Biomarker panels associated with progression of renal disease in type 1 diabetes

Application of a validated prognostic plasma protein biomarker test for renal decline in type 2 diabetes to type 1 diabetes: the Fremantle Diabetes Study Phase II

BACKGROUND

There are scant data relating to prognostic biomarkers for chronic kidney disease (CKD) complicating type 1 diabetes. The aim of this study was to assess the performance of the plasma protein biomarker-based PromarkerD test developed and validated for predicting renal decline in type 2 diabetes in the context of type 1 diabetes.

METHODS

The baseline PromarkerD test score was determined in 91 community-based individuals (mean age 46.2 years, 56.5% males) with confirmed type 1 diabetes recruited to the longitudinal observational Fremantle Diabetes Study Phase II. The performance of the PromarkerD test in predicting the risk of incident CKD (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m2 in people without CKD at baseline) or an eGFR decline of ≥ 30% over the next four years was determined. The score can range from 0 to 100%, and is categorized as representing low (< 10%), moderate (10% to < 20%) or high (≥ 20%) risk.

RESULTS

The area under the receiver operating characteristic curve was 0.93 (95% confidence interval 0.87-0.99) for the composite renal endpoint, indicating strong predictive accuracy. The positive and negative predictive values at moderate (10% to < 20%) and high (≥ 20%) risk PromarkerD cut-offs were 46.7-50.0% and ≥ 92.0%, respectively.

CONCLUSIONS

These preliminary data suggest that PromarkerD is at least as good a prognostic test for renal decline in type 1 as type 2 diabetes.

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.

Metabolomics in diabetic nephropathy: Unveiling novel biomarkers for diagnosis (Review)

Diabetic nephropathy (DN) also known as diabetic kidney disease, is a major microvascular complication of diabetes and a leading cause of end‑stage renal disease (ESRD), which affects the morbidity and mortality of patients with diabetes. Despite advancements in diabetes care, current diagnostic methods, such as the determination of albuminuria and the estimated glomerular filtration rate, are limited in sensitivity and specificity, often only identifying kidney damage after considerable morphological changes. The present review discusses the potential of metabolomics as an approach for the early detection and management of DN. Metabolomics is the study of metabolites, the small molecules produced by cellular processes, and may provide a more sensitive and specific diagnostic tool compared with traditional methods. For the purposes of this review, a systematic search was conducted on PubMed and Google Scholar for recent human studies published between 2011 and 2023 that used metabolomics in the diagnosis of DN. Metabolomics has demonstrated potential in identifying metabolic biomarkers specific to DN. The ability to detect a broad spectrum of metabolites with high sensitivity and specificity may allow for earlier diagnosis and better management of patients with DN, potentially reducing the progression to ESRD. Furthermore, metabolomics pathway analysis assesses the pathophysiological mechanisms underlying DN. On the whole, metabolomics is a potential tool in the diagnosis and management of DN. By providing a more in‑depth understanding of metabolic alterations associated with DN, metabolomics could significantly improve early detection, enable timely interventions and reduce the healthcare burdens associated with this condition.

Minireview: Understanding and targeting inflammatory, hemodynamic and injury markers for cardiorenal protection in type 1 diabetes

The coexistence of cardiovascular disease (CVD) and diabetic kidney disease (DKD) is common in people with type 1 diabetes (T1D) and is strongly associated with an increased risk of morbidity and mortality. Hence, it is imperative to explore robust tools that can accurately reflect the development and progression of cardiorenal complications. Several cardiovascular and kidney biomarkers have been identified to detect at-risk individuals with T1D. The primary aim of this review is to highlight biomarkers of injury, inflammation, or renal hemodynamic changes that may influence T1D susceptibility to CVD and DKD. We will also examine the impact of approved pharmacotherapies for type 2 diabetes, including renin-angiotensin-aldosterone system (RAAS) inhibitors, sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1RAs) on candidate biomarkers for cardiorenal complications in people with T1D and discuss how these changes may potentially mediate kidney and cardiovascular protection. Identifying predictive and prognostic biomarkers for DKD and CVD may highlight potential drug targets to attenuate cardiorenal disease progression, implement novel risk stratification measures in clinical trials, and improve the assessment, diagnosis, and treatment of at-risk individuals with T1D.

Investigating HMGB1 as a potential serum biomarker for early diabetic nephropathy monitoring by quantitative proteomics

Current diagnostic methods for diabetic nephropathy (DN) lack precision, especially in early stages and monitoring progression. This study aims to find potential biomarkers for DN progression and evaluate their accuracy. Using serum samples from healthy controls (NC), diabetic patients (DM), early-medium stage DN (DN-EM), and late-stage DN (DN-L), researchers employed quantitative proteomics and Mfuzz clustering analysis revealed 15 proteins showing increased expression during DN progression, hinting at their biomarker potential. Combining Mfuzz clustering with weighted gene co-expression network analysis (WGCNA) highlighted five candidates (HMGB1, CD44, FBLN1, PTPRG, and ADAMTSL4). HMGB1 emerged as a promising biomarker, closely correlated with renal function changes. Experimental validation supported HMGB1's upregulation under high glucose conditions, reinforcing its potential as an early detection biomarker for DN. This research advances DN understanding and identifies five potential biomarkers, notably HMGB1, as a promising early monitoring target. These findings set the stage for future clinical diagnostic applications in DN.

Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage

The effects of coronavirus disease 2019 (COVID-19) primarily concern the respiratory tract and lungs; however, studies have shown that all organs are susceptible to infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 may involve multiorgan damage from direct viral invasion through angiotensin-converting enzyme 2 (ACE2), through inflammatory cytokine storms, or through other secondary pathways. This study involved the analysis of publicly accessible transcriptome data from the Gene Expression Omnibus (GEO) database for identifying significant differentially expressed genes related to COVID-19 and an investigation relating to the pathways associated with mitochondrial, cardiac, hepatic, and renal toxicity in COVID-19. Significant differentially expressed genes were identified and ranked by statistical approaches, and the genes derived by biological meaning were ranked by feature importance; both were utilized as machine learning features for verification. Sample set selection for machine learning was based on the performance, sample size, imbalanced data state, and overfitting assessment. Machine learning served as a verification tool by facilitating the testing of biological hypotheses by incorporating gene list adjustment. A subsequent in-depth study for gene and pathway network analysis was conducted to explore whether COVID-19 is associated with cardiac, hepatic, and renal impairments via mitochondrial infection. The analysis showed that potential cardiac, hepatic, and renal impairments in COVID-19 are associated with ACE2, inflammatory cytokine storms, and mitochondrial pathways, suggesting potential medical interventions for COVID-19-induced multiorgan damage.

Serum metabolomics detected by LDI-TOF-MS can be used to distinguish between diabetic patients with and without diabetic kidney disease

Diabetic kidney disease (DKD) is an important cause of end-stage renal disease with changes in metabolic characteristics. The objective of this study was to study changes in serum metabolic characteristics in patients with DKD and to examine metabolite panels to distinguish DKD from diabetes with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). We recruited 40 type II diabetes mellitus (T2DM) patients with or without DKD from a single center for a cross-sectional study. Serum metabolic profiling was performed with MALDI-TOF-MS using a vertical silicon nanowire array. Differential metabolites between DKD and diabetes patients were selected, and their relevance to the clinical parameters of DKD was analyzed. We applied machine learning methods to the differential metabolite panels to distinguish DKD patients from diabetes patients. Twenty-four differential serum metabolites between DKD patients and diabetes patients were identified, which were mainly enriched in butyrate metabolism, TCA cycle, and alanine, aspartate, and glutamate metabolism. Among the metabolites, l-kynurenine was positively correlated with urinary microalbumin, urinary microalbumin/creatinine ratio (UACR), creatinine, and urea nitrogen content. l-Serine, pimelic acid, 5-methylfuran-2-carboxylic acid, 4-methylbenzaldehyde, and dihydrouracil were associated with the estimated glomerular filtration rate (eGFR). The panel of differential metabolites could be used to distinguish between DKD and diabetes patients with an AUC value reaching 0.9899-0.9949. Among the differential metabolites, l-kynurenine was related to the progression of DKD. The differential metabolites exhibited excellent performance at distinguishing between DKD and diabetes. This study provides a novel direction for metabolomics-based clinical detection of DKD.

Rate of Kidney Function Decline is Associated With Kidney and Heart Failure in Individuals With Type 1 Diabetes

Introduction

Diabetes is the most common cause of chronic kidney disease (CKD). Urinary albumin excretion rate (AER) and estimated glomerular filtration rate (eGFR) are commonly used to monitor the onset and progression of diabetic kidney disease (DKD). We studied if the preceding rate of kidney function decline, that is, the eGFR slope, is independently associated with incident clinical cardiorenal events.

Methods

This study included longitudinal data for 2498 Finnish individuals with type 1 diabetes (T1D). The eGFR slope was calculated from 5 years preceding the study visit. Data on kidney failure, coronary heart disease (CHD), stroke, 3-point major adverse cardiovascular events (MACE), heart failure, and death were obtained from national registries. The associations between the eGFR slope and incident events were assessed with multivariable competing risk models during the average follow-up of 9.2 years.

Results

The eGFR slopes were associated (P ≤ 0.001) with all outcomes when adjusted for age, sex, and HbA1c. However, eGFR slope remained associated only with the composite outcome of kidney failure or death when the albuminuria group and eGFR at the study visit were included in the model (P = 0.041). In addition, eGFR slope was independently associated with kidney failure in individuals without CKD (eGFR > 60 ml/min per 1.73 m2; P = 0.044), and with heart failure in those with CKD (P = 0.033). However, eGFR slope did not markedly improve the model C-index.

Conclusion

The eGFR slope was independently associated with kidney failure in those without CKD, and with heart failure in those with CKD. However, it is unlikely to have major relevance for clinical practice when the current eGFR and albuminuria status are known.

Progression of Diabetic Kidney Disease and Gastrointestinal Symptoms in Patients with Type I Diabetes

(1) Background: Little research is conducted on the link between diabetic kidney disease (DKD) progression and diabetic gastroenteropathy in type 1 diabetes (T1D). (2) Methods. We performed a cross-sectional study with 100 T1D patients; 27 of them had progressive DKD, defined as an estimated glomerular filtration rate (eGFR) decline ≥3 mL/min/year or increased albuminuria stage, over a mean follow-up time of 5.89 ± 1.73 years. A newly developed score with 17 questions on gastrointestinal (GI) symptoms was used. Faecal calprotectin was measured by ELISA. Lower GI endoscopies were performed in 21 patients. (3) Results: The gastrointestinal symptom score demonstrated high reliability (Cronbach's α = 0.78). Patients with progressive DKD had higher GI symptom scores compared to those with stable DKD (p = 0.019). The former group demonstrated more frequent bowel movement disorders (p < 0.01). The scores correlated negatively with eGFR (r = -0.335; p = 0.001), positively with albuminuria (r = 0.245; p = 0.015), Hba1c (r = 0.305; p = 0.002), and diabetes duration (r = 0.251; p = 0.012). Faecal calprotectin levels did not differ between DKD groups significantly. The most commonly reported histopathological findings of enteric mucosa were infiltration with eosinophils, lymphocytes, plasmacytes, the presence of lymphoid follicles, and lymphoid aggregates. Conclusion: The progression of DKD is positively correlated with gastrointestinal symptoms; however, more research is needed to clarify the causal relationships of the gut-kidney axis in T1D.

