Optimization of kidney dysfunction prediction in diabetic kidney disease using 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.

Deficiency of thiosulfate sulfurtransferase mediates the dysfunction of renal tubular mitochondrial fatty acid oxidation in diabetic kidney disease

One of the main characteristics of diabetic kidney disease (DKD) is abnormal renal tubular fatty acid metabolism, especially defective fatty acid oxidation (FAO), accelerating tubular injury and tubulointerstitial fibrosis. Thiosulfate sulfurtransferase (TST), a mitochondrial enzyme essential for sulfur transfer, is reduced in metabolic diseases like diabetes and obesity. However, the potential role of TST in regulating fatty acid metabolic abnormalities in DKD remains unclear. Here, our data revealed decreased TST expression in the renal cortex of DKD patients. TST deficiency exacerbated tubular impairment in both diabetic and renal fibrosis mouse models, while sodium thiosulfate treatment or TST overexpression mitigated renal tubular injury with high-glucose exposure. TST downregulation mediated the decrease in S-sulfhydration of very long-chain specific acyl-CoA dehydrogenase, resulting in mitochondrial FAO dysfunction. This sequence of events exacerbates the progression of tubulointerstitial injury in DKD. Together, our findings demonstrate TST as a regulator of renal tubular injury in DKD.

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.

High levels of high-density lipoprotein cholesterol may increase the risk of diabetic kidney disease in patients with type 2 diabetes

Studies have indicated that low high-density lipoprotein cholesterol (HDL-C) level is an important risk factor for diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D). However, whether higher HDL-C levels decrease the risk of developing DKD remains unclear. This study aimed to clarify the relationship between HDL-C levels and DKD risk in individuals with T2D in China. In total, 936 patients with T2D were divided into DKD and non-DKD groups. The association between HDL-C levels and DKD risk was evaluated using logistic regression analysis and restricted cubic spline curves adjusted for potential confounders. Threshold effect analysis of HDL-C for DKD risk was also performed. Higher HDL-C levels did not consistently decrease the DKD risk. Furthermore, a nonlinear association with threshold interval effects between HDL-C levels and the incidence of DKD was observed. Patients with HDL-C ≤ 0.94 mmol/L or HDL-C > 1.54 mmol/L had significantly higher DKD risk after adjusting for confounding factors. Interestingly, the association between high HDL-C levels and increased DKD risk was more significant in women. A U-shaped association between HDL-C levels and DKD risk was observed; therefore, low and high HDL-C levels may increase the DKD risk in patients with T2D.

Metabolite Biomarkers for Early Ischemic-Hypoxic Encephalopathy: An Experimental Study Using the NeoBase 2 MSMS Kit in a Rat Model

Hypoxic-ischemic encephalopathy (HIE) is one of the most common causes of childhood disability. Hypothermic therapy is currently the only approved neuroprotective approach. However, early diagnosis of HIE can be challenging, especially in the first hours after birth when the decision to use hypothermic therapy is critical. Distinguishing HIE from other neonatal conditions, such as sepsis, becomes a significant problem in diagnosis. This study explored the utility of a metabolomic-based approach employing the NeoBase 2 MSMS kit to diagnose HIE using dry blood stains in a Rice-Vannucci model of HIE in rats. We evaluated the diagnostic fidelity of this approach in a range between 3 and 6 h after the onset of HIE, including in the context of systemic inflammation and concomitant hypothermic therapy. Discriminant analysis revealed several metabolite patterns associated with HIE. A logistic regression model using glycine levels achieved high diagnostic fidelity with areas under the receiver operating characteristic curve of 0.94 at 3 h and 0.96 at 6 h after the onset of HIE. In addition, orthogonal partial least squares discriminant analysis, which included five metabolites, achieved 100% sensitivity and 80% specificity within 3 h of HIE. These results highlight the significant potential of the NeoBase 2 MSMS kit for the early diagnosis of HIE and could improve patient management and outcomes in this serious illness.

The association and prediction value of acylcarnitine on diabetic nephropathy in Chinese patients with type 2 diabetes mellitus

BACKGROUND

Acylcarnitines play a role in type 2 diabetes mellitus (T2DM), but the relationship between acylcarnitine and diabetic nephropathy was unclear. We aimed to explore the association of acylcarnitine metabolites with diabetic nephropathy and estimate the predictive value of acylcarnitine for diabetic nephropathy.

