Sphingomyelin is associated with kidney disease in type 1 diabetes (The FinnDiane Study)

Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies.

METHODS

We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN.

RESULTS

Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care.

CONCLUSION

Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Urinary sphingolipids in adolescents and young adults with youth-onset diabetes

BACKGROUND

This study evaluated urinary sphingolipids as a marker of diabetic kidney disease (DKD) in adolescents and young adults with youth-onset type 1 and type 2 diabetes.

METHODS

A comprehensive panel of urinary sphingolipids, including sphingomyelin (SM), glucosylceramide (GC), ceramide (Cer), and lactosylceramide (LC) species, was performed in patients with youth-onset diabetes from the SEARCH for Diabetes in Youth cohort. Sphingolipid levels, normalized to urine creatinine, were compared in 57 adolescents and young adults with type 1 diabetes, 59 with type 2 diabetes, and 44 healthy controls. The association of sphingolipids with albumin-to-creatinine (ACR) ratio and estimated glomerular filtration rate (eGFR) was evaluated.

RESULTS

The median age (interquartile range [IQR]) of participants was 23.1 years (20.9, 24.9) and the median duration of diabetes was 9.3 (8.5, 10.2) years. Urinary sphingolipid concentrations in patients with and without DKD (ACR ≥ 30 mg/g) were significantly elevated compared to healthy controls. There were no significant differences in sphingolipid levels between participants with type 1 and type 2 diabetes. In multivariable analysis, many sphingolipid species were positively correlated with ACR. Most significant associations were evident for the following species: C18 SM, C24:1 SM, C24:1 GC, and C24:1 Cer (all p < 0.001). Sphingolipid levels were not associated with eGFR. However, several interaction terms (diabetes type*sphingolipid) were significant, indicating diabetes type may modify the association of sphingolipids with eGFR.

CONCLUSION

Urinary sphingolipids are elevated in adolescents and young adults with youth-onset diabetes and correlate with ACR. Urinary sphingolipids may therefore represent an early biomarker of DKD.

Metabolomics Characterization of Disease Markers in Diabetes and Its Associated Pathologies

With the change in lifestyle of people, there has been a considerable increase in diabetes, which brings with it certain follow-up pathological conditions, which lead to a substantial medical burden. Identifying biomarkers that aid in screening, diagnosis, and prognosis of diabetes and its associated pathologies would help better patient management and facilitate a personalized treatment approach for prevention and treatment. With the advancement in techniques and technologies, metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression. Diabetes and metabolomics keywords were searched in correspondence with the assigned keywords, including kidney, cardiovascular diseases and critical illness from PubMed and Scopus, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study. This review is focused on the biomarkers identified in diabetes, diabetic kidney disease, diabetes-related development of CVD, and its role in critical illness.

Plasma Metabolomics of Dietary Intake of Protein-Rich Foods and Kidney Disease Progression in Children

OBJECTIVE

Evidence regarding the efficacy of a low-protein diet for patients with CKD is inconsistent and recommending a low-protein diet for pediatric patients is controversial. There is also a lack of objective biomarkers of dietary intake. The purpose of this study was to identify plasma metabolites associated with dietary intake of protein and to assess whether protein-related metabolites are associated with CKD progression.

METHODS

Nontargeted metabolomics was conducted in plasma samples from 484 Chronic Kidney Disease in Children (CKiD) participants. Multivariable linear regression estimated the cross-sectional association between 949 known, nondrug metabolites and dietary intake of total protein, animal protein, plant protein, chicken, dairy, nuts and beans, red and processed meat, fish, and eggs, adjusting for demographic, clinical, and dietary covariates. Cox proportional hazards models assessed the prospective association between protein-related metabolites and CKD progression defined as the initiation of kidney replacement therapy or 50% eGFR reduction, adjusting for demographic and clinical covariates.

RESULTS

One hundred and twenty-seven (26%) children experienced CKD progression during 5 years of follow-up. Sixty metabolites were significantly associated with dietary protein intake. Among the 60 metabolites, 10 metabolites were significantly associated with CKD progression (animal protein: n = 1, dairy: n = 7, red and processed meat: n = 2, nuts and beans: n = 1), including one amino acid, one cofactor and vitamin, 4 lipids, 2 nucleotides, one peptide, and one xenobiotic. 1-(1-enyl-palmitoyl)-2-oleoyl-glycerophosphoethanolamine (GPE, P-16:0/18:1) was positively associated with dietary intake of red and processed meat, and a doubling of its abundance was associated with 88% higher risk of CKD progression. 3-ureidopropionate was inversely associated with dietary intake of red and processed meat, and a doubling of its abundance was associated with 48% lower risk of CKD progression.

CONCLUSIONS

Untargeted plasma metabolomic profiling revealed metabolites associated with dietary intake of protein and CKD progression in a pediatric population.

Nuclear Magnetic Resonance-Based Metabolomics and Risk of CKD

RATIONALE & OBJECTIVE

Chronic kidney disease (CKD) leads to lipid and metabolic abnormalities, but a comprehensive investigation of lipids, lipoprotein particles, and circulating metabolites associated with the risk of CKD has been lacking. We examined the associations of nuclear magnetic resonance (NMR)-based metabolomics data with CKD risk in the UK Biobank study.

STUDY DESIGN

Observational cohort study.

SETTING & PARTICIPANTS

A total of 91,532 participants in the UK Biobank Study without CKD and not receiving lipid-lowering therapy.

EXPOSURE

Levels of metabolites including lipid concentration and composition within 14 lipoprotein subclasses, as well as other metabolic biomarkers were quantified via NMR spectroscopy.

OUTCOME

Incident CKD identified using ICD codes in any primary care data, hospital admission records, or death register records.

ANALYTICAL APPROACH

Cox proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals.

RESULTS

We identified 2,269 CKD cases over a median follow-up period of 13.1 years via linkage with the electronic health records. After adjusting for covariates and correcting for multiple testing, 90 of 142 biomarkers were significantly associated with incident CKD. In general, higher concentrations of very-low-density lipoprotein (VLDL) particles were associated with a higher risk of CKD whereas higher concentrations of high-density lipoprotein (HDL) particles were associated with a lower risk of CKD. Higher concentrations of cholesterol, phospholipids, and total lipids within VLDL were associated with a higher risk of CKD, whereas within HDL they were associated with a lower risk of CKD. Further, higher triglyceride levels within all lipoprotein subclasses, including all HDL particles, were associated with greater risk of CKD. We also identified that several amino acids, fatty acids, and inflammatory biomarkers were associated with risk of CKD.

LIMITATIONS

Potential underreporting of CKD cases because of case identification via electronic health records.

CONCLUSIONS

Our findings highlight multiple known and novel pathways linking circulating metabolites to the risk of CKD.

