Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy

Metabolomic approaches to dissect dysregulated metabolism in the progression of pre-diabetes to T2DM

Many individuals with pre-diabetes eventually develop diabetes. Therefore, profiling of prediabetic metabolic disorders may be an effective targeted preventive measure. We aimed to elucidate the metabolic mechanism of progression of pre-diabetes to type 2 diabetes mellitus (T2DM) from a metabolic perspective. Four sets of plasma samples (20 subjects per group) collected according to fasting blood glucose (FBG) concentration were subjected to metabolomic analysis. An integrative approach of metabolome and WGCNA was employed to explore candidate metabolites. Compared with the healthy group (FBG < 5.6 mmol L-1), 113 metabolites were differentially expressed in the early stage of pre-diabetes (5.6 mmol L-1 ⩽ FBG < 6.1 mmol L-1), 237 in the late stage of pre-diabetes (6.1 mmol L-1 ⩽ FBG < 7.0 mmol L-1), and 245 in the T2DM group (FBG ⩾ 7.0 mmol L-1). A total of 27 differentially expressed metabolites (DEMs) were shared in all comparisons. Among them, L-norleucine was downregulated, whereas ethionamide, oxidized glutathione, 5-methylcytosine, and alpha-D-glucopyranoside beta-D-fructofuranosyl were increased with the rising levels of FBG. Surprisingly, 15 (11 lyso-phosphatidylcholines, L-norleucine, oxidized glutathione, arachidonic acid, and 5-oxoproline) of the 27 DEMs were ferroptosis-associated metabolites. WGCNA clustered all metabolites into 8 modules and the pathway enrichment analysis of DEMs showed a significant annotation to the insulin resistance-related pathway. Integrated analysis of DEMs, ROC and WGCNA modules determined 12 potential biomarkers for pre-diabetes and T2DM, including L-norleucine, 8 of which were L-arginine or its metabolites. L-Norleucine and L-arginine could serve as biomarkers for pre-diabetes. The inventory of metabolites provided by our plasma metabolome offers insights into T2DM physiology metabolism.

Construction and evaluation of a metabolic correlation diagnostic model for diabetes based on machine learning algorithms

BACKGROUND

Diabetes mellitus (DM) is a prevalent chronic disease marked by significant metabolic dysfunctions. Understanding its molecular mechanisms is vital for early diagnosis and treatment strategies.

METHODS

We used datasets GSE7014, GSE25724, and GSE156248 from the GEO database to build a diagnostic model for DM using Random Forest (RF) and LASSO regression models. GSE20966 served as a validation cohort. DM patients were classified into two subtypes for functional enrichment analysis. Expression levels of key diagnostic genes were validated using quantitative real-time PCR (qRT-PCR) on Peripheral Blood Mononuclear Cells (PBMCs) from DM patients and healthy controls, focusing on CXCL12 and PPP1R12B with GAPDH as the internal control.

RESULTS

After de-batching the datasets, we identified 131 differentially expressed genes (DEGs) between DM and control groups, with 70 up-regulated and 61 down-regulated. Enrichment analysis revealed significant down-regulation in the IL-12 signaling pathway, JAK signaling post-IL-12 stimulation, and the ferroptosis pathway in DM. Five genes (CXCL12, MXRA5, UCHL1, PPP1R12B, and C7) were identified as having diagnostic value. The diagnostic model showed high accuracy in both the training and validation cohorts. The gene set also enabled the subclassification of DM patients into groups with distinct functional traits. qRT-PCR results confirmed the bioinformatics findings, particularly the up-regulation of CXCL12 and PPP1R12B in DM patients.

CONCLUSION

Our study pinpointed seven energy metabolism-related genes differentially expressed in DM and controls, with five holding diagnostic value. Our model accurately diagnosed DM and facilitated patient subclassification, offering new insights into DM pathogenesis.

Lipid metabolism disorder in diabetic kidney disease

Diabetic kidney disease (DKD), a significant complication associated with diabetes mellitus, presents limited treatment options. The progression of DKD is marked by substantial lipid disturbances, including alterations in triglycerides, cholesterol, sphingolipids, phospholipids, lipid droplets, and bile acids (BAs). Altered lipid metabolism serves as a crucial pathogenic mechanism in DKD, potentially intertwined with cellular ferroptosis, lipophagy, lipid metabolism reprogramming, and immune modulation of gut microbiota (thus impacting the liver-kidney axis). The elucidation of these mechanisms opens new potential therapeutic pathways for DKD management. This research explores the link between lipid metabolism disruptions and DKD onset.

β-Thalassemia and Diabetes Mellitus: Current State and Future Directions

β-Thalassemia major is a congenital hemoglobin disorder that requires regular blood transfusion. The disease is often associated with iron overload and diabetes mellitus, among other complications. Pancreatic iron overload in β-thalassemia patients disrupts β-cell function and insulin secretion and induces insulin resistance. Several risk factors, including family history of diabetes, sedentary lifestyle, obesity, gender, and advanced age increase the risk of diabetes in β-thalassemia patients. Precautionary measures such as blood glucose monitoring, anti-diabetic medications, and healthy living in β-thalassemia patients notwithstanding, the prevalence of diabetes in β-thalassemia patients continues to rise. This review aims to address the relationship between β-thalassemia and diabetes in an attempt to understand how the pathology and management of β-thalassemia precipitate diabetes mellitus. The possible employment of surrogate biomarkers for early prediction and intervention is discussed. More work is still needed to better understand the molecular mechanism(s) underlying the link between β-thalassemia and diabetes and to identify novel prognostic and therapeutic targets.

Metabolite signature of diabetes remission in individuals with obesity undergoing weight loss interventions

OBJECTIVE

This observational study investigated metabolomic changes in individuals with type 2 diabetes (T2D) after weight loss. We hypothesized that metabolite changes associated with T2D-relevant phenotypes are signatures of improved health.

METHODS

Fasting plasma samples from individuals undergoing bariatric surgery (n = 71 Roux-en-Y gastric bypass [RYGB], n = 22 gastric banding), lifestyle intervention (n = 66), or usual care (n = 14) were profiled for 139 metabolites before and 2 years after weight loss. Principal component analysis grouped correlated metabolites into factors. Association of preintervention metabolites was tested with preintervention clinical features and changes in T2D markers. Association between change in metabolites/metabolite factors and change in T2D remission markers, homeostasis model assessment of β-cell function, homeostasis model assessment of insulin resistance, and glycated hemoglobin (HbA1c) was assessed.

RESULTS

Branched-chain amino acids (BCAAs) were associated with preintervention adiposity. Changes in BCAAs (valine, leucine/isoleucine) and branched-chain ketoacids were positively associated with change in HbA1c (false discovery rate q value ≤ 0.001) that persisted after adjustment for percentage weight change and RYGB (p ≤ 0.02). In analyses stratified by RYGB or other weight loss method, some metabolites showed association with non-RYGB weight loss.

CONCLUSIONS

This study confirmed known metabolite associations with obesity/T2D and showed an association of BCAAs with HbA1c change after weight loss, independent of the method or magnitude of weight loss.

