In vitro method, biomarker panel, kit for prediction, screening, monitoring and / or diagnosis of diabetic kidney disease and method of treatment

Abstract

The present invention pertains to the fields of molecular biology and medicine. More specifically, the present invention discloses the use of TNFR1 as a biomarker for predicting, screening, monitoring and / or diagnosing diabetic kidney disease in mammals. A kit and a biomarker panel comprising two or more genes selected from among the following are disclosed: TNFR1, MCT1, MCT4, CD147, HIF-1α and VEGFα, SMAD1 and NGAL. An in vitro method for predicting, screening, monitoring and / or diagnosing diabetic kidney disease is also disclosed, comprising quantifying the gene expression of TNFR1, MCT1, MCT4, CD147, HIF-1α, VEGFA, SMAD1 and / or NGAL and comparing it with the level of a control sample. The present invention provides a new and improved strategy for tracking early renal changes resulting from the progression of diabetes mellitus.

Description

IN VITRO METHOD, BIOMARKER PANEL, KIT FOR PREDICTION, SCREENING, MONITORING AND / OR DIAGNOSIS OF DIABETIC KIDNEY DISEASE AND TREATMENT METHOD Descriptive Report of Invention Patent Field of Invention

[0001] The present invention is situated in the field of Molecular Biology and Medicine. More specifically, the present invention discloses the use of TNFR1 as a biomarker for the prediction, screening, monitoring, and / or diagnosis of diabetic kidney disease in mammals. A kit and a biomarker panel comprising two or more genes selected from among TNFR1, MCT1, MCT4, CD147, HIF-1a, VEGFa, SMAD1, and NGAL are disclosed. An in vitro method for the prediction, screening, monitoring, and / or diagnosis of diabetic kidney disease is also disclosed, comprising the quantification of gene expression of TNFR1, MCT1, MCT4, CD147, HIF-1a, VEGFa, SMAD1, and / or NGAL and comparison with the level of a control sample. The present invention provides a novel and improved strategy for screening for early renal changes resulting from the progression of diabetes mellitus. Background of the Invention

[0002] Diabetes mellitus (DM) is a syndrome of multiple etiologies resulting from insulin deficiency and / or the inability of insulin to adequately exert its effects. As a consequence, chronic hyperglycemia occurs, accompanied by disturbances in the metabolism of carbohydrates, lipids, and proteins. [921 The Brazilian Diabetes Society (SBD) recommends classification based on the etiopathogenesis of the disease, which includes type 1 diabetes (DM1), type 2 diabetes (DM2), gestational diabetes (GDM), and other types of diabetes (such as monogenic defects in pancreatic beta-cell function, genetic defects in insulin action, exocrine pancreatic diseases, among others). others).

[0003] Type 1 diabetes (DM1) is more common in children and adolescents and is characterized by severe insulin deficiency due to autoimmune destruction of pancreatic beta cells. Its clinical presentation is abrupt, with a propensity for ketosis and ketoacidosis, and requires full insulin therapy from diagnosis or after a short period. Type 2 diabetes mellitus (DM2) is the most prevalent form, with a multifactorial, endocrine, and metabolic character, frequently associated with obesity and aging. It is characterized by insulin resistance and partial deficiency in insulin secretion by pancreatic beta cells, in addition to alterations in incretin secretion. Its onset is insidious and frequently manifests clinical characteristics associated with insulin resistance, such as acanthosis nigricans and hypertriglyceridemia.

[0093] This condition is also characterized by sustained hyperglycemia as an initial and maintaining factor in the continuous aggression of target tissues and organs, promoting vasculopathies and interstitial inflammation. [2-4] .

[0004] In all cases, hyperglycemia is associated with microvascular damage (such as retinopathy, nephropathy, and neuropathy) and macrovascular damage (such as acute myocardial infarction, cerebrovascular events, and peripheral vascular disease). Although the development of such complications is more significant in patients with associated comorbidities, such as obesity and systemic hypertension, glycemic levels also play a crucial role in the pathogenesis of these conditions! 94More specifically, there is the formation of advanced glycation end products (AGEs) which, through binding to their receptors (RAGE), increase the generation of reactive oxygen species (ROS). ROS favor the formation of free radicals and promote oxidative stress.

[0005] Thus, type 2 diabetes is typically associated with several chronic complications. In 2021, it was estimated that there were 537 million adults aged 20 to 79 worldwide, representing 10.5% of the adult population in that age group, with diabetes. This number is expected to increase to 643 million by [year missing]. The projected number of people with diabetes is 783 million by 2030 and 783 million by 2045. While the estimated global population growth for this period is 20%, the number of people with diabetes is expected to increase by 46%. Furthermore, global healthcare spending related to diabetes has grown considerably, from US$232 billion in 2007 to US$966 billion in 2021 for adults aged 20 to 79, representing a 316% increase in 15 years. [5 '951 These data make type 2 diabetes a global public health problem.

[0006] Given this scenario, it is known that individuals with diabetes mellitus (DM) have a higher risk of developing various complications. Chronic hyperglycemia is associated with damage to the heart, blood vessels, eyes, nerves, and kidneys. In almost all high-income countries, DM is a leading cause of cardiovascular disease, blindness, kidney failure, and lower limb amputation. Regarding the kidneys, data from the UK and the US indicate that up to 40% of individuals with DM will develop chronic kidney disease. Furthermore, an analysis of data from 54 countries revealed that more than 80% of end-stage renal disease cases are caused by DM, hypertension, or a combination of both. [961 .

[0007] In the kidneys, hyperglycemic damage involves several mechanisms and is called Diabetic Kidney Disease (DKD). [6 ' 71. DRD is characterized by glomerular sclerosis and fibrosis, resulting from metabolic and hemodynamic changes induced by type 2 diabetes mellitus (DM). 97 l. Clinically, it manifests as persistent albuminuria (albumin excretion rate greater than 300 mg / day or 200 pg / min) and progressive reduction in glomerular filtration rate (GFR), frequently associated with increased blood pressure, culminating in end-stage renal disease. Therefore, it is essential to identify patients more susceptible to developing RDD, allowing for more effective control of disease progression. However, albuminuria, the main biomarker used to assess glomerular damage and increased glomerular permeability to macromolecules, has limitations such as high variability, low sensitivity, and late detection of the disease. [91 Of complex pathogenesis and still While not widely known, the development of diabetic kidney disease (DKD) involves environmental and genetic components, with chronic hyperglycemia and hypertension being the main risk factors. 7 ' 8 These factors, together, generate a cascade of molecular mechanisms involving oxidative stress, activation of the renin-angiotensin-aldosterone system (RAAS) and diacylglycerol (DAG), activation of pro-inflammatory mediators, non-enzymatic glycosylation, changes in membrane electronegativity, alteration of endothelial and mesangial function, among others. [9 ' 1 1.

[0008] The diagnosis of RD is established by considering the presence of albuminuria and / or a decrease in the estimated glomerular filtration rate (eGFR), in the absence of signs and symptoms of other causes of renal dysfunction or damage. Typically, it is marked by a prolonged duration of DM, encompassing retinopathy, albuminuria without macroscopic hematuria, and a progressive loss of eGFR.6 ' 7 l. Classic renal involvement begins with an increase in the mesangial lumen and endothelium of the glomerular capsule, causing initial hypoplasia, which progresses to glomerulosclerosis, followed by a reduction in the number of functional glomeruli and, consequently, the onset of chronic kidney disease (CKD) and increased cardiovascular risk! 11 ' 12 For these reasons, DRD is one of the most severe and frequent complications of DM, being the main cause of increased morbidity and mortality in these patients! 13 DRD is a syndrome that affects glomerular endothelial cells, tubular epithelial cells, and the renal interstitium through a diffuse process! 6 It is defined by a progressive decline in GFR, increased blood pressure levels, and persistent albuminuria (>30 mg / g creatinine, confirmed in two out of three samples, over a 3-6 month period)! 9 - 141. These manifestations are consequences of three main alterations in renal histology: mesangial expansion, glomerulosclerosis, and interstitial fibrosis with thickening of the glomerular basement membrane (GBM). [12 1. Dysfunction in these factors is interconnected and influences genetic regulation, activation or inhibition of transcription factors, and affects molecular pathways.

[0009] DRD is considered the leading cause of CKD worldwide, and the second leading cause in Brazil, after hypertension. 61 Progression to end-stage renal disease (ESRD) occurs in up to 40% of patients who develop ESRD and follows a natural progression divided into stages: glomerular hyperfiltration, silent phase (microalbuminuric), incipient nephropathy (macroalbuminuric), and finally, renal failure, evolving or not to end-stage renal disease. 15 ' 19], a phase in which Renal Replacement Therapy (RRT) becomes necessary. Because it is an initially asymptomatic disease with high prevalence and morbidity and mortality, the use of new tests with greater accuracy is necessary for early diagnosis and screening.

[0010] The International Society of Nephrology recommends an annual screening approach for diabetic patients as a measure to monitor possible changes in renal function. This monitoring involves regular assessment of albuminuria and eGFR to promptly identify the onset of diabetic kidney disease (DKD). 19The choice of this evaluation method is based on the direct correlation between albuminuria and the aforementioned structural changes in the nephron, which manifest as diabetes mellitus (DM) progresses. However, despite being considered the main diagnostic tool for the early detection of renal alterations in diabetic patients, it is recognized that renal impairment can manifest even in the absence of albuminuria! 9 ]

[0011] In clinical practice, the assessment of glomerular function is essential for the diagnosis, monitoring, and treatment of patients with kidney disease. Furthermore, it can determine not only renal but also cardiovascular outcomes, manage the onset and outcome of acute kidney injury, contribute to the adjustment of medications excreted by the kidneys, and influence decisions regarding renal replacement therapy (RRT). The eGFR (electroglomerular filtration rate) is the most commonly used measure for this assessment, as its reduction is directly correlated with a decrease in the number of nephrons. However, it is important to emphasize that this parameter may remain clinically stable even when there is a decrease in the number of nephrons. [2 °l.

[0012] The most common formulas for estimating GFR include the Cockcroft-Gault formula, the Modification of Diet in Renal Disease (MDRD) formula, and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. 21This is the formula recommended for use by KDIGO. It is worth noting that these formulas only provide an estimate of GFR and are not able to accurately quantify the glomerular filtration rate. Serum creatinine is one of the most widely used markers to estimate GFR, as it is freely filtered by the glomeruli. However, there are known limitations regarding creatinine clearance. 22 The formulas used to calculate GFR show limited accuracy in diabetic patients, especially when creatinine is included as a predictive variable. [11 ' 20 ' 21 i.

[0013] Creatinine is a waste product of creatine ae and phosphocreatine a, which is freely filtered by the glomeruli and is neither reabsorbed nor metabolized by the kidneys. However, this marker not only varies with the decline in GFR, but can also be influenced by the individual's muscle mass, since it originates from muscle catabolism. Thus, similar serum creatinine values ​​may correspond to different GFR levels in individuals of different age groups, genders, or ethnicities! 23 Additionally, creatinine levels are influenced by drugs such as trimethoprim and cimetidine. [24 ' 25 Factors that impact tubular secretion can elevate serum creatinine levels, even with a constant GFR. Its quantification can also be affected by diet, as protein-rich meals can increase serum creatinine levels. [26i. The production of creatinine, derived from creatine muscle, can also be negatively influenced by severe liver disease and positively by rhabdomyolysis[i2],

[0014] As mentioned earlier, a widely used marker in clinical practice is albuminuria or microalbuminuria, a condition characterized by the presence of elevated amounts of albumin in the urine. Albumin is a protein that should not normally be detected in significant quantities in urine; however, when the glomerular barrier in the kidneys is compromised, it can be detected. When albumin and other proteins are compromised, they can pass into the urine, resulting in microalbuminuria. 120 ' 221 Therefore, albuminuria is often considered an early sign of kidney dysfunction and may be an indicator of underlying health problems such as diabetes mellitus or hypertension.

