Metabolomic profile related to cardiovascular disease in patients with type 2 diabetes mellitus: A pilot study

Effects of multi-walled carbon nanotubes and halloysite nanotubes on plasma lipid profiles and autophagic lipolysis pathways in mouse aortas and hearts

Multi-walled carbon nanotubes (MWCNTs) and halloysite nanotubes (HNTs) are widely used tubular-structured nanomaterials (NMs), but their cardiovascular effects are not clear. This study compared the effects of MWCNTs and HNTs on lipid profiles in mouse plasma and gene expression profiles in aortas and hearts. Mice were intravenously injected with 50 μg NMs, once a day, for 5 days. Then, the plasma was collected for lipidomics analysis, and aortas and hearts were collected for RNA-sequencing analysis. While MWCNTs or HNTs did not induce obvious pathological changes in aortas or hearts, the lipid profiles in mouse plasma were altered. Further analysis revealed that MWCNTs more effectively upregulated sphingolipids and sterol lipids, whereas HNTs more effectively upregulated glycerophospholipids and fatty acyls. Consistently, RNA-sequencing data indicated that MWCNTs and HNTs altered signaling pathways related with lipid synthesis and metabolism, as well as those related with endoplasmic reticulum, lysosomes and autophagy, more significantly in aortas than in hearts. We further verified the changes of proteins involved in autophagic lipolysis, that MWCNTs were more effectively to suppress the autophagic biomarker LC3, whereas HNTs were more effectively to affect lipid metabolism proteins. These results may provide novel understanding about the influences of MWCNTs and HNTs on lipid profiles and lipid signaling pathways in cardiovascular systems. Importantly, previous studies considered HNTs as biocompatible materials, but the results from this study suggested that both MWCNTs and HNTs were capable to affect lipid profiles and autophagic lipolysis pathways in cardiovascular systems, although their exact influences were different.

Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study

BACKGROUND

Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge.

OBJECTIVES

To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management.

METHODS

In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM.

RESULTS

We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample.

CONCLUSIONS

These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.

Advancements in Omics and Breakthrough Gene Therapies: A Glimpse into the Future of Peripheral Artery Disease

Peripheral artery disease (PAD), a highly prevalent global disease, associates with significant morbidity and mortality in affected patients. Despite progress in endovascular and open revascularization techniques for advanced PAD, these interventions grapple with elevated rates of arterial restenosis and vein graft failure attributed to intimal hyperplasia (IH). Novel multiomics technologies, coupled with sophisticated analyses tools recently powered by advances in artificial intelligence, have enabled the study of atherosclerosis and IH with unprecedented single-cell and spatial precision. Numerous studies have pinpointed gene hubs regulating pivotal atherogenic and atheroprotective signaling pathways as potential therapeutic candidates. Leveraging advancements in viral and nonviral gene therapy (GT) platforms, gene editing technologies, and cutting-edge biomaterial reservoirs for delivery uniquely positions us to develop safe, efficient, and targeted GTs for PAD-related diseases. Gene therapies appear particularly fitting for ex vivo genetic engineering of IH-resistant vein grafts. This manuscript highlights currently available state-of-the-art multiomics approaches, explores promising GT-based candidates, and details GT delivery modalities employed by our laboratory and others to thwart mid-term vein graft failure caused by IH, as well as other PAD-related conditions. The potential clinical translation of these targeted GTs holds the promise to revolutionize PAD treatment, thereby enhancing patients' quality of life and life expectancy.

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.

Circulating palmitoyl sphingomyelin levels predict the 10-year increased risk of cardiovascular disease death in Chinese adults: findings from the Da Qing Diabetes Study

BACKGROUND

Higher levels of palmitoyl sphingomyelin (PSM, synonymous with sphingomyelin 16:0) are associated with an increased risk of cardiovascular disease (CVD) in people with diabetes. Whether circulating PSM levels can practically predict the long-term risk of CVD and all-cause death remains unclear. This study aimed to investigate whether circulating PSM is a real predictor of CVD death in Chinese adults with or without diabetes.

METHODS

A total of 286 and 219 individuals with and without diabetes, respectively, from the original Da Qing Diabetes Study were enrolled. Blood samples collected in 2009 were used as a baseline to assess circulating PSM levels. The outcomes of CVD and all-cause death were followed up from 2009 to 2020, and 178 participants died, including 87 deaths due to CVD. Cox proportional hazards regression was used to estimate HRs and their 95% CIs for the outcomes.

RESULTS

Fractional polynomial regression analysis showed a linear association between baseline circulating PSM concentration (log-2 transformed) and the risk of all-cause and CVD death (p < 0.001), but not non-CVD death (p > 0.05), in all participants after adjustment for confounders. When the participants were stratified by PSM-tertile, the highest tertile, regardless of diabetes, had a higher incidence of CVD death (41.5 vs. 14.7 and 22.2 vs. 2.9 per 1000 person-years in patients with and without diabetes, respectively, all log-rank p < 0.01). Individuals with diabetes in the highest tertile group had a higher risk of CVD death than those in the lowest tertile (HR = 2.73; 95%CI, 1.20-6.22).

CONCLUSIONS

Elevated PSM levels are significantly associated with a higher 10-year risk of CVD death, but not non-CVD death, in Chinese adults with diabetes. These findings suggest that PSM is a potentially useful long-term predictor of CVD death in individuals with diabetes.

Exploring the role of osteoglycin in type 2 diabetes: implications for insulin resistance and vascular pathophysiology

Osteoglycin, a fundamental proteoglycan within the vascular extracellular matrix, is expressed in vascular smooth muscle cells (VSMCs). Type 2 diabetes (T2D) is associated with cardiovascular disease (CVD) but the role of osteoglycin in the development of CVD is controversial to date. Therefore, our aims are to determine and compare the level of osteoglycin in T2D patients with/without CVD versus control subjects both at serum and vascular tissue and to analyze in vitro role of osteoglycin in VSMCs under calcified conditions. For this, serum osteoglycin levels were determined by enzyme-linked immunosorbent assay (ELISA) in 117 controls and 129 patients with T2D (46 with CVD and 83 without CVD), revealing a significant increase in patients with T2D compared with controls. Osteoglycin level was not an estimator of CVD but correlated with markers of insulin resistance (triglycerides and triglycerides/high-density lipoprotein cholesterol index) in patients with T2D. At the vascular level, osteoglycin expression was assessed by RT-qPCR and immunohistochemistry, and no significant differences were observed between calcified arteries from patients with T2D and noncalcified arteries from controls. In vitro experiments using VSMCs (mock and overexpressing osteoglycin) under calcifying conditions were performed to analyze the osteoglycin function. The overexpression of osteoglycin in VMSCs under calcifying conditions revealed an increase of cell proliferation without effect on apoptosis and an upregulation of the expression of autotaxin (ATX) involved in inflammatory processes. In conclusion, osteoglycin could play a role in glycemic homeostasis, being a potential biomarker of insulin resistance in patients with T2D. Furthermore, osteoglycin could indirectly participate in the development of atherosclerosis through its regulatory effect on ATX and by proliferating VSMCs.NEW & NOTEWORTHY This study uncovers an increase of serum osteoglycin levels in patients with type 2 diabetes, which does not appear to be associated with the development of atherosclerosis, but rather with insulin resistance in this population. Overexpression of osteoglycin increased proliferation and upregulated the expression of autotaxin in vascular smooth muscle cells within calcified environments. Osteoglycin could be a biomarker of insulin resistance for type 2 diabetes and could be indirectly involved in the development of atherosclerosis.

