1,5-Anhydroglucitol predicts CKD progression in macroalbuminuric diabetic kidney disease: results from non-targeted metabolomics

Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease

AIM

Diabetes is increasing in prevalence worldwide, with a 20% rise in prevalence predicted between 2021 and 2030, bringing an increased burden of complications, such as diabetic kidney disease (DKD). DKD is a leading cause of end-stage kidney disease, with significant impacts on patients, families and healthcare providers. DKD often goes undetected until later stages, due to asymptomatic disease, non-standard presentation or progression, and sub-optimal screening tools and/or provision. Deeper insights are needed to improve DKD diagnosis, facilitating the identification of higher-risk patients. Improved tools to stratify patients based on disease prognosis would facilitate the optimisation of resources and the individualisation of care. This review aimed to identify how multiomic approaches provide an opportunity to understand the complex underlying biology of DKD.

METHODS

This review explores how multiomic analyses of DKD are improving our understanding of DKD pathology, and aiding in the identification of novel biomarkers to detect disease earlier or predict trajectories.

RESULTS

Effective multiomic data integration allows novel interactions to be uncovered and empathises the need for harmonised studies and the incorporation of additional data types, such as co-morbidity, environmental and demographic data to understand DKD complexity. This will facilitate a better understanding of kidney health inequalities, such as social-, ethnicity- and sex-related differences in DKD risk, onset and progression.

CONCLUSION

Multiomics provides opportunities to uncover how lifetime exposures become molecularly embodied to impact kidney health. Such insights would advance DKD diagnosis and treatment, inform preventative strategies and reduce the global impact of this disease.

The clinical potential of 1,5-anhydroglucitol as biomarker in diabetes mellitus

A crucial measure of diabetes management is to monitor blood glucose, which often requires continuous blood collection, leading to economic burden and discomfort. Blood glucose and glycated hemoglobin A1c serve as traditional indicators of glucose monitoring. But now glycated albumin, fructosamine, and 1,5-anhydroglucitol (1,5-AG) have been gaining more attention. 1,5-AG is a chemically stable monosaccharide that exists in the human body. Its serum concentration remains stable when blood glucose levels are normal. However, it decreases when blood glucose exceeds the renal glucose threshold. Studies have shown that 1.5-AG reflects blood glucose changes in 1 to 2 weeks; therefore, decreased levels of serum 1,5-AG can serve as a clinical indicator of short-term blood glucose disturbances. Recent studies have shown that 1,5-AG can be used not only for the screening and managing of diabetes but also for predicting diabetes-related adverse events and islet β cell function in prediabetic patients. In addition, saliva 1,5-AG demonstrates potential value in the screening and diagnosis of diabetes. This review focuses on the biological characteristics, detection methods, and clinical application of 1,5-AG to promote understanding and applicable research of 1,5-AG in the future.

The Potential Use of Targeted Proteomics and Metabolomics for the Identification and Monitoring of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes mellitus and is associated with a significantly worse prognosis compared to diabetic patients without kidney involvement, other microvascular complications, or non-diabetic chronic kidney disease, due to its higher risk of cardiovascular events, faster progression to end-stage kidney disease, and increased mortality. In clinical practice, diagnosis is based on estimated glomerular filtration rate (eGFR) and albuminuria. However, given the limitations of these diagnostic markers, novel biomarkers must be identified. Omics is a new field of study involving the comprehensive analysis of various types of biological data at the molecular level. In different fields, they have shown promising results in (early) detection of diseases, personalized medicine, therapeutic monitoring, and understanding pathogenesis. DKD is primarily utilized in scientific research and has not yet been implemented in routine clinical practice. The aim of this review is to provide an overview of currently available data on targeted omics. After an extensive literature search, 25 different (panels of) omics were withheld and analyzed. Both serum/plasma and urine proteomics and metabolomics have been described with varying degrees of evidence. For all omics, there is still a relative paucity of data from large, prospective, longitudinal cohorts, presumably because of the heterogeneity of DKD and the lack of patient selection in studies, the complexity of omics technologies, and various practical and ethical considerations (e.g., limited accessibility, cost, and privacy concerns).

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.

Longitudinal Plasma Metabolome Patterns and Relation to Kidney Function and Proteinuria in Pediatric CKD

Key Points

Longitudinal untargeted metabolomics. Children with CKD have a circulating metabolome that changes over time.

Background

Understanding plasma metabolome patterns in relation to changing kidney function in pediatric CKD is important for continued research for identifying novel biomarkers, characterizing biochemical pathophysiology, and developing targeted interventions. There are a limited number of studies of longitudinal metabolomics and virtually none in pediatric CKD.

