Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study

Metabolism and bioenergetics in the pathophysiology of organ fibrosis

Fibrosis is the tissue scarring characterized by excess deposition of extracellular matrix (ECM) proteins, mainly collagens. A fibrotic response can take place in any tissue of the body and is the result of an imbalanced reaction to inflammation and wound healing. Metabolism has emerged as a major driver of fibrotic diseases. While glycolytic shifts appear to be a key metabolic switch in activated stromal ECM-producing cells, several other cell types such as immune cells, whose functions are intricately connected to their metabolic characteristics, form a complex network of pro-fibrotic cellular crosstalk. This review purports to clarify shared and particular cellular responses and mechanisms across organs and etiologies. We discuss the impact of the cell-type specific metabolic reprogramming in fibrotic diseases in both experimental and human pathology settings, providing a rationale for new therapeutic interventions based on metabolism-targeted antifibrotic agents.

Immediate drop of urine osmolality upon tolvaptan initiation predicts impact on renal prognosis in patients with ADPKD

BACKGROUND

Tolvaptan, a vasopressin V2 receptor antagonist, is used for treating autosomal dominant polycystic kidney disease (ADPKD). We focused on changes in urinary osmolality (U-Osm) after tolvaptan initiation to determine whether they were associated with the therapeutic response to tolvaptan.

METHODS

This was a single-centre, prospective, observational cohort study. Seventy-two patients with ADPKD who received tolvaptan were recruited. We analysed the relationship between changes in U-Osm and annual estimated glomerular filtration rate (eGFR) in terms of renal prognostic value using univariable and multivariable linear regression analyses.

RESULTS

The mean value of U-Osm immediately before tolvaptan initiation was 351.8 ± 142.2 mOsm/kg H2O, which decreased to 97.6 ± 23.8 mOsm/kg H2O in the evening. The decrease in U-Osm was maintained in the outpatient clinic 1 month later. However, the 1-month values of U-Osm showed higher variability (160.2 ± 83.8 mOsm/kg H2O) than did those in the first evening of tolvaptan administration. Multivariate analysis revealed that the baseline eGFR, baseline urinary protein and U-Osm change in the evening of the day of admission (initial U-Osm drop) were significantly correlated with the subsequent annual change in eGFR.

CONCLUSIONS

U-Osm can be measured easily and rapidly, and U-Osm change within a short time after tolvaptan initiation may be a useful index for the renal prognosis in actual clinical practice.

Metabolomics Characterization of Disease Markers in Diabetes and Its Associated Pathologies

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

A two-way street - cellular metabolism and myofibroblast contraction

Tissue fibrosis is characterised by the high-energy consumption associated with myofibroblast contraction. Although myofibroblast contraction relies on ATP production, the role of cellular metabolism in myofibroblast contraction has not yet been elucidated. Studies have so far only focused on myofibroblast contraction regulators, such as integrin receptors, TGF-β and their shared transcription factor YAP/TAZ, in a fibroblast-myofibroblast transition setting. Additionally, the influence of the regulators on metabolism and vice versa have been described in this context. However, this has so far not yet been connected to myofibroblast contraction. This review focuses on the known and unknown of how cellular metabolism influences the processes leading to myofibroblast contraction and vice versa. We elucidate the signalling cascades responsible for myofibroblast contraction by looking at FMT regulators, mechanical cues, biochemical signalling, ECM properties and how they can influence and be influenced by cellular metabolism. By reviewing the existing knowledge on the link between cellular metabolism and the regulation of myofibroblast contraction, we aim to pinpoint gaps of knowledge and eventually help identify potential research targets to identify strategies that would allow switching tissue fibrosis towards tissue regeneration.

Effects of continuous intravenous infusion with propofol on intestinal metabolites in rats

Microbial metabolites play an important role in regulating intestinal homeostasis and immune responses. Propofol is a common anesthetic in clinic, but it is not clear whether it affects intestinal metabolites in rats. Tail vein puncture was performed after adaptive feeding for 1 month in eight 2-month-old rats and they were given continuous intravenous infusion of propofol for 3 h. The feces of rats were divided into different groups based on time periods, with before and after anesthesia with propofol on days 1, 3 and 7 labeled as groups P, A1, A3 and A7, respectively. The effect of continuous intravenous infusion with propofol on rat fecal metabolites was determined using the non-targeted metabolomics technique gas chromatography coupled with a time-of-flight mass spectrometer analysis. The types and contents of metabolites in rat feces were changed after continuous intravenous infusion with propofol, but the changes were not statistically significant. The contents of the metabolites 3-hydroxyphenylacetic acid and palmitic acid increased from day 3 to 7, and it was shown that the two metabolites were positively correlated at a statistically significant level. Linoleic acid decreased to its lowest level on day 3, and it returned to pre-anesthesia level on day 7. At the same time, linoleic acid metabolism was a metabolic pathway that was co-enriched 7 days after infusion with propofol. Spearman correlation analysis showed that there was significant correlation between some differential metabolites and differential microorganisms. It was observed that zymosterol 1, cytosin and elaidic acid were negatively correlated with Alloprevotella in the A3 vs. P group. In the A7 vs. P group, cortexolone 3 and coprostan-3-one were positively correlated with Faecalibacterium, whilst aconitic acid was negatively correlated with it. In conclusion, the present study revealed statistically insignificant effects of continuous intravenous propofol on the intestinal metabolites in rats.

Crosstalk between proximal tubular epithelial cells and other interstitial cells in tubulointerstitial fibrosis after renal injury

The energy needs of tubular epithelial components, especially proximal tubular epithelial cells (PTECs), are high and they heavily depend on aerobic metabolism. As a result, they are particularly vulnerable to various injuries caused by factors such as ischemia, proteinuria, toxins, and elevated glucose levels. Initial metabolic and phenotypic changes in PTECs after injury are likely an attempt at survival and repair. Nevertheless, in cases of recurrent or prolonged injury, PTECs have the potential to undergo a transition to a secretory state, leading to the generation and discharge of diverse bioactive substances, including transforming growth factor-β, Wnt ligands, hepatocyte growth factor, interleukin (IL)-1β, lactic acid, exosomes, and extracellular vesicles. By promoting fibroblast activation, macrophage recruitment, and endothelial cell loss, these bioactive compounds stimulate communication between epithelial cells and other interstitial cells, ultimately worsening renal damage. This review provides a summary of the latest findings on bioactive compounds that facilitate the communication between these cellular categories, ultimately leading to the advancement of tubulointerstitial fibrosis (TIF).

