Biomarkers of rapid chronic kidney disease progression in type 2 diabetes

Urinary biomarkers in diabetic nephropathy

Diabetic nephropathy (DN), a significant consequence of diabetes, is associated with adverse cardiovascular and renal disease as well as mortality. Although microalbuminuria is considered the best non-invasive marker for DN, better predictive markers are needed of sufficient sensitivity and specificity to detect disease in general and in early disease specifically. Even prior to appearance of microalbuminuria, urinary biomarkers increase in diabetics and can serve as accurate nephropathy biomarkers even in normoalbuminuria. In this review, a number of novel urine biomarkers including those reflecting kidney damage caused by glomerular/podocyte damage, tubular damage, oxidative stress, inflammation, and intrarenal renin-angiotensin system activation are discussed. Our review also includes emerging biomarkers such as urinary microRNAs. These short noncoding miRNAs regulate gene expression and could be utilized to identify potential novel biomarkers in DN development and progression. .

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.

The therapeutic effect of mesenchymal stem cells in diabetic kidney disease

Diabetes mellitus (DM) often causes chronic kidney damage despite best medical practices. Diabetic kidney disease (DKD) arises from a complex interaction of factors within the kidney and the whole body. Targeting specific disease-causing agents using drugs has not been effective in treating DKD. However, stem cell therapies offer a promising alternative by addressing multiple disease pathways and promoting kidney regeneration. Mesenchymal stem cells (MSCs) offer great promise due to their superior accessibility ratio from adult tissues and remarkable modes of action, such as the production of paracrine anti-inflammatory and cytoprotective substances. This review critically evaluates the development of MSC treatment for DKD as it moves closer to clinical application. Results from animal models suggest that systemic MSC infusion may positively impact DKD progression. However, few registered and completed clinical trials exist, and whether the treatments are effective in humans is still being determined. Significant knowledge gaps and research opportunities exist, including establishing the ideal source, dose, and timing of MSC delivery, better understanding of in vivo mechanisms, and developing quantitative indicators to obtain a more significant therapeutic response. This paper reviews recent literature on using MSCs in preclinical and clinical trials in DKD. Potent biomarkers related to DKD are also highlighted, which may help better understand MSCs' action in this disease progression. KEY MESSAGES: Mesenchymal stem cells have anti-inflammatory and paracrine effects in diabetic kidney disease. Mesenchymal stem cells alleviate in animal models having diabetic kidney disease. Mesenchymal stem cells possess promise for the treatment of diabetic kidney disease.

Endostatin, soluble tumour necrosis factor receptor 1 and soluble tumour necrosis factor receptor 2 cannot predict new onset of microalbuminuria in patients with type 2 diabetes

AIMS

Inflammation and angiogenesis play an important role in the development of early diabetic kidney disease. We investigated the association of soluble Tumour Necrosis Factor Receptor 1 (sTNF-R1), sTNF-R2 and endostatin with new onset microalbuminuria in normoalbuminuric patients with diabetes mellitus type 2.

METHODS

We conducted a case control study to assess serum levels of sTNF-R1, sTNF-R2 and endostatin in 169 patients with new onset microalbuminuria and in 188 matched normoalbuminuric, diabetic controls. Baseline serum samples from participants of the ROADMAP (Randomized Olmesartan and Diabetes Microalbuminuria Prevention) and observational follow-up (ROADMAP-OFU) studies were used.

RESULTS

Endostatin and sTNF-R1 but not sTNF-R2 were increased at baseline in patients with future microalbuminuria. In the multivariate analysis, each log2 increment in endostatin levels was associated with an increase of only 6% in the risk of development of microalbuminuria (adjusted HR (95% CI) 1.006 (1.001-1011). sTNF-R1 and sTNF-R2 levels were conversely associated with microalbuminuria, but the results did not reach statistical significance. The respective adjusted HRs (95% CI) were 1.305 (0.928-1.774) and 0.874 (0.711-1.074).

CONCLUSIONS

sTNF-R1 and sTNF-R2 failed to predict the occurrence of microalbuminuria in normoalbuminuric patients with type 2 diabetes. Likewise, the utility of endostatin in predicting new onset proteinuria is limited.

The therapeutic effect of mesenchymal stem cells in diabetic kidney disease

Abstract

Diabetes mellitus (DM) often causes chronic kidney damage despite best medical practices. Diabetic kidney disease (DKD) arises from a complex interaction of factors within the kidney and the whole body. Targeting specific disease-causing agents using drugs has not been effective in treating DKD. However, stem cell therapies offer a promising alternative by addressing multiple disease pathways and promoting kidney regeneration. Mesenchymal stem cells (MSCs) offer great promise due to their superior accessibility ratio from adult tissues and remarkable modes of action, such as the production of paracrine anti-inflammatory and cytoprotective substances. This review critically evaluates the development of MSC treatment for DKD as it moves closer to clinical application. Results from animal models suggest that systemic MSC infusion may positively impact DKD progression. However, few registered and completed clinical trials exist, and whether the treatments are effective in humans is still being determined. Significant knowledge gaps and research opportunities exist, including establishing the ideal source, dose, and timing of MSC delivery, better understanding of in vivo mechanisms, and developing quantitative indicators to obtain a more significant therapeutic response. This paper reviews recent literature on using MSCs in preclinical and clinical trials in DKD. Potent biomarkers related to DKD are also highlighted, which may help better understand MSCs’ action in this disease progression.

Key messages

Utility of serum β2-microglobulin for prediction of kidney outcome among patients with biopsy-proven diabetic nephropathy

AIM

To examine whether serum β2-microglobulin (β2-MG) could improve the prediction performance for kidney failure with replacement therapy (KFRT) among patients with diabetic nephropathy (DN).

METHODS

Patients with biopsy-proven DN at Nara Medical University Hospital were included. The exposure of interest was log-transformed serum β2-MG levels measured at kidney biopsy. The outcome variable was KFRT. Multivariable Cox regression models and competing-risk regression models, with all-cause mortality as a competing event, were performed. Model fit by adding serum β2-MG levels was calculated using the Akaike information criterion (AIC). The net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indexes were used to evaluate the improvement of predictive performance for 5-year cumulative incidence of KFRT by serum β2-MG levels.

RESULTS

Among 408 patients, 99 developed KFRT during a median follow-up period of 6.7 years. A higher serum β2-MG level (1-unit increase in log-transformed serum β2-MG level) was associated with a higher incidence of KFRT, even after adjustments for previously known clinical and histological risk factors (hazard ratio [95% confidence interval {CI}]: 3.30 [1.57-6.94] and subdistribution hazard ratio [95% CI]: 3.07 [1.55-6.06]). The addition of log-transformed serum β2-MG level reduced AIC and improved the prediction of KFRT (NRI and IDI: 0.32 [0.09-0.54] and 0.03 [0.01-0.56], respectively).

CONCLUSIONS

Among patients with biopsy-proven DN, serum β2-MG was an independent predictor of KFRT and improved prediction performance. In addition to serum creatinine, serum β2-MG should probably be measured for DN.

Plasma Tryptophan-Kynurenine Pathway Metabolites and Risk for Progression to End-Stage Kidney Disease in Patients With Type 2 Diabetes

OBJECTIVE

We sought to study the associations between plasma metabolites in the tryptophan-kynurenine pathway and the risk of progression to end-stage kidney disease (ESKD) in patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Plasma tryptophan, kynurenine, 3-hydroxykynurenine, kynurenic acid, and xanthurenic acid concentrations were measured in discovery (n = 1,915) and replication (n = 346) cohorts. External validation was performed in Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes (n = 1,312). The primary outcome was a composite of incident ESKD (progression to estimated glomerular filtration rate [eGFR] <15 mL/min/1.73 m2, sustained dialysis, or renal death). The secondary outcome was annual eGFR decline.

