WO2020064995A1 - Use of biomarkers representing cardiac, vascular and inflammatory pathways for the prediction of acute kidney injury in patients with type 2 diabetes - Google Patents

Abstract

Acute kidney injury (AKI) is a related to chronic kidney disease and death in patients from the general population, with or without type 2 diabetes. Nevertheless AKI biomarkers are rarely validated in diabetes population. The inventors aimed to explore the individual and combined prognostic value of 7 circulating candidate markers for AKI. This include markers of cardiac and endothelial dysfunction (mid-regional-pro-adrenomedullin [MRproADM], angiopoietinlike-2 [ANGPTL2], N-terminal prohormone brain natriuretic peptide [NTproBNP]) oxidative stress (fluorescent advanced glycation endproducts [AGE], carbonyls), cardio-renal pathways (copeptin [CTproAVP]), and inflammation (soluble TNF receptor 1 [TNFR1]). They prospectively followed-up 1345 (565 women/780 men) type 2 diabetes participants of a French single-centre hospital-based cohort (SURDIAGENE). In univariate analysis, each biomarker was significantly associated with AKI, and 6 remained associated after multivariable adjustment. The addition of a multimarker score summing standardized and weighted values of these 6 markers to the model including usual risk factors significantly improved C-statistics (0.724 to 0.759, P<0.0001), and 5-year risk-predictive performance (relative integrated discrimination improvement index=0.435, P<0.0001). Thus the panel of 6 biomarkers representing cardiac, vascular and inflammatory pathways improves the prediction of AKI over usual risk factors in patients with type 2 diabetes.

Description

USE OF BIOMARKERS REPRESENTING CARDIAC, VASCULAR AND INFLAMMATORY PATHWAYS FOR THE PREDICTION OF ACUTE KIDNEY INJURY IN PATIENTS WITH TYPE 2 DIABETES

FIELD OF THE INVENTION:

The present invention relates to use of biomarkers representing cardiac, vascular and inflammatory pathways for the prediction of acute kidney injury in patients with type 2 diabetes.

BACKGROUND OF THE INVENTION:

Acute kidney injury (AKI) is a related to chronic kidney disease and death in patients from the general population, with or without type 2 diabetes. Nevertheless AKI biomarkers are rarely validated in diabetes population.

MR-proADM concentrations are associated with the doubling of serum creatinine and progression to ESRD [2], and with mortality [3] in type 2 diabetes patients. Circulating sTNFR is associated in numerous epidemiological studies cross-sectionally with GFR and DKD [4-6] and prospectively with DKD progression and ESRD occurrence [7-12] in both type 1 diabetes and type 2 diabetes patients. Moreover, sTNFR2 has been associated with GFR variation in type 2 diabetes patients [13] as well as in type 1 diabetes patients. [14]. sTNFR2 and TNFR1 have also been associated with DKD structural lesions and especially with early glomerular lesions in type 2 diabetes [15] . However, even TNFR2 and TNFR1 shows partially overlapping biological effects, we only had access to sTNFRl assay for the present study. NT-proBNP has been reported to be associated with rapid kidney decline in elderly adults [16] and with ESRD in the general population [17]. A post-hoc analysis of a clinical trial also reported an association of NT-proBNP with ESRD in type 2 diabetes patients [18]. Interestingly, all 3 of these biomarkers were associated with rapid progression of eGFR in type 2 diabetes in a recent biomarker-panel study, and they were included in our study to further evaluate their combined value [19]. However the panel of said 6 biomarkers representing cardiac, vascular and inflammatory pathways has never been investigated for the prediction of AKI over usual risk factors in patients with type 2 diabetes.

SUMMARY OF THE INVENTION:

The present invention relates to use of biomarkers representing cardiac, vascular and inflammatory pathways for the prediction of acute kidney injury in patients with type 2 diabetes. In particular, the present invention is defined by the claims.

DETAILED DESCRIPTION OF THE INVENTION: The inventors aimed to explore the individual and combined prognostic value of 7 circulating candidate markers for AKI. This include markers of cardiac and endothelial dysfunction (mid-regional-pro-adrenomedullin [MRproADM], angiopoietinlike-2 [ANGPTL2], N-terminal prohormone brain natriuretic peptide [NTproBNP]) oxidative stress (fluorescent advanced glycation endproducts [AGE], carbonyls), cardio-renal pathways (copeptin [CTproAVP]), and inflammation (soluble TNF receptor 1 [TNFR1]).

They prospectively followed-up 1345 (565 women/780 men) type 2 diabetes participants of a French single-centre hospital-based cohort (SURDIAGENE) with baseline GFR>30 ml/min/l.73m2 and no renal replacement to onset of AKI, death, or December 31, 2015, whichever came first. Intrahospital AKI was diagnosed and staged using the KDIGO criteria (increase in serum creatinine concentration by 0.3 mg/dF or increase in serum creatinine to >1.5 times baseline). Cox models were used to estimate the risk of AKI for each biomarker at baseline after adjustement for usual risk factors: sex, diabetes duration, HbAlc, systolic blood pressure, GFR, ACR, use of antihypertensive, and history of cardiovascular disease. Hazard ratios were reported per 1 SD increment of the logarithm of the biomarker concentration.

