or Cushing cohorts (AUROC > 0.7). More importantly, we identified three phosphatidylcholine (PC) lipid species, PC (14:0_18:3), PC (31:1), and PC (32:2) that were significantly associated with weight change in three studies. Our results highlight potential lipidomic biomarkers that, in the future, could be used in personalized approaches involving weight loss interventions.