Interferon-Alpha Decreases Cancer Stem Cell Properties and Modulates Exosomes in Malignant Melanoma

Malignant melanoma (MM) can spread to other organs and is resistant in part due to the presence of cancer stem cell subpopulations (CSCs). While a controversial high dose of interferon-alpha (IFN-α) has been used to treat non-metastatic high-risk melanoma, it comes with undesirable side effects. In this study, we evaluated the effect of low and high doses of IFN-α on CSCs by analyzing ALDH activity, side population and specific surface markers in established and patient-derived primary cell lines. We also assessed the clonogenicity, migration and tumor initiation capacities of IFN-α treated CSCs. Additionally, we investigated genomic modulations related to stemness properties using microRNA sequencing and microarrays. The effect of IFN-α on CSCs-derived exosomes was also analyzed using NanoSight and liquid chromatography (LC-HRMS)-based metabolomic analysis, among others. Our results showed that even low doses of IFN-α reduced CSC formation and stemness properties, and led to a significant decrease in the ability to form tumors in mice xenotransplants. IFN-α also modulated the expression of genes and microRNAs involved in several cancer processes and metabolomics of released exosomes. Our work suggests the utility of low doses of interferon, combined with the analysis of metabolic biomarkers, as a potential clinical approach against the aggressiveness of CSCs in melanoma.

Interferon-Alpha Decreases Cancer Stem Cell Properties and Modulates Exosomes in Malignant Melanoma

Malignant melanoma (MM) can spread to other organs and is resistant in part due to the presence of cancer stem cell subpopulations (CSCs). While a controversial high dose of interferon-alpha (IFN-α) has been used to treat non-metastatic high-risk melanoma, it comes with undesirable side effects. In this study, we evaluated the effect of low and high doses of IFN-α on CSCs by analyzing ALDH activity, side population and specific surface markers in established and patient-derived primary cell lines. We also assessed the clonogenicity, migration and tumor initiation capacities of IFN-α treated CSCs. Additionally, we investigated genomic modulations related to stemness properties using microRNA sequencing and microarrays. The effect of IFN-α on CSCs-derived exosomes was also analyzed using NanoSight and liquid chromatography (LC-HRMS)-based metabolomic analysis, among others. Our results showed that even low doses of IFN-α reduced CSC formation and stemness properties, and led to a significant decrease in the ability to form tumors in mice xenotransplants. IFN-α also modulated the expression of genes and microRNAs involved in several cancer processes and metabolomics of released exosomes. Our work suggests the utility of low doses of interferon, combined with the analysis of metabolic biomarkers, as a potential clinical approach against the aggressiveness of CSCs in melanoma.

Discovery and validation of glucose-sensitive peptide biomarkers from human serum albumin to diagnose type 2 diabetes mellitus

Glycated albumin (GA), which represents the global glycation level of albumin, has emerged as a biomarker for diagnosing prediabetes and diabetes. In our previous study, we developed a peptide-based strategy and found three putative peptide biomarkers from the tryptic peptides of GA to diagnose type 2 diabetes mellitus (T2DM). However, the trypsin cleavage sites at the carboxyl side of lysine (K) and arginine (R) are consistent with the nonenzymatic glycation modification site residues, which considerably increases the number of missed cleavage sites and half-cleaved peptides. To solve this problem, the endoproteinase Glu-C was used to digest GA from human serum to screen putative peptides to diagnose T2DM. In the discovery phase, we found eighteen and fifteen glucose-sensitive peptides from purified albumin and human serum incubated with ¹³C glucose in vitro, respectively. In the validation phase, eight glucose-sensitive peptides were screened and validated in 72 clinical samples (28 healthy controls and 44 patients with diabetes) using label-free LC-ESI-MRM. Three putative sensitive peptides (VAHRFKDLGEE, FKPLVEEPQNLIKQNCE and NQDSISSKLKE) from albumin exhibited good specificity and sensitivity based on receiver operating characteristic analysis. In summary, three peptides were found as promising biomarkers for the diagnosis and assessment of T2DM based on mass spectrometry.

Metabolic remodeling of glycerophospholipids acts as a signature of dulaglutide and liraglutide treatment in recent-onset type 2 diabetes mellitus

Aims

As metabolic remodeling is a pathological characteristic in type 2 diabetes (T2D), we investigate the roles of newly developed long-acting glucagon-like peptide-1 receptor agonists (GLP-1RAs) such as dulaglutide and liraglutide on metabolic remodeling in patients with recent-onset T2D.

Methods

We recruited 52 cases of T2D and 28 control cases in this study. In the patient with T2D, 39 cases received treatment with dulaglutide and 13 cases received treatment with liraglutide. Using untargeted metabolomics analysis with broad-spectrum LC-MS, we tracked serum metabolic changes of the patients from the beginning to the end of follow-up (12th week).

Results

We identified 198 metabolites that were differentially expressed in the patients with T2D, compared to the control group, in which 23 metabolites were significantly associated with fasting plasma glucose. Compared to pre-treatment, a total of 46 and 45 differentially regulated metabolites were identified after treatments with dulaglutide and liraglutide, respectively, in which the most differentially regulated metabolites belong to glycerophospholipids. Furthermore, a longitudinal integration analysis concurrent with diabetes case-control status revealed that metabolic pathways, such as the insulin resistance pathway and type 2 diabetes mellitus, were enriched after dulaglutide and liraglutide treatments. Proteins such as GLP-1R, GNAS, and GCG were speculated as potential targets of dulaglutide and liraglutide.

Conclusions

In total, a metabolic change in lipids existed in the early stage of T2D was ameliorated after the treatments of GLP-1RAs. In addition to similar effects on improving glycemic control, remodeling of glycerophospholipid metabolism was identified as a signature of dulaglutide and liraglutide treatments.