At baseline, meaniSD age was 64±l l years, diabetes duration 14±10 years, HbAlc 7.8±l.6 %, and eGFR 77±2l ml/min/l.73m², and median (IQR) ACR 3 (1-10) mg/mmol. During a median follow-up of 4.7 years, 449 (33%) patients developed an AKI. In univariate analysis, each biomarker was significantly associated with AKI, and 6 remained associated after multivariable adjustment. The addition of a multimarker score summing standardized and weighted values of these 6 markers to the model including usual risk factors significantly improved C-statistics (0.724 to 0.759, P<0.000l), and 5-year risk-predictive performance (relative integrated discrimination improvement index=0.435, P<0.000l).

Thus a panel of 6 biomarkers representing cardiac, vascular and inflammatory pathways improved the prediction of AKI over usual risk factors in patients with type 2 diabetes.

Accordingly, the present invention relates to a method of determining whether a patient suffering from type 2 diabetes is at risk of having acute kidney injury (AKI) comprising i) measuring the level of at least one biomarker representing cardiac, vascular or inflammatory pathways in a plasma sample obtained from the patient, ii) comparing the level measured at step i) with its corresponding predetermined reference value wherein detecting differential between the level measured at step i) and its corresponding predetermined reference value indicates whether the patient is or is not at risk of having acute kidney injury.

As used herein, the term "type 2 diabetes" or“non-insulin dependent diabetes mellitus (NIDDM)” has its general meaning in the art. Type 2 diabetes often occurs when levels of insulin are normal or even elevated and appears to result from the inability of tissues to respond appropriately to insulin. Most of the type 2 diabetics are obese. As used herein the term "obesity" refers to a condition characterized by an excess of body fat. The operational definition of obesity is based on the Body Mass Index (BMI), which is calculated as body weight per height in meter squared (kg/m²). Obesity refers to a condition whereby an otherwise healthy subject has a BMI greater than or equal to 30 kg/m², or a condition whereby a subject with at least one co-morbidity has a BMI greater than or equal to 27 kg/m². An "obese subject" is an otherwise healthy subject with a BMI greater than or equal to 30 kg/m² or a subject with at least one co-morbidity with a BMI greater than or equal 27 kg/m². A "subject at risk of obesity" is an otherwise healthy subject with a BMI of 25 kg/m² to less than 30 kg/m² or a subject with at least one co-morbidity with a BMI of 25 kg/m² to less than 27 kg/m². The increased risks associated with obesity may occur at a lower BMI in people of Asian descent. In Asian and Asian-Pacific countries, including Japan, "obesity" refers to a condition whereby a subject with at least one obesity-induced or obesity-related co-morbidity that requires weight reduction or that would be improved by weight reduction, has a BMI greater than or equal to 25 kg/m². An "obese subject" in these countries refers to a subject with at least one obesity-induced or obesity- related co-morbidity that requires weight reduction or that would be improved by weight reduction, with a BMI greater than or equal to 25 kg/m². In these countries, a "subject at risk of obesity" is a person with a BMI of greater than 23 kg/m² to less than 25 kg/m².

As used herein, the term "acute kidney injury" or "acute kidney failure" is typically identified by a rapid deterioration in renal function sufficient to result in the accumulation of nitrogenous wastes in the body (see, e.g., Anderson and Schrier (1994), in Harrison's Principles of Internal Medicine, l3th edition, Isselbacher et al, eds., McGraw Hill Text, New York). Rates of increase in BUN of at least 4 to 8 mmol/L/day (10 to 20 mg/dL/day), and rates of increase of serum creatinine of at least 40 to 80 mihoI/L/day (0.5 to 1.0 mg/dL/day), are typical in acute renal failure.