PLAIN-LANGUAGE SUMMARY

The relationship between individual lipoprotein particle subclasses and lipid-related traits and risk of chronic kidney disease (CKD) in general population is unclear. Using data from 91,532 participants in the UK Biobank, we evaluated the associations of metabolites measured using nuclear magnetic resonance testing with the risk of CKD. We identified that 90 out of 142 lipid biomarkers were significantly associated with incident CKD. We found that very-low-density lipoproteins, high-density lipoproteins, the lipid concentration and composition within these lipoproteins, triglycerides within all the lipoprotein subclasses, fatty acids, amino acids, and inflammation biomarkers were associated with CKD risk. These findings advance our knowledge about mechanistic pathways that may contribute to the development of CKD.

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.

High-Density Lipoprotein Lipidomics and Mortality in CKD

Rationale & Objective

Patients with chronic kidney disease (CKD) have dysfunctional high-density lipoprotein (HDL) particles that lack cardioprotective properties; altered lipid composition may be associated with these changes. To investigate HDL lipids as potential cardiovascular risk factors in CKD, we tested the associations of HDL ceramides, sphingomyelins, and phosphatidylcholines with mortality.

Study Design

We leveraged data from a longitudinal prospective cohort of participants with CKD.

Setting & Participants

We included participants aged greater than 21 years with CKD, excluding those on maintenance dialysis or with prior kidney transplant.

Exposure

HDL particles were isolated using density gradient ultracentrifugation. We quantified the relative abundance of HDL ceramides, sphingomyelins, and phosphatidylcholines via liquid chromatography tandem mass spectrometry (LC-MS/MS).

Outcomes

Our primary outcome was all-cause mortality.

Analytical Approach

We tested associations using Cox regressions adjusted for demographics, comorbid conditions, laboratory values, medication use, and highly correlated lipids with opposed effects, controlling for multiple comparisons with false discovery rates (FDR).

Results

There were 168 deaths over a median follow-up of 6.12 years (interquartile range, 3.71-9.32). After adjustment, relative abundance of HDL ceramides (HR, 1.22 per standard deviation; 95% CI, 1.06-1.39), sphingomyelins with long fatty acids (HR, 1.44; 95% CI, 1.05-1.98), and saturated and monounsaturated phosphatidylcholines (HR, 1.22; 95% CI, 1.06-1.41) were significantly associated with increased risk of all-cause mortality (FDR < 5%).

Limitations

We were unable to test associations with cardiovascular disease given limited power. HDL lipidomics may not reflect plasma lipidomics. LC-MS/MS is unable to differentiate between glucosylceramides and galactosylceramides. The cohort was comprised of research volunteers in the Seattle area with CKD.

Conclusions

Greater relative HDL abundance of 3 classes of lipids was associated with higher risk of all-cause mortality in CKD; sphingomyelins with very long fatty acids were associated with a lower risk. Altered lipid composition of HDL particles may be a novel cardiovascular risk factor in CKD.

Plain-Language Summary

Patients with chronic kidney disease have abnormal high-density lipoprotein (HDL) particles that lack the beneficial properties associated with these particles in patients with normal kidney function. To investigate if small lipid molecules found on the surface of HDL might be associated with these changes, we tested the associations of lipid molecules found on HDL with death among patients with chronic kidney disease. We found that several lipid molecules found on the surface of HDL were associated with increased risk of death among these patients. These findings suggest that lipid molecules may be risk factors for death among patients with chronic kidney disease.

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.

Evaluation of plasma sphingolipids as mediators of the relationship between kidney disease and cardiovascular events

BACKGROUND

Sphingolipids are a family of circulating lipids with regulatory and signaling roles that are strongly associated with both eGFR and cardiovascular disease. Patients with chronic kidney disease (CKD) are at high risk for cardiovascular events, and have different plasma concentrations of certain plasma sphingolipids compared to patients with normal kidney function. We hypothesize that circulating sphingolipids partially mediate the associations between eGFR and cardiovascular events.

METHODS

We measured the circulating concentrations of 8 sphingolipids, including 4 ceramides and 4 sphingomyelins with the fatty acids 16:0, 20:0, 22:0, and 24:0, in plasma from 3,463 participants in a population-based cohort (Cardiovascular Health Study) without prevalent cardiovascular disease. We tested the adjusted mediation effects by these sphingolipids of the associations between eGFR and incident cardiovascular disease via quasi-Bayesian Monte Carlo method with 2,000 simulations, using a Bonferroni correction for significance.

FINDINGS

The mean (±SD) eGFR was 70 (±16) mL/min/1.73 m2; 62% of participants were women. Lower eGFR was associated with higher plasma ceramide-16:0 and sphingomyelin-16:0, and lower ceramides and sphingomyelins-20:0 and -22:0. Lower eGFR was associated with risk of incident heart failure and ischemic stroke, but not myocardial infarction. Five of eight sphingolipids partially mediated the association between eGFR and heart failure. The sphingolipids associated with the greatest proportion mediated were ceramide-16:0 (proportion mediated 13%, 95% CI 8-22%) and sphingomyelin-16:0 (proportion mediated 10%, 95% CI 5-17%). No sphingolipids mediated the association between eGFR and ischemic stroke.

INTERPRETATION

Plasma sphingolipids partially mediated the association between lower eGFR and incident heart failure. Altered sphingolipids metabolism may be a novel mechanism for heart failure in patients with CKD.

FUNDING

This study was supported by T32 DK007467 and a KidneyCure Ben J. Lipps Research Fellowship (Dr. Lidgard). Sphingolipid measurements were supported by R01 HL128575 (Dr. Lemaitre) and R01 HL111375 (Dr. Hoofnagle) from the National Heart, Lung, and Blood Institute (NHLBI).

High-Density Lipoprotein Lipidomics in Chronic Kidney Disease

BACKGROUND

Patients with chronic kidney disease (CKD) have dysfunctional high-density lipoprotein (HDL) particles as compared with the general population. Understanding the lipid composition of HDL may provide mechanistic insight. We tested associations of estimated glomerular filtration rate (eGFR) and albuminuria with relative HDL abundance of ceramides, sphingomyelins, and phosphatidylcholines in participants with CKD.

METHODS

We studied 490 participants with CKD from the Seattle Kidney Study. HDL was isolated from plasma; targeted lipidomics was used to quantify the relative abundance of ceramides, sphingomyelins, and phosphatidylcholines per 10 µg of total HDL protein. We evaluated the associations of eGFR and albuminuria with levels of individual lipids and lipid classes (including 7 ceramides, 6 sphingomyelins, and 24 phosphatidylcholines) using multivariable linear regression, controlling for multiple comparisons via the false discovery rate.

