Lipidomics: Potential role in risk prediction and therapeutic monitoring for diabetes and cardiovascular disease

Lipidomics and cardiovascular disease

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating innovative approaches for early detection and personalized interventions. Lipidomics, leveraging advanced mass spectrometry techniques, has become instrumental in deciphering lipid-mediated mechanisms in CVDs. This review explores the application of lipidomics in identifying biomarkers for myocardial infarction, heart failure, stroke, and calcific aortic valve stenosis (CAVS). This review examines the technological advancements in shotgun lipidomics and LC/MS, which provide unparalleled insights into lipid composition and function. Key lipid biomarkers, including ceramides and lysophospholipids, have been linked to disease progression and therapeutic outcomes. Integrating lipidomics with genomic and proteomic data reveals the molecular underpinnings of CVDs, enhancing risk prediction and intervention strategies. This review positions lipidomics as a transformative tool in reshaping cardiovascular research and clinical practice.

Early Changes in the Plasma Lipidome of People at Very High Cardiovascular Risk: A New Approach to Assessing the Risk of Cardiovascular Changes

Background/Objectives: Cardiovascular disease (CVD) remains the leading cause of death worldwide and requires a deeper understanding of its pathogenesis for effective prevention and treatment. Familial hypercholesterolemia (FH), characterized by high levels of LDL cholesterol, is a significant risk factor for CVD. FH background remains unexplained despite advances in genetic testing. The aim was identification early changes in the plasma lipidome of individuals at high cardiovascular risk (HCVR) using liquid chromatography coupled with mass spectrometry. Methods: The lipidomic analysis examined over 400 compounds. Twenty individuals with suspected FH, very high cardiovascular risk (VHCVR), and undetectable mutations in the LDLR, APOB, or PCSK9 genes were compared to control group in a qualitative-quantitative analysis. Results: Multivariate analyses revealed statistically significant alterations in glycerophospholipids (GC), with a notable increase in phosphatidylcholines ((O-36:0/16:0), OR (95% CI): 1.246 (1.042-1.490), p = 0.0157), phosphatidylethanolamines ((O-40:7/22:6), OR (95% CI): 1.119 (1.039-1.205), p = 0.0028), and phosphatidylglycerol ((40:8/20:4), OR (95% CI): 1.053 (1.008-1.101), p = 0.0219) only in patients with HCVR. These changes, particularly in major classes of GC, underscored their potential as biomarkers for early assessment of cardiovascular risk. Lipidomic profiling revealed associations between specific lipid species and the comorbidities of arterial hypertension, atherosclerosis, and insulin resistance, implicating their role in atherosclerotic cardiovascular disease (ASCVD). Conclusions: This study points early changes in the plasma lipidome in individuals at HCVR, underline potential biomarkers, therapeutic targets for ASCVD, and offer opportunities to improve ASCVD diagnosis, therapy, and risk management strategies through detailed personalized medical approach.

Lipidomics-driven drug discovery and delivery strategies in glioblastoma

With few viable treatment options, glioblastoma (GBM) is still one of the most aggressive and deadly types of brain cancer. Recent developments in lipidomics have demonstrated the potential of lipid metabolism as a therapeutic target in GBM. The thorough examination of lipids in biological systems, or lipidomics, is essential to comprehending the changed lipid profiles found in GBM, which are linked to the tumor's ability to grow, survive, and resist treatment. The use of lipidomics in drug delivery and discovery is examined in this study, focusing on how it may be used to find new biomarkers, create multi-target directed ligands, and improve drug delivery systems. We also cover the use of FDA-approved medications, clinical trials that use lipid-targeted medicines, and the integration of lipidomics with other omics technologies. This study emphasizes lipidomics as a possible tool in developing more effective treatment methods for GBM by exploring various lipid-centric techniques.

The prognostic value of remnant cholesterol to adverse renal outcomes in patients with type 2 diabetes

BACKGROUND

Type 2 diabetes (T2DM) is known to have detrimental effects on renal health. Our study aimed to investigate the relationship between remnant cholesterol (remnant-C) and adverse renal outcomes in patients with T2DM.

METHODS

We utilized data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, which included 10,196 participants with T2DM to investigate the relationship between remnant-C and adverse renal outcomes by performing Kaplan-Meier survival analysis, Cox proportional regression and Restricted cubic spline (RCS) analysis. Finally, several sensitivity analyses were conducted to assess the robustness of our findings.

RESULTS

Over a 7-year follow-up period, 2039 patients (23.2%) developed albuminuria, 5824 patients (57.1%) experienced worsening renal function, and 280 patients (2.7%) progressed to renal failure. After adjusting for multiple confounding factors, we found that remnant-C was significantly associated with the development of albuminuria (P = 0.007) and worsening renal function (P = 0.002). However, there was no discernible connection between remnant-C and renal faiure (P = 0.621). In sensitivity analyses, the association between remnant-C and the risk of adverse renal outcomes remained robust.

CONCLUSION

Our findings highlight the association between remnant-C and the risk of adverse renal outcomes in patients with T2DM. This easily calculable index can provide valuable information to physicians for predicting the risk of adverse renal outcomes in patients with T2DM.

Serum lipidomic signatures in patients with varying histological severity of metabolic-dysfunction associated steatotic liver disease

BACKGROUND & AIMS

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a spectrum of pathologies ranging from simple steatosis to steatohepatitis, fibrosis and cirrhosis. Patients with metabolic associated steatohepatitis (MASH) with fibrosis are at greatest risk of liver and cardiovascular complications. To identify such at-risk MASLD patients, physicians are still reliant on invasive liver biopsies. This study aimed to identify circulating lipidomic signatures to better identify patients with MASH in a multi-ethnic Asian cohort.

APPROACH & RESULTS

A lipidomic approach was used to quantify a total of 481 serum lipids from 151 Singaporean patients paired with protocolized liver biopsies. Lipidomic signatures for MASLD, at-risk MASH and advanced fibrosis were identified. 210 lipids showed significant differences for varying histological subtypes of MASLD. Majority of these lipids were associated with liver steatosis (198/210). We identified a panel of 13 lipids associated with lobular inflammation, ballooning and significant fibrosis. Of note, dihexosylceramides were novel markers for significant fibrosis. Using the serum lipidome alone, we could stratify patients with MASLD (AUROC 0.863), as well as those with at-risk MASH (AUROC 0.912) and advanced fibrosis (AUROC 0.95). The lipidomic at-risk MASH predictor, using 14 markers, was independently validated (n = 105) with AUROC 0.76.

CONCLUSIONS

The dynamic shift in serum lipid profile was associated with progressive histological stages of MASLD, providing surrogate markers for distinguishing stages of MASLD as well as identifying novel pathways in the pathogenesis.

Four-Dimensional Lipidomic Analysis Using Comprehensive Online UHPLC × UHPSFC/Tandem Mass Spectrometry

Multidimensional chromatography offers enhanced chromatographic resolution and peak capacity, which are crucial for analyzing complex samples. This study presents a novel comprehensive online multidimensional chromatography method for the lipidomic analysis of biological samples, combining lipid class and lipid species separation approaches. The method combines optimized reversed-phase ultrahigh-performance liquid chromatography (RP-UHPLC) in the first dimension, utilizing a 150 mm long C18 column, with ultrahigh-performance supercritical fluid chromatography (UHPSFC) in the second dimension, using a 10 mm long silica column, both with sub-2 μm particles. A key advantage of employing UHPSFC in the second dimension is its ability to perform ultrafast analysis using gradient elution with a sampling time of 0.55 min. This approach offers a significant increase in the peak capacity. Compared to our routinely used 1D methods, the peak capacity of the 4D system is 10 times higher than RP-UHPLC and 18 times higher than UHPSFC. The entire chromatographic system is coupled with a high-resolution quadrupole-time-of-flight (QTOF) mass analyzer using electrospray ionization (ESI) in both full-scan and tandem mass spectrometry (MS/MS) and with positive- and negative-ion polarities, enabling the detailed characterization of the lipidome. The confident identification of lipid species is achieved through characteristic ions in both polarity modes, information from MS elevated energy (MSE) and fast data-dependent analysis scans, and mass accuracy below 5 ppm. This analytical method has been used to characterize the lipidomic profile of the total lipid extract from human plasma, which has led to the identification of 298 lipid species from 16 lipid subclasses.