As used herein, the term "Risk" in the context of the present invention, relates to the probability (i.e. at least 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99% of risk) that an event will occur over a specific time period (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 years), as in the conversion to AKI, and can mean a subject's "absolute" risk or "relative" risk. Absolute risk can be measured with reference to either actual observation post measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant time period. Relative risk refers to the ratio of absolute risks of a subject compared either to the absolute risks of low risk cohorts or an average population risk, which can vary by how clinical risk factors are assessed. Odds ratios, the proportion of positive events to negative events for a given test result, are also commonly used (odds are according to the formula p/(l-p) where p is the probability of event and (1- p) is the probability of no event) to no- conversion. "Risk evaluation," or "evaluation of risk" in the context of the present invention encompasses making a prediction of the probability, odds, or likelihood that an event or disease state may occur, the rate of occurrence of the event or conversion from one disease state to another, i.e., from a normal condition to loss of renal function or to one at risk of developing loss of renal function. Risk evaluation can also comprise prediction of future clinical parameters, traditional laboratory risk factor values, or other indices of loss of renal function, either in absolute or relative terms in reference to a previously measured population. The methods of the present invention may be used to make continuous or categorical measurements of the risk of conversion to loss of renal function, thus diagnosing and defining the risk spectrum of a category of subjects defined as being at risk of having loss of renal function. In the categorical scenario, the invention can be used to discriminate between normal and other subject cohorts at higher risk of having loss of renal function. Thus, the terms "high risk", "intermediate risk" and "low risk" refers to differences in the individual predisposition for developing a disease, disorder, complication or susceptibility therefor, preferably after a subject has been treated by one of the therapies referred to below, such as high-dose chemotherapy. Said high, intermediate or low risk can be statistically analyzed. Preferably, the differences between a subject and a group of subjects having a high, intermediate or low risk are statistically significant. This can be evaluated by well-known statistic techniques including Student's t-Test, Chi2-Test, Wilcoxon-Mann-Whitney Test, Kurskal- Wallis Test or Fisher's exact Test, log-rank test, logistic regression analysis, or Cox models. Most preferably, the risk groups are analyzed as described in the accompanied Examples whereby explorative data analysis is carried out and the risk groups are formed with respect to the median, the 25% and the 75% percentiles. Differences in continuous variables of the groups are tested by Wilcoxon-Mann-Whitney Test or Kurskal-Wallis Test depending on the number of groups to be compared. For nominal or ordered categories, Fisher's exact or Chi2-Test for trend are applied. Without further ado, the person skilled in the art can carry out multivariant analysis with stratified versions of the aforementioned tests or Cox models in order to examine the independent impact of predictive factors and to establish the different risk groups.

Typically the plasma sample may be obtained using methods well known in the art. Plasma may then be obtained from the plasma sample following standard procedures of the field including, but not limited to, centrifuging the plasma sample, followed by pipetting of the plasma layer. Platelet-free plasma (PFP) can be obtained following appropriate centrifugation. More preferably, the plasma sample obtained from the patient is a platelet free plasma sample.

According to the present invention, the biomarkers are selected from markers of cardiac and endothelial dysfunction (mid-regional-pro-adrenomedullin [MRproADM], angiopoietinlike-2 [ANGPTL2], N-terminal prohormone brain natriuretic peptide [NTproBNP]) oxidative stress (fluorescent advanced glycation endproducts [AGE], carbonyls), cardio-renal pathways (copeptin [CTproAVP]), and inflammation (soluble TNF receptor 1 [TNFR1]).

As used herein, the term“Mid-regional-pro-adrenomedullin” or“MR-proADM” has its general meaning in the art and refers to a fragmend of adrenomedullin of unknown function and with high ex vivo stability (Struck et al. (2004), Peptides 25(8): 1369-72). More particularly, mid-regional proANP comprises at least amino acid residues 53-90 of proadrenomedullin.

As used herein, the term“ANGPTF2” has its general meaning in the art and refers to the angiopoietin-related protein 2 also known as angiopoietin-like protein 2 is a protein that in humans is encoded by the ANGPTF2 gene.

As used herein, the term“NT-proBNP” has its general meaning in the art and relates to a polypeptide comprising, preferably, 76 amino acids in length corresponding to the N-terminal portion of the human NT-proBNP molecule. The structure of the human BNP and NT-proBNP has been described already in detail in the prior art, e.g., WO 02/089657, WO 02/083913, Bonow 1996, New Insights into the cardiac natriuretic peptides. Circulation 93: 1946-1950. Human NT-proBNP as disclosed in EP 0 648 228 Bl or under GeneBank accession number NP-002512.1; GL4505433.

As used herein, the term“AGE” refers to the compound which it modifies as the reaction product of either an advanced glycosylation endproduct or a compound which forms AGEs and the compound so modified, such as the bovine serum albumin (BSA). Thus, AGEs include, but are not limited to, AGE-proteins (such as BSA-AGE), AGE-lipids, AGE-peptides, and AGE- DNA.

As used herein, the term“CTproAVP” has its general meaning in the art and refers to a 39-amino acid-long peptide derived from the C-terminus of pre-pro-hormone of arginine vasopressin, neurophysin II and copeptin. The term is also known as copeptin.

As used herein, the term "TNFR1" has its general meaning in the art and is used herein to denote the human soluble tumour necrosis factor receptor type 1. Typically sTNFRl comprises the extracellular domain of the intact receptor and exhibits an approximate molecular weight of 30KDa.

In some embodiments, the level of 1, 2, 3, 4, 5, or 6 biomarkers is determined in the plasma sample. In some embodiments, the level of MR-proADM, sTNFRl and NT-proBNP is determined in the plasma sample.

