Clinical lipidomics: realizing the potential of lipid profiling

Establishment of MS LOC platform and its pilot application in clinical lipidomics

Lipidomics has demonstrated significant potential for disease diagnosis and prediction. The development and optimization of a robust mass spectrometry (MS) platform for lipidome analysis is critically important, as it can facilitate biomarker discovery, cohort testing, and performance evaluation in clinical lipidomics studies. In this work, we developed a high-throughput and reliable platform, termed MS Lab on a Chip (MS LOC), which integrates the MetArray chip, an automated lipidomics pretreatment protocol, and the reflectron matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) instrument. The MetArray chip, produced through a mass production process, exhibited exceptional stability as an MS substrate. The integration of automated lipid pretreatment and MS detection processes ensures high throughput, stability and efficiency during sample preparation. The analysis of various lipid standards and different types of biological samples enabled comprehensive investigation of lipid features and annotation using the MS LOC. Furthermore, a small cohort study, consisting of hepatocellular carcinoma (HCC) and non-HCC groups, was conducted on this platform, providing preliminary validation of its performance and suggesting that this platform offers a comprehensive protocol for clinical lipidomics testing.

Lipids dysregulation in diseases: core concepts, targets and treatment strategies

Lipid metabolism is a well-regulated process essential for maintaining cellular functions and energy homeostasis. Dysregulation of lipid metabolism is associated with various conditions, including cardiovascular diseases, neurodegenerative disorders, and metabolic syndromes. This review explores the mechanisms underlying lipid metabolism, emphasizing the roles of key lipid species such as triglycerides, phospholipids, sphingolipids, and sterols in cellular physiology and pathophysiology. It also examines the genetic and environmental factors contributing to lipid dysregulation and the challenges of diagnosing and managing lipid-related disorders. Recent advancements in lipid-lowering therapies, including PCSK9 inhibitors, ezetimibe, bempedoic acid, and olpasiran, provide promising treatment options. However, these advancements are accompanied by challenges related to cost, accessibility, and patient adherence. The review highlights the need for personalized medicine approaches to address the interplay between genetics and environmental factors in lipid metabolism. As lipidomics and advanced diagnostic tools continue to progress, a deeper understanding of lipid-related disorders could pave the way for more effective therapeutic strategies.

Matrix Linear Models for Connecting Metabolite Composition to Individual Characteristics

Background/Objectives: High-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how association of metabolite levels with individual (sample) characteristics, such as sex or treatment, depend on metabolite characteristics such as pathways. Typically, this is done using a two-step process. In the first step, we assess the association of each metabolite with individual characteristics. In the second step, an enrichment analysis is performed by metabolite characteristics. Methods: We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework previously developed for high-throughput genetic screens. Our method can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). Results: We demonstrate the flexibility and interoperability of MLMs by applying them to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglyceride characteristics, such as the number of double bonds and the number of carbon atoms. Conclusion: The matrix linear model offers a flexible, efficient, and interpretable framework for integrating external information and examining complex relationships in metabolomics data. Our method has been implemented in the open-source Julia package, MatrixLM. Data analysis scripts with example data analyses are also available.

Clinical prediction of wound re-epithelisation outcomes in non-severe burn injury using the plasma lipidome

Whilst wound repair in severe burns has received substantial research attention, non-severe burns (<20 % total body surface area) remain relatively understudied, despite causing considerable physiological impact and constituting most of the hospital admissions for burns. Early prediction of healing outcomes would decrease financial and patient burden, and aid in preventing long-term complications from poor wound healing. Lipids have been implicated in inflammation and tissue repair and may play essential roles in burn wound healing. In this study, plasma samples were collected from 20 non-severe burn patients over six weeks from admission, including surgery, and analysed by liquid chromatography-tandem mass spectrometry and nuclear magnetic resonance spectroscopy to identify 850 lipids and 112 lipoproteins. Orthogonal projections to latent structures-discriminant analysis was performed to identify changes associated with re-epithelialisation and delayed re-epithelisation. We demonstrated that the lipid and lipoprotein profiles at admission could predict re-epithelisation outcomes at two weeks post-surgery, and that these discriminatory profiles were maintained up to six weeks post-surgery. Inflammatory markers GlycB and C-reactive protein indicated divergent systemic responses to the burn injury at admission. Triacylglycerols, diacylglycerols and low-density lipoprotein subfractions were associated with re-epithelisation (p-value <0.02, Cliff's delta >0.7), whilst high-density lipoprotein subfractions, phosphatidylinositols, phosphatidylcholines, and phosphatidylserines were associated with delayed wound closure at two weeks post-surgery (p-value <0.01, Cliff's delta <-0.7). Further model validation will potentially lead to personalised intervention strategies to reduce the risk of chronic complications post-burn injury.

NAFPD exacerbation by hyperlipidemia combined with hyperuricemia: a pilot rat experiment in lipidomics

Background

Hyperuricemia and non-alcoholic fatty pancreas disease (NAFPD) are prevalent metabolic diseases, but the relationship between them remains underexplored.

Methods

Eighteen Sprague-Dawley rats were randomly assigned to three groups: normal (CON), high-fat (PO), and high-fat high-uric acid (PH). After 12 weeks, serum uric acid (SUA) and triacylglycerol levels were measured. Pathological changes in the pancreas were assessed using hematoxylin-eosin (HE) staining. Serum samples were analyzed using lipidomics technology, and multivariate statistical analysis was employed to identify differences in lipid metabolism.

Results

SUA levels in the PO group were not significantly different from those in the CON group (p > 0.05). However, from the 4th week onward, SUA levels in the PH group were significantly higher than those in both the PO and CON groups (p < 0.05). HE staining revealed that most rats in the CON group exhibited normal pancreatic islet and acinar cell morphology. The pathological NAFPD score in the PH group was higher than that in the PO group. Lipidomics analysis identified 34 potential serum biomarkers in the CON and PO groups, 38 in the CON and PH groups, and 32 in the PH and PO groups. These metabolites primarily included sphingolipids, cholesterol esters, fatty acids, triacylglycerols, phosphatidylcholines, lysophosphatidylcholine, phosphatidylethanolamine, and lysophosphatidylethanolamine.

Conclusion

Hyperlipidemia combined with hyperuricemia might exacerbates NAFPD. Glycerophospholipids may serve as key biomarkers in this process, potentially linked to a chronic inflammatory response mediated by glycerophospholipids.

Personalized Profiling of Lipoprotein and Lipid Metabolism Based on 1018 Measures from Combined Quantitative NMR and LC-MS/MS Platforms

Applications of advanced omics methodologies are increasingly popular in biomedicine. However, large-scale studies aiming at clinical translation are typically siloed to single technologies. Here, we present the first comprehensive large-scale population data combining 209 lipoprotein measures from a quantitative NMR spectroscopy platform and 809 lipid classes and species from a quantitative LC-MS/MS platform. These data with 1018 molecular measures were analyzed in two population cohorts totaling 7830 participants. The association and cluster analyses revealed excellent coherence between the methodologically independent data domains and confirmed their quantitative compatibility and suitability for large-scale studies. The analyses elucidated the detailed molecular characteristics of the heterogeneous circulatory macromolecular lipid transport system and the underlying structural and compositional relationships. Unsupervised neural network analysis─the so-called self-organizing maps (SOMs)─revealed that these deep molecular and metabolic data are inherently related to key physiological and clinical population characteristics. The data-driven population subgroups uncovered marked differences in the population distribution of multiple cardiometabolic risk factors. These include, e.g., multiple lipoprotein lipids, apolipoprotein B, ceramides, and oxidized lipids. All 79 structurally unique triglyceride species showed similar associations over the entire lipoprotein cascade and indicated systematically increased risk for carotid intima media thickening and other atherosclerosis risk factors, including obesity and inflammation. The metabolic attributes for 27 individual cholesteryl ester species, which formed six distinct clusters, were more intricate with associations both with higher─e.g., CE(16:1)─and lower─e.g., CE(20:4)─cardiometabolic risk. The molecular details provided by these combined data are unprecedented for molecular epidemiology and demonstrate a new potential avenue for population studies.