Biomarker-Guided Dietary Supplementation: A Narrative Review of Precision in Personalized Nutrition

Background: Dietary supplements (DS) are widely used to address nutritional deficiencies and promote health, yet their indiscriminate use often leads to reduced efficacy, adverse effects, and safety concerns. Biomarker-driven approaches have emerged as a promising strategy to optimize DS prescriptions, ensuring precision and reducing risks associated with generic recommendations. Methods: This narrative review synthesizes findings from key studies on biomarker-guided dietary supplementation and the integration of artificial intelligence (AI) in biomarker analysis. Key biomarker categories-genomic, proteomic, metabolomic, lipidomic, microbiome, and immunological-were reviewed, alongside AI applications for interpreting these biomarkers and tailoring supplement prescriptions. Results: Biomarkers enable the identification of deficiencies, metabolic imbalances, and disease predispositions, supporting targeted and safe DS use. For example, genomic markers like MTHFR polymorphisms inform folate supplementation needs, while metabolomic markers such as glucose and insulin levels guide interventions in metabolic disorders. AI-driven tools streamline biomarker interpretation, optimize supplement selection, and enhance therapeutic outcomes by accounting for complex biomarker interactions and individual needs. Limitations: Despite these advancements, AI tools face significant challenges, including reliance on incomplete training datasets and a limited number of clinically validated algorithms. Additionally, most current research focuses on clinical populations, limiting generalizability to healthier populations. Long-term studies remain scarce, raising questions about the sustained efficacy and safety of biomarker-guided supplementation. Regulatory ambiguity further complicates the classification of supplements, especially when combinations exhibit pharmaceutical-like effects. Conclusions: Biomarker-guided DS prescription, augmented by AI, represents a cornerstone of personalized nutrition. While offering significant potential for precision and efficacy, advancing these strategies requires addressing challenges such as incomplete AI data, regulatory uncertainties, and the lack of long-term studies. By overcoming these obstacles, clinicians can better meet individual health needs, prevent diseases, and integrate precision nutrition into routine care.

Biomarkers of insulin sensitivity/resistance

In recent years, remarkable advancements in elucidating the intricate molecular underpinnings of type 2 diabetes mellitus (T2D) have been achieved. Insulin resistance (IR) has been unequivocally acknowledged as the driving pathogenetic mechanism of T2D, preceding disease onset by several years. Nonetheless, diagnostic tools for ascertaining IR are lacking in current clinical practice, representing a critical unmet need; use of the hyperinsulinemic-euglycemic glucose clamp, widely accepted as the gold standard method for evaluating IR at present, is cumbersome in a clinical setting. Thus, the development of well-validated, reliable, and affordable biomarkers of IR has attracted considerable attention from the research community. The biomarkers under investigation can be divided into two major categories: (1) indices or ratios, comprising parameters obtained from a basic or comprehensive metabolic panel and/or derived from anthropometric measurements, and (2) circulating molecules implicated in pathophysiological processes associated with IR. Furthermore, numerous novel biomarkers, including markers of β-cell dysfunction, radiographic quantification of excess visceral adipose tissue, T2D prediction models, certain microRNAs and metabolomic biomarkers, have also provided promising preliminary results. This narrative review aims to present current evidence pertaining to the most notable and exciting biomarkers of IR that are under rigorous evaluation.

Regulatory effect of β-glucan secreted by Rhizobium pusense on triglyceride metabolism and their relationships with the modulation of intestinal microbiota in mice fed a high-fat diet

The present study investigated the regulatory effects of β-glucan secreted by Rhizobium pusense (RPG) on triglyceride metabolism and gut microbiota in mice fed a high-fat diet. The results indicated that supplementation with RPG significantly reduced body weight gain, blood glucose levels, and the tissue index of epididymal white adipose tissue (eWAT) and subcutaneous adipose tissue (SAT). Conversely, it increased the tissue index of brown adipose tissue (BAT). Furthermore, RPG supplementation effectively decreased the levels of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) in the serum. Regarding its influence on the triglyceride (TG) mechanism, RPG decreased TG levels in both serum and liver, while elevating TG levels in feces. Moreover, it moderated the composition of gut microbiota in mice fed a high-fat diet, particularly altering functionally relevant intestinal microbial phylotypes, leading to enhanced levels of short-chain fatty acids (SCFAs) in feces. Additionally, RPG treatment regulated the mRNA and protein levels of genes responsible for TG metabolism in the AMPK pathway, indicating an impact on TG synthesis and excretion in the liver. Pearson's correlation network analysis demonstrated strong correlations between key microbial phylotypes responsive to RPG intervention and parameters associated with TG metabolic disorders. SCFA levels were also found to correlate with the mRNA expression levels of genes involved in TG metabolism. Finally, lipidomics analyses were performed to investigate the underlying mechanisms of RPG intervention (glycerophospholipid metabolic pathway) and to identify potential lipid biomarkers, such as TG (18:2/20:4/22:6), TG (18:1/20:4/22:6), TG (20:1/18:1/22:4), PC (17:0/20:4), TG (18:1/20:4/22:5), PC (22:4/22:6), PC (20:0/22:6), PC (20:0e/20:4), DG (18:3e/18:2), DG (10:0/18:2), DG (18:2/14:2), TG (10:0/18:2/20:4), TG (16:1/14:3/18:2) and TG (16:0/14:2/22:6). Overall, our results suggest that RPG could activate the hepatic AMPK signaling pathway by regulating gut microbiota and metabolites through gut-liver crosstalk to exert a lipid-lowering effect in mice fed a high-fat diet and improve obesity.

Enhancement of weak signals by applying a suppression method to high-intense methyl and methylene signals of lipids in NMR spectroscopy

Lipids play crucial roles in human biology, serving as energy stores, cell membranes, hormone production, and signaling molecules. Accordingly, their study under lipidomics has advanced the study of living organisms. 1-Dimensional (D) and 2D NMR methods, particularly 1D 1H and 2D 1H-1H Total Correlation Spectroscopy (TOCSY), are commonly used in lipidomics for quantification and structural identification. However, these NMR methods suffer from low sensitivity, especially in cases of low concentrated molecules such as protons attached to hydroxy, esters, aliphatic, or aromatic unsaturated carbons. Such molecules are common in complex mixtures such as dairy products and plant oils. On the other hand, lipids have highly populated fractions of methyl and methylene groups that result in intense peaks that overwhelm lower peaks and cause inhomogeneities in 2D TOCSY spectra. In this study, we applied a method of suppression to suppress these intense peaks of methyl and methylene groups to detect weaker peaks. The suppression method was investigated on samples of cheese, butter, a mixture of lipids, coconut oil, and olive oil. A significant improvement in peak sensitivity and visibility of cross-peaks was observed, leading to enhanced comparative quantification and structural identification of a greater number of lipids. Additionally, the enhanced sensitivity reduced the time required for the qualitative and comparative quantification of other lipid compounds and components. This, in turn, enables faster and more reliable structural identification and comparative quantification of a greater number of lipids. Additionally, it reduces the time required for the qualitative, and comparative quantification due to the enhancement of sensitivity.

Effect of traditional Chinese medicine on metabolism disturbance in ischemic heart diseases

ETHNOPHARMACOLOGICAL RELEVANCE

Ischemic heart diseases (IHD), characterized by metabolic dysregulation, contributes majorly to the global morbidity and mortality. Glucose, lipid and amino acid metabolism are critical energy production for cardiomyocytes, and disturbances of these metabolism lead to the cardiac injury. Traditional Chinese medicine (TCM), widely used for treating IHD, have been demonstrated to effectively and safely regulate the cardiac metabolism reprogramming.

AIM OF THE REVIEW

This study discussed and analyzed the disturbed cardiac metabolism induced by IHD and development of formulas, extracts, single herb, bioactive compounds of TCM ameliorating IHD injury via metabolism regulation, with the aim of providing a basis for the development of clinical application of therapeutic strategies for TCM in IHD.

MATERIALS AND METHODS

With "ischemic heart disease", "myocardial infarction", "myocardial ischemia", "metabolomics", "Chinese medicine", "herb", "extracts" "medicinal plants", "glucose", "lipid metabolism", "amino acid" as the main keywords, PubMed, Web of Science, and other online search engines were used for literature retrieval.

RESULTS

IHD exhibits a close association with metabolism disorders, including but not limited to glycolysis, the TCA cycle, oxidative phosphorylation, branched-chain amino acids, fatty acid β-oxidation, ketone body metabolism, sphingolipid and glycerol-phospholipid metabolism. The therapeutic potential of TCM lies in its ability to regulate these disturbed cardiac metabolisms. Additionally, the active ingredients of TCM have depicted wonderful effects in cardiac metabolism reprogramming in IHD.