Methods

The CKD in Children study is a multi-institutional, prospective cohort that enrolled children aged 6 months to 16 years with eGFR 30–90 ml/min per 1.73 m2. Untargeted metabolomics profiling was performed on plasma samples from the baseline, 2-, and 4-year study visits. There were technologic updates in the metabolomic profiling platform used between the baseline and follow-up assays. Statistical approaches were adopted to avoid direct comparison of baseline and follow-up measurements. To identify metabolite associations with eGFR or urine protein-creatinine ratio (UPCR) among all three time points, we applied linear mixed-effects (LME) models. To identify metabolites associated with time, we applied LME models to the 2- and 4-year follow-up data. We applied linear regression analysis to examine associations between change in metabolite level over time (∆level) and change in eGFR (∆eGFR) and UPCR (∆UPCR). We reported significance on the basis of both the false discovery rate (FDR) <0.05 and P < 0.05.

Results

There were 1156 person-visits (N : baseline=626, 2-year=254, 4-year=276) included. There were 622 metabolites with standardized measurements at all three time points. In LME modeling, 406 and 343 metabolites associated with eGFR and UPCR at FDR <0.05, respectively. Among 530 follow-up person-visits, 158 metabolites showed differences over time at FDR <0.05. For participants with complete data at both follow-up visits (n =123), we report 35 metabolites with ∆level–∆eGFR associations significant at FDR <0.05. There were no metabolites with significant ∆level–∆UPCR associations at FDR <0.05. We report 16 metabolites with ∆level–∆UPCR associations at P < 0.05 and associations with UPCR in LME modeling at FDR <0.05.

Conclusions

We characterized longitudinal plasma metabolomic patterns associated with eGFR and UPCR in a large pediatric CKD population. Many of these metabolite signals have been associated with CKD progression, etiology, and proteinuria in previous CKD Biomarkers Consortium studies. There were also novel metabolite associations with eGFR and proteinuria detected.

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Angiotensin-converting-enzyme inhibitors and angiotensin receptor blockers for preventing the progression of diabetic kidney disease

BACKGROUND

Guidelines suggest that adults with diabetes and kidney disease receive treatment with angiotensin-converting-enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARB). This is an update of a Cochrane review published in 2006.

OBJECTIVES

We compared the efficacy and safety of ACEi and ARB therapy (either as monotherapy or in combination) on cardiovascular and kidney outcomes in adults with diabetes and kidney disease.

SEARCH METHODS

We searched the Cochrane Kidney and Transplants Register of Studies to 17 March 2024 through contact with the Information Specialist using search terms relevant to this review. Studies in the Register are identified through searches of CENTRAL, MEDLINE, and EMBASE, conference proceedings, the International Clinical Trials Registry Platform (ICTRP) Search Portal, and ClinicalTrials.gov.

SELECTION CRITERIA

We included studies evaluating ACEi or ARB alone or in combination, compared to each other, placebo or no treatment in people with diabetes and kidney disease.

DATA COLLECTION AND ANALYSIS

Two authors independently assessed the risk of bias and extracted data. Summary estimates of effect were obtained using a random-effects model, and results were expressed as risk ratios (RR) and their 95% confidence intervals (CI) for dichotomous outcomes and mean difference (MD) or standardised mean difference (SMD) and 95% CI for continuous outcomes. Confidence in the evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.