Untargeted Metabolomics by Liquid Chromatography-Mass Spectrometry in Biomedical Research

Metabolomics, alone or in combination with other omics sciences, has shown great relevance in a large number of investigations in different branches of biomedicine, often providing novel discoveries and helping to expand the knowledge. Metabolomics analyses are carried out using different techniques, but in this chapter, we focus on liquid chromatography coupled to high-resolution mass spectrometry. The designated methodology consists of an untargeted approach for the analysis of plasma samples. The use of this method, with a reverse-phase column and electrospray ionization in positive mode, covers the detection of a broad range of metabolites, mainly of nonpolar and of intermediate polarity. This chapter also reviews the mass fragmentation spectra for the identification of bile acids, acylcarnitines, and glycerophospholipids.

Study on the mechanism of American ginseng extract for treating type 2 diabetes mellitus based on metabolomics

American ginseng extract (AGE) is an efficient and low-toxic adjuvant for type 2 diabetes mellitus (T2DM). However, the metabolic mechanisms of AGE against T2DM remain unknown. In this study, a rat model of T2DM was created and administered for 28 days. Their biological (body weight and serum biochemical indicators) and pathological (pancreatic sections stained with HE) information were collected for further pharmacodynamic evaluation. Moreover, an ultra-performance liquid chromatography–mass spectrometry–based (UHPLC–MS/MS–based) untargeted metabolomics method was used to identify potential biomarkers of serum samples from all rats and related metabolic pathways. The results indicated that body weight, fasting blood glucose (FBG), fasting blood insulin (FINS), blood triglyceride concentration (TG), high-density lipoprotein cholesterol (HDL-C), insulin resistance index (HOMA-IR) and insulin sensitivity index (ISI), and impaired islet cells were significantly improved after the high dose of AGE (H_AGE) and metformin treatment. Metabolomics analysis identified 101 potential biomarkers among which 94 metabolites had an obvious callback. These potential biomarkers were mainly enriched in nine metabolic pathways linked to amino acid metabolism and lipid metabolism. Tryptophan metabolism and glutathione metabolism, as differential metabolic pathways between AGE and metformin for treating T2DM, were further explored. Further analysis of the aforementioned results suggested that the anti-T2DM effect of AGE was closely associated with inflammation, oxidative stress, endothelial dysfunction, dyslipidemia, immune response, insulin resistance, insulin secretion, and T2DM-related complications. This study can provide powerful support for the systematic exploration of the mechanism of AGE against T2DM and a basis for the clinical diagnosis of T2DM.

Profiling of lysophosphatidylethanolamine molecular species in human serum and in silico prediction of the binding site on albumin

Lysophosphatidylethanolamine (LPE) is a major lysophospholipid produced by phospholipids and binds to human serum albumin (HSA). LPEs may play various roles in vivo depending on the differences in their acyl chains. However, only few reports have been published on the biological functions of LPEs. Hence, we determined the exact relative abundance of the major LPEs in the serum of healthy participants (n = 8) using liquid chromatography-tandem mass spectrometry. Consequently, LPE 18:2 (24.1 ± 5.2%) was found to be the most abundant in serum. To understand the distribution of LPEs, the serum separated via gel-filtration high-performance liquid chromatography was subjected to quantitative measurement. LPEs were more observed in the albumin fraction than the lipoprotein fraction. We also performed a fluorescence displacement assay and an in silico molecular docking experiment using AutoDock to confirm the affinity and binding sites of the LPEs on HSA. The binding affinities of the LPEs for drug sites 1 and 2 on HSA were relatively low, with Ki values of approximately 11 and 3.8 μM, respectively. AutoDock analysis revealed the conformation of the LPEs bound to drug sites and the possibility of LPEs binding to other HSA sites. These findings could help to elucidate the biological and pathological functions of LPEs.

Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach

Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed.

Butter-Derived Ruminant Trans Fatty Acids Do Not Alleviate Atherosclerotic Lesions in High-Fat Diet-Fed ApoE-/- Mice

Atherosclerosis (AS) is the most common cardiovascular disease (CVD). Currently, it is widely believed that R-TFA and I-TFA may cause different biological effects. In the present study, we aim to elucidate the effect of mixed R-TFA derived from butter on the development of AS in high-fat diet-fed ApoE^-/- mice and find the possible mechanism. It was shown that butter-derived R-TFA promoted dyslipidemia, reduced thoracic and abdominal aorta diameters, and induced aortic lipid deposition and atherosclerotic lesions in high-fat diet-fed ApoE^-/- mice. Meanwhile, butter-derived R-TFA affected the serum lipid profile of high-fat diet-fed ApoE^-/- mice and the lipid metabolism of human umbilical vein endothelial cells (HUVECs). Through lipidomic techniques, we found that butter-derived R-TFA had a significant effect on the glycerophospholipid metabolic pathway. In conclusion, our results demonstrated that butter-derived R-TFA does not alleviate but promotes atherosclerotic lesions in high-fat diet-fed ApoE^-/- mice and that the glycerophospholipid metabolic pathway plays a major role in this pro-atherosclerotic effect.

The Modulatory Effect of Cyclocarya paliurus Flavonoids on Intestinal Microbiota and Hypothalamus Clock Genes in a Circadian Rhythm Disorder Mouse Model

Circadian rhythm disruption is detrimental and results in adverse health consequences. We used a multi-omics profiling approach to investigate the effects of Cyclocarya paliurus flavonoid (CPF)-enriched diets on gut microbiota, metabolites, and hypothalamus clock genes in mice with induced circadian rhythm disruption. It was observed that CPF supplementation altered the specific composition and function of gut microbiota and metabolites induced by circadian rhythm disruption. Analysis showed that the abundance of Akkermansia increased, while the abundance of Clostridiales and Ruminiclostridium displayed a significant downward trend after the CPF intervention. Correlation analysis also revealed that these gut microbes had certain correlations with the metabolites, suggesting that CPFs help the intestinal microbiota to repair the intestinal environment and modulate the release of some beneficial metabolites. Notably, single-cell RNA-seq revealed that CPF supplementation significantly regulated the expression of genes associated with circadian rhythm, myelination, and neurodegenerative diseases. Altogether, these findings highlight that CPFs may represent a promising dietary therapeutic strategy for treating circadian rhythm disruption.

Serum metabolomic profiles associated with subclinical and clinical cardiovascular phenotypes in people with type 2 diabetes

BACKGROUND

Atherosclerotic cardiovascular diseases (CVD) is the leading cause of death in diabetes, but the full range of biomarkers reflecting atherosclerotic burden and CVD risk in people with diabetes is unknown. Metabolomics may help identify novel biomarkers potentially involved in development of atherosclerosis. We investigated the serum metabolomic profile of subclinical atherosclerosis, measured using ankle brachial index (ABI), in people with type 2 diabetes, compared with the profile for symptomatic CVD in the same population.