HIF-1α: A potential therapeutic opportunity in renal fibrosis

Renal fibrosis is a common outcome of various renal injuries, leading to structural destruction and functional decline of the kidney, and is also a critical prognostic indicator and determinant in renal diseases therapy. Hypoxia is induced in different stress and injuries in kidney, and the hypoxia inducible factors (HIFs) are activated in the context of hypoxia in response and regulation the hypoxia in time. Under stress and hypoxia conditions, HIF-1α increases rapidly and regulates intracellular energy metabolism, cell proliferation, apoptosis, and inflammation. Through reprogramming cellular metabolism, HIF-1α can directly or indirectly induce abnormal accumulation of metabolites, changes in cellular epigenetic modifications, and activation of fibrotic signals. HIF-1α protein expression and activity are regulated by various posttranslational modifications. The drugs targeting HIF-1α can regulate the downstream cascade signals by inhibiting HIF-1α activity or promoting its degradation. As the renal fibrosis is affected by renal diseases, different diseases may trigger different mechanisms which will affect the therapy effect. Therefore, comprehensive analysis of the role and contribution of HIF-1α in occurrence and progression of renal fibrosis, and determination the appropriate intervention time of HIF-1α in the process of renal fibrosis are important ideas to explore effective treatment strategies. This study reviews the regulation of HIF-1α and its mediated complex cascade reactions in renal fibrosis, and lists some drugs targeting HIF-1α that used in preclinical studies, to provide new insight for the study of the renal fibrosis mechanism.

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

Metabolomic profiling of amino acids study reveals a distinct diagnostic model for diabetic kidney disease

Diabetic kidney disease (DKD), a highly prevalent complication of diabetes mellitus, is a major cause of mortality in patients. However, identifying circulatory markers to diagnose DKD requires a thorough understanding of the metabolic mechanisms of DKD. In this study, we performed ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) to reveal altered metabolic profiles of amino acids (AAs) in patients with DKD. We found decreased plasma levels of histidine and valine, increased urine levels of proline, decreased urine levels of histidine and valine, and increased saliva levels of arginine in patients with DKD compared with the levels in patients with type 2 diabetes mellitus (T2DM) and in healthy controls. Our analyses of the key metabolites and metabolic enzymes involved in histidine and valine metabolism indicated that the AAs level alterations may be due to enhanced carnosine hydrolysis, decreased degradation of homocarnosine and anserine, enhanced histidine methylation, and systemic enhancement of valine metabolism in patients with DKD. Notably, we generated a distinct diagnostic model with an AUC of 0.957 and an accuracy up to 92.2% on the basis of the AA profiles in plasma, urine and saliva differing in patients with DKD using logistic regression and receiver operating characteristic analyses. In conclusion, our results suggest that altered AA metabolic profiles are associated with the progression of DKD. Our DKD diagnostic model on the basis of AA levels in plasma, urine, and saliva may provide a theoretical basis for innovative strategies to diagnose DKD that may replace cumbersome kidney biopsies.

Precision Medicine in Diabetic Kidney Disease: A Narrative Review Framed by Lived Experience

Purpose of review

Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD) for which many treatments exist that have been shown to prevent CKD progression and kidney failure. However, DKD is a complex and heterogeneous etiology of CKD with a spectrum of phenotypes and disease trajectories. In this narrative review, we discuss precision medicine approaches to DKD, including genomics, metabolomics, proteomics, and their potential role in the management of diabetes mellitus and DKD. A patient and caregivers of patients with lived experience with CKD were involved in this review.

Sources of information

Original research articles were identified from MEDLINE and Google Scholar using the search terms "diabetes," "diabetic kidney disease," "diabetic nephropathy," "chronic kidney disease," "kidney failure," "dialysis," "nephrology," "genomics," "metabolomics," and "proteomics."

Methods

A focused review and critical appraisal of existing literature regarding the precision medicine approaches to the diagnosis, prognosis, and treatment of diabetes and DKD framed by a patient partner's/caregiver's lived experience.

Key findings

Distinguishing diabetic nephropathy from CKD due to other types of DKD and non-DKD is challenging and typically requires a kidney biopsy for a diagnosis. Biomarkers have been identified to assist with the prediction of the onset and progression of DKD, but they have yet to be incorporated and evaluated relative to clinical standard of care CKD and kidney failure risk prediction tools. Genomics has identified multiple causal genetic variants for neonatal diabetes mellitus and monogenic diabetes of the young that can be used for diagnostic purposes and to specify antiglycemic therapy. Genome-wide-associated studies have identified genes implicated in DKD pathophysiology in the setting of type 1 and 2 diabetes but their translational benefits are lagging beyond polygenetic risk scores. Metabolomics and proteomics have been shown to improve diagnostic accuracy in DKD, have been used to identify novel pathways involved in DKD pathogenesis, and can be used to improve the prediction of CKD progression and kidney failure as well as predict response to DKD therapy.

Limitations

There are a limited number of large, high-quality prospective observational studies and no randomized controlled trials that support the use of precision medicine based approaches to improve clinical outcomes in adults with or at risk of diabetes and DKD. It is unclear which patients may benefit from the clinical use of genomics, metabolomics and proteomics along the spectrum of DKD trajectory.

Implications

Additional research is needed to evaluate the role of the use of precision medicine for DKD management, including diagnosis, differentiation of diabetic nephropathy from other etiologies of DKD and CKD, short-term and long-term risk prognostication kidney outcomes, and the prediction of response to and safety of disease-modifying therapies.

The intellectual base and global trends in inflammation of diabetic kidney disease: a bibliometric analysis

Diabetic kidney disease (DKD) is a severe complication of diabetes mellitus (DM). The literature on DKD inflammation research has experienced substantial growth. However, there is a lack of bibliometric analyses. This study aimed to examine the existing research on inflammation in DKD by analyzing articles published in the Web of Science Core Collection (WOSCC) over the past 30 years. We conducted a visualization analysis using several software, including CiteSpace and VOSviewer. We found that the literature on inflammation research in DKD has experienced substantial growth, indicating a rising interest in this developing area of study. In this field, Navarro-Gonzalez, JF is the most frequently cited author, Kidney International is the most frequently cited journal, China had the highest number of publications in the field of DKD inflammation, and Monash University emerged as the institution with the most published research. The research area on inflammation in DKD primarily centers around the investigation of 'Glycation end-products', 'chronic kidney disease', and 'diabetic nephropathy'. The emerging research trends in this field will focus on the 'Gut microbiota', 'NLRP3 inflammasome', 'autophagy', 'pyroptosis', 'sglt2 inhibitor', and 'therapeutic target'. Future research on DKD may focus on further exploring the inflammatory response, identifying specific therapeutic targets, studying biomarkers, investigating stem cell therapy and tissue engineering, and exploring gene therapy and gene editing. In summary, this study examines the main areas of study, frontiers, and trends in DKD inflammation, which have significant implications for future research.

Metabolic Differences in Diabetic Kidney Disease Patients with Normoalbuminuria versus Moderately Increased Albuminuria

Key Points

The pathophysiological mechanisms of diabetic kidney disease (DKD) with normal (nonalbuminuric DKD) versus moderately increased albuminuria (A-DKD) are not well-understood. Fatty acid biosynthesis and oxydation, gluconeogenesis, TCA cycle, and glucose-alanine cycle were more disturbed in patients with A-DKD compared with those with nonalbuminuric DKD with identical eGFR. DKD patients with and without microalbuminuria could represent different clinical phenotypes.

Background

The pathophysiological mechanisms of diabetic kidney disease (DKD) with normal versus moderately increased albuminuria (nonalbuminuric DKD [NA-DKD] and A-DKD) are currently not well-understood and could have implications for diagnosis and treatment.