[0015] Although it has been an integral part of renal assessment for many years, it is essential to consider albuminuria assessment in parallel with GFR, since, in addition to its role in detecting renal alterations, albuminuria also shows elevated values ​​in situations of generalized vascular damage, establishing itself as a reliable marker for cardiovascular prognosis! 23 ' 981 Furthermore, it is important to highlight that the increase in albuminuria levels is not exclusively linked to hyperglycemia. Factors such as high blood pressure, gender, age, race, smoking, and obesity also contribute to its increase. 123 ' 241 Additionally, studies have indicated that histopathological changes can occur with the onset of CKD, even in patients with normal albuminuria levels. 123 ' 241Therefore, although it is an indicator of CKD progression, albuminuria has limitations, such as low sensitivity and specificity in assessing the risk of developing diabetic nephropathy (DN). 1251 and the inability to identify the early stages of kidney changes 1221 Currently, the recommended method is the albuminuria / creatinine ratio in a single urine sample (KDIGO 2022), which should be altered in two out of three samples within a 3 to 6 month interval. 1271 .

[0016] Among the most recent biomarkers for detecting kidney disease, cystatin C is the least affected by age, race, or muscle mass. This protein is synthesized by all nucleated cells, filtered by the glomerular membrane, and reabsorbed in the proximal tubule, where it is almost completely catabolized. Cystatin C correlates strongly with a worse prognosis for CKD; therefore, its quantification increases in parallel with the worsening of the disease and a higher risk of mortality. However, there are factors that can affect its quantification, such as BMI, diabetes mellitus, and inflammation, which cause its levels to increase. Regardless of renal function. Given this, considering the high prevalence of obesity in the population with diabetes mellitus (DM) and the suggestion of a possible differentiated performance of cystatin C in patients with DM, there are controversies regarding the use of cystatin C and creatinine in the evaluation of renal function in diabetic patients!28 ]

[0017] Given these limitations, the search for alternative biomarkers that can effectively reflect the initial phases of renal alterations induced by hyperglycemia has expanded. Biomarkers are calculated and evaluated to reflect both physiological and pathological processes, including responses to pharmacotherapy. [26 i. To be relevant in the diagnosis of RD, biomarkers must have high sensitivity, specificity, and accuracy (>80%), allowing the detection of subtle changes in renal function, for example. Furthermore, they must demonstrate a strong correlation with histopathological findings from biopsies, imaging results, and glycated hemoglobin. [26 ' 28 i. Although the most recent biomarkers are related to the pathogenesis of DRD! 13They fail to reflect the more subtle molecular changes present at the beginning of the 'tubular stage', which precedes the onset of albuminuria! 26 ' 28 ]

[0018] Among the markers studied in relation to the pathogenesis of kidney disease in patients with diabetes mellitus are monocarboxylate transporters (MCTs), membrane proteins whose main function is to transport lactates, pyruvates, and ketones! 30 This family consists of fourteen members, with distinct transport properties—not fully elucidated—and functionally located in different tissues and cells! 31 Isoform 1 is the most abundant and is functionally active in various tissues.

[3132] Isoform 4 is widely expressed in tissues responsible for glycolytic metabolism! 33The expression and activity, especially of MCT4, depend directly on the co-expression of the cluster of differentiation 147 (CD147), a chaperone known as an inducer of extracellular matrix metalloproteinases (EMMPRIN)! 34 Through the induction of Metalloproteinases (CD147) have been shown to generate accumulation and remodeling of the extracellular matrix, contributing to renal fibrosis. [35 CD147 is a plasma membrane glycoprotein, and its expression is regulated by stimuli from cellular metabolic activity, enabling glycolysis and cellular respiration! 33 ]

[0019] MCT activity also depends on oxygen regulation, a function mediated by hypoxia-inducible factor (HIF), with isoform 1a typically expressed in most tissues! 36 ' 38HIF-1α activity is induced by decreased oxygen bioavailability to cells – classified as a state of hypoxia – triggering significant metabolic changes in diabetes mellitus (DM), which are still poorly understood. 39 ' 42 In this state, there are also alterations in the expression of vascular endothelial growth factor (VEGF), a highly specific dimeric glycoprotein that promotes migration and inhibits the process of cell apoptosis! 43 ' 45 ]

[0020] In response to inflammatory stimuli, such as those caused by oxidative stress, cells release soluble tumor necrosis factor (TNFR) receptors, an inflammatory marker that is linked to the stimulation of kidney injury molecule 1 (KIM-1)! 46l. Studies in humans have observed that circulating levels of tumor necrosis factor receptor type 1 (TNFR1) are good predictors of CKD in patients with type 2 DM with or without proteinuria. This marker is specific to diabetic kidney disease and does not show the same relationship with other kidney diseases! 47 In an animal model, increased expression of TNFR1 mRNA and protein was observed in the renal tissue of rats with established diabetic nephropathy. [48 i.

[0021] In this context, studies also highlight the importance of two biomarkers in particular: the transcription factor "suppressor of mothers against decapentaplegic 1" (SMAD1) and neutrophil gelatinase-associated lipocalin (NGAL).

[0022] SMAD1 acts as a transcription factor for the α1 and α2 chains of type IV collagen, the main component of the expanded mesangial matrix in DRDl 100Under physiological conditions, this collagen is essential for the permeability of the glomerular filtration barrier. However, its excessive thickness compromises the function of renal podocytes, triggering glomerular diseases. Recent studies demonstrate that this molecule can be detected in the urine of patients with RD, allowing for the early recognition of renal fibrotic processes. [ "i.

[0023] NGAL, on the other hand, is a glycoprotein involved in various pathophysiological mechanisms and antimicrobial defense, as well as regulating cell division, cell adhesion, and cell survival. NGAL is a marker of acute tubular injury, and its level correlates positively with the severity of kidney damage and the risk of mortality. Both urinary and serum NGAL are highly sensitive and specific predictors of acute kidney injury. [101i. Like SMAD1, NGAL can be detected in the urine of diabetic patients independently of albuminuria, being a highly sensitive early marker for tubular lesions. [991 .

[0024] In addition to the articles cited above, the following documents addressing the topic were found in the search for the state of the art in scientific and patent literature:

[0025] The article “NGAL and SMAD 1 gene expression. In the early detection of diabetic nephropathy by liquid biopsy”, which shares some authors with the present invention, refers to a study of the expression profile of the maternal suppressor genes against diabetic nephropathy type 1 (SMAD1), neutrophil gelatinase-associated lipocalin (NGAL), and type IV collagen (COLIV1A) in peripheral blood and urinary sediment of participants with or without DM. This study observed increased expression of the NGAL and SMAD1 genes in diabetic patients compared to healthy individuals, suggesting their potential as markers of renal alterations. This article uses a TRIzole-based extraction methodology in the laboratory and does not standardize urine collection with RNA stabilization, which compromises the transposition of the method to clinical settings. The biomarker panel, as well as the kit... The elements revealed by the present invention are not revealed or suggested by the cited document. Furthermore, the content of that reference is incorporated into the present application.

[0026] The document “Novel biomarkers of diabetic kidney disease: Current status and potential clinical application. Barutta et al. (2021)” is a literature review that explores candidate biomarkers involved in the pathophysiological mechanisms of inflammation, fibrosis, endothelial dysfunction, and kidney injury. The studies reviewed in the research are predominantly related to chronic kidney disease derived from diabetes mellitus (types 1 and 2), that is, evaluated in advanced stages of kidney disease, in which there is more than 30% loss of renal function and the classic markers of renal alterations, such as albumin, proteinuria, and creatinine, are already altered.

[0027] Patent document W02020064995A1 discloses the use of biomarkers representing cardiac, vascular, and inflammatory pathways for the prediction of acute kidney injury in patients with type 2 diabetes. This document proposes protein expression methods and does not detail pre-analytical protocols to ensure sample integrity. The document also does not disclose a biomarker panel or kit such as that of the present invention, thus differing from that disclosed in the present invention.

[0028] Patent document US20140171335A1 discloses a method for evaluating renal or hepatic toxicity caused by the administration of a supposedly toxic compound in an individual by measuring a set of biomarkers in a sample and comparing the measured amount of biomarker with the corresponding amount in a healthy individual. Biomarkers mentioned include Vascular Endothelial Growth Factor (VEGF) and alpha-2u globulin-related protein (Alpha-2u), also known as lipocalin 2 (LCN2) or neutrophil gelatinase-associated lipocalin (NGAL). In this study, NGAL was quantified by protein expression only in renal tissue samples. Analysis using NGAL in blood or urine was included for toxicity assessment. The document does not focus on the study of diabetic kidney disease, which is a different area from that disclosed in this patent application. Furthermore, it does not disclose a biomarker panel or a kit such as the one described in the present invention.

[0029] The document “TGF-β signaling in diabetic nephropathy: An update. Wang et al. (2022)” is a literature review that reveals the possible use of the TGF-β[3] / SMAD1 signaling pathway as a therapeutic target to combat diabetic nephropathy. The article discusses the pathophysiology of the cited markers and indicates them as possible therapeutic targets, and not as diagnostic and / or prognostic markers. The document does not disclose a method such as that of the present invention, nor does it disclose a biomarker panel or a kit such as that of the present invention, thus being different from that disclosed in the present invention.

[0030] The present invention is an alternative to strategies for tracking, diagnosing, and monitoring DRD.

[0031] Thus, based on the literature reviewed, no documents were found that anticipated or suggested the teachings of the present invention, so the solution proposed here has novelty and inventive activity compared to the state of the art.

[0032] Therefore, there remains a need for methods that promote the acquisition of information about the renal status of a diabetic mammal, in order to assist in the early detection of diabetic kidney disease, and the implementation of timely therapeutic measures to prevent the progression of renal damage in this pathophysiological condition. Summary of the Invention

[0033] The present invention solves the problems of the prior art by providing a biomarker panel comprising genes involved in tissue oxidation and inflammation, angiogenesis, and glucose metabolism. This panel is capable of detecting alterations in gene expression in different biological matrices in the early stages of DM, when DRD has not yet been established. To this end, the present invention also provides a standardized method comprising pre-analytical steps that ensure the quality and reproducibility of molecular analyses even after the transport and storage of samples.

[0034] The present invention is useful, as an intermediate result, in both clinical and research settings, for screening and monitoring the early stages of DRD. Thus, the present invention transforms theoretical knowledge of molecular pathways into an applicable and validated diagnostic tool using biological samples, contributing to the early detection of DRD.

[0035] In a first object, the present invention provides an in vitro method for screening indicators of renal alterations in samples from diabetic mammals comprising the steps of: a) preparing the analyte from mammalian samples; b) measuring the expression level of the TNFR1 gene and at least 1 gene selected from MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL in the analyte sample; and c) comparing the expression level of the genes measured in a) with the expression level of the respective genes in a control sample, wherein renal alteration is indicated when the expression level of TNFR1 and at least 1 gene selected from MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL deviate from the expression level in the control sample.

[0036] In a second object, the present invention provides an in vitro method for predicting, screening, monitoring and / or diagnosing diabetic kidney disease comprising the steps of: a) preparing the analyte from mammalian samples; b) measuring the expression level of the TNFR1 gene and at least 1 gene selected from MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL in a mammalian sample in a sample of the analyte; and c) comparing the expression level of the genes measured in a) with the expression level of the respective genes in a control sample.

[0037] In a third object, the present invention provides a biomarker panel for prediction, screening and / or monitoring of diabetic kidney disease comprising two or more genes selected from: TNFR1, MCT1, MCT4, CD147, HIF-1a, VEGFa, SMAD1 and NGAL, wherein when the panel consists of two genes, one of which is SMAD1, the other gene is different from NGAL.