Circulating Metabolites Associated with Albuminuria in a Hispanic/Latino Population

BACKGROUND

Albuminuria is associated with metabolic abnormalities, but these relationships are not well understood. We studied the association of metabolites with albuminuria in Hispanic/Latino people, a population with high risk for metabolic disease.

METHODS

We used data from 3736 participants from the Hispanic Community Health Study/Study of Latinos, of which 16% had diabetes and 9% had an increased urine albumin-to-creatinine ratio (UACR). Metabolites were quantified in fasting serum through nontargeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Spot UACR was inverse normally transformed and tested for the association with each metabolite or combined, correlated metabolites, in covariate-adjusted models that accounted for the study design. In total, 132 metabolites were available for replication in the Hypertension Genetic Epidemiology Network study ( n =300), and 29 metabolites were available for replication in the Malmö Offspring Study ( n =999).

RESULTS

Among 640 named metabolites, we identified 148 metabolites significantly associated with UACR, including 18 novel associations that replicated in independent samples. These metabolites showed enrichment for D-glutamine and D-glutamate metabolism and arginine biosynthesis, pathways previously reported for diabetes and insulin resistance. In correlated metabolite analyses, we identified two modules significantly associated with UACR, including a module composed of lipid metabolites related to the biosynthesis of unsaturated fatty acids and alpha linolenic acid and linoleic acid metabolism.

CONCLUSIONS

Our study identified associations of albuminuria with metabolites involved in glucose dysregulation, and essential fatty acids and precursors of arachidonic acid in Hispanic/Latino population.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_02_08_CJN09070822.mp3.

Circulating β2 and α1 microglobulins predict progression of nephropathy in diabetic patients: a meta-analysis of prospective cohort studies

AIMS

To study the association of circulating β2 (B2M) and α1 microglobulins (A1M) with diabetic nephropathy (DN) progression, a meta-analysis was performed on the prospective cohort studies.

METHODS

Up to October 2021, a comprehensive search of the PubMed, EMBASE, Scopus, Web of Science, and Cochrane Library databases was performed. The primary outcome (progression of DN) was defined as a decrease in eGFR or the occurrence of end stage renal disease or DN-related mortality. Eligible studies were included in a pooled analysis that used either fixed-effect or random-effect models to compensate for variation in measurement standards between studies. The funnel plot and Egger's test were used to assess publication bias.

RESULTS

The meta-analysis included 4398 people from 9 prospective trials (8 cohorts) for B2M and 3110 people from 4 prospective trials (3 cohorts) for A1M. Diabetic individuals with higher B2M levels had an increased risk for DN (relative risk [RR]: 1.81, 95% confidence interval [CI]: 1.56-2.09). Likewise, higher A1M was associated with augmented probability of DN (RR: 1.96, 95% CI: 1.46-2.62). The funnel plot and Egger's tests indicated no publication bias for A1M. Additionally, to compensate for putative publication bias for B2M, using trim and fill analysis, four studies were filled for this marker and the results remained significant (RR: 1.62, 95% CI: 1.37-1.92).

CONCLUSIONS

The elevated serum levels of B2M and A1M could be considered as potential predictors of DN progression in diabetic patients.

PROTOCOL REGISTRATION

PROSPERO CRD42021278300.

Absence of long-term changes in urine biomarkers after AKI: findings from the CRIC study

BACKGROUND

Mechanisms by which AKI leads to CKD progression remain unclear. Several urine biomarkers have been identified as independent predictors of progressive CKD. It is unknown whether AKI may result in long-term changes in these urine biomarkers, which may mediate the effect of AKI on CKD progression.

METHODS

We selected 198 episodes of hospitalized AKI (defined as peak/nadir inpatient serum creatinine values ≥ 1.5) among adult participants in the Chronic Renal Insufficiency Cohort (CRIC) Study. We matched the best non-AKI hospitalization (unique patients) for each AKI hospitalization using pre-hospitalization characteristics including eGFR and urine protein/creatinine ratio. Biomarkers were measured in banked urine samples collected at annual CRIC study visits.

RESULTS

Urine biomarker measurements occurred a median of 7 months before and 5 months after hospitalization. There were no significant differences in the change in urine biomarker-to-creatinine ratio between the AKI and non-AKI groups: KIM-1/Cr + 9% vs + 7%, MCP-1/Cr + 4% vs + 1%, YKL-40/Cr + 7% vs -20%, EGF/Cr -11% vs -8%, UMOD/Cr -2% vs -7% and albumin/Cr + 17% vs + 13% (all p > 0.05).

CONCLUSION

In this cohort of adults with CKD, AKI did not associate with long-term changes in urine biomarkers.

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.

Acute Kidney Injury Associates with Long-Term Increases in Plasma TNFR1, TNFR2, and KIM-1: Findings from the CRIC Study

BACKGROUND

Some markers of inflammation-TNF receptors 1 and 2 (TNFR1 and TNFR2)-are independently associated with progressive CKD, as is a marker of proximal tubule injury, kidney injury molecule 1 (KIM-1). However, whether an episode of hospitalized AKI may cause long-term changes in these biomarkers is unknown.