RESULTS

In the discovery cohort, tryptophan was inversely associated with risk for ESKD, and kynurenine-to-tryptophan ratio (KTR) was positively associated with risk for ESKD after adjustment for clinical risk factors, including baseline eGFR and albuminuria (adjusted hazard ratios [HRs] 0.62 [95% CI 0.51, 0.75] and 1.48 [1.20, 1.84] per 1 SD). High levels of kynurenic acid and xanthurenic acid were associated with low risks of ESKD (0.74 [0.60, 0.91] and 0.74 [0.60, 0.91]). Consistently, high levels of tryptophan, kynurenic acid, and xanthurenic acid were independently associated with a slower eGFR decline, while a high KTR was predictive of a faster eGFR decline. Similar outcomes were obtained in the replication cohort. Furthermore, the inverse association between kynurenic acid and risk of ESKD was externally validated in CRIC participants with diabetes (adjusted HR 0.78 [0.65, 0.93]).

CONCLUSIONS

Accelerated catabolism of tryptophan in the kynurenine pathway may be involved in progressive loss of kidney function. However, shunting the kynurenine pathway toward the kynurenic acid branch may potentially slow renal progression.

Combination Therapy of RAS Inhibition and SGLT2 Inhibitors Decreases Levels of Endotrophin in Persons with Type 2 Diabetes

We investigated for the first time the effect of combination therapy of renin-angiotensin system inhibition (RASi) and sodium-glucose co-transporter-2 inhibitors (SGLT2is) on endotrophin (ETP), a pro-fibrotic signaling molecule reflecting collagen type VI formation, measured in the plasma of persons with type 2 diabetes (T2D). ETP was measured using the PRO-C6 ELISA in 294 individuals from the "Drug combinations for rewriting trajectories of renal pathologies in type 2 diabetes" (DC-ren) project. In the DC-ren study, kidney disease progression was defined as a >10% decline in the estimated glomerular filtration rate (eGFR) to an eGFR < 60 mL/min/1.73 m2. Among the investigated circulating markers, ETP was the most significant predictor of future eGFR. Combination therapy of RASi and SGLT2is led to a significant reduction in ETP levels compared to RASi monotherapy (p for slope difference = 0.002). Higher levels of baseline plasma ETP were associated with a significantly increased risk of kidney disease progression (p = 0.007). In conclusion, plasma ETP identified individuals at higher risk of kidney disease progression. The observed decreased levels of plasma ETP with combination therapy of RASi and SGLT2is in persons with T2D may reflect a reduced risk of kidney disease progression following treatment with SGLT2is.

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.

Diagnostic challenges of diabetic kidney disease

Diabetic kidney disease (DKD) is one of the most common microvascular complications of both type 1 and type 2 diabetes and the most common cause of the end-stage renal disease (ESRD). It has been evidenced that targeted interventions at an early stage of DKD can efficiently prevent or delay the progression of kidney failure and improve patient outcomes. Therefore, regular screening for DKD has become one of the fundamental principles of diabetes care. Long-established biomarkers such as serum-creatinine-based estimates of glomerular filtration rate and albuminuria are currently the cornerstone of diagnosis and risk stratification in routine clinical practice. However, their immanent biological limitations and analytical variations may influence the clinical interpretation of the results. Recently proposed new predictive equations without the variable of race, together with the evidence on better accuracy of combined serum creatinine and cystatin C equations, and both race- and sex-free cystatin C-based equation, have enabled an improvement in the detection of DKD, but also require the harmonization of the recommended laboratory tests, wider availability of cystatin C testing and specific approach in various populations. Considering the complex pathophysiology of DKD, particularly in type 2 diabetes, a panel of biomarkers is needed to classify patients in terms of the rate of disease progression and/or response to specific interventions. With a personalized approach to diagnosis and treatment, in the future, it will be possible to respond to DKD better and enable improved outcomes for numerous patients worldwide.

The potential of cardiac biomarkers, NT-ProBNP and troponin T, in predicting the progression of nephropathy in diabetic patients: A meta-analysis of prospective cohort studies

AIMS

A meta-analysis was done to investigate the association of two cardiac biomarkers of N-terminal prohormone of B-type natriuretic peptide (NT-proBNP) and circulating troponin T (TnT) with the progression of diabetic nephropathy (DN).

METHODS

A thorough search of the PubMed, Scopus, and Web of Science databases was done until June 2022. The outcome (progression of DN) was described as either of the followings: a) eGFR decline, b) albuminuria, c) end-stage renal disease, or d) mortality. A pooled analysis of eligible studies was performed using random-effect models to compensate for the differences in measurement standards between the studies. We further carried out subgroup analyses to examine our results' robustness and find the source of heterogeneity. A sensitivity analysis was performed to assess the influence of individual studies on the pooled result and the funnel plot and Egger's test were used to assess publication bias.

RESULTS

For NT-proBNP, 8741 participants from 14 prospective cohorts, and for TnT, 7292 participants from 9 prospective cohorts were included in the meta-analysis. Higher NT-proBNP levels in diabetic patients were associated with a higher probability of DN progression (relative risk [RR]: 1.67, 95% confidence interval [CI]: 1.44 to 1.92). Likewise, elevated levels of TnT were associated with an increased likelihood of DN (RR: 1.57, 95% CI: 1.34 to 1.83). The predictive power of both biomarkers for DN remained significant when the subgroup analyses were performed. The risk estimates were sensitive to none of the studies. The funnel plot and Egger's tests indicated publication bias for both biomarkers. Hence, trim and fill analysis was performed to compensate for this putative bias and the results remained significant both for NT-proBNP (RR: 1.50, 95% CI: 1.31 to 1.79) and TnT (RR: 1.35, 95% CI 1.15 to 1.60).

CONCLUSIONS

The increased blood levels of TnT and NT-proBNP can be considered as predictors of DN progression in diabetic individuals. PROSPERO registration code: CRD42022350491.

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.

Quantitative, Targeted Analysis of Gut Microbiota Derived Metabolites Provides Novel Biomarkers of Early Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Diabetic kidney disease (DKD) is one of the most debilitating complications of type 2 diabetes mellitus (T2DM), as it progresses silently to end-stage renal disease (ESRD). The discovery of novel biomarkers of early DKD becomes acute, as its incidence is reaching catastrophic proportions. Our study aimed to quantify previously identified metabolites from serum and urine through untargeted ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-ESI+-MS) techniques, such as the following: arginine, dimethylarginine, hippuric acid, indoxyl sulfate, p-cresyl sulfate, L-acetylcarnitine, butenoylcarnitine and sorbitol. The study concept was based on the targeted analysis of selected metabolites, using the serum and urine of 20 healthy subjects and 90 T2DM patients with DKD in different stages (normoalbuminuria-uACR < 30 mg/g; microalbuminuria-uACR 30-300 mg/g; macroalbuminuria-uACR > 300 mg/g). The quantitative evaluation of metabolites was performed with pure standards, followed by the validation methods such as the limit of detection (LOD) and the limit of quantification (LOQ). The following metabolites from this study resulted as possible biomarkers of early DKD: in serum-arginine, dimethylarginine, hippuric acid, indoxyl sulfate, butenoylcarnitine and sorbitol and in urine-p-cresyl sulfate.

