Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease

Metabolomics Based Exploration of the Mechanism of Action of Tripterygium Glycosides in Diabetic Kidney Disease

Tripterygium glycosides (TGs), the primary active components of Tripterygium wilfordii, have demonstrated therapeutic efficacy in treating diabetic kidney disease (DKD). However, the precise mechanisms underlying their action remain elusive, limiting the full realization of their medicinal potential. This study employed serum metabolomics based on liquid chromatography-mass spectrometry (LC-MS) analysis to elucidate the mechanisms by which TGs combat DKD. We evaluated the protective effects of TGs on DKD following treatment. Serum samples were collected before and after treatment, and their metabolic profiles were analyzed using LC-MS. Our metabolomics analysis revealed that TGs significantly modulated the hedgehog signaling pathway, a key metabolic pathway implicated in DKD pathogenesis. This study represents the first comprehensive investigation of the metabolic pathways regulated by TGs in the context of DKD using a metabolomics approach. Our findings provide a robust theoretical foundation for the more effective utilization and potential combination therapies involving TGs in the management of DKD. These insights pave the way for further research and development of targeted therapeutic strategies for this challenging condition.

Predicting diabetic kidney disease with serum metabolomics and gut microbiota

This study aims to identify biomarkers for reliably predicting diabetic kidney disease (DKD), systematically characterize serum metabolites and gut microbiota in DKD patients, and investigate the correlation between differential serum metabolites and gut microbiota. From September 2021 to January 2023, 90 subjects were recruited: 30 with DKD, 30 with type 2 diabetes mellitus (T2DM), and 30 normal controls (NCs). Serum metabolites, including 180 different metabolites, were analyzed using untargeted metabolomics UPLC-MS/MS, and gut microbiota were assessed via 16S rRNA sequencing. Differential metabolites were identified through univariate (t-test or Mann-Whitney U-test, P < 0.05) and multivariate analyses (OPLS-DA model, VIP > 1, P < 0.05), followed by selection using the least absolute shrinkage and selection operator (LASSO). The selected overlapping serum metabolites, along with DKD-associated differential gut microbiota, were used to develop a logistic regression prediction model for DKD based on six markers. In the DKD group compared to the DM and NC groups, 39 and 60 differential serum metabolites were identified, respectively (VIP > 1, P < 0.01). Among these, 36 serum metabolites, including alpha-Hydroxyisobutyric acid, were significantly elevated in DKD patients compared to those with DM. Of these, 28 metabolites showed a negative correlation with estimated glomerular filtration rate (eGFR), while 29 showed a positive correlation with urine albumin creatinine ratio (UACR). Patients with DKD were further categorized into subgroups (DKD middle and DKD early) based on eGFR (eGFR < 90 ml/min/1.73m2, eGFR ≥ 90 ml/min/1.73m2), revealing 23 differential metabolites. Dysbiosis of the gut microbiota was evident in DKD patients, with lower relative abundances of g_Prevotella and g_Faecalibacterium compared to the DM and NC groups. Subgroup analysis indicated decreased relative abundances of g_Prevotella and g_Faecalibacterium in the DKD middle group, along with a decrease in g_Klebsiella compared to the DKD early group, which correlated positively with DKD patients' eGFR. There were 11 common metabolites among the three groups of differential metabolites. Among these, three serum metabolites-imidazolepropionic acid, adipoylcarnitine, and 1-methylhistidine-were identified as predictive serum metabolic markers. Disease prediction models (logistic regression models) were constructed based on these three metabolites combined with three genera of bacteria. These models demonstrated strong discriminatory power for diagnosing patients with DKD compared to patients with DM (area under the receiver operating characteristic curve (AUROC) = 0.939 and precision-recall curve (AUPR) = 0.940). The models also effectively discriminated between patients with DKD and NCs (0.976, 0.973). This study revealed distinctive serum metabolites and gut microbiota in patients with DKD. It demonstrated the potential utility of three specific serum metabolites and three genera of bacteria in diagnosing patients with DKD and assessing their renal dysfunction.

Urine Tricarboxylic Acid Cycle Metabolites and Risk of End-stage Kidney Disease in Patients With Type 2 Diabetes

CONTEXT

Metabolites in the tricarboxylic acid (TCA) pathway have pleiotropic functions.