METHODS

Among adult participants in the Chronic Renal Insufficiency Cohort (CRIC) study, we identified 198 episodes of hospitalized AKI (defined as peak/nadir inpatient serum creatinine values ≥1.5). For each AKI hospitalization, we found the best matched non-AKI hospitalization (unique patients), using prehospitalization characteristics, including eGFR and urine protein/creatinine ratio. We measured TNFR1, TNFR2, and KIM-1 in banked plasma samples collected at annual CRIC study visits before and after the hospitalization (a median of 7 months before and 5 months after hospitalization).

RESULTS

In the AKI and non-AKI groups, we found similar prehospitalization median levels of TNFR1 (1373 pg/ml versus 1371 pg/ml, for AKI and non-AKI, respectively), TNFR2 (47,141 pg/ml versus 46,135 pg/ml, respectively), and KIM-1 (857 pg/ml versus 719 pg/ml, respectively). Compared with matched study participants who did not experience AKI, study participants who did experience AKI had greater increases in TNFR1 (23% versus 10%, P<0.01), TNFR2 (10% versus 3%, P<0.01), and KIM-1 (13% versus -2%, P<0.01).

CONCLUSIONS

Among patients with CKD, AKI during hospitalization was associated with increases in plasma TNFR1, TNFR2, and KIM-1 several months after their hospitalization. These results highlight a potential mechanism by which AKI may contribute to more rapid loss of kidney function months to years after the acute insult.

The Role of Amino Acids in Endothelial Biology and Function

The vascular endothelium acts as an important component of the vascular system. It is a barrier between the blood and vessel wall. It plays an important role in regulating blood vessel tone, permeability, angiogenesis, and platelet functions. Several studies have shown that amino acids (AA) are key regulators in maintaining vascular homeostasis by modulating endothelial cell (EC) proliferation, migration, survival, and function. This review summarizes the metabolic and signaling pathways of AAs in ECs and discusses the importance of AA homeostasis in the functioning of ECs and vascular homeostasis. It also discusses the challenges in understanding the role of AA in the development of cardiovascular pathophysiology and possible directions for future research.

Effect of serum sample storage temperature on metabolomic and proteomic biomarkers

Prospective biomarker studies can be used to identify biomarkers predictive of disease onset. However, if serum biomarkers are measured years after their collection, the storage conditions might affect analyte concentrations. Few data exists concerning which metabolites and proteins are affected by storage at − 20 °C vs − 80 °C. Our objectives were to document analytes affected by storage of serum samples at − 20 °C vs − 80 °C, and to identify those indicative of the storage temperature. We utilized liquid chromatography tandem mass spectrometry and Luminex to quantify 300 analytes from serum samples of 16 Finnish individuals with type 1 diabetes, with split-aliquot samples stored at − 80 °C and − 20 °C for a median of 4.2 years. Results were validated in 315 Finnish and 916 Scottish individuals with type 1 diabetes, stored at − 20 °C and at − 80 °C, respectively. After quality control, we analysed 193 metabolites and proteins of which 120 were apparently unaffected and 15 clearly susceptible to storage at − 20 °C vs − 80 °C. Further, we identified serum glutamate/glutamine ratio greater than 0.20 as a biomarker of storage at − 20 °C vs − 80 °C. The results provide a catalogue of analytes unaffected and affected by storage at − 20 °C vs − 80 °C and biomarkers indicative of sub-optimal storage.

A Contemporary Insight of Metabolomics Approach for Type 1 Diabetes: Potential for Novel Diagnostic Targets

High-throughput omics has been widely applied in metabolic disease, type 1 diabetes (T1D) was one of the most typical diseases. Effective prevention and early diagnosis are very important because of infancy and persistent characteristics of T1D. The occurrence and development of T1D is a chronic and continuous process, in which the production of autoantibodies (ie serum transformation) occupies the central position. Metabolomics can evaluate the metabolic characteristics of serum before seroconversion, the changes with age and T1D complications. And the addition of natural drug metabolomics is more conducive to the systematic and comprehensive diagnosis and treatment of T1D. This paper reviewed the metabolic changes and main pathogenesis from pre-diagnosis to treatment in T1D. The metabolic spectrum of significant abnormal energy and glucose-related metabolic pathway, down-regulation of lipid metabolism and up-regulation of some antioxidant pathways has appeared before seroconversion, indicating that the body has been in the dual state of disease progression and disease resistance before T1D onset. Some metabolites (such as methionine) are closely related to age, and the types of autoantibodies produced are age-specific. Some metabolites may jointly predict DN with eGFR, and metabolomics can further contribute to the pathogenesis based on the correlation between DN and DR. Many natural drug components have been proved to act on abnormal metabolic pathways of T1D and have a positive impact on some metabolic levels, which is very important for further finding therapeutic targets and developing new drugs with small side effects. Metabolomics can provide auxiliary value for the diagnosis of T1D and provide a new direction to reveal the pathogenesis of T1D and find new therapeutic targets. The development of T1D metabolomics shows that high-throughput research methods are expected to be introduced into clinical practice.

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.

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.

Novel Cardiovascular Risk Factors in Patients with Diabetic Kidney Disease

Patients with diabetic kidney disease (DKD) are at very high risk for cardiovascular events. Only part of this increased risk can be attributed to the presence of diabetes mellitus (DM) and to other DM-related comorbidities, including hypertension and obesity. The identification of novel risk factors that underpin the association between DKD and cardiovascular disease (CVD) is essential for risk stratification, for individualization of treatment and for identification of novel treatment targets.In the present review, we summarize the current knowledge regarding the role of emerging cardiovascular risk markers in patients with DKD. Among these biomarkers, fibroblast growth factor-23 and copeptin were studied more extensively and consistently predicted cardiovascular events in this population. Therefore, it might be useful to incorporate them in risk stratification strategies in patients with DKD to identify those who would possibly benefit from more aggressive management of cardiovascular risk factors.