The measurement of the level of a biomarker (e.g. TNFR1) in the blood sample is typically carried out using standard protocols known in the art. For example, the method may comprise contacting the blood sample with a binding partner capable of selectively interacting with the biomarker (e.g. TNFR1) in the sample. In some embodiments, the binding partners are antibodies, such as, for example, monoclonal antibodies or even aptamers. For example the binding may be detected through use of a competitive immunoassay, a non-competitive assay system using techniques such as western blots, a radioimmunoassay, an ELISA (enzyme linked immunosorbent assay), a“sandwich” immunoassay, an immunoprecipitation assay, a precipitin reaction, a gel diffusion precipitin reaction, an immunodiffusion assay, an agglutination assay, a complement fixation assay, an immunoradiometric assay, a fluorescent immunoassay, a protein A immunoassay, an immunoprecipitation assay, an immunohistochemical assay, a competition or sandwich ELISA, a radioimmunoassay, a Western blot assay, an immunohistological assay, an immunocytochemical assay, a dot blot assay, a fluorescence polarization assay, a scintillation proximity assay, a homogeneous time resolved fluorescence assay, a IAsys analysis, and a BIAcore analysis. The aforementioned assays generally involve the binding of the partner (ie. antibody or aptamer) to a solid support. Solid supports which can be used in the practice of the invention include substrates such as nitrocellulose (e.g., in membrane or microtiter well form); polyvinylchloride (e.g., sheets or microtiter wells); polystyrene latex (e.g., beads or microtiter plates); polyvinylidine fluoride; diazotized paper; nylon membranes; activated beads, magnetically responsive beads, and the like. An exemplary biochemical test for identifying specific proteins employs a standardized test format, such as ELISA test, although the information provided herein may apply to the development of other biochemical or diagnostic tests and is not limited to the development of an ELISA test (see, e.g., Molecular Immunology: A Textbook, edited by Atassi et al. Marcel Dekker Inc., New York and Basel 1984, for a description of ELISA tests). Therefore ELISA method can be used, wherein the wells of a microtiter plate are coated with a set of antibodies which recognize the biomarker (e.g. TNFR1). A sample containing or suspected of containing the biomarker (e.g. TNFR1) is then added to the coated wells. After a period of incubation sufficient to allow the formation of antibody-antigen complexes, the plate(s) can be washed to remove unbound moieties and a detectably labelled secondary binding molecule added. The secondary binding molecule is allowed to react with any captured sample marker protein, the plate washed and the presence of the secondary binding molecule detected using methods well known in the art. Measuring the level of a biomarker (e.g. TNFR1) (with or without immunoassay-based methods) may also include separation of the compounds: centrifugation based on the compound’s molecular weight; electrophoresis based on mass and charge; HPLC based on hydrophobicity; size exclusion chromatography based on size; and solid-phase affinity based on the compound's affinity for the particular solid-phase that is used. Once separated, said one or two biomarkers proteins may be identified based on the known "separation profile" e.g., retention time, for that compound and measured using standard techniques. Alternatively, the separated compounds may be detected and measured by, for example, a mass spectrometer. Typically, levels of immunoreactive biomarker (e.g. TNFR1) in a sample may be measured by an immunometric assay on the basis of a double-antibody "sandwich" technique, with a monoclonal antibody specific for a biomarker (e.g. TNFR1) (Cayman Chemical Company, Ann Arbor, Michigan). According to said embodiment, said means for measuring a biomarker (e.g. TNFR1) level are for example i) the biomarker (e.g. TNFR1) buffer, ii) a monoclonal antibody that interacts specifically with the biomarker (e.g. TNFR1), iii) an enzyme-conjugated antibody specific for the biomarker (e.g. TNFR1) and a predetermined reference value of the biomarker (e.g. TNFR1).

Typically, the level of biomarkers are determined in the plasma sample by any method well known in the art and more preferably as described in the EXAMPLE.

A predetermined reference value can be relative to a number or value derived from population studies, including without limitation, such subjects having similar body mass index, total cholesterol levels, LDL/HDL levels, systolic or diastolic blood pressure, subjects of the same or similar age range, subjects in the same or similar ethnic group, and subjects having the same severity of type 2 diabetes. Such predetermined reference values can be derived from statistical analyses and/or risk prediction data of populations obtained from mathematical algorithms and computed indices of metabolic syndrome. In some embodiments, the predetermined reference values are derived from the level of a biomarker in a control sample derived from one or more subjects who were not subjected to the event. Furthermore, retrospective measurement of the level of a biomarker in properly banked historical subject samples may be used in establishing these predetermined reference values. The predetermined reference value has to be determined in order to obtain the optimal sensitivity and specificity according to the function of the test and the benefit/risk balance (clinical consequences of false positive and false negative). Typically, the optimal sensitivity and specificity (and so the predetermined reference value) can be determined using a Receiver Operating Characteristic (ROC) curve based on experimental data. For example, after determining the level of the marker in a group of reference, one can use algorithmic analysis for the statistic treatment of the measured levels of the marker in samples to be tested, and thus obtain a classification standard having significance for sample classification. The full name of ROC curve is receiver operator characteristic curve, which is also known as receiver operation characteristic curve. It is mainly used for clinical biochemical diagnostic tests. ROC curve is a comprehensive indicator that reflects the continuous variables of true positive rate (sensitivity) and false positive rate (1- specificity). It reveals the relationship between sensitivity and specificity with the image composition method. A series of different cut-off values (thresholds or critical values, boundary values between normal and abnormal results of diagnostic test) are set as continuous variables to calculate a series of sensitivity and specificity values. Then sensitivity is used as the vertical coordinate and specificity is used as the horizontal coordinate to draw a curve. The higher the area under the curve (AUC), the higher the accuracy of diagnosis. On the ROC curve, the point closest to the far upper left of the coordinate diagram is a critical point having both high sensitivity and high specificity values. The AUC value of the ROC curve is between 1.0 and 0.5. When AUC>0.5, the diagnostic result gets better and better as AUC approaches 1. When AUC is between 0.5 and 0.7, the accuracy is low. When AUC is between 0.7 and 0.9, the accuracy is moderate. When AUC is higher than 0.9, the accuracy is quite high. This algorithmic method is preferably done with a computer. Existing software or systems in the art may be used for the drawing of the ROC curve, such as: MedCalc 9.2.0.1 medical statistical software, SPSS 9.0, ROCPOWER.SAS, DESIGNROC.FOR, MULTIREADER POWER.SAS, CREATE-ROC.SAS, GB STAT VIO.O (Dynamic Microsystems, Inc. Silver Spring, Md., USA), etc.