Application of Human Plasma Targeted Lipidomics and Analysis of Toxic Elements to Capture the Metabolic Complexities of Hypothyroidism

BACKGROUND

Hypothyroidism (HT) affects millions worldwide and can lead to various lipid disorders. The metabolic complexity and the influence of toxic elements in autoimmune and non-autoimmune HT subtypes are not fully understood. This study aimed to investigate the relationships between plasma lipidome, toxic elements, and clinical classifications of HT in unexposed individuals.

METHODS

Samples were collected from 120 adults assigned to a study group with Hashimoto's disease and non-autoimmune HT, and a healthy control group. Quantification of 145 pre-defined lipids was performed by using triple quadrupole tandem mass spectrometry (TQ MS/MS) in multiple reactions monitoring (MRM) mode via positive electrospray ionization (ESI). Levels of toxic elements were determined using inductively coupled plasma mass spectrometry (ICP-MS).

RESULTS

Significant associations between altered levels of several components of the plasma lipidome and Al, Cd, Ni, As, and Pb with HT were found. We show metabolic differences in lysophosphatidylcholines (LPC) and phosphatidylcholines (PC) between HT and controls, with distinct predicted activation patterns for lysolecithin acyltransferase and phospholipase A2.

CONCLUSIONS

There are significant changes in the lipidome profiles of healthy subjects compared to euthyroid HT patients treated with L-thyroxine, which are related to the type of hypothyroidism and non-occupational exposure to toxic elements.

Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards

In this community effort, we compare measurements between 34 laboratories from 19 countries, utilizing mixtures of labelled authentic synthetic standards, to quantify by mass spectrometry four clinically used ceramide species in the NIST (National Institute of Standards and Technology) human blood plasma Standard Reference Material (SRM) 1950, as well as a set of candidate plasma reference materials (RM 8231). Participants either utilized a provided validated method and/or their method of choice. Mean concentration values, and intra- and inter-laboratory coefficients of variation (CV) were calculated using single-point and multi-point calibrations, respectively. These results are the most precise (intra-laboratory CVs ≤ 4.2%) and concordant (inter-laboratory CVs < 14%) community-derived absolute concentration values reported to date for four clinically used ceramides in the commonly analyzed SRM 1950. We demonstrate that calibration using authentic labelled standards dramatically reduces data variability. Furthermore, we show how the use of shared RM can correct systematic quantitative biases and help in harmonizing lipidomics. Collectively, the results from the present study provide a significant knowledge base for translation of lipidomic technologies to future clinical applications that might require the determination of reference intervals (RIs) in various human populations or might need to estimate reference change values (RCV), when analytical variability is a key factor for recall during multiple testing of individuals.

Lipidomic profiling in patients with familial hypercholesterolemia: Abnormalities in glycerolipids and oxysterols

OBJECTIVES AND AIM

This study aimed to identify precise biomarkers and develop targeted therapeutic strategies for preventing premature atherosclerotic cardiovascular disease in patients with familial hypercholesterolemia (FH) by investigating the quantitative and qualitative abnormalities in the metabolic network of lipids in these patients using an advanced lipidomics platform.

DESIGN & METHODS

The study population comprised 18 homozygous (HoFH), 18 heterozygous (HeFH) FH patients, and 20 healthy controls. Cholesterol oxidation products (oxysterol, COPs) and main lipid classes were determined using gas chromatography-mass spectrometry. Results were expressed as percentages of total fat matter for lipid classes and percentages of total COPs for oxysterols. The principal component analysis (PCA) was also carried out, to highlight the correlation between studied parameters and groups investigated.

RESULTS

Patients (both HoFH and HeFH) showed lower content of free fatty acids (FFAs) and greater values of triacylglycerols (TAGs) in comparison to controls. HoFH showed lower monoacylglycerols (P<0.01) and higher free cholesterol (FC) (P<0.05) when compared to HeFH and controls. The total content of COPs ranged from 1.96 to 4.25 mg/dL, from 2.27 to 4.05 mg/dL, and from 0.79 to 4.12 mg/dL in healthy controls, HoFH and HeFH groups, respectively, with no significant differences between patients and controls. In general, the 7α-hydroxycholesterol (7α-HC) was greater than other COPs. However, no significant differences were found between the three studied groups. Moreover, an opposite trend was observed between 7α-HC and 7-ketocholesterol (7-KC). Additionally, when PCA was carried out, the first two PCs explained 92.13 % of the total variance, of which the PC1 describes 53.94 % of variance mainly correlated to TAGs, diacylglycerols (DAGs), and 7-KC. On the other hand, the PC2 was correlated primarily for FFAs, FC and esterified sterols (E-STE).

CONCLUSIONS

In conclusion, abnormal levels of TAGs, DAGs and 7-KC were associated with HeFH while HoFH was associated with the abnormal amount of E-STE.

Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention

BACKGROUND

Accurate risk stratification is vital for primary prevention of cardiovascular disease (CVD). However, traditional tools such as the Framingham Risk Score (FRS) may underperform within the diverse intermediate-risk group, which includes individuals requiring distinct management strategies.

OBJECTIVES

This study aimed to develop a lipidomic-enhanced risk score (LRS), specifically targeting risk prediction and reclassification within the intermediate group, benchmarked against the FRS.

METHODS

The LRS was developed via a machine learning workflow using ridge regression on the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab; n = 10,339). It was externally validated with the Busselton Health Study (n = 4,492), and its predictive utility for coronary artery calcium scoring (CACS)-based outcomes was independently validated in the BioHEART cohort (n = 994).

RESULTS

LRS significantly improved discrimination metrics for the intermediate-risk group in both AusDiab and Busselton Health Study cohorts (all P < 0.001), increasing the area under the curve for CVD events by 0.114 (95% CI: 0.1123-0.1157) and 0.077 (95% CI: 0.0755-0.0785), with a net reclassification improvement of 0.36 (95% CI: 0.21-0.51) and 0.33 (95% CI: 0.15-0.49), respectively. For CACS-based outcomes in BioHEART, LRS achieved a significant area under the curve improvement of 0.02 over the FRS (0.76 vs 0.74; P < 1.0 × 10-5). A simplified, clinically applicable version of LRS was also created that had comparable performance to the original LRS.

CONCLUSIONS

LRS, augmenting the FRS, presents potential to improve intermediate-risk stratification and to predict atherosclerotic markers using a simple blood test, suitable for clinical application. This could facilitate the triage of individuals for noninvasive imaging such as CACS, fostering precision medicine in CVD prevention and management.