Importance of Identifying Novel Biomarkers of Microvascular Damage in Type 1 Diabetes

Microvascular complications of type 1 diabetes, which primarily include diabetic kidney disease, retinopathy, and neuropathy, are characterized by damage to the microvasculature of the kidney, retina, and neurons. The pathogenesis of these complications is multifactorial, and several pathways are implicated. These complications are often silent during their early stages, and once symptoms develop, there might be little to be done to cure them. Thus, there is a strong need for novel biomarkers to identify individuals at risk of microvascular complications at an early stage and guide the implementation of new therapeutic options for preventing their development and progression. Recent advancements in proteomics, metabolomics, and other 'omics' have led to the identification of several potential biomarkers of microvascular complications. However, biomarker discovery has met several challenges and, up to now, there are no new biomarkers that have been implemented into clinical practice. This highlights the need for further work in this area to move towards better diagnostic and prognostic approaches.

T-Cell Expression and Release of Kidney Injury Molecule-1 in Response to Glucose Variations Initiates Kidney Injury in Early Diabetes

Half of the mortality in diabetes is seen in individuals <50 years of age and commonly predicted by the early onset of diabetic kidney disease (DKD). In type 1 diabetes, increased urinary albumin-to-creatinine ratio (uACR) during adolescence defines this risk, but the pathological factors responsible remain unknown. We postulated that early in diabetes, glucose variations contribute to kidney injury molecule-1 (KIM-1) release from circulating T cells, elevating uACR and DKD risk. DKD risk was assigned in youth with type 1 diabetes (n = 100; 20.0 ± 2.8 years; males/females, 54:46; HbA_1c 66.1 [12.3] mmol/mol; diabetes duration 10.7 ± 5.2 years; and BMI 24.5 [5.3] kg/m²) and 10-year historical uACR, HbA_1c, and random blood glucose concentrations collected retrospectively. Glucose fluctuations in the absence of diabetes were also compared with streptozotocin diabetes in apolipoprotein E ^-/- mice. Kidney biopsies were used to examine infiltration of KIM-1-expressing T cells in DKD and compared with other chronic kidney disease. Individuals at high risk for DKD had persistent elevations in uACR defined by area under the curve (AUC; uACR_AUC0-10yrs, 29.7 ± 8.8 vs. 4.5 ± 0.5; P < 0.01 vs. low risk) and early kidney dysfunction, including ∼8.3 mL/min/1.73 m² higher estimated glomerular filtration rates (modified Schwartz equation; P_adj < 0.031 vs. low risk) and plasma KIM-1 concentrations (∼15% higher vs. low risk; P < 0.034). High-risk individuals had greater glycemic variability and increased peripheral blood T-cell KIM-1 expression, particularly on CD8⁺ T cells. These findings were confirmed in a murine model of glycemic variability both in the presence and absence of diabetes. KIM-1⁺ T cells were also infiltrating kidney biopsies from individuals with DKD. Healthy primary human proximal tubule epithelial cells exposed to plasma from high-risk youth with diabetes showed elevated collagen IV and sodium-glucose cotransporter 2 expression, alleviated with KIM-1 blockade. Taken together, these studies suggest that glycemic variations confer risk for DKD in diabetes via increased CD8⁺ T-cell production of KIM-1.

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.

Circulating short and medium chain fatty acids are associated with normoalbuminuria in type 1 diabetes of long duration

A substantial number of subjects with Type 1 Diabetes (T1D) of long duration never develop albuminuria or renal function impairment, yet the underlying protective mechanisms remain unknown. Therefore, our study included 308 Joslin Kidney Study subjects who had T1D of long duration (median: 24 years), maintained normal renal function and had either normoalbuminuria or a broad range of albuminuria within the 2 years preceding the metabolomic determinations. Serum samples were subjected to global metabolomic profiling. 352 metabolites were detected in at least 80% of the study population. In the logistic analyses adjusted for multiple testing (Bonferroni corrected α = 0.000028), we identified 38 metabolites associated with persistent normoalbuminuria independently from clinical covariates. Protective metabolites were enriched in Medium Chain Fatty Acids (MCFAs) and in Short Chain Fatty Acids (SCFAs) and particularly involved odd-numbered and dicarboxylate Fatty Acids. One quartile change of nonanoate, the top protective MCFA, was associated with high odds of having persistent normoalbuminuria (OR (95% CI) 0.14 (0.09, 0.23); p < 10-12). Multivariable Random Forest analysis concordantly indicated to MCFAs as effective classifiers. Associations of the relevant Fatty Acids with albuminuria seemed to parallel associations with tubular biomarkers. Our findings suggest that MCFAs and SCFAs contribute to the metabolic processes underlying protection against albuminuria development in T1D that are independent from mechanisms associated with changes in renal function.

A Validated Prediction Model for End-Stage Kidney Disease in Type 1 Diabetes

OBJECTIVE

End-stage kidney disease (ESKD) is a life-threatening complication of diabetes that can be prevented or delayed by intervention. Hence, early detection of people at increased risk is essential.

RESEARCH DESIGN AND METHODS

From a population-based cohort of 5,460 clinically diagnosed Danish adults with type 1 diabetes followed from 2001 to 2016, we developed a prediction model for ESKD accounting for the competing risk of death. Poisson regression analysis was used to estimate the model on the basis of information routinely collected from clinical examinations. The effect of including an extended set of predictors (lipids, alcohol intake, etc.) was further evaluated, and potential interactions identified in a survival tree analysis were tested. The final model was externally validated in 9,175 adults from Denmark and Scotland.

