Identification of Diabetic Nephropathy Biomarkers Through Transcriptomics

Study Overview
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Brief Summary
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According to the different epidemiological studies in Mexico the prevalence of diabetic nephropathy is 9.1%-40% in diabetic patients, however the complication is subdiagnosed when we see the numbers of uncontrolled diabetics (75%) and patients that are under continuous screening to prevent complications development (only 12.6% had an annual albuminuria measurement). In addition, Mexican have an increased susceptibility to developing diabetic nephropathy. These data highlight the need to identify new biomarkers that could help us to identify those patients at high risk for developing diabetic nephropathy, in order to take preventing measures to delay the progress of the disease to CKD and improve the quality of the patients. Thus, the comparison of transcriptomic profile between diabetic patients with and without diabetic nephropathy is the first step to characterize this complication. In addition, we will be able to identify diabetic nephropathy biomarkers for development of new diagnostic tools and even to find therapeutic targets in Mexican from Hospital Juárez de México.

Detailed Description
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Type 2 diabetes (T2D) is defined as a group of metabolic diseases characterized by chronic hyperglycemia, resulting from defects in insulin secretion, insulin action or both. According to the National Health Survey 2012, in Mexico, approximately 1 out of 10 persons is affected with T2D, though is important to mention that these data only reflect subjects previously diagnosed with the disease, but we expect a two fold increase in this number when including newly diagnosed T2D patients. In addition, the prevalence of T2D in younger age groups has increased (25% of diabetes cases in Mexico occurs in young adults <43 years of age), which implies >20 years living with the disease. In consequence, T2D is found among the leading causes of death, which represents a health burden for the country. It has been estimated that more than 40% of people with diabetes will develop chronic kidney disease (CKD), accounting for about 40% of all patients beginning renal replacement therapy. In the Instituto Mexicano del Seguro Social, nephropathy is among the five leading causes of medical care in general hospitals in the area and in high-specialty hospitals. A study in Tuxtla Gutiérrez, Chiapas, informed a 35% incidence of nephropathy was observed in diabetic patients. Cueto-Manzano et al., reported a 40% incidence of early nephropathy and 29% of established nephropathy in 756 diabetic patients from Jalisco. Other study in Mexico that included 3,609 diabetic patients in Guanajuato, reported a 23.8% of diabetic nephropathy. A recent study conducted in the State of Mexico, which included 44 458 subjects diagnosed with T2D, registered the presence of diabetic nephropathy in 9.1% . Diabetic kidney disease is uncommon if diabetes is less than one decade duration. The highest incidence rates of 3% per year are on average seen 10 to 20 years after diabetes onset, after which the rate of nephropathy tapers off. It is important to say that a diabetic patient for 20 to 25 years without clinical signs of diabetic nephropathy has low chance to develop such complication. The progression of T2D to diabetic nephropathy has become a health problem, not only for the costs to health sector, but to the worsening of life quality of the patient and the outcomes. The main risk factors of progression to diabetic nephropathy includes: hyperglycemia, response to drugs, and long duration of diabetes, high blood pressure, obesity and dyslipidemia. Most of these factors are modifiable by drugs or changes in life style. Therefore, the management of the modifiable risk factors is a key for preventing and delaying the decline in renal function. Early diagnosis of diabetic nephropathy is another essential component in the management of diabetes and its complications such as nephropathy. The American Diabetes Association (ADA) recommends the routine screening to diabetic subjects with progressive diabetic nephropathy and CKD. The most widely accepted guidelines of National Kidney Foundation were implicating in measuring glomerular filtration rate (GFR) and stages of CKD using serum creatinine in patients. However, due to creatinine undergo tubular secretion in addition to glomerular filtration and its extrarenal elimination via the gastrointestinal tract, particularly in advanced renal failure, the GFR could be overestimated. In case of GFR, the techniques are overwhelming due to invasive methods and some markers are difficult to handle. Another marker used in the clinic is microalbuminuria, in most patients, the first sign of diabetic nephropathy is the moderate increase of urinary albumin excretion, i.e. 30-300 mg/g creatinine in a urine sample (also termed microalbuminuria). Patients who develop macroalbuminuria (>30-300 mg/g creatinine) are at high risk for developing diabetic nephropathy. Nonetheless, approximately up to 40% of patients with moderate albuminuria returns to normoalbuminuria. Moreover, up to 50% of patients with type 1 diabetes or T2D experience a decline in eGFR, despite the presence of only moderate albuminuria or even normoalbuminuria. Consequently, the actual markers available in the clinic are inaccurate, so it is necessary the identification of new markers that can recognize those patients at high risk for developing diabetic nephropathy to delay the progress of the complications taking the adequate measures. The transcriptomics The development of new technologies in the genomic era had allow the accelerated advance in system biology and the generation of knowledge in kidney development, homeostasis, and disease. In this context, transcriptome signatures associated with specific disease states can provide great information about pathogenic mechanisms and bring to light priority gene expression biomarker candidates . In addition, comparison of transcriptomes allows the identification of genes that are differentially expressed in distinct populations. In general, the RNA-Seq technology is very useful for differential expression analysis, in which is commonly adopted five steps. First, the RNA samples are fragmented into small complementary DNA sequences (cDNA) and then sequenced from a high throughput platform. Second, the small generated sequences are mapped to a transcriptome. Third, the expression levels for each gene or isoform are estimated. Fourth, the mapped data are normalized and, e.g. using statistical and machine learning methods, the differentially expressed genes (DEGs) are identified. Finally, the relevance of the produced data is finally evaluated from a biological context. A recent study by O´Conell et al. identified a set of 13 genes that was predictive for the development of renal fibrosis at 1 year of renal transplant, through microarray expression analysis of renal allograft recipients' biopsies. Thus, the authors suggest that the set of 13 genes could be used to identify kidney transplant recipients at risk of allograft loss before the development of irreversible damage. A study by Ju et al., revealed that epidermal growth factor (EGF) a tubule-specific protein critical for cell differentiation and regeneration, predicted eGFR, throughout transcriptome analysis on microdissected tubulointerstitial components of human renal biopsies of patients with CKD. In addition, the amount of EGF protein in urine (uEGF) showed significant correlation with intrarenal EGF mRNA, interstitial fibrosis/tubular atrophy, eGFR and rate of eGFR loss, suggesting that uEGF could be a good predictor of CKD progression. Other study demonstrated that serum miRNA profile is affected by hemodialysis contributing to subfertility and increased risk for cancer development. Therefore, transcriptomics could provide better diagnostic tools, prognostic biomarkers, and signaling pathways amenable to therapeutic targeting.