Methods

Fourteen patients with NA-DKD with urine albumin–creatinine ratio <3 mg/mmol, 26 patients with A-DKD with albumin–creatinine ratio 3–29 mg/mmol, and 60 age- and sex-matched healthy controls were randomly chosen from a population-based cohort study (Nord-Trøndelag Health Study-3, Norway). Seventy-four organic acids, 21 amino acids, 21 biogenic acids, 40 acylcarnitines, 14 sphingomyelins, and 88 phosphatidylcholines were quantified in urine. One hundred forty-six patients with diabetes from the US-based Chronic Renal Insufficiency Cohort study were used to verify main findings.

Results

Patients with NA-DKD and A-DKD had similar age, kidney function, diabetes treatment, and other traditional risk factors. Still, partial least-squares discriminant analysis showed strong metabolite-based separation (R2, 0.82; Q2, 0.52), with patients with NA-DKD having a metabolic profile positioned between the profiles of healthy controls and patients with A-DKD. Seventy-five metabolites contributed significantly to separation between NA-DKD and A-DKD (variable importance in projection scores ≥1.0) with propionylcarnitine (C3), phosphatidylcholine C38:4, medium-chained (C8) fatty acid octenedioic acid, and lactic acid as the top metabolites (variable importance in projection scores, 2.7–2.2). Compared with patients with NA-DKD, those with A-DKD had higher levels of short-chained acylcarnitines, higher long-chained fatty acid levels with more double bounds, higher branched-chain amino acid levels, and lower TCA cycle intermediates. The main findings were similar by random forest analysis and in the Chronic Renal Insufficiency Cohort study. Formal enrichment analysis indicated that fatty acid biosynthesis and oxydation, gluconeogenesis, TCA cycle, and glucose-alanine cycle were more disturbed in patients with A-DKD compared with those with NA-DKD with identical eGFR. We also found indications of a Warburg-like effect in patients with A-DKD (i.e. , metabolism of glucose to lactate despite adequate oxygen).

Conclusion

DKD patients with normoalbuminuria differ substantially in their metabolic disturbances compared with patients with moderately increase albuminuria and could represent different clinical phenotypes.

CKD Urine Metabolomics: Modern Concepts and Approaches

One of the primary challenges regarding chronic kidney disease (CKD) diagnosis is the absence of reliable methods to detect early-stage kidney damage. A metabolomic approach is expected to broaden the current diagnostic modalities by enabling timely detection and making the prognosis more accurate. Analysis performed on urine has several advantages, such as the ease of collection using noninvasive methods and its lower protein and lipid content compared with other bodily fluids. This review highlights current trends in applied analytical methods, major discoveries concerning pathways, and investigated populations in the context of urine metabolomic research for CKD over the past five years. Also, we are presenting approaches, instrument upgrades, and sample preparation modifications that have improved the analytical parameters of methods. The onset of CKD leads to alterations in metabolism that are apparent in the molecular composition of urine. Recent works highlight the prevalence of alterations in the metabolic pathways related to the tricarboxylic acid cycle and amino acids. Including diverse patient cohorts, using numerous analytical techniques with modifications and the appropriate annotation and explanation of the discovered biomarkers will help develop effective diagnostic models for different subtypes of renal injury with clinical applications.

Urinary metabolites associate with the presence of diabetic kidney disease in type 2 diabetes and mediate the effect of inflammation on kidney complication

AIMS

Diabetic kidney disease (DKD) is the one of the leading causes of end-stage kidney disease. Unraveling novel biomarker signatures capable to identify patients with DKD is favorable for tackle the burden. Here, we investigated the possible association between urinary metabolites and the presence of DKD in type 2 diabetes (T2D), and further, whether the associated metabolites improve discrimination of DKD and mediate the effect of inflammation on kidney involvement was evaluated.

METHODS

Two independent cohorts comprising 192 individuals (92 DKD) were analyzed. Urinary metabolites were analyzed by targeted metabolome profiling  and inflammatory cytokine IL-18 were measured by ELISA. Differentially expressed metabolites were selected and mediation analysis was carried out.

RESULTS

Seven potential metabolite biomarkers (i.e., S-Adenosyl-L-homocysteine, propionic acid, oxoadipic acid, leucine, isovaleric acid, isobutyric acid, and indole-3-carboxylic acid) were identified using the discovery and validation design. In the pooled analysis, propionic acid, oxoadipic acid, leucine, isovaleric acid, isobutyric acid, and indole-3-carboxylic acid were markedly and independently associated with DKD. The composite index of 7 potential metabolite biomarkers (CMI) mediated 32.99% of the significant association between the inflammatory IL-18 and DKD. Adding the metabolite biomarkers improved the discrimination of DKD.

CONCLUSIONS

In T2D, several associated urinary metabolites were identified to improve the prediction of DKD. Whether interventions aimed at reducing CMI also reduce the risk of DKD especially in patients with high IL-18 needs further investigations.

Metabolic reprogramming heterogeneity in chronic kidney disease

Fibrosis driven by excessive accumulation of extracellular matrix (ECM) is the hallmark of chronic kidney disease (CKD). Myofibroblasts, which are the cells responsible for ECM production, are activated by cross talk with injured proximal tubule and immune cells. Emerging evidence suggests that alterations in metabolism are not only a feature of but also play an influential role in the pathogenesis of renal fibrosis. The application of omics technologies to cell-tracing animal models and follow-up functional data suggest that cell-type-specific metabolic shifts have particular roles in the fibrogenic response. In this review, we cover the main metabolic reprogramming outcomes in renal fibrosis and provide a future perspective on the field of renal fibrometabolism.

Integration of metabolomics and peptidomics reveals distinct molecular landscape of human diabetic kidney disease

Diabetic kidney disease (DKD) is the most common microvascular complication of diabetes, and there is an urgent need to discover reliable biomarkers for early diagnosis. Here, we established an effective urine multi-omics platform and integrated metabolomics and peptidomics to investigate the biological changes during DKD pathogenesis. Methods: Totally 766 volunteers (221 HC, 198 T2DM, 175 early DKD, 125 overt DKD, and 47 grey-zone T2DM patients with abnormal urinary mALB concentration) were included in this study. Non-targeted metabolic fingerprints of urine samples were acquired on matrix-free LDI-MS platform by the tip-contact extraction method using fluorinated ethylene propylene coated silicon nanowires chips (FEP@SiNWs), while peptide profiles hidden in urine samples were uncovered by MALDI-TOF MS after capturing urine peptides by porous silicon microparticles. Results: After multivariate analysis, ten metabolites and six peptides were verified to be stepwise regulated in different DKD stages. The altered metabolic pathways and biological processes associated with the DKD pathogenesis were concentrated in amino acid metabolism and cellular protein metabolic process, which were supported by renal transcriptomics. Interestingly, multi-omics significantly increased the diagnostic accuracy for both early DKD diagnosis and DKD status discrimination. Combined with machine learning, a stepwise prediction model was constructed and 89.9% of HC, 75.5% of T2DM, 69.6% of early DKD and 75.7% of overt DKD subjects in the external validation cohort were correctly classified. In addition, 87.5% of grey-zone patients were successfully distinguished from T2DM patients. Conclusion: This multi-omics platform displayed a satisfactory ability to explore molecular information and provided a new insight for establishing effective DKD management.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Intestinal microbiome diversity of diabetic and non-diabetic kidney disease: Current status and future perspective

