High-Throughput Metabolomics and Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency (CRIC) Study

Urine Metabolomics Reveals the Intervention Effects and Mechanism of Shenhua Tablets in IgA Nephropathy

Shenhua tablets (SHT), a traditional Chinese medicine (TCM), have shown significant clinical efficacy in treating IgA nephropathy (IgAN), but the underlying mechanisms are not fully understood. This study aims to elucidate the renoprotective effects of SHT on IgAN and explore the potential mechanisms of its action using metabolomics approaches. The renoprotective effects of SHT on IgAN were evaluated in a Thy-1 antibody-induced IgAN rat model. Metabolomics techniques were employed to detect and analyze urine biomarkers of IgAN, and to identify SHT targets and metabolic pathways. SHT significantly reduced the levels of 24-h urine protein (Upro), albumin-to-creatinine ratio (ACR), Interleukin 1β (IL-1β), tumor necrosis factor-α (TNF-α), and interleukin 6 (IL-6), alleviated kidney tissue damage, and inhibited mesangial cell proliferation. Seventeen urine metabolites were identified as biomarkers for IgAN, 14 of which were restored by SHT. SHT primarily modulated metabolic pathways, including the tricarboxylic acid (TCA) cycle, glycolysis/gluconeogenesis, pyruvate metabolism, and β-alanine metabolism, upregulating citric acid and succinic acid while downregulating pyruvic acid, L-lactic acid, uracil, and malonic semialdehyde. SHT exerts renoprotective effects in IgAN by modulating key metabolic pathways and normalizing abnormal metabolites levels.

Spatial Metabolomics and Lipidomics in Kidney Disease

Kidney disease is a global health issue that affects over 850 million people, and early detection is key to preventing severe disease and complications. Kidney diseases are associated with complex and dysregulation of lipid metabolism. Spatial metabolomics through mass spectrometry imaging (MSI) enables spatial mapping of the lipids in tissue and includes a variety of techniques that can be used to image lipids. In the kidney, MSI studies often seek to resolve individual functional tissue units such as glomeruli and proximal tubules. Several different MSI techniques, such as matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), have been used to characterize lipids and small molecules in chronic kidney disease, acute kidney injury, genetic kidney disease, and cancer. In this review we provide several examples of how spatial metabolomics data can provide critical information concerning the localization of changes in various disease states. Additionally, when combined with pathology, transcriptomics, or proteomics, the metabolomic changes can illuminate underlying mechanisms and provide new clinical insights into disease mechanisms. Semin Nephrol 36:x-xx © 20xx Elsevier Inc. All rights reserved.

Quinolinic acid potentially links kidney injury to brain toxicity

Kidney dysfunction often leads to neurological impairment, yet the complex kidney-brain relationship remains elusive. We employed spatial and bulk metabolomics to investigate a mouse model of rapid kidney failure induced by mouse double minute 2 (Mdm2) conditional deletion in the kidney tubules to interrogate kidney and brain metabolism. Pathway enrichment analysis of a focused plasma metabolomics panel pinpointed tryptophan metabolism as the most altered pathway with kidney failure. Spatial metabolomics showed toxic tryptophan metabolites in the kidneys and brains, revealing a connection between advanced kidney disease and accelerated kynurenine degradation. In particular, the excitotoxic metabolite quinolinic acid was localized in ependymal cells in the setting of kidney failure. These findings were associated with brain inflammation and cell death. Separate mouse models of ischemia-induced acute kidney injury and adenine-induced chronic kidney disease also exhibited systemic inflammation and accumulating toxic tryptophan metabolites. Patients with advanced chronic kidney disease (stage 3b-4 and stage 5) similarly demonstrated elevated plasma kynurenine metabolites, and quinolinic acid was uniquely correlated with fatigue and reduced quality of life. Overall, our study identifies the kynurenine pathway as a bridge between kidney decline, systemic inflammation, and brain toxicity, offering potential avenues for diagnosis and treatment of neurological issues in kidney disease.

Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential

Omics technologies have significantly advanced the prediction and therapeutic approaches for chronic kidney disease (CKD) by providing comprehensive molecular insights. This is a review of the current state and future prospects of integrating biomarkers into the clinical practice for CKD, aiming to improve patient outcomes by targeted therapeutic interventions. In fact, the integration of genomic, transcriptomic, proteomic, and metabolomic data has enhanced our understanding of CKD pathogenesis and identified novel biomarkers for an early diagnosis and targeted treatment. Advanced computational methods and artificial intelligence (AI) have further refined multi-omics data analysis, leading to more accurate prediction models for disease progression and therapeutic responses. These developments highlight the potential to improve CKD patient care with a precise and individualized treatment plan .