METHODS

A total of 1032 (mean age: 57.24 ± 13.82) T2DM participants were derived from Liaoning Medical University First Affiliated Hospital. Mass Spectrometry was utilized to measure levels of 25 acylcarnitine metabolites in fasting plasma. Diabetic nephropathy was ascertained based on the medical records. Factor analysis was used to reduce the dimensions and extract factors of the 25 acylcarnitine metabolites. Logistic regression was used to estimate the relationship between factors extracted from the 25 acylcarnitine metabolites and diabetic nephropathy. Receiver operating characteristic curves were used to test the predictive values of acylcarnitine factors for diabetic nephropathy.

RESULTS

Among all T2DM participants, 138 (13.37%) patients had diabetic nephropathy. Six factors were extracted from 25 acylcarnitines, which account for 69.42% of the total variance. In multi-adjusted logistic regression models, the odds ratio (OR, 95% confidence interval [CI]) of diabetic nephropathy on factor 1 (including butyrylcarnitine/glutaryl-carnitine/hexanoylcarnitine/octanoylcarnitine/decanoylcarnitine/lauroylcarnitine/tetradecenoylcarnitine), factor 2 (including propionylcarnitine/palmitoylcarnitine/hydroxypalmitoleyl-carnitine/octadecanoylcarnitine/arachidiccarnitine), and factor 3 (including tetradecanoyldiacylcarnitine/behenic carnitine/tetracosanoic carnitine/hexacosanoic carnitine) were 1.33 (95%CI 1.12-1.58), 0.76 (95%CI 0.62-0.93), and 1.24 (95%CI 1.05-1.47), respectively. The area under the curve for diabetic nephropathy prediction was significantly increased after the complement of factors 1, 2, and 3 in traditional factors model (P < 0.01).

CONCLUSIONS

Some plasma acylcarnitine metabolites extracted in factors 1 and 3 were higher in diabetic nephropathy, while factor 2 was lower in diabetic nephropathy among T2DM patients. The addition of acylcarnitine to traditional factors model improved the predictive value for diabetic nephropathy.

Metabolite Profiling of the Gut–Renal–Cerebral Axis Reveals a Particular Pattern in Early Diabetic Kidney Disease in T2DM Patients

Type 2 diabetes mellitus (T2DM) represents an important microvascular disease concerning the kidney and the brain. Gut dysbiosis and microbiota-derived metabolites may be in relation with early pathophysiological changes in diabetic kidney disease (DKD). The aim of the study was to find new potential gut-derived biomarkers involved in the pathogenesis of early DKD, with a focus on the complex interconnection of these biomarkers with podocyte injury, proximal tubule dysfunction, renal and cerebrovascular endothelial dysfunction. The study design consisted of metabolite profiling of serum and urine of 90 T2DM patients (subgroups P1-normoalbuminuria, P2-microalbuminuria, P3-macroalbuminuria) and 20 healthy controls (group C), based on ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry analysis (UHPLC-QTOF-ESI+-MS). By multivariate and univariate analyses of serum and urine, which included Partial Least Squares Discriminant Analysis (PLSDA), Variable Importance Plots (VIP), Random Forest scores, One Way ANOVA and Biomarker analysis, there were discovered metabolites belonging to nitrogen metabolic pathway and retinoic acid signaling pathway which differentiate P1 group from P2, P3, C groups. Tyrosine, phenylalanine, indoxyl sulfate, serotonin sulfate, and all-trans retinoic acid express the metabolic fingerprint of P1 group vs. P2, P3, C groups, revealing a particular pattern in early DKD in T2DM patients.

Potential Plasma Metabolite Biomarkers of Diabetic Nephropathy: Untargeted Metabolomics Study

Diabetic nephropathy (DN) is one of the specific complications of diabetes mellitus and one of the leading kidney-related disorders, often requiring renal replacement therapy. Currently, the tests commonly used for the diagnosis of DN, albuminuria (AU) and glomerular filtration rate (GFR), have limited sensitivity and specificity and can usually be noted when typical morphological changes in the kidney have already been manifested. That is why the extreme urgency of the problem of early diagnosis of this disease exists. The untargeted metabolomics analysis of blood plasma samples from 80 patients with type 1 diabetes and early and late stages of DN according to GFR was performed using direct injection mass spectrometry and bioinformatics analysis for diagnosing signatures construction. Among the dysregulated metabolites, combinations of 15 compounds, including amino acids and derivatives, monosaccharides, organic acids, and uremic toxins were selected for signatures for DN diagnosis. The selected metabolite combinations have shown high performance for diagnosing of DN, especially for the late stage (up to 99%). Despite the metabolite signature determined for the early stage of DN being characterized by a diagnostic performance of 81%, these metabolites as potential biomarkers might be useful in the evaluation of treatment of the disease, especially at early stages that may reduce the risk of kidney failure development.