Lipidomics Profiling Reveals Serum Phospholipids Associated with Albuminuria in Early Type 2 Diabetic Kidney Disease

Early screening and administration of DKD are beneficial for renal outcomes of type 2 diabetic patients. However, the current early diagnosis using the albuminuria/creatine ratio (ACR) contains limitations. This study aimed to compare serum lipidome variation between type 2 diabetes and early DKD patients with increased albuminuria through an untargeted lipidomics method to explore the potential lipid biomarkers for DKD identification. 92 type 2 diabetic patients were enrolled and divided into two groups: DM group (ACR < 3 mg/mmol, n = 49) and early DKD group (3 mg/mmol ≤ ACR < 30 mg/mmol, n = 43). Fasting serum was analyzed through an ultraperformance liquid mass spectrometry tandem chromatography system (LC-MS). Orthogonal partial least-squares discriminant analysis (OPLS-DA) and univariate and multivariate analysis were performed to filter differentially depressed lipids. Receiver operating characteristic (ROC) curves were used to estimate the diagnostic capability of potential lipid biomarkers. We found that serum phospholipids including phosphatidylserine (PS), sphingomyelin (SM), and phosphatidylcholine (PC) were significantly upregulated in the DKD group and were highly correlated with the ACR. In addition, a panel of two phospholipids including PS(27:0)-H and PS(30:2e)-H showed good performance to help clinical lipids in early DKD identification, which increased the area under the curve (AUC) from 0.568 to 0.954. The study exhibited the serum lipidome variation in early DKD patients, and the increased phospholipids might participate in the development of albuminuria. The panel of PS(27:0)-H and PS(30:2e)-H could be a potential biomarker for DKD diagnosis.

Circulating Sphingolipids in Insulin Resistance, Diabetes and Associated Complications

Sphingolipids play an important role in the development of diabetes, both type 1 and type 2 diabetes, as well as in the development of both micro- and macro-vascular complications. Several reviews have been published concerning the role of sphingolipids in diabetes but most of the emphasis has been on the possible mechanisms by which sphingolipids, mainly ceramides, contribute to the development of diabetes. Research on circulating levels of the different classes of sphingolipids in serum and in lipoproteins and their importance as biomarkers to predict not only the development of diabetes but also of its complications has only recently emerged and it is still in its infancy. This review summarizes the previously published literature concerning sphingolipid-mediated mechanisms involved in the development of diabetes and its complications, focusing on how circulating plasma sphingolipid levels and the relative content carried by the different lipoproteins may impact their role as possible biomarkers both in the development of diabetes and mainly in the development of diabetic complications. Further studies in this field may open new therapeutic avenues to prevent or arrest/reduce both the development of diabetes and progression of its complications.

Comparison of Local Metabolic Changes in Diabetic Rodent Kidneys Using Mass Spectrometry Imaging

Understanding the renal region-specific metabolic alteration in different animal models of diabetic nephropathy (DN) is critical for uncovering the underlying mechanisms and for developing effective treatments. In the present study, spatially resolved metabolomics based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was used to compare the local metabolic changes in the kidneys of HFD/STZ-induced diabetic rats and db/db mice. As a result, a total of 67 and 59 discriminating metabolites were identified and visualized in the kidneys of the HFD/STZ-induced diabetic rats and db/db mice, respectively. The result showed that there were significant region-specific changes in the glycolysis, TCA cycle, lipid metabolism, carnitine metabolism, choline metabolism, and purine metabolism in both DN models. However, the regional levels of the ten metabolites, including glucose, AMP, eicosenoic acid, eicosapentaenoic acid, Phosphatidylserine (36:1), Phosphatidylserine (36:4), Phosphatidylethanolamine (34:1), Phosphatidylethanolamine (36:4), Phosphatidylcholine (34:2), Phosphatidylinositol (38:5) were changed in reversed directions, indicating significant differences in the local metabolic phenotypes of these two commonly used DN animal models. This study provides comprehensive and in-depth analysis of the differences in the tissue and molecular pathological features in diabetic kidney injury in HFD/STZ-induced diabetic rats and db/db mice.

Investigation of the mechanism of Shen Qi Wan prescription in the treatment of T2DM via network pharmacology and molecular docking

Shen Qi Wan (SQW) prescription has been used to treat type 2 diabetes mellitus (T2DM) for thousands of years, but its pharmacological mechanism is still unclear. The network pharmacology method was used to reveal the potential pharmacological mechanism of SQW in the treatment of T2DM in this study. Nine core targets were identified through protein-protein interaction (PPI) network analysis and KEGG pathway enrichment analysis, which were AKT1, INSR, SLC2A1, EGFR, PPARG, PPARA, GCK, NOS3, and PTPN1. Besides, this study found that SQW treated the T2DM through insulin resistance (has04931), insulin signaling pathway (has04910), adipocytokine signaling pathway (has04920), AMPK signaling pathway (has04152) and FoxO signaling pathway (has04068) via ingredient-hub target-pathway network analysis. Finally, molecular docking was used to verify the drug-target interaction network in this research. This study provides a certain explanation for treating T2DM by SQW prescription, and provides a certain angle and method for researchers to study the mechanism of TCM in the treatment of complex diseases.

Supplementary information

The online version contains supplementary material available at 10.1007/s40203-022-00124-2.

Sphingolipid metabolism plays a key role in diabetic peripheral neuropathy

INTRODUCTION

As the most common chronic complication of diabetes mellitus (DM), diabetic peripheral neuropathy (DPN) seriously affects the quality of life of DM patients. So, it is of great significance for the diagnosis and treatment of DPN. In recent years, there have been numerous studies on pathogenesis and biomarkers of DM, but there are few studies on the biomarkers of DPN.

OBJECTIVES

This research is intended to identify abnormal metabolic pathways, search for potential biomarkers of DPN, and provide a metabolic basis for the diagnosis and mechanism of DPN.

METHODS

Serum samples from 23 healthy controls (HC), 42 DM patients and 30 DPN patients and urine samples from 42 HC, 40 DM patients, and 30 DPN patients were collected. UPLC-Q-TOF/MS was used to analyze the samples. Potential biomarkers were screened from principal component analysis (PCA) to orthogonal partial least squares discriminant analysis (OPLS-DA) and further evaluated by receiver operating characteristic analysis (ROC). The biomarkers were then enriched and pathway analyzed.

RESULTS

12 potential DPN biomarkers were identified from patient's serum. 11 potential DPN biomarkers were identified from the patient's urine. Among them, the diagnostic ability of gluconic acid, lipoic acid, sphinganine, bilirubin, sphingosine and 4-hydroxybenzoic acid was increased by ROC analysis. Potential biomarkers suggest that the disorder of DPN metabolism may be linked to sphingolipid metabolism.

CONCLUSIONS

This research laid a theoretical foundation for the diagnosis and pathogenesis of DPN.

Metabolic Profiling Analysis of the Effect and Mechanism of Gushiling Capsule in Rabbits With Glucocorticoid-Induced Osteonecrosis of the Femoral Head

Gushiling capsule (GSLC) is an effective traditional Chinese medicine for the treatment of glucocorticoid-induced osteonecrosis of the femoral head (GIONFH). This study established the serum metabolite profiles of GSLC in rabbits and explored the metabolic mechanism and effect of GSLC on GIONFH. Seventy-five Japanese white rabbits were randomly divided into the control, model, and GSLC groups. The rabbits in the model group and the GSLC group received injection of prednisolone acetate. Meanwhile, rabbits in the GSLC group were treated by gavage at a therapeutic dose of GSLC once a day. The control group and the model group received the same volume of normal saline gavage. Three groups of serum samples were collected at different time points, and the changes in the metabolic spectrum were analyzed by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The resulting data set was analyzed using multivariate statistical analysis to identify potential biomarkers related to GSLC treatment. The metabolic pathway was analyzed by MetaboAnalyst 4.0 and a heatmap was constructed using the HEML1.0.3.7 software package. In addition, histopathological and radiography studies were carried out to verify the anti-GIONFH effects of GSLC. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) score plots revealed a significant separation trend between the control group and the model group and the GSLC group (1-3 weeks), but there were no significant differences in the GSLC group (4-6 weeks). Orthogonal PLS-DA (OPLS-DA) score plots also revealed an obvious difference between the model and the GSLC groups (4-6 weeks). Ten potential metabolite biomarkers, mainly phospholipids, were identified in rabbit serum samples and demonstrated to be associated with GIONFH. Hematoxylin and eosin staining and magnetic resonance imaging indicated that the pathological changes in femoral head necrosis in the GSLC group were less than in the model group, which was consistent with the improved serum metabolite spectrum. GSLC regulated the metabolic disorder of endogenous lipid components in GIONFH rabbits. GSLC may prevent and treat GIONFH mainly by regulating phospholipid metabolism in vivo.