Typically, when the level of the biomarker is higher than its corresponding predetermined reference value, it is concluded that the patient is a risk of having AKI. On the contrary, when the level of the biomarker is lower than its corresponding predetermined reference value, it is concluded that the patient is a risk of having AKI.

Once it is concluded that the patient is at risk of having AKI, treatment options may be prescribed. Typical treatment position include weight management, physical activity, smoking cessation and medications. Medicines for preventing typically includes Examples of drug suitable for the prevention of loss of renal function include but is not limited to inhibitors of the renin-angiotensin system (RAS), including angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs) or antidiabetic drugs such as insulin or Sodium-glucose co-transporter 2 (SGLT2) inhibitors among patients with diabetes.

A further object relates to a kit suitable for performing the method of the present invention which comprises means for measuring the biomarker of the present invention. In some embodiments, the kit comprises binding partner specific for the biomarker(s). Said binding partners are antibodies as described above. In some embodiments, these antibodies are labelled as described above. Typically, the kits described above will also comprise one or more other containers, containing for example, wash reagents, and/or other reagents capable of quantitatively detecting the presence of bound antibodies. Typically compartmentalised kit includes any kit in which reagents are contained in separate containers, and may include small glass containers, plastic containers or strips of plastic or paper. Such containers may allow the efficient transfer of reagents from one compartment to another compartment whilst avoiding cross-contamination of the samples and reagents, and the addition of agents or solutions of each container from one compartment to another in a quantitative fashion. Such kits may also include a container which will accept the tumor tissue sample, a container which contains the antibody(s) used in the assay, containers which contain wash reagents (such as phosphate buffered saline, Tris-buffers, and like), and containers which contain the detection reagent.

The invention will be further illustrated by the following figures and examples. However, these examples and figures should not be interpreted in any way as limiting the scope of the present invention.

EXAMPLE:

Methods

Study patients

The SURDIAGENE study is a French single-center inception cohort of type 2 diabetes patients regularly visiting the diabetes department at Poitiers University Hospital, France [20]. Patients were consecutively enrolled from 2002 to 2012 and outcome updates were performed every 2 years since 2007. Since this is a referral population, some participants may be more complicated than those in the general diabetes population. The Poitiers University Hospital Ethics Committee approved the design (CPP Ouest III). All participants in the study gave their informed written consent.

At baseline, all patients were examined to collect relevant clinical and biological data. A history of cardiovascular disease at baseline was defined as a personal history of myocardial infarction, and/or stroke. Patients with a baseline eGFR <30 ml/min/l.73m² and/or prior renal replacement therapy were excluded from the present analysis. Definition of outcomes

The primary outcome in the longitudinal analyses was AKI defined according to The Kidney Disease: Improving Global Outcomes (KDIGO) guidelines criteria [21]. Only serum creatinine criteria were used to diagnose and stage AKI, and, therefore, urinary output criteria were omitted. We considered the lowest creatinine value found between the dates of hospital admission and discharge as the reference creatinine value. We identified and classified AKI by comparing the highest creatinine value found during full hospitalization to the reference serum creatinine value. AKI was defined as an increase in serum creatinine by > 0.3mg/dL (>26.5 pmol/L) or > 1.5 baseline versus the reference serum creatinine level. AKI was further classified by stage according to this ratio (stage 1 1.5-1.9%, stage 2 2.0-2.9%, stage 3 >300%). Stage 3 AKI was also defined by a serum creatinine increase of >4.0 mg/dL (>353.6 mmol/L). For each patient, we considered only the first episode of AKI.

The vital status of all study participants was confirmed through December 31, 2015.

Biomarker selection process

Biomarker selection was based on existing evidence from the literature (manual literature review) and the availability of reliable validated assays to measure biomarker concentrations in small volumes of serum.

Assays

Blood samples and second morning urine samples were obtained in patients after an overnight fast. Serum and urine creatinine and urinary albumin were measured by colorimetry and immunoturbidimetry tests, respectively, on a COBAS System analyzer (Roche Diagnostics GmbH, Mannheim, Germany). Glomerular filtration rate was estimated using the Chronic Kidney Disease Epidemiology (2009 CKD-EPI) creatinine equation. Glycated hemoglobin was determined using a high-performance liquid chromatography method with a HA- 8160 analyzer (Menarini, Flrence, Italy).