RESULTS

The mean (SD) eGFR was 45 (24) mL/min/1.73 m2; the median (IQR[interquartile range]) albuminuria was 108 (16, 686) mg/g (12.2 [1.8, 77.6] mg/mmol) urine creatinine. After adjusting for demographics, past medical history, laboratory values, and medication use, eGFR was not associated with higher relative abundance of any class of lipids or individual lipids. Greater albuminuria was significantly associated with a higher relative abundance of total ceramides and moderate-long R-chain sphingomyelins, ceramides 22:0 and 24:1, hexosylceramide 16:0, sphingomyelin 16:0, and phosphatidylcholines 29:0, 30:1, and 38:2; the strongest association was for hexosylceramide 16:0 (increase per doubling of urine albumin to creatinine ratio 0.022 (95% CI, 0.012-0.032).

CONCLUSIONS

Greater albuminuria was significantly associated with specific alterations in the lipid composition of HDL in participants with CKD.

Nuclear Magnetic Resonance-Based Lipidomics in the Assessment of Cardiometabolic Risk in Type 1 Diabetes: An Exploratory Analysis

INTRODUCTION

Cardiovascular disease (CVD) is the leading cause of mortality in type 1 diabetes (T1D). However, there is a need for daily practice tools for identifying those more prone to suffer from these events. We aimed to assess the relationships between nuclear magnetic resonance (¹H NMR)-based lipidomic analysis and several CVD risk variables (including preclinical carotid atherosclerosis) in individuals with T1D at high risk.

METHODS

We included patients with T1D without CVD, with at least one of the following: age ≥ 40 years, diabetic kidney disease, or ≥ 10 years of evolution with another risk factor. The presence of plaque (intima-media thickness > 1.5 mm) was determined by standardized ultrasonography protocol. Lipidomic analysis was performed by ¹H NMR. Bivariate and multivariate-adjusted differences in ¹H NMR lipidomics were evaluated.

RESULTS

We included n = 131 participants (49.6% female, age 46.4 ± 10.3 years, diabetes duration 27.0 ± 9.5 years, 47.3% on statins). Carotid plaques were present in 28.2% of the individuals (n = 12, with ≥ 3 plaques). Glucose (HbA1c), anthropometric (body mass index and waist circumference), and insulin resistance-related (fatty liver index and estimated glucose disposal rate) variables were those most associated with ¹H NMR-derived lipidomic analysis (p < 0.01 for all). Regarding preclinical atherosclerosis, sphingomyelin was independently associated with carotid plaque presence (for 0.1 mmol/L increase, OR 0.50 [0.28-0.86]; p = 0.013), even after adjusting for age, sex, hypertension, statin use, mean 5-year HbA1c and diabetes duration. Furthermore, linoleic acid and ω-6 fatty acids remained independently associated with higher plaque burden (≥ 3 plaques) in multivariate models (0.17 [0.03-0.93] and 0.27 [0.07-0.97], respectively; p < 0.05 for both).

CONCLUSION

In our preliminary study of individuals with T1D at high risk, several ¹H NMR-derived lipidomic parameters were independently associated with preclinical atherosclerosis. Specifically, ω-6 fatty acids and linoleic acid seem promising for identifying those with higher plaque burden.

Identification of Serum Metabolites for Predicting Chronic Kidney Disease Progression according to Chronic Kidney Disease Cause

Early detection and proper management of chronic kidney disease (CKD) can delay progression to end-stage kidney disease. We applied metabolomics to discover novel biomarkers to predict the risk of deterioration in patients with different causes of CKD. We enrolled non-dialytic diabetic nephropathy (DMN, n = 124), hypertensive nephropathy (HTN, n = 118), and polycystic kidney disease (PKD, n = 124) patients from the KNOW-CKD cohort. Within each disease subgroup, subjects were categorized as progressors (P) or non-progressors (NP) based on the median eGFR slope. P and NP pairs were randomly selected after matching for age, sex, and baseline eGFR. Targeted metabolomics was performed to quantify 188 metabolites in the baseline serum samples. We selected ten progression-related biomarkers for DMN and nine biomarkers each for HTN and PKD. Clinical parameters showed good ability to predict DMN (AUC 0.734); however, this tendency was not evident for HTN (AUC 0.659) or PKD (AUC 0.560). Models constructed with selected metabolites and clinical parameters had better ability to predict CKD progression than clinical parameters only. When selected metabolites were used in combination with clinical indicators, random forest prediction models for CKD progression were constructed with AUCs of 0.826, 0.872, and 0.834 for DMN, HTN, and PKD, respectively. Select novel metabolites identified in this study can help identify high-risk CKD patients who may benefit from more aggressive medical treatment.

Lipidomic profiling in the Strong Heart Study identified American Indians at risk of chronic kidney disease

Dyslipidemia associates with and usually precedes the onset of chronic kidney disease (CKD), but a comprehensive assessment of molecular lipid species associated with risk of CKD is lacking. Here, we sought to identify fasting plasma lipids associated with risk of CKD among American Indians in the Strong Heart Family Study, a large-scale community-dwelling of individuals, followed by replication in Mexican Americans from the San Antonio Family Heart Study and Caucasians from the Australian Diabetes, Obesity and Lifestyle Study. We also performed repeated measurement analysis to examine the temporal relationship between the change in the lipidome and change in kidney function between baseline and follow-up of about five years apart. Network analysis was conducted to identify differential lipid classes associated with risk of CKD. In the discovery cohort, we found that higher baseline level of multiple lipid species, including glycerophospholipids, glycerolipids and sphingolipids, was significantly associated with increased risk of CKD, independent of age, sex, body mass index, diabetes and hypertension. Many lipid species were replicated in at least one external cohort at the individual lipid species and/or the class level. Longitudinal change in the plasma lipidome was significantly associated with change in the estimated glomerular filtration rate after adjusting for covariates, baseline lipids and the baseline rate. Network analysis identified distinct lipidomic signatures differentiating high from low-risk groups. Thus, our results demonstrated that disturbed lipid metabolism precedes the onset of CKD. These findings shed light on the mechanisms linking dyslipidemia to CKD and provide potential novel biomarkers for identifying individuals with early impaired kidney function at preclinical stages.

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.

Impact of Metabolomics Technologies on the Assessment of Peritoneal Membrane Profiles in Peritoneal Dialysis Patients: A Systematic Review

Peritoneal dialysis (PD) is an effective and frequent dialysis modality in adults, particularly preferred in infants and young children with end-stage renal disease (ESRD). Long-term exposure of the peritoneal membrane to dialysis solutions results in severe morphologic and functional alterations. Peritoneal dialysis effluent biomarkers are based on omics technologies, which could predict the onset or confirm the diagnosis of peritoneal membrane dysfunction, would allow the development of accurate early prognostic tools and, potentially, the identification of future therapeutic targets. The purpose of our study was to critically review the literature on the impact and the effectiveness of metabolomics technologies in peritoneal health. The main search was performed in electronic databases (PubMed/MEDLINE, Embase and Cochrane Central Register of Controlled Trials) from inception to December 2020, using various combinations of Medical Subject Headings (MeSH). The main search highlighted nine studies, of which seven were evaluated in detail. Metabolomics technologies may provide significant input in the recognition of peritoneal membrane dysfunction in PD patients and provide evidence of early intervention strategies that could protect peritoneum health and function.