Metabolomics as a tool for the early diagnosis and prognosis of diabetic kidney disease

Diabetic kidney disease (DKD) is one of the most prevalent comorbidities of diabetes mellitus and the leading cause of the end-stage renal disease (ESRD). DKD results from chronic exposure to hyperglycemia, leading to progressive alterations in kidney structure and function. The early development of DKD is clinically silent and when albuminuria is detected the lesions are often at advanced stages, leading to rapid kidney function decline towards ESRD. DKD progression can be arrested or substantially delayed if detected and addressed at early stages. A major limitation of current methods is the absence of albuminuria in non-albuminuric phenotypes of diabetic nephropathy, which becomes increasingly prevalent and lacks focused therapy. Metabolomics is an ever-evolving omics technology that enables the study of metabolites, downstream products of every biochemical event that occurs in an organism. Metabolomics disclosures complex metabolic networks and provide knowledge of the very foundation of several physiological or pathophysiological processes, ultimately leading to the identification of diseases' unique metabolic signatures. In this sense, metabolomics is a promising tool not only for the diagnosis but also for the identification of pre-disease states which would confer a rapid and personalized clinical practice. Herein, the use of metabolomics as a tool to identify the DKD metabolic signature of tubule interstitial lesions to diagnose or predict the time-course of DKD will be discussed. In addition, the proficiency and limitations of the currently available high-throughput metabolomic techniques will be discussed.

Comparative Evaluation of the Effect of Metformin and Insulin on Gut Microbiota and Metabolome Profiles of Type 2 Diabetic Rats Induced by the Combination of Streptozotocin and High-Fat Diet

Lately, an increasing number of studies have investigated the relationship between metformin and gut microbiota, suggesting that metformin exerts part of its hypoglycemic effect through the microbes. However, its underlying mechanism remains largely undetermined. In the present study, we investigated the effects of metformin on gut microbiota and metabolome profiles in serum and compared it with insulin treatment in rats with type 2 diabetes mellitus (T2DM). Diabetic rats (DM group) were induced by a combination of streptozotocin and high-fat diet (HFD). After 7 days, DM rats were treated with metformin (MET group) or insulin (INS group) for 3 weeks. The 16S rRNA sequencing of the gut microbiota and non-targeted metabolomics analysis of serum were conducted. A total of 13 bile acids (BAs) in serum were further determined and compared among different groups. The rat model of T2DM was well established with the typical diabetic symptoms, showing significantly increased blood glucose, AUC of OGTT, HOMA-IR, TC, TG, LDL-C and TBA. Metformin or insulin treatment could ameliorate symptoms of diabetes and partly recover the abnormal biochemical indicators. Compared with DM rats, the relative abundances of 13 genera were significantly changed after metformin treatment, while only three genera were changed after insulin treatment. The metformin and insulin treatments also exhibited different serum metabolome profiles in T2DM rats. Moreover, 64 differential metabolites were identified between MET and DM groups, whereas 206 were identified between INS and DM groups. Insulin treatment showed greater influence on amino acids, glycerophospholipids/glycerolipids, and acylcarnitine compared with the metformin treatment, while metformin had an important impact on BAs. Furthermore, metformin could significantly decrease the serum levels of CA, GCA, UDCA, and GUDCA, but increase the level of TLCA in DM rats. Insulin treatment significantly decreased the levels of CA, UDCA, and CDCA. Besides, several metabolites in serum or microbiota were positively or negatively correlated with some bacteria. Collectively, our findings indicated that metformin had a stronger effect on gut microbiota than insulin, while insulin treatment showed greater influence on serum metabolites, which provided novel insights into the therapeutic effects of metformin on diabetes.

Metabolomics-based evidence of the hypoglycemic effect and alleviation of diabetic complications by Ficus racemosa fruit in diabetic mice

The hypoglycemic and metabolic effects of Ficus racemosa fruit were studied in diabetic mice, and its potential mechanisms of hypoglycemic activity and its alleviation of diabetic complications were explored using...

Urine proteomics identifies biomarkers for diabetic kidney disease at different stages

BACKGROUND

Type 2 diabetic kidney disease is the most common cause of chronic kidney diseases (CKD) and end-stage renal diseases (ESRD). Although kidney biopsy is considered as the 'gold standard' for diabetic kidney disease (DKD) diagnosis, it is an invasive procedure, and the diagnosis can be influenced by sampling bias and personal judgement. It is desirable to establish a non-invasive procedure that can complement kidney biopsy in diagnosis and tracking the DKD progress.

METHODS

In this cross-sectional study, we collected 252 urine samples, including 134 uncomplicated diabetes, 65 DKD, 40 CKD without diabetes and 13 follow-up diabetic samples, and analyzed the urine proteomes with liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We built logistic regression models to distinguish uncomplicated diabetes, DKD and other CKDs.

RESULTS

We quantified 559 ± 202 gene products (GPs) (Mean ± SD) on a single sample and 2946 GPs in total. Based on logistic regression models, DKD patients could be differentiated from the uncomplicated diabetic patients with 2 urinary proteins (AUC = 0.928), and the stage 3 (DKD3) and stage 4 (DKD4) DKD patients with 3 urinary proteins (AUC = 0.949). These results were validated in an independent dataset. Finally, a 4-protein classifier identified putative pre-DKD3 patients, who showed DKD3 proteomic features but were not diagnosed by clinical standards. Follow-up studies on 11 patients indicated that 2 putative pre-DKD patients have progressed to DKD3.

CONCLUSIONS

Our study demonstrated the potential for urinary proteomics as a noninvasive method for DKD diagnosis and identifying high-risk patients for progression monitoring.

Multi-Omics Analysis Reveals Disturbance of Nanosecond Pulsed Electric Field in the Serum Metabolic Spectrum and Gut Microbiota

Nanosecond pulsed electric field (nsPEF) is a novel ablation technique that is based on high-intensity electric voltage to achieve tumour-killing effect in the target region, and increasingly considered for treating tumours of the liver, kidneys and other organs with rich blood supply. This study aims to observe effect of nsPFE treatment on serum metabolites and gut microbiota. The serum and faecal specimens of the pigs were collected pre- and post-treatment. The gut microbiota of pigs was sequenced by Illumina Miseq platform for analysing the diversity and alterations of gut microbiota. Liquid chromatography-mass spectrometry (LC-MS)-based metabonomic analysis and Pearson coefficient method were also used to construct the interaction system of different metabolites, metabolic pathways and flora. A total of 1,477 differential metabolites from the serum were identified by four cross-comparisons of different post-operative groups with the control group. In addition, an average of 636 OTUs per sample was detected. Correlation analysis also revealed the strong correlation between intestinal bacteria and differential metabolites. The nsPEF ablation of the liver results in a degree of liver damage that affects various metabolic pathways, mainly lipid metabolism, as well as gut microbiota. In conclusion, our study provided a good point for the safety and feasibility of applying nsPEF on liver through the integrated analysis of metabolomics and microbiomes, which is beneficial for the improvement of nsPEF in clinical use.