METHODS

The Edinburgh Type 2 Diabetes Study is a cohort of 1,066 individuals with type 2 diabetes. ABI was measured at baseline, years 4 and 10, with cardiovascular events assessed at baseline and during 10 years of follow-up. A panel of 228 metabolites was measured at baseline using nuclear magnetic resonance spectrometry, and their association with both ABI and prevalent CVD was explored using univariate regression models and least absolute shrinkage and selection operator (LASSO). Metabolites associated with baseline ABI were further explored for association with follow-up ABI and incident CVD.

RESULTS

Mean (standard deviation, SD) ABI at baseline was 0.97 (0.18, N = 1025), and prevalence of CVD was 35.0%. During 10-year follow-up, mean (SD) change in ABI was + 0.006 (0.178, n = 436), and 257 CVD events occurred. Lactate, glycerol, creatinine and glycoprotein acetyls levels were associated with baseline ABI in both univariate regression [βs (95% confidence interval, CI) ranged from - 0.025 (- 0.036, - 0.015) to - 0.023 (- 0.034, - 0.013), all p < 0.0002] and LASSO analysis. The associations remained nominally significant after adjustment for major vascular risk factors. In prospective analyses, lactate was nominally associated with ABI measured at years 4 and 10 after adjustment for baseline ABI. The four ABI-associated metabolites were all positively associated with prevalent CVD [odds ratios (ORs) ranged from 1.29 (1.13, 1.47) to 1.49 (1.29, 1.74), all p < 0.0002], and they were also positively associated with incident CVD [ORs (95% CI) ranged from 1.19 (1.02, 1.39) to 1.35 (1.17, 1.56), all p < 0.05].

CONCLUSIONS

Serum metabolites relating to glycolysis, fluid balance and inflammation were independently associated with both a marker of subclinical atherosclerosis and with symptomatic CVD in people with type 2 diabetes. Additional investigation is warranted to determine their roles as possible etiological and/or predictive biomarkers for atherosclerotic CVD.

Comprehensive Metabolic Profiling of Inflammation Indicated Key Roles of Glycerophospholipid and Arginine Metabolism in Coronary Artery Disease

Background

Systemic immune inflammation is a key mediator in the progression of coronary artery disease (CAD), concerning various metabolic and lipid changes. In this study, the relationship between the inflammatory index and metabolic profile in patients with CAD was investigated to provide deep insights into metabolic disturbances related to inflammation.

Methods

Widely targeted plasma metabolomic and lipidomic profiling was performed in 1,234 patients with CAD. Laboratory circulating inflammatory markers were mainly used to define general systemic immune and low-grade inflammatory states. Multivariable-adjusted linear regression was adopted to assess the associations between 860 metabolites and 7 inflammatory markers. Least absolute shrinkage and selection operator (LASSO) logistic-based classifiers and multivariable logistic regression were applied to identify biomarkers of inflammatory states and develop models for discriminating an advanced inflammatory state.

Results

Multiple metabolites and lipid species were linearly associated with the seven inflammatory markers [false discovery rate (FDR) <0.05]. LASSO and multivariable-adjusted logistic regression analysis identified significant associations between 45 metabolites and systemic immune-inflammation index, 46 metabolites and neutrophil-lymphocyte ratio states, 32 metabolites and low-grade inflammation score, and 26 metabolites and high-sensitivity C-reactive protein states (P < 0.05). Glycerophospholipid metabolism and arginine and proline metabolism were determined as key altered metabolic pathways for systemic immune and low-grade inflammatory states. Predictive models based solely on metabolite combinations showed feasibility (area under the curve: 0.81 to 0.88) for discriminating the four parameters that represent inflammatory states and were successfully validated using a validation cohort. The inflammation-associated metabolite, namely, β-pseudouridine, was related to carotid and coronary arteriosclerosis indicators (P < 0.05).

Conclusions

This study provides further information on the relationship between plasma metabolite profiles and inflammatory states represented by various inflammatory markers in CAD. These metabolic markers provide potential insights into pathological changes during CAD progression and may aid in the development of therapeutic targets.

Circulating Palmitoyl Sphingomyelin Is Associated With Cardiovascular Disease in Individuals With Type 2 Diabetes: Findings From the China Da Qing Diabetes Study

OBJECTIVE

To investigate the association of potential cardiovascular disease (CVD) biomarkers in patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

We enrolled 120 participants (aged 61.5-69.5 years) with type 2 diabetes and 60 (aged 62.5-73.5 years) with normal glucose tolerance in the discovery group from the original Da Qing Diabetes Study. Their diabetes status was confirmed in 1986; then, the participants were followed over 23 years to collect CVD outcome data. Untargeted and targeted metabolomics analyses based on ultra-high-performance liquid chromatography-tandem mass spectrometry were used to identify potential markers. Multivariable regression analysis was used to evaluate the association between metabolites and CVD outcomes. An independent group of 335 patients (aged 67.0-77.0 years) with diabetes was used for biomarker validation.

RESULTS

In the discovery group, untargeted metabolomics analysis found 16 lipids and fatty acids metabolites associated with CVD risk in patients with diabetes, with palmitoyl sphingomyelin (PSM) having the strongest association. Plasma PSM concentrations were significantly higher in cases of diabetes with CVD than without (41.68 ± 10.47 vs. 9.69 ± 1.47 μg/mL; P < 0.0001). The odds ratio (OR) of CVD for 1 µg/mL PSM change was 1.19 (95% CI 1.13-1.25) after adjustment of clinical confounders. The validation study confirmed that PSM was significantly associated with increased CVD risk in diabetes (OR 1.22 [95% CI 1.16-1.30]).

CONCLUSIONS

Changes in lipid and fatty acid content were significantly associated with CVD risk in the Chinese population with diabetes. PSM is a potential biomarker of increased CVD risk in diabetes.

Metabolic Interactions and Differences between Coronary Heart Disease and Diabetes Mellitus: A Pilot Study on Biomarker Determination and Pathogenesis

Comprehensive understanding of plasma metabotype of diabetes mellitus (DM), coronary heart disease (CHD), and especially diabetes mellitus with coronary heart disease (CHDDM) is still lacking. In this work, the plasma metabolic differences and links of DM, CHD, and CHDDM patients were investigated by the strategy of comparative metabolomics based on ¹H NMR spectroscopy combined with network analysis for revealing their metabolic differences. A total of 17 metabolites are related to three diseases, among which valine, alanine, leucine, isoleucine, and N-acetyl-glycoprotein are positively correlated with CHD and CHDDM (odds ratios (OR) > 1). The trimethylamine oxide, glycerol, lactose, indoleacetate, and scyllo-inositol are closely related to the development of DM to CHDDM (OR > 1), and indoleactate (OR: 1.06, 95% confidence interval (CI): 1.01-1.12) and lactose (OR: 2.46, 95% CI: 1.67-3.25) are particularly prominent in CHDDM. We identified three multi-biomarkers types that were significantly associated with glycosylated hemoglobin (HbA1C) at baseline. All diseases demonstrated dysregulated glycolysis/gluconeogenesis and amino acid biosynthesis pathway. In addition, enrichment in tryptophan metabolism observed in CHDDM, enrichment in inositol phosphate metabolism observed in DM, and the metabolites related to microbiota metabolism were dysregulated in both DM and CHDDM. The comparative metabolomics strategy of multi-diseases offers a new perspective in disease-specific markers and pathogenic pathways.