[0038] In a fourth object, the present invention provides a kit for the prediction, tracking and / or monitoring of diabetic kidney disease comprising: - reagents for stabilizing sample mRNA; - enzyme to enable the formation of cDNA from mRNA; - primers, probes, dNTPs for amplification of TNFR1 cDNA and reference marker gene cDNA; - optionally, primers, probes, dNTPs for amplification of cDNA from MCT1, MCT4, CD147, HIF-1a, VEGFa, SMAD1 and NGAL; - reagents for the quantitative visualization of amplified cDNAs.

[0039] In a fifth object, the present invention provides an in vitro method for predicting diabetic kidney disease comprising the steps of: a) preparing the analyte from mammalian samples; b) measuring the expression levels of TNFR1 and at least one of the genes selected from NGAL and SMAD1 in the analyte sample; and c) comparing the expression levels measured in a) with the expression levels in a control sample, wherein the sample with expression levels of the TNFR1 and NGAL and / or SMAD1 genes that deviate from the expression levels in the control sample indicates a risk of developing diabetic kidney disease.

[0040] In a sixth object, the present invention provides an in vitro method for evaluating the effectiveness of a treatment in controlling diabetic kidney disease comprising the steps of: i) measuring TNFR1 gene expression in individual samples in treatment; ii) measure the expression level of one or more selected genes among: MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL; iii) detect an increase or decrease in the expression of the analyzed genes in the treated sample compared to the control sample as an indication of the effectiveness of the treatment in controlling diabetic kidney disease.

[0041] In a seventh object, the present invention provides a method for treating diabetic kidney disease comprising administering to a mammal a drug acting on a target selected from: TNFR1, MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1, NGAL and combinations thereof.

[0042] These and other objects of the invention will be immediately appreciated by those skilled in the art and will be described in detail below. Brief Description of the Figures

[0043] The following figures are presented:

[0044] Figure i refers to the graphs representing the gene expression of TNFR1, MCT1, CD147, and VEGFa in the bulb of the adult rat group (items A, B, C, and D, respectively) and in the aged rat group (items E, F, G, and H, respectively). Data are expressed as mean ± SD. Mann-Whitney tests. *p<0.05 vs. control. 95% CI.

[0045] Figure 2 refers to graphs representing the gene expression of TNFR1, CD147, and VEGFa in the hearts of the adult rat group (items A, B, and C, respectively) and the aged rat group (items D, E, and F, respectively). Data are expressed as mean ± SD. Mann-Whitney tests. *p<0.05 vs. control.

[0046] Figure 3 refers to the representative graph of the expression of the TNFR1, MCT1, CD147, and VEGFa genes in the liver of the adult rat group (items A, B, C, and D, respectively) and the aged rat group (items E, F, G, and H, respectively). Data are expressed as mean ± SD. Mann-Z tests were performed. Whitney. *p<0.05 vs. control.

[0047] Figure 4 refers to the representative graphs of gene expression of TNFR1, MCT1, MCT4, CD147, VEGFa and PTX3 in the kidney of the adult rat group (items A, C, D, E and F respectively) and of CD / 47 in the aged rat group (item G). Data are expressed as mean ± SDVP. Mann-Whitney tests. *p<0.05 vs. control.

[0048] Figure 5 refers to graphs representing the expression of the TNFR1, MCT1, MCT4, and CD147 genes in the peripheral blood of the adult rat group (items A, B, C, and D, respectively) and of TNFR1 and CD147 in the aged rat group (items E and F, respectively). Data are expressed as mean ± SD. Mann-Whitney tests. *p<0.05 vs. control. 95% CI.

[0049] Figure 6 refers to the graphs representing the positive correlations of TNFR1 and MCT1 expression in the medulla oblongata (items A and B, respectively), VEGFa in the heart (item C), and TNFR1 in the blood (item G) in the DMA group (adult diabetic rats), and the negative correlations of CD 147 expression in the heart and kidney (items D and E, respectively) and VEGFa in the kidney (item F) in the DMA group. Data are expressed as mean ± SD. Spearman correlation test. *p<0.05.

[0050] Figure 7 refers to the graphs representing the positive correlations of TNFR1 expression in blood (item A) and CD147 in the medulla oblongata (item B) in the DMI group (elderly diabetic rats) and the negative correlations of CD147 expression in the heart (items C and D) in the DMI group. Data are expressed as mean ± SEM. Spearman correlation. *p<0.05.

[0051] Figure 8 refers to the representative graphs of the gene expression correlation of the markers MCT1, MCT4, CD147, HIF-1a and VEGF in heart samples between the groups of rats induced to Diabetes Mellitus (DM) and simulated Non-Diabetes (NDS) through intergroup progressive temporal analysis in the different sample groups. Gene expression was evaluated using formula (II). One-way ANOVA test, *p < 0.05.

[0052] Figure 9 refers to the graphs representing the correlation of Gene expression of the markers MCT1, MCT4, CD147, HIF-1a and VEGF in brain tissue samples between groups of rats induced to Diabetes Mellitus (DM) and sham Non-Diabetic (NDS) rats by means of intergroup forward temporal analysis in the different sample groups. Gene expression was assessed using formula (II). One-way ANOVA test, *p < 0.05.

[0053] Figure 10 refers to the representative graphs of the correlation of gene expression of the markers MCT1, MCT4, CD147, HIF-1a and VEGF in renal samples between the groups of rats induced to Diabetes Mellitus (DM) and simulated Non-Diabetes (NDS) through intergroup progressive temporal analysis in the different sample groups. Gene expression was evaluated using formula (II). One-way ANOVA test, *p < 0.05.

[0054] Figure 11 refers to the representative graphs of the correlation of gene expression of the markers MCT1, MCT4, CD147, HIF-1a and VEGF in blood samples between the groups of rats induced to Diabetes Mellitus (DM) and simulated Non-Diabetes (NDS) through intergroup progressive temporal analysis in the different sample groups. Gene expression was evaluated using formula (II). One-way ANOVA test, *p < 0.05. Detailed Description of the Invention

[0055] The current state of the art lacks alternatives for the early detection of diabetic kidney disease. There are broad diagnostic methods that are limited to protein detection and present significant disadvantages such as susceptibility to various external factors and lack of diagnostic precision. Some platforms reveal gene expression analyses of biomarkers, but they rely on microfluidic systems and biosensors and do not exploit the specific challenges of the urinary matrix for messenger RNA analysis. Therefore, they lack a standardized methodology for collection and preservation that prevents the degradation of biological samples and ensures the quality and reproducibility of molecular analyses.

[0056] The biomarkers of the present invention are applicable, as a result Intermediate methods allow for the early detection of subtle changes in renal function, even before noticeable changes are observed in classic renal function markers such as albuminuria, creatinine, and proteinuria, and provide valuable information about the patient's health status. Gene expression analysis of these specific biomarkers ensures greater sensitivity and specificity of the present invention. Furthermore, the analytical methods disclosed herein, comprising pre-analytical steps, enable the analysis of nucleic acids, preferably messenger RNA, in biologically unstable and underutilized matrices such as urine.

[0057] In the case of kidney disease in patients with type 2 diabetes mellitus (DM2), the present invention, in all its modalities, provides, among other technical advantages, an intermediate result for understanding disease progression, treatment response, and the risk of complications. One of the main advantages of using these new molecular biomarkers for kidney disease is their ability to identify renal changes in vitro early, even before clinical symptoms manifest. This allows for early intervention, enabling more effective disease management and, consequently, reducing the risk of serious complications such as chronic kidney failure and the need for hemodialysis.Unlike conventionally used clinical markers, such as serum creatinine and estimated glomerular filtration rate (eGFR), which can be influenced by various external factors, the markers revealed here provide direct information about the pathophysiological processes underlying kidney injury, making in vitro prognostic assessment more reliable.

[0058] In a first object, the present invention provides an in vitro method for screening indicators of renal alterations in samples from diabetic mammals comprising the steps of: a) preparing the analyte from mammalian samples; b) measuring the expression level of the TNFR1 gene and at least 1 gene selected from MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL in analyte sample; and c) compare the expression level of the genes measured in a) with the expression level of the respective genes in a control sample, where renal alteration is indicated when the expression level of TNFR1 and at least 1 gene selected from MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL deviate from the expression level in the control sample.

[0059] In a second object, the present invention provides an in vitro method for predicting, screening, monitoring and / or diagnosing diabetic kidney disease comprising the steps of: a) preparing the analyte from mammalian samples; b) measuring the expression level of the TNFR1 gene and at least 1 gene selected from MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL in an analyte sample; and c) comparing the expression level of the genes measured in a) with the expression level of the respective genes in a control sample.

[0060] In an in vitro method realization, reduced levels of TNFR1 gene expression in analyte samples, compared to expression levels in control samples, are indicative of chronic kidney disease.

[0061] In another embodiment, the in vitro method further comprises the calculation of the ratio of measured expression levels of the SMAD1 and NGAL genes, where a reduced ratio in the analyte sample, compared to a ratio in the control sample, is indicative of diabetic kidney disease.

[0062] Non-limiting examples of techniques for measuring gene expression levels include dPCR, qPCR, RT-PCR, and microarray. Preferably, gene expression measurement is performed by qPCR, and even more preferably by dPCR.

[0063] In a third object, the present invention provides a biomarker panel for the prediction, screening, and / or monitoring of diabetic kidney disease comprising two or more genes selected from: TNFR1, MCT1, MCT4, CD147, HIF-1a, VEGFa, SMAD1, and NGAL. where the panel consists of two genes, one of which is SMAD1, the other gene is different from NGAL.

[0064] In a fourth object, the present invention provides a kit for the prediction, tracking and / or monitoring of diabetic kidney disease comprising: - reagents for stabilizing sample mRNA; - enzyme to enable the formation of cDNA from mRNA; - primers, probes, dNTPs for amplification of TNFRI cDNA and reference marker gene cDNA; -Optionally, primers, probes, dNTPs for cDNA amplification of MCT1, MCT4, CD147, HIF-1a, VEGFa, SMAD1 and NGAL; - reagents for the quantitative visualization of amplified cDNAs.

[0065] The reagents for stabilizing mRNA samples are stored in containers suitable for each sample type. For urine samples, the kit includes a sterile collector containing a solution of reagents for stabilizing nucleic acids, with buffer and RNase inhibitors, capable of stabilizing RNA for up to 2 years at room temperature. The sterile collector holds up to 15 mL of urine and has a fill indicator. For blood samples, the vacuum tube is compatible with standard venous collection and contains reagents for RNA / DNA stabilization, which ensures immediate stabilization of total RNA in peripheral leukocytes, even during transport. Stabilization for blood samples maintains the genetic material for up to three days at room temperature or five days at 2-8°C. The tubes can be frozen at temperatures of -20°C to -70°C for long-term storage.

[0066] The kit for prediction, screening and / or monitoring of diabetic kidney disease additionally comprises reagents for the extraction of total RNA from the sample, including cell lysis buffers with RNase inactivation, reagents or columns for purification based on silica or organic phase, washing and elution solutions, and components optimized for both sedimentation. both urinary and preserved whole blood.

[0067] The kit further comprises reverse transcription reagents including an enzyme to enable cDNA formation from mRNA, preferably thermostable reverse transcriptase, dNTPs, Oligo(dT) and / or random primers, and RT (reversed transcription) buffer with MgCl2.