A significant portion of the health burden of diabetic kidney disease (DKD) is caused by both type 1 and type 2 diabetes which leads to morbidity and mortality globally. It is one of the most common diabetic complications characterized by loss of renal function with high prevalence, often leading to acute kidney disease (AKD). Inflammation triggered by gut microbiota is commonly associated with the development of DKD. Interactions between the gut microbiota and the host are correlated in maintaining metabolic and inflammatory homeostasis. However, the fundamental processes through which the gut microbiota affects the onset and progression of DKD are mainly unknown. In this narrative review, we summarised the potential role of the gut microbiome, their pathogenicity between diabetic and non-diabetic kidney disease (NDKD), and their impact on host immunity. A well-established association has already been seen between gut microbiota, diabetes and kidney disease. The gut-kidney interrelationship is confirmed by mounting evidence linking gut dysbiosis to DKD, however, it is still unclear what is the real cause of gut dysbiosis, the development of DKD, and its progression. In addition, we also try to distinguish novel biomarkers for early detection of DKD and the possible therapies that can be used to regulate the gut microbiota and improve the host immune response. This early detection and new therapies will help clinicians for better management of the disease and help improve patient outcomes.

A generalized covariate-adjusted top-scoring pair algorithm with applications to diabetic kidney disease stage classification in the Chronic Renal Insufficiency Cohort (CRIC) Study

BACKGROUND

The growing amount of high dimensional biomolecular data has spawned new statistical and computational models for risk prediction and disease classification. Yet, many of these methods do not yield biologically interpretable models, despite offering high classification accuracy. An exception, the top-scoring pair (TSP) algorithm derives parameter-free, biologically interpretable single pair decision rules that are accurate and robust in disease classification. However, standard TSP methods do not accommodate covariates that could heavily influence feature selection for the top-scoring pair. Herein, we propose a covariate-adjusted TSP method, which uses residuals from a regression of features on the covariates for identifying top scoring pairs. We conduct simulations and a data application to investigate our method, and compare it to existing classifiers, LASSO and random forests.

RESULTS

Our simulations found that features that were highly correlated with clinical variables had high likelihood of being selected as top scoring pairs in the standard TSP setting. However, through residualization, our covariate-adjusted TSP was able to identify new top scoring pairs, that were largely uncorrelated with clinical variables. In the data application, using patients with diabetes (n = 977) selected for metabolomic profiling in the Chronic Renal Insufficiency Cohort (CRIC) study, the standard TSP algorithm identified (valine-betaine, dimethyl-arg) as the top-scoring metabolite pair for classifying diabetic kidney disease (DKD) severity, whereas the covariate-adjusted TSP method identified the pair (pipazethate, octaethylene glycol) as top-scoring. Valine-betaine and dimethyl-arg had, respectively, ≥ 0.4 absolute correlation with urine albumin and serum creatinine, known prognosticators of DKD. Thus without covariate-adjustment the top-scoring pair largely reflected known markers of disease severity, whereas covariate-adjusted TSP uncovered features liberated from confounding, and identified independent prognostic markers of DKD severity. Furthermore, TSP-based methods achieved competitive classification accuracy in DKD to LASSO and random forests, while providing more parsimonious models.

CONCLUSIONS

We extended TSP-based methods to account for covariates, via a simple, easy to implement residualizing process. Our covariate-adjusted TSP method identified metabolite features, uncorrelated from clinical covariates, that discriminate DKD severity stage based on the relative ordering between two features, and thus provide insights into future studies on the order reversals in early vs advanced disease states.

Multi-Scalar Data Integration Links Glomerular Angiopoietin-Tie Signaling Pathway Activation With Progression of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD). Prognostic biomarkers reflective of underlying molecular mechanisms are critically needed for effective management of DKD. A three-marker panel was derived from a proteomics analysis of plasma samples by an unbiased machine learning approach from participants (N = 58) in the Clinical Phenotyping and Resource Biobank study. In combination with standard clinical parameters, this panel improved prediction of the composite outcome of ESKD or a 40% decline in glomerular filtration rate. The panel was validated in an independent group (N = 68), who also had kidney transcriptomic profiles. One marker, plasma angiopoietin 2 (ANGPT2), was significantly associated with outcomes in cohorts from the Cardiovascular Health Study (N = 3,183) and the Chinese Cohort Study of Chronic Kidney Disease (N = 210). Glomerular transcriptional angiopoietin/Tie (ANG-TIE) pathway scores, derived from the expression of 154 ANG-TIE signaling mediators, correlated positively with plasma ANGPT2 levels and kidney outcomes. Higher receptor expression in glomeruli and higher ANG-TIE pathway scores in endothelial cells corroborated potential functional effects in the kidney from elevated plasma ANGPT2 levels. Our work suggests that ANGPT2 is a promising prognostic endothelial biomarker with likely functional impact on glomerular pathogenesis in DKD.

Metabolite profiling of CKD progression in the chronic renal insufficiency cohort study

BACKGROUNDMetabolomic profiling in individuals with chronic kidney disease (CKD) has the potential to identify novel biomarkers and provide insight into disease pathogenesis.METHODSWe examined the association between blood metabolites and CKD progression, defined as the subsequent development of end-stage renal disease (ESRD) or estimated glomerular filtrate rate (eGFR) halving, in 1,773 participants of the Chronic Renal Insufficiency Cohort (CRIC) study, 962 participants of the African-American Study of Kidney Disease and Hypertension (AASK), and 5,305 participants of the Atherosclerosis Risk in Communities (ARIC) study.RESULTSIn CRIC, more than half of the measured metabolites were associated with CKD progression in minimally adjusted Cox proportional hazards models, but the number and strength of associations were markedly attenuated by serial adjustment for covariates, particularly eGFR. Ten metabolites were significantly associated with CKD progression in fully adjusted models in CRIC; 3 of these metabolites were also significant in fully adjusted models in AASK and ARIC, highlighting potential markers of glomerular filtration (pseudouridine), histamine metabolism (methylimidazoleacetate), and azotemia (homocitrulline). Our findings also highlight N-acetylserine as a potential marker of kidney tubular function, with significant associations with CKD progression observed in CRIC and ARIC.CONCLUSIONOur findings demonstrate the application of metabolomics to identify potential biomarkers and causal pathways in CKD progression.FUNDINGThis study was supported by the NIH (U01 DK106981, U01 DK106982, U01 DK085689, R01 DK108803, and R01 DK124399).

Novel biomarkers for diabetic kidney disease

Although diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in patients with diabetes mellitus; its prevalence has failed to decline over the past 30 years. To identify those at high risk of developing DKD and disease progression at an early stage, extensive research has been ongoing in the search for prognostic and surrogate endpoint biomarkers for DKD. Although biomarkers are not used routinely in clinical practice or prospective clinical trials, many biomarkers have been developed to improve the early identification and prognostication of patients with DKD. Novel biomarkers that capture one specific mechanism of the DKD disease process have been developed, and studies have evaluated the prognostic value of assay-based biomarkers either in small sets or in combinations involving multiple biomarkers. More recently, several studies have assessed the prognostic value of omics- based biomarkers that include proteomics, metabolomics, and transcriptomics. This review will first describe the biomarkers used in current practice and their limitations, and then summarize the current status of novel biomarkers for DKD with respect to assay- based protein biomarkers, proteomics, metabolomics, and transcriptomics.