MAIN RESULTS

One hundred and nine studies (28,341 randomised participants) were eligible for inclusion. Overall, the risk of bias was high. Compared to placebo or no treatment, ACEi may make little or no difference to all-cause death (24 studies, 7413 participants: RR 0.91, 95% CI 0.73 to 1.15; I2 = 23%; low certainty) and with similar withdrawals from treatment (7 studies, 5306 participants: RR 1.03, 95% CI 0.90 to 1.19; I2 = 0%; low certainty). ACEi may prevent kidney failure (8 studies, 6643 participants: RR 0.61, 95% CI 0.39 to 0.94; I2 = 0%; low certainty). Compared to placebo or no treatment, ARB may make little or no difference to all-cause death (11 studies, 4260 participants: RR 0.99, 95% CI 0.85 to 1.16; I2 = 0%; low certainty). ARB have uncertain effects on withdrawal from treatment (3 studies, 721 participants: RR 0.85, 95% CI 0.58 to 1.26; I2 = 2%; low certainty) and cardiovascular death (6 studies, 878 participants: RR 3.36, 95% CI 0.93 to 12.07; low certainty). ARB may prevent kidney failure (3 studies, 3227 participants: RR 0.82, 95% CI 0.72 to 0.94; I2 = 0%; low certainty), doubling of serum creatinine (SCr) (4 studies, 3280 participants: RR 0.84, 95% CI 0.72 to 0.97; I2 = 32%; low certainty), and the progression from microalbuminuria to macroalbuminuria (5 studies, 815 participants: RR 0.44, 95% CI 0.23 to 0.85; I2 = 74%; low certainty). Compared to ACEi, ARB had uncertain effects on all-cause death (15 studies, 1739 participants: RR 1.13, 95% CI 0.68 to 1.88; I2 = 0%; low certainty), withdrawal from treatment (6 studies, 612 participants: RR 0.91, 95% CI 0.65 to 1.28; I2 = 0%; low certainty), cardiovascular death (13 studies, 1606 participants: RR 1.15, 95% CI 0.45 to 2.98; I2 = 0%; low certainty), kidney failure (3 studies, 837 participants: RR 0.56, 95% CI 0.29 to 1.07; I2 = 0%; low certainty), and doubling of SCr (2 studies, 767 participants: RR 0.88, 95% CI 0.52 to 1.48; I2 = 0%; low certainty). Compared to ACEi plus ARB, ACEi alone has uncertain effects on all-cause death (6 studies, 1166 participants: RR 1.08, 95% CI 0.49 to 2.40; I2 = 20%; low certainty), withdrawal from treatment (2 studies, 172 participants: RR 0.78, 95% CI 0.33 to 1.86; I2 = 0%; low certainty), cardiovascular death (4 studies, 994 participants: RR 3.02, 95% CI 0.61 to 14.85; low certainty), kidney failure (3 studies, 880 participants: RR 1.36, 95% CI 0.79 to 2.32; I2 = 0%; low certainty), and doubling of SCr (2 studies, 813 participants: RR 1.14, 95% CI 0.70 to 1.85; I2 = 0%; low certainty). Compared to ACEi plus ARB, ARB alone has uncertain effects on all-cause death (7 studies, 2607 participants: RR 1.02, 95% CI 0.76 to 1.37; I2 = 0%; low certainty), withdrawn from treatment (3 studies, 1615 participants: RR 0.81, 95% CI 0.53 to 1.24; I2 = 0%; low certainty), cardiovascular death (4 studies, 992 participants: RR 3.03, 95% CI 0.62 to 14.93; low certainty), kidney failure (4 studies, 2321 participants: RR 1.15, 95% CI 0.67 to 1.95; I2 = 29%; low certainty), and doubling of SCr (3 studies, 2252 participants: RR 1.18, 95% CI 0.85 to 1.64; I2 = 0%; low certainty). Comparative effects of different ACEi or ARB and low-dose versus high-dose ARB were rarely evaluated. No study compared different doses of ACEi. Adverse events of ACEi and ARB were rarely reported.

AUTHORS' CONCLUSIONS

ACEi or ARB may make little or no difference to all-cause and cardiovascular death compared to placebo or no treatment in people with diabetes and kidney disease but may prevent kidney failure. ARB may prevent the doubling of SCr and the progression from microalbuminuria to macroalbuminuria compared with a placebo or no treatment. Despite the international guidelines suggesting not combining ACEi and ARB treatment, the effects of ACEi or ARB monotherapy compared to dual therapy have not been adequately assessed. The limited data availability and the low quality of the included studies prevented the assessment of the benefits and harms of ACEi or ARB in people with diabetes and kidney disease. Low and very low certainty evidence indicates that it is possible that further studies might provide different results.

Deliverables from Metabolomics in Kidney Disease: Adenine, New Insights, and Implication for Clinical Decision-Making

BACKGROUND

Chronic kidney disease (CKD) presents a persistent global health challenge, characterized by complex pathophysiology and diverse progression patterns. Metabolomics has emerged as a valuable tool in unraveling the intricate molecular mechanisms driving CKD progression.

SUMMARY

This comprehensive review provides a summary of recent progress in the field of metabolomics in kidney disease with a focus on spatial metabolomics to shed important insights to enhancing our understanding of CKD progression, emphasizing its transformative potential in early disease detection, refined risk assessment, and the development of targeted interventions to improve patient outcomes.

KEY MESSAGE

Through an extensive analysis of metabolic pathways and small-molecule fluctuations, bulk and spatial metabolomics offers unique insights spanning the entire spectrum of CKD, from early stages to advanced disease states. Recent advances in metabolomics technology have enabled spatial identification of biomarkers to provide breakthrough discoveries in predicting CKD trajectory and enabling personalized risk assessment. Furthermore, metabolomics can help decipher the complex molecular intricacies associated with kidney diseases for exciting novel therapeutic approaches. A recent example is the identification of adenine as a key marker of kidney fibrosis for diabetic kidney disease using both untargeted and targeted bulk and spatial metabolomics. The metabolomics studies were critical to identify a new biomarker for kidney failure and to guide new therapeutics for diabetic kidney disease. Similar approaches are being pursued for acute kidney injury and other kidney diseases to enhance precision medicine decision-making.