[0068] Finally, the present kit comprises reagents for real-time PCR (RT-qPCR), including a standard mix containing thermostable DNA polymerase, buffer, dNTPs, and fluorophore (e.g., SYBR Green / N',N'-dimethyl-N-[4-[(E)-(3-methyl-1,3-benzothiazol-2-ylidene)methyl]-1-phenylquinolin-1-io-2-yl]-N-propylpropane-1,3-diamine or TaqMan probes). Primers and probes specific for all genes of interest and the reference gene (e.g., RPL13A) are also included in the kit. Furthermore, the kit includes a technical manual with a standardized protocol for both matrices (urine and blood) and standard curves, when applicable, for relative or absolute quantification.All components are supplied in packaging compatible with multiple scales (e.g., 24, 48, 96 reactions). The kit is compatible with open RT-qPCR platforms (Applied Biosystems, Bio-Rad, among others) and is developed for clinical application, focusing on the robustness of the process from collection to analysis.

[0069] In a fifth object, the present invention provides an in vitro method for predicting diabetic kidney disease comprising the steps of: a) preparing the analyte from mammalian samples; b) measuring the expression levels of TNFR1 and at least one of the genes selected from NGAL and SMAD1 in a sample of the analyte; and c) comparing the expression levels measured in a) with the expression levels in a control sample, wherein the sample has expression levels of the TNFR1 and NGAL genes. and / or SMAD1 that deviate from the expression levels in the control sample indicate a risk of developing diabetic kidney disease.

[0070] In a sixth object, the present invention provides an in vitro method for evaluating the effectiveness of a treatment in controlling diabetic kidney disease comprising the steps of: i) measuring TNFR1 gene expression in samples from the treated individual; ii) measuring the expression level of one or more genes selected from: MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL; iii) detecting an increase or decrease in the expression of the analyzed genes in the treated sample compared to the control sample as an indication of the effectiveness of the treatment in controlling diabetic kidney disease.

[0071] In a seventh object, the present invention provides a method for treating diabetic kidney disease comprising administering to a mammal a drug acting on a target selected from: TNFR1, MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1, NGAL and combinations thereof.

[0072] In the context of the present invention, the mammalian sample is preferably liquid, more preferably blood or urine.

[0073] Definitions

[0074] The term "chronic kidney disease" or "CKD" generally refers to chronic kidney diseases. The term "diabetic kidney disease" or "DRD" represents a more restricted group of kidney disorders caused by diabetes and includes patients with both albuminuric and non-albuminuric phenotypes. The term "diabetic nephropathy" or "DN" is even more restricted, reserved for patients who have CKD demonstrably caused by diabetes, either through biopsy or an unequivocal history of the 5 stages of kidney involvement, including albuminuria and loss of kidney function.

[0075] The term "control sample," except where otherwise indicated, refers to samples from mammals that do not have and are not susceptible to the disease. diabetic kidney disease.

[0076] The term "reference gene" or "reference marker gene" refers to a gene that serves as a point of comparison for evaluating the expression of other genes. This gene can be used to normalize the relative expression values ​​of target genes. Non-limiting examples of reference marker genes include the ribosomal gene RPL13A (18S) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH).

[0077] The present invention provides evidence that TNFR1, MCT1, MCT4, CD147, HIF-1a, VEGFa, SMAD1, and NGAL exhibit altered expression even before the detection of changes in biochemical parameters usually evaluated clinically. Therefore, the use of these genes in in vitro processes for predicting, screening, and / or monitoring diabetic kidney disease allows for the early identification of renal alterations and, consequently, this intermediate result is useful in assisting therapeutic decision-making. The multi-marker approach proposed here is capable of capturing damage in multiple organs in an integrated and early manner, significantly expanding its diagnostic and prognostic power. Furthermore, the use of the SMAD1 / NGAL ratio calculation allows for the development of a patient classification score. Example

[0078] The examples shown here are intended only to illustrate one of the numerous ways of carrying out the invention, without, however, limiting its scope.

[0079] Example 1

[0080] With the aim of investigating new biomarkers for the early detection of diabetes complications, specifically diabetic retinopathy (DRI), and their expression profiles during aging, the expression levels of the markers TNFR1, VEGFa, CD147, MCT1, MCT4, FGF23, and PTX3 were analyzed in male Wistar rats (Rattus norvegicus). Furthermore, Correlations were identified between gene expression and biochemical parameters already used in the routine diagnosis and control of DRD.

[0081] For this study, the rats were housed under the following conditions: (a) 12-hour light / dark cycle; (b) ambient temperature of 21 ±2°C; (c) ad libitum supply of water and rodent food. A period of at least 5 days before the experiments was stipulated for the habituation of these animals to the environment. At 2 months of age, this species enters the reproductive phase and is considered a young adult. At 6 months, this phase declines, characterizing a more advanced age. Thus, the rats were divided into the following experimental groups: DMA: adult animals approximately 3 months old, evaluated 30 days after DM induction with alloxan; DMI: elderly animals approximately ? months old, evaluated 30 days after DM induction with alloxan; CSA: adult animals approximately 3 months old, evaluated 30 days after saline injection; CSI: Elderly animals approximately 7 months old, evaluated 30 days after saline injection.

[0082] Alloxan cytotoxicity is specific to pancreatic cells [3], causing damage to pancreatic islets and blood vessels, as well as cell death. Thus, this molecule is capable of inducing DM depending on the dose, infusion rate, route of administration, diet, fasting time, and animal weight. [63 ' 641 At the end of the habituation period, alloxan at a dose of 120 mg / kg was administered by intraperitoneal injection. 65 ' 661 Next, rats that presented with blood glucose levels above 250 mg / dL after 7 days of DM induction were selected. These animals were monitored for 30 days without treatment, during which their weight and blood glucose levels were measured every 7 days using a commercial glucometer (Accu-chek Advantage, Roche Diagnostics, Indiana, USA), in blood collected by caudal vein puncture.

[0083] After the 30-day monitoring period, the animals were Animals were placed in individual metabolic cages for a period of 24 hours for urine collection for biochemical analyses. Urine collection was performed using containers containing RNA latex, which were positioned at the conical outlet located below the cage. Furthermore, these animals underwent a 12-hour fasting period before collection to minimize urine contamination by feed. Water intake (ml) and urinary excretion (ml) were quantified. Before the 24-hour collection, the animals underwent a 2-day habituation period during which they remained in the metabolic cages for 12 hours.

[0084] After urine collection over 24 hours, the rats were euthanized with thiopental. Immediately after administration, while the animal was still alive, blood samples were collected in tubes containing EDTA for gene expression studies and HbA1c dosage, and in dry tubes for serum separation and determination of biochemical parameters. The left kidney was extracted and fixed in formalin for subsequent histological and histochemical analysis. In addition, the right kidney, liver, heart, and bulbar region of the brainstem were collected to study the expression of the genes of interest mentioned above.

[0085] The biochemical tests performed include quantification of blood glucose and glycated hemoglobin levels, plasma urea, creatinine (serum and urine), creatinine clearance, proteinuria, microalbuminuria, cystatin C, ApoA and ApoB (plasma and urine).

[0086] As mentioned previously, blood glucose was determined weekly using the photometric method and confirmed with saturated plasma using the automated enzymatic-colorimetric method, BioSystems Glucose Ref. COD 12503. HbA1c was determined by the immunoturbidimetry method, BioSystems Hemoglobin A1c-Turbi kit Ref. COD 13044. Measurements were performed using a COBAS 8000 analyzer. Urea was determined by the enzymatic / colorimetric method using the Elitech Urea UV SL Cat. n kit. s URSL-0500 (Elitech Group Clinical Systems, France) according to the manufacturer's protocol, in a COBAS 8000 instrument with an absorbance of 600 nm.

[0087] The biochemical analyses of serum and urinary creatinine were determined by the alkaline Jaffé picric kinetic-colorimetric method, Creatinine Jafe Elitech Cat. n sCRCO-0600 (Elitech Group Clinical Systems, France), according to the manufacturer's protocol, in a COBAS 8000 apparatus with an absorbance of 510 nm. The urine was previously homogenized and centrifuged at 2,500 rpm for 5 min at room temperature to precipitate the sediment. The supernatant was diluted (1:25) in distilled water using glass tubes. Creatinine clearance was calculated using formula (I): Formula (I) Ccr=(Ucr x Vu / Pcr) x Ma Where: Ucr = urinary creatinine concentration (mg / dl); Vu = urine volume (ml / min); Pcr = plasma creatinine concentration (mg / dl); Ma = animal weight (kg) 67 l.

[0088] The determination of urinary protein excretion was performed by reaction with copper (II) ions in an alkaline medium, resulting in a colored complex whose intensity was quantified using a COBAS 8000 instrument at an absorbance of 545 nm. The kit used was Protein (Total) BioSystems (BioSystems SA, Spain) Cat. N sCODE 12500. The determination of microalbuminuria in urine collected over 24 hours was performed using the immunoturbidimetry method, with the BioSystems Albumin (Microalbuminuria) kit (BioSystems SA, Spain) Cat. N. sCOD 13324, according to the manufacturer's protocol, on a COBAS 8000 instrument. Apo B and apo Al determination was performed using the turbidimetric method, with BioSystems Apolipoprotein B Turbidimetry Ref. COD 31099 and BioSystems Apolipoprotein Al Turbidimetry Ref. COD 31096 reagents, respectively. All dosages were determined using a COBAS 8000 device, using control sera to verify assay performance. Cystatin C was quantified by the Enzyme-Linked Immunosorbent Assay (ELISA) method, with the Cystatin C Kit, catalog ALX-850-292, Enzo Life Sciences brand. The test was based on the identification of antigens by antibodies labeled with an enzyme, which acts on its substrate and causes a change in the color of the chromogen (a colorless substance that changes color when oxidized by the enzyme).

[0089] Regarding molecular analyses, blood and tissue samples were properly prepared for RNA extraction, cDNA synthesis, and quantification of the expression of the genes TNFR1, VEGFa, CD147, MCT1, MCT4, FGF23, and PTX3 by real-time PCR (qPCR), in order to ensure the stability and suitability of the sample for analysis.

[0090] For this purpose, 1x blood lysis buffer (NH4Cl 1550 mM, KHCO3 100 mM, EDTA 10 nM, pH 7.4) was added to EDTA tubes containing blood samples in a 3:1 ratio to remove red blood cells. Each tube was then homogenized for 30 min under refrigeration and centrifuged twice at 2,500 rpm for 15 min at 4 °C, followed by discarding the supernatant. The leukocyte pellet obtained after centrifugation was dissolved in 1000 µL of Invitrogen Trizol Reagent Cat. n s15596026 (Invitrogen Termo Fisher Scientific - USA) and the RNA extraction protocol was followed. Tissues from the right kidney, heart, and bulbar region were collected in cryogenic vials containing 1% PBS and immediately frozen at -80°C. Subsequently, the tissues were macerated in buffer solution with TissueRuptor II Cat. Equipment n s / ID: 990890 (Qiagen, Germany) and 300 pL were added to 1000 pL of TRIzol reagent and the RNA extraction protocol was followed. The total RNA concentration and the 260 / 280 ratio were estimated by spectrophotometric reading on the NanoDrop Lite instrument (GE Health Care). Then, the RNA samples were diluted to a concentration of 1 pg and cDNA synthesis was performed using the QuantiTect Reverse Transcription Kit Cat No. / ID: 205313 (Qiagen, Germany), according to the manufacturer's protocol.

[0091] To normalize the expression values ​​of the target genes, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression was used as a reference gene. Specific primers for each selected gene were designed using Primer3 Input 0.4.0 software, available at http: / / frodo.wi.mit.edu / primer3 / . The designed primer sequences were checked for specificity using the Primer-BLAST program, available at http: / / www.ncbi.nlm.nih.gov / tools / primer-blast (Table 1).

[0092] Table 1 - Specific primer sequences and their amplicons.