CONCLUSION

Drawing from the principles of TCM, we have pinpointed specific herbal remedies for the treatment of IHD, and leveraged advanced metabolomics technologies to uncover the effect of these TCMs on metabolomics alteration. In the future, further clinical experimental studies should be included to explore whether more TCM medicines can play a therapeutic role in IHD by reversing cardiac metabolism disorders; multi-omics would be conducted to explore more pathways and genes targeting such metabolism reprogramming by TCMs, and to seek more TCM therapies for IHD.

An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit

The cardiovascular disease (CVD) is the dangerous disease in the world. Most of the people around the world are affected by this dangerous CVD. In under-developed countries, the prediction of CVD remains the toughest job and it takes more time and cost. Diagnosing this illness is an intricate task that has to be performed precisely to save the life span of the human. In this research, an advanced deep model-based CVD prediction and risk analysis framework is proposed to minimize the death rate of humans all around the world. The data required for the prediction of CVD is collected from online data sources. Then, the input data is preprocessed using data cleaning, data scaling, and Nan and null value removal techniques. From the preprocessed data, three sets of features are extracted. The three sets of features include deep features, Principal Component Analysis (PCA), and Support Vector Machine (SVM)-based features. A Multi-scale Weighted Feature Fusion-based Deep Structure Network (MWFF-DSN) is developed to predict CVD. This structure is composed of a Multi-scale weighted Feature fusion-based Convolutional Neural Network (CNN) with a Residual Gated Recurrent Unit (GRU). The retrieved features are given as input to MWFF-DSN, and for optimizing weights, a Modernized Plum Tree Algorithm (MPTA) is developed. From the overall analysis, the developed model has attained an accuracy of 96% and it achieves a specificity of 95.95%. The developed model takes minimum time for the CVD and it gives highly accurate detection results.

Sphingolipid changes in mouse brain and plasma after mild traumatic brain injury at the acute phases

BACKGROUND

Traumatic brain injury (TBI) causes neuroinflammation and can lead to long-term neurological dysfunction, even in cases of mild TBI (mTBI). Despite the substantial burden of this disease, the management of TBI is precluded by an incomplete understanding of its cellular mechanisms. Sphingolipids (SPL) and their metabolites have emerged as key orchestrators of biological processes related to tissue injury, neuroinflammation, and inflammation resolution. No study so far has investigated comprehensive sphingolipid profile changes immediately following TBI in animal models or human cases. In this study, sphingolipid metabolite composition was examined during the acute phases in brain tissue and plasma of mice following mTBI.

METHODS

Wildtype mice were exposed to air-blast-mediated mTBI, with blast exposure set at 50-psi on the left cranium and 0-psi designated as Sham. Sphingolipid profile was analyzed in brain tissue and plasma during the acute phases of 1, 3, and 7 days post-TBI via liquid-chromatography-mass spectrometry. Simultaneously, gene expression of sphingolipid metabolic markers within brain tissue was analyzed using quantitative reverse transcription-polymerase chain reaction. Significance (P-values) was determined by non-parametric t-test (Mann-Whitney test) and by Tukey's correction for multiple comparisons.

RESULTS

In post-TBI brain tissue, there was a significant elevation of 1) acid sphingomyelinase (aSMase) at 1- and 3-days, 2) neutral sphingomyelinase (nSMase) at 7-days, 3) ceramide-1-phosphate levels at 1 day, and 4) monohexosylceramide (MHC) and sphingosine at 7-days. Among individual species, the study found an increase in C18:0 and a decrease in C24:1 ceramides (Cer) at 1 day; an increase in C20:0 MHC at 3 days; decrease in MHC C18:0 and increase in MHC C24:1, sphingomyelins (SM) C18:0, and C24:0 at 7 days. Moreover, many sphingolipid metabolic genes were elevated at 1 day, followed by a reduction at 3 days and an absence at 7-days post-TBI. In post-TBI plasma, there was 1) a significant reduction in Cer and MHC C22:0, and an increase in MHC C16:0 at 1 day; 2) a very significant increase in long-chain Cer C24:1 accompanied by significant decreases in Cer C24:0 and C22:0 in MHC and SM at 3 days; and 3) a significant increase of C22:0 in all classes of SPL (Cer, MHC and SM) as well as a decrease in Cer C24:1, MHC C24:1 and MHC C24:0 at 7 days.

CONCLUSIONS

Alterations in sphingolipid metabolite composition, particularly sphingomyelinases and short-chain ceramides, may contribute to the induction and regulation of neuroinflammatory events in the early stages of TBI, suggesting potential targets for novel diagnostic, prognostic, and therapeutic strategies in the future.

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Phospholipidomics in Clinical Trials for Brain Disorders: Advancing our Understanding and Therapeutic Potentials

Phospholipidomics is a specialized branch of lipidomics that focuses on the characterization and quantification of phospholipids. By using sensitive analytical techniques, phospholipidomics enables researchers to better understand the metabolism and activities of phospholipids in brain disorders such as Alzheimer's and Parkinson's diseases. In the brain, identifying specific phospholipid biomarkers can offer valuable insights into the underlying molecular features and biochemistry of these diseases through a variety of sensitive analytical techniques. Phospholipidomics has emerged as a promising tool in clinical studies, with immense potential to advance our knowledge of neurological diseases and enhance diagnosis and treatment options for patients. In the present review paper, we discussed numerous applications of phospholipidomics tools in clinical studies, with a particular focus on the neurological field. By exploring phospholipids' functions in neurological diseases and the potential of phospholipidomics in clinical research, we provided valuable insights that could aid researchers and clinicians in harnessing the full prospective of this innovative practice and improve patient outcomes by providing more potent treatments for neurological diseases.

Untargeted lipidomics analysis in women with morbid obesity and type 2 diabetes mellitus: A comprehensive study

There is a phenotype of obese individuals termed metabolically healthy obese that present a reduced cardiometabolic risk. This phenotype offers a valuable model for investigating the mechanisms connecting obesity and metabolic alterations such as Type 2 Diabetes Mellitus (T2DM). Previously, in an untargeted metabolomics analysis in a cohort of morbidly obese women, we observed a different lipid metabolite pattern between metabolically healthy morbid obese individuals and those with associated T2DM. To validate these findings, we have performed a complementary study of lipidomics. In this study, we assessed a liquid chromatography coupled to a mass spectrometer untargeted lipidomic analysis on serum samples from 209 women, 73 normal-weight women (control group) and 136 morbid obese women. From those, 65 metabolically healthy morbid obese and 71 with associated T2DM. In this work, we find elevated levels of ceramides, sphingomyelins, diacyl and triacylglycerols, fatty acids, and phosphoethanolamines in morbid obese vs normal weight. Conversely, decreased levels of acylcarnitines, bile acids, lyso-phosphatidylcholines, phosphatidylcholines (PC), phosphatidylinositols, and phosphoethanolamine PE (O-38:4) were noted. Furthermore, comparing morbid obese women with T2DM vs metabolically healthy MO, a distinct lipid profile emerged, featuring increased levels of metabolites: deoxycholic acid, diacylglycerol DG (36:2), triacylglycerols, phosphatidylcholines, phosphoethanolamines, phosphatidylinositols, and lyso-phosphatidylinositol LPI (16:0). To conclude, analysing both comparatives, we observed decreased levels of deoxycholic acid, PC (34:3), and PE (O-38:4) in morbid obese women vs normal-weight. Conversely, we found elevated levels of these lipids in morbid obese women with T2DM vs metabolically healthy MO. These profiles of metabolites could be explored for the research as potential markers of metabolic risk of T2DM in morbid obese women.