Multiomics signatures of type 1 diabetes with and without albuminuria

AIMS/HYPOTHESIS

To identify novel pathophysiological signatures of longstanding type 1 diabetes (T1D) with and without albuminuria we investigated the gut microbiome and blood metabolome in individuals with T1D and healthy controls (HC). We also mapped the functional underpinnings of the microbiome in relation to its metabolic role.

METHODS

One hundred and sixty-one individuals with T1D and 50 HC were recruited at the Steno Diabetes Center Copenhagen, Denmark. T1D cases were stratified based on levels of albuminuria into normoalbuminuria, moderate and severely increased albuminuria. Shotgun sequencing of bacterial and viral microbiome in stool samples and circulating metabolites and lipids profiling using mass spectroscopy in plasma of all participants were performed. Functional mapping of microbiome into Gut Metabolic Modules (GMMs) was done using EggNog and KEGG databases. Multiomics integration was performed using MOFA tool.

RESULTS

Measures of the gut bacterial beta diversity differed significantly between T1D and HC, either with moderately or severely increased albuminuria. Taxonomic analyses of the bacterial microbiota identified 51 species that differed in absolute abundance between T1D and HC (17 higher, 34 lower). Stratified on levels of albuminuria, 10 species were differentially abundant for the moderately increased albuminuria group, 63 for the severely increased albuminuria group while 25 were common and differentially abundant both for moderately and severely increased albuminuria groups, when compared to HC. Functional characterization of the bacteriome identified 23 differentially enriched GMMs between T1D and HC, mostly involved in sugar and amino acid metabolism. No differences in relation to albuminuria stratification was observed. Twenty-five phages were differentially abundant between T1D and HC groups. Six of these varied with albuminuria status. Plasma metabolomics indicated differences in the steroidogenesis and sugar metabolism and circulating sphingolipids in T1D individuals. We identified association between sphingolipid levels and Bacteroides sp. abundances. MOFA revealed reduced interactions between gut microbiome and plasma metabolome profiles albeit polar metabolite, lipids and bacteriome compositions contributed to the variance in albuminuria levels among T1D individuals.

CONCLUSIONS

Individuals with T1D and progressive kidney disease stratified on levels of albuminuria show distinct signatures in their gut microbiome and blood metabolome.

Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies

The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to explore the potential pathways modulated by medications. Investigating diabetes using a systems biology approach by integrating metabolomics with other omics data, such as genetics, transcriptomics, proteomics, and clinical data can present a comprehensive metabolic network and facilitate causal inference. In this regard, metabolomics can deepen the molecular understanding, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The current review focused on metabolomic biomarkers for kidney and cardiovascular disease in T2D identified from epidemiological studies, and will also provide a brief overview on metabolomic investigations for T2D.

Lipid responses to environmental perfluoroalkyl substance exposure in a Taiwanese Child cohort

Although recent epidemiologic studies have focused on some of the health effects of perfluoroalkyl substance (PFASs) exposure in humans, the associations between PFASs exposure and the lipidome in children are still unclear. The purpose of this study was to assess lipid changes in children to understand possible molecular events of environmental PFASs exposure and suggest potential health effects. A total of 290 Taiwanese children (8-10 years old) were included in this study. Thirteen PFASs were analyzed in their serum by high-performance liquid chromatography-tandem mass spectrometry (LC-MS). MS-based lipidomic approaches were applied to examine lipid patterns in the serum of children exposed to different levels of PFASs. LC coupling with triple quadrupole MS technology was conducted to analyze phosphorylcholine-containing lipids. Multivariate analyses, such as partial least squares analysis along with univariate analyses, including multiple linear regression, were used to analyze associations between s exposure and unique lipid patterns. Our results showed that different lipid patterns were discovered in children exposed to different levels of specific PFASs, such as PFTrDA, PFOS, and PFDA. These changes in lipid levels may be involved in hepatic lipid metabolism, metabolic disorders, and PFASs-membrane interactions. This study showed that lipidomics is a powerful approach to identify critical PFASs that cause metabolite perturbation in the serum of children and suggest possible adverse health effects of these chemicals in children.

A Mediation Approach to Discovering Causal Relationships between the Metabolome and DNA Methylation in Type 1 Diabetes

Environmental factors including viruses, diet, and the metabolome have been linked with the appearance of islet autoimmunity (IA) that precedes development of type 1 diabetes (T1D). We measured global DNA methylation (DNAm) and untargeted metabolomics prior to IA and at the time of seroconversion to IA in 92 IA cases and 91 controls from the Diabetes Autoimmunity Study in the Young (DAISY). Causal mediation models were used to identify seven DNAm probe-metabolite pairs in which the metabolite measured at IA mediated the protective effect of the DNAm probe measured prior to IA against IA risk. These pairs included five DNAm probes mediated by histidine (a metabolite known to affect T1D risk), one probe (cg01604946) mediated by phostidyl choline p-32:0 or o-32:1, and one probe (cg00390143) mediated by sphingomyelin d34:2. The top 100 DNAm probes were over-represented in six reactome pathways at the FDR <0.1 level (q = 0.071), including transport of small molecules and inositol phosphate metabolism. While the causal pathways in our mediation models require further investigation to better understand the biological mechanisms, we identified seven methylation sites that may improve our understanding of epigenetic protection against T1D as mediated by the metabolome.

Lipidomics reveals association of circulating lipids with body mass index and outcomes in IgA nephropathy patients

IgA nephropathy (IgAN) is a leading cause of chronic kidney disease (CKD), which are commonly accompanied by dyslipidemia. Obesity is also associated with dyslipidemia and risk of CKD, but the relation of the dyslipidemia patterns with obesity and disease progression in IgAN patients remains unknown. Traditional Chinese medicine (TCM) and the combined treatment with corticosteroids and TCM have been shown to be of benefit for IgAN patients, but predictive markers for guiding these treatments are lacking. Here, we quantified 545 lipid species in the plasma from 196 participants, including 140 IgAN patients and 56 healthy volunteers, and revealed an altered plasma lipidome in IgAN patients as compared to healthy participants. Association analysis showed that a sub-group of glycerides, particularly triacylglycerols (TGs) containing docosahexaenoic acid, were positively associated with high body mass index (BMI) in under- or normal weight IgAN patients, while several free fatty acids and sphingomyelins were positively associated with high BMI in overweight or obese IgAN patients. Further, our study suggested that elevated levels of eight lipids, mainly TG species containing linolenic acid, were independent risk factors for IgAN progression and also reported the prospective association of circulating lipids with treatment outcomes in IgAN. Taken together, our findings may not only help to achieve precision medicine but also provide a knowledge base for dietary intervention in the treatment of IgAN.

