Identification of Diabetic Nephropathy Biomarkers Through Transcriptomics

Type 2 Diabetes Clinical Trial

Official title:

Identification of Diabetic Nephropathy Biomarkers Through Transcriptomics

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05378282
• Other study ID #: HJM 0513/18-1
• Status: Completed
• Start date: September 3, 2021
• Est. completion date: February 28, 2023

Study information

• Verified date: February 2023
• Source: Hospital Juarez de Mexico
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Description:

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 40
• Est. completion date: February 28, 2023
• Est. primary completion date: February 28, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 65 Years

Eligibility

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

Related Conditions & MeSH terms

• Diabetes Mellitus
• Diabetes Mellitus, Type 2
• Diabetic Nephropathies
• Type 2 Diabetes
• Type 2 Diabetes With Renal Manifestations

Locations

Country	Name	City	State
Mexico	Hospital Juárez de México	Mexico City

Sponsors (1)

Lead Sponsor	Collaborator
Hospital Juarez de Mexico

Country where clinical trial is conducted

Mexico, 

References & Publications (32)

American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010 Jan;33 Suppl 1(Suppl 1):S62-9. doi: 10.2337/dc10-S062. No abstract available. Erratum In: Diabetes Care. 2010 Apr;33(4):e57. — View Citation

American Diabetes Association. Standards of medical care in diabetes--2010. Diabetes Care. 2010 Jan;33 Suppl 1(Suppl 1):S11-61. doi: 10.2337/dc10-S011. No abstract available. Erratum In: Diabetes Care. 2010 Mar;33(3):692. — View Citation

American Diabetes Association. Standards of medical care in diabetes--2014. Diabetes Care. 2014 Jan;37 Suppl 1:S14-80. doi: 10.2337/dc14-S014. No abstract available. — View Citation

Bello-Chavolla OY, Rojas-Martinez R, Aguilar-Salinas CA, Hernandez-Avila M. Epidemiology of diabetes mellitus in Mexico. Nutr Rev. 2017 Jan;75(suppl 1):4-12. doi: 10.1093/nutrit/nuw030. — View Citation

Benjamín; T-D, Antonio; M-MM, Eugenia; C-PM, A D-EMT, Lucia; r-D, Patricia; R-C, et al. DETECCIÓN DE MICROALBUMINURIA EN PACIENTES DIABÉTICOS TIPO II. Bioquimia. 2007(SA126)

Cueto-Manzano AM, Cortes-Sanabria L, Martinez-Ramirez HR, Rojas-Campos E, Barragan G, Alfaro G, Flores J, Anaya M, Canales-Munoz JL. Detection of early nephropathy in Mexican patients with type 2 diabetes mellitus. Kidney Int Suppl. 2005 Aug;(97):S40-5. doi: 10.1111/j.1523-1755.2005.09707.x. — View Citation

de Boer IH, Rue TC, Cleary PA, Lachin JM, Molitch ME, Steffes MW, Sun W, Zinman B, Brunzell JD; Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study Research Group; White NH, Danis RP, Davis MD, Hainsworth D, Hubbard LD, Nathan DM. Long-term renal outcomes of patients with type 1 diabetes mellitus and microalbuminuria: an analysis of the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications cohort. Arch Intern Med. 2011 Mar 14;171(5):412-20. doi: 10.1001/archinternmed.2011.16. — View Citation

Gheith O, Farouk N, Nampoory N, Halim MA, Al-Otaibi T. Diabetic kidney disease: world wide difference of prevalence and risk factors. J Nephropharmacol. 2015 Oct 9;5(1):49-56. eCollection 2016. — View Citation

Gonzalez-Villalpando C, Davila-Cervantes CA, Zamora-Macorra M, Trejo-Valdivia B, Gonzalez-Villalpando ME. Incidence of type 2 diabetes in Mexico: results of the Mexico City Diabetes Study after 18 years of follow-up. Salud Publica Mex. 2014 Jan-Feb;56(1):11-7. doi: 10.21149/spm.v56i1.7318. — View Citation