Insights into the Anti-Adipogenic and Anti-Inflammatory Potentialities of Probiotics against Obesity

Functional foods with probiotics are safe and effective dietary supplements to improve overweight and obesity. Thus, altering the intestinal microflora may be an effective approach for controlling or preventing obesity. This review aims to summarize the experimental method used to study probiotics and obesity, and recent advances in probiotics against obesity. In particular, we focused on studies (in vitro and in vivo) that used probiotics to treat obesity and its associated comorbidities. Several in vitro and in vivo (animal and human clinical) studies conducted with different bacterial species/strains have reported that probiotics promote anti-obesity effects by suppressing the differentiation of pre-adipocytes through immune cell activation, maintaining the Th1/Th2 cytokine balance, altering the intestinal microbiota composition, reducing the lipid profile, and regulating energy metabolism. Most studies on probiotics and obesity have shown that probiotics are responsible for a notable reduction in weight gain and body mass index. It also increases the levels of anti-inflammatory adipokines and decreases those of pro-inflammatory adipokines in the blood, which are responsible for the regulation of glucose and fatty acid breakdown. Furthermore, probiotics effectively increase insulin sensitivity and decrease systemic inflammation. Taken together, the intestinal microbiota profile found in overweight individuals can be modified by probiotic supplementation which can create a promising environment for weight loss along enhancing levels of adiponectin and decreasing leptin, tumor necrosis factor (TNF)-α, interleukin (IL)-6, monocyte chemotactic protein (MCP)-1, and transforming growth factor (TGF)-β on human health.

Plasma Lipidomic Profiles in cART-Treated Adolescents with Perinatally Acquired HIV Compared to Matched Controls

Children with perinatally acquired human immunodeficiency virus (PHIV) are growing into adulthood with HIV and treatment-associated comorbidities, such as dyslipidemia and insulin resistance. HIV is identified as independent risk factor for cardiovascular disease (CVD). The hypothesis behind increased CVD risk associated with HIV includes vascular inflammation, dyslipidemia and combination antiretroviral therapy (cART) metabolomic toxicity. To investigate differences in lipid profiles and pathophysiological mechanisms of CVD risk in adolescents with PHIV, we compared the plasma lipidome of PHIV adolescents and HIV-negative controls. We additionally investigated the influence of current cART regimens and increased lipoprotein(a) (Lp(a)) levels on the plasma lipidome. We included 20 PHIV-infected adolescents and 20 HIV-negative controls matched for age, sex, ethnic origin and socio-economic status. Plasma lipidome was measured using Thermo Scientific Ultimate 3000 binary high-performance liquid chromatography (HPLC)-mass spectrometry. We evaluated the plasma lipidome in PHIV adolescents using different cART regimens (including those known to be associated with lipid alterations). The median age was 17.5 years (15.5-20.7) and 16.5 years (15.7-19.8) for PHIV adolescents and controls, respectively. Of PHIV adolescents, 45% used a non-nucleotide reverse transcriptase inhibitor (NNRTI)-based (25%) or protease inhibitor (PI)-based (20%) cART regimen. In this pilot study, we observed no significant differences between lipidomic profiles between PHIV adolescents and controls. We observed no differences in the plasma lipidome in participants with increased versus normal Lp(a) levels. Different cART regimens appear to influence chain length differences in the plasma lipidome of PHIV adolescents; however, the significance and causality of this observation remains undetermined. Further research on the influence of cART on lipid composition could further identify these alterations.

Lipidomics reveals new lipid-based lung adenocarcinoma early diagnosis model

Lung adenocarcinoma (LUAD) continues to pose a significant mortality risk with a lack of dependable biomarkers for early noninvasive cancer detection. Here, we find that aberrant lipid metabolism is significantly enriched in lung cancer cells. Further, we identified four signature lipids highly associated with LUAD and developed a lipid signature-based scoring model (LSRscore). Evaluation of LSRscore in a discovery cohort reveals a robust predictive capability for LUAD (AUC: 0.972), a result further validated in an independent cohort (AUC: 0.92). We highlight one lipid signature biomarker, PE(18:0/18:1), consistently exhibiting altered levels both in cancer tissue and in plasma of LUAD patients, demonstrating significant predictive power for early-stage LUAD. Transcriptome analysis reveals an association between increased PE(18:0/18:1) levels and dysregulated glycerophospholipid metabolism, which consistently displays strong prognostic value across two LUAD cohorts. The combined utility of LSRscore and PE(18:0/18:1) holds promise for early-stage diagnosis and prognosis of LUAD.

Comprehensive analysis of phospholipid in milk and their biological roles as nutrients and biomarkers

Phospholipids (PL) have garnered significant attention due to their physiological activities. Milk and other dairy products are important dietary sources for humans and have been extensively used to analyze the presence of PL by various analytical techniques. In this paper, the analysis techniques of PL were reviewed with the eight trigrams of phospholipidomics and a comprehensive fingerprint of 1295 PLs covering 8 subclasses in milk and other dairy products, especially. Technology is the primary productive force. Based on phospholipidomics technology, we further review the relationship between the composition of PL and factors that may be involved in processing and experimental operation, and emphasized the significance of the biological role played by PL in dietary supplements and biomarkers (production, processing and clinical research), and providing the future research directions.

Lipidomics based on UHPLC/Q-TOF-MS to characterize lipid metabolic profiling in patients with newly diagnosed type 2 diabetes mellitus with dyslipidemia

Dyslipidemia often accompanies type 2 diabetes mellitus (T2DM). Elevated blood glucose in patients commonly leads to high levels of lipids. Lipid molecules can play a crucial role in early detection, treatment, and prognosis of T2DM with dyslipidemia. Previous lipid studies on T2DM mainly focused on Western diabetic populations with elevated blood glucose. In this research, we investigate both high blood sugar and high lipid levels to better understand changes in plasma lipid metabolism in newly diagnosed Chinese T2DM patients with dyslipidemia (NDDD). We used a plasma lipid analysis method based on ultra-high performance liquid chromatography coupled with mass spectrometry technology (UHPLC-MS) and statistical analysis to characterize lipid profiles and identify potential biomarkers in NDDD patients compared to healthy control (HC) subjects. Additionally, we examined the differences in lipid profiles between hyperlipidemia (HL) patients and HC subjects. We found significant changes in 15 and 23 lipid molecules, including lysophosphatidylcholine (LysoPC), phosphatidylcholine (PC), phosphatidylethanolamine (PE), sphingomyelin (SM), and ceramide (Cer), in the NDDD and HL groups compared to the HC group. These altered lipid molecules are associated with five metabolic pathways, with sphingolipid metabolism and glycerophospholipid metabolism being the most relevant to glucose and lipid metabolism changes. These lipid biomarkers are strongly correlated with traditional markers of glucose and lipid metabolism. Notably, Cer(d18:1/24:0), SM(d18:1/24:0), SM(d18:1/16:1), SM(d18:1/24:1), and SM(d18:2/24:1) were identified as essential potential biomarkers closely linked to clinical parameters through synthetic analysis of receiver operating characteristic curves, random forest analysis, and Pearson matrix correlation. These lipid biomarkers can enhance the risk prediction for the development of T2DM in individuals with dyslipidemia but no clinical signs of high blood sugar. Furthermore, they offer insights into the pathological mechanisms of T2DM with dyslipidemia.

Plasma Sphingolipid Profile of Healthy Black and White Adults Differs Based on Their Parental History of Type 2 Diabetes

CONTEXT

Ceramides and sphingolipids have been linked to type 2 diabetes (T2D). The Ceramides and Sphingolipids as Predictors of Incident Dysglycemia (CASPID) study is designed to determine the association of plasma sphingolipids with the pathophysiology of human T2D.

OBJECTIVE

A comparison of plasma sphingolipids profiles in Black and White adults with (FH+) and without (FH-) family history of T2D.

DESIGN

We recruited 100 Black and White FH- (54 Black, 46 White) and 140 FH+ (75 Black, 65 White) adults. Fasting plasma levels of 58 sphingolipid species, including 18 each from 3 major classes (ceramides, monohexosylceramides, and sphingomyelins, all with 18:1 sphingoid base) and 4 long-chain sphingoid base-containing species, were measured by liquid chromatography/mass spectrometry.

RESULTS

Sphingomyelin was the most abundant sphingolipid in plasma (89% in FH-), and was significantly elevated in FH+ subjects (93%). Ceramides and monohexosylceramides comprised 5% and 6% of total sphingolipids in the plasma of FH- subjects, and were reduced significantly in FH+ subjects (3% and 4%, respectively). In FH+ subjects, most ceramide and monohexosylceramide species were decreased but sphingomyelin species were increased. The level of C18:1 species of all 3 classes was elevated in FH+ subjects.

CONCLUSION

Elevated levels of sphingomyelin, the major sphingolipids of plasma, and oleic acid-containing sphingolipids in healthy FH+ subjects compared with healthy FH- subjects may reflect heritable elements linking sphingolipids and the development of T2D.