Comprehensive metabolomic study of the response of HK-2 cells to hyperglycemic hypoxic diabetic-like milieu

Diabetic nephropathy (DN) is the leading cause of chronic kidney disease. Although hyperglycaemia has been determined as the most important risk factor, hypoxia also plays a relevant role in the development of this disease. In this work, a comprehensive metabolomic study of the response of HK-2 cells, a human cell line derived from normal proximal tubular epithelial cells, to hyperglycemic, hypoxic diabetic-like milieu has been performed. Cells simultaneously exposed to high glucose (25 mM) and hypoxia (1% O2) were compared to cells in control conditions (5.5 mM glucose/18.6% O2) at 48 h. The combination of advanced metabolomic platforms (GC-TOF MS, HILIC- and CSH-QExactive MS/MS), freely available metabolite annotation tools, novel databases and libraries, and stringent cut-off filters allowed the annotation of 733 metabolites intracellularly and 290 compounds in the extracellular medium. Advanced bioinformatics and statistical tools demonstrated that several pathways were significantly altered, including carbohydrate and pentose phosphate pathways, as well as arginine and proline metabolism. Other affected metabolites were found in purine and lipid metabolism, the protection against the osmotic stress and the prevention of the activation of the β-oxidation pathway. Overall, the effects of the combined exposure of HK-cells to high glucose and hypoxia are reasonably compatible with previous in vivo works.

Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals With Prediabetes and Type 2 Diabetes

Early and precise identification of individuals with prediabetes and type 2 diabetes (T2D) at risk for progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin C18:1 and phosphatidylcholine diacyl C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors, and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in people with prediabetes and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.

Uncovering the mechanism of Astragali Radix against nephrotic syndrome by intergrating lipidomics and network pharmacology

BACKGROUND

Astragali Radix (AR), a common Traditional Chinese Medicine (TCM), is commonly used for treating nephrotic syndrome (NS) in China. At present, the research on the efficacy of AR against NS is relative clearly, but there are fewer researches on the mechanism.

PURPOSE

The aim of this study was to evaluate the potential beneficial effects of AR in an adriamycin-induced nephropathy rat model, as well as investigate the possible mechanisms of action and potential lipid biomarkers.

METHODS

In this work, a rat model of NS was established by two injections of ADR (3.5 + 1 mg/kg) into the tail vein. The potential metabolites and targets involved in the anti-NS effects of AR were predicted by lipidomics coupled with the network pharmacology approach, and the crucial metabolite and protein were further validated by western blotting and ELISA.

RESULTS

The results showed that 22 metabolites such as l-carnitine, LysoPC (20:3), and SM (d18:1/16:0) were associated with renal injury. Moreover, SMPD1, CPT1A and LCAT were predicted as lipids linked targets of AR against NS, whilst glycerophospholipid, sphingolipid and fatty acids metabolism were involved as key pathways of AR against NS. Besides, AR could play a critical role in NS by improving oxidative stress, inhibiting apoptosis and reducing inflammation. Interestingly, our results indicated that key metabolite l-carnitine and target CPT1 were one of the important metabolites and targets for AR to exert anti-NS effects.

CONCLUSION

In summary, this study offered a new understanding of the protection mechanism of AR against NS by network pharmacology and lipidomic method.

Sphingomyelin and progression of renal and coronary heart disease in individuals with type 1 diabetes

AIMS/HYPOTHESIS

Lipid abnormalities are associated with diabetic kidney disease and CHD, although their exact role has not yet been fully explained. Sphingomyelin, the predominant sphingolipid in humans, is crucial for intact glomerular and endothelial function. Therefore, the objective of our study was to investigate whether sphingomyelin impacts kidney disease and CHD progression in individuals with type 1 diabetes.

METHODS

Individuals (n = 1087) from the Finnish Diabetic Nephropathy (FinnDiane) prospective cohort study with serum sphingomyelin measured using a proton NMR metabolomics platform were included. Kidney disease progression was defined as change in eGFR or albuminuria stratum. Data on incident end-stage renal disease (ESRD) and CHD were retrieved from national registries. HRs from Cox regression models and regression coefficients from the logistic or linear regression analyses were reported per 1 SD increase in sphingomyelin level. In addition, receiver operating curves were used to assess whether sphingomyelin improves eGFR decline prediction compared with albuminuria.

RESULTS

During a median (IQR) 10.7 (6.4, 13.5) years of follow-up, sphingomyelin was independently associated with the fastest eGFR decline (lowest 25%; median [IQR] for eGFR change: <-4.4 [-6.8, -3.1] ml min^-1 [1.73 m^-2] year^-1), even after adjustment for classical lipid variables such as HDL-cholesterol and triacylglycerols (OR [95% CI]: 1.36 [1.15, 1.61], p < 0.001). Similarly, sphingomyelin increased the risk of progression to ESRD (HR [95% CI]: 1.53 [1.19, 1.97], p = 0.001). Moreover, sphingomyelin increased the risk of CHD (HR [95% CI]: 1.24 [1.01, 1.52], p = 0.038). However, sphingomyelin did not perform better than albuminuria in the prediction of eGFR decline.

CONCLUSIONS/INTERPRETATION

This study demonstrates for the first time in a prospective setting that sphingomyelin is associated with the fastest eGFR decline and progression to ESRD in type 1 diabetes. In addition, sphingomyelin is a risk factor for CHD. These data suggest that high sphingomyelin level, independently of classical lipid risk factors, may contribute not only to the initiation and progression of kidney disease but also to CHD. Graphical abstract.