[0093] Real-time amplification reactions were performed using an Applied Biosystems 7500 Real Time PCR Systems thermocycler (Applied Biosystems, USA), in a final reaction volume of 15 pL, containing: 1 xSYBR Green mix (Quantitec SYBR Green PCR Cat kit No. / ID: 204343, Qiagen, Germany), 1.5 pL of cDNA, and 10 pmol of each specific primer at the following concentrations: GAPDH, VEGFa, and MCT1 at 0.2 pmol; TNFRI and CD147 at 0.15 pmol. The thermal profile was determined with an initial heating step at 95 °C for 10 min, followed by 45 replicates at 95 °C for 15 s and 60 °C for 25 s. Genes that did not yet have standardized concentrations were previously determined, and amplicon sizes were confirmed by electrophoresis on a 2% agarose gel. Calibration curves for each gene were performed with serial dilutions (1, 1:10, and 1:100) of cDNA synthesized from a concentration of 1 pg of total RNA obtained from control leukocytes.Gene expression was calculated using formula (II) í. 68 - 69 ]; Formula (II) 2 (-ACq) .

[0094] Finally, gene expression results and biochemical values ​​were expressed as mean ± standard deviation (SD / SDV). The Student's t-test was performed for the CSA group versus (vs.) DMA and between the CSI group vs. DMI for parametric data, and the Mann-Whitney test for non-parametric data. Spearman's correlation analysis was performed in the DMA and DMI groups, between the expression of the genes investigated and the biochemical dosages performed. For multivariate comparison analysis between all groups studied, the Kruskal-Wallis one-way analysis of variance test was used. All analyses were performed using the GraphPad Prism computer program (GraphPad, version 7.0, USA). The significance level established was 5% (descriptive value of p<0.05), 95% confidence interval (CI).

[0095] Results

[0096] In the literature, mortality rates of up to 30% have been observed after induction with alloxan, due to its toxicity. [701In the present study, the DMA group showed a mortality rate of 23% and the DMI group of 25%. No difference in food intake was found between the groups during the 12-hour period, however, there was a significant increase in water intake during the 24-hour period in the DMA group versus the control group (59.2±33.4 n=9 vs. 22.6±7.8 n=5 *p 0.0425). Animals in the DMA and DMI groups showed a decrease in average weight gain over 5 weeks compared to their respective control groups. Initial weight was measured on the day of induction – alloxan or saline solution – and monitored for 4 weeks after DM confirmation. No difference was observed in the weight of the right kidney and heart between the diabetic groups and their controls on the day of euthanasia, but in the comparison of the DMD and DMD groups this difference was significant in the heart (1.03±0.1 n=9 vs. 1.3±0.2 n=1 1 *p 0.0049).

[0097] After confirmation of the onset of DM, diabetic rats showed higher glycemic and HbA1c values ​​compared to controls. Regarding data evaluating renal function, all The groups presented values ​​within the reference range, with the exception of urea, indicating that in the present model there was no development of DRD. However, plasma concentrations of urea and creatinine were statistically higher in the DMA and DMI groups, and urinary creatinine showed lower values ​​compared to their respective control groups. Creatinine clearance showed significantly lower values ​​only in the DMI group compared to the controls (Table 2). The increase in urea reflects the increased reabsorption due to the larger urine volume and lower urinary excretion, and the consequent higher concentration of plasma creatinine, leading to a reduction in its clearance.

[0098] Table 2 - Laboratory parameters. a Most microalbuminuria values ​​were zero. Values ​​presented as Mean ± SD and number of samples. Mann-Whitney and Student's test*P<0.05. 95% CI. Reference values ​​for Wistar rats: Blood glucose: 72-193 mg / dL; HbA1c: 4-6%; Urea: 45-80 mg / dL; Plasma creatinine: 0.24-1.20 mg / dL; Urinary creatinine: 32-97.4 mg / dL; Creatinine clearance: 0.2-1.8 mL / min; Proteinuria: 8.8-33.2 mg / dL; Microalbuminuria: <20 mg / 24; Apo B / A ratio: 0.7-1.2; Cystatin C: 0.18-1.5 mg / dL30-35.

[0099] Surprising data were obtained in molecular studies of gene expression of interest. In the bulbar region of the DMA group, no alteration in VEGFA expression was observed; however, there was a decrease in the expression of TNFR1 (0.1 ± 0.2 n=5 vs. 0.03 ± 0.08 n=9 * p 0.001), MCT1 (0.6 ± 0.4 n = 5 vs. 0.05 ± 0.1 n = 9 * p 0.0380) and CD147 (3.8 ± 1.2 n = 5 vs. 1.1 ± 2.04 n = 9 * p 0.0420), as shown in Figure 1, items A, B, C, and D. The DMI group did not show statistical differences between the genes studied (Figure 1, items E, F, G, and H). It was not possible to obtain expression values ​​for the MCT4, FGF23, and PTX3 genes in some groups, making comparative statistical analysis unfeasible.

[0100] Furthermore, positive correlations were observed in the DMA group between TNFR1 in the bulbar region and HbA1c (R 0.7610 and *p 0.0405 n=8) and between MCT1 in the bulbar region and plasma creatinine (R 0.8235 and p* 0.0472 n=6), as shown in Figure 6, items A and B. In the DMI group, a positive correlation was observed for CD / 47 in the bulbar region and urinary creatinine (R 0.9856, *p 0.0056 n=6), as shown in Figure 7, item B.

[0101] In DM, it is known that the increased production of AGEs is directly related to the formation of ROS and the establishment of persistent inflammatory processes, in which the activation of the TNF system plays an important role. [71 ' 721 Furthermore, AGEs also stimulate the CD147 protein, which promotes increased expression of matrix metalloproteinases (MMPs), involved in the degradation of the extracellular matrix (ECM) and alteration of its tissue deposition, leading to remodeling and stimulation of monocyte migration. [731Surprisingly, the gene expression levels of TNFR1 and CD147 levels are decreased in samples from the bulbar region of the adult diabetic rat group.

[0102] CD147 also plays an important role in the neuronal metabolic pathway. In astrocytes, lactate production is derived from glucose metabolism, which is stored and delivered to neurons as a metabolic source. The lactate transporter proteins MCT1 and MCT4 are present in astrocytes, while MCT2 is present in neurons. Since CD147 acts as a chaperone for MCT1 and 4, decreased expression of MCT1 and its chaperone indicates reduced lactate transport and lower energy supply to neurons.

[0103] In the heart, TNFR1 expression was lower in the DMA group compared to the CSA group (0.9 ± 0.8 n=5 vs. 0.3 ± 0.5 n=9 *p 0.0420) (Figure 2 item A), however, no changes in expression were observed for the other genes (Figure 2 items B and C). The DMI group did not show altered expression of the evaluated genes, as can be seen in Figure 2 items D, E, and F. It was not possible to obtain expression values ​​for the genes MCT1, MCT4, FGF23, and PTX3 in some groups, making comparative statistical analysis unfeasible.

[0104] Furthermore, in the DMA group, a positive correlation was observed between VEGFA expression in the heart and urinary creatinine (R 0.6979 and *p 0.0439 n=9) and a negative correlation between CD147 in the heart and urea (R -0.8697 and *p 0.0333 n=6), as shown in Figure 6, items C and D. In the DMI group, negative correlations were observed between CD147 in the heart versus urea (R -0.8895 and *p 0.0143 n = 7) and glycemia (R -0.9266 and *p 0.0095 n=7) (Figure 7, items C and D).

[0105] As mentioned earlier, the metabolic changes involved in DM contribute to a pro-inflammatory environment, of which TNFR is a part. In the heart, TNFR1 also plays an important role in DM complications and the development of cardiovascular diseases, and is related to increased induction of apoptosis. [741 Surprisingly, the expression of the gene related to TNFR1 is also decreased in the hearts of adult diabetic rats, when compared to healthy rats.

[0106] In the kidneys of the DMA group, decreased expression of CD147 (6.7 ± 5.3 n = 5 vs. 1.3 ± 3.4 n = 9 *p 0.0287) and VEGFA (1.1 ± 0.8 n=5 vs 0.2 ± 0.3 n=9 *p 0.0120) was observed, as shown in Figure 4, items D and E, while the other genes did not show differences when compared to the controls (Figure 4, items A, B, C, and F). In the DMI group, it was only possible to evaluate the expression of CD147 (Figure 4, item G) in the kidney, and the genes TNFR1, VEGFA, FGF23, MCT4, and MCT1 did not show expression in some tissues of the CSI group, making comparative statistical analysis in the elderly group unfeasible.

[0107] Furthermore, the DMA group showed negative correlations between CD147 in the kidney and urea (R - 1000 and *p 0.0167 n=5) and VEGFA in renal tissue and urea (R - 0.9856 and *p 0.0056 n=6), as can be seen in figure 6 items E and F.

[0108] VEGFA is known to be a pro-angiogenic factor that increases vascular permeability and contributes to the endothelial dysfunction that occurs in DM. In rats with ND, increased VEGFA expression has been shown to be related to increased cell proliferation and permeability of endothelial cells, which contribute to accumulation in the ECM. 75 l. Furthermore, an increase in VEGFA expression was observed in podocytes in rats 35 days after DM induction with streptozotocin. 76 l. However, these animals already presented proteinuria, while in the present study no alteration in this parameter was observed. On the other hand, Veron et al. demonstrated that diabetic mice knockdown for VEGF develop diffuse glomerulosclerosis and increased thickness of the glomerular basement membrane. Furthermore, CD147 positively regulates VEGF production in adriamycin-induced nephropathy, indicating a co-regulatory mechanism. 77l. In humans, plasma CD147 concentration has been directly associated with the progression of ND í 78 l. Thus, the reduced expression level of CD147 and VEGF genes in adult diabetic rats is also surprising in the context of the kidneys.

[0109] In blood samples, TNFR1 expression was lower in the DMA group compared to the CSA group (3.5 ± 3.3 n=5 vs. 0.7 ± 1.0 n=9 *p 0.0295) (Figure 5 item A) and no significant changes were observed in the expression of other genes (Figure 5 items B, C and D). The DMI group did not show changes in the expression of the studied genes compared to the control group (Figure 5 items E and F). We did not obtain expression of VEGFA, FGF23 and PTX3 in some tissues of adult rats, nor of MC1, MCT4, VEGFA, FGF23 and PTX3 in some samples from elderly rats, making comparative statistical analysis unfeasible. Finally, in the DMA and DMI groups, we observed a positive correlation between TNFR1 in blood and glycemia (R 0.8857 and *p 0.0333 n=6), as can be seen in figures 6 item G and 7 item A.

[0110] TNFR1 is a soluble receptor that has been shown to have increased plasma concentrations in obese diabetic mice, along with TNF-α levels. Furthermore, in vitro stimulation of TNF-α has been shown to increase TNFR expression in renal tubular cells. [791In this sense, it has also been shown that increased concentrations of circulating TNFRs are related to lower GFR values, with the potential to predict a decline in renal function.

[0080] On the other hand, the present study demonstrated reduced levels of TNFR1 expression in diabetic rats, indicating surprising molecular alterations in the early stages of diabetes. Furthermore, the homogeneous profile of altered expression of this marker in adult rats, both in blood and in the bulbar region and heart, presents a scenario that demonstrates the usefulness of TNFR1 as a marker in these tissues. It is also interesting to note that the specificity of TNFRs in predicting the progression of diabetic nephropathy (DN) is not observed in other renal pathologies, making it a good predictive biomarker. [471 .

[0111] In the liver, no changes were observed in the expression of the studied genes in the DMA and DMI groups compared to their respective controls (Figure 3). It was not possible to obtain expression values ​​for the MCT4 and evc genes in any of the groups; furthermore, there was no expression of FGF23 in the CSA, DMA, and CSI groups, and of PTXS in the CSA and CSI groups, making comparative statistical analysis unfeasible.