Pitavastatin treatment remodels the HDL subclass lipidome and proteome in hypertriglyceridemia

HDL particles vary in lipidome and proteome, which dictate their individual physicochemical properties, metabolism, and biological activities. HDL dysmetabolism in nondiabetic hypertriglyceridemia (HTG) involves subnormal HDL-cholesterol and apoAI levels. Metabolic anomalies may impact the qualitative features of both the HDL lipidome and proteome. Whether particle content of bioactive lipids and proteins may differentiate HDL subclasses (HDL2b, 2a, 3a, 3b, and 3c) in HTG is unknown. Moreover, little is known of the effect of statin treatment on the proteolipidome of hypertriglyceridemic HDL and its subclasses. Nondiabetic, obese, HTG males (n = 12) received pitavastatin calcium (4 mg/day) for 180 days in a single-phase, unblinded study. ApoB-containing lipoproteins were normalized poststatin. Individual proteolipidomes of density-defined HDL subclasses were characterized prestatin and poststatin. At baseline, dense HDL3c was distinguished by marked protein diversity and peak abundance of surface lysophospholipids, amphipathic diacylglycerol and dihydroceramide, and core cholesteryl ester and triacylglycerol, (normalized to mol phosphatidylcholine), whereas light HDL2b showed peak abundance of free cholesterol, sphingomyelin, glycosphingolipids (monohexosylceramide, dihexosylceramide, trihexosylceramide, and anionic GM3), thereby arguing for differential lipid transport and metabolism between subclasses. Poststatin, bioactive lysophospholipid (lysophosphatidylcholine, lysoalkylphosphatidylcholine, lysophosphatidylethanolamine, and lysophosphatidylinositol) cargo was preferentially depleted in HDL3c. By contrast, baseline lipidomic profiles of ceramide, dihydroceramide and related glycosphingolipids, and GM3/phosphatidylcholine were maintained across particle subclasses. All subclasses were depleted in triacylglycerol and diacylglycerol/phosphatidylcholine. The abundance of apolipoproteins CI, CII, CIV, and M diminished in the HDL proteome. Statin treatment principally impacts metabolic remodeling of the abnormal lipidome of HDL particle subclasses in nondiabetic HTG, with lesser effects on the proteome.

HILIC/MS quantitation of low-abundant phospholipids and sphingolipids in human plasma and serum: Dysregulation in pancreatic cancer

A new hydrophilic interaction liquid chromatography - mass spectrometry method is developed for low-abundant phospholipids and sphingolipids in human plasma and serum. The optimized method involves the Cogent Silica type C hydride column, the simple sample preparation by protein precipitation, and the removal of highly abundant lipid classes using the postcolumn valve directed to waste during two elution windows. The method allows a highly confident and sensitive identification of low-abundant lipid classes in human plasma (246 lipid species from 24 lipid subclasses) based on mass accuracy and retention dependencies in both polarity modes. The method is validated for quantitation using two internal standards (if available) for each lipid class and applied to human plasma and serum samples obtained from patients with pancreatic ductal adenocarcinoma (PDAC), healthy controls, and NIST SRM 1950. Multivariate data analysis followed by various statistical projection methods is used to determine the most dysregulated lipids. Significant downregulation is observed for lysophospholipids with fatty acyl composition 16:0, 18:0, 18:1, and 18:2. Distinct trends are observed for phosphatidylethanolamines (PE) in relation to the bonding type of fatty acyls, where most PE with acyl bonds are upregulated, while ether/plasmenyl PE are downregulated. For the sphingolipid category, sphingolipids with very long N-acyl chains are downregulated, while sphingolipids with shorter N-acyl chains were upregulated in PDAC. These changes are consistently observed for various classes of sphingolipids, ranging from ceramides to glycosphingolipids, indicating a possible metabolic disorder in ceramide biosynthesis caused by PDAC.

The role of lipid dysregulation in gestational diabetes mellitus: Early prediction and postpartum prognosis

Gestational diabetes mellitus (GDM) is a pathological condition during pregnancy characterized by impaired glucose tolerance, and the failure of pancreatic beta-cells to respond appropriately to an increased insulin demand. However, while the majority of women with GDM will return to normoglycemia after delivery, they have up to a seven times higher risk of developing type 2 diabetes during midlife, compared with those with no history of GDM. Gestational diabetes mellitus also increases the risk of multiple metabolic disorders, including non-alcoholic fatty liver disease, obesity, and cardiovascular diseases. Lipid metabolism undergoes significant changes throughout the gestational period, and lipid dysregulation is strongly associated with GDM and the progression to future type 2 diabetes. In addition to common lipid variables, discovery-based omics techniques, such as metabolomics and lipidomics, have identified lipid biomarkers that correlate with GDM. These lipid species also show considerable potential in predicting the onset of GDM and subsequent type 2 diabetes post-delivery. This review aims to update the current knowledge of the role that lipids play in the onset of GDM, with a focus on potential lipid biomarkers or metabolic pathways. These biomarkers may be useful in establishing predictive models to accurately predict the future onset of GDM and type 2 diabetes, and early intervention may help to reduce the complications associated with GDM.

Longitudinal lipidomic signature of carotid atherosclerosis in American Indians: Findings from the Strong Heart Family Study

BACKGROUND AND AIMS

Dyslipidemia is an independent risk factor for atherosclerosis and atherosclerotic cardiovascular disease (ASCVD). To date, a comprehensive assessment of individual lipid species associated with atherosclerosis is lacking in large-scale epidemiological studies, especially in a longitudinal setting. We investigated the association of circulating lipid species and its longitudinal changes with carotid atherosclerosis.

METHODS

Using liquid chromatograph-mass spectrometry, we repeatedly measured 1542 lipid species in 3687 plasma samples from 1918 unique American Indians attending two visits (mean ∼5 years apart) in the Strong Heart Family Study. Carotid atherosclerotic plaques were assessed by ultrasonography at each visit. We identified lipids associated with prevalence or progression of carotid plaques, adjusting age, sex, BMI, smoking, hypertension, diabetes, and eGFR. Then we examined whether longitudinal changes in lipids were associated with changes in cardiovascular risk factors. Multiple testing was controlled at false discovery rate (FDR) < 0.05.

RESULTS

Higher levels of sphingomyelins, ether-phosphatidylcholines, and triacylglycerols were significantly associated with prevalence or progression of carotid plaques (odds ratios ranged from 1.15 to 1.34). Longitudinal changes in multiple lipid species (e.g., acylcarnitines, phosphatidylcholines, triacylglycerols) were associated with changes in cardiometabolic traits (e.g., BMI, blood pressure, fasting glucose, eGFR). Network analysis identified differential lipid networks associated with plaque progression.

CONCLUSIONS

Baseline and longitudinal changes in multiple lipid species were significantly associated with carotid atherosclerosis and its progression in American Indians. Some plaque-related lipid species were also associated with risk for CVD events.

Metabolic Pathway of Monounsaturated Lipids Revealed by In-Depth Structural Lipidomics by Mass Spectrometry

The study of lipid metabolism relies on the characterization of the lipidome, which is quite complex due to the structure variations of the lipid species. New analytical tools have been developed recently for characterizing fine structures of lipids, with C=C location identification as one of the major improvements. In this study, we studied the lipid metabolism reprograming by analyzing glycerol phospholipid compositions in breast cancer cell lines with structural specification extended to the C=C location level. Inhibition of the lipid desaturase, stearoyl-CoA desaturase 1, increased the proportion of n-10 isomers that are produced via an alternative fatty acid desaturase 2 pathway. However, there were different variations of the ratio of n-9/n-7 isomers in C18:1-containing glycerol phospholipids after stearoyl-CoA desaturase 1 inhibition, showing increased tendency in MCF-7 cells, MDA-MB-468 cells, and BT-474 cells, but decreased tendency in MDA-MB-231 cells. No consistent change of the ratio of n-9/n-7 isomers was observed in SK-BR-3 cells. This type of heterogeneity in reprogrammed lipid metabolism can be rationalized by considering both lipid desaturation and fatty acid oxidation, highlighting the critical roles of comprehensive lipid analysis in both fundamental and biomedical applications.