Potential progression biomarkers of diabetic kidney disease determined using comprehensive machine learning analysis of non-targeted metabolomics

Diabetic kidney disease is the main cause of end-stage renal disease worldwide. The prediction of the clinical course of patients with diabetic kidney disease remains difficult, despite the identification of potential biomarkers; therefore, novel biomarkers are needed to predict the progression of the disease. We conducted non-targeted metabolomics using plasma and urine of patients with diabetic kidney disease whose estimated glomerular filtration rate was between 30 and 60 mL/min/1.73 m². We analyzed how the estimated glomerular filtration rate changed over time (up to 30 months) to detect rapid decliners of kidney function. Conventional logistic analysis suggested that only one metabolite, urinary 1-methylpyridin-1-ium (NMP), was a promising biomarker. We then applied a deep learning method to identify potential biomarkers and physiological parameters to predict the progression of diabetic kidney disease in an explainable manner. We narrowed down 3388 variables to 50 using the deep learning method and conducted two regression models, piecewise linear and handcrafted linear regression, both of which examined the utility of biomarker combinations. Our analysis, based on the deep learning method, identified systolic blood pressure and urinary albumin-to-creatinine ratio, six identified metabolites, and three unidentified metabolites including urinary NMP, as potential biomarkers. This research suggests that the machine learning method can detect potential biomarkers that could otherwise escape identification using the conventional statistical method.

Impaired Amino Acid Metabolism and Its Correlation with Diabetic Kidney Disease Progression in Type 2 Diabetes Mellitus

Background: Metabolomics is useful in elucidating the progression of diabetes; however, the follow-up changes in metabolomics among health, diabetes mellitus, and diabetic kidney disease (DKD) have not been reported. This study was aimed to reveal metabolomic signatures in diabetes development and progression. Methods: In this cross-sectional study, we compared healthy (n = 30), type 2 diabetes mellitus (T2DM) (n = 30), and DKD (n = 30) subjects with the goal of identifying gradual altering metabolites. Then, a prospective study was performed in T2DM patients to evaluate these altered metabolites in the onset of DKD. Logistic regression was conducted to predict rapid eGFR decline in T2DM subjects using altered metabolites. The prospective association of metabolites with the risk of developing DKD was examined using logistic regression and restricted cubic spline regression models. Results: In this cross-sectional study, impaired amino acid metabolism was the main metabolic signature in the onset and development of diabetes, which was characterized by increased N-acetylaspartic acid, L-valine, isoleucine, asparagine, betaine, and L-methionine levels in both the T2DM and DKD groups. These candidate metabolites could distinguish the DKD group from the T2DM group. In the follow-up study, higher baseline levels of L-valine and isoleucine were significantly associated with an increased risk of rapid eGFR decline in T2DM patients. Of these, L-valine and isoleucine were independent risk factors for the development of DKD. Notably, nonlinear associations were also observed for higher baseline levels of L-valine and isoleucine, with an increased risk of DKD among patients with T2DM. Conclusion: Amino acid metabolism was disturbed in diabetes, and N-acetylaspartic acid, L-valine, isoleucine, asparagine, betaine, and L-methionine could be biomarkers for the onset and progression of diabetes. Furthermore, high levels of L-valine and isoleucine may be risk factors for DKD development.

Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research

Chronic kidney disease, including renal failure (RF), is a global public health problem. The clinical diagnosis mainly depends on the change of estimated glomerular filtration rate, which usually lags behind disease progression and likely has limited clinical utility for the early detection of this health problem. Now, we employed Q-Exactive HFX Orbitrap LC-MS/MS based metabolomics to reveal the metabolic profile and potential biomarkers for RF screening. 27 RF patients and 27 healthy controls were included as the testing groups, and comparative analysis of results using different techniques, such as multivariate pattern recognition and univariate statistical analysis, was applied to screen and elucidate the differential metabolites. The dot plots and receiver operating characteristics curves of identified different metabolites were established to discover the potential biomarkers of RF. The results exhibited a clear separation between the two groups, and a total of 216 different metabolites corresponding to 13 metabolic pathways were discovered to be associated with RF; and 44 metabolites showed high levels of sensitivity and specificity under curve values of close to 1, thus might be used as serum biomarkers for RF. In summary, for the first time, our untargeted metabolomics study revealed the distinct metabolic profile of RF, and 44 metabolites with high sensitivity and specificity were discovered, 3 of which have been reported and were consistent with our observations. The other metabolites were first reported by us. Our findings might provide a feasible diagnostic tool for identifying populations at risk for RF through detection of serum metabolites.

Characteristics of Normalization Methods in Quantitative Urinary Metabolomics—Implications for Epidemiological Applications and Interpretations

A systematic comparison is presented for the effects of seven different normalization schemes in quantitative urinary metabolomics. Morning spot urine samples were analyzed with nuclear magnetic resonance (NMR) spectroscopy from a population-based group of 994 individuals. Forty-four metabolites were quantified and the metabolite–metabolite associations and the associations of metabolite concentrations with two representative clinical measures, body mass index and mean arterial pressure, were analyzed. Distinct differences were observed when comparing the effects of normalization for the intra-urine metabolite associations with those for the clinical associations. The metabolite–metabolite associations show quite complex patterns of similarities and dissimilarities between the different normalization methods, while the epidemiological association patterns are consistent, leading to the same overall biological interpretations. The results indicate that, in general, the normalization method appears to have only minor influences on standard epidemiological regression analyses with clinical/physiological measures. Multimetabolite normalization schemes showed consistent results with the customary creatinine reference. Nevertheless, interpretations of intra-urine metabolite associations and nuanced understanding of the epidemiological associations call for comparisons with different normalizations and accounting for the physiology, metabolism and kidney function related to the normalization schemes.

Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan

OBJECTIVES

Trajectories of estimated glomerular filtration rate (eGFR) decline vary highly among patients with chronic kidney disease (CKD). It is clinically important to identify patients who have high risk for eGFR decline. We aimed to identify clusters of patients with extremely rapid eGFR decline and develop a prediction model using a machine learning approach.

DESIGN

Retrospective single-centre cohort study.

SETTINGS

Tertiary referral university hospital in Toyoake city, Japan.

PARTICIPANTS

A total of 5657 patients with CKD with baseline eGFR of 30 mL/min/1.73 m² and eGFR decline of ≥30% within 2 years.

PRIMARY OUTCOME

Our main outcome was extremely rapid eGFR decline. To study-complicated eGFR behaviours, we first applied a variation of group-based trajectory model, which can find trajectory clusters according to the slope of eGFR decline. Our model identified high-level trajectory groups according to baseline eGFR values and simultaneous trajectory clusters. For each group, we developed prediction models that classified the steepest eGFR decline, defined as extremely rapid eGFR decline compared with others in the same group, where we used the random forest algorithm with clinical parameters.