[0112] The pattern of reduced expression of the TNFR1, MCT1, CD147, and VEGFA genes observed in the DMA group was not observed in the DMI group. This result is related to the absence of expression of these genes in some tissues of the elderly group; however, the endogenous gene was expressed, indicating tissue viability.

[0113] In diabetic groups, analysis of the correlation between the expression of the studied genes and biochemical parameters indicates that the expression of TNFR1, CD147, MCT1, and VEGF is modulated by glycemia, creatinine, and urea in the bulbar region, cardiac and renal tissues, and blood. This shows that the metabolic disturbances present at the onset of DM are important in the molecular alterations associated with the pathophysiology of RD. In the present study, TNFR1 showed a positive correlation with glycemic values, and studies have already demonstrated that this marker is positively modulated in DM. 81 - 82However, the present study showed reduced levels of TNFR1 expression, as well as other target genes. These results indicate that in the initial phases of inflammation, harmful cellular stimuli trigger inhibitory mechanisms in order to attenuate cellular damage resulting from the exacerbated activation of pathways related to the genes studied here. Finally, the altered expression of these genes, before the onset of DRD, shows that these biomarkers are useful in monitoring changes in an early phase, with special attention to TNFR1, which has specificity for DRD.

[0114] Example 2

[0115] With the aim of identifying a panel of markers that allows monitoring changes related to the progression of diabetes mellitus (DM) and its complications in the different affected organs, an experimental, case-control study was conducted, measuring the expression of five distinct genes involved in tissue oxidation, angiogenesis, and biological processes of glucose metabolism in 68 Wistar rats (120-180 grams). Furthermore, this analysis incorporated a temporal perspective crucial for understanding the... changes.

[0116] For this purpose, the animals were housed in boxes containing up to 4 animals. The 12-hour light / dark cycle was respected, and the ambient temperature was maintained at 21 (± 2 SC) and there was ad libitum provision of water and commercial feed. The animals were randomly distributed into two experimental groups: Simulated Non-Diabetics (NDS) and those induced with Diabetes Mellitus (DM). Furthermore, to observe the progressive development of the disease, these animals were subdivided into four subgroups: ? days (NDS7, n=1 and DM7, n=13); 21 days (NDS21, n=10 and DM21, n=8); 30 days (NDS30, n=6 and DM30, n=8) and 40 days (NDS40, n=7 and DM40, n=5). These timeframes correspond to intervals equivalent to 7 months, 1 to 2 years, 2 years and 6 months, and 3 years, respectively, of the development of DM in humans.

[0117] All rats underwent a 12-hour fasting period before the experimental procedures began, following the methodology of Sheriff et al. (2019). 83l. For DM induction, alloxan [2,4,5,6-tetraoxypyrimidine; 2,4,5,6-pyrimidinetetron] monohydrate (Sigma-Aldrich®, USA) was used at a concentration of 120 mg / kg, dissolved in 0.9% sodium chloride solution for intraperitoneal (ip) injection. In the NDS group, animals underwent ip injections of sodium chloride (0.9%) to simulate the applications performed on treated animals. Seven days after alloxan administration, blood measurements were taken from the tail vein using commercial glucose meters (Accu-chek Advantage, Roche Diagnostics, Indiana, USA). Only animals with blood glucose levels above 250 mg / dL and maintaining this level during the designated time periods in this study were considered diabetic (Day 1). The animals in both groups (NDS and DM) were monitored with weekly measurements of blood glucose levels and weight.At the end of the experimental period for each group, as determined in this study, the corresponding animals were euthanized with an anesthetic overdose of thiopental (100 mg / kg, i.p.). Tissue samples (heart, kidney, and brain) were collected, sectioned, and placed in cryotubes, which were then stored in ultra-low temperature freezers at -80°C. Blood samples were also collected by venipuncture, immediately processed, and analyzed.

[0118] For gene expression analysis by RT-qPCR, the evaluated tissues were first macerated using a TissueRuptorll apparatus (Qiagen®, DE), and 50-100 mg was used to obtain RNA with the TRIzol reagent (LS TRIzol Reagent, Thermo Fisher®, USA). For peripheral blood, collected by venipuncture in an EDTA tube, genetic material isolation was performed with the TRIzol reagent, according to the manufacturer's instructions, following a standard protocol. All materials were quantified using the NanoDrop Spectrophotometer Lite (Thermo Fisher Scientific®, USA). For cDNA synthesis, RNA samples (initially 1 pg) were converted to cDNA using the QuantiTect Reverse Transcription kit (Qiagen®, DE), according to the manufacturer's protocol.

[0119] To normalize the relative expression of the target genes, the average expression values ​​of the reference gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were used. The specific primers for each selected gene are shown in Table 3.

[0120] Table 3 - primer and amplicon sequences of the MCT1, MCT4, CD147, HIF-1a, VEGFa and GAPDH genes.

[0121] Gene expression and appropriate amplification patterns were determined by qPCR. Reactions were performed using an Applied Biosystems 7500 Real Time PCR Systems thermocycler (Applied Biosystems®, USA) with a final reaction volume of 15 pL, containing proportionally: 1 x SYBR Green mix (Quantitec SYBR Green PCR kit, Qiagen®, USA), 10 pmol of each specific primer, and 1.5 pL of cDNA. Cyclic parameters were determined with an initial hot step of 95°C. e C for 10 minutes, followed by 40 repetitions at 95 eC for 15 seconds and at 60 e C for 25 seconds. The calibration curve for each gene was performed using serial dilutions of synthesized cDNA (1 pg of mRNA) from whole blood and lung tissue of normal animals. Gene expression was determined by formula (II) and its results presented as expression difference followed by range (minimum and maximum).

[0122] In addition to molecular analyses, analyses of biochemical parameters were performed, including urea, proteinuria, albuminuria, serum creatinine, and glycemic levels, aiming to confirm the DM model established in this study (values ​​>250 mg / dL were considered abnormal). Plasma glucose determination, after euthanasia of the animals, was performed by an automated enzymatic-colorimetric method using fluoridated plasma, BioSystems® Glucose Reference Code 12503. Serum creatinine biochemical analyses were determined by the Jaffé alkaline picric kinetic-colorimetric method, Creatinine Jaffe Elitech Catalog Number CRCO-0600 (Elitech Group Clinical Systems®, France), according to the manufacturer's protocol, in a COBAS 23 8,000 instrument with an absorbance of 510 nm.Urea was determined by the enzymatic / colorimetric method using the Urea UV SL Elitech kit with catalog code URSL - 0500 (Elitech Group Clinical Systems ®, France) according to the manufacturer's protocol, in a COBAS 8000 instrument with an absorbance of 600 nm.

[0123] The results obtained are presented as mean and standard deviation. (SD) with a 95% confidence interval (significance level of 5%). Quantitative variables were compared using the unpaired Student's t-test for parametric values ​​(mean blood glucose and plasma biochemical parameters) and the Mann-Whitney test for non-parametric values ​​(gene expression), with the distribution being analyzed using Shapiro-Wilk (p > 0.05). A one-way ANOVA test was performed to analyze the temporal correlation between the studied genes and the experimental subgroups. The software used for analysis was GraphPad Prism (GraphPad®, version 7.0, USA).

[0124] Results

[0125] The diabetogenic drug alloxan showed induction capacity in the experimental group, with a mortality rate of 21% (n = 9) (Table 4). Animals in the DM group presented blood glucose levels above 250 mg / dL, statistically high values ​​compared to the NDS group and within normal parameters.

[0084] .

[0126] Table 4 - Analysis of plasma biochemical parameters between the NDS and DM groups through progressive analysis in the different sampling time groups (7, 21, 30 and 40 days). Values ​​expressed as mean and standard deviation. Student's t-test (95% confidence interval: CI95%), *p < 0.05 vs. NDS.

[0127] Regarding plasma markers that may reflect renal impairment during the onset of DM, a significant increase in plasma creatinine was observed for group 7 (DM7 0.80 ± 1.24* vs. NDS7 0.42 ± 0.06 mg / dL, p=0.04) and the 30-day group (DM30 1.23 ± 0.45* vs. NDS30 0.86 ± 0.12 mg / dL, p=0.03); Plasma urea in group 7 (DM7 99.78 ± 26.25* vs. NDS7 58.01 ± 13.14 mg / dL, p=<0.0002), group 21 (DM21 168.30 ± 44.13* vs. NDS21 68.31 ± 5.08 mg / dL, p=<0.0001), group 30 (DM30 190.40 ± 44.41* vs. NDS30 83.75 ± 20.68 mg / dL, p=<0.0007) and group 40 days (DM40 173.10 ± 42.71* vs. NDS40 90.60 ± 17.48 mg / dL, p=<0.01) ), all with values ​​above those of their respective control groups.

[0128] In the gene expression analyses studied in cardiac and brain tissues, there was a significant increase in monocarboxylates, causing alterations in cellular energy metabolism. In the heart, an increase in MCT1 expression was obtained in the 21-day group (DM21 7.1 1 ± 1 1 ,02, n=7) compared to the 7-day group (DM7 1 ,21 ± 3.57, n=13), as shown in Figure 8.

[0129] Among the tissues evaluated, the brain showed the greatest alterations during the onset of DM. In this study, a statistically significant difference was obtained in the different groups in the genes / WCT4, CD147, HIF-1 / and VEGF. More specifically, there were significant decreases in MCT4 expression in the 21-day groups (DM7 7.85 ± 9.80, n=13 vs. DM21 3.18 ± 5.33*, n=8, p<0.05) and in the 40-day groups (DM7 7.85 ± 9.80, n=13 vs. DM40 0.30 ± 0.43*, n=5, p<0.05). There were reductions in the expression of CD147 (DM7 18.90 ± 20.20, n=11 vs. DM30 1.62 ± 2.77*, n=8, p<0.05), HIF-1A (DM21 6.73 ± 11.97, n=7 vs. DM30 0.25 ± 0.19*, n=8, p<0.05) and VEGF between the 7-day group (DM7 17.36 ± 27.53, n=9) compared to the 30-day (DM30 0.08 ± 0.12*, n=8, p.0.05) and 40-day (DM40 0.11 ± 0.08, n=5) groups, as can be seen in Figure 9.

[0130] In the kidneys, a decrease in the expression of HIF-1A and VEGF genes was observed, demonstrating that DM causes alterations in this organ. A significant decrease in the expression of HIF-1A (DM21 7.85 ± 9.80, n=6 vs. DM30 0.30 ± 0.43*, n=8, *p<0.05) and VEGF was observed between the 21-day (DM21 28.19 ± 43.48, n=7), 30-day (DM30 0.44 ± 0.86*, n=8) and 40-day (DM40 0.01 ± 0.01*, n=5) groups, as can be seen in Figure 10.

[0131] In the expression of the studied genes in peripheral blood, a decrease in MCT1 expression was observed in the 40-day group (DM40 0.12 ± 0.26*, n=5, *p<0.05) compared to the 7-day group (DM7 4.02 ± 4.43, n=13), as shown in Figure 11.

[0132] Thus, in the temporal evaluation of the onset of DM, the tissues in which the alterations were most pronounced were the brain and kidney, with a significant decrease in the initial stages of DM. The activity of the MCT1 and MCT4 genes was observed in all tissues evaluated, as well as in the blood profile, important information still unexplored in the literature, since there is a direct association of tissues with the standard families of monocarboxylates in studies. MCTs play a central role in the cellular influx / efflux of metabolic products – mainly pyruvate, lactate, butyrate, and ketone bodies – and in the ionic regulation of H+ protons. 85 - 87Low availability and / or unavailability of insulin in the brain triggers dysfunction in energy homeostasis and in the modulation of brain functions. [88 ' 891 In diabetics, the hyperglycemic state leads to metabolic incapacity in cellular uptake and energy production, triggering a significant metabolic imbalance in monocarboxylates. However, studies indicate that neurons and peripheral nerves preferentially consume lactate in environments with bioavailability of glucose molecules [90 ' 911 ...and this consumption is mediated by MCT1. In accordance with the cited studies, it was observed that isoform 4 demonstrated less adaptive capacity in the presented tissue environment when compared to isoform 1.

