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Plasma Biomarkers and Kidney Function Decline in Early and Established Diabetic Kidney Disease.

ABSTRACT: Biomarkers of diverse pathophysiologic mechanisms may improve risk stratification for incident or progressive diabetic kidney disease (DKD) in persons with type 2 diabetes. To evaluate such biomarkers, we performed a nested case-control study (n=190 cases of incident DKD and 190 matched controls) and a prospective cohort study (n=1156) using banked baseline plasma samples from participants of randomized, controlled trials of early (ACCORD) and advanced (VA NEPHRON-D) DKD. We assessed the association and discrimination obtained with baseline levels of plasma TNF receptor-1 (TNFR-1), TNFR-2, and kidney injury molecule-1 (KIM-1) for the outcomes of incident DKD (ACCORD) and progressive DKD (VA-NEPHRON-D). At baseline, median concentrations of TNFR-1, TNFR-2, and KIM-1 were roughly two-fold higher in the advanced DKD population (NEPHRON-D) than in the early DKD population (ACCORD). In both cohorts, patients who reached the renal outcome had higher baseline levels than those who did not reach the outcome. Associations between doubling in TNFR-1, TNFR-2, and KIM-1 levels and risk of the renal outcomes were significant for both cohorts. Inclusion of these biomarkers in clinical models increased the area under the curve (SEM) for predicting the renal outcome from 0.68 (0.02) to 0.75 (0.02) in NEPHRON-D. Systematic review of the literature illustrated high consistency in the association between these biomarkers of inflammation and renal outcomes in DKD. In conclusion, TNFR-1, TNFR-2, and KIM-1 independently associated with higher risk of eGFR decline in persons with early or advanced DKD. Moreover, addition of these biomarkers to clinical prognostic models significantly improved discrimination for the renal outcome.

SUBMITTER: Coca SG

PROVIDER: S-EPMC5576932 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Plasma Biomarkers and Kidney Function Decline in Early and Established Diabetic Kidney Disease.

Coca Steven G SG Nadkarni Girish N GN Huang Yuan Y Moledina Dennis G DG Rao Veena V Zhang Jane J Ferket Bart B Crowley Susan T ST Fried Linda F LF Parikh Chirag R CR

Journal of the American Society of Nephrology : JASN 20170505 9

Biomarkers of diverse pathophysiologic mechanisms may improve risk stratification for incident or progressive diabetic kidney disease (DKD) in persons with type 2 diabetes. To evaluate such biomarkers, we performed a nested case-control study (<i>n</i>=190 cases of incident DKD and 190 matched controls) and a prospective cohort study (<i>n</i>=1156) using banked baseline plasma samples from participants of randomized, controlled trials of early (ACCORD) and advanced (VA NEPHRON-D) DKD. We assess ...[more]

PMID: 28476763

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Project description:Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0291-6) contains supplementary material, which is available to authorized users.

Project description:BackgroundCrucial to the success of clinical trials targeting early Alzheimer's disease (AD) is recruiting participants who are more likely to progress over the course of the trials. We hypothesize that a combination of plasma and structural MRI biomarkers, which are less costly and non-invasive, is predictive of longitudinal progression measured by atrophy and cognitive decline in early AD, providing a practical alternative to PET or cerebrospinal fluid biomarkers.MethodsLongitudinal T1-weighted MRI, cognitive (memory-related test scores and clinical dementia rating scale), and plasma measurements of 245 cognitively normal (CN) and 361 mild cognitive impairment (MCI) patients from ADNI were included. Subjects were further divided into β-amyloid positive/negative (Aβ+/Aβ-)] subgroups. Baseline plasma (p-tau181 and neurofilament light chain) and MRI-based structural medial temporal lobe subregional measurements and their association with longitudinal measures of atrophy and cognitive decline were tested using stepwise linear mixed effect modeling in CN and MCI, as well as separately in the Aβ+/Aβ- subgroups. Receiver operating characteristic (ROC) analyses were performed to investigate the discriminative power of each model in separating fast and slow progressors (first and last terciles) of each longitudinal measurement.ResultsA total of 245 CN (35.0% Aβ+) and 361 MCI (53.2% Aβ+) participants were included. In the CN and MCI groups, both baseline plasma and structural MRI biomarkers were included in most models. These relationships were maintained when limited to the Aβ+ and Aβ- subgroups, including Aβ- CN (normal aging). ROC analyses demonstrated reliable discriminative power in identifying fast from slow progressors in MCI [area under the curve (AUC): 0.78-0.93] and more modestly in CN (0.65-0.73).ConclusionsThe present data support the notion that plasma and MRI biomarkers, which are relatively easy to obtain, provide a prediction for the rate of future cognitive and neurodegenerative progression that may be particularly useful in clinical trial stratification and prognosis. Additionally, the effect in Aβ- CN indicates the potential use of these biomarkers in predicting a normal age-related decline.

Dataset Information

Plasma Biomarkers and Kidney Function Decline in Early and Established Diabetic Kidney Disease.

Publications

Plasma Biomarkers and Kidney Function Decline in Early and Established Diabetic Kidney Disease.

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