Classification of Common Food Lipid Sources Regarding Healthiness Using Advanced Lipidomics: A Four-Arm Crossover Study

Prospective studies have failed to establish a causal relationship between animal fat intake and cardiovascular diseases in humans. Furthermore, the metabolic effects of different dietary sources remain unknown. In this four-arm crossover study, we investigated the impact of consuming cheese, beef, and pork meat on classic and new cardiovascular risk markers (obtained from lipidomics) in the context of a healthy diet. A total of 33 young healthy volunteers (23 women/10 men) were assigned to one out of four test diets in a Latin square design. Each test diet was consumed for 14 days, with a 2-week washout. Participants received a healthy diet plus Gouda- or Goutaler-type cheeses, pork, or beef meats. Before and after each diet, fasting blood samples were withdrawn. A reduction in total cholesterol and an increase in high density lipoprotein particle size were detected after all diets. Only the pork diet upregulated plasma unsaturated fatty acids and downregulated triglycerides species. Improvements in the lipoprotein profile and upregulation of circulating plasmalogen species were also observed after the pork diet. Our study suggests that, within the context of a healthy diet rich in micronutrients and fiber, the consumption of animal products, in particular pork meat, may not induce deleterious effects, and reducing the intake of animal products should not be regarded as a way of reducing cardiovascular risk in young individuals.

Guiding the choice of informatics software and tools for lipidomics research applications

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

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.

Small LDL subfractions are associated with coronary atherosclerosis despite no differences in conventional lipids

Lipoprotein subfractions currently represent a new source of cardiovascular disease (CVD) risk markers that may provide more information than conventional lipid measures. We aimed to investigate whether lipoprotein subfractions are associated with coronary atherosclerosis in patients without prior known CVD. Fasting serum samples from 60 patients with suspected coronary artery disease (CAD) were collected before coronary angiography and analyzed by nuclear magnetic resonance (NMR) spectroscopy. The severity of coronary atherosclerosis was quantified by the Gensini score (≤20.5 = nonsignificant coronary atherosclerosis, 20.6-30.0 = intermediate coronary atherosclerosis, ≥30.1 = significant CAD). Differences in lipoprotein subfractions between the three Gensini groups were assessed by two-way ANOVA, adjusted for statin use. Despite no differences in conventional lipid measures between the three Gensini groups, patients with significant CAD had higher apolipoprotein-B/apolipoprotein-A1 ratio, 30% more small and dense low-density lipoprotein 5 (LDL-5) particles, and increased levels of cholesterol, triglycerides, and phospholipids within LDL-5 compared with patients with nonsignificant coronary atherosclerosis and intermediate coronary atherosclerosis (P ≤ 0.001). In addition, the low-density lipoprotein (LDL) cholesterol/high-density lipoprotein cholesterol ratio, and triglyceride levels of LDL 4 were significantly increased in patients with significant CAD compared with patients with nonsignificant coronary atherosclerosis. In conclusion, small and dense lipoprotein subfractions were associated with coronary atherosclerosis in patients without prior CVD. Additional studies are needed to explore whether lipoprotein subfractions may represent biomarkers offering a clinically meaningful improvement in the risk prediction of CAD.

Lipoprotein subfraction profiling in the search of new risk markers for myocardial infarction: The HUNT study

BACKGROUND

Traditional biomarkers used to measure risk of myocardial infarction (MI) only explain a modest proportion of the incidence. Lipoprotein subfractions have the potential to improve risk prediction of MI.

AIM

We aimed to identify lipoprotein subfractions that were associated with imminent MI risk.

METHODS

We identified apparently healthy participants with a predicted low 10-year risk of MI from The Trøndelag Health Survey 3 (HUNT3) that developed MI within 5 years after inclusion (cases, n = 50) and 100 matched controls. Lipoprotein subfractions were analyzed in serum by nuclear magnetic resonance spectroscopy at time of inclusion in HUNT3. Lipoprotein subfractions were compared between cases and controls in the full population (N = 150), and in subgroups of males (n = 90) and females (n = 60). In addition, a sub analysis was performed in participants that experienced MI within two years and their matched controls (n = 56).

RESULTS

None of the lipoprotein subfractions were significantly associated with future MI when adjusting for multiple testing (p<0.002). At nominal significance level (p<0.05), the concentration of apolipoprotein A1 in the smallest high-density lipoprotein (HDL) subfractions was higher in cases compared to controls. Further, in sub analyses based on sex, male cases had lower lipid concentration within the large HDL subfractions and higher lipid concentration within the small HDL subfractions compared to male controls (p<0.05). No differences were found in lipoprotein subfractions between female cases and controls. In sub analysis of individuals suffering from MI within two years, triglycerides in low-density lipoprotein were higher among cases (p<0.05).

CONCLUSION

None of the investigated lipoprotein subfractions were associated with future MI after adjustment for multiple testing. However, our findings suggests that HDL subfractions may be of interest in relation to risk prediction for MI, especially in males. This need to be further investigated in future studies.

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.

Effectiveness and safety of Tiaogan formula in the treatment of coronary heart disease: A protocol for systematic review and meta-analysis

BACKGROUND

The morbidity and mortality of coronary heart disease (CHD) has remained high, which greatly increases people's economic burden. Several studies have showed that Tiaogan formula (TGF), as a kind of Chinese herbal medicine, was of benefit to relieving angina pectoris symptoms and improving the quality of life for CHD patients. However, the intensity of evidence has been poor, limiting the further clinical application of TGF to CHD. This systematic review and meta-analysis will assess the effectiveness and safety of studies of TGF in CHD patients.

METHODS

A systematic search for literature up to December 2022 will be conducted in following public electronic databases: PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, Chinese Scientific Journals Database Database, and Wanfang Database. Inclusion criteria are randomized controlled trials of Tiaogan formula in the treatment of coronary heart disease. The primary outcome measures will be mortality, acute cardiovascular events, total efficacy rate, and improvement of angina symptoms. The secondary outcome measures will be electrocardiogram, levels of blood lipid, and adverse events. RevMan 5.4 software Cochrane Collaboration (London, United Kingdom) will be applied for data synthesis, sensitivity analysis, subgroup analysis, and risk of bias assessment. A funnel plot will be developed to evaluate reporting bias and Egger tests will be used to assess funnel plot symmetries. We will use the Grading of Recommendations Assessment, Development and Evaluation system to assess the quality of evidence.

RESULTS

This study will provide a systematic review of Tiaogan formula in the treatment of CHD.

CONCLUSION

This study will provide a high-quality synthesis of the effects and safety of Tiaogan formula in the treatment of CHD patients.

Quantitative lipidomics reveals lipid perturbation in the liver of fatty liver hemorrhagic syndrome in laying hens

Fatty liver hemorrhagic syndrome (FLHS) is a metabolic disease that causes decreased egg production and even death in laying hens, which brings huge economic losses to the poultry industry. However, the pathogenesis of FLHS is unclear. The purpose of the present study was to identify the changes in lipid profile and the lipid species related to FLHS. In the present study, the FLHS disease model in Chinese commercial Jing Fen laying hens was induced by a high-energy low-protein diet. A lipidomics approach based on ultra-performance liquid chromatography-mass spectrometry coupled with multivariate statistical analysis was performed for the qualitative and quantitative analyses of the liver lipids. The results showed that a total of 29 lipid subclasses, including 1,302 lipid species, were detected and identified. Among them, the proportions of phosphatidylserine (Control/FLHS, 33.1% vs. 29.1%), phosphatidylethanolamine (22.7% vs. 15.5%), phosphatidylcholine (15.7% vs. 11.7%) and phosphatidylinositol (7% vs. 6%) were reduced, while triacylglycerol (7.1% vs. 18.3%) and diglyceride (3.9% vs. 11.7%) were increased. Between the Control and FLHS groups, distinct changes in lipid profile were observed in the score plots of principal component analysis and orthogonal partial least squares discriminant analysis. Twelve differential lipid species mainly involved in glycerophospholipid metabolism and linoleic acid metabolism were identified and considered to be related to the pathogenesis of FLHS. Fatty acid chain length and unsaturation were reduced, while the mRNA levels of elongation of very long chain fatty acids-2 (ELOVL2) were increased in the liver of laying hens with FLHS. Collectively, this study characterized the liver lipid profile and explored the changes in lipid species related to FLHS, which provided insights into the pathogenesis of FLHS from the view of lipid metabolism.