Remaining samples were processed under standardized conditions and stored at -80°C in the Poitiers Biological Resource Center (BRC BB-0033-00068) undergoing only one prior freeze-thaw cycle prior to assay. The fluorescence intensity of AGEs and the levels of carbonyls, ANGPTL2, CTproAVP, MR-proADM and NT-proBNP were measured in stored plasma-EDTA samples while sTNFRl was measured in stored serum. Detailed method of the assays were already published but briefly, AGEs were measured by spectrofluorometer, carbonyls [22], ANGPTL2[23] and sTNFRl by ELISA, and CTproAVP, MR-proADM and NT-proBNP [24]by immunoassay. Concentrations below the limit of detection were replaced by the LOD/V2 [25] Choice of covariates in the model

Clinical covariates included in the models for biomarker selection were selected based upon their inclusion in known associations AKI or CKD: age, sex, diabetes duration, HbAi_c, systolic blood pressure (SBP), use of antihypertensive, history of cardiovascular disease, eGFR, uACR...

Statistical analysis

Quantitative variables were expressed as means ± standard deviation (SD) or medians (25^lh-75^lh percentile) for skewed distributions; qualitative variables were presented as frequencies and percentages. Because of non-Gaussian distribution, concentrations of biomarkers were log-transformed. Spearman’s correlations were used to assess the relationship of biomarkers with each other and with clinical variables. A complete case method was used to handle missing data. Thus, 2 subjects with at least one missing value for biomarkers were omitted in the present study. Patients with follow up less than 1 month were omitted, living 1342 participants included in the complete case study.

The hazard ratio (HR) of AKI for each biomarker measured at baseline was determined by using Cox proportional hazards regression. We tested each model for log-linearity and proportionality assumptions using Schoenfeld residuals. Results were given with HR and 95% confidence intervals and expressed for a l-SD increase in the distribution of the logarithm of the biomarker concentration.

Two sets of models were used for individual biomarkers: univariate models (model 1), models adjusted for age, sex, diabetes duration, HbAi_c, systolic blood pressure (SBP), use of antihypertensive, history of cardiovascular disease, eGFR and uACR (model 2) as they represent established key markers associated with renal outcomes [26]. Interactions between sex and biomarkers for the association between biomarkers and AKI were evaluated by the addition of interaction terms into the corresponding regression model. The Akaike's information criterion (AIC) was used to compare global fit among models (nested or not nested), the model with the smallest AIC was considered as the best model. Comparisons of model adequacy were assessed using the likelihood ratio Chi2 tests.

Generalized c-statistics were calculated for model 2 accounting for variable follow-up times [27] The relative integrated discrimination improvement (ID I) index was calculated to assess the improvement in 5-year risk prediction of each biomarker in addition to traditional risk factors (model 2) [28]. Five-year risk was selected because it clinical relevance. The 95% CIs for the changes in the c-statistic and the relative IDI were computed based on 10,000 bootstrap samples. Areas under the curve of Five-year receiver-operating-characteristic (ROC) curves were also generated for models with traditional risk factors (model 2) and traditional risk factors plus biomarkers.

To examine the combined effects of biomarkers we considered biomarkers significantly associated with AKI after model 2 adjustment to compute a weighted AKI biomarker risk score (AKI-BRS). The AKI-BRS which derived from the following equation:

where X^k are log of biomarker concentration and p^k are beta coefficients were derived from the model 2 Cox regression model and correspond to the log (HR) of the biomarker.

We also computed a score (RFL-BRS) for the combination of MR-proADM, NT- proBNP and sTNFRl since we published its prognostic value for renal function loss in the SURDIAGENE cohort [24] The time to event was plotted as Kaplan-Meier cumulative incidence curves according to quartiles of biomarkers and biomarker risk score, and comparison was made using the log-rank test.

We conducted a series of sensitivity analyses.

( ) We used the competing risk model of Fine and Gray to estimate the subdistribution hazard ratios for AKI, while accounting for the competing risk of all-cause deaths [29].

(2) In order to verify that intrahospital AKI was independent from hospitalization risk we tested for an interaction between number of all-cause hospital admissions over time and risk of AKI when considering first hospitalization as a time-dependent variable.

(3) Finally, we modeled the risk of first intrahospital AKI grade 1 and AKI grade2+ using the model 2 Cox regression model.

P values < 0.05 were considered statistically significant. Statistical analyses were performed with SAS version 9.4 (SAS Institute, Cary, NC).

Results

Baseline characteristics

The study population included 1,343 patients with available samples and follow-up data. The clinical and biological characteristics of the patients are presented in Table 1.