Official Title
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Identification of Diabetic Nephropathy Biomarkers Through Transcriptomics

Conditions
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Type 2 Diabetes, Type 2 Diabetes With Renal Manifestations

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients with ≥ 20 years of T2D evolution with normoalbuminuria Patients without a personal or family history of kidney disease in 1st degree relatives Age ≥ 18 years T2D diagnosed at least 5 years before initiating renal replacement therapy Background or diabetic retinopathy by self-report to ensure that albuminuria was the consequence of diabetic nephropathy rather than a non-diabetic glomerulopathy albuminuria ≥ 300 mg/24 h in at least two out of three sterile urine samples no hematuria or signs (including cellular casts), history or predisposition to other kidney or urinary tract disease. Exclusion Criteria: Diabetic patients without diabetic nephropathy Patients with type 1 diabetes, gesta- tional diabetes, uncontrollable hypertension, active cancer, heart failure, liver or kidney disease, cotreatment with corticosteroids or estrogens, conditions that can cause hyperglycemia, addiction to alcohol or illegal drugs, and dementia or severe psychiatric disor- ders were not included in this study

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Diabetic patients without diabetic nephropathy<br>i. Patients with ≥ 20 years of T2D evolution with normoalbuminuria ii. Patients without a personal or family history of kidney disease in 1st degree relatives iii. Age ≥ 18 years | |
| Diabetic patients with diabetic nephropathy<br>i. T2D diagnosed at least 5 years before initiating renal replacement therapy ii. Background or diabetic retinopathy by self-report to ensure that albuminuria was the consequence of diabetic nephropathy rather than a non-diabetic glomerulopathy iii. albuminuria ≥ 300 mg/24 h in at least two out of three sterile urine samples iv. no hematuria or signs (including cellular casts), history or predisposition to other kidney or urinary tract disease. v. Age ≥ 18 years | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Differential Expression in blood and urine of diabetic patiens with and without diabetic nephropathy assesed by RNA seq | Different expression of down regulated and upregulated genes in blood and urine between patients with type II diabetes with and without diabetic nephropathy | through study completion, an average of 1 year | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Demographic data of participants | Age, Serum creatinine, age and weight will be combined to report glomerular filtration rate by Cockcroft-Gault formula | through study completion, an average of 1 year | 
| Data of pharmacological treatment of participants | Pharmacological treatment, This information will be use as covariate in RNASeq | through study completion, an average of 1 year | 
| Habits data, Smoking | This information will be use as covariate in RNASeq | through study completion, an average of 1 year | 
| Habits data, Exercise | This information will be use as covariate in RNASeq | through study completion, an average of 1 year | 
| Habits data, Special Diet | (proteic diet, low in calories), This information will be use as covariate in RNASeq | through study completion, an average of 1 year | 
| Anthropometric data of participants, Height | Weight and height will be combined to report BMI in kg/m^2 | through study completion, an average of 1 year | 
| Anthropometric data of participants, Weight | Weight and height will be combined to report BMI in kg/m^2 | through study completion, an average of 1 year | 
| Biochemical data of participants, Glucose | To observe glycemic control of patients, due to have diagnostic of diabetes | through study completion, an average of 1 year | 
| Biochemical data of participants, Glycated hemoglobin | To observe glycemic control of patients, due to have diagnostic of diabetes | through study completion, an average of 1 year | 
| Biochemical data of participants, Serum creatinine | Serum creatinine, age and weight will be combined to report glomerular filtration rate by Cockcroft-Gault formula | through study completion, an average of 1 year | 
| Biochemical data of participants, Triglycerides | To observe metabolic control in patients | through study completion, an average of 1 year | 
| Biochemical data of participants, Total Cholesterol | To observe metabolic control in patients | through study completion, an average of 1 year | 
| Biochemical data of participants, High density lipoproteins (HDL) | To observe metabolic control in patients | through study completion, an average of 1 year | 
| Biochemical data of participants, Low density lipoproteins (LDL) | To observe metabolic control in patients, risk of cardiometabolic disease | through study completion, an average of 1 year | 
| Biochemical data of participants, Uric acid | To observe metabolism in patients | through study completion, an average of 1 year | 
| Biochemical data of participants, Blood urea nitrogen (BUN) | To observe renal function in patients | through study completion, an average of 1 year | 
| Biochemical data of participants, Urea | To observe renal function in patients | through study completion, an average of 1 year | 
| Molecular data, Whole messenger RNA sequencing | Data of whole messenger in blood and urine | through study completion, an average of 1 year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Type 2 Diabetes, transcriptomics, microalbuminuria, biomarkers