Trans triacylglycerols from dairy products and industrial hydrogenated oil exhibit different effects on the function of human umbilical vein endothelial cells via modulating phospholipase A2/arachidonic acid metabolism pathways

Dairy fat intake has been considered as a risk factor for cardiovascular disease. Rodent models show that trans fatty acids in industrial hydrogenated oil and ruminant milk have different effects on cardiovascular diseases. One of the main reasons is that the distributions of trans fatty acids in triacylglycerols from dairy products and from industrial hydrogenated oil are different, which affects lipid absorption and metabolism. This study investigated the effects of 1,3-olein-2-elaidin (OEO, representing industrial hydrogenated oil triacylglycerols) and 1-vaccenic-2,3-olein (OOV, representing ruminant triacylglycerols in dairy products) on the function of human umbilical vein endothelial cells (HUVEC), including cell viability, lactate dehydrogenase (LDH) exudation rate, and nitric oxide secretory and nitric oxide synthase relative activity. We found that the detrimental effect of OEO on HUVEC was significantly greater than that of OOV. The results also showed that the absorption rate of OEO in HUVEC (78.25%) was significantly greater than that of OOV (63.32%). Mechanistically, based on phospholipidomics analysis, we found that calcium-independent phospholipase A2 (iPLA2) played a key role with regard to the OOV-mediated arachidonic acid (ARA)/COX-2/PG pathway, whereas secretory phospholipase A2 (sPLA2) and cytoplasmic phospholipase A2 (cPLA2) are responsible for the OEO-mediated ARA/COX-2/PG pathway. Moreover, OEO had a greater effect on the protein expression of COX-2 and PG secretion than OOV. In addition, iPLA2, sPLA2, and cPLA2 could mediate the ARA/CYP4A11 pathway in OOV-treated HUVEC, but only iPLA2 could mediate this pathway in HUVEC treated with OEO. We also found that sPLA2 could mediate the ARA/5-LOX pathway in HUVEC treated with OOV, but none of these 3 forms of PLA2 could mediate this pathway in HUVEC treated with OEO. On the other hand, after OOV treatment, trans-11 C18:1 was converted to beneficial forms of fatty acids in HUVEC, including conjugated linoleic acid (CLA) and trans-9 C16:1. In conclusion, we elucidated the potential mechanisms that might account for the diverse effects of triacylglycerols from industrial hydrogenated oil and ruminant milk on the function of HUVEC.

Characterization of the COPD Salivary Fingerprint through Surface Enhanced Raman Spectroscopy: A Pilot Study

Chronic Obstructive Pulmonary Disease (COPD) is a debilitating pathology characterized by reduced lung function, breathlessness and rapid and unrelenting decrease in quality of life. The severity rate and the therapy selection are strictly dependent on various parameters verifiable after years of clinical observations, missing a direct biomarker associated with COPD. In this work, we report the methodological application of Surface Enhanced Raman Spectroscopy combined with Multivariate statistics for the analysis of saliva samples collected from 15 patients affected by COPD and 15 related healthy subjects in a pilot study. The comparative Raman analysis allowed to determine a specific signature of the pathological saliva, highlighting differences in determined biological species, already studied and characterized in COPD onset, compared to the Raman signature of healthy samples. The unsupervised principal component analysis and hierarchical clustering revealed a sharp data dispersion between the two experimental groups. Using the linear discriminant analysis, we created a classification model able to discriminate the collected signals with accuracies, specificities, and sensitivities of more than 98%. The results of this preliminary study are promising for further applications of Raman spectroscopy in the COPD clinical field.

Metabolite biomarkers of type 2 diabetes mellitus and pre-diabetes: a systematic review and meta-analysis

BACKGROUND

We aimed to explore metabolite biomarkers that could be used to identify pre-diabetes and type 2 diabetes mellitus (T2DM) using systematic review and meta-analysis.

METHODS

Four databases, the Cochrane Library, EMBASE, PubMed and Scopus were selected. A random effect model and a fixed effect model were applied to the results of forest plot analyses to determine the standardized mean difference (SMD) and 95% confidence interval (95% CI) for each metabolite. The SMD for every metabolite was then converted into an odds ratio to create an metabolite biomarker profile.

RESULTS

Twenty-four independent studies reported data from 14,131 healthy individuals and 3499 patients with T2DM, and 14 included studies reported 4844 healthy controls and a total of 2139 pre-diabetes patients. In the serum and plasma of patients with T2DM, compared with the healthy participants, the concentrations of valine, leucine, isoleucine, proline, tyrosine, lysine and glutamate were higher and that of glycine was lower. The concentrations of isoleucine, alanine, proline, glutamate, palmitic acid, 2-aminoadipic acid and lysine were higher and those of glycine, serine, and citrulline were lower in prediabetic patients. Metabolite biomarkers of T2DM and pre-diabetes revealed that the levels of alanine, glutamate and palmitic acid (C16:0) were significantly different in T2DM and pre-diabetes.

CONCLUSIONS

Quantified multiple metabolite biomarkers may reflect the different status of pre-diabetes and T2DM, and could provide an important reference for clinical diagnosis and treatment of pre-diabetes and T2DM.

Untargeted metabolomics reveals the effect of lovastatin on steroid-induced necrosis of the femoral head in rabbits

Purpose

Lovastatin is an important medicine and it shows a significant effect against glucocorticoid-induced necrosis of the femoral head. This study aimed to investigate the effect of lovastatin on preventing necrosis of the femoral head of by serum metabolomics strategy.

Methods

Adult healthy adult Japanese white rabbits were divided into three groups: control group, model group, and drug group. The pathologic changes of femoral head were assessed with magnetic resonance imaging and microscope. Metabolomics based on ultra-high performance liquid chromatography tandem mass spectrometry analysis was used to analyze the collected serum sample. Data were analyzed using principal component analysis, partial least squares-discriminate analysis, and orthogonal partial least squares-discriminant analysis. All potential metabolites were identified by comparing with human metabolome database, Metlin database, lipid maps, and chemspider database.

Results

Eleven potential biomarkers were noted and identified as potential biomarkers. The change of biomarkers suggested that lovastatin on preventing necrosis of the femoral head may affect glycerophospholipid metabolism, linoleic acid metabolism, sphingolipid metabolism, alpha-linolenic acid metabolism, pyrimidine metabolism, and arachidonic acid metabolism.

Conclusion

The study suggested that lovastatin could prevent the glucocorticoid-induced necrosis of the femoral head of rabbits. The possible reasons were closely associated with adjusting the lipid metabolism, inhibiting adipogenesis, and delaying the osteocyte apoptosis.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s13018-020-02026-5.

Comprehensive Lipidome Profiling of the Kidney in Early-Stage Diabetic Nephropathy

Metabolic changes associated with diabetes are reported to lead to the onset of early-stage diabetic nephropathy (DN). Furthermore, lipotoxicity is implicated in renal dysfunction. Most studies of DN have focused on a single or limited number of lipids, and the lipidome of the kidney during early-stage DN remains to be elucidated. In the present study, we aimed to comprehensively identify lipid abnormalities during early-stage DN; to this end, we established an early-stage DN rat model by feeding a high-sucrose and high-fat diet combined with administration of low-dose streptozotocin. Using a high-coverage, targeted lipidomic approach, we established the lipid profile, comprising 437 lipid species and 25 lipid classes, of the kidney cortex in normal rats and the DN rat model. Our findings additionally confirmed that the DN rat model had been successfully established. We observed distinct lipidomic signatures in the DN kidney, with characteristic alterations in side chain composition and degree of unsaturation. Glyceride lipids, especially cholesteryl esters, showed a significant increase in the DN kidney cortex. The levels of most phospholipids exhibited a decline, except those of phospholipids with side chain of 36:1. Furthermore, the levels of lyso-phospholipids and sphingolipids, including ceramide and its derivatives, were dramatically elevated in the present DN rat model. Our findings, which provide a comprehensive lipidome of the kidney cortex in rats with DN, are expected to be useful for the identification of pathologically relevant lipid species in DN. Furthermore, the results represent novel insights into the mechanistic basis of DN.