Identification of a New RNA and Protein Integrated Biomarker Panel Associated with Kidney Function Impairment in DKD: Translational Implications

Diabetic kidney disease (DKD) is a complication that strongly increases the risk of end-stage kidney disease and cardiovascular events. The identification of novel, highly sensitive, and specific early biomarkers to identify DKD patients and predict kidney function decline is a pivotal aim of translational medicine. In a previous study, after a high-throughput approach, we identified in 69 diabetic patients 5 serum mitochondrial RNAs (MT-ATP6, MT-ATP8, MT-COX3, MT-ND1, and MT-RNR1) progressively downregulated with increasing eGFR stages. Here, we analyzed the protein serum concentrations of three well-validated biomarkers: TNFRI, TNFRII, and KIM-1. The protein biomarkers were gradually upregulated from G1 to G2 and G3 patients. All protein biomarkers correlated with creatinine, eGFR, and BUN. Performing multilogistic analyses, we found that, with respect to single protein biomarkers, the combination between (I) TNFRI or KIM-1 with each RNA transcript and (II) TNFRII with MT-ATP8, MT-ATP6, MT-COX-3, and MT-ND1 determined an outstanding improvement of the diagnostic performance of G3 versus G2 patient identification, reaching values in most cases above 0.9 or even equal to 1. The improvement of AUC values was also evaluated in normoalbuminuric or microalbuminuric patients considered separately. This study proposes a novel, promising multikind marker panel associated with kidney impairment in DKD.

Surface-enhanced Raman spectroscopy of centrifuged blood serum samples of diabetic type II patients by using 50KDa filter devices

Blood serum contains essential biochemical information which are used for early disease diagnosis. Blood serum consisted of higher molecular weight fractions (HMWF) and lower molecular weight fractions (LMWF). The disease biomarkers are lower molecular weight fraction proteins, and their contribution to disease diagnosis is suppressed due to higher molecular weight fraction proteins. To diagnose diabetes in early stages are difficult because of the presence of huge amount of these HMWF. In the current study, surface-enhanced Raman spectroscopy (SERS) are employed to diagnose diabetes after centrifugation of serum samples using Amicon ultra filter devices of 50 kDa which produced two fractions of whole blood serum of filtrate, low molecular weight fraction, and residue, high molecular weight fraction. Furthermore SERS is employed to study the LMW fractions of healthy and diseased samples. Some prominent SERS bands are observed at 725 cm^-1, 842 cm^-1, 1025 cm^-1, 959 cm^-1, and 1447 cm^-1 due to small molecular weight proteins, and these biomarkers helped to diagnose the disease early stage. Moreover, chemometric techniques such as principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are employed to check the potential of surface-enhanced Raman spectroscopy for the differentiation and classifications of the blood serum samples. SERS can be employed for the early diagnosis and screening of biochemical changes during type II diabetes.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Novel Biomarkers of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is a highly prevalent condition worldwide. It represents one of the most common complications arising from diabetes mellitus (DM) and is the leading cause of end-stage kidney disease (ESKD). Its development involves three fundamental components: the hemodynamic, metabolic, and inflammatory axes. Clinically, persistent albuminuria in association with a progressive decline in glomerular filtration rate (GFR) defines this disease. However, as these alterations are not specific to DKD, there is a need to discuss novel biomarkers arising from its pathogenesis which may aid in the diagnosis, follow-up, therapeutic response, and prognosis of the disease.

Different roles of protein biomarkers predicting eGFR trajectories in people with chronic kidney disease and diabetes mellitus: a nationwide retrospective cohort study

BACKGROUND

Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory.

METHODS

We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation.

RESULTS

The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline.

CONCLUSIONS

Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway.

Using Data to Improve the Management of Diabetes: The Tayside Experience

Tayside is a region in the East of Scotland and forms one of nine local government regions in the country. It is home to approximately 416,000 individuals who fall under the National Health Service (NHS) Tayside health board, which provides health care services to the population. In Tayside, Scotland, a comprehensive informatics network for diabetes care and research has been established for over 25 years. This has expanded more recently to a comprehensive Scotland-wide clinical care system, Scottish Care Information - Diabetes (SCI-Diabetes). This has enabled improved diabetes screening and integrated management of diabetic retinopathy, neuropathy, nephropathy, cardiovascular health, and other comorbidities. The regional health informatics network links all of these specialized services with comprehensive laboratory testing, prescribing records, general practitioner records, and hospitalization records. Not only do patients benefit from the seamless interconnectedness of these data, but also the Tayside bioresource has enabled considerable research opportunities and the creation of biobanks. In this article we describe how health informatics has been used to improve care of people with diabetes in Tayside and Scotland and, through anonymized data linkage, our understanding of the phenotypic and genotypic etiology of diabetes and associated complications and comorbidities.

Glutathione Peroxidase 4 Is a Predictor of Diabetic Kidney Disease Progression in Type 2 Diabetes Mellitus

BACKGROUND

Diabetic kidney disease (DKD) represents a heavy burden in type 2 diabetes mellitus (T2DM). Ferroptosis plays an important role in DKD, and it thus provides new perspectives to pursue more related biomarkers to assess the disease severity and prognosis. Glutathione peroxidase 4 (GPX4) is the mainstay in regulating ferroptosis. The current study investigated the predictive value of kidney GPX4 expression level in DKD progression.

METHODS

We measured GPX4 levels in kidney paraffin sections of 85 biopsy-proven DKD patients by immunohistochemistry staining. The associations between the GPX4 level and clinicopathological parameters as well as renal outcomes were analyzed.

RESULTS

GPX4 is mainly expressed in kidney tubulointerstitium, especially in tubular epithelial cells of DKD patients. The GPX4 expression level was significantly lower in DKD patients than healthy controls. Besides, GPX4 level significantly correlated with proteinuria (r = -0.42, p < 0.001), urinary albumin-to-creatinine ratio (uACR) (r = -0.40, p < 0.01), serum creatinine (Scr) (r = -0.59, p < 0.001), estimated glomerular filtration rate (eGFR) (r = 0.66, p < 0.001), and the percentage of sclerosed glomeruli (r = -0.42, p < 0.001) in renal specimens. During follow-up, the GPX4 level positively correlated with eGFR slope (r = 0.48, p < 0.001), and GPX4-low patients showed a significantly higher probability of developing end-stage kidney disease (ESKD) compared with GPX4-high patients (p < 0.01). Moreover, after adjusting for other potential predictors, the GPX4 level was still an independent predictor of developing ESKD (HR 2.15, 95% CI 1.08 to 4.28, p < 0.05).

CONCLUSIONS

Kidney tubulointerstitial GPX4 expression level was associated with the disease severity and progression of DKD.

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.

Review of potential biomarkers of inflammation and kidney injury in diabetic kidney disease

Diabetic kidney disease is expected to increase rapidly over the coming decades with rising prevalence of diabetes worldwide. Current measures of kidney function based on albuminuria and estimated glomerular filtration rate do not accurately stratify and predict individuals at risk of declining kidney function in diabetes. As a result, recent attention has turned towards identifying and assessing the utility of biomarkers in diabetic kidney disease. This review explores the current literature on biomarkers of inflammation and kidney injury focussing on studies of single or multiple biomarkers between January 2014 and February 2020. Multiple serum and urine biomarkers of inflammation and kidney injury have demonstrated significant association with the development and progression of diabetic kidney disease. Of the inflammatory biomarkers, tumour necrosis factor receptor-1 and -2 were frequently studied and appear to hold most promise as markers of diabetic kidney disease. With regards to kidney injury biomarkers, studies have largely targeted markers of tubular injury of which kidney injury molecule-1, beta-2-microglobulin and neutrophil gelatinase-associated lipocalin emerged as potential candidates. Finally, the use of a small panel of selective biomarkers appears to perform just as well as a panel of multiple biomarkers for predicting kidney function decline.