Triglyceride profiles are associated with subacute exposure to bisphenol A in healthy young adults

Conflicted results from previous epidemiological studies call for mechanistic evidence to associate exposure to bisphenol A (BPA) with cardiometabolic diseases. In this natural experiment among healthy travelers from Los Angeles (LA) to Beijing, we collected paired urine and blood samples before their departure, 6-8 weeks after their arrival to Beijing, and 4-7 weeks after their return to LA for the assessment of urinary BPA and lipidome in the serum fraction of blood, to study the effects of drastically changed BPA exposure on the lipid metabolism in relation to the development of cardiometabolic disorders. We used linear mixed-effects models with random intercepts for participant and phase to examine the associations between urinary BPA and serum lipidome. Among 744 lipid species from seven classes, triglyceride (TGs) species showed the strongest associations with BPA exposure. The elevation in BPA exposure was associated with increases in TGs with short carbon chains or few double bonds, and decreases in TGs with long carbon chains or many double bonds. A significant linear relationship was observed between BPA-associated TG changes and the number of carbons and double-bonds in the acyl chain. No modification effects of gender but of body mass index (BMI) were observed on the associations between BPA exposure and TGs. This interdisciplinary environmental research substantiated the cardiometabolic effects of BPA according to the perturbations of TG profiling.

Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.

High Throughput UHPLC-MS-Based Lipidomics Using Vacuum Jacketed Columns

Reversed-phase UHPLC-MS is extensively employed for both the profiling of biological fluids and tissues to characterize lipid dysregulation in disease and toxicological studies. With conventional LC-MS systems the chromatographic performance and throughput are limited due to dispersion from the fluidic connections as well as radial and longitudinal thermal gradients in the LC column. In this study vacuum jacketed columns (VJC), positioned at the source of the mass spectrometer, were applied to the lipidomic analysis of plasma extracts. Compared to conventional UHPLC, the VJC-based methods offered greater resolution, faster analysis, and improved peak intensity. For a 5 min VJC analysis, the peak capacity increased by 66%, peak tailing reduced by up to 34%, and the number of lipids detected increased by 30% compared to conventional UHPLC. The narrower peaks, and thus increased resolution, compared to the conventional system resulted in a 2-fold increase in peak intensity as well a significant improvement in MS and MS/MS spectral quality resulting in a 22% increase in the number of lipids identified. When applied to mouse plasma samples, reproducibility of the lipid intensities in the pooled QC ranged from 1.8-12%, with no related drift in t_R observed.

A reversed phase ultra-high-performance liquid chromatography-data independent mass spectrometry method for the rapid identification of mycobacterial lipids

A rapid reversed-phase ultra-high-performance liquid chromatography-high resolution mass spectrometry based mycobacterial lipidomics approach is described. This method enables the separation of various lipid classes including lipids specific to mycobacterial, such as methoxy mycolic acid and α-mycolic acid. Lipid separation occurs during a relatively short runtime of 14 min on a charged surface hybrid C₁₈ column. A high-resolution quadrupole-time of flight mass spectrometer and a data independent acquisition mode allowed for the simultaneous acquisition of the full scan and collision induced dissociation fragmentation. The proposed method provides lipid detection results equivalent to or better than existing methods, but with a faster throughput and an overall higher sensitivity. The reversed-phase ultra-high-performance liquid chromatography-high resolution mass spectrometry method was shown to obtain structural information for lipids extracted from Mycobacterium smegmatis, but the method is applicable to the analysis of lipids from various bacterial and mammalian cell lines.

Genomics of Postprandial Lipidomics in the Genetics of Lipid-Lowering Drugs and Diet Network Study

Postprandial lipemia (PPL) is an important risk factor for cardiovascular disease. Inter-individual variation in the dietary response to a meal is known to be influenced by genetic factors, yet genes that dictate variation in postprandial lipids are not completely characterized. Genetic studies of the plasma lipidome can help to better understand postprandial metabolism by isolating lipid molecular species which are more closely related to the genome. We measured the plasma lipidome at fasting and 6 h after a standardized high-fat meal in 668 participants from the Genetics of Lipid-Lowering Drugs and Diet Network study (GOLDN) using ultra-performance liquid chromatography coupled to (quadrupole) time-of-flight mass spectrometry. A total of 413 unique lipids were identified. Heritable and responsive lipid species were examined for association with single-nucleotide polymorphisms (SNPs) genotyped on the Affymetrix 6.0 array. The most statistically significant SNP findings were replicated in the Amish Heredity and Phenotype Intervention (HAPI) Heart Study. We further followed up findings from GOLDN with a regional analysis of cytosine-phosphate-guanine (CpGs) sites measured on the Illumina HumanMethylation450 array. A total of 132 lipids were both responsive to the meal challenge and heritable in the GOLDN study. After correction for multiple testing of 132 lipids (α = 5 × 10-8/132 = 4 × 10-10), no SNP was statistically significantly associated with any lipid response. Four SNPs in the region of a known lipid locus (fatty acid desaturase 1 and 2/FADS1 and FADS2) on chromosome 11 had p < 8.0 × 10-7 for arachidonic acid FA(20:4). Those SNPs replicated in HAPI Heart with p < 3.3 × 10-3. CpGs around the FADS1/2 region were associated with arachidonic acid and the relationship of one SNP was partially mediated by a CpG (p = 0.005). Both SNPs and CpGs from the fatty acid desaturase region on chromosome 11 contribute jointly and independently to the diet response to a high-fat meal.

Clinical lipidomics: realizing the potential of lipid profiling

Dysregulation of lipid metabolism plays a major role in the etiology and sequelae of inflammatory disorders, cardiometabolic and neurological diseases, and several forms of cancer. Recent advances in lipidomic methodology allow comprehensive lipidomic profiling of clinically relevant biological samples, enabling researchers to associate lipid species and metabolic pathways with disease onset and progression. The resulting data serve not only to advance our fundamental knowledge of the underlying disease process but also to develop risk assessment models to assist in the diagnosis and management of disease. Currently, clinical applications of in-depth lipidomic profiling are largely limited to the use of research-based protocols in the analysis of population or clinical sample sets. However, we foresee the development of purpose-built clinical platforms designed for continuous operation and clinical integration-assisting health care providers with disease risk assessment, diagnosis, and monitoring. Herein, we review the current state of clinical lipidomics, including the use of research-based techniques and platforms in the analysis of clinical samples as well as assays already available to clinicians. With a primary focus on MS-based strategies, we examine instrumentation, analysis techniques, statistical models, prospective design of clinical platforms, and the possible pathways toward implementation of clinical lipidomics.

Hepatic Lipidomics Analysis Reveals the Ameliorative Effects of Highland Barley β-Glucan on Western Diet-Induced Nonalcoholic Fatty Liver Disease Mice

Nonalcoholic fatty liver disease (NAFLD) is characterized by marked imbalances in lipid storage and metabolism. Because the beneficial health effects of cereal β-glucan (BG) include lowering cholesterol and regulating lipid metabolism, BG may alleviate the imbalances in lipid metabolism observed during NAFLD. The aim of our study was to investigate whether BG from highland barley has an effect on western diet-induced NAFLD in mice. Using lipidomics, we investigated the underlying mechanisms of BG intervention, and identified potential lipid biomarkers. The results reveal that BG (300 mg/kg body weight) significantly alleviated liver steatosis. Lipidomics analysis demonstrated that BG also altered lipid metabolic patterns. We were able to identify 13 differentially regulated lipid species that may be useful as lipid biomarkers. Several genes in the hepatic lipid and cholesterol metabolism pathways were also modulated. These findings provide evidence that BG ameliorates NAFLD by altering liver lipid metabolites and regulating lipid metabolism-related genes.

Screening of lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography-high resolution mass spectrometry

Coronary heart disease (CHD) has a high mortality worldwide. This study aimed to screen lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography-high resolution mass spectrometry. Extraction and reconstitution solvents, liquid chromatographic and mass spectrometry conditions were optimized to detect more plasma lipid metabolites. In this study, the chromatographic and mass spectra characteristics of lipid metabolites were summarized. A total of 316 lipid metabolites were annotated via diagnostic fragment ion filtration, nitrogen rule filtration, and neutral loss filtration. Glycerophospholipid metabolism and sphingolipid metabolism were revealed as the main lipid disorders of CHD. This study provides a novel insight for high-throughput detection of lipid metabolites in plasma and provides a further understanding of the occurrence of CHD, which can provide valuable suggestions for the prevention of CHD.