Separation of Glycolipids/Sphingolipids from Glycerophospholipids on TiO2 Coating in Aprotic Solvent for Rapid Comprehensive Lipidomic Analysis with Liquid Microjunction Surface Sampling-Mass Spectrometry

In lipidomic analysis by direct mass spectrometry (MS), high abundance lipids with high ionizability (such as glycerophospholipids) would cause ion suppression to lipids with poor ionizability and low abundance (such as glycolipids, sphingolipids, or glycerides), which largely limits the detection coverage for lipidomics. In this work, TiO₂-based liquid microjunction surface sampling (LMJSS) coupled with MS was used for separation of glycerides, phospholipids and glycolipids/sphingolipids in biological samples and rapid analysis of lipids in different classes with high lipidome coverage. We found that, in nonaqueous aprotic solvents, lipids with a glycosyl or sphingosine group could be selectively separated from lipids with a phosphate group (selectivity >10) after being coenriched on TiO₂ by tuning the solvent composition. Accordingly, a selective multistep extraction method was developed by loading the biosamples on TiO₂ slides in neutral aprotic solvent, and sequentially eluting glycerides in pure acetonitrile, glycerophospholipids in 6% ammonia-94% acetonitrile (v/v) and glycolipids/sphingolipids in 5% formic acid-95% methanol (v/v) by LMJSS probe from TiO₂ slide. Each eluate from TiO₂ slide was directly delivered by LMJSS to MS for analysis. The total detection time with three desorption steps would be controlled in 3 min. The method performance for each lipid class was evaluated using lipid standards, including matrix effects (107-128%), RSDs (0.4-16%), linearity (0.98-0.99), detection limits (5-3000 ng/mL), the adsorption equilibrium constants (10²-10⁴) and adsorption capacity (1-38 μg/mm²) of TiO₂ coated slides to lipids. Finally, the TiO₂-based-LMJSS-MS method was applied to lipidomic analysis for blood plasma and brain tissue, and compared with direct infusion MS. Results showed that (2-5)-fold more sphingolipids/glycolipids and 40-50 more glycerophospholipids/glycerides were identified in both plasma and brain extract by the new method comparing with direct infusion MS method. Detected lipids were quantified with standard addition calibration method, and the absolute quantitation results measured by TiO₂-based-LMJSS-MS were verified with that by the traditional LC-MS method (correlation coefficient >0.98, slope of correlation line = 0.87-1.05).

Lipidomic Abnormalities During the Pathogenesis of Type 1 Diabetes: a Quantitative Review

PURPOSE OF REVIEW

The underlying factors triggering a cascade of autoimmune response that leads to the death of pancreatic beta cells and type 1 diabetes are to large extent unknown. Aberrations in the lipid balance have been suggested, either as factors directly contributing to autoimmunity or as a reflection of external factors, such as the diet or chemical exposure, which may increase the risk or even trigger the autoimmunity cascade.

RECENT FINDINGS

A small number of recent studies have investigated the blood lipidome before and after the onset of type 1 diabetes with a goal of identifying biomarkers of disease progression. Phosphatidylcholine levels in particular have been suggested to be reduced prior to the onset of type 1 diabetes. In this review, we approach this question through a quantitative analysis of the reported lipids. We quantify the extent of consensus between these heterogeneous studies, describe the overall lipidomic pattern that has been reported, and call for more independent replication of the findings that we highlight in this review.

Rotten to the Cortex: Ceramide-Mediated Lipotoxicity in Diabetic Kidney Disease

Diabetic kidney disease (DKD) is a prevalent and progressive comorbidity of diabetes mellitus that increases one's risk of developing renal failure. Progress toward development of better DKD therapeutics is limited by an incomplete understanding of forces driving and connecting the various features of DKD, which include renal steatosis, fibrosis, and microvascular dysfunction. Herein we review the literature supporting roles for bioactive ceramides as inducers of local and systemic DKD pathology. In rodent models of DKD, renal ceramides are elevated, and genetic and pharmacological ceramide-lowering interventions improve kidney function and ameliorate DKD histopathology. In humans, circulating sphingolipid profiles distinguish human DKD patients from diabetic controls. These studies highlight the potential for ceramide to serve as a central and therapeutically tractable lipid mediator of DKD.

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.

Activated NF-κB/Nrf2 and Wnt/β-catenin pathways are associated with lipid metabolism in CKD patients with microalbuminuria and macroalbuminuria

Early diagnosis of CKD patients at risk for microalbuminuria or macroalbuminuria could facilitate clinical outcomes and long-term survival. Considering the few and limited efficacy of current biomarkers in early detection, we aim to discover plasma lipids that effectively predict the development of CKD paitents with microalbuminuria or macroalbuminuria. A total of 380 healthy controls and 1156 patients with CKD stages 3 to 5 were stratified by urine albumin-creatinine ratio as microalbuminuria (30-300 mg/g) and macroalbuminuria (>300 mg/g). Fasting plasma samples were determined by UPLC-HDMS based on lipidomics. Quantitative real-time polymerase chain reaction, Western blot and immunohistochemical analyses were used to validate the lipid metabolism-associated pathways. Pathway analysis demonstrated that these lipids were closely associated with PPARγ, inflammatory mediator regulation of TRP channels and RAS signaling, which were intimately involved in activated NF-κB and Nrf2 pathways. We further carried out pathway validation and demonstrated that NF-κB pathway was activated in patients with macroalbuminuria compared with CKD patients with microalbuminuria, while Nrf2-associated protein expression was downregulated, which was accompanied by the up-regulation of Wnt/β-catenin signaling pathway. Four lipids including DTA, 5,8-TDA, GGD3 and DHA that showed great potential in the discrimination of CKD patients with microalbuminuria and healthy controls were selected by logistic regression analysis. Additionally, six lipid species including CDCA, glucosylceramide, GGD2, TTA, DHA and EDA that contributed to the discrimination of CKD patients with microalbuminuria and macroalbuminuria were selected by logistic LASSO regression Gangliosides were first identified and might be promising therapeutic targets for CKD patients with the different degree of albuminuria. Collectively, this study first demonstrates the association of plasma inflammation, oxidative stress, Wnt/β-catenin and lipid metabolism in CKD patients with microalbuminuria and macroalbuminuria.

Glycosylated sphingolipids and progression to kidney dysfunction in type 1 diabetes

BACKGROUND

Glycosphingolipids are important components of cell membranes, modulators of cell-cell interactions and cell recognition, and have recently emerged as bioactive molecules and important players in nearly all cell biological processes. We previously have shown that decreased plasma levels of long and very long species of ceramides were able to predict the development of macroalbuminuria (MA) in type 1 diabetes.

OBJECTIVE

This study proposed to examine whether plasma glycosphingolipids could predict development of diabetic nephropathy, assessed as MA or chronic kidney disease (CKD).

METHODS

Measurement of plasma hexosylceramides (H) and lactosylceramides (L) were conducted in the Lipidomics Core Facility of our Institution in a subcohort of 432 patients from the DCCT/Epidemiology of Diabetes Interventions and Complications cohort in plasma collected at entry into the study. Inverse probability weighted Cox proportional hazards regression models were used to assess the effect of glycosphingolipids levels on the risk of developing MA (albumin excretion rate ≥300 mg/24 hours) or CKD (glomerular filtration rate <60 mL/min) over a period of 21 to 28 years.

RESULTS

Decreases of several long and very long chain lactosylceramides were significantly associated with increased risk of progression to MA but not CKD. Among the hexosylceramides, the only significant association observed was between one of its minor species C18:1-H and CKD.

CONCLUSION

Our findings showed that decreased levels of long and very long lactosylceramides were able to predict the development of MA in type 1 diabetes. This finding is similar to previous findings showing that low levels of long and very long ceramides were also able to predict development of MA in the same cohort. Further studies are needed to determine the changes in sphingolipid metabolism leading to the development of complications.