Type 2 diabetes preventive effects with a 12-months sardine-enriched diet in elderly population with prediabetes: An interventional, randomized and controlled trial

BACKGROUND

Fish could play a role in preventing type 2 diabetes (T2D) but there has been little specification about the type of fish and the preventive mechanism involved in its health claim. The sardine is a source of omega-3 and taurine that, in isolation or in synergy, would produce T2D-delaying through different molecular mechanism.

HYPOTHESIS

The consumption of twice a week of sardine, during one year would reduce T2D-developing risk in a population with prediabetes (preDM) and old age.

DESIGN

152 subjects with fasting glucose between 100-124 mg/dL aged ≥65 yo were recruited from three primary care centers in Barcelona and were randomly distributed among two interventional groups: control group (CG) and sardine group (SG). Both groups received same T2D-prevention nutritional during a year but only SG had to add 200 g of sardine per week. All variables were collected before to start and at the end of the diet. (ClinicalTrials.gov: NCT03557541).

RESULTS

152 people were randomized into CG (n=77) and SG (n=75) with 18 and 12 drop outs respectively. Subjects in SG, significantly compared to CG, decreased percentage classified-individuals in a very high risk group to develop T2D according to FINDRISC (p=0.035). In addition to increasing HDL-cholesterol and adiponectin and decreasing triglycerides (p<0.05) and blood pressure (<0.05), SG showed a lower HOMA-IR (p=0.032). The consumption of sardine characteristics nutrients as omega-3, EPA and DHA, vitamin D, fluorine and taurine were higher for SG (p<0.05). These results agreed with the increased of taurine, fatty acid (FA) omega-3 and bile acids circulating metabolites (p<0.05). Changes erythrocyte membrane FA were detected only in SG with a decrease of 5 omega-6 FA (p<0.001) and an increase of 3 omega-3 FA types (p<0.001).

CONCLUSION

We conclude that a year T2D-prevention diet with sardine supplementation has a greater protective effect against developing T2D and CV events.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Metabolomic profile of cancer stem cell-derived exosomes from patients with malignant melanoma

Malignant melanoma (MM) is the most aggressive and life‐threatening form of skin cancer. It is characterized by an extraordinary metastasis capacity and chemotherapy resistance, mainly due to melanoma cancer stem cells (CSCs). To date, there are no suitable clinical diagnostic, prognostic or predictive biomarkers for this neoplasia. Therefore, there is an urgent need for new MM biomarkers that enable early diagnosis and effective disease monitoring. Exosomes represent a novel source of biomarkers since they can be easily isolated from different body fluids. In this work, a primary patient‐derived MM cell line enriched in CSCs was characterized by assessing the expression of specific markers and their stem‐like properties. Exosomes derived from CSCs and serums from patients with MM were characterized, and their metabolomic profile was analysed by high‐resolution mass spectrometry (HRMS) following an untargeted approach and applying univariate and multivariate statistical analyses. The aim of this study was to search potential biomarkers for the diagnosis of this disease. Our results showed significant metabolomic differences in exosomes derived from MM CSCs compared with those from differentiated tumour cells and also in serum‐derived exosomes from patients with MM compared to those from healthy controls. Interestingly, we identified similarities between structural lipids differentially expressed in CSC‐derived exosomes and those derived from patients with MM such as the glycerophosphocholine PC 16:0/0:0. To our knowledge, this is the first metabolomic‐based study aimed at characterizing exosomes derived from melanoma CSCs and patients' serum in order to identify potential biomarkers for MM diagnosis. We conclude that metabolomic characterization of CSC‐derived exosomes sets an open door to the discovery of clinically useful biomarkers in this neoplasia.

Non-Targeted Metabolomic Analysis Reveals Serum Phospholipid Alterations in Patients with Early Stages of Diabetic Foot Ulcer

Diabetic foot ulcer (DFU) is a common complication of type 2 diabetes mellitus (T2DM) characterized by ulcer formation, which can lead to the amputation of lower extremities. However, the metabolic alterations related to this complication are not completely elucidated. Therefore, we carried out a metabolomic analysis of serum samples obtained from T2DM adult patients diagnosed with diabetic foot ulcer in a cross-sectional, observational, and comparative study. Eighty-four volunteers were classified into the following groups: without T2DM (control group, n = 30) and with T2DM and different stages of diabetic foot ulcer according to Wagner-Meggitt classification system: DFU G0 (n = 11), DFU G1 (n = 14), DFU G2 (n = 16), and DFU G3 (n = 13). The non-target metabolomic profile followed by chemometric analysis revealed that lysophosphatidylethanolamine (16:1) could be proposed as key metabolite related to the onset of diabetic foot ulcer; however, this phospholipid was not affected by diabetic foot ulcer progression. Therefore, further studies are necessary to validate these phospholipids as biomarker candidates for the early diagnosis of diabetic foot ulcer in T2DM patients.

Altered Metabolome of Lipids and Amino Acids Species: A Source of Early Signature Biomarkers of T2DM

Diabetes mellitus, a disease of modern civilization, is considered the major mainstay of mortalities around the globe. A great number of biochemical changes have been proposed to occur at metabolic levels between perturbed glucose, amino acid, and lipid metabolism to finally diagnoe diabetes mellitus. This window period, which varies from person to person, provides us with a unique opportunity for early detection, delaying, deferral and even prevention of diabetes. The early detection of hyperglycemia and dyslipidemia is based upon the detection and identification of biomarkers originating from perturbed glucose, amino acid, and lipid metabolism. The emerging “OMICS” technologies, such as metabolomics coupled with statistical and bioinformatics tools, proved to be quite useful to study changes in physiological and biochemical processes at the metabolic level prior to an eventual diagnosis of DM. Approximately 300–400 such metabolites have been reported in the literature and are considered as predicting or risk factor-reporting metabolic biomarkers for this metabolic disorder. Most of these metabolites belong to major classes of lipids, amino acids and glucose. Therefore, this review represents a snapshot of these perturbed plasma/serum/urinary metabolic biomarkers showing a significant correlation with the future onset of diabetes and providing a foundation for novel early diagnosis and monitoring the progress of metabolic syndrome at early symptomatic stages. As most metabolites also find their origin from gut microflora, metabolism and composition of gut microflora also vary between healthy and diabetic persons, so we also summarize the early changes in the gut microbiome which can be used for the early diagnosis of diabetes.