Serum metabolomics detected by LDI-TOF-MS can be used to distinguish between diabetic patients with and without diabetic kidney disease

Diabetic kidney disease (DKD) is an important cause of end-stage renal disease with changes in metabolic characteristics. The objective of this study was to study changes in serum metabolic characteristics in patients with DKD and to examine metabolite panels to distinguish DKD from diabetes with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). We recruited 40 type II diabetes mellitus (T2DM) patients with or without DKD from a single center for a cross-sectional study. Serum metabolic profiling was performed with MALDI-TOF-MS using a vertical silicon nanowire array. Differential metabolites between DKD and diabetes patients were selected, and their relevance to the clinical parameters of DKD was analyzed. We applied machine learning methods to the differential metabolite panels to distinguish DKD patients from diabetes patients. Twenty-four differential serum metabolites between DKD patients and diabetes patients were identified, which were mainly enriched in butyrate metabolism, TCA cycle, and alanine, aspartate, and glutamate metabolism. Among the metabolites, l-kynurenine was positively correlated with urinary microalbumin, urinary microalbumin/creatinine ratio (UACR), creatinine, and urea nitrogen content. l-Serine, pimelic acid, 5-methylfuran-2-carboxylic acid, 4-methylbenzaldehyde, and dihydrouracil were associated with the estimated glomerular filtration rate (eGFR). The panel of differential metabolites could be used to distinguish between DKD and diabetes patients with an AUC value reaching 0.9899-0.9949. Among the differential metabolites, l-kynurenine was related to the progression of DKD. The differential metabolites exhibited excellent performance at distinguishing between DKD and diabetes. This study provides a novel direction for metabolomics-based clinical detection of DKD.

Dispersion of Serum 1,5 Anhydroglucitol Values in patients with Type 2 Diabetes at goal of HbA1c

AIM

To investigate the relationship of 1,5 anhydroglucitol (1,5 AG) with HbA1c in patients with type 2 diabetes (T2D) with different ranges of glycemic control.

METHODS

One hundred outpatients with T2D ≥ 18 years old were studied. In addition, HbA1c, glycemia, 1,5 AG, lipids, albuminuria, estimated glomerular filtration rate, and clinical data were registered.

RESULTS

The patient's median age was 62.5 years, with a median of 10 years with T2D. Those with HbA1c <7 % had higher 1,5 AG than those with HbA1c ≥ 7 %, 16.8 ug/ml vs. 4.90 (p=0.00001).1,5 AG correlated inversely with HbA1c (r= -0.7910, p=0.00001), glycemia (r= -0.6307, p=0.00001), cholesterol (r= -0.2257, p= 0.0239), LDL-cholesterol (r= -0.2240 , p=0.0266), albuminuria (r= -0.3644, p=0.0002) and heart rate (r= -0.267 ,p=0.0072). Those on insulin therapy also had lower 1,5 AG (p=0.000). The scatter plot of 1,5 AG and HbA1c fitted a second-degree fractional polynomic regression model, with dispersion of 1 5 AG when HbA1c < 7.5%. An HbA1c ≥ 7.5 % predicted a 1,5 AG <10 ug/ml CONCLUSION: Dispersion of 1,5 AG values at HbA1c < 7.5 % indicates postprandial glucose excursions that may impair glucose control and increase the cardiovascular risk in these patients.

Circulating Metabolites Associated with Albuminuria in a Hispanic/Latino Population

BACKGROUND

Albuminuria is associated with metabolic abnormalities, but these relationships are not well understood. We studied the association of metabolites with albuminuria in Hispanic/Latino people, a population with high risk for metabolic disease.

METHODS

We used data from 3736 participants from the Hispanic Community Health Study/Study of Latinos, of which 16% had diabetes and 9% had an increased urine albumin-to-creatinine ratio (UACR). Metabolites were quantified in fasting serum through nontargeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Spot UACR was inverse normally transformed and tested for the association with each metabolite or combined, correlated metabolites, in covariate-adjusted models that accounted for the study design. In total, 132 metabolites were available for replication in the Hypertension Genetic Epidemiology Network study ( n =300), and 29 metabolites were available for replication in the Malmö Offspring Study ( n =999).

RESULTS

Among 640 named metabolites, we identified 148 metabolites significantly associated with UACR, including 18 novel associations that replicated in independent samples. These metabolites showed enrichment for D-glutamine and D-glutamate metabolism and arginine biosynthesis, pathways previously reported for diabetes and insulin resistance. In correlated metabolite analyses, we identified two modules significantly associated with UACR, including a module composed of lipid metabolites related to the biosynthesis of unsaturated fatty acids and alpha linolenic acid and linoleic acid metabolism.