Lipids: biomarkers of healthy aging

Life expectancy, and longevity have been increasing in recent years. However, this is, in most cases, accompanied by age-related diseases. Thus, it became essential to better understand the mechanisms inherent to aging, and to establish biomarkers that characterize this physiological process. Among all biomolecules, lipids appear to be a good target for the study of these biomarkers. In fact, some lipids have already been associated with age-related diseases. With the development of analytical techniques such as Mass Spectrometry, and Nuclear Magnetic Resonance, Lipidomics has been increasingly used to study pathological, and physiological states of an organism. Thus, the study of serum, and plasma lipidome in centenarians, and elderly individuals without age-related diseases can be a useful tool for the identification of aging biomarkers, and to understand physiological aging, and longevity. This review focus on the importance of lipids as biomarkers of aging, and summarize the changes in the lipidome that have been associated with aging, and longevity.

Potential probiotics Lactobacillus casei K11 combined with plant extracts reduce markers of type 2 diabetes mellitus in mice

AIMS

Probiotics and plant extracts have been used to prevent the development of type 2 diabetes mellitus (T2DM). The study aimed to explore the effect of the interaction between potential probiotics and bitter gourd extract (BGE) or mulberry leaf extract (MLE) on T2DM.

METHODS AND RESULTS

Potential probiotics were tested for their gastrointestinal tract viability and growth situation combined with BGE and MLE in vitro. The diabetes model was constructed in C57BL/6 mice, and the potential effect and mechanism of regulating blood glucose were verified. Hematoxylin-eosin staining (HE), gas chromatography (GC), ELISA, and RT-PCR were also used for analysis. The results showed that Lactobacillus casei K11 had outstanding gastrointestinal tract viability and growth situation with plant extracts. Administration of L. casei K11 combined with BGE and MLE significantly reduced blood glucose levels and ameliorated insulin resistance in diabetic mice than the administration of Lactobacillus paracasei J5 combined with BGE and MLE. Moreover, in L. casei K11 combined with BGE and MLE groups, lipid metabolism, oxidative stress, and proinflammatory cytokine levels were regulated. Furthermore, the results indicated that L. casei K11 combined with BGE and MLE improved free fatty acid receptor 2 (FFAR2) upregulation, glucagon-like peptide-1 (GLP-1) secretion, and short-chain fatty acid (SCFA) levels.

CONCLUSIONS

These findings showed that L. casei K11 combined with BGE and MLE modified the SCFA-FFAR2-GLP-1 pathway to improve T2DM.

SIGNIFICANCE AND IMPACT OF THE STUDY

This study identified a new modality for evaluating interactions between potential probiotics and plant extracts. Our findings revealed that L. casei K11 combined with BGE and MLE significantly promoted the SCFA-FFAR2-GLP-1 pathway to inhibit T2DM.

Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program

INTRODUCTION

Although various lipid and non-lipid analytes measured by nuclear magnetic resonance (NMR) spectroscopy have been associated with type 2 diabetes, a structured comparison of the ability of NMR-derived biomarkers and standard lipids to predict individual diabetes risk has not been undertaken in larger studies nor among individuals at high risk of diabetes.

RESEARCH DESIGN AND METHODS

Cumulative discriminative utilities of various groups of biomarkers including NMR lipoproteins, related non-lipid biomarkers, standard lipids, and demographic and glycemic traits were compared for short-term (3.2 years) and long-term (15 years) diabetes development in the Diabetes Prevention Program, a multiethnic, placebo-controlled, randomized controlled trial of individuals with pre-diabetes in the USA (N=2590). Logistic regression, Cox proportional hazards model and six different hyperparameter-tuned machine learning algorithms were compared. The Matthews Correlation Coefficient (MCC) was used as the primary measure of discriminative utility.

RESULTS

Models with baseline NMR analytes and their changes did not improve the discriminative utility of simpler models including standard lipids or demographic and glycemic traits. Across all algorithms, models with baseline 2-hour glucose performed the best (max MCC=0.36). Sophisticated machine learning algorithms performed similarly to logistic regression in this study.

CONCLUSIONS

NMR lipoproteins and related non-lipid biomarkers were associated but did not augment discrimination of diabetes risk beyond traditional diabetes risk factors except for 2-hour glucose. Machine learning algorithms provided no meaningful improvement for discrimination compared with logistic regression, which suggests a lack of influential latent interactions among the analytes assessed in this study.

TRIAL REGISTRATION NUMBER

Diabetes Prevention Program: NCT00004992; Diabetes Prevention Program Outcomes Study: NCT00038727.

Association of cardiac autonomic dysfunction with higher levels of plasma lipid metabolites in recent-onset type 2 diabetes

AIMS/HYPOTHESIS

Emerging evidence suggests that in addition to hyperglycaemia, dyslipidaemia could represent a contributing pathogenetic factor to diabetic neuropathy, while obesity and insulin resistance play a role in the development of diabetic cardiac autonomic neuropathy (CAN) characterised by reduced heart rate variability (HRV), particularly in type 2 diabetes. We hypothesised that distinct lipid metabolites are associated with diminished HRV in recent-onset type 2 diabetes rather than type 1 diabetes.

METHODS

We analysed 127 plasma lipid metabolites (11 acylcarnitines, 39 NEFA, 12 sphingomyelins (SMs), 56 phosphatidylcholines and nine lysophosphatidylcholines) using MS in participants from the German Diabetes Study baseline cohort recently diagnosed with type 1 (n = 100) and type 2 diabetes (n = 206). Four time-domain HRV indices (number of normal-to-normal (NN) intervals >50 ms divided by the number of all NN intervals [pNN50]; root mean square of successive differences [RMSSD]; SD of NN intervals [SDNN]; and SD of differences between adjacent NN intervals) and three frequency-domain HRV indices (very-low-frequency [VLF], low-frequency [LF] and high-frequency [HF] power spectrum) were computed from NN intervals recorded during a 3 h hyperinsulinaemic-euglycaemic clamp at baseline and in subsets of participants with type 1 (n = 60) and type 2 diabetes (n = 95) after 5 years.

RESULTS

In participants with type 2 diabetes, after Bonferroni correction and rigorous adjustment, SDNN was inversely associated with higher levels of diacyl-phosphatidylcholine (PCaa) C32:0, PCaa C34:1, acyl-alkyl-phosphatidylcholine (PCae) C36:0, SM C16:0 and SM C16:1. SD of differences between NN intervals was inversely associated with PCaa C32:0, PCaa C34:1, PCaa C34:2, PCae C36:0 and SM C16:1, and RMSSD with PCae C36:0. For VLF power, inverse associations were found with PCaa C30:0, PCaa C32:0, PCaa C32:1, PCaa C34:2 and SM C16:1, and for LF power inverse associations were found with PCaa C32:0 and SM C16:1 (r = -0.242 to r = -0.349; p ≤ 0.0005 for all correlations). In contrast, no associations of lipid metabolites with measures of cardiac autonomic function were noted in participants recently diagnosed with type 1 diabetes. After 5 years, HRV declined due to ageing rather than diabetes, whereby prediction analyses for lipid metabolites were hampered.

CONCLUSIONS/INTERPRETATION

Higher plasma levels of specific lipid metabolites are closely linked to cardiac autonomic dysfunction in recent-onset type 2 diabetes but not type 1 diabetes, suggesting a role for perturbed lipid metabolism in the early development of CAN in type 2 diabetes. Graphical abstract.

Identification of Circulating Endocan-1 and Ether Phospholipids as Biomarkers for Complications in Thalassemia Patients

Despite advances in our knowledge and attempts to improve therapies, β-thalassemia remains a prevalent disorder with increased risk for the development of cardiomyopathy. Using an untargeted discovery-based lipidomic workflow, we uncovered that transfusion-dependent thalassemia (TDT) patients had a unique circulating lipidomic signature consisting of 387 lipid features, allowing their significant discrimination from healthy controls (Q-value < 0.01). In particular, TDT patients had elevated triacylglycerols and long-chain acylcarnitines, albeit lower ether phospholipids or plasmalogens, sphingomyelins, and cholesterol esters, reminiscent of that previously characterized in cardiometabolic diseases resulting from mitochondrial and peroxisomal dysfunction. Discriminating lipid (sub)classes correlated differentially with clinical parameters, reflecting blood (ether phospholipids) and iron (cholesterol ester) status or heart function (triacylglycerols). We also tested 15 potential serum biomarkers related to cardiometabolic disease and found that both lipocalin-2 and, for the first time, endocan-1 levels were significantly elevated in TDT patients and showed a strong correlation with blood parameters and three ether diacylglycerophosphatidylcholine species. In conclusion, this study identifies new characteristics of TDT patients which may have relevance in developing biomarkers and therapeutics.