Feature selection for OPLS discriminant analysis of cancer tissue lipidomics data

The mass spectrometry-based molecular profiling can be used for better differentiation between normal and cancer tissues and for the detection of neoplastic transformation, which is of great importance for diagnostics of a pathology, prognosis of its evolution trend, and development of a treatment strategy. The aim of the present study is the evaluation of tissue classification approaches based on various data sets derived from the molecular profile of the organic solvent extracts of a tissue. A set of possibilities are considered for the orthogonal projections to latent structures discriminant analysis: all mass spectrometric peaks over 300 counts threshold, subset of peaks selected by ranking with support vector machine algorithm, peaks selected by random forest algorithm, peaks with the statistically significant difference of the intensity determined by the Mann-Whitney U test, peaks identified as lipids, and both identified and significantly different peaks. The best predictive potential is obtained for OPLS-DA model built on nonpolar glycerolipids (Q² = 0.64, area under curve [AUC] = 0.95); the second one is OPLS-DA model with lipid peaks selected by random forest algorithm (Q² = 0.58, AUC = 0.87). Moreover, models based on particular molecular classes are more preferable from biological point of view, resulting in new explanatory mechanisms of pathophysiology and providing a pathway analysis. Another promising features for OPLS-DA modeling are phosphatidylethanolamines (Q² = 0.48, AUC = 0.86).

Altered Metabolomic Profile in Patients with Peripheral Artery Disease

Peripheral artery disease (PAD) is a common atherosclerotic disease characterized by narrowed or blocked arteries in the lower extremities. Circulating serum biomarkers can provide significant insight regarding the disease progression. Here, we explore the metabolomics signatures associated with different stages of PAD and investigate potential mechanisms of the disease. We compared the serum metabolites of a cohort of 26 PAD patients presenting with claudication and 26 PAD patients presenting with critical limb ischemia (CLI) to those of 26 non-PAD controls. A difference between the metabolite profiles of PAD patients from non-PAD controls was observed for several amino acids, acylcarnitines, ceramides, and cholesteryl esters. Furthermore, our data demonstrate that patients with CLI possess an altered metabolomic signature different from that of both claudicants and non-PAD controls. These findings provide new insight into the pathophysiology of PAD and may help develop future diagnostic procedures and therapies for PAD patients.

Comparative Metabolomics between Mycobacterium tuberculosis and the MTBVAC Vaccine Candidate

MTBVAC is a live attenuated M. tuberculosis vaccine constructed by genetic deletions in the phoP and fadD26 virulence genes. The MTBVAC vaccine is currently in phase 2 clinical trials with newborns and adults in South Africa, one of the countries with the highest incidence. Although MTBVAC has been extensively characterized by genomics, transcriptomics, lipidomics, and proteomics, its metabolomic profile is yet unknown. Accordingly, in this study we aim to identify differential metabolites between M. tuberculosis and MTBVAC. To this end, an untargeted metabolomics approach based on liquid chromatography coupled to high-resolution mass spectrometry was implemented in order to explore the main metabolic differences between M. tuberculosis and MTBVAC. As an outcome, we identified a set of 34 metabolites involved in diverse bacterial biosynthetic pathways. A consistent increase in the phosphatidylinositol species was observed in the vaccine candidate relative to its parental strain. This phenotype resulted in an increased production of phosphatidylinositol mannosides, a novel PhoP-regulated phenotype in the most widespread lineages of M. tuberculosis. This study represents a step ahead in our understanding of the MTBVAC vaccine, and some of the differential metabolites identified in this work might be used as potential vaccination biomarkers.

Targeted metabolomic analysis of plasma metabolites in patients with coronary heart disease in southern China

Coronary heart disease (CHD), one of the leading causes of death in the world, is a complex metabolic disorder due to genetic and environmental interactions. The potential mechanisms and diagnostic biomarkers for different types of coronary heart disease remain unclear. Metabolomics is increasingly considered to be a promising technology with the potential to identify metabolomic features in an attempt to distinguish the different stages of CHD.We aimed to investigate serum metabolite profiling between CHD patients and normal coronary artery (NCA) subjects and identify metabolic biomarkers associated with CHD progression in an ethnic Hakka population in southern China.Using a novel targeted metabolomics approach, we explored the metabolic characteristics of CHD patients. Blood samples from 302 patients with CHD and 59 NCA subjects were collected that analyses using targeted liquid-chromatography coupled with tandem mass spectrometry (LC-MS).A total of 361 blood samples were determined using targeted LC-MS. Plasma concentrations for trimetlylamine oxide (TMAO), choline, creatinine, and carnitine were significantly higher in patients with CHD compared to the NCA cohort. Further, we observed that the concentration of the 4 metabolites were higher than that of the NCA group in any group of CHD, which including acute myocardial infarction (AMI), unstable angina (UA), and stable angina (SA). In addition, the diagnostic model was constructed based on the metabolites identified and the ROC curve of the NCA subjects and CHD patients were performed. For choline and creatinine, the AUCs ranged from 0.720 to 0.733. For TMAO and carnitine, the AUCs ranged from 0.568 to 0.600.In conclusion, the current study illustrates the distribution of 4 metabolites between CHD patients and NCA subjects. Metabolomics analysis may yield novel predictive biomarkers that will potentially provide value for clinical diagnosis of CHD.

The role of mass spectrometry in studies of glycation processes and diabetes management

In the last decade, mass spectrometry has been widely employed in the study of diabetes. This was mainly due to the development of new, highly sensitive, and specific methods representing powerful tools to go deep into the biochemical and pathogenetic processes typical of the disease. The aim of this review is to give a panorama of the scientifically valid results obtained in this contest. The recent studies on glycation processes, in particular those devoted to the mechanism of production and to the reactivity of advanced glycation end products (AGEs, AGE peptides, glyoxal, methylglyoxal, dicarbonyl compounds) allowed to obtain a different view on short and long term complications of diabetes. These results have been employed in the research of effective markers and mass spectrometry represented a precious tool allowing the monitoring of diabetic nephropathy, cardiovascular complications, and gestational diabetes. The same approaches have been employed to monitor the non-insulinic diabetes pharmacological treatments, as well as in the discovery and characterization of antidiabetic agents from natural products. © 2018 Wiley Periodicals, Inc. Mass Spec Rev 38:112-146, 2019.

Plasma Lipidomic Profiling and Risk of Type 2 Diabetes in the PREDIMED Trial

OBJECTIVE

Specific lipid molecular changes leading to type 2 diabetes (T2D) are largely unknown. We assessed lipidome factors associated with future occurrence of T2D in a population at high cardiovascular risk.