RESULTS

During a median follow-up of 10.4 years (interquartile limits 5.1; 14.7), 303 (5.5%) of the participants (mean [SD] age 42.3 [16.5] years) developed ESKD, and 764 (14.0%) died without having developed ESKD. The final ESKD prediction model included age, male sex, diabetes duration, estimated glomerular filtration rate, micro- and macroalbuminuria, systolic blood pressure, hemoglobin A_1c, smoking, and previous cardiovascular disease. Discrimination was excellent for 5-year risk of an ESKD event, with a C-statistic of 0.888 (95% CI 0.849; 0.927) in the derivation cohort and confirmed at 0.865 (0.811; 0.919) and 0.961 (0.940; 0.981) in the external validation cohorts from Denmark and Scotland, respectively.

CONCLUSIONS

We have derived and validated a novel, high-performing ESKD prediction model for risk stratification in the adult type 1 diabetes population. This model may improve clinical decision making and potentially guide early intervention.

Current Challenges and Future Perspectives of Renal Tubular Dysfunction in Diabetic Kidney Disease

Over decades, substantial progress has been achieved in understanding the pathogenesis of proteinuria in diabetic kidney disease (DKD), biomarkers for DKD screening, diagnosis, and prognosis, as well as novel hypoglycemia agents in clinical trials, thereby rendering more attention focused on the role of renal tubules in DKD. Previous studies have demonstrated that morphological and functional changes in renal tubules are highly involved in the occurrence and development of DKD. Novel tubular biomarkers have shown some clinical importance. However, there are many challenges to transition into personalized diagnosis and guidance for individual therapy in clinical practice. Large-scale clinical trials suggested the clinical relevance of increased proximal reabsorption and hyperfiltration by sodium-glucose cotransporter-2 (SGLT2) to improve renal outcomes in patients with diabetes, further promoting the emergence of renal tubulocentric research. Therefore, this review summarized the recent progress in the pathophysiology associated with involved mechanisms of renal tubules, potential tubular biomarkers with clinical application, and renal tubular factors in DKD management. The mechanism of kidney protection and impressive results from clinical trials of SGLT2 inhibitors were summarized and discussed, offering a comprehensive update on therapeutic strategies targeting renal tubules.

Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease

Chronic kidney disease (CKD) patients typically progress to kidney failure, but the rate of progression differs per patient or may not occur at all. Current CKD screening methods are sub-optimal at predicting progressive kidney function decline. This investigation develops a model for predicting progressive CKD based on a panel of biomarkers representing the pathophysiological processes of CKD, kidney function, and common CKD comorbidities. Two patient cohorts are utilised: The CKD Queensland Registry (n = 418), termed the Biomarker Discovery cohort; and the CKD Biobank (n = 62), termed the Predictive Model cohort. Progression status is assigned with a composite outcome of a ≥30% decline in eGFR from baseline, initiation of dialysis, or kidney transplantation. Baseline biomarker measurements are compared between progressive and non-progressive patients via logistic regression. In the Biomarker Discovery cohort, 13 biomarkers differed significantly between progressive and non-progressive patients, while 10 differed in the Predictive Model cohort. From this, a predictive model, based on a biomarker panel of serum creatinine, osteopontin, tryptase, urea, and eGFR, was calculated via linear discriminant analysis. This model has an accuracy of 84.3% when predicting future progressive CKD at baseline, greater than eGFR (66.1%), sCr (67.7%), albuminuria (53.2%), or albumin-creatinine ratio (53.2%).

Biomarkers of Inflammation and Glomerular Filtration Rate in Individuals with Recent-Onset Type 1 and Type 2 Diabetes

CONTEXT

While inflammation has been associated with kidney function in long-standing diabetes, its possible association in newly diagnosed diabetes is unknown.

OBJECTIVE

To investigate cross-sectional and prospective associations between biomarkers of inflammation and kidney function in recent-onset diabetes.

METHODS

The study included individuals with type 1 and type 2 diabetes with known diabetes duration of <1 year from the German Diabetes Study. Baseline serum concentrations of 74 biomarkers were measured using proximity extension assay technology and their associations with estimated glomerular filtration rate (eGFR) and kidney function decline over 5 years were tested using multiple linear and logistic regression analysis.

RESULTS

The cross-sectional analysis included 165 individuals with type 1 diabetes and 291 with type 2 diabetes. Baseline eGFR was higher in type 1 compared with type 2 diabetes (102 ± 15 vs 90 ± 16 mL/min/1.73 m2; P < 0.0001). After full adjustment for covariates and multiple testing, 7 biomarkers were associated with lower baseline eGFR in type 1 diabetes and 24 were associated with lower baseline eGFR in type 2 diabetes. Among these biomarkers, 6 biomarkers (CD5, CCL23, CST5, IL-10RB, PD-L1, TNFRSF9) were inversely associated with eGFR in both diabetes types. The prospective analysis did not detect associations between inflammatory biomarkers and kidney function decline. No evidence of an interaction between diabetes type and inflammatory biomarkers was found.

CONCLUSION

Several biomarkers of inflammation associate with lower baseline eGFR in recent-onset type 1 and type 2 diabetes, but do not associate with kidney function loss during the first 5 years after the diagnosis of diabetes.