Up-Date on Diabetic Nephropathy

Diabetes is one of the leading causes of kidney disease. Diabetic kidney disease (DKD) is a major cause of end-stage kidney disease (ESKD) worldwide, and it is linked to an increase in cardiovascular (CV) risk. Diabetic nephropathy (DN) increases morbidity and mortality among people living with diabetes. Risk factors for DN are chronic hyperglycemia and high blood pressure; the renin-angiotensin-aldosterone system blockade improves glomerular function and CV risk in these patients. Recently, new antidiabetic drugs, including sodium-glucose transport protein 2 inhibitors and glucagon-like peptide-1 agonists, have demonstrated additional contribution in delaying the progression of kidney disease and enhancing CV outcomes. The therapeutic goal is regression of albuminuria, but an atypical form of non-proteinuric diabetic nephropathy (NP-DN) is also described. In this review, we provide a state-of-the-art evaluation of current treatment strategies and promising emerging treatments.

Evaluation of the clinical utility of the PromarkerD in-vitro test in predicting diabetic kidney disease and rapid renal decline through a conjoint analysis

Background

Early identification of patients at risk of developing diabetic kidney disease or rapid renal decline is imperative for appropriate patient management, but traditional methods of predicting renal decline are limited.

Objective

This study evaluated the impact of PromarkerD, a biomarker-based blood test predicting the risk of diabetic kidney disease (DKD) and rapid renal decline.

Methods

Conjoint analysis clarified the importance of PromarkerD and other patient attributes to physician decisions for type 2 diabetes patients. Forty-two patient profiles were generated, with varying levels of albuminuria, estimated glomerular filtration rate (eGFR), blood pressure, hemoglobin A1c (HbA1c), age, and PromarkerD result. A web-based survey asked each physician to make monitoring/treatment decisions about eight randomly selected profiles. Data were analyzed using multivariable logit models.

Results

Two hundred three primary care physicians and 197 endocrinologists completed the survey. PromarkerD result was most important for increasing the frequency of risk factor monitoring. PromarkerD was second to HbA1c in importance for deciding to prescribe sodium/glucose cotransporter-2 inhibitors (SGLT2s) with a DKD indication, second to blood pressure for increasing the dose of lisinopril, and second to eGFR for replacing ibuprofen with a non-nephrotoxic medication. Compared with no PromarkerD results, a high-risk PromarkerD result was associated with significantly higher odds of increasing monitoring frequency (odds ratio [OR]: 2.56, 95% confidence interval: 1.90–3.45), prescribing SGLT2s (OR: 1.98 [1.56–2.52]), increasing lisinopril dose (OR: 1.48 [1.17–1.87]), and replacing ibuprofen (OR: 1.78 [1.32–2.40]). A low-risk PromarkerD result was associated with significantly lower odds of increasing monitoring frequency (OR: 0.48 [0.37–0.64]), prescribing SGLT2s (OR: 0.70 [0.56–0.88]), and replacing ibuprofen (OR: 0.75 [0.57–0.99]).

Conclusion

PromarkerD could increase adoption of renoprotective interventions in patients at high risk for renal decline and lower the likelihood of aggressive treatment in those at low risk. Further studies are needed to assess patient outcomes with PromarkerD in real-world practice.

Lifting the Veil: Characteristics, Clinical Significance, and Application of β-2-Microglobulin as Biomarkers and Its Detection with Biosensors

Because β-2-microglobulin (β₂M) is a surface protein that is present on most nucleated cells, it plays a key role in the human immune system and the kidney glomeruli to regulate homeostasis. The primary clinical significance of β₂M is in dialysis-related amyloidosis, a complication of end-stage renal disease caused by a gradual accumulation of β₂M in the blood. Therefore, the function of β₂M in kidney-related diseases has been extensively studied to evaluate its glomerular and tubular functions. Because increased β₂M shedding due to rapid cell turnover may indicate other underlying medical conditions, the possibility to use β₂M as a versatile biomarker rose in prominence across multiple disciplines for various applications. Therefore, this work has reviewed the recent use of β₂M to detect various diseases and its progress as a biomarker. While the use of state-of-the-art β₂M detection requires sophisticated tools, high maintenance, and labor cost, this work also has reported the use of biosensor to quantify β₂M over the past decade. It is hoped that a portable and highly efficient β₂M biosensor device will soon be incorporated in point-of-care testing to provide safe, rapid, and reliable test results.

Fibroblast Growth Factor 21 Levels Exhibit the Association With Renal Outcomes in Subjects With Type 2 Diabetes Mellitus

Background

Whether microalbuminuria predicts renal outcomes in patients with type 2 diabetes mellitus (T2DM) is argued. Fibroblast growth factor 21 (FGF-21) levels were elevated by the pathogenic process of diabetic kidney disease. The purpose of the study was to evaluate the associations of FGF-21 and renal outcomes in subjects with T2DM.

Methods

Chinese patients with T2DM were enrolled and then observed prospectively, and FGF-21 levels at baseline were measured. The associations of FGF-21 levels and renal composite events, defined by a drop > 30% of eGFR or worsening category of albuminuria, were evaluated using Cox analysis. The appropriate cut-off value of FGF-21 was mapped by the receiver operating characteristic (ROC) curve.

Results

Among 312 subjects, higher FGF-21 levels were associated with higher risks of renal events in Cox analysis. The area under the curve of FGF-21 levels in the ROC curve was optimal (0.67, p < 0.001), and the cut-off value of 1.40 pg/dl exhibited the best sensitivity (76.2%) and specificity (53.5%). The frequency of renal composite events was higher in subjects with FGF-21 ≥ 1.40 pg/dl than in others (30% vs. 10%, p<0.001 by the log-rank test). The worse renal outcomes predicted by FGF-21 ≥ 1.40 pg/dl were confirmed using the adjustments of Cox sequential models (hazard ratio 2.28, 95% confidence interval 1.23-4.24, p=0.009) and consistent across subjects with different status of baseline characteristics and renal risks.

Conclusion

FGF-21 levels were proportional to the risks of renal events in broad- spectrum Chinese T2DM subjects, making it a potential biomarker to predict the renal outcomes of T2DM.

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.

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.

Stem cell-derived and circulating exosomal microRNAs as new potential tools for diabetic nephropathy management

BACKGROUND

Despite major advances in the treatment of diabetic nephropathy (DN) in recent years, it remains the most common cause of end-stage renal disease. An early diagnosis and therapy may slow down the DN progression. Numerous potential biomarkers are currently being researched. Circulating levels of the kidney-released exosomes and biological molecules, which reflect the DN pathology including glomerular and tubular dysfunction as well as mesangial expansion and fibrosis, have shown the potential for predicting the occurrence and progression of DN. Moreover, many experimental therapies are currently being investigated, including stem cell therapy and medications targeting inflammatory, oxidant, or pro-fibrotic pathways activated during the DN progression. The therapeutic potential of stem cells is partly depending on their secretory capacity, particularly exosomal microRNAs (Exo-miRs). In recent years, a growing line of research has shown the participation of Exo-miRs in the pathophysiological processes of DN, which may provide effective therapeutic and biomarker tools for DN treatment.

