Clinical trial NCT05378282: Identification of Diabetic Nephropathy Biomarkers Through Transcriptomics
Official title: Identification of Diabetic Nephropathy Biomarkers Through Transcriptomics
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Type 2 Diabetes
Type 2 Diabetes With Renal Manifestations
Keywords: Type 2 Diabetes
transcriptomics
microalbuminuria
biomarkers
Lead sponsor: Hospital Juarez de Mexico
Sponsor class: OTHER_GOV
First posted: 2022-05-18
Enrollment: 40
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Mexican patients with Type 2 Diabetes with or without Diabetic Nephropathy

Brief 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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: Differential Expression in blood and urine of diabetic patiens with and without diabetic nephropathy assesed by RNA seq
timeFrame: through study completion, an average of 1 year
Secondary outcomes:
measure: Demographic data of participants
timeFrame: through study completion, an average of 1 year
measure: Data of pharmacological treatment of participants
timeFrame: through study completion, an average of 1 year
measure: Habits data, Smoking
timeFrame: through study completion, an average of 1 year
measure: Habits data, Exercise
timeFrame: through study completion, an average of 1 year
measure: Habits data, Special Diet
timeFrame: through study completion, an average of 1 year
measure: Anthropometric data of participants, Height
timeFrame: through study completion, an average of 1 year
measure: Anthropometric data of participants, Weight
timeFrame: through study completion, an average of 1 year
measure: Biochemical data of participants, Glucose
timeFrame: through study completion, an average of 1 year
measure: Biochemical data of participants, Glycated hemoglobin
timeFrame: through study completion, an average of 1 year
measure: Biochemical data of participants, Serum creatinine
timeFrame: through study completion, an average of 1 year
measure: Biochemical data of participants, Triglycerides
timeFrame: through study completion, an average of 1 year
measure: Biochemical data of participants, Total Cholesterol
timeFrame: through study completion, an average of 1 year
measure: Biochemical data of participants, High density lipoproteins (HDL)
timeFrame: through study completion, an average of 1 year
measure: Biochemical data of participants, Low density lipoproteins (LDL)
timeFrame: through study completion, an average of 1 year
measure: Biochemical data of participants, Uric acid
timeFrame: through study completion, an average of 1 year
measure: Biochemical data of participants, Blood urea nitrogen (BUN)
timeFrame: through study completion, an average of 1 year
measure: Biochemical data of participants, Urea
timeFrame: through study completion, an average of 1 year
measure: Molecular data, Whole messenger RNA sequencing
timeFrame: through study completion, an average of 1 year
Other outcomes:
Locations:
city: Mexico City
country: Mexico
facility: Hospital Juárez de México
Study officials:
References:
citation: 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.
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.
citation: Secretaría de Salud. Instituto Nacional de Salud Pública. Encuesta Nacional de Salud y Nutrición 2012. 2012
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.
citation: 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.
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)
citation: 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.
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.
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.
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.
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.
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.
citation: 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
citation: Viloria; AT, Castillo RZ. Nefropatía diabética. Rev Hosp Gral Dr M Gea González. 2002;5(1 and 2):24-32