Development and validation of a plasmalogen score as an independent modifiable marker of metabolic health: population based observational studies and a placebo-controlled cross-over study

BACKGROUND

Decreased levels of circulating ethanolamine plasmalogens [PE(P)], and a concurrent increase in phosphatidylethanolamine (PE) are consistently reported in various cardiometabolic conditions. Here we devised, a plasmalogen score (Pls Score) that mirrors a metabolic signal that encompasses the levels of PE(P) and PE and captures the natural variation in circulating plasmalogens and perturbations in their metabolism associated with disease, diet, and lifestyle.

METHODS

We utilised, plasma lipidomes from the Australian Obesity, Diabetes and Lifestyle study (AusDiab; n = 10,339, 55% women) a nationwide cohort, to devise the Pls Score and validated this in the Busselton Health Study (BHS; n = 4,492, 56% women, serum lipidome) and in a placebo-controlled crossover trial involving Shark Liver Oil (SLO) supplementation (n = 10, 100% men). We examined the association of the Pls Score with cardiometabolic risk factors, type 2 diabetes mellitus (T2DM), cardiovascular disease and all-cause mortality (over 17 years).

FINDINGS

In a model, adjusted for age, sex and BMI, individuals in the top quintile of the Pls Score (Q5) relative to Q1 had an OR of 0.31 (95% CI 0.21-0.43), 0.39 (95% CI 0.25-0.61) and 0.42 (95% CI 0.30-0.57) for prevalent T2DM, incident T2DM and prevalent cardiovascular disease respectively, and a 34% lower mortality risk (HR = 0.66; 95% CI 0.56-0.78). Significant associations between diet and lifestyle habits and Pls Score exist and these were validated through dietary supplementation of SLO that resulted in a marked change in the Pls Score.

INTERPRETATION

The Pls Score as a measure that captures the natural variation in circulating plasmalogens, was not only inversely related to cardiometabolic risk and all-cause mortality but also associate with diet and lifestyle. Our results support the potential utility of the Pls Score as a biomarker for metabolic health and its responsiveness to dietary interventions. Further research is warranted to explore the underlying mechanisms and optimise the practical implementation of the Pls Score in clinical and population settings.

FUNDING

National Health and Medical Research Council (NHMRC grant 233200), National Health and Medical Research Council of Australia (Project grant APP1101320), Health Promotion Foundation of Western Australia, and National Health and Medical Research Council of Australia Senior Research Fellowship (#1042095).

Implementation of Modern Therapeutic Drug Monitoring and Lipidomics Approaches in Clinical Practice: A Case Study with Colistin Treatment

Nowadays, lipidomics plays a crucial role in the investigation of novel biomarkers of various diseases. Its implementation into the field of clinical analysis led to the identification of specific lipids and/or significant changes in their plasma levels in patients suffering from cancer, Alzheimer's disease, sepsis, and many other diseases and pathological conditions. Profiling of lipids and determination of their plasma concentrations could also be helpful in the case of drug therapy management, especially in combination with therapeutic drug monitoring (TDM). Here, for the first time, a combined approach based on the TDM of colistin, a last-resort antibiotic, and lipidomic profiling is presented in a case study of a critically ill male patient suffering from Pseudomonas aeruginosa-induced pneumonia. Implementation of innovative analytical approaches for TDM (online combination of capillary electrophoresis with tandem mass spectrometry, CZE-MS/MS) and lipidomics (liquid chromatography-tandem mass spectrometry, LC-MS/MS) was demonstrated. The CZE-MS/MS strategy confirmed the chosen colistin drug dosing regimen, leading to stable colistin concentrations in plasma samples. The determined colistin concentrations in plasma samples reached the required minimal inhibitory concentration of 1 μg/mL. The complex lipidomics approach led to monitoring 545 lipids in collected patient plasma samples during and after the therapy. Some changes in specific individual lipids were in good agreement with previous lipidomics studies dealing with sepsis. The presented case study represents a good starting point for identifying particular individual lipids that could correlate with antimicrobial and inflammation therapeutic management.

Exploring the significance of lipids in Alzheimer's disease and the potential of extracellular vesicles

Lipids play a significant role in maintaining central nervous system (CNS) structure and function, and the dysregulation of lipid metabolism is known to occur in many neurological disorders, including Alzheimer's disease. Here we review what is currently known about lipid dyshomeostasis in Alzheimer's disease. We propose that small extracellular vesicle (sEV) lipids may provide insight into the pathophysiology and progression of Alzheimer's disease. This stems from the recognition that sEV likely contributes to disease pathogenesis, but also an understanding that sEV can serve as a source of potential biomarkers. While the protein and RNA content of sEV in the CNS diseases have been studied extensively, our understanding of the lipidome of sEV in the CNS is still in its infancy.

Nanoparticle-Mediated Drug Delivery Systems for Precision Targeting in Oncology

Nanotechnology has emerged as a transformative force in oncology, facilitating advancements in site-specific cancer therapy and personalized oncomedicine. The development of nanomedicines explicitly targeted to cancer cells represents a pivotal breakthrough, allowing the development of precise interventions. These cancer-cell-targeted nanomedicines operate within the intricate milieu of the tumour microenvironment, further enhancing their therapeutic efficacy. This comprehensive review provides a contemporary perspective on precision cancer medicine and underscores the critical role of nanotechnology in advancing site-specific cancer therapy and personalized oncomedicine. It explores the categorization of nanoparticle types, distinguishing between organic and inorganic variants, and examines their significance in the targeted delivery of anticancer drugs. Current insights into the strategies for developing actively targeted nanomedicines across various cancer types are also provided, thus addressing relevant challenges associated with drug delivery barriers. Promising future directions in personalized cancer nanomedicine approaches are delivered, emphasising the imperative for continued optimization of nanocarriers in precision cancer medicine. The discussion underscores translational research's need to enhance cancer patients' outcomes by refining nanocarrier technologies in nanotechnology-driven, site-specific cancer therapy.

Distinct Lipidomic Profiles between People Living with HIV Treated with E/C/F/TAF or B/F/TAF: An Open-Label Prospective Cohort Study

INTRODUCTION

Elvitegravir/cobicistat/emtricitabine/tenofovir alafenamide (E/C/F/TAF) has been increasingly replaced by bictegravir/emtricitabine/tenofovir alafenamide (B/F/TAF) in the treatment of human immunodeficiency virus (HIV) owing to its more favorable pharmacokinetics and fewer drug-drug interactions. However, the effect of this switch on plasma lipids and lipidomic profiles remains poorly characterized.

METHODS

HIV infected patients on an E/C/F/TAF regimen were recruited into the study and followed up every 12 weeks. Participants were divided into E/C/F/TAF and B/F/TAF groups depending on whether they were switched to B/F/TAF during follow-up. Clinical information and blood samples were collected at 0, 12, and 24 weeks, and lipidomic analysis was performed using liquid chromatography mass spectrometry.

RESULTS

No significant differences were observed between the groups at baseline. At week 24, patients switched to B/F/TAF had lower triglyceride [mmol/L; 1.23 (0.62) versus 2.03 (0.75), P = 0.001] and very low-density lipoprotein cholesterol [mmol/L; 0.64 (0.26) versus 0.84 (0.32), P = 0.037) compared with patients who continued E/C/F/TAF therapy. Small decrease from baseline in Framingham general cardiovascular risk score (FRS) was observed in the B/F/TAF arm [week (W) 0: 2.59 (1.57) versus W24: 2.18 (1.01), P = 0.043]. Lipidomic analysis indicated that E/C/F/TAF treatment increased the levels of several diglycerides (DGs), triacylglycerols (TAGs), and lyso-phosphatidylcholines (LPCs), whereas switching to B/F/TAF led to increased sphingolipids and glycerophospholipids. After adjusting for demographic and clinical parameters, only DG (16:0/18:2), DG (18:2/22:6), DG (18:3/18:2), DG (20:5/18:2), TAG (18:3/18:2/21:5), TAG (20:5/18:2/22:6), and LPC (22:6) were found to be significantly associated with FRS (regression coefficient of 0.17-6.02, P < 0.05). Most of these FRS associate lipid species were significantly elevated in individuals treated with E/C/F/TAF instead of individuals treated with B/F/TAF.