All the biomarkers did not have any significant correlation with PAS or HbAlc. However, all biomarkers showed a negative correlation with baseline eGFR (Rho = -0.63 to - 0.06, all P O.OOO 1 , a positive correlation with ACR eGFR (Rho = -0.25 to -0.06, all <0.005). All biomarkers showed a positive correlation with age (Rho = -0.52 to -0.06, all P<0.005) and all biomarkers - except AGEs and carbonyls - with diabetes duration (Rho = -0.32 to -0.08, all P<0.005). Compared to women, men had significantly higher concentrations of CTproAVP (7.4 [4.6-12.2] vs 4.8 [2.8-8.5] pmol/L; P<0.000l) and significantly lower concentrations of MR- proADM (0.70 [0.56-0.90] nmol/L vs 0.75[0.62-0.92] nmol/L, P = 0.0008). Men had no significance difference in AGEs (111294[92585-130403] vs 108919[92585-130403], P=0.449), carbonyls (28[25-30] vs 28 [26-31], P= 0.959), ANGPTL2 (l4.5[l l.1-19.8] vs 15.2 [11.2-19.1], P=0.458), NT-ProBNP (107 [45-286] pg/mL vs 99 [49-240] pg/mL, P=0.500),and sTNFRl (1,804 [1,520-2,257] pg/mL vs 1,822 [1,564-2,200] pg/mL, P = 0.458)

We observed no significant statistical interaction between sex and biomarkers for the association of biomarkers with AKI incidence so we reported findings for men and women together.

Biomarkers and acute kidney injury

Patients were followed for AKI for a median of 4.7 years (2.4-8.1 years), during which 451 cases of AKI occurred (incidence rate 65 per 1,000 person-years, 95% confidence interval, 59 to 71) and 361 deaths occurred (incidence rate 45 per 1,000 person-years, 95%CI 40 to 49) including 155 before AKI.

Each biomarker except carbonyls predicted AKI in univariate and multivariate models (Table 2). RFL-BRS and AKI-BRS scores were also associated with a significant increased risk of AKI: adjusted HR per l-SD 2.24 (1.96-2.55); P< 0.0001 and 2.36 (2.06-2.71); P< 0.0001 for RFL-BRS and AKI-BRS, respectively.

Discrimination

We assessed goodness of fit and improvement in risk discrimination for each biomarker score compared with the model with traditional risk factors (model 2). The inclusion of AKI- RFL and RFL-BRS increased c-statistic of 0.032, P < 0.0001 and of 0.034, P < 0.0001, respectively; whereas the rIDI was 0.324, P <0.0001 and 0.436, P <0.0001, respectively. (Table 3).

Sensitivity analyses

After accounting for the competing risk of all-cause mortality in a Fine and Gray analysis, each BRS remained independently associated with an increased risk of AKI ; both P<0.000l.

When considering first non-AKI hospitalization as a time-dependent variable in multiadjusted model 2, we confirmed that each BRS remained independently associated with an increased risk of AKI both P<0.000l.

A total 92 cases of AKI grade 2+ occurred during follow up (incidence rate 13 per 1,000 person-years, 95% confidence interval, 11 to 16). Modeling the risk of first intrahospital AKI grade2+ using the model 2 Cox regression model retrieved similar findings for RFL-BRS and AKI-BRS (both P<0.000l).

Conclusion:

In this study, we investigated 7 biomarkers in a hospital-based sample of 1,135 type 2 diabetes patients with normal to mildly impaired renal function (eGFR > 30 ml/min/l 73m² and no history of RRT) who were followed for up to 11.8 years for AKI. In conclusion, we found, in a prospective cohort study, that beyond traditional risk factors, an increased circulating level of a combination of MR-proADM, sTNFRl and NT-proBNP improves risk prediction of AKI as it was for RFL in a type 2 diabetes population.

TABLES

Table 1 - Clinical and biological characteristics in the SURDIAGENE study.

Variables All

_ n=l,343 _

Male 778 (58)

Age (years) 64 + 11

Body mass index (kg/m²) 31 + 6

Active smoking 148 (11)

Known diabetes duration (years) 14 + 10

History of cardiovascular disease 255 (19)

Systolic blood pressure (inmHg) 132 + 17

Diastolic blood pressure (inmHg) 72 + 11

Resting heart rate (beats per min) 71 + 14

Therapeutics

Any antihypertensive dmg 1112 (83)

Diuretics 597 (44)

RAAS blockers 847 (63)

Beta blockers 231 (17)

Calcium antagonists 400 (30)

Insulin 790 (59)

OAD agents 893 (66)

Biological determinations

HbAlc (%) 7.8 + 1.6

HbAlc (mmol/mol) 62 + 16.4 eGFR (ml/min/l.73m²) 77 + 21 uACR (mg/mmol) 2.6 (1.0-10.4)

Normoalbuminuria, microalbuminuria, macroalbuminuria* 544 (45)/429 (36)/226 (19) Fluorescent AGE (AU) 110132 (91635-130196)

ELISA carbonyls (mmol/mg) 28 (26-31)

ANGPTL2 (ng/ml) 15 (11-20)

MR-proADM (nmol/L) 6 (4-11)

CT -pro A VP (pmol/L) 0.7 (0.6-0.9) NT-pro BNP (pg/mL) 103 (47-262) sTNFRl (pg/mL) 1816 (1544-2236)