OBJECTIVE

To study the association between urine TCA cycle metabolites and the risk for chronic kidney disease progression in individuals with type 2 diabetes.

DESIGN, SETTING AND PARTICIPANTS

A prospective study in a discovery (n = 1826) and a validation (n = 1235) cohort of people with type 2 diabetes in a regional hospital and a primary care facility.

EXPOSURE AND OUTCOME

Urine lactate, pyruvate, citrate, alpha-ketoglutarate, succinate, fumarate, and malate were measured by mass spectrometry. Chronic kidney disease progression was defined as a composite of sustained estimated glomerular filtration rate below 15 mL/min/1.73 m2, dialysis, renal death, or doubling of serum creatinine.

RESULTS

During a median of 9.2 (interquartile range 8.1-9.7) and 4.0 (3.2-5.1) years of follow-up, 213 and 107 renal events were identified. Cox regression suggested that urine lactate, fumarate, and malate were associated with an increased risk (adjusted hazard ratio, [95% CI] 1.63 [1.16-2.28], 1.82 [1.17-2.82], and 1.49 [1.05-2.11], per SD), whereas citrate was associated with a low risk (aHR 0.83 [0.72-0.96] per SD) for the renal outcome after adjustment for cardiorenal risk factors. These findings were reproducible in the validation cohort. Noteworthy, fumarate and citrate were independently associated with the renal outcome after additional adjustment for other metabolites.

CONCLUSION

Urine fumarate and citrate predict the risk for progression to end-stage kidney disease independent of clinical risk factors and other urine metabolites. These 2 metabolites in TCA cycle pathway may play important roles in the pathophysiological network, underpinning progressive loss of kidney function in patients with type 2 diabetes.

Decoding Kidney Pathophysiology: Omics-Driven Approaches in Precision Medicine

Chronic kidney disease (CKD) is a major worldwide health concern because of its progressive nature and complex biology. Traditional diagnostic and therapeutic approaches usually fail to account for disease heterogeneity, resulting in low efficacy. Precision medicine offers a novel approach to studying kidney disease by combining omics technologies such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics. By identifying discrete disease subtypes, molecular biomarkers, and therapeutic targets, these technologies pave the way for personalized treatment approaches. Multi-omics integration has enhanced our understanding of CKD by revealing intricate molecular linkages and pathways that contribute to treatment resistance and disease progression. While pharmacogenomics offers insights into expected responses to personalized treatments, single-cell and spatial transcriptomics can be utilized to investigate biological heterogeneity. Despite significant development, challenges persist, including data integration concerns, high costs, and ethical quandaries. Standardized data protocols, collaborative data-sharing frameworks, and advanced computational tools such as machine learning and causal inference models are required to address these challenges. With the advancement of omics technology, nephrology may benefit from improved diagnostic accuracy, risk assessment, and personalized care. By overcoming these barriers, precision medicine has the potential to develop novel techniques for improving patient outcomes in kidney disease treatment.

Diabetic Kidney Disease: Contribution of Phenyl Sulfate Derived from Dietary Tyrosine upon Gut Microbiota Catabolism

Deranged gut microbiota can release increased levels of uremic toxins leading to exacerbated kidney injury. In diabetic kidney disease (DKD), phenyl sulfate (PS) derived from tyrosine catabolism by gut microbiota has been demonstrated to be both an early diagnostic marker and a therapeutic target. In this perspective article, we summarize PS generation pathways and recent findings on PS and kidney injury in DKD. Increasing evidence has shown that the underlying mechanisms of PS-induced kidney injury mainly involve oxidative stress, redox imbalance, and mitochondrial dysfunction, which all may be targeted to attenuate PS-induced kidney injury. For future research directions, we think that a deeper understanding of the pathogenic role of PS in kidney injury using a variety of diabetic animal models should be investigated. Moreover, we also suggest beneficial approaches that could be used to mitigate the deleterious effect of PS on the kidney. These approaches include caloric restriction, tyrosine restriction, and administration of ketogenic drugs, ketogenic diets or natural products; all of which should be conducted under obese and diabetic conditions.