CONCLUSION

E/C/F/TAF promotes the accumulation of lipid species closely associated with cardiovascular disease (CVD) risk among people living with HIV, whereas B/F/TAF has a decreased impact on CVD-related lipid profile and is associated with lower CVD risk. A graphical abstract is available with this article.

TRIAL REGISTRATION

ClinicalTrials.gov; identifier, NCT06019273.

The intersection of host in vivo metabolism and immune responses to infection with kinetoplastid and apicomplexan parasites

SUMMARYProtozoan parasite infection dramatically alters host metabolism, driven by immunological demand and parasite manipulation strategies. Immunometabolic checkpoints are often exploited by kinetoplastid and protozoan parasites to establish chronic infection, which can significantly impair host metabolic homeostasis. The recent growth of tools to analyze metabolism is expanding our understanding of these questions. Here, we review and contrast host metabolic alterations that occur in vivo during infection with Leishmania, trypanosomes, Toxoplasma, Plasmodium, and Cryptosporidium. Although genetically divergent, there are commonalities among these pathogens in terms of metabolic needs, induction of the type I immune responses required for clearance, and the potential for sustained host metabolic dysbiosis. Comparing these pathogens provides an opportunity to explore how transmission strategy, nutritional demand, and host cell and tissue tropism drive similarities and unique aspects in host response and infection outcome and to design new strategies to treat disease.

Clinical advances in analytical profiling of signature lipids: implications for severe non-communicable and neurodegenerative diseases

BACKGROUND

Lipids play key roles in numerous biological processes, including energy storage, cell membrane structure, signaling, immune responses, and homeostasis, making lipidomics a vital branch of metabolomics that analyzes and characterizes a wide range of lipid classes. Addressing the complex etiology, age-related risk, progression, inflammation, and research overlap in conditions like Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and Cancer poses significant challenges in the quest for effective therapeutic targets, improved diagnostic markers, and advanced treatments. Mass spectrometry is an indispensable tool in clinical lipidomics, delivering quantitative and structural lipid data, and its integration with technologies like Liquid Chromatography (LC), Magnetic Resonance Imaging (MRI), and few emerging Matrix-Assisted Laser Desorption Ionization- Imaging Mass Spectrometry (MALDI-IMS) along with its incorporation into Tissue Microarray (TMA) represents current advances. These innovations enhance lipidomics assessment, bolster accuracy, and offer insights into lipid subcellular localization, dynamics, and functional roles in disease contexts.

AIM OF THE REVIEW

The review article summarizes recent advancements in lipidomic methodologies from 2019 to 2023 for diagnosing major neurodegenerative diseases, Alzheimer's and Parkinson's, serious non-communicable cardiovascular diseases and cancer, emphasizing the role of lipid level variations, and highlighting the potential of lipidomics data integration with genomics and proteomics to improve disease understanding and innovative prognostic, diagnostic and therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical lipidomic studies are a promising approach to track and analyze lipid profiles, revealing their crucial roles in various diseases. This lipid-focused research provides insights into disease mechanisms, biomarker identification, and potential therapeutic targets, advancing our understanding and management of conditions such as Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and specific cancers.

Perspectives on the success of plasma lipidomics in cardiovascular drug discovery and future challenges

INTRODUCTION

Plasma lipidomics has emerged as a powerful tool in cardiovascular drug discovery by providing insights into disease mechanisms, identifying potential biomarkers for diagnosis and prognosis, and discovering novel targets for drug development. Widespread application of plasma lipidomics is hampered by technological limitations and standardization and requires a collaborative approach to maximize its use in cardiovascular drug discovery.

AREAS COVERED

This review provides an overview of the utility of plasma lipidomics in cardiovascular drug discovery and discusses the challenges and future perspectives of this rapidly evolving field. The authors discuss the role of lipidomics in understanding the molecular mechanisms of CVD, identifying novel biomarkers for diagnosis and prognosis, and discovering new therapeutic targets for drug development. Furthermore, they highlight the challenges faced in data analysis, standardization, and integration with other omics approaches and propose future directions for the field.

EXPERT OPINION

Plasma lipidomics holds great promise for improving the diagnosis, treatment, and prevention of CVD. While challenges remain in standardization and technology, ongoing research and collaboration among scientists and clinicians will undoubtedly help overcome these obstacles. As lipidomics evolves, its impact on cardiovascular drug discovery and clinical practice is expected to grow, ultimately benefiting patients and healthcare systems worldwide.

PCPro: a clinically accessible, circulating lipid biomarker signature for poor-prognosis metastatic prostate cancer

BACKGROUND

Using comprehensive plasma lipidomic profiling from men with metastatic castration-resistant prostate cancer (mCRPC), we have previously identified a poor-prognostic lipid profile associated with shorter overall survival (OS). In order to translate this biomarker into the clinic, these men must be identifiable via a clinically accessible, regulatory-compliant assay.

METHODS

A single regulatory-compliant liquid chromatography-mass spectrometry assay of candidate lipids was developed and tested on a mCRPC Discovery cohort of 105 men. Various risk-score Cox regression prognostic models of OS were built using the Discovery cohort. The model with the highest concordance index (PCPro) was chosen for validation and tested on an independent Validation cohort of 183 men.

RESULTS

PCPro, the lipid biomarker, contains Cer(d18:1/18:0), Cer(d18:1/24:0), Cer(d18:1/24:1), triglycerides and total cholesterol. Within the Discovery and Validation cohorts, men who were PCPro positive had significantly shorter OS compared to those who were PCPro negative (Discovery: median OS 12.0 months vs 24.2 months, hazard ratio (HR) 3.75 [95% confidence interval (CI) 2.29-6.15], p < 0.001, Validation: median OS 13.0 months vs 25.7 months, HR = 2.13 [95% CI 1.46-3.12], p < 0.001).

CONCLUSIONS

We have developed PCPro, a lipid biomarker assay capable of prospectively identifying men with mCRPC with a poor prognosis. Prospective clinical trials are required to determine if men who are PCPro positive will benefit from therapeutic agents targeting lipid metabolism.

Lipid storage disease in 4 sibling superb birds-of-paradise (Lophorina superba)

Pedigree analysis, clinical, gross, microscopic, ultrastructural, and lipidomic findings in 4 female superb bird-of-paradise (SBOP, Lophorina superba) siblings led to the diagnosis of a primary inherited glycerolipid storage disease. These birds were the offspring of a related breeding pair (inbreeding coefficient = 0.1797) and are the only known SBOPs to display this constellation of lesions. The birds ranged from 0.75 to 4.3 years of age at the time of death. Two birds were euthanized and 1 died naturally due to the disease, and 1 died of head trauma with no prior clinical signs. Macroscopic findings included hepatomegaly and pallor (4/4), cardiac and renal pallor (2/4), and coelomic effusion (1/4). Microscopic examination found marked tissue distortion due to cytoplasmic lipid vacuoles in hepatocytes (4/4), cardiomyocytes (4/4), renal tubular epithelial cells (4/4), parathyroid gland principal cells (2/2), exocrine pancreatic cells (3/3), and the glandular cells of the ventriculus and proventriculus (3/3). Ultrastructurally, the lipids were deposited in single to coalescing or fused droplets lined by an inconspicuous or discontinuous monolayer membrane. Lipidomic profiling found that the cytoplasmic lipid deposits were primarily composed of triacylglycerols. Future work, including sequencing of the SBOP genome and genotyping, will be required to definitively determine the underlying genetic mechanism of this disease.