Quantitative levels of serum N-glycans in type 1 diabetes and their association with kidney disease

We investigated associations of quantitative levels of N-glycans with hemoglobin A1c (HbA1c), renal function and renal function decline in type 1 diabetes. We measured 46 total N-glycan peaks (GPs) on 1565 serum samples from the Scottish Diabetes Research Network Type 1 Bioresource Study (SDRNT1BIO) and a pool of healthy donors. Quantitation of absolute abundance of each GP used 2AB-labeled mannose-3 as a standard. We studied cross-sectional associations of GPs and derived measures with HbA1c, albumin/creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR), and prospective associations with incident albuminuria and final eGFR. All GPs were 1.4 to 3.2 times more abundant in SDRTN1BIO than in the healthy samples. Absolute levels of all GPs were slightly higher with higher HbA1c, with strongest associations for triantennary trigalactosylated disialylated, triantennary trigalactosylated trisialylated structures with core or outer arm fucose, and tetraantennary tetragalactosylated trisialylated glycans. Most GPs showed increased abundance with worsening ACR. Lower eGFR was associated with higher absolute GP levels, most significantly with biantennary digalactosylated disialylated glycans with and without bisect, triantennary trigalactosylated trisialylated glycans with and without outer arm fucose, and core fucosylated biantennary monogalactosylated monosialylated glycans. Although several GPs were inversely associated prospectively with final eGFR, cross-validated multivariable models did not improve prediction beyond clinical covariates. Elevated HbA1c is associated with an altered N-glycan profile in type 1 diabetes. Although we could not establish GPs to be prognostic of future renal function decline independently of HbA1c, further studies to evaluate their impact in the pathogenesis of diabetic kidney disease are warranted.

Biomarkers associated with early stages of kidney disease in adolescents with type 1 diabetes

OBJECTIVES

To identify biomarkers of renal disease in adolescents with type 1 diabetes (T1D) and to compare findings in adults with T1D.

METHODS

Twenty-five serum biomarkers were measured, using a Luminex platform, in 553 adolescents (median [interquartile range] age: 13.9 [12.6, 15.2] years), recruited to the Adolescent Type 1 Diabetes Cardio-Renal Intervention Trial. Associations with baseline and final estimated glomerular filtration rate (eGFR), rapid decliner and rapid increaser phenotypes (eGFR slopes <-3 and > 3 mL/min/1.73m² /year, respectively), and albumin-creatinine ratio (ACR) were assessed. Results were also compared with those obtained in 859 adults (age: 55.5 [46.1, 64.4) years) from the Scottish Diabetes Research Network Type 1 Bioresource.

RESULTS

In the adolescent cohort, baseline eGFR was negatively associated with trefoil factor-3, cystatin C, and beta-2 microglobulin (B2M) (B coefficient[95%CI]: -0.19 [-0.27, -0.12], P = 7.0 × 10^-7 ; -0.18 [-0.26, -0.11], P = 5.1 × 10^-6 ; -0.12 [-0.20, -0.05], P = 1.6 × 10^-3 ), in addition to clinical covariates. Final eGFR was negatively associated with osteopontin (-0.21 [-0.28, -0.14], P = 2.3 × 10^-8 ) and cystatin C (-0.16 [-0.22, -0.09], P = 1.6 × 10^-6 ). Rapid decliner phenotype was associated with osteopontin (OR: 1.83 [1.42, 2.41], P = 7.3 × 10^-6 ), whereas rapid increaser phenotype was associated with fibroblast growth factor-23 (FGF-23) (1.59 [1.23, 2.04], P = 2.6 × 10^-4 ). ACR was not associated with any of the biomarkers. In the adult cohort similar associations with eGFR were found; however, several additional biomarkers were associated with eGFR and ACR.

CONCLUSIONS

In this young population with T1D and high rates of hyperfiltration, osteopontin was the most consistent biomarker associated with prospective changes in eGFR. FGF-23 was associated with eGFR increases, whereas trefoil factor-3, cystatin C, and B2M were associated with baseline eGFR.

Protective factors as biomarkers and targets for prevention and treatment of diabetic nephropathy: From current human evidence to future possibilities

Although hyperglycemia, high blood pressure and aging increase the risk of developing kidney complications, some diabetes patients exposed to these risk factors do not develop kidney disease, suggesting the presence of endogenous protective factors. There is a growing need to understand these factors determining protection of the kidney in order to improve the design of preventive strategies and to enhance the processes responsible for renoprotection. The aim of this review was to present the existing molecular and epidemiological data on factors showing protective effects in diabetic kidney disease, and to summarize the evidence regarding their potential in the area of future clinical diagnostics, therapeutics and early preventive strategies. These include transcriptomic and proteomic studies regarding the anti-inflammatory, anti-fibrotic and regenerative factors that were associated with slower progression of renal function loss. Another focus is the new evidence regarding the evaluation of alterations in the regulatory epigenome and its involvement in the risk of diabetic kidney disease. Further effort is required to validate and extend these findings, and to define their potential for clinical implementation in the future.

Comparison of serum and urinary biomarker panels with albumin/creatinine ratio in the prediction of renal function decline in type 1 diabetes

AIMS/HYPOTHESIS

We examined whether candidate biomarkers in serum or urine can improve the prediction of renal disease progression in type 1 diabetes beyond prior eGFR, comparing their performance with urinary albumin/creatinine ratio (ACR).

METHODS

From the population-representative Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) we sampled 50% and 25% of those with starting eGFR below and above 75 ml min-1 [1.73 m]-2, respectively (N = 1629), and with median 5.1 years of follow-up. Multiplexed ELISAs and single molecule array technology were used to measure nine serum biomarkers and 13 urine biomarkers based on our and others' prior work using large discovery and candidate studies. Associations with final eGFR and with progression to <30 ml min-1 [1.73] m-2, both adjusted for baseline eGFR, were tested using linear and logistic regression models. Parsimonious biomarker panels were identified using a penalised Bayesian approach, and their performance was evaluated through tenfold cross-validation and compared with using urinary ACR and other clinical record data.