CONCLUSIONS

Our study identified associations of albuminuria with metabolites involved in glucose dysregulation, and essential fatty acids and precursors of arachidonic acid in Hispanic/Latino population.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_02_08_CJN09070822.mp3.

Revealing the Pathogenesis of Salt-Sensitive Hypertension in Dahl Salt-Sensitive Rats through Integrated Multi-Omics Analysis

Salt-induced renal metabolism dysfunction is an important mechanism of salt-sensitive hypertension. Given that the gut-liver axis is the first hit of a high-salt diet (HSD), we aimed to identify the extra-renal mechanism from hepatic metabolism and gut microbiota, and attempted to relieve the salt-induced metabolic dysfunctions by curcumin. Untargeted metabolomics analysis was performed to identify the changes in hepatic metabolic pathways, and integrated analysis was employed to reveal the relationship between hepatic metabolic dysfunction and gut microbial composition. HSD induced significant increase in fumaric acid, l-lactic acid, creatinine, l-alanine, glycine, and l-cysteine levels, and amino acids metabolism pathways associated with glycolysis were significantly altered, including alanine, aspartate, and glutamate metabolism; glycine, serine, and threonine metabolism, which were involved in the regulation of blood pressure. Integrated multi-omics analysis revealed that changes in Paraprevotella, Erysipelotrichaceae, and genera from Clostridiales are associated with metabolic disorders. Gene functional predication analysis based on 16S Ribosomal RNA sequences showed that the dysfunction in hepatic metabolism were correlated with enhanced lipopolysaccharide (LPS) biosynthesis and apoptosis in gut microbes. Curcumin (50 mg/kg/d) might reduce gut microbes-associated LPS biosynthesis and apoptosis, partially reverse metabolic dysfunction, ameliorate renal oxidative stress, and protect against salt-sensitive hypertension.

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.

Metabolomics-driven of relationships among kidney, bone marrow and bone of rats with postmenopausal osteoporosis

As a global public health problem, postmenopausal osteoporosis (PMOP) poses a great threat to old women's health. Bone is the target organ of PMOP, and the dynamic changes of bone marrow could affect the bone status. Kidney is the main organ regulating calcium and phosphorus homeostasis. Kidney, bone marrow and bone play crucial roles in PMOP, but the relationships of the three tissues in the disease have not been completely described. Here, metabolomics was employed to investigate the disease mechanism of PMOP from the perspectives of kidney, bone marrow and bone, and the relationships among the three tissues were also discussed. Six-month-old female Sprague-Dawley (SD) rats were randomly divided into ovariectomized (OVX) group (with bilateral ovariectomy) and sham group (with sham surgery). 13 weeks after surgery, gas chromatography-mass spectrometry (GC-MS) was performed to analyze the metabolic profiling of two groups. Multivariate statistical analysis revealed that the number of differential metabolites in kidney, bone marrow and bone between the two groups were 37, 16 and 17, respectively. The common differential metabolites of the three tissues were N-methyl-L-alanine. Kidney and bone marrow had common differential metabolites, including N-methyl-L-alanine, 2-hydroxybutyric acid, (R)-3-hydroxybutyric acid (β-hydroxybutyric acid, βHBA), urea and dodecanoic acid. There were three common differential metabolites between kidney and bone, including N-methyl-L-alanine, α-tocopherol and isofucostanol. The common differential metabolite of bone marrow and bone was N-methyl-L-alanine. Some common metabolic pathways were disturbed in multiple tissues of OVX rats, such as glycine, serine and threonine metabolism, purine metabolism, tryptophan metabolism, ubiquinone and other terpenoid-quinone biosynthesis and fatty acid biosynthesis. In conclusion, our study demonstrated that profound metabolic changes have taken place in the kidney, bone marrow and bone, involving common differential metabolites and metabolic pathways. The evaluation of differential metabolites strengthened the understanding of the kidney-bone axis and the metabolic relationships among the three tissues of OVX rats.