METHODS

A systematic literature search was performed in MEDLINE, Scopus, and Google Scholar to collect published findings regarding therapeutic stem cell-derived Exo-miRs for DN treatment as well as circulating Exo-miRs as potential DN-associated biomarkers.

FINDINGS

Glomerular mesangial cells and podocytes are the most important culprits in the pathogenesis of DN and, thus, can be considered valuable therapeutic targets. Preclinical investigations have shown that stem cell-derived exosomes can exert beneficial effects in DN by transferring renoprotective miRs to the injured mesangial cells and podocytes. Of note, renoprotective Exo-miR-125a secreted by adipose-derived mesenchymal stem cells can improve the injured mesangial cells, while renoprotective Exo-miRs secreted by adipose-derived stem cells (Exo-miR-486 and Exo-miR-215-5p), human urine-derived stem cells (Exo-miR-16-5p), and bone marrow-derived mesenchymal stem cells (Exo-miR-let-7a) can improve the injured podocytes. On the other hand, clinical investigations have indicated that circulating Exo-miRs isolated from urine or serum hold great potential as promising biomarkers in DN.

Urinary levels of pro-fibrotic transglutaminase 2 (TG2) may help predict progression of chronic kidney disease

Renal clinical chemistry only detects kidney dysfunction after considerable damage has occurred and is imperfect in predicting long term outcomes. Consequently, more sensitive markers of early damage and better predictors of progression are being urgently sought, to better support clinical decisions and support shorter clinical trials. Transglutaminase 2 (TG2) is strongly implicated in the fibrotic remodeling that drives chronic kidney disease (CKD). We hypothesized that urinary TG2 and its ε-(γ-glutamyl)-lysine crosslink product could be useful biomarkers of kidney fibrosis and progression. Animal models: a rat 4-month 5/6th subtotal nephrectomy model of CKD and a rat 8-month streptozotocin model of diabetic kidney disease had 24-hour collection of urine, made using a metabolic cage, at regular periods throughout disease development. Patients: Urine samples from patients with CKD (n = 290) and healthy volunteers (n = 33) were collected prospectively, and progression tracked for 3 years. An estimated glomerular filtration rate (eGFR) loss of 2–5 mL/min/year was considered progressive, with rapid progression defined as > 5 mL/min/year. Assays: TG2 was measured in human and rat urine samples by enzyme-linked immunosorbent assay (ELISA) and ε-(γ-glutamyl)-lysine by exhaustive proteolytic digestion and amino acid analysis. Urinary TG2 and ε-(γ-glutamyl)-lysine increased with the development of fibrosis in both animal model systems. Urinary TG2 was 41-fold higher in patients with CKD than HVs, with levels elevated 17-fold by CKD stage 2. The urinary TG2:creatinine ratio (UTCR) was 9 ng/mmol in HV compared with 114 ng/mmol in non-progressive CKD, 1244 ng/mmol in progressive CKD and 1898 ng/mmol in rapidly progressive CKD. Both urinary TG2 and ε-(γ-glutamyl)-lysine were significantly associated with speed of progression in univariate logistic regression models. In a multivariate model adjusted for urinary TG2, ε-(γ-glutamyl)-lysine, age, sex, urinary albumin:creatinine ratio (UACR), urinary protein:creatinine ratio (UPCR), and CKD stage, only TG2 remained statistically significant. Receiver operating characteristic (ROC) curve analysis determined an 86.4% accuracy of prediction of progression for UTCR compared with 73.5% for UACR. Urinary TG2 and ε-(γ-glutamyl)-lysine are increased in CKD. In this pilot investigation, UTCR was a better predictor of progression in patients with CKD than UACR. Larger studies are now warranted to fully evaluate UTCR value in predicting patient outcomes.

Examining the factors contributing to the association between non-albuminuric CKD and a low rate of kidney function decline in diabetes

BACKGROUND

Studies have shown that among people with diabetes, those with non-albuminuric chronic kidney disease (CKD) have a slower rate of reduction in renal function than do those with normal renal function. This suggests the presence of protective factors, the identification of which may open up targets for intervention. The aim of this study was to identify protective clinical factors and nonclinical biomarkers that contribute to the association between non-albuminuric CKD and the low rate of progression of CKD.

METHODS

We tested for significant associations of several clinical factors and 33 nonclinical biomarkers with (1) normoalbuminuria and (2) a low rate of CKD progression among participants with diabetes and CKD enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study in the United States. Factors significantly associated with both normoalbuminuria and a low rate of CKD progression were assessed in linear regression to estimate their potential contributions to the association between non-albuminuric CKD and rate of CKD progression.

RESULTS

Systolic blood pressure (SBP), glycated A1c (HbA1c), estimated glomerular filtration rate (eGFR) and six biomarkers [β-trace protein (BTP), kidney injury molecule (KIM-1), fibrinogen, fractalkine, brain natriuretic peptide (BNP) and high-sensitivity troponin-T (hsTnT)] were associated with both normoalbuminuria and a low rate of eGFR decline. The univariate β-coefficient for normoalbuminuria was 0.93 [95% confidence interval (CI): 0.82, 1.05]. When all associated factors and biomarkers were included, the regression coefficient decreased to 0.54 (95% CI: 0.40, 0.67). The factors that contributed to the association between non-albuminuric CKD and low rate of eGFR were lower levels of SBP, HbA1c, BTP, KIM-1, hsTnT, BNP, fibrinogen and fractalkine.

CONCLUSION

Lower levels of SBP and biomarkers that have pro-inflammatory and vascular modulating features may explain up to 40% of the association between non-albuminuric CKD and low rate of CKD progression. Further investigation of these biomarkers may lead to therapeutic interventions.

REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Clinical features are traditionally used to predict DKD, yet with low diagnostic efficacy. Most of the recent biomarkers used to predict DKD are based on transcriptomics and metabolomics; however, they also should be used in combination with many other predictive indicators. The purpose of this study was thus to identify a simplified class of blood biomarkers capable of predicting the risk of developing DKD. The Gene Expression Omnibus database was screened for DKD biomarkers, and differentially expressed genes (DEGs) in human blood and kidney were identified via gene expression analysis and the Least Absolute Shrinkage and Selection Operator regression. A comparison of the area under the curve (AUC) profiles on multiple receiver operating characteristic curves of the DEGs in DKD and other renal diseases revealed that REG1A and RUNX3 had the highest specificity for DKD diagnosis. The AUCs of the combined expression of REG1A and RUNX3 in kidney (AUC = 0.929) and blood samples (AUC = 0.917) of DKD patients were similar to each other. The AUC of blood samples from DKD patients and healthy individuals obtained for external validation further demonstrated that REG1A combined with RUNX3 had significant diagnostic efficacy (AUC=0.948). REG1A and RUNX3 expression levels were found to be positively and negatively correlated with urinary albumin creatinine ratio and estimated glomerular filtration rate, respectively. Kaplan-Meier curves also revealed the potential of REG1A and RUNX3 for predicting the risk of DKD. In conclusion, REG1A and RUNX3 may serve as biomarkers for predicting the risk of developing DKD.

Correlation between Acylcarnitine and Peripheral Neuropathy in Type 2 Diabetes Mellitus

OBJECTIVE

In patients with type 2 diabetes mellitus (T2DM), it is unknown whether acylcarnitine changes in the patient's plasma as diabetic peripheral neuropathy (DPN) occurs. The purpose of the present study was to investigate the correlation between acylcarnitines and DPN in Chinese patients with T2DM.