Hovind P, Tarnow L, Rossing P, Jensen BR, Graae M, Torp I, Binder C, Parving HH. Predictors for the development of microalbuminuria and macroalbuminuria in patients with type 1 diabetes: inception cohort study. BMJ. 2004 May 8;328(7448):1105. doi: 10.1136/bmj.38070.450891.FE. Epub 2004 Apr 19. — View Citation

Ju W, Nair V, Smith S, Zhu L, Shedden K, Song PXK, Mariani LH, Eichinger FH, Berthier CC, Randolph A, Lai JY, Zhou Y, Hawkins JJ, Bitzer M, Sampson MG, Thier M, Solier C, Duran-Pacheco GC, Duchateau-Nguyen G, Essioux L, Schott B, Formentini I, Magnone MC, Bobadilla M, Cohen CD, Bagnasco SM, Barisoni L, Lv J, Zhang H, Wang HY, Brosius FC, Gadegbeku CA, Kretzler M; ERCB, C-PROBE, NEPTUNE, and PKU-IgAN Consortium. Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker. Sci Transl Med. 2015 Dec 2;7(316):316ra193. doi: 10.1126/scitranslmed.aac7071. — View Citation

Kramer HJ, Nguyen QD, Curhan G, Hsu CY. Renal insufficiency in the absence of albuminuria and retinopathy among adults with type 2 diabetes mellitus. JAMA. 2003 Jun 25;289(24):3273-7. doi: 10.1001/jama.289.24.3273. — View Citation

Levin A, Stevens PE. Summary of KDIGO 2012 CKD Guideline: behind the scenes, need for guidance, and a framework for moving forward. Kidney Int. 2014 Jan;85(1):49-61. doi: 10.1038/ki.2013.444. Epub 2013 Nov 27. — View Citation

Li P, Piao Y, Shon HS, Ryu KH. Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data. BMC Bioinformatics. 2015 Oct 28;16:347. doi: 10.1186/s12859-015-0778-7. — View Citation

MacIsaac RJ, Tsalamandris C, Panagiotopoulos S, Smith TJ, McNeil KJ, Jerums G. Nonalbuminuric renal insufficiency in type 2 diabetes. Diabetes Care. 2004 Jan;27(1):195-200. doi: 10.2337/diacare.27.1.195. — View Citation

Napierala JS, Li Y, Lu Y, Lin K, Hauser LA, Lynch DR, Napierala M. Comprehensive analysis of gene expression patterns in Friedreich's ataxia fibroblasts by RNA sequencing reveals altered levels of protein synthesis factors and solute carriers. Dis Model Mech. 2017 Nov 1;10(11):1353-1369. doi: 10.1242/dmm.030536. — View Citation

National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002 Feb;39(2 Suppl 1):S1-266. No abstract available. — View Citation

O'Connell PJ, Zhang W, Menon MC, Yi Z, Schroppel B, Gallon L, Luan Y, Rosales IA, Ge Y, Losic B, Xi C, Woytovich C, Keung KL, Wei C, Greene I, Overbey J, Bagiella E, Najafian N, Samaniego M, Djamali A, Alexander SI, Nankivell BJ, Chapman JR, Smith RN, Colvin R, Murphy B. Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study. Lancet. 2016 Sep 3;388(10048):983-93. doi: 10.1016/S0140-6736(16)30826-1. Epub 2016 Jul 22. — View Citation

Oshlack A, Robinson MD, Young MD. From RNA-seq reads to differential expression results. Genome Biol. 2010;11(12):220. doi: 10.1186/gb-2010-11-12-220. Epub 2010 Dec 22. — View Citation

Perkins BA, Ficociello LH, Silva KH, Finkelstein DM, Warram JH, Krolewski AS. Regression of microalbuminuria in type 1 diabetes. N Engl J Med. 2003 Jun 5;348(23):2285-93. doi: 10.1056/NEJMoa021835. — View Citation

Secretaría de Salud. Instituto Nacional de Salud Pública. Encuesta Nacional de Salud y Nutrición 2012. 2012

Slocum JL, Heung M, Pennathur S. Marking renal injury: can we move beyond serum creatinine? Transl Res. 2012 Apr;159(4):277-89. doi: 10.1016/j.trsl.2012.01.014. Epub 2012 Feb 3. — View Citation