New insight into the mechanism of POP-induced obesity: Evidence from DDE-altered microbiota

Although epidemiological studies demonstrate that persistent organic pollutants (POPs) could lead to metabolic syndrome, the mechanism has remained unclear. The dysbiosis of gut microbiota and the lipid metabolome have been put forward in the pathophysiology of metabolic syndrome. In this study, we used dichlorodiphenyldichloroethylene (DDE) as an example to study the effects of POP-impaired microbial composition and metabolome homeostasis on metabolic syndrome. The results showed that DDE exposure increased body weight and fat content and impaired glucose homeostasis. Further investigation revealed that DDE induced gut dysbiosis as indicated by an increased Firmicutes-to-Bacteroidetes ratio, which may impact energy harvest efficiency. Meanwhile, the plasma lipid metabolome profile was significantly altered by DDE. Furthermore, phosphatidylcholine, phosphatidylserine, phosphatidylethanolamine, and triacylglycerol were identified as key metabolites affected by DDE treatment, and these altered lipid metabolites were highly correlated with changed microbiota composition. This study provides novel insight into the underlying mechanism of POP-induced obesity and diabetes, pointing to gut microbiota as one of the targets.

DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules

INTRODUCTION

Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have been demonstrated to provide valuable insights for DKD. However, limited information on spatial distributions of metabolites in kidney tissues have been reported.

OBJECTIVES

In this work, we employed an ambient desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) coupled to a novel bioinformatics platform (METASPACE) to characterize the metabolome in a mouse model of DKD.

METHODS

DESI-MSI was performed for spatial untargeted metabolomics analysis in kidneys of mouse models (F1 C57BL/6J-Ins2Akita male mice at 17 weeks of age) of type 1 diabetes (T1D, n = 5) and heathy controls (n = 6).

RESULTS

Multivariate analyses (i.e., PCA and PLS-DA (a 2000 permutation test: P < 0.001)) showed clearly separated clusters for the two groups of mice on the basis of 878 measured m/z's in kidney cortical tissues. Specifically, mice with T1D had increased relative abundances of pseudouridine, accumulation of free polyunsaturated fatty acids (PUFAs), and decreased relative abundances of cardiolipins in cortical proximal tubules when compared with healthy controls.

CONCLUSION

Results from the current study support potential key roles of pseudouridine and cardiolipins for maintaining normal RNA structure and normal mitochondrial function, respectively, in cortical proximal tubules with DKD. DESI-MSI technology coupled with METASPACE could serve as powerful new tools to provide insight on fundamental pathways in DKD.

Identification of Potential Serum Metabolic Biomarkers of Diabetic Kidney Disease: A Widely Targeted Metabolomics Study

Background and Objectives. Diabetic kidney disease is a leading cause of chronic kidney disease and end-stage renal disease across the world. Early identification of DKD is vitally important for the effective prevention and control of it. However, the available indicators are doubtful in the early diagnosis of DKD. This study is aimed at determining novel sensitive and specific biomarkers to distinguish DKD from their counterparts effectively based on the widely targeted metabolomics approach. Materials and Method. This case-control study involved 44 T2DM patients. Among them, 24 participants with DKD were defined as the cases and another 20 without DKD were defined as the controls. The ultraperformance liquid chromatography-electrospray ionization-tandem mass spectrometry system was applied for the assessment of the serum metabolic profiles. Comprehensive analysis of metabolomics characteristics was conducted to detect the candidate metabolic biomarkers and assess their capability and feasibility.

RESULT

A total of 11 differential metabolites, including Hexadecanoic Acid (C16:0), Linolelaidic Acid (C18:2N6T), Linoleic Acid (C18:2N6C), Trans-4-Hydroxy-L-Proline, 6-Aminocaproic Acid, L-Dihydroorotic Acid, 6-Methylmercaptopurine, Piperidine, Azoxystrobin Acid, Lysopc 20:4, and Cuminaldehyde, were determined as the potential biomarkers for the DKD early identification, based on the multivariable generalized linear regression model and receiver operating characteristic analysis.

CONCLUSION

Serum metabolites might act as sensitive and specific biomarkers for DKD early detection. Further longitudinal studies are needed to confirm our findings.

Lipidomic analysis reveals sphingomyelin and phosphatidylcholine species associated with renal impairment and all-cause mortality in type 1 diabetes

There is an urgent need for a better molecular understanding of the pathophysiology underlying development and progression of diabetic nephropathy. The aim of the current study was to identify novel associations between serum lipidomics and diabetic nephropathy. Non-targeted serum lipidomic analyses were performed with mass spectrometry in 669 individuals with type 1 diabetes. Cross-sectional associations of lipid species with estimated glomerular filtration rate (eGFR) and urinary albumin excretion were assessed. Moreover, associations with register-based longitudinal follow-up for progression to a combined renal endpoint including ≥30% decline in eGFR, ESRD and all-cause mortality were evaluated. Median follow-up time was 5.0–6.4 years. Adjustments included traditional risk factors and multiple testing correction. In total, 106 lipid species were identified. Primarily, alkyl-acyl phosphatidylcholines, triglycerides and sphingomyelins demonstrated cross-sectional associations with eGFR and macroalbuminuria. In longitudinal analyses, thirteen lipid species were associated with the slope of eGFR or albuminuria. Of these lipids, phosphatidylcholine and sphingomyelin species, PC(O-34:2), PC(O-34:3), SM(d18:1/24:0), SM(d40:1) and SM(d41:1), were associated with lower risk of the combined renal endpoint. PC(O-34:3), SM(d40:1) and SM(d41:1) were associated with lower risk of all-cause mortality while an SM(d18:1/24:0) was associated with lower risk of albuminuria group progression. We report distinct associations between lipid species and risk of renal outcomes in type 1 diabetes, independent of traditional markers of kidney function.

Impaired Amino Acid and TCA Metabolism and Cardiovascular Autonomic Neuropathy Progression in Type 1 Diabetes

While diabetes is characterized by hyperglycemia, nutrient metabolic pathways like amino acid and tricarboxylic acid (TCA) cycle are also profoundly perturbed. As glycemic control alone does not prevent complications, we hypothesized that these metabolic disruptions are responsible for the development and progression of diabetic cardiovascular autonomic neuropathy (CAN). We performed standardized cardiovascular autonomic reflex tests and targeted fasting plasma metabolomic analysis of amino acids and TCA cycle intermediates in subjects with type 1 diabetes and healthy control subjects followed for 3 years. Forty-seven participants with type 1 diabetes (60% female and mean ± SD age 35 ± 13 years, diabetes duration 13 ± 7 years, and HbA_1c 7.9 ± 1.2%) had lower fumarate levels and higher threonine, serine, proline, asparagine, aspartic acid, phenylalanine, tyrosine, and histidine levels compared with 10 age-matched healthy control subjects. Higher baseline fumarate levels and lower baseline amino acid levels-asparagine and glutamine-correlate with CAN (lower baseline SD of normal R-R interval [SDNN]). Baseline glutamine and ornithine levels also associated with the progression of CAN (lower SDNN at 3 years) and change in SDNN, respectively, after adjustment for baseline HbA_1c, blood glucose, BMI, cholesterol, urine microalbumin-to- creatinine ratio, estimated glomerular filtration rate, and years of diabetes. Therefore, significant changes in the anaplerotic flux into the TCA cycle could be the critical defect underlying CAN progression.

Metabolic profiling of serum and tissue from the rotator interval and anterior capsule in shoulder stiffness: a preliminary study

Background

Using mass spectrometry, we evaluated the metabolic profiles of patients who had rotator cuff tears with shoulder stiffness, or shoulder stiffness only, and compared these with samples from a control group.

Methods

This study enrolled 28 patients, including 10 patients with shoulder stiffness only (group I), nine patients with rotator cuff tear and stiffness (group II), and nine controls selected from patients diagnosed with impingement syndrome or long head of the biceps lesions without evident limitation of joint motion or rotator cuff tears. Serum and tissue from the rotator interval and anterior capsule were collected. In all, 82 samples were analyzed for metabolite profiling using the AbsoluteIDQ™p180 Kit.