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.

Unraveling lipid and inflammation interplay in cancer, aging and infection for novel theranostic approaches

The synergistic relationships between Cancer, Aging, and Infection, here referred to as the CAIn Triangle, are significant determinants in numerous health maladies and mortality rates. The CAIn-related pathologies exhibit close correlations with each other and share two common underlying factors: persistent inflammation and anomalous lipid concentration profiles in the membranes of affected cells. This study provides a comprehensive evaluation of the most pertinent interconnections within the CAIn Triangle, in addition to examining the relationship between chronic inflammation and specific lipidic compositions in cellular membranes. To tackle the CAIn-associated diseases, a suite of complementary strategies aimed at diagnosis, prevention, and treatment is proffered. Our holistic approach is expected to augment the understanding of the fundamental mechanisms underlying these diseases and highlight the potential of shared features to facilitate the development of novel theranostic strategies.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Matrix Linear Models for connecting metabolite composition to individual characteristics

High-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how the association of metabolite levels with individual (sample) characteristics such as sex or treatment may depend on metabolite characteristics such as pathway. Typically this is one in a two-step process: In the first step we assess the association of each metabolite with individual characteristics. In the second step an enrichment analysis is performed by metabolite characteristics among significant associations. We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework we have previously developed for high-throughput genetic screens. Our framework can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). We demonstrate how MLM offers flexibility and interpretability by applying our method to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglycerides characteristics, such as the number of double bonds and the number of carbon atoms. The proposed method have been implemented in the open-source Julia package, MatrixLM. Data analysis scripts with example data analyses are also available.

Expanding Lipidomic Coverage in Multisegment Injection-Nonaqueous Capillary Electrophoresis-Mass Spectrometry via a Convenient and Quantitative Methylation Strategy

Orthogonal separation techniques coupled to high-resolution mass spectrometry are required for characterizing the human lipidome, given its inherent chemical and structural complexity. However, electrophoretic separations remain largely unrecognized in contemporary lipidomics research compared to established chromatographic and ion mobility methods. Herein, we introduce a novel derivatization protocol based on 3-methyl-1-p-tolyltriazene (MTT) as a safer alternative to diazomethane for quantitative phospholipid (PL) methylation (∼90%), which enables their rapid analysis by multisegment injection-nonaqueous capillary electrophoresis-mass spectrometry (MSI-NACE-MS). Isobaric interferences and ion suppression effects were minimized by performing an initial reaction using 9-fluorenylmethyoxycarbonyl chloride prior to MTT and a subsequent back extraction in hexane. This charge-switch derivatization strategy expands lipidome coverage when using MSI-NACE-MS under positive ion mode with improved resolution, greater sensitivity, and higher throughput (∼3.5 min/sample), notably for zwitterionic PLs that are analyzed as their cationic phosphate methyl esters. Our method was validated by analyzing methyl-tert-butyl ether extracts of reference human plasma, which enabled a direct comparison of 48 phosphatidylcholine and 27 sphingomyelin species previously reported in an interlaboratory lipidomics harmonization study. The potential for plasma PL quantification by MSI-NACE-MS via a serial dilution of NIST SRM-1950 was also demonstrated based on estimation of relative response factors using their reported consensus concentrations. Moreover, lipid identification was supported by modeling predictable changes in the electrophoretic mobility for cationic PLs in conjunction with MS/MS. Overall, this work offers a practical derivatization protocol to expand lipidome coverage in CE-MS beyond the analysis of hydrophilic/polar metabolites under aqueous buffer conditions.

What can we learn from the platelet lipidome?

Besides their proteome, platelets use, in all responses to the environmental cues, a huge and diverse family of hydrophobic and amphipathic small molecules involved in structural, metabolic and signaling functions; the lipids. Studying how platelet lipidome changes modulate platelet function is an old story constantly renewed through the impressive technical advances allowing the discovery of new lipids, functions and metabolic pathways. Technical progress in analytical lipidomic profiling by top-of-the-line approaches such as nuclear magnetic resonance and gas chromatography or liquid chromatography coupled to mass spectrometry enables either large-scale analysis of lipids or targeted lipidomics. With the support of bioinformatics tools and databases, it is now possible to investigate thousands of lipids over a concentration range of several orders of magnitude. The lipidomic landscape of platelets is considered a treasure trove, not only able to expand our knowledge of platelet biology and pathologies but also to bring diagnostic and therapeutic opportunities. The aim of this commentary article is to summarize the advances in the field and to highlight what lipidomics can tell us about platelet biology and pathophysiology.

Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts

Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of "metabolically healthy obese". We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify "at risk" individuals for targeted intervention and monitoring.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

DBLiPro: A Database for Lipids and Proteins in Human Lipid Metabolism

To help researchers in the field of biology, medicine, chemistry, and materials science to use lipidomic data conveniently, there is an urgent need to develop a platform that provides a systematic knowledgebase of human lipid metabolism and lipidome-centric omics analysis tools. DBLiPro is a user-friendly webserver allowing for access to human metabolism-related lipids and proteins knowledge database and an interactive bioinformatics integrative analysis workflow for lipidomics, transcriptomics, and proteomics data. In DBLiPro, there are 3109 lipid-associated proteins (LAPs) and 2098 lipid metabolites in the knowledge base section, which were obtained from Uniprot, Kyoto Encyclopedia of Genes and Genomes (KEGG) and were further annotated by information from other public resources in the knowledge base section, such as RaftProt and PubChem. DBLiPro offers a step-by-step interactive analysis workflow for lipidomics, transcriptomics, proteomics, and their integrating multi-omics analysis focusing on the human lipid metabolism. In summary, DBLiPro is capable of helping users discover key molecules (lipids and proteins) in human lipid metabolism and investigate lipid-protein functions underlying mechanisms based on their own omics data. The DBLiPro is freely available at http://lipid.cloudna.cn/home.

Lipid signatures of West Nile virus infection unveil alterations of sphingolipid metabolism providing novel biomarkers

West Nile virus (WNV) is a neurotropic flavivirus transmitted by the bites of infected mosquitoes. Severe forms of West Nile disease (WND) can curse with meningitis, encephalitis or acute flaccid paralysis. A better understanding of the physiopathology associated with disease progression is mandatory to find biomarkers and effective therapies. In this scenario, blood derivatives (plasma and serum) constitute the more commonly used biofluids due to its ease of collection and high value for diagnostic purposes. Therefore, the potential impact of this virus in the circulating lipidome was addressed combining the analysis of samples from experimentally infected mice and naturally WND patients. Our results unveil dynamic alterations in the lipidome that define specific metabolic fingerprints of different infection stages. Concomitant with neuroinvasion in mice, the lipid landscape was dominated by a metabolic reprograming that resulted in significant elevations of circulating sphingolipids (ceramides, dihydroceramides and dihydrosphingomyelins), phosphatidylethanolamines and triacylglycerols. Remarkably, patients suffering from WND also displayed an elevation of ceramides, dihydroceramides, lactosylceramides and monoacylglycerols in their sera. The dysregulation of sphingolipid metabolism by WNV may provide new therapeutic opportunities and supports the potential of certain lipids as novel peripheral biomarkers of WND progression.