[0133] Additionally, a decrease in HIF-1A and VEGF expression was found in renal and brain tissues in the correlation between groups 7, 21, 30, and 40. Similar alterations in VEGF expression in renal tissue were also observed in Example 1 and correlate with diffuse glomerular changes caused by possible vasoconstriction and renal tubular inflammation through VEGF modulation. The inactivation of this anti-apoptotic system is directly related to the imbalance of cellular processes and, consequently, to cell death.

[0134] The results presented demonstrate that the genes studied enable the detection of subtle molecular alterations in mammals with DM, even in the early stages. Therefore, these genes, especially MCT1, can be applied to methods for early diagnosis and screening of diabetic kidney disease.

[0135] Example 3

[0136] To evaluate ways to improve the quantification of gene expression and, consequently, improve the methods, panels, and kits for prediction, screening, monitoring, and / or diagnosis of diabetic kidney disease, studies were conducted using probes, as shown in Table 5.

[0137] Table 5: Characteristics of the specific primers (F and R) and probes (P) and their amplicons.

[0138] A total of 160 patients were recruited by the Specialty Clinic of the FMABC University Center. Patients who agreed to participate in the study received an Informed Consent Form (ICF) with explanations about the protocols adopted. The study groups were:

[0139] Healthy individuals (Group A - Control, CTL): Group A consisted of 48 healthy, non-diabetic individuals with no prior history of kidney disease, liver disease, or carcinoma before the study. Individuals eligible for this group were 21 years of age or older and were not users of illicit drugs.

[0140] Patients with DM (Group B): Group B consisted of 55 patients diagnosed with diabetes mellitus (fasting blood glucose >140 mg / dL and glycated hemoglobin >7%) for at least 5 years, without known renal impairment, aged 21 years or older, and undergoing pharmacological treatment. Individuals in this group were randomly selected from patients undergoing dialysis at the Nephrology clinic.

[0141] The study exclusion criteria were: expressed wish of the patient and hospitalization for any reason in the last 30 days.

[0142] Sample Collection:

[0143] Sample collection was standardized to ensure sample preservation and the quality and reproducibility of molecular analyses. Before collection, the area was cleaned with an antiseptic solution. For urine samples, the first stream of urine into the toilet was discarded, and preference was given to collecting the first urine of the morning. Other samples were also obtained, provided the patient retained urine for at least 2 hours before the test. All samples were collected in a sterile container containing a solution of reagents for nucleic acid stabilization, with buffer and RNase inhibitors capable of stabilizing RNA; and were properly stored under refrigeration until the time of analysis. Blood samples for the determination of biochemical parameters were obtained by vacuum venipuncture after an 8-hour fast.

[0144] Assessment of Glycemic Levels in Patients with Type 2 Diabetes:

[0145] Fasting plasma glucose levels were determined to confirm diabetes mellitus (DM) status, being evaluated after overnight fasting. Measurements were performed using an automated enzymatic method with fluoridated serum. Glycemic control was assessed by fasting glucose levels, considering values ​​>140 mg / dL as abnormal.

[0146] Evaluation of Glycated Hemoglobin (HbA1c) Levels in Patients with Type 2 Diabetes Mellitus due to Polycystic Occult Blood:

[0147] Glycated hemoglobin (HbA1c) was determined by low-performance liquid chromatography (LPLC) using the DiaStat - Bio-Rad analyzer. The equipment expresses the percentage of total hemoglobin and evaluates average blood glucose over a period of 3 months. The collected material consisted of 5 mL of whole blood, of which 1 mL was treated with a hemolyzing reagent. Values ​​above 7% were considered altered.

[0148] Assessment of Renal Function in Patients with Diabetes Mellitus:

[0149] To assess renal function in patients, serum and urinary creatinine levels were measured using the Jaffé method, as described below. Following this measurement, the estimated glomerular filtration rate (eGFR) was calculated using the MDRD formula. For this serum creatinine test and for performing creatinine clearance, patients were instructed to fast for a minimum of 8 hours and to discontinue, if possible, the use of medications, particularly cephalosporins. Patients were well hydrated and followed a meat-free diet. Samples collected included serum or plasma (heparin, EDTA, fluoride, oxalate, citrate), urine (collected over a 24-hour period), and amniotic fluid. The anticoagulant Glistab (Labtest Cat. 29) allowed for the collection of a single sample for creatinine, glucose, and urea measurements. Urine and amniotic fluid were centrifuged.The 24-hour urine sample was kept refrigerated during the collection period, up to the present time. Regarding dosage, the ideal urine volume was established as 10 ml and the minimum as 5 ml, with samples containing less than this volume being rejected. The analyte is stable for 7 days between 2-8 e W.

[0150] Serum and Urinary Creatinine Levels:

[0151] The collected urine was homogenized and centrifuged at 5000 RPM for 5 minutes to precipitate the sediment. The supernatant was diluted 1:25 with distilled water in glass tubes. The kit used was Elitech® (Vitória, Brazil), following the manufacturer's protocol. Serum creatinine biochemical analyses were performed in duplicate using the Jaffé kinetic-colorimetric method. Creatinine clearance was calculated by dividing urinary creatinine by serum creatinine, multiplying by minute urine volume, and the value was corrected for body surface area.

[0152] Determination of Microalbuminuria:

[0153] The determination of microalbuminuria in isolated urine samples was performed using the Biosystems immunoturbidimetry method (http: / / www.biosystems.com.br). The reference value adopted was up to 15 mg / L for normoalbuminurics and between 30 and 300 mg / 24h for microalbuminurics.

[0154] Gene Expression of NGAL and SMAD-1 in Peripheral Blood:

[0155] Total RNA extraction: Total RNA was isolated from peripheral blood using TRIzol reagent (TRIzol LS Reagent, Thermo Fisher, cat. no. 10296-010), according to the manufacturer's protocol. Total RNA concentration was estimated by spectrophotometric reading on the NanoVue Plus instrument (GE Health Care).

[0156] cDNA synthesis: Total RNA samples (initial 1 pg) were converted to cDNA using the SSIII First Strand qPCR Supermix kit (Invitrogen, cat. no. 11752050), according to the manufacturer's protocol.

[0157] qPCR: The expression of the NGAL and SMAD1 genes was evaluated by real-time PCR (RT-qPCR). Specific primers for each gene were designed using Primer3 Input 0.4.0 software (http: / / frodo.wi.mit.edu / primer3 / ) and checked for specificity using Primer-BLAST software. (http: / / www.ncbi.nlm.nih.gov / tools / primer-blast). The conserved gene region used for primer design was carefully selected based on sequence alignment analyses, ensuring high specificity, amplification efficiency, and reproducibility of results.

[0158] To normalize the relative expression of the target genes, the ribosomal gene RPL13A (18S) was used as an endogenous control due to its stable expression and widespread use in gene quantification studies, providing greater precision and reliability in data analysis. The specific sequences are described in Table 5.

[0159] The relationship between the values ​​of the two biomarkers (SMAD1 and NGAL) was calculated by dividing the SMAD1 expression values ​​by the NGAL expression values, according to formula (III) below: Formula (III) Value of SMAD1 expression (2-ACq) / Value of NGAL expression (2-ACq)=X

[0160] Statistical Analysis:

[0161] Results were expressed as mean ± standard deviation (SD). Comparisons were performed using the unpaired Student's t-test for parametric values ​​and the Mann-Whitney test for non-parametric values. The correlation between the studied genes and biochemical parameters was analyzed using Spearman's correlation test.

[0162] To assess the relationship between the two biomarkers, a calculation was applied in which the SMAD-1 expression value was divided by the NGAL expression value.

[0163] The analyses were performed using GraphPad Prism® software (GraphPad®, version 9, USA). The significance level was set at 5% (p-value < 0.05).

[0164] Results:

[0165] In this study, participants were divided into two groups: one composed of healthy individuals without type 2 diabetes mellitus (CTL group), and the other of patients diagnosed with the disease (DM2 group). In the CTL group, The majority of participants were female (55.3%), while men represented 44.7%. The average age was 45.1 years, and the predominant ethnicity was Caucasian (76.6%), with 23.4% of individuals being mixed-race or black.

[0166] In the type 2 diabetes group, there was an even greater predominance of women (65.5%) compared to men (34.5%). The average age was 64.2 years, and the ethnic distribution remained similar to that of the control group, with 66.4% Caucasian individuals and 26.3% mixed-race or black individuals. Regarding the time since diagnosis, the majority of patients (72.7%) had diabetes for less than five years, while 23.6% had the disease for between 5 and 10 years, and 9.1% had diabetes for more than 10 years. This higher percentage of diabetic patients who have had the disease for less than five years reinforces the importance of early biomarkers in the detection of renal complications associated with diabetes mellitus.

[0167] Furthermore, the study included hemodialysis patients, who were primarily composed of women (66%), while men represented 44% of this group. The average age of the dialysis patients was 61.7 years.

[0168] The anthropometric data of the participants in this study were represented in absolute and relative values ​​with the values ​​of the participants in the CTL and DM groups, respectively. The quantification data of the biochemical parameters are presented in Tables 6-13. The ratio between the SMAD1 / NGAL transcript values ​​is presented in Table 14.

[0169] Table 6 - Values ​​for quantifying blood glucose levels in the studied groups. CTL: control; DM: diabetes mellitus; DLT: dialysis.

[0170] Table 7 - HbA1c quantification values ​​in the studied groups. CTL: control; DM: diabetes mellitus; DLT: dialysis.

[0171] Table 8 - Creatinine quantification values ​​in the studied groups. CTL: control; DM: diabetes mellitus; DLT: dialysis.

[0172] Table 9 - Urea quantification values ​​in the studied groups. CTL: control; DM: diabetes mellitus; DLT: dialysis.

[0173] Table 10 - Proteinuria quantification values ​​in the studied groups. CTL: control; DM: diabetes mellitus; DLT: dialysis.

[0174] Table 11 - Albuminuria quantification values ​​in the studied groups. CTL: control; DM: diabetes mellitus; DLT: dialysis.

[0175] Table 12 - NGAL quantification values ​​in blood in the studied groups. CTL: control; DM: diabetes mellitus; DLT: dialysis.

[0176] Table 13 - SMAD1 quantification values ​​in blood in the studied groups. CTL: control; DM: diabetes mellitus; DLT: dialysis.

[0177] Table 14 - SMAD1 / NGAL transcript values ​​in the studied groups. CTL: control; DM: diabetes mellitus; DLT: dialysis patients.