A global perspective on the status of clinical metabolomics in laboratory medicine - a survey by the IFCC metabolomics working group

OBJECTIVES

Metabolomics aims for comprehensive characterization and measurement of small molecule metabolites (<1700 Da) in complex biological matrices. This study sought to assess the current understanding and usage of metabolomics in laboratory medicine globally and evaluate the perception of its promise and future implementation.

METHODS

A survey was conducted by the IFCC metabolomics working group that queried 400 professionals from 79 countries. Participants provided insights into their experience levels, knowledge, and usage of metabolomics approaches, along with detailing the applications and methodologies employed.

RESULTS

Findings revealed a varying level of experience among respondents, with varying degrees of familiarity and utilization of metabolomics techniques. Targeted approaches dominated the field, particularly liquid chromatography coupled to a triple quadrupole mass spectrometer, with untargeted methods also receiving significant usage. Applications spanned clinical research, epidemiological studies, clinical diagnostics, patient monitoring, and prognostics across various medical domains, including metabolic diseases, endocrinology, oncology, cardiometabolic risk, neurodegeneration and clinical toxicology.

CONCLUSIONS

Despite optimism for the future of clinical metabolomics, challenges such as technical complexity, standardization issues, and financial constraints remain significant hurdles. The study underscores the promising yet intricate landscape of metabolomics in clinical practice, emphasizing the need for continued efforts to overcome barriers and realize its full potential in patient care and precision medicine.

An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples

Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze-thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage.

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.

Quinolinic acid links kidney injury to brain toxicity

Kidney dysfunction often leads to neurological impairment, yet the complex kidney-brain relationship remains elusive. We employed spatial and bulk metabolomics to investigate a mouse model of rapid kidney failure induced by mouse double minute 2 ( Mdm2) conditional deletion in the kidney tubules to interrogate kidney and brain metabolism. Pathway enrichment analysis of focused plasma metabolomics panel pinpointed tryptophan metabolism as the most altered pathway with kidney failure. Spatial metabolomics showed toxic tryptophan metabolites in the kidneys and brains, revealing a novel connection between advanced kidney disease and accelerated kynurenine degradation. In particular, the excitotoxic metabolite quinolinic acid was localized in ependymal cells adjacent to the ventricle in the setting of kidney failure. These findings were associated with brain inflammation and cell death. A separate mouse model of acute kidney injury also had an increase in circulating toxic tryptophan metabolites along with altered brain inflammation. Patients with advanced CKD similarly demonstrated elevated plasma kynurenine metabolites and quinolinic acid was uniquely correlated with fatigue and reduced quality of life in humans. Overall, our study identifies the kynurenine pathway as a bridge between kidney decline, systemic inflammation, and brain toxicity, offering potential avenues for diagnosis and treatment of neurological issues in kidney disease.

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

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

Branched-chain amino acid levels are inversely associated with incident and prevalent chronic kidney disease in people with type 2 diabetes

AIM

To investigate the association of plasma metabolites with incident and prevalent chronic kidney disease (CKD) in people with type 2 diabetes and establish whether this association is causal.

MATERIALS AND METHODS

The Hoorn Diabetes Care System cohort is a large prospective cohort consisting of individuals with type 2 diabetes from the northwest part of the Netherlands. In this cohort we assessed the association of baseline plasma levels of 172 metabolites with incident (Ntotal = 462/Ncase = 81) and prevalent (Ntotal = 1247/Ncase = 120) CKD using logistic regression. Additionally, replication in the UK Biobank, body mass index (BMI) mediation and causality of the association with Mendelian randomization was performed.

RESULTS

Elevated levels of total and individual branched-chain amino acids (BCAAs)-valine, leucine and isoleucine-were associated with an increased risk of incident CKD, but with reduced odds of prevalent CKD, where BMI was identified as an effect modifier. The observed inverse effects were replicated in the UK Biobank. Mendelian randomization analysis did not provide evidence for a causal relationship between BCAAs and prevalent CKD.

CONCLUSIONS

Our study shows the intricate relationship between plasma BCAA levels and CKD in individuals with type 2 diabetes. While an association exists, its manifestation varies based on disease status and BMI, with no definitive evidence supporting a causal link between BCAAs and prevalent CKD.