METHODS

A total of 508 patients admitted to the First Affiliated Hospital of Jinzhou Medical University were included in this study, and all of whom were hospitalized for T2DM from January 2018 to December 2020. The diagnostic criteria for DPN were based on the 2017 Chinese Guidelines for the Prevention of Type 2 Diabetes. The contents of 25 acylcarnitine metabolites in fasting blood were determined by mass spectrometry. The measured acylcarnitines were classified by factor analysis, and the factors were extracted. To determine the correlation between acylcarnitines and DPN, binary logistic regression analysis was applied.

RESULTS

Among the 508 T2DM patients, 270 had DPN. Six factors were extracted from 25 acylcarnitines, and the cumulative contribution rate of variance was 61.02%. After the adjustment for other potential confounding factors, such as other carnitines and conventional risk factors, Factor 2 was positively associated with an increased risk of DPN (OR: 1.38, 95% CI: 1.13-1.69). Factor 2 contained acetylcarnitine (C2), propionylcarnitine (C3), butylcarnitine (C4), and isovalerylcarnitine (C5).

CONCLUSIONS

Plasma levels of short-chain acylcarnitines (C2, C3, C4, and C5) were positively associated with DPN risk.

Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease

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

Serological and cellular inflammatory signatures in end-stage kidney disease and latent tuberculosis

Objectives

Tuberculosis comorbidity with chronic diseases including diabetes, HIV and chronic kidney disease is of rising concern. In particular, latent tuberculosis infection (LTBI) comorbidity with end‐stage kidney disease (ESKD) is associated with up to 52.5‐fold increased risk of TB reactivation to active tuberculosis infection (ATBI). The immunological mechanisms driving this significant rise in TB reactivation are poorly understood. To contribute to this understanding, we performed a comprehensive assessment of soluble and cellular immune features amongst a unique cohort of patients comorbid with ESKD and LTBI.

Methods

We assessed the plasma and cellular immune profiles from patients with and without ESKD and/or LTBI (N = 40). We characterised antibody glycosylation, serum complement and cytokine levels. We also assessed classical and non‐classical monocytes and T cells with flow cytometry. Using a systems‐based approach, we identified key immunological features that discriminate between the different disease states.

Results

Individuals with ESKD exhibited a highly inflammatory plasma profile and an activated cellular state compared with those without ESKD, including higher levels of inflammatory antibody Fc glycosylation structures and activated CX3CR1⁺ monocytes that correlate with increased inflammatory plasma cytokines. Similar elevated inflammatory signatures were also observed in ESKD⁺/LTBI⁺ compared with ESKD⁻/LTBI⁺, suggesting that ESKD induces an overwhelming inflammatory immune state. In contrast, no significant inflammatory differences were observed when comparing LTBI⁺ and LTBI⁻ individuals.

Conclusion

Our study highlights the highly inflammatory state induced by ESKD. We hypothesise that this inflammatory state could contribute to the increased risk of TB reactivation in ESKD patients.

DNA methylation profiling identifies epigenetic differences between early versus late stages of diabetic chronic kidney disease

BACKGROUND

We investigated a cross-sectional epigenome-wide association study of patients with early and late diabetes-associated chronic kidney disease (CKD) to identify possible epigenetic differences between the two groups as well as changes in methylation across all stages of diabetic CKD. We also evaluated the potential of using a panel of identified 5'-C-phosphate-G-3' (CpG) sites from this cohort to predict the progression of diabetic CKD.

METHODS

This cross-sectional study recruited 119 adults. DNA was extracted from blood using the Qiagen QIAampDNA Mini Spin Kit. Genome-wide methylation analysis was performed using Illumina Infinium MethylationEPIC BeadChips (HM850K). Intensity data files were processed and analysed using the minfi and MissMethyl packages for R. We examined the degree of methylation of CpG sites in early versus late diabetic CKD patients for CpG sites with an unadjusted P-value <0.01 and an absolute change in methylation of 5% (n = 239 CpG sites).

RESULTS

Hierarchical clustering of the 239 CpG sites largely separated the two groups. A heat map for all 239 CpG sites demonstrated distinct methylation patterns in the early versus late groups, with CpG sites showing evidence of progressive change. Based on our differentially methylated region (DMR) analysis of the 239 CpG sites, we highlighted two DMRs, namely the cysteine-rich secretory protein 2 (CRISP2) and piwi-like RNA-mediated gene silencing 1 (PIWIL1) genes. The best predictability for the two groups involved a receiver operating characteristics curve of eight CpG sites alone and achieved an area under the curve of 0.976.

CONCLUSIONS

We have identified distinct DNA methylation patterns between early and late diabetic CKD patients as well as demonstrated novel findings of potential progressive methylation changes across all stages (1-5) of diabetic CKD at specific CpG sites. We have also identified associated genes CRISP2 and PIWIL1, which may have the potential to act as stage-specific diabetes-associated CKD markers, and showed that the use of a panel of eight identified CpG sites alone helps to increase the predictability for the two groups.

Trajectories of kidney function in diabetes: a clinicopathological update

Diabetic nephropathy has been traditionally diagnosed based on persistently high albuminuria and a subsequent decline in glomerular filtration rate (GFR), which is widely recognized as the classical phenotype of diabetic kidney disease (DKD). Several studies have emphasized that trajectories of kidney function in patients with diabetes (specifically, changes in GFR and albuminuria over time) can differ from this classical DKD phenotype. Three alternative DKD phenotypes have been reported to date and are characterized by albuminuria regression, a rapid decline in GFR, or non-proteinuric or non-albuminuric DKD. Although kidney biopsies are not typically required for the diagnosis of DKD, a few studies of biopsy samples from patients with DKD have demonstrated that changes in kidney function associate with specific histopathological findings in diabetes. In addition, various clinical and biochemical parameters are related to trajectories of GFR and albuminuria. Collectively, pathological and clinical characteristics can be used to predict trajectories of GFR and albuminuria in diabetes. Furthermore, cohort studies have suggested that the risks of kidney and cardiovascular outcomes might vary among different phenotypes of DKD. A broader understanding of the clinical course of DKD is therefore crucial to improve risk stratification and enable early interventions that prevent adverse outcomes.

Oxidized LDL Is Associated with eGFR Decline in Proteinuric Diabetic Kidney Disease: A Cohort Study

Diabetic kidney disease (DKD) is a highly heterogenous disease, including the proteinuric and the nonproteinuric pattern. Oxidized low-density lipoprotein (ox-LDL) is progressively increased in DKD and causes direct damage to kidney tubular epithelial cells through a mechanism similar to that underlying the deleterious effect of lipid peroxides in the vascular endothelium. We aimed to examine the association between plasma ox-LDL cholesterol and clinical endpoints in DKD patients. Ninety-one patients with established proteinuric DKD and diabetic retinopathy were enrolled and prospectively followed for 10 years or the occurrence of death, or at least 30% decline in eGFR, or progression to end-stage kidney disease (ESKD) requiring renal replacement therapy (primary outcome). At the end of the study, both eGFR and proteinuria were reassessed. Secondary outcomes of the study were the percentage change in eGFR and proteinuria over time for each patient. At baseline, patients were divided into 2 groups according to the median ox-LDL value (i.e., below or equal and above 66.22 U/L). Both Kaplan-Meier curves (p = 0.001, log-rank test) and univariate Cox regression analysis showed that high ox-LDL was associated with the primary outcome (HR = 3.42, 95%CI = 1.55 − 7.56, p = 0.002). After adjustment for various well-known cofounders, multivariate Cox analysis showed that the association between increased circulating ox-LDL levels and the composite kidney endpoint remained significant (HR = 2.87, 95%CI = 1.14–7.20, p = 0.025). Regarding the secondary outcome of eGFR decline, the assessment of areas under the curves (AUC) showed that ox-LDL outperformed several cofounding factors (AUC 71%, 95%CI = 0.59 − 0.83, p = 0.001) and had better accuracy to predict deterioration of eGFR over time than baseline proteinuria (AUC 67%, 95%CI = 0.54 − 0.79, p = 0.014). Increased ox-LDL might be associated with disease progression in proteinuric DKD.