Multi-Omics Profiling for Health

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Equivalent carbon number based targeted odd chain fatty acyl lipidomics reveals triacylglycerol profiling in clinical colon cancer

Odd chain fatty acids (OCFAs) present in very low level at nearly 1% of total fatty acids in human plasma and thus their functions were usually ignored. Recent epidemiological studies have shown that OCFAs are inversely associated with a variety of disease risks. However, the contribution of OCFAs incorporated into complex lipids remains elusive. Here, we developed a targeted odd chain fatty acyl containing lipidomics method based on equivalent carbon number and retention time prediction. The method displayed good reproducibility and robustness as shown by peak width at half height within 0.7 min and coefficient of variation (CV) under 20%. A total number of 776 lipid species with odd chain fatty acyl residues could be detected in the electrospray ionization (ESI) mode of reverse phase liquid chromatography-mass spectrometry, of which 309 lipids were further validated using multiple MRM transitiions. Using this method, we quantified odd chain fatty acyl containing lipidome in tissues from 12 colon cancer patients, revealing the remodeling of triacylglycerol (TAG). The dynamics of odd chain fatty acyl lipids were further consolidated by the association with genomic and proteomic feature of altered catabolism of branched chain amino acids and TAG endogenous synthesis in colon cancer. This lipidomics approach will be applicable for screening of dysregulated odd chain fatty acyl lipids, which enriches and improves the methods for diagnosis and prognosis evaluation of cancer using lipidomics.

Schizandrin C regulates lipid metabolism and inflammation in liver fibrosis by NF-κB and p38/ERK MAPK signaling pathways

Liver fibrosis is considered a sustained wound healing response and metabolic syndrome, and its therapy is of great significance for chronic liver disease. Schizandrin C, as one lignan from hepatic protectant Schisandra chinensis, can depress the oxidative effect and lipid peroxidation, and protect against liver injury. In this study, C57BL/6J mice were used to estimate a liver fibrosis model by CCl₄, and Schizandrin C exerted an anti-hepatic fibrosis effect, as evidenced by decreased alanine aminotransferase, aspartate aminotransferase and total bilirubin activities in serum, lower hydroxyproline content, recuperative structure and less collagen accumulation in the liver. In addition, Schizandrin C reduced the expressions of alpha-smooth muscle actin and type Ι collagen in the liver. In vitro experiments also revealed that Schizandrin C attenuated hepatic stellate cell activation in both LX-2 and HSC-T6 cells. Furthermore, lipidomics and quantitative real-time PCR analysis revealed that Schizandrin C regulated the lipid profile and related metabolic enzymes in the liver. In addition, the mRNA levels of inflammation factors were downregulated by Schizandrin C treatment, accompanied by lower protein levels of IκB-Kinase-β, nuclear factor kappa-B p65, and phospho-nuclear factor kappa-B p65. Finally, Schizandrin C inhibited the phosphorylation of p38 MAP kinase and extracellular signal-regulated protein kinase, which were activated in the CCl₄ fibrotic liver. Taken together, Schizandrin C can regulate lipid metabolism and inflammation to ameliorate liver fibrosis by nuclear factor kappa-B and p38/ERK MAPK signaling pathways. These findings supported Schizandrin C as a potential drug for liver fibrosis.

Chewing the fat: How lipidomics is changing our understanding of human health and disease in 2022

Lipids are biological molecules that play vital roles in all living organisms. They perform many cellular functions, such as 1) forming cellular and subcellular membranes, 2) storing and using energy, and 3) serving as chemical messengers during intra- and inter-cellular signal transduction. The large-scale study of the pathways and networks of cellular lipids in biological systems is called "lipidomics" and is one of the fastest-growing omics technologies of the last two decades. With state-of-the-art mass spectrometry instrumentation and sophisticated data handling, clinical studies show how human lipid composition changes in health and disease, thereby making it a valuable medium to collect for clinical applications, such as disease diagnostics, therapeutic decision-making, and drug development. This review gives a comprehensive overview of current workflows used in clinical research, from sample collection and preparation to data and clinical interpretations. This is followed by an appraisal of applications in 2022 and a perspective on the exciting future of clinical lipidomics.

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry-Mass Spectrometry Lipidomics Data

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.

Accumulation of polyunsaturated fatty acid-derived metabolites in the sarcopenic muscle of aging mice

AIM

Although it is known that advanced age alters skeletal muscle lipid metabolism, the role(s) of polyunsaturated fatty acid-derived metabolites (mostly eicosanoids and docosanoids) in sarcopenia are not clear. We therefore examined the changes in the metabolites of arachidonic acid, eicosapentaenoic acid and docosahexaenoic acid in the sarcopenic muscle of aged mice.

METHODS

We used 6- and 24-month-old male C57BL/6J mice as healthy and sarcopenic muscle models, respectively. Skeletal muscles were removed from the lower limb and subjected to a liquid chromatography-tandem mass spectrometry analysis.

RESULTS

The liquid chromatography-tandem mass spectrometry analysis detected distinct changes of metabolites in the muscles of the aged mice. Of the 63 metabolites identified, nine were significantly higher in the sarcopenic muscle of aged mice compared with the healthy muscle of young mice. In particular, prostaglandin E2 , prostaglandin F2a , thromboxane B2 , 5-hydroxyeicosatetraenoic acid, and 15-oxo-eicosatetraenoic acid (arachidonic acid-derived metabolites), 12-hydroxy-eicosapentaenoic acid and 14,15-epoxy-eicosatetraenoic acid (eicosapentaenoic acid-derived metabolites) and 10-hydroxydocosa-hexaenoic acid and 14-hydroxyoctadeca-pentaenoic acid (docosahexaenoic acid-derived metabolites) were significantly higher in aged tissue compared with young tissue (all P < 0.05).

CONCLUSIONS

We observed the accumulation of metabolites in the sarcopenic muscle of aged mice. Our results may provide new insights into the pathogenesis and progression of aging- or disease-related sarcopenia. Geriatr Gerontol Int 2023; 23: 297-303.

Effects of Different Storage Conditions on Lipid Stability in Mice Tissue Homogenates

Lipids are biomolecules involved in numerous (patho-)physiological processes and their elucidation in tissue samples is of particular interest. However, tissue analysis goes hand in hand with many challenges and the influence of pre-analytical factors can intensively change lipid concentrations ex vivo, compromising the results of the whole research project. Here, we study the influence of pre-analytical factors on lipid profiles during the processing of homogenized tissues. Homogenates from four different mice tissues (liver, kidney, heart, spleen) were stored at room temperature as well as in ice water for up to 120 min and analyzed via ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). Lipid class ratios were calculated since their suitability as indicators for sample stability has been previously illustrated. Only approx. 40% of lipid class ratios were unchanged after 35 min, which was further reduced to 25% after 120 min during storage at room temperature. In contrast, lipids in tissue homogenates were generally stable when samples were kept in ice water, as more than 90% of investigated lipid class ratios remained unchanged after 35 min. Ultimately, swift processing of tissue homogenates under cooled conditions represents a viable option for lipid analysis and pre-analytical factors require more attention to achieve reliable results.