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

BACKGROUND

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

SUMMARY

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

KEY MESSAGE

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Ketone production and excretion even during mild hyperglycemia and the impact of sodium-glucose co-transporter inhibition in type 1 diabetes

AIMS

We aimed to determine if ketone production and excretion are increased even at mild fasting hyperglycemia in type 1 diabetes (T1D) and if these are modified by ketoacidosis risk factors, including sodium-glucose co-transporter inhibition (SGLTi) and female sex.

METHODS

In secondary analysis of an 8-week single-arm open-label trial of empagliflozin (NCT01392560) we evaluated ketone concentrations during extended fasting and clamped euglycemia (4-6 mmol/L) and mild hyperglycemia (9-11 mmol/L) prior to and after treatment. Plasma and urine beta-hydroxybutyrate (BHB) concentrations and fractional excretion were analyzed by metabolomic analysis.

RESULTS

Forty participants (50 % female), aged 24 ± 5 years, HbA1c 8.0 ± 0.9 % (64 ± 0.08 mmol/mol) with T1D duration of 17.5 ± 7 years, were studied. Increased BHB production even during mild hyperglycemia (median urine 6.3[3.5-13.6] vs. 3.5[2.2-7.0] µmol/mmol creatinine during euglycemia, p < 0.001) was compensated by increased fractional excretion (0.9 % [0.3-1.6] vs. 0.4 % [0.2-0.9], p < 0.001). SGLTi increased production and attenuated the increased BHB fractional excretion (decreased to 0.3 % during mild hyperglycemia, p < 0.001), resulting in higher plasma concentrations (increased to 0.21 [0.05-0.40] mmol/L, p < 0.001), particularly in females (interaction p < 0.001).

CONCLUSIONS

Even mild hyperglycemia is associated with greater ketone production, compensated by urinary excretion, in T1D. However, SGLTi exaggerates production and partially reduces compensatory excretion, particularly in women.

Adenine crosses the biomarker bridge: from 'omics to treatment in diabetic kidney disease

Enabling the early detection and prevention of diabetic kidney damage has potential to substantially reduce the global burden of kidney failure. There is a critical need for identification of mechanistic biomarkers that can predict progression and serve as therapeutic targets. In this issue of the JCI, Sharma and colleagues used an integrated multiomics approach to identify the metabolite adenine as a noninvasive biomarker of progression in early diabetic kidney disease (DKD). The highest tertile of urine adenine/creatinine ratio (UAdCR) was associated with higher risk for end-stage kidney disease and mortality across independent cohorts, including participants with early DKD without macroalbuminuria. Spatial metabolomics, single-cell transcriptomics, and experimental studies localized adenine to regions of tubular pathology and implicated the mTOR pathway in adenine-mediated tissue fibrosis. Inhibition of endogenous adenine production was protective in a diabetic model. These findings exemplify the potential for multiomics to uncover mechanistic biomarkers and targeted therapies in DKD.

Endogenous adenine mediates kidney injury in diabetic models and predicts diabetic kidney disease in patients

Diabetic kidney disease (DKD) can lead to end-stage kidney disease (ESKD) and mortality; however, few mechanistic biomarkers are available for high-risk patients, especially those without macroalbuminuria. Urine from participants with diabetes from the Chronic Renal Insufficiency Cohort (CRIC) study, the Singapore Study of Macro-angiopathy and Micro-vascular Reactivity in Type 2 Diabetes (SMART2D), and the American Indian Study determined whether urine adenine/creatinine ratio (UAdCR) could be a mechanistic biomarker for ESKD. ESKD and mortality were associated with the highest UAdCR tertile in the CRIC study and SMART2D. ESKD was associated with the highest UAdCR tertile in patients without macroalbuminuria in the CRIC study, SMART2D, and the American Indian study. Empagliflozin lowered UAdCR in nonmacroalbuminuric participants. Spatial metabolomics localized adenine to kidney pathology, and single-cell transcriptomics identified ribonucleoprotein biogenesis as a top pathway in proximal tubules of patients without macroalbuminuria, implicating mTOR. Adenine stimulated matrix in tubular cells via mTOR and stimulated mTOR in mouse kidneys. A specific inhibitor of adenine production was found to reduce kidney hypertrophy and kidney injury in diabetic mice. We propose that endogenous adenine may be a causative factor in DKD.

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.