RESULTS

Our clustering model first identified three high-level groups according to the baseline eGFR (G1, high GFR, 99.7±19.0; G2, intermediate GFR, 62.9±10.3 and G3, low GFR, 43.7±7.8); our model simultaneously found three eGFR trajectory clusters for each group, resulting in nine clusters with different slopes of eGFR decline. The areas under the curve for classifying the extremely rapid eGFR declines in the G1, G2 and G3 groups were 0.69 (95% CI, 0.63 to 0.76), 0.71 (95% CI 0.69 to 0.74) and 0.79 (95% CI 0.75 to 0.83), respectively. The random forest model identified haemoglobin, albumin and C reactive protein as important characteristics.

CONCLUSIONS

The random forest model could be useful in identifying patients with extremely rapid eGFR decline.

TRIAL REGISTRATION

UMIN 000037476; This study was registered with the UMIN Clinical Trials Registry.

Prediction of the Short-Term Risk of New-Onset Renal Dysfunction in Patients with Type 2 Diabetes: A Longitudinal Observational Study

Background

Studies in the past decade have reported many novel biomarkers for predicting the new-onset or progression risk of renal dysfunction in patients with type 2 diabetes (T2D) based on the genomic, metabolomic, and proteomic technologies. These novel predictive markers, however, are difficult to be widely used in clinical practice over the short term due to their high technology content, instability, and high cost. This study was aimed at evaluating the associations of clinical features and six traditional renal markers with the short-term risk of new-onset renal dysfunction in patients with T2D.

Methods

This study involved 213 participants with T2D and normal renal function at baseline. The baseline levels of the albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), alpha-1-microglobulin-to-creatinine ratio (A1MCR), neutrophil gelatinase-associated lipocalin-to-creatinine ratio, transferrin-to-creatinine ratio (UTRF/Cr), and retinol-binding protein-to-creatinine ratio (URBP/Cr) were analyzed. Multivariate logistic models were established and validated.

Results

During the two-year follow-up period, 23.01% participants progressed to renal dysfunction. The basal levels of ACR, A1MCR, UTRF/Cr, and URBP/Cr were the independent risk factors of new-onset renal dysfunction (P < 0.05). Several logistic models incorporating clinical characteristics and these renal markers were constructed for predicting the short-term risk of new-onset renal dysfunction. Comparatively, the model including age, glycated hemoglobin (HbA1c), hypertension, ACR, A1MCR, UTRF/Cr, and URBP/Cr levels at baseline had the highest potential (C − index = 0.785, P < 0.001). This model was validated using the K-fold cross-validation method; the accuracy was 0.815 ± 0.013 in training sets and 0.784 ± 0.019 in validation sets, indicating a good consistency for predicting the new-onset renal dysfunction risk. Finally, a nomogram based on this model was constructed to provide a quantitative tool to assess the individualized risk of short-term new-onset renal dysfunction.

Conclusion

The model incorporating these markers and clinical features may have a high potential to predict the short-term risk of new-onset renal dysfunction.

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.

Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease

Although diabetic kidney disease (DKD) remains the leading cause of end-stage kidney disease eventually requiring chronic kidney replacement therapy, the prevalence of DKD has failed to decline over the past 30 years. In order to reduce disease prevalence, extensive research has been ongoing to improve prediction of DKD onset and progression. Although the most commonly used markers of DKD are albuminuria and estimated glomerular filtration rate, their limitations have encouraged researchers to search for novel biomarkers that could improve risk stratification. Considering that DKD is a complex disease process that involves several pathophysiologic mechanisms such as hyperglycemia induced inflammation, oxidative stress, tubular damage, eventually leading to kidney damage and fibrosis, many novel biomarkers that capture one specific mechanism of the disease have been developed. Moreover, the increasing use of high-throughput omic approaches to analyze biological samples that include proteomics, metabolomics, and transcriptomics has emerged as a strong tool in biomarker discovery. This review will first describe recent advances in the understanding of the pathophysiology of DKD, and second, describe the current clinical biomarkers for DKD, as well as the current status of multiple potential novel biomarkers with respect to protein biomarkers, proteomics, metabolomics, and transcriptomics.

Metabolome-wide association study of estimated glomerular filtration rates in Hispanics

Circulating metabolites are by-products of endogenous metabolism or exogenous sources and may inform disease states. Our study aimed to identify the source of variability in the association of metabolites with estimated glomerular filtration rate (eGFR) in Hispanics/Latinos with low chronic kidney disease prevalence by testing the association of 640 metabolites in 3,906 participants of the Hispanic Community Health Study/Study of Latinos. Metabolites were quantified in fasting serum through non-targeted mass spectrometry analysis. eGFR was regressed on inverse normally transformed metabolites in models accounting for study design and covariates. To identify the source of variation on eGFR associations, we tested the interaction of metabolites with lifestyle and clinical risk factors, and results were integrated with genotypes to identify metabolite genetic regulation. The mean age was 46 years, 43% were men, 22% were current smokers, 47% had a Caribbean Hispanic background, 19% had diabetes and the mean cohort eGFR was 96.4 ml/min/1.73 m². We identified 404 eGFR-metabolite associations (False Discovery Rate under 0.05). Of these, 69 were previously reported, and 79 were novel associations with eGFR replicated in one or more published studies. There were significant interactions with lifestyle and clinical risk factors, with larger differences in eGFR-metabolite associations within strata of age, urine albumin to creatinine ratio, diabetes and Hispanic/Latino background. Several newly identified metabolites were genetically regulated, and variants were located at genomic regions previously associated with eGFR. Thus, our results suggest complex mechanisms contribute to the association of eGFR with metabolites and provide new insights into these associations.

Decreased Urine N6-methyladenosine level is closely associated with the presence of diabetic nephropathy in type 2 diabetes mellitus

BACKGROUND

To investigate the dynamic changes of urine N6-methyladenosine (m6A) levels in patients with type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN) and evaluate the clinical significance.

METHODS

First, the levels of urine m6A were examined and compared among 62 patients with T2DM, 70 patients with DN, and 52 age- and gender-matched normal glucose tolerant subjects (NGT) by using a MethyIFIashTM Urine m6A Quantification Kit. Subsequently, we compared the concentrations of urine m6A between different stages of DN. Moreover, statistical analysis was performed to evaluate the association of urine m6A with DN.

RESULTS

The levels of m6A were significantly decreased in patients with DN [(16.10 ± 6.48) ng/ml], compared with NGT [(23.12 ± 7.52) ng/ml, P < 0.0001] and patients with T2DM [(20.39 ± 7.16) ng/ml, P < 0.0001]. Moreover, the concentrations of urine m6A were obviously reduced with the deterioration of DN. Pearson rank correlation and regression analyses revealed that m6A was significantly associated with DN (P < 0.05). The areas under the receiver operator characteristics curve (AUC) were 0.783 (95% CI, 0.699 - 0.867, P < 0.0001) for the DN and NGT groups, and 0.737 (95% CI, 0.639 - 0.835, P < 0.0001) for the macroalbuminuria and normoalbuminuria groups, and the optimal cutoff value for m6A to distinguish the DN from NGT and the macroalbuminuria from normoalbuminuria cases was 0.4687 (diagnostic sensitivity, 71%; diagnostic specificity, 76%) and 0.4494 (diagnostic sensitivity, 79%; diagnostic specificity, 66%), respectively.

CONCLUSIONS

The levels of urine m6A are significantly decreased in patients with DN and change with the deterioration of DN, which could serve as a prospective biomarker for the diagnosis of DN.