Results

Comparison of 186 metabolites revealed that groups I and II had significantly higher concentrations of sphingolipids in serum (SM C24:1; group I = 65.16 μm, group II = 68.07 μm) than controls (55.37 μm, p = 0.005 & 0.015, respectively). Higher concentrations of sphingolipids were also present in the rotator interval tissue (SM C22:3) of groups 1 (0.0197 μm) and 2 (0.0144 μm) than controls (0.0081 μm, p = 0.012 & 0.014, respectively). The concentration of glycerophospholipid (PC aa C30:0) was higher in the anterior capsule tissue of groups I (0.850 μm) and II (1.164 μm) than controls (0.572 μm; p = 0.007) Total cholesterol was positively correlated with sphingolipid concentration in serum (SM C24:1, rho = 0.782, p = 0.008) and rotator interval tissue (SM C22:3, rho = 0.750, p = 0.017). There was no significant difference in the metabolites evaluated in groups I and II.

Conclusion

Metabolic profiling showed that levels of lipid-related metabolites were increased in the anterior capsule tissue and rotator interval tissue of patients with shoulder stiffness. Sphingomyelin (SM C22:3) in the tissue of the rotator interval was positively correlated with the serum level of total cholesterol in patients with shoulder stiffness only. The level of glycerophospholipid (PC30:0) in the anterior capsule was positively correlated with the serum level of total cholesterol in patients who had rotator cuff tear with shoulder stiffness. The results indicate that serum total cholesterol may be related to shoulder stiffness. Future studies are needed to evaluate the role of serum cholesterol in the pathogenesis of shoulder stiffness.

Trial registration

KC12OISI0532. Registered Nov 15, 2012. approval by the Institutional Review Board of Seoul St. Mary’s Hospital, the Catholic University of Korea.

Proteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease

Chronic kidney disease (CKD) is an aging-related disorder that represents a major global public health burden. Current biochemical biomarkers, such as serum creatinine and urinary albumin, have important limitations when used to identify the earliest indication of CKD or in tracking the progression to more advanced CKD. These issues underline the importance of finding and testing new molecular biomarkers that are capable of successfully meeting this clinical need. The measurement of changes in nature and/or levels of proteins and metabolites in biological samples from patients provide insights into pathophysiological processes. Proteomic and metabolomic techniques provide opportunities to record dynamic chemical signatures in patients over time. This review article presents an overview of the recent developments in the fields of metabolomics and proteomics in relation to CKD. Among the many different proteomic biomarkers proposed, there is particular interest in the CKD273 classifier, a urinary proteome biomarker reported to predict CKD progression and with implementation potential. Other individual non-invasive peptidomic biomarkers that are potentially relevant for CKD detection include type 1 collagen, uromodulin and mucin-1. Despite the limited sample sizes and variability of the metabolomics studies, some metabolites such as trimethylamine N-oxide, kynurenine and citrulline stand out as potential biomarkers in CKD.

A metabolomics research based on UHPLC-ESI-Q-TOF-MS coupled with metabolic pathway analysis: Treatment effects of stir-frying Xanthii Fructus on allergic rhinitis in mice model

Xanthii Fructus (XF), a well-known herb in traditional Chinese medicine, has been frequently used for the treatment of allergic rhinitis in the clinic. Its therapeutic metabolic mechanism, however, remains undetermined. In this work, a metabolomics research coupled with metabolic pathway analysis has been employed to screen out the potential mechanism in its effects on allergic rhinitis. Specifically, mouse serum samples containing XF were analyzed based on ultra-high performance liquid chromatography equipped with electrospray ionization quadruple time-of-flight mass spectrometry detection (UHPLC-ESI-Q-TOF-MS) in both positive and negative polarity. In addition, the raw data gained from UHPLC-ESI-Q-TOF-MS were processed by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) in order to discover remarkable metabolites. Twenty-seven potential biomarkers in mouse serum were filtered from free databases like HMDB. Interestingly, this study filtered the potential metabolic pathways including glycerophospholipid metabolism and branch-chain amino acid metabolism. We hope that this paper will provide a feasible strategy for revealing the therapeutic mechanism of XF in allergic rhinitis mice model.

Metabolomics window into diabetic complications

Diabetes has become a major global health problem. The elucidation of characteristic metabolic alterations during the diabetic progression is critical for better understanding its pathogenesis, and identifying potential biomarkers and drug targets. Metabolomics is a promising tool to reveal the metabolic changes and the underlying mechanism involved in the pathogenesis of diabetic complications. The present review provides an update on the application of metabolomics in diabetic complications, including diabetic coronary artery disease, diabetic nephropathy, diabetic retinopathy and diabetic neuropathy, and this review provides notes on the prevention and prediction of diabetic complications.

A metabolomic study on early detection of steroid-induced avascular necrosis of the femoral head

The early and accurate diagnosis of steroid-induced avascular necrosis of the femoral head (SANFH) is appealing considering its irreversible progression and serious consequence for the patients. The purpose of this study was to investigate the metabolic change of SANFH for its early detection. Two stages were designed in this study, namely discovery and verification. Except the biochemical index anomaly and the accidental death, 30 adult healthy adult Japanese white rabbits were used for screening out the potential metabolites in discovery experiment and 13 rabbits were used in verification experiment. The femoral heads were assessed with magnetic resonance imaging and transmission electron microscopy. The metabolomic profiling of serum samples were analysis by UHPLC-MS/MS. Metabolomic cluster analysis enable us to differentiate the rabbits without and with injection of the glucocorticoid in 1 week even when there is no obvious abnormal symptom in behaviors or imaging diagnosis. The majority of differential metabolites were identified as phospholipids which were observed significant change after injection of glucocorticoid in 1, 2, 3 weeks. And the results obtained in verification experiment of 6 weeks showed that these differential metabolites exhibited consistent trends in late progression with that in early-stage. At the end of 6 weeks the damage of SANFH could be verified by pathological imaging. Therefore the finding of serum metabolite profile links to the progression of SANFH and provides the potential of early detection of SANFH.

Immediate reduction of serum citrulline but no change of steroid profile after initiation of metformin in individuals with type 2 diabetes

Metformin is the most important first-line treatment for type 2 diabetes mellitus (T2DM) but its exact mode of action remains unknown. In this study, we used targeted metabolomics to gain new insights into the metabolic effects of metformin in humans with T2DM. We also examined changes in the serum steroid hormone profile. We quantified 167 serum metabolites and 19 steroid hormones using liquid chromatography-tandem mass spectrometry at three time points in individuals with previously untreated T2DM: before the start of metformin therapy (time point A), after the first dose (B) and after short-term therapy for 4-6 weeks (C). For metabolite analysis, we split the study cohort into a discovery and a replication study of 88 and 45 subjects, respectively. The statistical analysis was done using linear mixed-effects models. Among the metabolites quantified, citrulline showed the most pronounced changes. Compared to its baseline serum concentration, citrulline was reduced by 17% after the first dose of metformin (p=1.34E-07) and by 24% after short-term therapy (p=2.84E-08) in the discovery study. These results were confirmed in the replication study. The only other metabolite significantly changed after correction for multiple testing was PC ae C36:4 between baseline and 4-6 weeks. The serum steroid hormone profile showed no significant changes after metformin intake. In summary, we observed an immediate and sustained reduction of serum citrulline by metformin in humans. This may be relevant for some of the wanted or unwanted effects of the drug.

Muscle Sympathetic Nerve Activity Is Associated With Elements of the Plasma Lipidomic Profile in Young Asian Adults

BACKGROUND

Asian subjects are at increased cardio-metabolic risk at comparatively lower body mass index (BMI) compared with white subjects. Sympathetic nervous system activation and dyslipidemia, both characteristics of increased adiposity, appear to be related. We therefore analyzed the association of muscle sympathetic nerve activity (MSNA) with the plasma lipidomic profile in young adult Asian and white subjects.

METHODS

Blood samples were collected from 101 participants of either Asian or white background (age, 18 to 30 years; BMI, 28.1 ± 5.9 kg/m2). Lipids were extracted from plasma and analyzed using electrospray ionization-tandem mass spectrometry. MSNA was quantified using microneurography. The association of MSNA and obesity with lipid species was examined using linear regression analysis.