Data are mean ± standard deviation, median (25^th-75^th percentile) or n (%)

History of cardiovascular disease was defined as history of stroke and/or myocardial infarction prior to baseline

AU, arbitrary unit; RAAS blocker, Renin Angiotensin aldosterone system blocker (Angiotensin receptor blocker and/or ACE inhibitor); OAD agent, oral antidiabetic agent; eGFR, estimated glomerular filtration rate by CKD EPI equation; uACR, urinary albumin-to-creatinine ratio; MR- proADM, Mid-regional-pro-adrenomedullin; NT-proBNP; sTNFRl, soluble Tumor Necrosis Factor receptor 1; N-terminal of the prohormone brain natriuretic peptide

*Normoalbuminuria was defined as uACR< 30mg/g, microalbuminuria as uACR 30-299 mg/g and macroalbuminuria as uACR >300 mg/g

Missing values: diabetes duration, 2; PAS/PAD, 7; HR, 6; HbAlc, 1; uACR, 4; smoking status, 18; albuminuria status, 145.

Table 2- Risk of acute kidney injury according to biomarkers in patients of the SURDIAGENE cohort

Variables univariate Adjusted

Hazard ratio for AKI *

Fluorescent AGE 1.30 (1.18-1.43) <0.0001 1.12 (1.02-1.23) 0.024 ELISA carbonyls 1.09 (0.99-1.19) 0.072 1.02 (0.94-1.11) 0.590 ANGPTL2 1.61 (1.47-1.75) <0.0001 1.29 (1.16-1.43) <0.0001 CT -pro A VP 1.62 (1.48-1.78) <0.0001 1.33 (1.19-1.48) <0.0001 MR-proADM 2.13 (1.95-2.33) <0.0001 1.93 (1.71-2.18) <0.0001 NT-pro BNP 1.86 (1.70-2.04) <0.0001 1.52 (1.36-1.70) <0.0001 sTNFRl 2.12 (1.93-2.33) <0.0001 1.79 (1.58-2.03) <0.0001 RFL-BRS 2.32 <0.0001 2.08 (1.83-2.35) <0.0001 AKI-BRS 2.39 <0.0001 2,36 (2,06-2.71) <0,0001

Ratios are presented for 1 SD increment with 95% confidence interval and P Value.

* Cox model,† Fine and Gray model (competing risk = all-cause death)

Adjustment for age, sex, diabetes duration, systolic blood pressure, hbalc, eGFR, UACR

MR-proADM, Mid-regional-pro-adrenomedullin; sTNFRl, soluble Tumor Necrosis Factor receptor 1; NT-proBNP; N-terminal prohormone brain natriuretic peptide Table 3-C-statistics, relative integrated discrimination improvement index (rIDI) using individual biomarkers or their combination for the prediction of renal function loss (> 40% GFR drop) and of rapid renal function decline (<-5ml/min/year)

AIC Likelihood Difference Likelihood Relative

Variables ratio in ratio IDI P value

P value C-statistics P value (95% Cl)

Reference model 5687

+ AKI-BRS 5555 <0.0001 0.032 <0.0001 0.324 <0.0001

+ RFL-BRS 5546 <0.0001 0.034 <0.0001 0.436 0.001

Reference Model = age, sex, diabetes duration, systolic blood pressure, hbalc, eGFR, UACR

C-statistics reference = 0.725

AIC, Akaike information criterion; Relative IDI, relative integrated discrimination improvement index; MR-proADM, Mid-regional-pro-adrenomedullin; sTNFRl, soluble Tumor Necrosis Factor receptor 1; NT-proBNP; N-terminal prohormone brain natriuretic peptide

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Throughout this application, various references describe the state of the art to which this invention pertains. The disclosures of these references are hereby incorporated by reference into the present disclosure.

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Claims

CLAIMS:

1. A method of determining whether a patient suffering from type 2 diabetes is at risk of having acute kidney injury (AKI) comprising i) measuring the level of at least one biomarker representing cardiac, vascular or inflammatory pathways in a plasma sample obtained from the patient, ii) comparing the level measured at step i) with its corresponding predetermined reference value wherein detecting differential between the level measured at step i) and its corresponding predetermined reference value indicates whether the patient is or is not at risk of having acute kidney injury.

2. The method of claim 1 wherein the biomarker is selected from markers of cardiac and endothelial dysfunction (mid-regional-pro-adrenomedullin [MRproADM], angiopoietinlike-2 [ANGPTL2], N-terminal prohormone brain natriuretic peptide [NTproBNP]) oxidative stress (fluorescent advanced glycation endproducts [AGE], carbonyls), cardio-renal pathways (copeptin [CTproAVP]), and inflammation (soluble TNF receptor 1 [TNFR1]).

3. The method of claim 1 wherein level of 1, 2, 3, 4, 5, or 6 biomarkers is determined in the plasma sample.

4. The method of claim 1 wherein the level of MR-proADM, sTNFRl and NT-proBNP is determined in the plasma sample.