Urine tricarboxylic acid cycle signatures of early-stage diabetic kidney disease

INTRODUCTION

Urine tricarboxylic acid (TCA) cycle organic anions (OAs) are elevated in diabetes and may be biomarkers for diabetic kidney disease (DKD) progression.

OBJECTIVES

We assessed associations of 10 urine TCA cycle OAs with estimated glomerular filtration rate (eGFR) and eGFR slope.

METHODS

This study is ancillary to the Simultaneous Risk Factor Control Using Telehealth to SlOw Progression of Diabetic Kidney Disease (STOP-DKD) Trial-a randomized trial of pharmacist-led medication and behavior management in 281 patients with early to moderate DKD at Duke from 2014 to 2015. We used linear mixed models to assess associations of urine TCA cycle OAs with outcomes and modelled TCA cycle OAs as: (1) the average of z-scores for each OA; and (2) principal component (PC) scores derived by principal component analysis (PCA). Untargeted urine metabolomics were added for additional discovery.

RESULTS

Among 132 participants with 24 h urine samples (50% men; 58% Black; mean age 64 years [SD 9]; mean eGFR 74 ml/min/1.73m2 [SD 21] and median urine albumin-to-creatinine [UACR] 20 mg/g [IQR 8-95]), PCA identified 3 OA metabolite PCs. Malate, fumarate, pyruvate, α-ketoglutarate, lactate, succinate and citrate/isocitrate loaded positively on PC1; methylsuccinate, ethylmalonate and succinate loaded positively on PC2; and methylmalonate, ethylmalonate and citrate/isocitrate loaded negatively on PC3. Over a median follow-up of 1.8 years (IQR, 1.2 to 2.2), higher average OA z-score was strongly associated with higher eGFR after covariate adjustment (p = 0.01), but not with eGFR slope (p = 0.9). Higher PC3, but not other PCs, was associated with lower eGFR (p < 0.001). Conditional random forests and smooth clipped absolute deviation models confirmed methylmalonate, citrate/isocitrate, and ethylmalonate, and added lactate as top ranked metabolites in models of baseline eGFR (R-squared 0.32 and 0.33, respectively). Untargeted urine metabolites confirmed association of urine TCA cycle OAs with kidney function.

CONCLUSION

Thus, lower urine TCA cycle OAs, most notably lower methylmalonate, ethylmalonate and citrate/isocitrate, are potential indicators of kidney impairment in early stage DKD.

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.

Tubular Cell Glucose Metabolism Shift During Acute and Chronic Injuries

Acute and chronic kidney disease are responsible for large healthcare costs worldwide. During injury, kidney metabolism undergoes profound modifications in order to adapt to oxygen and nutrient shortage. Several studies highlighted recently the importance of these metabolic adaptations in acute as well as in chronic phases of renal disease, with a potential deleterious effect on fibrosis progression. Until recently, glucose metabolism in the kidney has been poorly studied, even though the kidney has the capacity to use and produce glucose, depending on the segment of the nephron. During physiology, renal proximal tubular cells use the beta-oxidation of fatty acid to generate large amounts of energy, and can also produce glucose through gluconeogenesis. In acute kidney injury, proximal tubular cells metabolism undergo a metabolic shift, shifting away from beta-oxidation of fatty acids and gluconeogenesis toward glycolysis. In chronic kidney disease, the loss of fatty acid oxidation is also well-described, and data about glucose metabolism are emerging. We here review the modifications of proximal tubular cells glucose metabolism during acute and chronic kidney disease and their potential consequences, as well as the potential therapeutic implications.

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.

Mitochondrial Regulation of Diabetic Kidney Disease

The role and nature of mitochondrial dysfunction in diabetic kidney disease (DKD) has been extensively studied. Yet, the molecular drivers of mitochondrial remodeling in DKD are poorly understood. Diabetic kidney cells exhibit a cascade of mitochondrial dysfunction ranging from changes in mitochondrial morphology to significant alterations in mitochondrial biogenesis, biosynthetic, bioenergetics and production of reactive oxygen species (ROS). How these changes individually or in aggregate contribute to progression of DKD remain to be fully elucidated. Nevertheless, because of the remarkable progress in our basic understanding of the role of mitochondrial biology and its dysfunction in DKD, there is great excitement on future targeted therapies based on improving mitochondrial function in DKD. This review will highlight the latest advances in understanding the nature of mitochondria dysfunction and its role in progression of DKD, and the development of mitochondrial targets that could be potentially used to prevent its progression.

Urinary 2-Hydroxyglutarate Enantiomers Are Markedly Elevated in a Murine Model of Type 2 Diabetic Kidney Disease

Metabolic reprogramming is a hallmark of diabetic kidney disease (DKD); nutrient overload leads to increased production of metabolic byproducts that may become toxic at high levels. One metabolic byproduct may be 2-hydroxyglutarate (2-HG), a metabolite with many regulatory functions that exists in both enantiomeric forms physiologically. We quantitatively determined the levels of L and D-2HG enantiomers in the urine, plasma, and kidney cortex of db/db mice, a pathophysiologically relevant murine model of type 2 diabetes and DKD. We found increased fractional excretion of both L and D-2HG enantiomers, suggesting increased tubular secretion and/or production of the two metabolites in DKD. Quantitation of TCA cycle metabolites in db/db cortex suggests that TCA cycle overload and an increase in 2-HG precursor substrate, α-ketoglutarate, drive the increased L and D-2HG production in DKD. In conclusion, we demonstrated increased 2-HG enantiomer production and urinary excretion in murine type 2 DKD, which may contribute to metabolic reprogramming and progression of diabetic kidney disease.

Urine Metabolite Levels, Adverse Kidney Outcomes, and Mortality in CKD Patients: A Metabolome-wide Association Study

RATIONALE & OBJECTIVE

Mechanisms underlying the variable course of disease progression in patients with chronic kidney disease (CKD) are incompletely understood. The aim of this study was to identify novel biomarkers of adverse kidney outcomes and overall mortality, which may offer insights into pathophysiologic mechanisms.

STUDY DESIGN

Metabolome-wide association study.

SETTING & PARTICIPANTS

5,087 patients with CKD enrolled in the observational German Chronic Kidney Disease Study.

EXPOSURES

Measurements of 1,487 metabolites in urine.

OUTCOMES

End points of interest were time to kidney failure (KF), a combined end point of KF and acute kidney injury (KF+AKI), and overall mortality.

ANALYTICAL APPROACH

Statistical analysis was based on a discovery-replication design (ratio 2:1) and multivariable-adjusted Cox regression models.

RESULTS

After a median follow-up of 4 years, 362 patients died, 241 experienced KF, and 382 experienced KF+AKI. Overall, we identified 55 urine metabolites whose levels were significantly associated with adverse kidney outcomes and/or mortality. Higher levels of C-glycosyltryptophan were consistently associated with all 3 main end points (hazard ratios of 1.43 [95% CI, 1.27-1.61] for KF, 1.40 [95% CI, 1.27-1.55] for KF+AKI, and 1.47 [95% CI, 1.33-1.63] for death). Metabolites belonging to the phosphatidylcholine pathway showed significant enrichment. Members of this pathway contributed to the improvement of the prediction performance for KF observed when multiple metabolites were added to the well-established Kidney Failure Risk Equation.