Multi Platforms Strategies and Metabolomics Approaches for the Investigation of Comprehensive Metabolite Profile in Dogs with Babesia canis Infection

Canine babesiosis is an important tick-borne disease worldwide, caused by parasites of the Babesia genus. Although the disease process primarily affects erythrocytes, it may also have multisystemic consequences. The goal of this study was to explore and characterize the serum metabolome, by identifying potential metabolites and metabolic pathways in dogs naturally infected with Babesia canis using liquid and gas chromatography coupled to mass spectrometry. The study included 12 dogs naturally infected with B. canis and 12 healthy dogs. By combining three different analytical platforms using untargeted and targeted approaches, 295 metabolites were detected. The untargeted ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) metabolomics approach identified 64 metabolites, the targeted UHPLC-MS/MS metabolomics approach identified 205 metabolites, and the GC-MS metabolomics approach identified 26 metabolites. Biological functions of differentially abundant metabolites indicate the involvement of various pathways in canine babesiosis including the following: glutathione metabolism; alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; cysteine and methionine metabolism; and phenylalanine, tyrosine, and tryptophan biosynthesis. This study confirmed that host–pathogen interactions could be studied by metabolomics to assess chemical changes in the host, such that the differences in serum metabolome between dogs with B. canis infection and healthy dogs can be detected with liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) methods. Our study provides novel insight into pathophysiological mechanisms of B. canis infection.

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.

Exosomes from mesenchymal stem cells expressing microribonucleic acid-125b inhibit the progression of diabetic nephropathy via the tumour necrosis factor receptor-associated factor 6/Akt axis

Diabetic nephropathy (DN) seriously threatens the health of patients with diabetes. Moreover, it has been reported that mesenchymal stem cell (MSC)-derived exosomal miRNAs can modulate the progression of multiple diseases, including DN. It has been suggested that miR-125b is involved in DN. However, the biological functions of exosomal miRNAs, especially miR-125b, in DN are still unclear. To establish a DN model in vitro, we used a model of human embryonic kidney epithelial cells (HKCs) injury induced by high glucose (HG). Then, miR-125b was delivered to the model cells in vitro via MSC-derived exosomes (MSC-Exos), and the effect of exosomal miR-125b on HKCs apoptosis was evaluated by flow cytometry. qRT-PCR or western blotting was performed to measure miR-125b or tumour necrosis factor receptor-associated factor 6 (TRAF6) expression in HKC. The effect of MSC-Exos on HKCs apoptosis after miR-125b knockdown was determined by flow cytometry. Moreover, dual-luciferase reporter assays were used to determine the targeting relationship between miR-125b and TRAF6 in HKCs. Our data revealed that MSC-Exos increased HG-induced autophagy in HKCs and reversed HKCs apoptosis. Moreover, our study found that miR-125b was enriched in MSC-Exos and directly targeted TRAF6 in HKCs. In addition, exosomally transferred miR-125b inhibited the apoptosis of HG-treated HKCs by mediating Akt signalling. In summary, MSC-derived exosomal miR-125b induced autophagy and inhibited apoptosis in HG-treated HKCs via the downregulation of TRAF6. Therefore, our study provided a new idea for DN treatment.

A review of applications of metabolomics in osteoarthritis

Osteoarthritis (OA) represents the most prevalent and disabling arthritis worldwide due to its heterogeneous and progressive articular degradation. However, effective and timely diagnosis and fundamental treatment for this disorder are lacking. Metabolomics, a growing field in life science research in recent years, has the potential to detect many metabolites and thus explains the underlying pathophysiological processes. Hence, new specific metabolic markers and related metabolic pathways can be identified for OA. In this review, we aimed to provide an overview of studies related to the metabolomics of OA in animal models and humans to describe the metabolic changes and related pathways for OA. The present metabolomics studies reveal that the pathogenesis of OA may be significantly related to perturbations of amino acid metabolism. These altered amino acids (e.g., branched-chain amino acids, arginine, and alanine), as well as phospholipids, were identified as potential biomarkers to distinguish patients with OA from healthy individuals.

New Potential Biomarkers for Chronic Kidney Disease Management-A Review of the Literature

The prevalence of chronic kidney disease (CKD) is increasing worldwide, and the mortality rate continues to be unacceptably high. The biomarkers currently used in clinical practice are considered relevant when there is already significant renal impairment compromising the early use of potentially successful therapeutic interventions. More sensitive and specific biomarkers to detect CKD earlier on and improve patients' prognoses are an important unmet medical need. The aim of this review is to summarize the recent literature on new promising early CKD biomarkers of renal function, tubular lesions, endothelial dysfunction and inflammation, and on the auspicious findings from metabolomic studies in this field. Most of the studied biomarkers require further validation in large studies and in a broad range of populations in order to be implemented into routine CKD management. A panel of biomarkers, including earlier biomarkers of renal damage, seems to be a reasonable approach to be applied in clinical practice to allow earlier diagnosis and better disease characterization based on the underlying etiologic process.