Clinical lipidomics in the era of the big data

Lipidomics as a branch of metabolomics provides unique information on the complex lipid profile in biological materials. In clinically focused studies, hundreds of lipids together with available clinical information proved to be an effective tool in the discovery of biomarkers and understanding of pathobiochemistry. However, despite the introduction of lipidomics nearly twenty years ago, only dozens of big data studies using clinical lipidomics have been published to date. In this review, we discuss the lipidomics workflow, statistical tools, and the challenges of standartisation. The consequent summary divided into major clinical areas of cardiovascular disease, cancer, diabetes mellitus, neurodegenerative and liver diseases is demonstrating the importance of clinical lipidomics. In these publications, the potential of lipidomics for prediction, diagnosis or finding new targets for the treatment of selected diseases can be seen. The first of these results have already been implemented in clinical practice in the field of cardiovascular diseases, while in other areas we can expect the application of the results summarized in this review in the near future.

Comprehensive Targeted Lipidomic Profiling for Research and Clinical Applications

Mass spectrometry remains one of the gold standard approaches in examining the lipidome in biological samples. Recently, advancements in chromatography and mass spectrometry approaches have enabled broad coverage of the lipidome. However, many limitations still exist, and lipidomic analysis often requires a fine balance between coverage of the lipidome, structural detail, and sample throughput. For biomedical and clinical research using human samples, the diversity and natural variation between different individuals necessitate larger sample numbers to identify significant associations with clinical outcomes and account for potential confounding factors. Here we describe a targeted lipidomics workflow that enables reproducible profiling of thousands of plasma samples in a systematic manner, while maintaining good structural detail and high coverage of the lipidome.

The lipidomes of C. elegans with mutations in asm-3/acid sphingomyelinase and hyl-2/ceramide synthase show distinct lipid profiles during aging

Lipid metabolism affects cell and physiological functions that mediate animal healthspan and lifespan. Lipidomics approaches in model organisms have allowed us to better understand changes in lipid composition related to age and lifespan. Here, using the model C. elegans, we examine the lipidomes of mutants lacking enzymes critical for sphingolipid metabolism; specifically, we examine acid sphingomyelinase (asm-3), which breaks down sphingomyelin to ceramide, and ceramide synthase (hyl-2), which synthesizes ceramide from sphingosine. Worm asm-3 and hyl-2 mutants have been previously found to be long- and short-lived, respectively. We analyzed longitudinal lipid changes in wild type animals compared to mutants at 1-, 5-, and 10-days of age. We detected over 700 different lipids in several lipid classes. Results indicate that wildtype animals exhibit increased triacylglycerols (TAG) at 10-days compared to 1-day, and decreased lysophoshatidylcholines (LPC). We find that 10-day hyl-2 mutants have elevated total polyunsaturated fatty acids (PUFA) and increased LPCs compared to 10-day wildtype animals. These changes mirror another short-lived model, the daf-16/FOXO transcription factor that is downstream of the insulin-like signaling pathway. In addition, we find that hyl-2 mutants have poor oxidative stress response, supporting a model where mutants with elevated PUFAs may accumulate more oxidative damage. On the other hand, 10-day asm-3 mutants have fewer TAGs. Intriguingly, asm-3 mutants have a similar lipid composition as the long-lived, caloric restriction model eat-2/mAChR mutant. Together, these analyses highlight the utility of lipidomic analyses to characterize metabolic changes during aging in C. elegans.

Targeting lipid metabolism in metastatic prostate cancer

Despite key advances in the treatment of prostate cancer (PCa), a proportion of men have de novo resistance, and all will develop resistance to current therapeutics over time. Aberrant lipid metabolism has long been associated with prostate carcinogenesis and progression, but more recently there has been an explosion of preclinical and clinical data which is informing new clinical trials. This review explores the epidemiological links between obesity and metabolic syndrome and PCa, the evidence for altered circulating lipids in PCa and their potential role as biomarkers, as well as novel therapeutic strategies for targeting lipids in men with PCa, including therapies widely used in cardiovascular disease such as statins, metformin and lifestyle modification, as well as novel targeted agents such as sphingosine kinase inhibitors, DES1 inhibitors and agents targeting FASN and beta oxidation.

Lipidomics-Paving the Road towards Better Insight and Precision Medicine in Rare Metabolic Diseases

Even though the application of Next-Generation Sequencing (NGS) has significantly facilitated the identification of disease-associated mutations, the diagnostic rate of rare diseases is still below 50%. This causes a diagnostic odyssey and prevents specific treatment, as well as genetic counseling for further family planning. Increasing the diagnostic rate and reducing the time to diagnosis in children with unclear disease are crucial for a better patient outcome and improvement of quality of life. In many cases, NGS reveals variants of unknown significance (VUS) that need further investigations. The delineation of novel (lipid) biomarkers is not only crucial to prove the pathogenicity of VUS, but provides surrogate parameters for the monitoring of disease progression and therapeutic interventions. Lipids are essential organic compounds in living organisms, serving as building blocks for cellular membranes, energy storage and signaling molecules. Among other disorders, an imbalance in lipid homeostasis can lead to chronic inflammation, vascular dysfunction and neurodegenerative diseases. Therefore, analyzing lipids in biological samples provides great insight into the underlying functional role of lipids in healthy and disease statuses. The method of choice for lipid analysis and/or huge assemblies of lipids (=lipidome) is mass spectrometry due to its high sensitivity and specificity. Due to the inherent chemical complexity of the lipidome and the consequent challenges associated with analyzing it, progress in the field of lipidomics has lagged behind other omics disciplines. However, compared to the previous decade, the output of publications on lipidomics has increased more than 17-fold within the last decade and has, therefore, become one of the fastest-growing research fields. Combining multiple omics approaches will provide a unique and efficient tool for determining pathogenicity of VUS at the functional level, and thereby identifying rare, as well as novel, genetic disorders by molecular techniques and biochemical analyses.

Alteration of Lipid Metabolism in Prostate Cancer: Multifaceted Oncologic Implications

Cancer is increasingly recognized as an extraordinarily heterogeneous disease featuring an intricate mutational landscape and vast intra- and intertumor variability on both genetic and phenotypic levels. Prostate cancer (PCa) is the second most prevalent malignant disease among men worldwide. A single metabolic program cannot epitomize the perplexing reprogramming of tumor metabolism needed to sustain the stemness of neoplastic cells and their prominent energy-consuming functional properties, such as intensive proliferation, uncontrolled growth, migration, and invasion. In cancerous tissue, lipids provide the structural integrity of biological membranes, supply energy, influence the regulation of redox homeostasis, contribute to plasticity, angiogenesis and microenvironment reshaping, mediate the modulation of the inflammatory response, and operate as signaling messengers, i.e., lipid mediators affecting myriad processes relevant for the development of the neoplasia. Comprehensive elucidation of the lipid metabolism alterations in PCa, the underlying regulatory mechanisms, and their implications in tumorigenesis and the progression of the disease are gaining growing research interest in the contemporary urologic oncology. Delineation of the unique metabolic signature of the PCa featuring major aberrant pathways including de novo lipogenesis, lipid uptake, storage and compositional reprogramming may provide novel, exciting, and promising avenues for improving diagnosis, risk stratification, and clinical management of such a complex and heterogeneous pathology.