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.

Kidney and plasma metabolomics provide insights into the molecular mechanisms of urate nephropathy in a mouse model of hyperuricemia

Hyperuricemia (HUA) is closely associated with kidney damage and kidney diseases in humans; however, the underlying mechanisms of HUA-induced kidney diseases remain unknown. In the present study, we examined the kidney and plasma metabolic profiles in a HUA mouse model constructed by knocking out (Ko) the urate oxidase (Uox) gene. The Uox-Ko mice were characterized by an increase in uric acid, glycine, 3'-adenosine monophosphate, citrate, N-acetyl-l-glutamate, l-kynurenine, 5-hydroxyindoleacetate, xanthurenic acid, cortisol, and (-)-prostaglandin e2 together with a decrease of inosine in the kidneys. These altered metabolites confirmed disturbances of purine metabolism, amino acid biosynthesis, tryptophan metabolism, and neuroactive ligand-receptor interaction in Uox-Ko mice. Betaine and biotin were related to kidney function and identified as the potential plasma metabolic biomarker for predicting urate nephropathy (UN). Taken together, these results revealed the underlying pathogenic mechanisms of UN. Investigating these pathways might provide novel targets for the therapeutic intervention of UN and can potentially lead to new treatment strategies.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

Acylcarnitines: Can They Be Biomarkers of Diabetic Nephropathy?

Diabetic nephropathy (DN), one of the most serious microvascular complications of diabetes mellitus (DM), may progress to end-stage renal disease (ESRD). Current biochemical biomarkers, such as urinary albumin excretion rate (UAER), have limitations for early screening and monitoring of DN. Recent studies have identified some metabolites as candidate biomarkers for early detection of DN. In this review, we summarize the role of dysregulated acylcarnitines (AcylCNs) in DN pathophysiology. Lower abundance of short- and medium-chain AcylCNs and higher long-chain AcylCNs often occurred in DM with normal albuminuria and microalbuminuria, compared with advanced stages of DN. The increase of long-chain AcylCNs was supposed to be an adaptive compensation in fat acids (FAs) oxidation in the early stage of DN. Conversely, the decrease of long-chain AcylCNs was due to incomplete oxidation of FAs in advanced stage of DN. Thus, AcylCNs may serve as sensitive biomarkers in predicting the risk of DN.

Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease

Dyslipidaemia is a hallmark of chronic kidney disease (CKD). The severity of dyslipidaemia not only correlates with CKD stage but is also associated with CKD-associated cardiovascular disease and mortality. Understanding how lipids are dysregulated in CKD is, however, challenging owing to the incredible diversity of lipid structures. CKD-associated dyslipidaemia occurs as a consequence of complex interactions between genetic, environmental and kidney-specific factors, which to understand, requires an appreciation of perturbations in the underlying network of genes, proteins and lipids. Modern lipidomic technologies attempt to systematically identify and quantify lipid species from biological systems. The rapid development of a variety of analytical platforms based on mass spectrometry has enabled the identification of complex lipids at great precision and depth. Insights from lipidomics studies to date suggest that the overall architecture of free fatty acid partitioning between fatty acid oxidation and complex lipid fatty acid composition is an important driver of CKD progression. Available evidence suggests that CKD progression is associated with metabolic inflexibility, reflecting a diminished capacity to utilize free fatty acids through β-oxidation, and resulting in the diversion of accumulating fatty acids to complex lipids such as triglycerides. This effect is reversed with interventions that improve kidney health, suggesting that targeting of lipid abnormalities could be beneficial in preventing CKD progression.

NADH/NAD+ Redox Imbalance and Diabetic Kidney Disease

Diabetic kidney disease (DKD) is a common and severe complication of diabetes mellitus. If left untreated, DKD can advance to end stage renal disease that requires either dialysis or kidney replacement. While numerous mechanisms underlie the pathogenesis of DKD, oxidative stress driven by NADH/NAD⁺ redox imbalance and mitochondrial dysfunction have been thought to be the major pathophysiological mechanism of DKD. In this review, the pathways that increase NADH generation and those that decrease NAD⁺ levels are overviewed. This is followed by discussion of the consequences of NADH/NAD⁺ redox imbalance including disruption of mitochondrial homeostasis and function. Approaches that can be applied to counteract DKD are then discussed, which include mitochondria-targeted antioxidants and mimetics of superoxide dismutase, caloric restriction, plant/herbal extracts or their isolated compounds. Finally, the review ends by pointing out that future studies are needed to dissect the role of each pathway involved in NADH-NAD⁺ metabolism so that novel strategies to restore NADH/NAD⁺ redox balance in the diabetic kidney could be designed to combat DKD.