[0178] Regarding the analysis of biochemical parameters, the following results were observed: blood glucose in patients with DM is significantly higher than the blood glucose levels found in patients in the control group CTL (DM: 160.3 ± 55.38 mg / dL x CTL: 88.47 ± 13.43 mg / dL, p < 0.0001) and patients with DM on dialysis DLT (DM: 160.3 ± 55.38 mg / dL x DLT: 100.5 ± 41.8, p < 0.0001); The quantification of glycated hemoglobin followed the behavior observed for glycemia, in which the values ​​are significantly higher in patients with DM, compared with the CTL group (DM: 7.365 ± 1.977% x CTL: 5.518 ± 0.3357%, p < 0.0001) and DLT patients (DM: 7.365 ± 1.977% x DLT: 5.947 ± 0.9941%, p < 0.0001); Creatinine values ​​were significantly higher for DLT patients compared to the CTL group (DLT: 8.71 ± 4.259 x CTL: 0.7357 ± 0.1650, p < 0.0001) and DM patients (DLT: 8.71 ± 4.259 x DM: 0.8186 ± 0.2681, p < 0.0001);as well as the urea values, DLT:148 ± 54.55 x CTL:28.82 ± 6.715, p< 0.0001 , DLT:148 ± 54.55 x DM:46.66 ± 58.97, p< 0.0001 ; for the proteinuria values ​​there was a significant difference only between the DM and DLT groups (DM: 18.98 ± 39.8 x 6.539 ± 0.6396, p = 0.0361 ); For albuminuria values, there were significant differences between DM patients and the control group (DM: 49.82 ± 79.1 x CTL: 9.5 ± 13.1, p = 0.0003) and between DM patients and DLT patients (DM: 49.82 ± 79.1 x DLT: 3.943 ± 0.3987, p < 0.0001); blood NGAL values ​​showed a significant difference between DLT patients and the CTL group (DLT: 39.02 ± 64.52 x CTL: 0.2374 ± 0.765, p < 0.0001) and between the; DLT patients and DM patients (DLT: 39.02 ± 64.52 x DM: 3.852 ± 13.76, p< 0.0001); blood SMAD1 values ​​showed a significant difference between DM patients and the CTL group (DM: 0.2633 ± 0.7592 x CTL: 0.00011 ± 0.0003, p< 0.0001) and between DM patients and DLT patients (DM: 0.2633 ± 0.7592 x DLT: 0.5307 ± 2.463, p< 0.000).

[0179] These results showed higher levels of glycemia, HbA1c, proteinuria, and albuminuria in diabetic patients compared to the control group. Furthermore, an increase in serum urea and creatinine levels was observed in DM patients, demonstrating that even in patients without a prior diagnosis of diabetic kidney disease, subtle alterations in renal function can occur. Additionally, the expression of the SMAD1 and NGAL genes was detected in the group of diabetic individuals, with significant differences between DM patients and DLT patients, demonstrating their relevance as molecular biomarkers in the context of early renal dysfunction associated with DM.

[0180] As mentioned earlier, diabetic kidney disease (DKD) is one of the most serious microvascular complications of diabetes mellitus (DM) and represents the leading cause of end-stage chronic kidney disease (CKD), being strongly associated with increased mortality. [101 ' 1021Kidney disease is characterized by glomerulosclerosis and renal fibrosis, resulting from metabolic and hemodynamic changes triggered by diabetes mellitus (DM). Clinically, RD manifests as persistent albuminuria (albumin excretion greater than 300 mg / day or 200 pg / min) and a progressive reduction in glomerular filtration rate (GFR), frequently accompanied by hypertension, culminating in renal failure. 101 ' 103 However, albuminuria, despite being widely used in clinical practice, has important limitations, including high interindividual variability and low sensitivity for the early detection of renal dysfunction. In this context, the identification of new molecular biomarkers is crucial to improve the risk stratification of CKD and allow for early therapeutic interventions.

[0181] SMAD1 is a transcription factor essential for the activation of α1 and α2 chains of type IV collagen, which play a central role in mesangial matrix expansion in the early stages of diabetic kidney disease (DKD). Therefore, early detection of increased SMAD1 expression provides early indications of fibrotic processes in the kidneys of diabetic patients. NGAL, in turn, is a glycoprotein involved in several pathophysiological mechanisms, including antimicrobial defense, regulation of cell division, cell-cell adhesion, and cell survival. Furthermore, it is widely recognized as a sensitive biomarker of acute tubular injury and has been used in the early identification of acute kidney injury (AKI)! 102 ' 104 ]

[0182] Experimental evidence reinforces the role of these molecules in the pathophysiology of RD. A study conducted by Chen et al. (2019) demonstrated that hyperglycemia induces the proliferation of mesangial cells through the activation of the BMP4 / SMAD1 signaling pathway, promoting the overproduction of type IV collagen, one of the main markers of renal fibrosis. Blocking the SMAD1 pathway, on the other hand, inhibited the synthesis of type IV collagen, confirming the participation of this pathway in the thickening of the glomerular basement membrane and in the progression of RD.

[0183] Given the importance of SMAD1 signaling in the mesangial processes characteristic of diabetic kidney disease (DKD), the detection of elevated SMAD1 levels in diabetic patients has proven to be an early indicator of nephropathy. Veiga et al. (2020) demonstrated a positive correlation between urinary SMAD1 expression and HbA1c levels, suggesting that the greater the chronic glycemic dysregulation, the greater the activation of molecular pathways associated with fibrosis and early kidney injury, such as the SMAD1 signaling pathway. Furthermore, Doi et al. (2018) reported that diabetic patients with increased urinary SMAD1 expression, even in the absence of macroalbuminuria, showed a progressive decline in GFR. These findings indicate that urinary SMAD1 expression precedes GFR reduction, reinforcing its potential as a predictive biomarker of incipient renal dysfunction. [100 ' 108] .

[0184] Veiga et al. (2020) also demonstrated a significant correlation between SMAD1 gene expression in urine and blood (r 2 = 0.4163, p = 0.024); and absence of correlation between NGAL gene expression in urine and blood (r 2 = 0.07147, p = 0.674). This divergence between the intermatrix stability of SMAD1 and NGAL reinforces the importance of combined assessment, as it reveals complementary pathophysiological characteristics — SMAD1 as a more systemically regulated marker, while NGAL may reflect distinct local stimuli.

[0185] In the evaluation of the ratio between the values ​​of SMAD1 / NGAL transcripts in the blood samples of the participants in this study, a reduction in this ratio was observed in patients with DM compared to the CTL group (DM: 0.02 ± 0.07 x CTL: 1.53 ± 3.83, p = 0.03). These data obtained in the present study demonstrate that the SMAD1 / NGAL ratio is associated with DM2 and that its reduction reflects specific alterations in the TGF-β signaling pathway. Furthermore, the increased expression of NGAL, a biomarker traditionally associated with acute kidney injury, indicates that early structural and functional changes are already occurring in the kidneys of patients with DM2, even before the development of macroalbuminuria and reduced GFR.

[0186] The identification of new predictive markers for the progression of diabetic kidney disease (DKD) is an active and constantly evolving area of ​​research. To date, there are no reports in the literature that have evaluated the SMAD1 / NGAL ratio as a predictor of renal function loss in diabetics. Thus, this study contributes to filling this gap, enabling the assignment of a patient classification score.

[0187] In addition to the SMAD1 / NGAL ratio, the present invention revealed a positive correlation between TNFR1 gene expression in peripheral blood and increased blood glucose (R 0.8857 and *p 0.0333), demonstrating an inflammatory modulating role that accompanies metabolic dysregulation in diabetic models with renal alterations. This direct association between a marker The correlation between circulating inflammatory markers (TNFR1) and a metabolic variable (glycemia) is not predictable from the isolated knowledge of each biomarker, especially considering the tissue of expression (blood vs. kidney). Relevant associations were also identified between other vascular and inflammatory biomarkers, such as: significant correlations between CD147 and urea (in the kidney and heart) and glycemia (heart), and between VEGFA and urinary creatinine or urea (in different tissues). The combination with perfusion (VEGFA) and tissue remodeling (CD147) biomarkers, associated with cross-correlations between tissues (kidney, blood, and heart), presents an unexpected technical effect: the ability to reflect not only isolated renal damage, but also interorgan interactions and systemic alterations in the pathophysiology of the disease.These findings reinforce that the combined analysis of the claimed biomarkers is not simply redundant, but rather complementary, sensitive, and pathophysiologically integrated, which would not be predicted by the individual analysis of each gene.

[0188] The results revealed in the present invention are surprising and demonstrate the usefulness of the studied genes as biomarkers of early renal alterations. These markers fill the existing gap between the onset of the most subtle and initial molecular alterations and tubular and glomerular changes, facilitating the anticipation of the onset of diabetic kidney disease (DKD) in time for significant clinical intervention for the patient. Therefore, the present invention allows the early detection of diabetic kidney disease (DKD) through the quantification of nucleic acid, preferably messenger RNA (mRNA), of specific biomarkers directly in urine and / or blood samples collected with reagents for the stabilization of genetic material, something that is neither described nor suggested by prior art documents.The panel and kit proposed here provide an integrated view of metabolic and functional damage in target organs, significantly expanding the diagnostic and prognostic power of these tools.

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Available from: https: / / www. eurchembull.com / uploads / paper / 0330fe01 bc74f2e8ca

[0190] Those skilled in the art will appreciate the knowledge presented here and will be able to reproduce the invention in the forms presented and in other variants and alternatives, covered by the scope of the following claims.

Claims

Claims 1. In vitro method for screening indicators of renal alterations in samples from diabetic mammals, characterized by comprising the steps of: a) preparing the analyte from mammalian samples; b) measuring the expression level of the TNFR1 gene and at least 1 gene selected from MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL in the analyte sample; and c) comparing the expression level of the genes measured in a) with the expression level of the respective genes in a control sample, where renal alteration is indicated when the expression level of TNFR1 and at least 1 gene selected from MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL deviate from the expression level in the control sample.

2. In vitro method for prediction, screening, monitoring and / or diagnosis of diabetic kidney disease characterized by comprising the steps of: a) preparation of the analyte from mammalian samples; b) measuring the expression level of the TNFR1 gene and at least 1 gene selected from MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL in an analyte sample; and c) comparing the expression level of the genes measured in a) with the expression level of the respective genes in a control sample.

3. In vitro method, according to claim 2, characterized by a reduced level of TNFR1 gene expression in an analyte sample, compared to the expression level in a control sample, being indicative of diabetic kidney disease.

4. In vitro method, according to claim 2, characterized by further comprising the calculation of the ratio of measured expression levels of the SMAD1 and NGAL genes, wherein a reduced ratio in the sample An elevated ratio of the analyte to a control sample ratio is indicative of diabetic kidney disease.

5. In vitro method, according to claims 2 to 4, characterized in that the measurement is performed by dPCR, qPCR, RT-PCR or microarray.

6. Biomarker panel for prediction, screening and / or monitoring of diabetic kidney disease characterized by comprising two or more genes selected from: TNFR1, MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1 and NGAL, wherein when the panel consists of two genes, one of which is SMAD1, the other gene is different from NGAL.

7. Kit for prediction, screening and / or monitoring of diabetic kidney disease characterized by comprising: - reagents for stabilizing sample mRNA; - enzyme to enable the formation of cDNA from mRNA; - primers, probes, dNTPs for amplification of TNFR1 cDNA and reference marker gene cDNA; -Optionally, primers, probes, dNTPs for cDNA amplification of MCT1, MCT4, CD 147, HIF-1a, VEGFA, SMAD1 and NGAL; - reagents for the quantitative visualization of amplified cDNAs.

8. In vitro method for predicting diabetic kidney disease characterized by comprising the steps of: a) preparing the analyte from mammalian samples; b) measuring the expression levels of TNFR1 and at least one of the selected genes NGAL and SMAD1 in the analyte sample; and c) comparing the expression levels measured in a) with the expression levels in a control sample, wherein the sample with expression levels of the TNFR1 and NGAL and / or SMAD1 genes that deviate from the expression levels in the control sample indicates a risk of developing diabetic kidney disease.

9. In vitro method for evaluating the effectiveness of a treatment in controlling Diabetic kidney disease is characterized by comprising the steps of: i) measuring TNFR1 gene expression in samples from the individual under treatment; ii) measuring the expression level of one or more genes selected from: MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1, and NGAL; iii) detecting an increase or decrease in the expression of the analyzed genes in the treated sample compared to the control sample as an indication of the effectiveness of the treatment in controlling diabetic kidney disease.

10. A method for treating diabetic kidney disease characterized by comprising the administration, to a mammal, of a drug acting on a target selected from: TNFR1, MCT1, MCT4, CD147, HIF-1a, VEGFA, SMAD1, NGAL and combinations thereof.

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