SOME NOVEL BIOMARKERS OF CARDIOVASCULAR DISEASE IN PATIENTS WITH CHRONIC KIDNEY DISEASE Dept. of nephrology, Clinic for internal medicine University Medical Centre Maribor Faculty of Medicine, University of Maribor 1
The ideal biomarker identifies early stages of disease or is indicative of disease prognosis or correlates well with progression, response to therapy and clinically meaningful endpoints 2 http://grants.nih.gov/grants/guide/rfa-files/rfa-dk-08-015.html#parti
Clinically useful biomarker should be highly sensitive and specific in detecting disease it should be reproducible and standardized across different clinical laboratories it should be relatively easy to perform so that the information is readily available to clinicians 3 http://grants.nih.gov/grants/guide/rfa-files/rfa-dk-08-015.html#parti
Hypothetical ROC: plot of the sensitivity of a test vs. 1-specificity for different cut-off values of a biomarker 4
Clinically useful biomarker the inherent error in the technical measurement and the coefficient of variation should be sufficiently low over the entire spectrum of values for the biomarker small changes in the biomarker should reflect true changes in the clinical condition of the patient 5 http://grants.nih.gov/grants/guide/rfa-files/rfa-dk-08-015.html#parti
Proposed mechanisms for increased biomarker levels in plasma and urine (1) Increased synthesis in extrarenal tissues (2) Release from circulating immune cells (3) Glomerular filtration Increased biomarker levels in plasma (4) Impaired reabsorption in the proximal tubule (5) Increased synthesis in tubular cells Increased biomarker levels in urine (6) Release from infiltrating immune cells 6 Martensson J et al BJA, 2012
7 Fassett RG et al. Kidney Int, 2011
Biomarkers in kidney disease 1. biomarkers for early diagnosis of AKI 2. biomarkers to distinguish patients with diff. diagnosis of AKI 3. biomarkers for stratification and prognosis of AKI 4. biomarkers for GFR measurement 5. biomarkers of inflammation and fibrosis 6. biomarkers for evaluation of CKD progression 7. biomarkers for cardiovascular disease prediction in CKD 8
CKD and CVD Searching for biomarkers to define the outcome CKD BIOMARKERS CVD 9
CKD and CVD CKD a strong and independent risk factor for CVD in rich countries the prevalence of CVD to 2/3 of CKD patients prevalence of CVD directly correlated with the stages of CKD correlation CKD RISK FACTORS CVD LVH 10
Evaluation of CKD patients with important LVH 2D echocardiography + Doppler imaging coronary angiography stress echocardiography, CT angiography, cardiac MRI (gadolinium) assessment of fluid status US inferior vena cava diameter bioelectrical impedance analysis (BIA) US detection of pulmonary congestion serum biomarkers brain natriuretic peptid (BNP) 11
NT-proBNP in CKD patients 56% of the 207 asymptomatic patients who had CKD had elevated NT-pro-BNP levels 12 defilippi et al. Am J Kidney Dis, 2005
Kaplan-Meier estimates of patients stratified by quartiles of N-terminal pro-brain natriuretic peptide (NT-pro-BNP) in relation to the composite end point of all fatal and nonfatal cardiovascular events including cardiovascular congestion. 13 Angela Yee-Moon Wang et al. JASN 2007;18:321-330 2007 by American Society of Nephrology
Multimarker strategy Relative risks for mortality and/or HF readmission according to the number of the elevated study biomarkers pts. with acute heart failure Biomarkers: Cystatin C NT- ProB NP Cardiac Troponin T Figure 2. Adverse clinical events according to the number of elevated biomarkers. *Adjusted for age, New York Heart Association functional class, diabetes mellitus, hyperlipidemia, previous ST-segment elevation myocardial infarction, anemia, and in-hospital inotrope use. 14 Manzano-Fernández S et al. Am J Cardiol, Sebastjan 2009 Bevc
ROC curves for NT-pro-BNP, cardiac troponin T and C-reactive protein in predicting (A) severe left ventricular (LV) hypertrophy and (B) systolic dysfunction in the overall population. Angela Yee-Moon Wang et al. Nephrol. Dial. Transplant. 2009;24:1962-1969 The Author [2009]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org 15
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Unlike creatinine, basal serum cystatin C was a predictor of overall mortality and predictor of the development of fatal cardiovascular events. 17
18 Bevc et al. 2017 unpublished
19 Bevc et al. 2017 unpublished
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Receiver operating characteristic (ROC) curves. Base model includes sex, age, systolic blood pressure, LDLcholesterol, smoking, HbA 1c, creatinine and urinary albumin excretion rate. Dashed line reference; Dotted line Base model; Full line Base model + symmetric dimethylarginine/asymmetric dimethylarginine 24
Endotelial dysfunction and ADMA ADMA is endogenous inhibitor of NO production (NO is generated by endothelium, is key substance for regulation of vasodilation) In patients with non-diabetic kidney disease, circulating ADMA levels have been demonstrated to be positively correlated with the degree of proteinuria and a prognostic marker of progression of renal dysfunction. In diabetes patients 25 Hanai et al. Nephrol Dial Transplant, 2009