RESEARCH DESIGN AND METHODS

We conducted a case-cohort study nested within the PREDIMED trial, with 250 incident T2D cases diagnosed during 3.8 years of median follow-up, and a random sample of 692 participants (639 noncases and 53 overlapping cases) without T2D at baseline. We repeatedly measured 207 plasma known lipid metabolites at baseline and after 1 year of follow-up. We built combined factors of lipid species using principal component analysis and assessed the association between these lipid factors (or their 1-year changes) and T2D incidence.

RESULTS

Baseline lysophosphatidylcholines and lysophosphatidylethanolamines (lysophospholipids [LPs]), phosphatidylcholine-plasmalogens (PC-PLs), sphingomyelins (SMs), and cholesterol esters (CEs) were inversely associated with risk of T2D (multivariable-adjusted P for linear trend ≤0.001 for all). Baseline triacylglycerols (TAGs), diacylglycerols (DAGs), and phosphatidylethanolamines (PEs) were positively associated with T2D risk (multivariable-adjusted P for linear trend <0.001 for all). One-year changes in these lipids showed associations in similar directions but were not significant after adjustment for baseline levels. TAGs with odd-chain fatty acids showed inverse associations with T2D after adjusting for total TAGs.

CONCLUSIONS

Two plasma lipid profiles made up of different lipid classes were found to be associated with T2D in participants at high cardiovascular risk. A profile including LPs, PC-PLs, SMs, and CEs was associated with lower T2D risk. Another profile composed of TAGs, DAGs, and PEs was associated with higher T2D risk.

Identification of Lysophosphatidylcholines and Sphingolipids as Potential Biomarkers for Acute Aortic Dissection via Serum Metabolomics

OBJECTIVES

Acute aortic dissection (AAD) is a severe clinical emergency with a high mortality, and is easily misdiagnosed in its early stage. This study aimed at discovering serum metabolomic markers with the potential to diagnose AAD and distinguish between two subtypes of AAD.

METHODS

Thirty-five patients with AAD, including 20 with Stanford type A and 15 with Stanford type B were enrolled in this study, together with 20 healthy controls. All patients with AAD were admitted within 72 h of onset. Serum metabolomics profiles were determined by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry and the data were analysed by principal component analysis and partial least squares discriminant analysis.

RESULTS

A total of 17 metabolites differing between the control and AAD groups were finally screened and identified as lysophosphatidylcholines (LPC) and sphingolipids including sphinganine, phytosphingosine, sphingomyelin, and ceramide. Compared with those in the healthy control group, LPC levels were significantly lower in both the Stanford type A and type B AAD groups. Interestingly, sphingolipids, including sphinganine, phytosphingosine, and ceramide, were remarkably reduced in the Stanford type A AAD group, but not in the Stanford type B AAD group. Subgroup analysis showed that the changes in LPC and sphingolipid levels were unrelated to hypertension or gender.

CONCLUSIONS

The present results indicate that LPCs and sphingolipids are significantly altered in patients with AAD, and several sphingolipids, such as sphinganine, phytosphingosine, and ceramide, were dramatically decreased in patients with Stanford type A AAD. A combination of these two families of metabolites could serve as a potential biomarker for the diagnosis of AAD and distinguishing between Stanford type A and Stanford type B.

Metabolic map of osthole and its effect on lipids

1. Osthole, a coumarin compound from plants, is a promising agent for the treatment of metabolic diseases, including hyperglycemia, fatty liver, and cancers. Studies indicate that the peroxisome proliferator-activated receptors (PPAR) α and γ are involved in the pharmacological effects of osthole. The in vitro and in vivo metabolism of osthole and its biological activity are not completely understood. 2. In this study, ultra-performance chromatography electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS)-based metabolomics was used to determine the metabolic pathway of osthole and its influence on the levels of endogenous metabolites. Forty-one osthole metabolites, including 23 novel metabolites, were identified and structurally elucidated from its metabolism in vitro and in vivo. Recombinant cytochrome P450s (CYPs) screening showed that CYP3A4 and CYP3A5 were the primary enzymes contributing to osthole metabolism. 3. More importantly, osthole was able to decrease the levels of lysophosphatidylethanolamine (LPE) and lysophosphatidylcholine (LPC) in the plasma, which explains in part its modulatory effects on metabolic diseases. 4. This study gives the insights about the metabolic pathways of osthole in vivo, including hydroxylation, glucuronidation, and sulfation. Furthermore, the levels of the lipids regulated by osthole indicated its potential effects on adipogenesis. These data contribute to the understanding of the disposition and pharmacological activity of osthole in vivo.

Identification of serum metabolites associated with obesity and traditional risk factors for metabolic disease in Chinese adults

BACKGROUND AND AIMS

Obesity is a major worldwide health problem and is often associated with many metabolic diseases. Levels of several serum-specific metabolites may be altered in patients with these metabolic diseases. We aimed to investigate the associations of serum metabolite levels with obesity and traditional risk factors for metabolic disease in Chinese individuals.

METHODS AND RESULTS

Six-hundred Chinese individuals undergoing annual physical exams were recruited and categorized into overweight/obese and control groups (1:1 ratio). We simultaneously quantified the serum lysophosphatidylcholine (LPC), branched-chain amino acids (BCAA), aromatic amino acids (AAA), 25-hydroxyvitamin D, glutamine (Gln), glutamic acid (Glu), and Gln/Glu ratio levels using our previously established targeted serum metabolomic method. The overweight/obesity group had significantly higher levels of BCAA, AAA, and Glu, as well as lower levels of unsaturated LPC, Gln, and Gln/Glu, than the control group. Correlation analyses revealed significant and positive relationships of saturated LPC, BCAA, AAA, and Glu with blood pressure, glucose, triglycerides, apolipoprotein B, and high-sensitivity C-reactive protein, while unsaturated LPC, Gln, Gln/Glu, and 25-hydroxyvitamin D exhibited an opposite trend. In the multifactor logistic regression model, low unsaturated LPC and Gln/Glu, as well as high BCAA and AAA levels, were found to be independent risk factors for obesity; the odds ratios (95% confidence interval) of the highest quartile compared to the lowest quartile were 0.241 (0.139-0.417), 0.436 (0.252-0.755), 3.944 (2.094-7.430), and 2.357 (1.274-4.361) (P < 0.01), respectively.

CONCLUSION

LPC, BCAA, AAA, and Gln/Glu are significantly related to obesity development and risk factors of some metabolic diseases.