Comprehensive lipidomic profiling by plasma separation cards

Large-scale lipidomic analyses have been limited by the cost and accessibility of traditional venipuncture sampling. Microsampling techniques offer a less-invasive and more accessible alternative. From a single drop of blood, plasma separation cards (PSC) deliver two volumetric dried plasma samples which are studied here for profiling endogenous blood lipids. Six lots of EDTA-treated human whole blood were used to compare PSC, dried blood spot analyses (DBS), and classic wet plasma extractions. Six replicate extractions were performed for each lot. Nontargeted lipidomics was performed by liquid chromatography-high resolution tandem mass spectrometry. Lipids were annotated by accurate mass/retention time matching and MS/MS spectral library matching using peak intensities for quantitation. Four hundred ninety-eight compounds covering 24 lipid subclasses were annotated. Inter-lot repeatability was evaluated by the percent relative standard deviation (%RSD) for each lot, giving median %RSD values across the lots at 14.6% for PSC, 9.3% for DBS, and 8.6% for wet plasma. Strong correlations of lipid peak intensities between wet plasma and PSCs were observed, but less for DBS. Lipid recovery and stability were comparable between the PSC and DBS samples, with roughly 60% of annotated lipids stable at room temperature after 28 days. Overall, PSCs provide a better alternative for quantitative blood lipidomic analyses compared to dried blood spots. However, problems with lipid stability for samples handled and shipped at room temperature are currently unavoidable outside of a clinical setting. Data transferability and comparability to standard plasma is lipid and lipid class dependent.

Landscape of lipidomics in cardiovascular medicine from 2012 to 2021: A systematic bibliometric analysis and literature review

Lipidomics has shaped our knowledge of how lipids play a central role in cardiovascular diseases (CVD), whereas there is a lack of a summary of existing research findings. This study performed a bibliometric analysis of lipidomics research in cardiovascular medicine to reveal the core countries, institutions, key researchers, important references, major journals, research hotspots and frontiers in this field. From 2012 to 2021, a total of 761 articles were obtained from the Web of Science Core Collection database. There is a steady increase of publications yearly. The United States and China are on the top of the list regarding article output. The institutions with the most publications were the Baker Heart and Diabetes Institute, the Chinese Academy of Sciences and Harvard Medical School. Peter J Meikle was both the most published and most co-cited author. The major journal in this field is Journal of lipid research. Keyword co-occurrence analysis indicated that coronary heart disease, mass spectrometry, risk, fatty acid, and insulin resistance have become hot topics in this field and keyword burst detection suggests that metabolomics, activation, liver, low density lipoprotein are the frontiers of research in recent years. Collectively, lipidomics in CVD is still in its infancy with a steady increase yearly. More in-depth studies in this area are warranted in the future.

Advances in methods to analyse cardiolipin and their clinical applications

Cardiolipin (CL) is a mitochondria-exclusive phospholipid, primarily localised within the inner mitochondrial membrane, that plays an essential role in mitochondrial architecture and function. Aberrant CL content, structure, and localisation have all been linked to impaired mitochondrial activity and are observed in the pathophysiology of cancer and neurological, cardiovascular, and metabolic disorders. The detection, quantification, and localisation of CL species is a valuable tool to investigate mitochondrial dysfunction and the pathophysiological mechanisms underpinning several human disorders. CL is measured using liquid chromatography, usually combined with mass spectrometry, mass spectrometry imaging, shotgun lipidomics, ion mobility spectrometry, fluorometry, and radiolabelling. This review summarises available methods to analyse CL, with a particular focus on modern mass spectrometry, and evaluates their advantages and limitations. We provide guidance aimed at selecting the most appropriate technique, or combination of techniques, when analysing CL in different model systems, and highlight the clinical contexts in which measuring CL is relevant.

Prognostic implication of lipidomics in patients with coronary total occlusion undergoing PCI

BACKGROUND

Predictors of prognosis in patients with coronary chronic total occlusion (CTO) undergoing elective percutaneous coronary intervention (PCI) have remained lacking. Lipidomic profiling enables researchers to associate lipid species with disease progression and may improve the prediction of cardiovascular events.

METHODS

In the present study, 781 lipids were measured by targeted lipidomic profiling in 350 individuals (50 healthy controls, 50 patients with coronary artery disease and 250 patients with CTO). L1-regularized logistic regression was used to identify lipid species associated with adverse cardiovascular events and create predicting models, which were verified by 10-fold cross-validation (200 repeats). Comparisons were made between a traditional model constructed with clinical characteristics alone and a combined model built with both lipidomic data and traditional factors.

RESULTS

Twenty-four lipid species were dysregulated exclusively in patients with CTO, most of which belonged to sphingomyelin (SM) and triacylglycerol (TAG). Compared with traditional risk factors, new model combining lipids and traditional factors had significantly improved performance in predicting adverse cardiovascular events in CTO patients after PCI (area under the curve, 0.870 vs. 0.726, p < .05; Akaike information criterion, 129 versus 156; net reclassification improvement, 0.312, p < .001; integrated discrimination improvement, 0.244, p < .001). Nomogram was built based on the incorporated model and proved efficient by Kaplan-Meier method.

CONCLUSIONS

Lipidomic profiling revealed lipid species which may participate in the formation of CTO and could contribute to the risk stratification in CTO patients undergoing PCI.

Lipidomic changes in the liver of beagle dogs associated with Toxocara canis infection

A global lipidomic analysis using liquid chromatography–tandem mass spectrometry was performed on the liver of beagle dogs infected with Toxocara canis to profile hepatic lipid species at 12 h post-infection (hpi), 24 hpi, and 36 days post-infection (dpi). This analysis identified six categories and 42 subclasses of lipids, including 173, 64, and 116 differentially abundant lipid species at 12 hpi, 24 hpi, and 36 dpi, respectively. Many of the identified lysophospholipids, such as lysophosphatidylglycerol, lysophosphatidylserine, and lysophosphatidylcholine, may contribute to the migration and development of T. canis during the early infection stage. Pathway analysis revealed significant alterations of several immune-inflammatory pathways, such as the B-cell receptor signaling pathway, the NF-kappa B signaling pathway, and the C-type lectin receptor signaling pathway at 12 and 24 hpi. These findings demonstrate the value of lipidomic profiling in revealing the extent of changes in the composition and abundance of hepatic lipidome caused by T. canis infection and their relevance to the pathophysiology of toxocariasis in beagle dogs.

Birth Weight and Early Postnatal Outcomes: Association with the Cord Blood Lipidome

Being born small or large for gestational age (SGA and LGA, respectively), combined with suboptimal early postnatal outcomes, can entail future metabolic alterations. The exact mechanisms underlying such risks are not fully understood. Lipids are a highly diverse class of molecules that perform multiple structural and metabolic functions. Dysregulation of lipid metabolism underlies the onset and progression of many disorders leading to pathological states. The aim of this pilot study was to investigate the relationships between birth weight, early postnatal outcomes, and cord blood serum lipidomes. We performed a non-targeted lipidomics-based approach to ascertain differences in cord blood lipid species among SGA, LGA, and appropriate-for-GA (AGA) newborns. Moreover, we longitudinally assessed (at birth and at ages of 4 and 12 months) weight and length, body composition (DXA), and clinical parameters. We disclosed distinct cord blood lipidome patterns in SGA, LGA, and AGA newborns; target lipid species distinctly modulated in each SGA, AGA, and LGA individual were associated with parameters related to growth and glucose homeostasis. The distinct lipidome patterns observed in SGA, AGA, and LGA newborns may play a role in adipose tissue remodeling and future metabolic risks. Maternal dietary interventions may potentially provide long-term benefits for the metabolic health of the offspring.