The Multiple Roles of Fibroblast Growth Factor in Diabetic Nephropathy

Diabetic nephropathy (DN) is a common microvascular complication in the late stages of diabetes. Currently, the etiology and pathogenesis of DN are not well understood. Even so, available evidence shows its development is associated with metabolism, oxidative stress, cytokine interaction, genetic factors, and renal microvascular disease. Diabetic nephropathy can lead to proteinuria, edema and hypertension, among other complications. In severe cases, it can cause life-threatening complications such as renal failure. Patients with type 1 diabetes, hypertension, high protein intake, and smokers have a higher risk of developing DN. Fibroblast growth factor (FGF) regulates several human processes essential for normal development. Even though FGF has been implicated in the pathological development of DN, the underlying mechanisms are not well understood. This review summarizes the role of FGF in the development of DN. Moreover, the association of FGF with metabolism, inflammation, oxidative stress and fibrosis in the context of DN is discussed. Findings of this review are expected to deepen our understanding of DN and generate ideas for developing effective prevention and treatments for the disease.

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.

Plasma activity of the antioxidant enzymes in predicting diabetic nephropathy progression

INTRODUCTION

The aim of the study was to examine whether biomarkers of oxidative stress are predictors of diabetic nephropathy (DN) progression.

METHODS

The study involved 45 patients with type 2 diabetes and DN and 15 healthy controls. Patients were followed for 3 years and the annual percentage change in eGFR was used to estimate the progression of DN. Patients with an annual percentage change in eGFR above the cutoff value of - 5.48%/year were classified in group 1, those with an annual percentage change in eGFR ≤ - 5.48%/year in group 2.

RESULTS

The 28 patients in group 1 had the annual percentage change in eGFR of - 4.78 and 39.12%/year, and for the 17 patients in group 2 it ranged from - 24.86 to - 6.18%/year. At the onset of the study no significant differences were found between the groups in demographic, clinical or laboratory parameters. Plasma activities of glutathione peroxidase (GPX) and superoxide dismutase (SOD) were significantly lower in patients than in the controls. During 3-year study kidney function and size changed insignificantly in group 1, while eGFR and kidney size decreased and proteinuria increased significantly in group 2. Multivariate linear regression analysis selected male gender, duration of diabetes, systolic blood pressure, fasting serum glucose, urine protein/creatinine ratio as factors associated with DN progression. Plasma activity of GPX and SOD were selected as positive predictors of annual percentage change in eGFR.

CONCLUSION

Besides already known factors, plasma activity of GPX and SOD were found to be significant independent predictors of DN progression.

Regulation and Potential Biological Role of Fibroblast Growth Factor 21 in Chronic Kidney Disease

Chronic kidney disease (CKD) is an incurable progressive disease with the progressive impairment of kidney function, which can accelerate the progression of cardiovascular disease, increase the risk of infection, and lead to related complications such as anemia and bone disease. CKD is to a great extent preventable and treatable, and it is particularly important to improve the early diagnosis, strengthen the research underlying the mechanism of disease occurrence and development, and innovate new intervention measures. Fibroblast growth factor 21 (FGF21) belongs to one of members of endocrine FGF subfamily with evolutionarily conserved functions and performs a vital role in the regulation of energy balance and adipose metabolism. FGF21 needs to rely on β-Klotho protein to specifically bind to FGF receptor (FGFR), which activates the FGF21 signaling exerting the biological function. FGF21 is deemed as an important regulatory factor extensively modulating many cellular functions under physiologic and pathologic conditions. Although the metabolic effect of FGF21 has been extensively studied, its potential biological role in the kidney has not been generally investigated. In this review, we summarize the biological characteristics, regulation and biological function of FGF21 based on the current studies, and briefly discuss the potential relationship with chronic kidney disease.

Diabetic kidney disease: new clinical and therapeutic issues. Joint position statement of the Italian Diabetes Society and the Italian Society of Nephrology on "The natural history of diabetic kidney disease and treatment of hyperglycemia in patients with type 2 diabetes and impaired renal function"

Aims

This joint document of the Italian Diabetes Society and the Italian Society of Nephrology reviews the natural history of diabetic kidney disease (DKD) in the light of the recent epidemiological literature and provides updated recommendations on anti-hyperglycemic treatment with non-insulin agents.

Data Synthesis

Recent epidemiological studies have disclosed a wide heterogeneity of DKD. In addition to the classical albuminuric phenotype, two new albuminuria-independent phenotypes have emerged, i.e., “nonalbuminuric renal impairment” and “progressive renal decline”, suggesting that DKD progression toward end-stage kidney disease (ESKD) may occur through two distinct pathways, albuminuric and nonalbuminuric. Several biomarkers have been associated with decline of estimated glomerular filtration rate (eGFR) independent of albuminuria and other clinical variables, thus possibly improving ESKD prediction. However, the pathogenesis and anatomical correlates of these phenotypes are still unclear. Also the management of hyperglycemia in patients with type 2 diabetes and impaired renal function has profoundly changed during the last two decades. New anti-hyperglycemic drugs, which do not cause hypoglycemia and weight gain and, in some cases, seem to provide cardiorenal protection, have become available for treatment of these individuals. In addition, the lowest eGFR safety thresholds for some of the old agents, particularly metformin and insulin secretagogues, have been reconsidered.

Conclusions

The heterogeneity in the clinical presentation and course of DKD has important implications for the diagnosis, prognosis, and possibly treatment of this complication. The therapeutic options for patients with type 2 diabetes and impaired renal function have substantially increased, thus allowing a better management of these individuals.

Oxidized LDL Modifies the Association between Proteinuria and Deterioration of Kidney Function in Proteinuric Diabetic Kidney Disease

Proteinuria is characterized by low accuracy for predicting onset and development of diabetic kidney disease (DKD) because it is not directly associated with molecular changes that promote DKD, but is a result of kidney damage. Oxidized low-density lipoprotein (ox-LDL) reflects oxidative stress and endothelial dysfunction, both underlying the development of proteinuria and loss of kidney function in DKD. We aimed to investigate whether ox-LDL modifies the association between proteinuria and progression of DKD in a cohort of 91 patients with proteinuric DKD and diabetic retinopathy, followed for 10 years. The primary endpoint was a combined kidney outcome of eGFR decline ≥30% or progression to end-stage kidney disease. After the end of the study, we considered the percentage change of eGFR over time as our secondary outcome. Proteinuria was associated with both outcomes, and ox-LDL amplified the magnitude of this link (p < 0.0001 for primary and p < 0.0001 for secondary outcome, respectively). After adjustment for duration of diabetes, history of cardiovascular disease and serum albumin, ox-LDL remained a significant effect modifier of the association between proteinuria and eGFR decline over time (p = 0.04). Our study shows that in proteinuric DKD, circulating ox-LDL levels amplified the magnitude of the association between proteinuria and progression of DKD.