Up-regulation of MMP-2 by histone H3K9 β-hydroxybutyrylation to antagonize glomerulosclerosis in diabetic rat

AIMS

Besides energy supply, β-hydroxybutyrate (BHB) acts as a bioactive molecule to play multiple protective roles, even in diabetes and its complications. The aim of this study was to investigate the antagonizing effects of BHB against diabetic glomerulosclerosis and the underlying mechanism.

METHODS

Male Sprague-Dawley rats were intraperitoneally injected with streptozotocin to induce diabetes and then treated with different concentrations of β-hydroxybutyrate. After 10 weeks, body weight, blood glucose, serum creatinine and 24-h urine protein were examined. Glomerular morphological changes and the contents of collagen type IV (COL IV) were evaluated. Then, transforming growth factor (TGF)-β/Smad3 contents and matrix metalloproteinase-2 (MMP-2) generation were detected. Moreover, the total contents of trans-activating histone H3K9 β-hydroxybutyrylation (H3K9bhb) and the contents of H3K9bhb in the Mmp-2 promoter were measured.

RESULTS

It was firstly confirmed that BHB treatments reduced renal biochemical indicators and attenuated glomerular morphological changes of the diabetic rats, with COL IV content decreased in a concentration-dependent manner. Then, BHB treatments were found to up-regulate renal MMP-2 generation of the diabetic rats significantly, while not affecting the increased TGF-β/Smad3 contents. Furthermore, the contents of H3K9bhb in the Mmp-2 promoter were elevated significantly for the middle and high concentrations of BHB treatments, up-regulating MMP-2 generation.

CONCLUSION

BHB treatments could up-regulate MMP-2 generation via causing elevated H3K9bhb in its promoter to antagonize glomerulosclerosis in the diabetic rats.

Metabolomics of the diabetic nephropathy: behind the fingerprint of development and progression indicators

Current diagnostic methods are not very sensitive to detect the initial stages diabetic nephropathy of type 2. In this work, a review of metabolomic approximation studies for the identification of biomarkers of this disease with potential to differentiate between early stages, evaluate and direct treatment and help slow kidney damage. Using public (Pubmed and Google Scholar) and private (Scopus and Web of Knowledge) databases, a systematic search of the information published related to metabolomics of diabetic nephropathy in different biospecimens (urine, serum, plasma and blood) was made. Later, the MetaboAnalyst 4.0 software was used to identify the metabolic pathways associated with these metabolites. Groups of potential metabolites were identified for monitoring diabetic nephropathy with the available literature data. In the urine, oxide-3-hydroxyisovalerate, TMAO, aconite and citrate and hydroxypropionate derivatives are highlighted; meanwhile, in the serum: citrate, creatinine, arginine and its derivatives; and in the plasma: amino acids such as histidine, methionine and arginine has a potential contribution. Using MetaboAnalyst 4.0 the metabolic pathways related to these metabolites were related. The search for biomarkers to measure the progression of diabetic nephropathy, together with analytical strategies for their detection and quantification, are the starting point for designing new methods of clinical chemistry analysis. The association between the metabolic pathway dysfunction could be useful for the overall assessment of the treatment and clinical follow-up of this disease.

Diabetic kidney diseases revisited: A new perspective for a new era

BACKGROUND

Globally, diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. As the most common microvascular complication of diabetes, DKD is a thorny, clinical problem in terms of its diagnosis and management. Intensive glucose control in DKD could slow down but not significantly halt disease progression. Revisiting the tremendous advances that have occurred in the field would enhance recognition of DKD pathogenesis as well as improve our understanding of translational science in DKD in this new era.

SCOPE OF REVIEW

In this review, we summarize advances in the understanding of the local microenvironmental changes in diabetic kidneys and discuss the involvement of genetic and epigenetic factors in the pathogenesis of DKD. We also review DKD prevalence changes and analyze the challenges in optimizing the diagnostic approaches and management strategies for DKD in the clinic. As we enter the era of 'big data', we also explore the possibility of linking systems biology with translational medicine in DKD in the current healthcare system.

MAJOR CONCLUSION

Newer understanding of the structural changes of diabetic kidneys and mechanisms of DKD pathogenesis, as well as emergent research technologies will shed light on new methods of dealing with the existing clinical challenges of DKD.