Integrative transcriptomics and metabolomics analyses provide hepatotoxicity mechanisms of asarum

Asarum is frequently applied in combination with other agents for prescriptions in practices of Traditional Chinese Medicine. A number of studies have previously indicated that asarum treatment induces lung toxicity by triggering inflammation. However, the potential effects of asarum in the liver and the underlying mechanisms have remained largely elusive. Therefore, transcriptomics and metabolomics approaches were used in the present study to examine the mechanisms of the hepatotoxicity of asarum. Specifically, mRNA and metabolites were obtained from rat liver samples following intragastric administration of asarum powder. RNA sequencing analysis was subsequently performed to screen for differentially expressed genes (DEGs), and a total of 434 DEGs were identified in liver tissue samples, 214 of which were upregulated and 220 were downregulated. Pathway enrichment analysis found that these genes were particularly enriched in processes including the regulation of p53 signaling, metabolic pathways and bile secretion. To investigate potential changes to the metabolic profile as a result of asarum treatment, a metabolomics analysis was performed, which detected 14 significantly altered metabolites in rat liver samples by gas chromatography-mass spectrometry. These metabolites were predominantly members of the taurine, hypotaurine and amino acid metabolic pathways. Metscape network analyses were subsequently performed to integrate the transcriptomics and metabolomics data. Integrative analyis revealed that the DEGs and metabolites were primarily associated with bile acid biosynthesis, amino acid metabolism and the p53 signaling pathway. Taken together, these results provide novel insight into the mechanism of asarum-mediated hepatotoxicity, which may potentially aid the clinical diagnosis and future therapeutic intervention of asarum poisoning.

Progress in the Treatment of Diabetes Mellitus Based on Intestinal Flora Homeostasis and the Advancement of Holistic Analysis Methods

Diabetes mellitus (DM) is a complex metabolic disorder characterized by abnormal glucose metabolism, which is accompanied by alterations in energy metabolism, intestinal bacterial metabolism, amino acid metabolism, lipid metabolism, nucleotide metabolism, and others. However, intestinal flora metabolism plays a fundamental role in host metabolism; they are complementary to each other and help maintain homeostasis, thus ensuring the normal operation of the host metabolic system. This suggests that a holistic analysis method would be of great use in the study of the overall metabolism in patients with DM. With this in mind, this review summarizes the mechanism of intestinal flora metabolism regarding the occurrence of DM and assesses the effects of drug treatments on the intestinal flora of patients with diabetes. Based on these results, we combined intestinal flora metabolism with host metabolism to evaluate the necessity and the advantages of holistic metabonomics analyses in the treatment of DM and its complications.

Targeted Clinical Metabolite Profiling Platform for the Stratification of Diabetic Patients

Several small molecule biomarkers have been reported in the literature for prediction and diagnosis of (pre)diabetes, its co-morbidities, and complications. Here, we report the development and validation of a novel, quantitative method for the determination of a selected panel of 34 metabolite biomarkers from human plasma. We selected a panel of metabolites indicative of various clinically-relevant pathogenic stages of diabetes. We combined these candidate biomarkers into a single ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method and optimized it, prioritizing simplicity of sample preparation and time needed for analysis, enabling high-throughput analysis in clinical laboratory settings. We validated the method in terms of limits of detection (LOD) and quantitation (LOQ), linearity (R²), and intra- and inter-day repeatability of each metabolite. The method’s performance was demonstrated in the analysis of selected samples from a diabetes cohort study. Metabolite levels were associated with clinical measurements and kidney complications in type 1 diabetes (T1D) patients. Specifically, both amino acids and amino acid-related analytes, as well as specific bile acids, were associated with macro-albuminuria. Additionally, specific bile acids were associated with glycemic control, anti-hypertensive medication, statin medication, and clinical lipid measurements. The developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.

Chronic kidney disease: Biomarker diagnosis to therapeutic targets

Chronic kidney disease (CKD), characterized as renal dysfunction, is recognized as a major public health problem with high morbidity and mortality worldwide. Unfortunately, there are no obvious clinical symptoms in early stage disease until severe damage has occurred. Further complicating early diagnosis and treatment is the lack of sensitive and specific biomarkers. As such, novel biomarkers are urgently needed. Metabolomics has shown an increasing potential for identifying underlying disease mechanisms, facilitating clinical diagnosis and developing pharmaceutical treatments for CKD. Recent advances in metabolomics revealed that CKD was closely associated with the dysregulation of numerous metabolites, such as amino acids, lipids, nucleotides and glycoses, that might be exploited as potential biomarkers. In this review, we summarize recent metabolomic applications based on animal model studies and in patients with CKD and highlight several biomarkers that may play important roles in diagnosis, intervention and development of new therapeutic strategies.