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.

Metabolomics Approaches for the Diagnosis and Understanding of Kidney Diseases

Diseases of the kidney are difficult to diagnose and treat. This review summarises the definition, cause, epidemiology and treatment of some of these diseases including chronic kidney disease, diabetic nephropathy, acute kidney injury, kidney cancer, kidney transplantation and polycystic kidney diseases. Numerous studies have adopted a metabolomics approach to uncover new small molecule biomarkers of kidney diseases to improve specificity and sensitivity of diagnosis and to uncover biochemical mechanisms that may elucidate the cause and progression of these diseases. This work includes a description of mass spectrometry-based metabolomics approaches, including some of the currently available tools, and emphasises findings from metabolomics studies of kidney diseases. We have included a varied selection of studies (disease, model, sample number, analytical platform) and focused on metabolites which were commonly reported as discriminating features between kidney disease and a control. These metabolites are likely to be robust indicators of kidney disease processes, and therefore potential biomarkers, warranting further investigation.

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.

Metabolomics and Lipidomics Study of Mouse Models of Type 1 Diabetes Highlights Divergent Metabolism in Purine and Tryptophan Metabolism Prior to Disease Onset

With the increase in incidence of type 1 diabetes (T1DM), there is an urgent need to understand the early molecular and metabolic alterations that accompany the autoimmune disease. This is not least because in murine models early intervention can prevent the development of disease. We have applied a liquid chromatography (LC-) and gas chromatography (GC-) mass spectrometry (MS) metabolomics and lipidomics analysis of blood plasma and pancreas tissue to follow the progression of disease in three models related to autoimmune diabetes: the nonobese diabetic (NOD) mouse, susceptible to the development of autoimmune diabetes, and the NOD-E (transgenic NOD mice that express the I-E heterodimer of the major histocompatibility complex II) and NOD-severe combined immunodeficiency (SCID) mouse strains, two models protected from the development of diabetes. All three analyses highlighted the metabolic differences between the NOD-SCID mouse and the other two strains, regardless of diabetic status indicating that NOD-SCID mice are poor controls for metabolic changes in NOD mice. By comparing NOD and NOD-E mice, we show the development of T1DM in NOD mice is associated with changes in lipid, purine, and tryptophan metabolism, including an increase in kynurenic acid and a decrease in lysophospholipids, metabolites previously associated with inflammation.

Cardiovascular Implications of Sphingomyelin Presence in Biological Membranes

Sphingomyelin (SM) is a type of sphingolipid found within plasma, cellular membranes and plasma lipoproteins. Here we highlight the basic biochemical features of SMs and their role in biological membranes. We further discuss evidence of the association between SM and cardiovascular diseases such as atherosclerosis, valvular disease, heart failure and diabetes mellitus.

HDL functionality in type 1 diabetes

Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by absence of insulin secretion due to destruction of the pancreatic beta-cells. Patients with T1D exhibit an increased risk for cardiovascular disease (CVD) compared with non-diabetic subjects. It has been established that low concentration of high-density lipoprotein cholesterol (HDL-C), an independent risk marker of CVD, coincides with a reduced protective capacity against oxidative stress. However, conflicting results have been reported on the prevalence of low HDL-C levels in T1D. Interestingly, changes in composition and function of HDL particles (abnormal ratio of cholesteryl ester-to-triglyceride, reduction in the phospholipid content, reduced capacity to promote cholesterol efflux from macrophages, impaired anti-inflammatory and anti-oxidant activities) have been described in patients with T1D. Hence, exploring HDL function, even in the presence of normal HDL-C levels, might provide additional insight into the underlying pathophysiology associated with increased CV risk in T1D. In the current review, we will provide a detailed overview of the current evidence for a role of HDL function as independent risk factor for the development of CVD in T1D.

Mitochondrial Dysfunction in the Diabetic Kidney

The role of mitochondria in diabetic complications has been viewed as a source of excess superoxide production leading to cell dysfunction. However, with the lack of benefit of non-specific anti-oxidant approaches this view needs to be re-evaluated. With recent studies using real-time imaging of superoxide, metabolomics, flux studies, transcriptomics and proteomics a new appreciation for the role of mitochondria in the evolution of diabetic kidney disease has emerged. Ongoing studies to further unravel the time course and mechanisms that reduce mitochondrial function will be relevant to novel therapies that could have a major impact on diabetic kidney disease and other diabetic complications.

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.

Mass Spectrometry Imaging Reveals Elevated Glomerular ATP/AMP in Diabetes/obesity and Identifies Sphingomyelin as a Possible Mediator

AMP-activated protein kinase (AMPK) is suppressed in diabetes and may be due to a high ATP/AMP ratio, however the quantitation of nucleotides in vivo has been extremely difficult. Via matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to localize renal nucleotides we found that the diabetic kidney had a significant increase in glomerular ATP/AMP ratio. Untargeted MALDI-MSI analysis revealed that a specific sphingomyelin species (SM(d18:1/16:0)) accumulated in the glomeruli of diabetic and high-fat diet-fed mice compared with wild-type controls. In vitro studies in mesangial cells revealed that exogenous addition of SM(d18:1/16:0) significantly elevated ATP via increased glucose consumption and lactate production with a consequent reduction of AMPK and PGC1α. Furthermore, inhibition of sphingomyelin synthases reversed these effects. Our findings suggest that AMPK is reduced in the diabetic kidney due to an increase in the ATP/AMP ratio and that SM(d18:1/16:0) could be responsible for the enhanced ATP production via activation of the glycolytic pathway.

•

MALDI-MSI revealed an increase in glomerular ATP/AMP ratio in the diabetic kidney.

•

SM(d18:1/16:0) is increased in the glomeruli of diabetic and high-fat diet-fed mice.

•

SM(d18:1/16:0) stimulated ATP production via enhanced aerobic glycolysis and reduced AMPK activity in mesangial cells.

AMPK is known to be suppressed in states of high ATP/AMP ratio but the measurement of nucleotides in vivo has been difficult. Miyamoto et al. utilize matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to investigate the distribution of nucleotides and find an increase in glomerular ATP/AMP ratio in the diabetic kidney. Untargeted MALDI-MSI revealed that sphingomyelin(d18:1/16:0) is accumulated in the glomeruli of diabetic and high-fat diet-fed mice compared with controls. Sphingomyelin(d18:1/16:0) promotes ATP production in mesangial cells via activation of the glycolytic pathway. The inhibition of sphingomyelin(d18:1/16:0) synthesis may lead to novel therapeutic targets for the treatment of caloric-induced CKD.

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.