Organelle communication maintains mitochondrial and endosomal homeostasis during podocyte lipotoxicity

Organelle stress exacerbates podocyte injury, contributing to perturbed lipid metabolism. Simultaneous organelle stresses can occur in the kidney in the diseased state; therefore, a thorough analysis of organelle communication is crucial for understanding the progression of kidney diseases. Although organelles closely interact with one another at membrane contact sites, limited studies have explored their involvement in kidney homeostasis. The endoplasmic reticulum (ER) protein, PDZ domain-containing 8 (PDZD8), is implicated in multiple-organelle-tethering processes and cellular lipid homeostasis. In this study, we aimed to elucidate the role of organelle communication in podocyte injury using podocyte-specific Pdzd8-knockout mice. Our findings demonstrated that Pdzd8 deletion exacerbated podocyte injury in an accelerated obesity-related kidney disease model. Proteomic analysis of isolated glomeruli revealed that Pdzd8 deletion exacerbated mitochondrial and endosomal dysfunction during podocyte lipotoxicity. Additionally, electron microscopy revealed the accumulation of abnormal, fatty endosomes in Pdzd8-deficient podocytes during obesity-related kidney diseases. Lipidomic analysis indicated that glucosylceramide accumulated in Pdzd8-deficient podocytes, owing to accelerated production and decelerated degradation. Thus, the organelle-tethering factor, PDZD8, plays a crucial role in maintaining mitochondrial and endosomal homeostasis during podocyte lipotoxicity. Collectively, our findings highlight the importance of organelle communication at the 3-way junction among the ER, mitochondria, and endosomes in preserving podocyte homeostasis.

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.

Current updates on metabolites and its interlinked pathways as biomarkers for diabetic kidney disease: A systematic review

Diabetic kidney disease (DKD) is a major microvascular complication of diabetes mellitus (DM) that poses a serious risk as it can lead to end-stage renal disease (ESRD). DKD is linked to changes in the diversity, composition, and functionality of the microbiota present in the gastrointestinal tract. The interplay between the gut microbiota and the host organism is primarily facilitated by metabolites generated by microbial metabolic processes from both dietary substrates and endogenous host compounds. The production of numerous metabolites by the gut microbiota is a crucial factor in the pathogenesis of DKD. However, a comprehensive understanding of the precise mechanisms by which gut microbiota and its metabolites contribute to the onset and progression of DKD remains incomplete. This review will provide a summary of the current scenario of metabolites in DKD and the impact of these metabolites on DKD progression. We will discuss in detail the primary and gut-derived metabolites in DKD, and the mechanisms of the metabolites involved in DKD progression. Further, we will address the importance of metabolomics in helping identify potential DKD markers. Furthermore, the possible therapeutic interventions and research gaps will be highlighted.

The AKI-to-CKD Transition: The Role of Uremic Toxins

After acute kidney injury (AKI), renal function continues to deteriorate in some patients. In a pro-inflammatory and profibrotic environment, the proximal tubules are subject to maladaptive repair. In the AKI-to-CKD transition, impaired recovery from AKI reduces tubular and glomerular filtration and leads to chronic kidney disease (CKD). Reduced kidney secretion capacity is characterized by the plasma accumulation of biologically active molecules, referred to as uremic toxins (UTs). These toxins have a role in the development of neurological, cardiovascular, bone, and renal complications of CKD. However, UTs might also cause CKD as well as be the consequence. Recent studies have shown that these molecules accumulate early in AKI and contribute to the establishment of this pro-inflammatory and profibrotic environment in the kidney. The objective of the present work was to review the mechanisms of UT toxicity that potentially contribute to the AKI-to-CKD transition in each renal compartment.

Omics and Artificial Intelligence in Kidney Diseases

Omics applications in nephrology may have relevance in the future to improve clinical care of kidney disease patients. In a short term, patients will benefit from specific measurement and computational analyses around biomarkers identified at various omics-levels. In mid term and long term, these approaches will need to be integrated into a holistic representation of the kidney and all its influencing factors for individualized patient care. Research demonstrates robust data to justify the application of omics for better understanding, risk stratification, and individualized treatment of kidney disease patients. Despite these advances in the research setting, there is still a lack of evidence showing the combination of omics technologies with artificial intelligence and its application in clinical diagnostics and care of patients with kidney disease.