Multiplex Serum Biomarker Assays Improve Prediction of Renal and Mortality Outcomes in Chronic Kidney Disease

BACKGROUND

We investigated the predictive value of 11 serum biomarkers for renal and mortality end points in people with CKD.

METHODS

Adults with CKD (n=139) were enrolled from outpatient clinics between February 2014 and November 2016. Biomarker quantification was performed using two multiplex arrays on a clinical-grade analyzer. Relationships between biomarkers and renal and mortality end points were investigated by random forests and Cox proportional hazards regression.

RESULTS

The cohort was 56% male. The mean age was 63 years and median (IQR) CKD-EPI eGFR was 33 (24-51) ml/min per BSA. A total of 56 (40%) people developed a composite end point defined as ≥40% decline in eGFR, doubling of serum creatinine, RRT, or death over median (IQR) follow-up of 5.4 (4.7-5.7) years. Prediction of the composite end point was better with random forests trained on serum biomarkers compared with clinical variables (area under the curve of 0.81 versus 0.78). The predictive performance of biomarkers was further enhanced when considered alongside clinical variables (area under the curve of 0.83 versus 0.81 for biomarkers alone). Patients (n=27, 19%) with high soluble TNF receptor-1 (≥3 ng/ml) and neutrophil gelatinase-associated lipocalin (≥156 ng/ml), coupled with low complement 3a des-arginine (<2368 ng/ml), almost universally (96%) developed the composite renal and mortality end point. C-reactive protein (adjusted hazard ratio, 1.4; 95% CI, 1.1 to 1.8), neutrophil gelatinase-associated lipocalin (adjusted hazard ratio, 2.8; 95% CI, 1.3 to 6.1) and complement 3a desarginine (adjusted hazard ratio, 0.6; 95% CI, 0.4 to 0.96) independently predicted time to the composite end point.

CONCLUSIONS

Outpatients with the triad of high soluble TNF receptor-1 and neutrophil gelatinase-associated lipocalin coupled with low complement 3a des-arginine had high adverse event rates over 5-year follow-up. Incorporation of serum biomarkers alongside clinical variables improved prediction of CKD progression and mortality. Our findings require confirmation in larger, more diverse patient cohorts.

Osteoglycin as a Potential Biomarker of Mild Kidney Function Impairment in Type 2 Diabetes Patients

Osteoglycin (OGN) could be a biomarker of mild kidney function impairment in type 2 diabetes (T2D). Our study aimed to determine the association between serum OGN and impaired kidney function risk in T2D patients and to analyze its potential role as an estimator of kidney disturbances in this population. This cross-sectional study included 147 T2D patients (65 ± 8 years, 58.5% males), and 75 healthy controls (63 ± 10 years, 36% males). Circulating OGN levels were determined by ELISA. Linear regression modeling was performed to determine the variables influencing circulating OGN, and an ROC curve was plotted to assess the usefulness of OGN as an estimator of diabetic kidney disease risk. Circulating OGN was significantly increased in T2D patients compared to controls (18.41 (14.45–23.27) ng/mL vs. 8.74 (7.03–12.35) ng/mL; p < 0.001). We found a progressive increase in serum OGN according to the severity of kidney impairment in T2D patients (normal kidney function: 16.14 (12.13–20.48) ng/mL; mildly impaired kidney function: 19.15 (15.78–25.90) ng/mL; moderate impaired kidney function: 21.80 (15.06–29.22) ng/mL; p = 0.006). Circulating OGN was an independent estimator of mildly impaired kidney function risk in T2D patients. We suggest that serum OGN could act as an albuminuria-independent biomarker of incipient kidney dysfunction in T2D patients.

Association between serum alkaline phosphatase and renal outcome in patients with type 2 diabetes mellitus

This retrospective study included 299 patients with type 2 diabetes mellitus and biopsy-confirmed diabetic nephropathy (DN) to investigate the prognostic value of alkaline phosphatase (ALP) for renal outcome. Cox proportional hazards models were used to estimate the hazard ratios (HRs) for the serum ALP level on renal outcome, which was defined as end-stage renal disease (ESRD) or a 50% decline in estimated glomerular filtration rate (eGFR) from baseline. The median baseline ALP was 80 IU/L with an interquartile range of 64–97 IU/L. Serum ALP was negatively associated with eGFR but positively associated with proteinuria and renal interstitial fibrosis. During a median follow-up period of 23 months, ESRD or a 50% declined in the eGFR occurred in 156 (52.2%) patients. The highest quartile of ALP was significantly associated with poor renal outcome, as defined above (HR 2.38, 95% confidence interval [CI] 1.09–5.17), when adjusted for sociodemographics, baseline eGFR, proteinuria, liver function parameters, parathyroid hormone levels, and renal pathological findings. Each standard deviation higher in the natural log-transformed ALP was associated with a 25% increased risk for poor renal outcome. Additionally, there was a graded increase in the risk for poor renal outcome with higher ALP in patients with nephrotic-range proteinuria. However, no significant associations were observed between serum ALP levels and renal outcome in patients with non-nephrotic-range proteinuria. In conclusion, an elevated ALP level was independently associated with poor renal outcome in patients with type 2 diabetes mellitus and nephrotic-range proteinuria after multivariate adjustment.

Discovering novel injury features in kidney transplant biopsies associated with TCMR and donor aging

We previously characterized the molecular changes in acute kidney injury (AKI) and chronic kidney disease (CKD) in kidney transplant biopsies, but parenchymal changes selective for specific types of injury could be missed by such analyses. The present study searched for injury changes beyond AKI and CKD related to specific scenarios, including correlations with donor age. We defined injury using previously defined gene sets and classifiers and used principal component analysis to discover new injury dimensions. As expected, Dimension 1 distinguished normal vs. injury, and Dimension 2 separated early AKI from late CKD, correlating with time posttransplant. However, Dimension 3 was novel, distinguishing a set of genes related to epithelial polarity (e.g., PARD3) that were increased in early AKI and decreased in T cell-mediated rejection (TCMR) but not in antibody-mediated rejection. Dimension 3 was increased in kidneys from older donors and was particularly important in survival of early kidneys. Thus high Dimension 3 scores emerge as a previously unknown element in the kidney response-to-injury that affects epithelial polarity genes and is increased in AKI but depressed in TCMR, indicating that in addition to general injury elements, certain injury elements are selective for specific pathologic mechanisms. (ClinicalTrials.gov NCT01299168).

FGF21 and Chronic Kidney Disease

The global nephrology community recognizes the increasing burden of kidney disease and its poor health outcomes in the general population. Given this, strategies to establish early diagnosis, improve understanding of the natural course and develop novel therapeutic interventions to slow progression and reduce complications are encouraged. Fibroblast growth factor 21 (FGF21), a member of the endocrine FGF subfamily, has emerged as a master homeostasis regulator of local and systemic lipid, glucose and energy metabolism. In addition, FGF21 should be considered an autonomic and endocrine regulator of stress responses in general. Promising results has been shown in both dysmetabolic animal models and metabolic disease patients after pharmacological administration of FGF21 analogs. The association of FGF21 with renal function has been studied for more than ten years. However, the functional role of FGF21 in the kidney is still poorly understood. This review summarizes the biological effects of FGF21 and discusses what is currently known about this hormone and chronic kidney disease, highlighting important gaps that warrant further research.