Shared and distinct lipid-lipid interactions in plasma and affected tissues in a diabetic mouse model

Lipids are ubiquitous metabolites with diverse functions; abnormalities in lipid metabolism appear to be related to complications from multiple diseases, including type 2 diabetes. Through technological advances, the entire lipidome has been characterized and researchers now need computational approaches to better understand lipid network perturbations in different diseases. Using a mouse model of type 2 diabetes with microvascular complications, we examined lipid levels in plasma and in renal, neural, and retinal tissues to identify shared and distinct lipid abnormalities. We used correlation analysis to construct interaction networks in each tissue, to associate changes in lipids with changes in enzymes of lipid metabolism, and to identify overlap of coregulated lipid subclasses between plasma and each tissue to define subclasses of plasma lipids to use as surrogates of tissue lipid metabolism. Lipid metabolism alterations were mostly tissue specific in the kidney, nerve, and retina; no lipid changes correlated between the plasma and all three tissue types. However, alterations in diacylglycerol and in lipids containing arachidonic acid, an inflammatory mediator, were shared among the tissue types, and the highly saturated cholesterol esters were similarly coregulated between plasma and each tissue type in the diabetic mouse. Our results identified several patterns of altered lipid metabolism that may help to identify pathogenic alterations in different tissues and could be used as biomarkers in future research into diabetic microvascular tissue damage.

Proteomics and metabolomics in ageing research: from biomarkers to systems biology

Age is the single greatest risk factor for a wide range of diseases, and as the mean age of human populations grows steadily older, the impact of this risk factor grows as well. Laboratory studies on the basic biology of ageing have shed light on numerous genetic pathways that have strong effects on lifespan. However, we still do not know the degree to which the pathways that affect ageing in the lab also influence variation in rates of ageing and age-related disease in human populations. Similarly, despite considerable effort, we have yet to identify reliable and reproducible 'biomarkers', which are predictors of one's biological as opposed to chronological age. One challenge lies in the enormous mechanistic distance between genotype and downstream ageing phenotypes. Here, we consider the power of studying 'endophenotypes' in the context of ageing. Endophenotypes are the various molecular domains that exist at intermediate levels of organization between the genotype and phenotype. We focus our attention specifically on proteins and metabolites. Proteomic and metabolomic profiling has the potential to help identify the underlying causal mechanisms that link genotype to phenotype. We present a brief review of proteomics and metabolomics in ageing research with a focus on the potential of a systems biology and network-centric perspective in geroscience. While network analyses to study ageing utilizing proteomics and metabolomics are in their infancy, they may be the powerful model needed to discover underlying biological processes that influence natural variation in ageing, age-related disease, and longevity.

Imaging and lipidomics methods for lipid analysis in metabolic and cardiovascular disease

Cardiometabolic diseases exhibit changes in lipid biology, which is important as lipids have critical roles in membrane architecture, signalling, hormone synthesis, homoeostasis and metabolism. However, Developmental Origins of Health and Disease studies of cardiometabolic disease rarely include analysis of lipids. This short review highlights some examples of lipid pathology and then explores the technology available for analysing lipids, focussing on the need to develop imaging modalities for intracellular lipids. Analytical methods for studying interactions between the complex endocrine and intracellular signalling pathways that regulate lipid metabolism have been critical in expanding our understanding of how cardiometabolic diseases develop in association with obesity and dietary factors. Biochemical methods can be used to generate detailed lipid profiles to establish links between lifestyle factors and metabolic signalling pathways and determine how changes in specific lipid subtypes in plasma and homogenized tissue are associated with disease progression. New imaging modalities enable the specific visualization of intracellular lipid traffic and distribution in situ. These techniques provide a dynamic picture of the interactions between lipid storage, mobilization and signalling, which operate during normal cell function and are altered in many important diseases. The development of methods for imaging intracellular lipids can provide a dynamic real-time picture of how lipids are involved in complex signalling and other cell biology pathways; and how they ultimately regulate metabolic function/homoeostasis during early development. Some imaging modalities have the potential to be adapted for in vivo applications, and may enable the direct visualization of progression of pathogenesis of cardiometabolic disease after poor growth in early life.

Comprehensive metabolomics identified lipid peroxidation as a prominent feature in human plasma of patients with coronary heart diseases

Coronary heart disease (CHD) is a complex human disease associated with inflammation and oxidative stress. The underlying mechanisms and diagnostic biomarkers for the different types of CHD remain poorly defined. Metabolomics has been increasingly recognized as an enabling technique with the potential to identify key metabolomic features in an attempt to understand the pathophysiology and differentiate different stages of CHD. We performed comprehensive metabolomic analysis in human plasma from 28 human subjects with stable angina (SA), myocardial infarction (MI), and healthy control (HC). Subsequent analysis demonstrated a uniquely altered metabolic profile in these CHD: a total of 18, 37 and 36 differential metabolites were identified to distinguish SA from HC, MI from SA, and MI from HC groups respectively. Among these metabolites, glycerophospholipid (GPL) metabolism emerged as the most significantly disturbed pathway. Next, we used a targeted metabolomic approach to systematically analyze GPL, oxidized phospholipid (oxPL), and downstream metabolites derived from polyunsaturated fatty acids (PUFAs), such as arachidonic acid and linoleic acid. Surprisingly, lipids associated with lipid peroxidation (LPO) pathways including oxidized PL and isoprostanes, isomers of prostaglandins, were significantly elevated in plasma of MI patients comparing to HC and SA, consistent with the notion that oxidative stress-induced LPO is a prominent feature in CHD. Our studies using the state-of-the-art metabolomics help to understand the underlying biological mechanisms involved in the pathogenesis of CHD; LPO metabolites may serve as potential biomarkers to differentiation MI from SA and HC.

Untargeted LC-HRMS-Based Metabolomics for Searching New Biomarkers of Pancreatic Ductal Adenocarcinoma: A Pilot Study

Pancreatic ductal adenocarcinoma is one of the most lethal tumors since it is usually detected at an advanced stage in which surgery and/or current chemotherapy have limited efficacy. The lack of sensitive and specific markers for diagnosis leads to a dismal prognosis. The purpose of this study is to identify metabolites in serum of pancreatic ductal adenocarcinoma patients that could be used as diagnostic biomarkers of this pathology. We used liquid chromatography-high-resolution mass spectrometry for a nontargeted metabolomics approach with serum samples from 28 individuals, including 16 patients with pancreatic ductal adenocarcinoma and 12 healthy controls. Multivariate statistical analysis, which included principal component analysis and partial least squares, revealed clear separation between the patient and control groups analyzed by liquid chromatography-high-resolution mass spectrometry using a nontargeted metabolomics approach. The metabolic analysis showed significantly lower levels of phospholipids in the serum from patients with pancreatic ductal adenocarcinoma compared with serum from controls. Our results suggest that the liquid chromatography-high-resolution mass spectrometry-based metabolomics approach provides a potent and promising tool for the diagnosis of pancreatic ductal adenocarcinoma patients using the specific metabolites identified as novel biomarkers that could be used for an earlier detection and treatment of these patients.