LIMITATIONS

Findings among patients of European ancestry with CKD may not be generalizable to the general population.

CONCLUSIONS

Our comprehensive screen of the association between urine metabolite levels and adverse kidney outcomes and mortality identifies metabolites that predict KF and represents a valuable resource for future studies of biomarkers of CKD progression.

A Metabolic Reprogramming of Glycolysis and Glutamine Metabolism Is a Requisite for Renal Fibrogenesis-Why and How?

Chronic Kidney Disease (CKD) is characterized by organ remodeling and fibrosis due to failed wound repair after on-going or severe injury. Key to this process is the continued activation and presence of matrix-producing renal fibroblasts. In cancer, metabolic alterations help cells to acquire and maintain a malignant phenotype. More recent evidence suggests that something similar occurs in the fibroblast during activation. To support these functions, pro-fibrotic signals released in response to injury induce metabolic reprograming to meet the high bioenergetic and biosynthetic demands of the (myo)fibroblastic phenotype. Fibrogenic signals such as TGF-β1 trigger a rewiring of cellular metabolism with a shift toward glycolysis, uncoupling from mitochondrial oxidative phosphorylation, and enhanced glutamine metabolism. These adaptations may also have more widespread implications with redirection of acetyl-CoA directly linking changes in cellular metabolism and regulatory protein acetylation. Evidence also suggests that injury primes cells to these metabolic responses. In this review we discuss the key metabolic events that have led to a reappraisal of the regulation of fibroblast differentiation and function in CKD.

A Targeted Multiomics Approach to Identify Biomarkers Associated with Rapid eGFR Decline in Type 1 Diabetes

BACKGROUND

Individuals with type 1 diabetes (T1D) demonstrate varied trajectories of estimated glomerular filtration rate (eGFR) decline. The molecular pathways underlying rapid eGFR decline in T1D are poorly understood, and individual-level risk of rapid eGFR decline is difficult to predict.

METHODS

We designed a case-control study with multiple exposure measurements nested within 4 well-characterized T1D cohorts (FinnDiane, Steno, EDC, and CACTI) to identify biomarkers associated with rapid eGFR decline. Here, we report the rationale for and design of these studies as well as results of models testing associations of clinical characteristics with rapid eGFR decline in the study population, upon which "omics" studies will be built. Cases (n = 535) and controls (n = 895) were defined as having an annual eGFR decline of ≥3 and <1 mL/min/1.73 m2, respectively. Associations of demographic and clinical variables with rapid eGFR decline were tested using logistic regression, and prediction was evaluated using area under the curve (AUC) statistics. Targeted metabolomics, lipidomics, and proteomics are being performed using high-resolution mass-spectrometry techniques.

RESULTS

At baseline, the mean age was 43 years, diabetes duration was 27 years, eGFR was 94 mL/min/1.73 m2, and 62% of participants were normoalbuminuric. Over 7.6-year median follow-up, the mean annual change in eGFR in cases and controls was -5.7 and 0.6 mL/min/1.73 m2, respectively. Younger age, longer diabetes duration, and higher baseline HbA1c, urine albumin-creatinine ratio, and eGFR were significantly associated with rapid eGFR decline. The cross-validated AUC for the predictive model incorporating these variables plus sex and mean arterial blood pressure was 0.74 (95% CI: 0.68-0.79; p < 0.001).

CONCLUSION

Known risk factors provide moderate discrimination of rapid eGFR decline. Identification of blood and urine biomarkers associated with rapid eGFR decline in T1D using targeted omics strategies may provide insight into disease mechanisms and improve upon clinical predictive models using traditional risk factors.

Urinary Biomarkers for Diagnosis and Prediction of Acute Kidney Allograft Rejection: A Systematic Review

Noninvasive tools for diagnosis or prediction of acute kidney allograft rejection have been extensively investigated in recent years. Biochemical and molecular analyses of blood and urine provide a liquid biopsy that could offer new possibilities for rejection prevention, monitoring, and therefore, treatment. Nevertheless, these tools are not yet available for routine use in clinical practice. In this systematic review, MEDLINE was searched for articles assessing urinary biomarkers for diagnosis or prediction of kidney allograft acute rejection published in the last five years (from 1 January 2015 to 31 May 2020). This review follows the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Articles providing targeted or unbiased urine sample analysis for the diagnosis or prediction of both acute cellular and antibody-mediated kidney allograft rejection were included, analyzed, and graded for methodological quality with a particular focus on study design and diagnostic test accuracy measures. Urinary C-X-C motif chemokine ligands were the most promising and frequently studied biomarkers. The combination of precise diagnostic reference in training sets with accurate validation in real-life cohorts provided the most relevant results and exciting groundwork for future studies.

Contribution of Predictive and Prognostic Biomarkers to Clinical Research on Chronic Kidney Disease

Chronic kidney disease (CKD), defined as the presence of albuminuria and/or reduction in estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m², is considered a growing public health problem, with its prevalence and incidence having almost doubled in the past three decades. The implementation of novel biomarkers in clinical practice is crucial, since it could allow earlier diagnosis and lead to an improvement in CKD outcomes. Nevertheless, a clear guidance on how to develop biomarkers in the setting of CKD is not yet available. The aim of this review is to report the framework for implementing biomarkers in observational and intervention studies. Biomarkers are classified as either prognostic or predictive; the first type is used to identify the likelihood of a patient to develop an endpoint regardless of treatment, whereas the second type is used to determine whether the patient is likely to benefit from a specific treatment. Many single assays and complex biomarkers were shown to improve the prediction of cardiovascular and kidney outcomes in CKD patients on top of the traditional risk factors. Biomarkers were also shown to improve clinical trial designs. Understanding the correct ways to validate and implement novel biomarkers in CKD will help to mitigate the global burden of CKD and to improve the individual prognosis of these high-risk patients.

Shaping Up Mitochondria in Diabetic Nephropathy

Mitochondrial medicine has experienced significant progress in recent years and is expected to grow significantly in the near future, yielding many opportunities to translate novel bench discoveries into clinical medicine. Multiple lines of evidence have linked mitochondrial dysfunction to a variety of metabolic diseases, including diabetic nephropathy (DN). Mitochondrial dysfunction presumably precedes the emergence of key histologic and biochemical features of DN, which provides the rationale to explore mitochondrial fitness as a novel therapeutic target in patients with DN. Ultimately, the success of mitochondrial medicine is dependent on a better understanding of the underlying biology of mitochondrial fitness and function. To this end, recent advances in mitochondrial biology have led to new understandings of the potential effect of mitochondrial dysfunction in a myriad of human pathologies. We have proposed that molecular mechanisms that modulate mitochondrial dynamics contribute to the alterations of mitochondrial fitness and progression of DN. In this comprehensive review, we highlight the possible effects of mitochondrial dysfunction in DN, with the hope that targeting specific mitochondrial signaling pathways may lead to the development of new drugs that mitigate DN progression. We will outline potential tools to improve mitochondrial fitness in DN as a novel therapeutic strategy. These emerging views suggest that the modulation of mitochondrial fitness could serve as a key target in ameliorating progression of kidney disease in patients with diabetes.