ST2 (growth STimulation expressed gene 2) is a member of the interleukin-1 receptor family is expressed as a transmembrane (ST2L) and soluble isoform (sst2) is expressed by fibroblasts in the heart and elevated in response to heart failure disease or injury 26
low sst2 group high sst2 group 117 pts on HDF Log Rank test: P<0.0001 27 Ekart et al. SLO NEF congress 2016
28 Fassett RG et al. Kidney Int, 2011
CKD and CVD Searching for biomarkers to define the outcome CKD BIOMARKERS CVD 29
The Association between Biomarker Profiles, Etiology of Chronic Kidney Disease, and Mortality Langsford et al. Am J Nephrol 2017;45:226-234 (DOI:10.1159/000454991) Table 1. Baseline demographic, renal-specific data and biomarker profiles by CKD etiological groups 30
The Association between Biomarker Profiles, Etiology of Chronic Kidney Disease, and Mortality Langsford et al. Am J Nephrol 2017;45:226-234 (DOI:10.1159/000454991) Table 2. Multivariate HRs (95% CIs) of traditional risk factors for mortality by CKD etiological groups 31
The Association between Biomarker Profiles, Etiology of Chronic Kidney Disease, and Mortality Langsford et al. Am J Nephrol 2017;45:226-234 (DOI:10.1159/000454991) Fig. 1. Kaplan-Meier analysis of survival stratified by CKD etiology. GN, glomerulonephritis; PCK/TIN, polycystic kidney disease, pyelonephritis, or chronic tubulointerstitial nephritis; DKD, diabetic kidney disease. 32
The Association between Biomarker Profiles, Etiology of Chronic Kidney Disease, and Mortality Langsford et al. Am J Nephrol 2017;45:226-234 (DOI:10.1159/000454991) Fig. 2. Biomarker profiles associated with an increased risk of mortality stratified by CKD etiology. Adjusted for statistically significant recognized risk factors: DKD: age, history of CVD, egfr at cohort entry, diastolic blood pressure, and albumin; GN: age; PCK/TIN: age and diastolic blood pressure. Shaded plots, p < 0.05. 33
CKD and CVD Searching for biomarkers to define the outcome Define the ETHIOLOGY and search for biomarkers to define the outcome CKD BIOMARKERS CVD 34
- omics informally refers to a field of study in biology such as genomics, proteinomics, metabolomics) 35 Atzler D et al. Nephrol Dial Transplant, 2014
Genomics is defined as the study of genes and their functions, and related techniques / the study of the genome and its action genome-wide association studies (GWAS) identify genes involved in human disease - this method searches the genome for small variations, single nucleotide polymorphisms (SNPs), that occur more frequently in people with a particular disease than in people without the disease each study can look at hundreds or thousands of SNPs at the same time 36 http://www.who.int/genomics/ http://ghr.nlm.gov/
UMOD - gene encoding uromodulin ELMO1- engulfment and cell motility 1 gene 37 Atzler D et al. Nephrol Dial Transplant, 2014
GoKinD = Genetics of Kidneys in Diabetes study collection 38 Pezzolesi et al. Diabetes, 2009
Proteomics proteomics studies the structure and function of proteins, the principal constituents of the protoplasm of all cells proteome is derived from PROTEins expressed by a genome, and it refers to all the proteins produced by an organism, much like the genome is the entire set of genes proteome often is defined as the proteins present in one sample (tissue, organism, cell culture) at a certain point in time 39 http://www.ama-assn.org
Proteomics the most commonly used technologies includes twodimensional gel electrophoresis (2-DE) and capillary electrophoresis coupled with mass spectrometry (CE-MS) proteomic approaches have identified potential biomarkers, including specific collagen fragments, β2 microglobulin, proinflammatory cytokines and retinol binding protein urinary peptide classifier, consisting of 273 defined urinary peptides, was recently discovered as a good classifier in patients with CKD 40
41 Papale et al. Diabetes Care, 2010
Zurbig et al. Diabetes, 2012 42 Argiles et al. PLoS OE, 2013
Metabolomics is analytical approaches that aim to identify (and quantify) small molecules (metabolites < 1.5 kda) in a single experiment there is no single-instrument platform that can cover all metabolites; therefore, two main technologies, i.e. nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), are applied 43 Atzler D et al. Nephrol Dial Transplant, 2014
2011 2013 44 FREE AVAILABLE at: http://www.serummetabolome.ca; http://www.urinemetabolome.ca
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microrna are endogenously produced short non-coding RNAs about 20 22 nucleotides in length that have been shown to play a key role in mammalian posttranscriptional gene expression by repressing translation or inducing target degradation, ultimately resulting in gene silencing mirnas are capable of regulating several key biological pathways and cellular functions there are over 1000 mirnas within the human genome and it is estimated that about 60% of the human proteincoding genes can be regulated by mirnas 47 Kato M et al. Free Radic Biol Med, 2013
microrna a number of mirnas have emerged as key players in diabetic nephropathy 48 Khella HWZ et al. Am J Kidney Dis, 2013
Conclusions Biomarkers TODAY searching for biomarkers to define the outcome define the ETHIOLOGY and search for biomarkers to define the outcome searching for associated GENES, PROTEINS, METABOLITES of new biomarkers identify the disease predict the patients' outcome 49
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