NMR-Based Metabolomics to Decipher the Molecular Mechanisms in the Action of Gut-Modulating Foods

Metabolomics deals with uncovering and characterizing metabolites present in a biological system, and is a leading omics discipline as it provides the nearest link to the biological phenotype. Within food and nutrition, metabolomics applied to fecal samples and bio-fluids has become an important tool to obtain insight into how food and food components may exert gut-modulating effects. This review aims to highlight how nuclear magnetic resonance (NMR)-based metabolomics in food and nutrition science may help us get beyond where we are today in understanding foods’ inherent, or added, biofunctionalities in relation to gut health.

In situ Metabolomics of Metabolic Reprogramming Involved in a Mouse Model of Type 2 Diabetic Kidney Disease

The in situ metabolic profiling of the kidney is crucial to investigate the complex metabolic reprogramming underlying diabetic kidney disease (DKD) and to allow exploration of potential metabolic targets to improve kidney function. However, as the kidney is a highly heterogeneous organ, traditional metabolomic methods based on bulk analysis that produce an averaged measurement are inadequate. Herein, we employed an in situ metabolomics approach to discover alternations of DKD-associated metabolites and metabolic pathways. A series of histology-specific metabolic disturbances were discovered in situ using airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI). In combination with integrated metabolomics analysis, five dysfunctional metabolic pathways were identified and located in the kidneys of type-2 DKD mice simultaneously for the first time, including taurine metabolism, arginine and proline metabolism, histidine metabolism, biosynthesis of unsaturated fatty acids, and fatty acid degradation pathways. As crucial nodes of metabolic pathways, five dysregulated rate-limiting enzymes related to altered metabolic pathways were further identified. These findings reveal alternations from metabolites to enzymes at the molecular level in the progression of DKD and provide insights into DKD-associated metabolic reprogramming.

The Role of Gut Microbiota and Microbiota-Related Serum Metabolites in the Progression of Diabetic Kidney Disease

Objective: Diabetic kidney disease (DKD) has become the major cause of end-stage renal disease (ESRD) associated with the progression of renal fibrosis. As gut microbiota dysbiosis is closely related to renal damage and fibrosis, we investigated the role of gut microbiota and microbiota-related serum metabolites in DKD progression in this study.

Methods: Fecal and serum samples obtained from predialysis DKD patients from January 2017 to December 2019 were detected using 16S rRNA gene sequencing and liquid chromatography-mass spectrometry, respectively. Forty-one predialysis patients were divided into two groups according to their estimated glomerular filtration rate (eGFR): the DKD non-ESRD group (eGFR ≥ 15 ml/min/1.73 m²) (n = 22), and the DKD ESRD group (eGFR < 15 ml/min/1.73 m²) (n = 19). The metabolic pathways related to differential serum metabolites were obtained by the KEGG pathway analysis. Differences between the two groups relative to gut microbiota profiles and serum metabolites were investigated, and associations between gut microbiota and metabolite concentrations were assessed. Correlations between clinical indicators and both microbiota-related metabolites and gut microbiota were calculated by Spearman rank correlation coefficient and visualized by heatmap.

Results: Eleven different intestinal floras and 239 different serum metabolites were identified between the two groups. Of 239 serum metabolites, 192 related to the 11 different intestinal flora were mainly enriched in six metabolic pathways, among which, phenylalanine and tryptophan metabolic pathways were most associated with DKD progression. Four microbiota-related metabolites in the phenylalanine metabolic pathway [hippuric acid (HA), L-(−)-3-phenylactic acid, trans-3-hydroxy-cinnamate, and dihydro-3-coumaric acid] and indole-3 acetic acid (IAA) in the tryptophan metabolic pathway positively correlated with DKD progression, whereas L-tryptophan in the tryptophan metabolic pathway had a negative correlation. Intestinal flora g_Abiotrophia and g_norank_f_Peptococcaceae were positively correlated with the increase in renal function indicators and serum metabolite HA. G_Lachnospiraceae_NC2004_Group was negatively correlated with the increase in renal function indicators and serum metabolites [L-(−)-3-phenyllactic acid and IAA].

Conclusions: This study highlights the interaction among gut microbiota, serum metabolites, and clinical indicators in predialysis DKD patients, and provides new insights into the role of gut microbiota and microbiota-related serum metabolites that were enriched in the phenylalanine and tryptophan metabolic pathways, which correlated with the progression of DKD.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.