Stevens LA, Coresh J, Greene T, Levey AS. Assessing kidney function--measured and estimated glomerular filtration rate. N Engl J Med. 2006 Jun 8;354(23):2473-83. doi: 10.1056/NEJMra054415. No abstract available. — View Citation

Stevens LA, Levey AS. Measurement of kidney function. Med Clin North Am. 2005 May;89(3):457-73. doi: 10.1016/j.mcna.2004.11.009. — View Citation

Trzybulska D, Eckersten D, Giwercman A, Christensson A, Tsatsanis C. Alterations in Serum MicroRNA Profile During Hemodialysis - Potential Biological Implications. Cell Physiol Biochem. 2018;46(2):793-801. doi: 10.1159/000488737. Epub 2018 Mar 29. — View Citation

Tziomalos K, Athyros VG. Diabetic Nephropathy: New Risk Factors and Improvements in Diagnosis. Rev Diabet Stud. 2015 Spring-Summer;12(1-2):110-8. doi: 10.1900/RDS.2015.12.110. Epub 2015 Aug 10. — View Citation

Vaidya VS, Niewczas MA, Ficociello LH, Johnson AC, Collings FB, Warram JH, Krolewski AS, Bonventre JV. Regression of microalbuminuria in type 1 diabetes is associated with lower levels of urinary tubular injury biomarkers, kidney injury molecule-1, and N-acetyl-beta-D-glucosaminidase. Kidney Int. 2011 Feb;79(4):464-70. doi: 10.1038/ki.2010.404. Epub 2010 Oct 27. — View Citation

Viloria; AT, Castillo RZ. Nefropatía diabética. Rev Hosp Gral Dr M Gea González. 2002;5(1 and 2):24-32

Vora JP, Ibrahim HA, Bakris GL. Responding to the challenge of diabetic nephropathy: the historic evolution of detection, prevention and management. J Hum Hypertens. 2000 Oct-Nov;14(10-11):667-85. doi: 10.1038/sj.jhh.1001058. — View Citation

Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009 Jan;10(1):57-63. doi: 10.1038/nrg2484. — View Citation

Zenteno-Castillo P, Munoz-Lopez DB, Merino-Reyes B, Vega-Sanchez A, Preciado-Puga M, Gonzalez-Yebra AL, Kornhauser C. Prevalence of diabetic nephropathy in Type 2 Diabetes Mellitus in rural communities of Guanajuato, Mexico. Effect after 6 months of Telmisartan treatment. J Clin Transl Endocrinol. 2015 Aug 18;2(4):125-128. doi: 10.1016/j.jcte.2015.08.001. eCollection 2015 Dec. — View Citation

Zhang ZH, Jhaveri DJ, Marshall VM, Bauer DC, Edson J, Narayanan RK, Robinson GJ, Lundberg AE, Bartlett PF, Wray NR, Zhao QY. A comparative study of techniques for differential expression analysis on RNA-Seq data. PLoS One. 2014 Aug 13;9(8):e103207. doi: 10.1371/journal.pone.0103207. eCollection 2014. — View Citation

* Note: There are 32 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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
Secondary	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
Secondary	Habits data, Smoking	This information will be use as covariate in RNASeq	through study completion, an average of 1 year
Secondary	Habits data, Exercise	This information will be use as covariate in RNASeq	through study completion, an average of 1 year
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	Biochemical data of participants, Triglycerides	To observe metabolic control in patients	through study completion, an average of 1 year
Secondary	Biochemical data of participants, Total Cholesterol	To observe metabolic control in patients	through study completion, an average of 1 year
Secondary	Biochemical data of participants, High density lipoproteins (HDL)	To observe metabolic control in patients	through study completion, an average of 1 year
Secondary	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
Secondary	Biochemical data of participants, Uric acid	To observe metabolism in patients	through study completion, an average of 1 year
Secondary	Biochemical data of participants, Blood urea nitrogen (BUN)	To observe renal function in patients	through study completion, an average of 1 year
Secondary	Biochemical data of participants, Urea	To observe renal function in patients	through study completion, an average of 1 year
Secondary	Molecular data, Whole messenger RNA sequencing	Data of whole messenger in blood and urine	through study completion, an average of 1 year