Serum and urine metabolomic biomarkers for predicting prognosis in patients with immunoglobulin A nephropathy

BACKGROUND

Immunoglobulin A nephropathy (IgAN) is the most prevalent form of glomerulonephritis worldwide. Prediction of disease progression in IgAN can help to provide individualized treatment based on accurate risk stratification.

METHODS

We performed proton nuclear magnetic resonance-based metabolomics analyses of serum and urine samples from healthy controls, non-progressor (NP), and progressor (P) groups to identify metabolic profiles of IgAN disease progression. Metabolites that were significantly different between the NP and P groups were selected for pathway analysis. Subsequently, we analyzed multivariate area under the receiver operating characteristic (ROC) curves to evaluate the predictive power of metabolites associated with IgAN progression.

RESULTS

We observed several distinct metabolic fingerprints of the P group involving the following metabolic pathways: glycolipid metabolism; valine, leucine, and isoleucine biosynthesis; aminoacyl-transfer RNA biosynthesis; glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism. In multivariate ROC analyses, the combinations of serum glycerol, threonine, and proteinuria (area under the curve [AUC], 0.923; 95% confidence interval [CI], 0.667-1.000) and of urinary leucine, valine, and proteinuria (AUC, 0.912; 95% CI, 0.667-1.000) showed the highest discriminatory ability to predict IgAN disease progression.

CONCLUSION

This study identified serum and urine metabolites profiles that can aid in the identification of progressive IgAN and proposed perturbed metabolic pathways associated with the identified metabolites.

Point-of-care applicable metabotyping using biofluid-specific electrospun MetaSAMPs directly amenable to ambient LA-REIMS

In recent years, ambient ionization mass spectrometry (AIMS) including laser ablation rapid evaporation IMS, has enabled direct biofluid metabolome analysis. AIMS procedures are, however, still hampered by both analytical, i.e., matrix effects, and practical, i.e., sample transport stability, drawbacks that impede metabolome coverage. In this study, we aimed at developing biofluid-specific metabolome sampling membranes (MetaSAMPs) that offer a directly applicable and stabilizing substrate for AIMS. Customized rectal, salivary, and urinary MetaSAMPs consisting of electrospun (nano)fibrous membranes of blended hydrophilic (polyvinylpyrrolidone and polyacrylonitrile) and lipophilic (polystyrene) polymers supported metabolite absorption, adsorption, and desorption. Moreover, MetaSAMP demonstrated superior metabolome coverage and transport stability compared to crude biofluid analysis and was successfully validated in two pediatric cohorts (MetaBEAse, n = 234 and OPERA, n = 101). By integrating anthropometric and (patho)physiological with MetaSAMP-AIMS metabolome data, we obtained substantial weight-driven predictions and clinical correlations. In conclusion, MetaSAMP holds great clinical application potential for on-the-spot metabolic health stratification.

Role of endogenous adenine in kidney failure and mortality with diabetes

Diabetic kidney disease (DKD) can lead to end-stage kidney disease (ESKD) and mortality, however, few mechanistic biomarkers are available for high risk patients, especially those without macroalbuminuria. Urine from participants with diabetes from Chronic Renal Insufficiency Cohort (CRIC), Singapore Study of Macro-Angiopathy and Reactivity in Type 2 Diabetes (SMART2D), and the Pima Indian Study determined if urine adenine/creatinine ratio (UAdCR) could be a mechanistic biomarker for ESKD. ESKD and mortality were associated with the highest UAdCR tertile in CRIC (HR 1.57, 1.18, 2.10) and SMART2D (HR 1.77, 1.00, 3.12). ESKD was associated with the highest UAdCR tertile in patients without macroalbuminuria in CRIC (HR 2.36, 1.26, 4.39), SMART2D (HR 2.39, 1.08, 5.29), and Pima Indian study (HR 4.57, CI 1.37-13.34). Empagliflozin lowered UAdCR in non-macroalbuminuric participants. Spatial metabolomics localized adenine to kidney pathology and transcriptomics identified ribonucleoprotein biogenesis as a top pathway in proximal tubules of patients without macroalbuminuria, implicating mammalian target of rapamycin (mTOR). Adenine stimulated matrix in tubular cells via mTOR and stimulated mTOR in mouse kidneys. A specific inhibitor of adenine production was found to reduce kidney hypertrophy and kidney injury in diabetic mice. We propose that endogenous adenine may be a causative factor in DKD.

Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol

BACKGROUND

Many potential prognostic factors for predicting kidney transplantation outcomes have been identified. However, in Switzerland, no widely accepted prognostic model or risk score for transplantation outcomes is being routinely used in clinical practice yet. We aim to develop three prediction models for the prognosis of graft survival, quality of life, and graft function following transplantation in Switzerland.

METHODS

The clinical kidney prediction models (KIDMO) are developed with data from a national multi-center cohort study (Swiss Transplant Cohort Study; STCS) and the Swiss Organ Allocation System (SOAS). The primary outcome is the kidney graft survival (with death of recipient as competing risk); the secondary outcomes are the quality of life (patient-reported health status) at 12 months and estimated glomerular filtration rate (eGFR) slope. Organ donor, transplantation, and recipient-related clinical information will be used as predictors at the time of organ allocation. We will use a Fine & Gray subdistribution model and linear mixed-effects models for the primary and the two secondary outcomes, respectively. Model optimism, calibration, discrimination, and heterogeneity between transplant centres will be assessed using bootstrapping, internal-external cross-validation, and methods from meta-analysis.

DISCUSSION

Thorough evaluation of the existing risk scores for the kidney graft survival or patient-reported outcomes has been lacking in the Swiss transplant setting. In order to be useful in clinical practice, a prognostic score needs to be valid, reliable, clinically relevant, and preferably integrated into the decision-making process to improve long-term patient outcomes and support informed decisions for clinicians and their patients. The state-of-the-art methodology by taking into account competing risks and variable selection using expert knowledge is applied to data from a nationwide prospective multi-center cohort study. Ideally, healthcare providers together with patients can predetermine the risk they are willing to accept from a deceased-donor kidney, with graft survival, quality of life, and graft function estimates available for their consideration.

STUDY REGISTRATION

Open Science Framework ID: z6mvj.

SUPPRESION OF MITOCHONDRIAL RESPIRATION IS A FEATURE OF CELLULAR GLUCOSE TOXICITY

Glucose toxicity is central to the myriad complications of diabetes and is now believed to encompass neurodegenerative diseases and cancer as well as microvascular and macrovascular disease. Due to the widespread benefits of SGLT2 inhibitors, which affect glucose uptake in the kidney proximal tubular cell, a focus on cell metabolism in response to glucose has important implications for overall health. We previously found that a -Warburg-type effect underlies diabetic kidney disease and involves metabolic reprogramming. This is now supported by quantitative measurements of superoxide measurement in the diabetic kidney and systems biology analysis of urine metabolites in patients. Further exploration of mechanisms underlying mediators of mitochondrial suppression will be critical in understanding the chronology of glucose-induced toxicity and developing new therapeutics to arrest the systemic glucose toxicity of diabetes.

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: "Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal" (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214-0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: "Cer(d18:1/24:1) i2", "PC(20:3-OH/P-18:1)", "Ganoderic acid C2", "TG(16:0/17:1/18:1)" and "GPEtn(18:0/20:4)".

A technology assisted precision ketogenic diet intervention for cardio-renal-metabolic health in overweight or obese adults: Protocol for a randomized controlled trial

BACKGROUND

The obesity epidemic is a public health concern, as it is associated with a variety of chronic conditions. The ketogenic diet has drawn much scientific and public attention. However, implementation is challenging and its effect on cardio-renal-metabolic health is inconclusive. This study will assess the feasibility, acceptability, and preliminary efficacy of a technology-assisted ketogenic diet on cardio-renal-metabolic health.

METHODS

This is a single center, 6-month, stratified, randomized controlled trial. A total of 60 overweight/obese adults (18+ years old) will be enrolled, including 20 without type 2 diabetes (T2D) and without chronic kidney disease (CKD); 20 with T2D, but without CKD; and 20 with early-stage CKD. Participants will be stratified based on health conditions and randomized into a ketogenic diet (n = 30) or a low-fat diet group (n = 30). Health education involving diet and physical activity will be delivered both digitally and in-person. Mobile and connected health technologies will be used to track lifestyle behaviors and health indicators, as well as provide weekly feedback. The primary outcome (weight) and the secondary outcomes (e.g., blood pressure, glycemic control, renal health) will be assessed with traditional measurements and metabolomics.

DISCUSSION

Mobile and connected health technologies provide new opportunities to improve chronic condition management, health education attendance, planned lifestyle changes and engagement, and health outcomes. The advancement of bioinformatics technology offers the possibility to profile and analyze omics data which may advance our understanding of the underlying mechanisms of intervention effects on health outcomes at the molecular level for personalized and precision lifestyle interventions.