Effects of Potassium Fertilization on Oil Palm Fruit Metabolism and Mesocarp Lipid Accumulation

Potassium fertilization is commonly practiced in oil palm (Elaeis guineensis) plantations to increase yield. However, its effects on fruit oil content and composition are not well documented. Here, we conducted bunch, metabolomics, and oil composition analyses in two contrasting crosses (Deli × La Mé and Deli × Yangambi) grown under different K fertilization conditions. K availability impacted bunch oil content, resulting in lower water content and higher oil proportion in fruit mesocarp, in Deli × La Mé only, thus showing differential responses of crosses to K. Oil composition at maturity did not significantly change under low K conditions despite clear alterations in fruit metabolism associated with lipid production during maturation, demonstrating the resilience of oil biosynthetic metabolism. However, the analysis of variance in oil content (across K treatments and crosses) demonstrates that sugar availability, lipid synthesis rates, and metabolic recycling are all important in determining the oil content.

An update of urine and blood metabolomics in chronic kidney disease

Chronic kidney disease is considered as a serious obstacle in global health, with increasing incidence and prevalence. In spite of numerous attempts by using recent omics technologies, specially metabolomics, for understanding pathophysiology, molecular mechanism and identification reliable consensus biomarkers for diagnosis and prognosis of this complex disease, the current biomarkers are still insensitive and many questions about its pathomechanism are still to be unanswered. This review is focused on recent findings about urine and serum/plasma metabolite biomarkers and changes in the pathways that occurs in the disease conditions. The urine and blood metabolome content in the normal and disease state is investigated based on the current metabolomics studies and well known metabolite candidate biomarkers for chronic kidney disease are discussed.

Metabolomics biomarkers and the risk of overall mortality and ESRD in CKD: Results from the Progredir Cohort

INTRODUCTION

Studies on metabolomics and CKD have primarily addressed CKD incidence defined as a decline on eGFR or appearance of albuminuria in the general population, with very few evaluating hard outcomes. In the present study, we investigated the association between metabolites and mortality and ESRD in a CKD cohort.

SETTING AND METHODS

Data on 454 participants of the Progredir Cohort Study, Sao Paulo, Brazil were used. Metabolomics was performed by GC-MS (Agilent MassHunter) and metabolites were identified using Agilent Fiehn GC/MS and NIST libraries. After excluding metabolites present in <50% of participants, 293 metabolites were analyzed. An FDR q value <0.05 criteria was applied in Cox models on the composite outcome (mortality or incident renal replacement therapy) adjusted for batch effect, resulting in 34 metabolites associated with the outcome. Multivariable-adjusted Cox models were then built for the composite outcome, death, and ESRD incident events. Competing risk analysis was also performed for ESRD.

RESULTS

Mean age was 68±12y, mean eGFR-CKDEPI was 38.4±14.6 ml/min/1.73m2 and 57% were diabetic. After adjustments (GC-MS batch, sex, age, DM and eGFR), 18 metabolites remained significantly associated with the composite outcome. Nine metabolites were independently associated with death: D-malic acid (HR 1.84, 95%CI 1.32-2.56, p = 0.0003), acetohydroxamic acid (HR 1.90, 95%CI 1.30-2.78, p = 0.0008), butanoic acid (HR 1.59, 95%CI 1.17-2.15, p = 0.003), and docosahexaenoic acid (HR 0.58, 95%CI 0.39-0.88, p = 0.009), among the top associations. Lactose (SHR 1.49, 95%CI 1.04-2.12, p = 0.03), 2-O-glycerol-α-D-galactopyranoside (SHR 1.76, 95%CI 1.06-2.92, p = 0.03), and tyrosine (SHR 0.52, 95%CI 0.31-0.88, p = 0.02) were associated to ESRD risk, while D-threitol, mannitol and myo-inositol presented strong borderline associations.

CONCLUSION

Our results identify specific metabolites related to hard outcomes in a CKD population. These findings point to the need of further exploration of these metabolites as biomarkers in CKD and the understanding of the underlying biological mechanisms related to the observed associations.

Metabolomics biotechnology, applications, and future trends: a systematic review

Given the highly increased incidence of human diseases, a better understanding of the related mechanisms regarding endogenous metabolism is urgently needed. Mass spectrometry-based metabolomics has been used in a variety of disease research areas. However, the deep research of metabolites remains a difficult and lengthy process. Fortunately, mass spectrometry is considered to be a universal tool with high specificity and sensitivity and is widely used around the world. Mass spectrometry technology has been applied to various basic disciplines, providing technical support for the discovery and identification of endogenous substances in living organisms. The combination of metabolomics and mass spectrometry is of great significance for the discovery and identification of metabolite biomarkers. The mass spectrometry tool could further improve and develop the exploratory research of the life sciences. This mini review discusses metabolomics biotechnology with a focus on recent applications of metabolomics as a powerful tool to elucidate metabolic disturbances and the related mechanisms of diseases.

Given the highly increased incidence of human diseases, a better understanding of the related mechanisms regarding endogenous metabolism is urgently needed.