RESULTS

Seven serum and seven urine biomarkers were strongly associated with either final eGFR or progression to <30 ml min-1 [1.73 m]-2, adjusting for baseline eGFR and other covariates (all at p<2.3 × 10-3). Of these, associations of four serum biomarkers were independent of ACR for both outcomes. The strongest associations with both final eGFR and progression to <30 ml min-1 [1.73 m]-2 were for serum TNF receptor 1, kidney injury molecule 1, CD27 antigen, α-1-microglobulin and syndecan-1. These serum associations were also significant in normoalbuminuric participants for both outcomes. On top of baseline covariates, the r2 for prediction of final eGFR increased from 0.702 to 0.743 for serum biomarkers, and from 0.702 to 0.721 for ACR alone. The area under the receiver operating characteristic curve for progression to <30 ml min-1 [1.73 m]-2 increased from 0.876 to 0.953 for serum biomarkers, and to 0.911 for ACR alone. Other urinary biomarkers did not outperform ACR.

CONCLUSIONS/INTERPRETATION

A parsimonious panel of serum biomarkers easily measurable along with serum creatinine may outperform ACR for predicting renal disease progression in type 1 diabetes, potentially obviating the need for urine testing.

Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus

AIMS/HYPOTHESIS

The aim of this study was to provide data from a contemporary population-representative cohort on rates and predictors of renal decline in type 1 diabetes.

METHODS

We used data from a cohort of 5777 people with type 1 diabetes aged 16 and older, diagnosed before the age of 50, and representative of the adult population with type 1 diabetes in Scotland (Scottish Diabetes Research Network Type 1 Bioresource; SDRNT1BIO). We measured serum creatinine and urinary albumin/creatinine ratio (ACR) at recruitment and linked the data to the national electronic healthcare records.

RESULTS

Median age was 44.1 years and diabetes duration 20.9 years. The prevalence of CKD stages G1, G2, G3 and G4 and end-stage renal disease (ESRD) was 64.0%, 29.3%, 5.4%, 0.6%, 0.7%, respectively. Micro/macroalbuminuria prevalence was 8.6% and 3.0%, respectively. The incidence rate of ESRD was 2.5 (95% CI 1.9, 3.2) per 1000 person-years. The majority (59%) of those with chronic kidney disease stages G3-G5 did not have albuminuria on the day of recruitment or previously. Over 11.6 years of observation, the median annual decline in eGFR was modest at -1.3 ml min-1 [1.73 m]-2 year-1 (interquartile range [IQR]: -2.2, -0.4). However, 14% experienced a more significant loss of at least 3 ml min-1 [1.73 m]-2. These decliners had more cardiovascular disease (OR 1.9, p = 5 × 10-5) and retinopathy (OR 1.3 p = 0.02). Adding HbA1c, prior cardiovascular disease, recent mean eGFR and prior trajectory of eGFR to a model with age, sex, diabetes duration, current eGFR and ACR maximised the prediction of final eGFR (r2 increment from 0.698 to 0.745, p < 10-16). Attempting to model nonlinearity in eGFR decline or to detect latent classes of decliners did not improve prediction.

CONCLUSIONS

These data show much lower levels of kidney disease than historical estimates. However, early identification of those destined to experience significant decline in eGFR remains challenging.

Animal Models and Renal Biomarkers of Diabetic Nephropathy

Diabetes mellitus (DM) is the first cause of end stage chronic kidney disease (CKD). Animal models of the disease can shed light on the pathogenesis of the diabetic nephropathy (DN) and novel and earlier biomarkers of the condition may help to improve diagnosis and prognosis. This review summarizes the most important features of animal models used in the study of DN and updates the most recent progress in biomarker research.

Increased Plasma Soluble Interleukin-2 Receptor Alpha Levels in Patients With Long-Term Type 1 Diabetes With Vascular Complications Associated With IL2RA and PTPN2 Gene Polymorphisms

Type 1 diabetes (T1D) is largely considered an autoimmune disease leading to the destruction of insulin-producing pancreatic β cells. Further, patients with T1D have 3-4-fold increased risk of developing micro- and macrovascular complications. However, the contribution of immune-related factors contributing to these diabetes complications are poorly understood. Individuals with long-term T1D who do not progress to vascular complications offer a great potential to evaluate end-organ protection. The aim of the present study was to investigate the association of inflammatory protein levels with vascular complications (retinopathy, nephropathy, cardiovascular disease) in individuals with long-term T1D compared to individuals who rapidly progressed to complications. We studied a panel of inflammatory markers in plasma of patients with long-term T1D with (n = 81 and 26) and without (n = 313 and 25) vascular complications from two cross-sectional Scandinavian cohorts (PROLONG and DIALONG) using Luminex technology. A subset of PROLONG individuals (n = 61) was screened for circulating immune cells using multicolor flow cytometry. We found that elevated plasma levels of soluble interleukin-2 receptor alpha (sIL-2R) were positively associated with the complication phenotype. Risk carriers of polymorphisms in the IL2RA and PTPN2 gene region had elevated plasma levels of sIL-2R. In addition, cell surface marker analysis revealed a shift from naïve to effector T cells in T1D individuals with vascular complications as compared to those without. In contrast, no difference between the groups was observed either in IL-2R cell surface expression or in regulatory T cell population size. In conclusion, our data indicates that IL2RA and PTPN2 gene variants might increase the risk of developing vascular complications in people with T1D, by affecting sIL-2R plasma levels and potentially lowering T cell responsiveness. Thus, elevated sIL-2R plasma levels may serve as a biomarker in monitoring the risk for developing diabetic complications and thereby improve patient care.