RESULTS

The plasma concentrations of total dihydroceramide, ceramide, GM3 ganglioside, lysoalkylphosphatidylcholine, alkenylphosphatidylethanolamine, and lysophosphatidylinositol were elevated in the Asian subjects relative to the white subjects. After adjustment for confounders, diacylglycerols and triacylglycerols, cholesterol esters, phosphatidylinositols, phosphatidylethanolamines, and phosphatidylglycerols bore significant associations with MSNA but only in the Asian subjects. These associations remained significant after further adjustment for the participants' degree of insulin resistance and appeared not to be related to differences in diet macronutrient content between groups.

CONCLUSIONS

The lipidomic profile differs between Asian and white subjects. There exists a strong relationship between certain lipid species and MSNA. The association is stronger in Asian subjects, despite their lower BMI. This study demonstrates an association between circulating lipids and central sympathetic outflow. Whether the stronger association between the lipid profile and sympathetic activation underpins the apparent greater risk posed by increased adiposity in Asian individuals merits further attention.

Profiling of Plasma Metabolites Suggests Altered Mitochondrial Fuel Usage and Remodeling of Sphingolipid Metabolism in Individuals With Type 2 Diabetes and Kidney Disease

Introduction

Pathophysiology of diabetic kidney disease (DKD) is incompletely understood. We aim to elucidate metabolic abnormalities associated with DKD in type 2 diabetes mellitus (T2DM) by targeted plasma metabolomics.

Methods

A total of 126 T2DM participants with early DKD (urinary albumin-to-creatinine ratio [ACR] 30−299 mg/g and eGFR ≥ 60 ml/min/1.73 m²), 154 overt DKD (ACR ≥ 300 mg/g or eGFR < 60 ml/min/1.73 m²), and 129 non-DKD T2DM controls (ACR < 30 mg/g and eGFR ≥ 60 ml/min/1.73 m²) were included in discovery study. Findings were subsequently validated in 149 T2DM with macroalbuminuria (ACR ≥ 300 mg/g) and 149 matched non-DKD T2DM controls. Plasma amino acid, acylcarnitine, Krebs cycle organic acid, and sphingolipids/ceramide levels were quantified by liquid chromatography−mass spectrometry and gas chromatography−mass spectrometry.

Results

Of 123 metabolites included in the data analysis, 24 differed significantly between DKD and controls in the same direction in both discovery and validation subpopulations. A number of short acylcarnitines including their dicarboxylic derivatives (C2−C6) were elevated in DKD, suggesting abnormalities in fatty acids and amino acids metabolic pathways. Five phosphatidylcholines were lower whereas 4 metabolites in the sphingomyelin−ceramide subfamily were higher in DKD. Principal component regression revealed that long-chain ceramides were independently associated with ACR but not eGFR. Conversely, essential amino acids catabolism and short dicarboxylacylcarnitine accumulation were associated with eGFR but not ACR.

Discussion

DKD is associated with altered fuel substrate use and remodeling of sphingolipid metabolism in T2DM with DKD. Associations of albuminuria and impaired filtration function with distinct metabolomic signatures suggest different pathophysiology underlying these 2 manifestations of DKD.

Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis

BACKGROUND

The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues.

METHODS

Our RSF approach was illustrated with data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, with concentrations of 127 serum metabolites as exposure variables and time to development of type 2 diabetes mellitus (T2D) as outcome variable. Out of this data set, Cox regression with a stepwise selection method was recently published. Replication of methodical comparison (RSF and Cox regression) was conducted in two independent cohorts. Finally, the R-code for implementing the metabolite selection procedure into the RSF-syntax is provided.

RESULTS

The application of the RSF approach in EPIC-Potsdam resulted in the identification of 16 incident T2D-associated metabolites which slightly improved prediction of T2D when used in addition to traditional T2D risk factors and also when used together with classical biomarkers. The identified metabolites partly agreed with previous findings using Cox regression, though RSF selected a higher number of highly correlated metabolites.

CONCLUSIONS

The RSF method appeared to be a promising approach for identification of disease-associated variables in complex data with time to event as outcome. The demonstrated RSF approach provides comparable findings as the generally used Cox regression, but also addresses the problem of multicollinearity and is suitable for high-dimensional data.

An integrated strategy for the rapid extraction and screening of phosphatidylcholines and lysophosphatidylcholines using semi-automatic solid phase extraction and data processing technology

This study attempts to establish a comprehensive strategy for the rapid extraction and screening of phosphatidylcholines (PCs) and lysophosphatidylcholines (LysoPCs) in biological samples using semi-automatic solid phase extraction (SPE) and data processing technology based on ultra-performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS). First, the Ostro sample preparation method (i.e., semi-automatic SPE) was compared with the Bligh-Dyer method in terms of substance coverage, reproducibility and sample preparation time. Meanwhile, the screening method for PCs and LysoPCs was built through mass range screening, mass defect filtering and diagnostic fragments filtering. Then, the Ostro sample preparation method and the aforementioned screening method were combined under optimal conditions to establish a rapid extraction and screening platform. Finally, this developed method was validated and applied to the preparation and data analysis of tissue samples. Through a systematic evaluation, this developed method was shown to provide reliable and high-throughput experimental results and was suitable for the preparation and analysis of tissue samples. Our method provides a novel strategy for the rapid extraction and analysis of functional phospholipids. In addition, this study will promote further study of phospholipids in disease research.

Early detection of diabetic kidney disease: Present limitations and future perspectives

Diabetic kidney disease (DKD) is one of the most common diabetic complications, as well as the leading cause of chronic kidney disease and end-stage renal disease around the world. To prevent the dreadful consequence, development of new assays for diagnostic of DKD has always been the priority in the research field of diabetic complications. At present, urinary albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) are the standard methods for assessing glomerular damage and renal function changes in clinical practice. However, due to diverse tissue involvement in different individuals, the so-called “non-albuminuric renal impairment” is not uncommon, especially in patients with type 2 diabetes. On the other hand, the precision of creatinine-based GFR estimates is limited in hyperfiltration status. These facts make albuminuria and eGFR less reliable indicators for early-stage DKD. In recent years, considerable progress has been made in the understanding of the pathogenesis of DKD, along with the elucidation of its genetic profiles and phenotypic expression of different molecules. With the help of ever-evolving technologies, it has gradually become plausible to apply the thriving information in clinical practice. The strength and weakness of several novel biomarkers, genomic, proteomic and metabolomic signatures in assisting the early diagnosis of DKD will be discussed in this article.

Evaluation of treadmill exercise effect on muscular lipid profiles of diabetic fatty rats by nanoflow liquid chromatography-tandem mass spectrometry

We compare comprehensive quantitative profiling of lipids at the molecular level from skeletal muscle tissues (gastrocnemius and soleus) of Zucker diabetic fatty rats and Zucker lean control rats during treadmill exercise by nanoflow liquid chromatography-tandem mass spectrometry. Because type II diabetes is caused by decreased insulin sensitivity due to excess lipids accumulated in skeletal muscle tissue, lipidomic analysis of muscle tissues under treadmill exercise can help unveil the mechanism of lipid-associated insulin resistance. In total, 314 lipid species, including phospholipids, sphingolipids, ceramides, diacylglycerols (DAGs), and triacylglycerols (TAGs), were analyzed to examine diabetes-related lipid species and responses to treadmill exercise. Most lysophospholipid levels increased with diabetes. While DAG levels (10 from the gastrocnemius and 13 from the soleus) were >3-fold higher in diabetic rats, levels of most of these decreased after exercise in soleus but not in gastrocnemius. Levels of 5 highly abundant TAGs (52:1 and 54:3 in the gastrocnemius and 48:2, 50:2, and 52:4 in the soleus) displaying 2-fold increases in diabetic rats decreased after exercise in the soleus but not in the gastrocnemius in most cases. Thus, aerobic exercise has a stronger influence on lipid levels in the soleus than in the gastrocnemius in type 2 diabetic rats.

Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes

Diabetic kidney disease occurs in ∼ 25-40% of patients with type 2 diabetes. Given the high risk of progressive renal function loss and end-stage renal disease, early identification of patients with a renal risk is important. Novel biomarkers may aid in improving renal risk stratification. In this review, we first focus on the classical panel of albuminuria and estimated glomerular filtration rate as the primary clinical predictors of renal disease and then move our attention to novel biomarkers, primarily concentrating on assay-based multiple/panel biomarkers, proteomics biomarkers and metabolomics biomarkers. We focus on multiple biomarker panels since the molecular processes of renal disease progression in type 2 diabetes are heterogeneous, rendering it unlikely that a single biomarker significantly adds to clinical risk prediction. A limited number of prospective studies of multiple biomarkers address the predictive performance of novel biomarker panels in addition to the classical panel in type 2 diabetes. However, the prospective studies conducted so far have small sample sizes, are insufficiently powered and lack external validation. Adequately sized validation studies of multiple biomarker panels are thus required. There is also a paucity of studies that assess the effect of treatments on novel biomarker panels and determine whether initial treatment-induced changes in novel biomarkers predict changes in long-term renal outcomes. Such studies can not only improve our healthcare but also our understanding of the mechanisms of actions of existing and novel drugs and may yield biomarkers that can be used to monitor drug response. We conclude that this will be an area to focus research on in the future.

A Systems Biology Overview on Human Diabetic Nephropathy: From Genetic Susceptibility to Post-Transcriptional and Post-Translational Modifications

Diabetic nephropathy (DN), a microvascular complication occurring in approximately 20-40% of patients with type 2 diabetes mellitus (T2DM), is characterized by the progressive impairment of glomerular filtration and the development of Kimmelstiel-Wilson lesions leading to end-stage renal failure (ESRD). The causes and molecular mechanisms mediating the onset of T2DM chronic complications are yet sketchy and it is not clear why disease progression occurs only in some patients. We performed a systematic analysis of the most relevant studies investigating genetic susceptibility and specific transcriptomic, epigenetic, proteomic, and metabolomic patterns in order to summarize the most significant traits associated with the disease onset and progression. The picture that emerges is complex and fascinating as it includes the regulation/dysregulation of numerous biological processes, converging toward the activation of inflammatory processes, oxidative stress, remodeling of cellular function and morphology, and disturbance of metabolic pathways. The growing interest in the characterization of protein post-translational modifications and the importance of handling large datasets using a systems biology approach are also discussed.

Phospholipidomic identification of potential serum biomarkers in dengue fever, hepatitis B and hepatitis C using liquid chromatography-electrospray ionization-tandem mass spectrometry

The serum phospholipid (PL) profiles of healthy volunteers (HE) and patients with recently diagnosed dengue fever (DF), hepatitis B (HBV), and hepatitis C (HCV) were investigated using liquid chromatography-ion trap-mass spectrometry (LC-IT-MS) and liquid chromatography-triple quad-mass spectrometry (LC-TQ-MS). Major PLs, including lyso-phosphatidylcholins (LPCs), phosphatidylcholins (PCs), phosphatidylinositols (PIs), phosphatidylethanolamines (PEs) and phosphatidylserines (PSs), were characterized in human serum using LC-IT-MS. Thirty-five PLs were quantified using seven non-endogenous odd-carbon PL standards. An MS search protocol for the identification of PLs is described. The analytical method was optimized to achieve maximum recovery and detection. PLs were detected with minimal ionization suppression. The PLs species were characterized on the basis of (i) MS(2) peaks due to polar head, (ii) precursor ion or neutral loss scans, (iii) identification of fatty acid, (iv) identification of sn-1 and sn-2 fatty acid. The quantitation data were subjected to principal component analysis (PCA), and a significant difference was observed between the PL profiles of the investigated diseases and those of HE subjects. The significance of the changes in each lipid among the four groups was statistically assessed using one-way analysis of variance (ANOVA) followed by Bonferroni post hoc multiple comparison. The serum profiles of 28 PLs were determined to be significantly different and enabled the discrimination between HE individuals and the studied patients. Potentially dysregulated PLs were considered as differentiating biomarkers to diagnose DF, HBV, and HCV.

Metabolomic biomarkers in diabetic kidney diseases--A systematic review

Diabetic kidney disease (DKD) is generally characterized by increasing albuminuria in diabetic patients; however, few biomarkers are available to facilitate early diagnosis of this disease. The application of metabolomics has shown promises addressing this need. In this review, we conducted a search about metabolomic biomarkers in DKD patients through MEDLINE, EMBASE, and Cochrane Database up to the end of March, 2015. 12 eligible studies were selected and evaluated subsequently through the use of QUADOMICS, a quality assessment tool. 7 of the 12 included studies were classified as 'high quality'. We also recorded specific study characteristics including participants' characteristics, metabolomic techniques, sample types, and significantly altered metabolites between DKD and control groups. Products of lipid metabolisms including esterified and non-esterified fatty acids, carnitines, phospholipids and metabolites involved in branch-chained amino acids and aromatic amino acids metabolisms were frequently affected biomarkers of DKD. Other differential metabolites were also found, while some of their associations with DKD were unclear. Further more studies are required to test these findings in larger, diverse ethnic populations with elaborate study designs, and finally we could translate them into the benefits of DKD patients.

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

Type 2 diabetes mellitus (T2DM) patients have an increased risk of cardiovascular disease (CVD) that represents one of the main causes of mortality in this population. The knowledge of the underlie factors involved in the development of CVD and the discovery of new biomarkers of the disease could help to early identification of high-risk patients. Using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) we analyzed the serum metabolomic profile of 30 subject distributed according three groups: (i) T2DM patients with CVD; (ii) T2DM patients without CVD; (iii) non-diabetic subjects as controls (C) in order to identify potential biomarkers of the CVD related to T2DM. A partial least squares discriminant analysis (PLS-DA) and one-way analysis of variance (ANOVA) were applied to identify differential metabolites between different groups. Four glycerophospholipids were further identified as potential biomarkers of CVD in T2DM patients. Specifically, a reduction in phosphatidylcholine, lysophosphatidylcholine and lysophosphatidylethanolamine (LPE) serum levels were found in T2DM patients compared to controls, presenting the patients with CVD the lowest serum levels of these metabolites. These results show a generalized reduction of circulating phospholipids species in T2DM patients which is more pronounced in those with CVD providing information of the pathways involved in the pathogenesis and progression of CVD associated to T2DM.

Increased Levels of Sphingosylphosphorylcholine (SPC) in Plasma of Metabolic Syndrome Patients

Recent developments in lipid mass spectrometry enable extensive lipid class and species analysis in metabolic disorders such as diabesity and metabolic syndrome. The minor plasma lipid class sphingosylphosphorylcholine (SPC) was identified as a ligand for lipid sensitive G-protein coupled receptors playing a key role in cell growth, differentiation, motility, calcium signaling, tissue remodeling, vascular diseases and cancer. However, information about its role in diabesity patients is sparse. In this study, we analyzed plasma lipid species in patients at risk for diabesity and the metabolic syndrome and compared them with healthy controls. Our data show that SPC is significantly increased in plasma samples from metabolic syndrome patients but not in plasma from patients at risk for diabesity. Detailed SPC species analysis showed that the observed increase is due to a significant increase in all detected SPC subspecies. Moreover, a strong positive correlation is observed between total SPC and individual SPC species with both body mass index and the acute phase low grade inflammation marker soluble CD163 (sCD163). Collectively, our study provides new information on SPC plasma levels in metabolic syndrome and suggests new avenues for investigation.