Targeted Lipidomic and Transcriptomic Analysis Identifies Dysregulated Renal Ceramide Metabolism in a Mouse Model of Diabetic Kidney Disease

Both type 1 and type 2 diabetes are associated with altered lipid metabolism, which might in part contribute to debilitating complications such as diabetic kidney disease (DKD). Ceramides are bioactive sphingolipids that have been implicated in a variety of diseases as they can regulate cellular responses to stress and invoke a myriad of downstream signaling responses. To investigate a potential role of altered ceramide metabolism in DKD, we utilized a highly sensitive and specific mass spectrometry (MS) method to quantitatively measure species in plasma and kidney cortex from the C57BLKS db/db mouse model of DKD and littermate controls. Long-chain ceramides (C14:0, C16:0, C18:0, C20:0) and a glucosylceramide (Glu-Cer C18:0) were increased in diabetic mouse plasma, while long-chain (C14:0, C16:0, C18:0) and very-long-chain (C24:0, C24:1) ceramides and a glucosylceramide (Glu-Cer C16:0) were decreased in diabetic mouse kidney tissue. Kidney and plasma ceramide levels correlated to functional and histopathological features of DKD. Transcriptomic analysis of mouse kidney tissue revealed expression changes indicative of decreased ceramide synthesis (Degs2, Smpd2) and increased conversion to sphingosine (Acer2) and downstream sphingosine-1-phosphate signaling. Correlation analysis identified a negative relationship between plasma and kidney tissue levels of ceramide C16:0 and ceramide C24:1. Overall, the findings suggest a previously unrecognized role for ceramide metabolism in DKD.

NMR metabolomics of renal cancer: an overview

This paper reviews the use of NMR metabolomics for the metabolic characterization of renal cancer. The existing challenges in the clinical management of this disease are first presented, followed by a brief introduction to the metabolomics approach, in the context of cancer research. A subsequent review of the literature on NMR metabolic studies of renal cancer reveals that the subject has been clearly underdeveloped, compared with other types of cancer, particularly regarding cultured cells and tissue analysis. NMR analysis of biofluids has focused on blood (plasma or serum) metabolomics, comprising no account of studies on human urine, in spite of its noninvasiveness and physiological proximity to the affected organs. Finally, some areas of potential future development are identified.

Epidemiology: disease associations and modulators of HDL-related biomarkers

Epidemiological studies have shown an inverse association between high-density lipoprotein cholesterol (HDL-C) levels and risk of ischemic heart disease. In addition, a low level of HDL-C has been shown to be a risk factor for other diseases not related to atherosclerosis. However, recent studies have not supported a causal effect of HDL-C in the development of atherosclerosis. Furthermore, new drugs markedly elevating HDL-C levels have been disappointing with respect to clinical endpoints. Earlier, most studies have focused almost exclusively on the total HDL-C without regard to the chemical composition or multiple subclasses of HDL particles. Recently, there have been efforts to dissect the HDL fraction into as many well-defined subfractions and individual molecules of HDL particles as possible. On the other hand, the focus is shifting from the structure and composition to the function of HDL particles. Biomarkers and mechanisms that could potentially explain the beneficial characteristics of HDL particles unrelated to their cholesterol content have been sought with sophisticated methods such as proteomics, lipidomics, metabonomics, and function studies including efflux capacity. These new approaches have been used in order to resolve the complex effects of diseases, conditions, environmental factors, and genes in relation to the protective role of HDL but high-throughput methods are still needed for large-scale epidemiological studies.

Decreased plasma levels of select very long chain ceramide species are associated with the development of nephropathy in type 1 diabetes

OBJECTIVE

Sphingolipid metabolism is altered in diabetes and we analyzed the plasma concentrations of sphingolipid species to investigate their association with the development of albuminuria in type 1 patients with diabetes.

MATERIALS AND METHODS

Samples were collected from 497 type 1 diabetic patients during their enrollment into the Diabetes Control and Complications Trial (DCCT). We determined plasma concentrations of multiple ceramide species and individual sphingoid bases and their phosphates using high performance liquid chromatography-tandem mass spectrometry and investigated their association with the development of albuminuria during 14-20 years of follow-up.

RESULTS

Patients exhibited normal albumin excretion rates (AER <40 mg/24h) at the time of plasma sampling. Although the majority of patients (N = 291; 59%) exhibited normal levels of albuminuria throughout follow-up, 141 patients (28%) progressed to microalbuminuria (40 mg/24h ≤ AER<300 mg/24h), while 65 (13%) progressed to macroalbuminuria (AER ≥ 300 mg/24h). To test the association of log transformed plasma sphingolipid level with the development of albuminuria, generalized logistic regression models were used where normal, micro- and macroalbuminuria were the outcomes of interest. Models were adjusted for DCCT treatment group, baseline retinopathy, gender, baseline HbA1c %, age, AER, lipid levels, diabetes duration, and the use of ACE/ARB drugs. Increased plasma levels of very long, but not long chain ceramide species measured at DCCT baseline were associated with decreased odds to develop macroalbuminuria during the subsequent nineteen years (DCCT Baseline to EDIC year 8).

CONCLUSION

These studies demonstrate, prospectively, that decreased plasma levels of select ceramide species are associated with the development of macroalbuminuria in type 1 diabetes.

Lipid profiling reveals different therapeutic effects of metformin and glipizide in patients with type 2 diabetes and coronary artery disease

OBJECTIVE

We recently demonstrated a beneficial effect of metformin compared with glipizide in type 2 diabetic patients regarding cardiovascular outcomes for 3-year treatment in the SPREAD-DIMCAD study. However, the potential mechanism for the clinical effects remains unclear. Here, we performed a comprehensive lipidomics study to evaluate the different lipid metabolites in serum samples obtained from participants in this study.

RESEARCH DESIGN AND METHODS

Liquid chromatography-quadrupole time of flight-mass spectrometry was used to evaluate the different lipid metabolites in serum samples obtained from the participants (21 patients in glipizide group and 23 patients in metformin group) before and after each year of treatment (at 0 [baseline], 1, 2, and 3 years of study drug administration).

RESULTS

A total of 118 serum lipid molecular species was identified and quantified. During treatment, metformin induced a substantially greater change in serum lipid species compared with glipizide, especially at the 2- and 3-year time points (with 2, 11, and 12 lipid species being significantly different between the groups after each year of treatment [1, 2, or 3 years], P < 0.05). Among the significantly changed lipid species, three lipid metabolites were linked to long-term composite cardiovascular events (adjusted P < 0.05). After treatment, triacylglycerols (TAGs) of a relatively higher carbon number showed a clearly increased trend in metformin group compared with the glipizide group, whereas the changes in TAGs with different double bonds were minimal.

CONCLUSIONS

Our findings revealed the differential therapeutic effects of metformin and glipizide on comprehensive lipidomics, which were comparable with their different long-term effects on cardiovascular outcomes.