Biomarkers in risk prediction of heart and kidney disease
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1 Biomarkers in risk prediction of heart and kidney disease Winfried März Medizinische Universität Graz, Medizinische Klinik V Medizinische Fakultät Mannheim, Universität Heidelberg, Synlab Akademie, Mannheim Privatärzte Mannheim Munich, G-PCS-0268/GLB.ALI Conflicts of Interest Research Grants and Honoraria: Abbott Diagnostics, Aegerion Pharmaceuticals, Astrazeneca, AMGEN, BASF, Danone Research, Sanofi, Siemens Diagnostics, Numares Employment: Synlab Holding Deutschland GmbH 2 G-PCS-0268/GLB.ALI
2 death per inhabitants 01/12/ G-PCS-0268/GLB.ALI The good news Age-adjusted mortality rates Germany 1998 to 2013 women men CAD CAD MammaCa COPD HI CVD HI CVD COPD lungca Gesundheitsberichterstattung des Bundes RKI destatis G-PCS-0268/GLB.ALI
3 and the yet bad news Causes of death Germany 2013 Rank Women % Men % 1 CAD (I20-I25) 13,3 CAD (I20-I25) 15,6 2 CVD (I60-I69) 7,6 Lung Cancer (C33-C34) 6,9 3 Heart failure (I50) 6,5 CVD (I69-I69) 5,4 4 Dementia (F01, F03, G30) 5,2 COPD (J40-J47) 4,2 5 Hypertension (I11,I13) 4,6 Heart failure (I50) 3,7 6 Breast Cancer (C50) 3,8 Colon Cancer (C18-C21) 3,2 7 Lung Cancer (C33-C34) 3,3 Prostate Cancer (C61) 3,1 8 COPD (J40-J47) 3,2 Accidents (V01-X59) 2,6 9 Diabetes mellitus (E0-E14) 3,0 Dementia (F01, F03, G30) 2,5 10 Colon Cancer (C18-C21) 2,6 Diabetes mellitus (E0-E14) 2,4 Total CVD 32,0 Total CVD 24,7 Gesundheitsberichterstattung des Bundes, RKI und destatis, G-PCS-0268/GLB.ALI Agenda Cardiovascular risk assessment Traditional risk factors and their limitations Emerging Biomarkers to improve risk assessment Renal markers impacting on cardiovascular risk Chronic kidney disease and risk of heart disease Lipoprotein metabolism 6 G-PCS-0268/GLB.ALI
4 Why risk prediction? High risk high (cost-) effectiveness 7 G-PCS-0268/GLB.ALI Kannel et al. Annals Intern Med 1961;55: G-PCS-0268/GLB.ALI
5 Inconsistency of Risk Scores DETECT Study 2003 PROCAM FRS-CVD ARRIBA PCE Reynolds FRS-CHD2 9 G-PCS-0268/GLB.ALI Limitations of current CVD risk Scientific Already used to guide therapy but Poor discrimination Inconsistency of prediction models Different endpoints (MACE vs. CVD mortality) Poor representation of genetic factors (e.g. family history) Intermediate risk groups ill defined Diabetes mellitus mostly treated as coronary risk equivalent Only one risk stratification algorithm available for patients with stable coronary disease (Marshner) Novel biomarkers not included 10 G-PCS-0268/GLB.ALI
6 Limitations of CVD risk prediction Practical Lack of knowledge Confusion due to different recommendations and guidelines Confusion due to different endpoints Require linking clinical and laboratory findings No straight forward tools available in primary care In Germany Ten out of 100 physicians know about risk algorithms One out of 100 uses them in daily practice 11 G-PCS-0268/GLB.ALI traditional Oxidative Stress NO Endothelial Dysfunction Calcification Factors Sympathetic activity BNP 12 G-PCS-0268/GLB.ALI
7 Development requirements Utilize the potential of novel biomarkers for improved risk prediction Non-redundancy of selected predictor variables Biomedical plausibility (representation of organ systems and pathophysiology) Actionable markers Ease and convenience of implementation on automated analyzers Transportability and ease of re-calibration Complete independence from anamnestic and clinical information 13 G-PCS-0268/GLB.ALI The evidence base LUdwigshafen RIsk and Cardiovascular Health (LURIC) Study One of the first German biobanks to investigate heart disease Heart Center Ludwigshafen, Universities of Freiburg, Ulm, and Graz Inclusion criteria Coronary angiogramm Clinically stable with the exception of acute coronary disease Recruitment 1997 to 2001 (n = 3500) Comprehenisve clinical, biochemical and genetic characterization Follow-up 9.9 years 14 G-PCS-0268/GLB.ALI
8 Coropredict 1.0 Variable Age Sex LDL-C HDL-C HbA1c Cotinin Cystatin C Vitamin D NT-pro-BNP TnT Galektin 3 CRP (ST2) Significance Not part of the risk function Not part of the risk function Diabetes mellitus Smoking Kidney function Lifestyle Cardiac function, volume regulation Myocardial necrosis Fibrosis Chronic Inflammation 15 G-PCS-0268/GLB.ALI Example for Discrimination patients with stable CAD CVD mortality Quartile AUC=0.801 Integrated Coropredict Scores ranked and stratified into quartiles, mean of 10 years of follow up 16 G-PCS-0268/GLB.ALI
9 Motrality (%, 7 years) 01/12/2016 Implementation 17 G-PCS-0268/GLB.ALI Replicated in an independent cohort VIVIT cohort - stable CAD CVD mortality risk predicted risk observed Deciles of predicted risk 18 G-PCS-0268/GLB.ALI
10 Extrapolation to subjects without history of CVD? 19 G-PCS-0268/GLB.ALI Recalibrated for primary prevention DETECT Study G-PCS-0268/GLB.ALI
![Count [%] Count [%] 01/12/2016 Prevalence of CKD in primary care Germany n = 4339 <29 [ml/min/1,73m²] 30-44 29 [ml/min/1,73m²] 45-59 29 [ml/min/1,73m²] >60 [ml/min/1,73m²] overall prevalence 29.](/docs-images/88/116013168/images/11-0.jpg "7% CKD-EPI 2004 <29 [ml/min/1,73m²] CKD-EPI 2007 30-44 29 [ml/min/1,73m²] 45-59 29 [ml/min/1,73m²] >60 [ml/min/1,73m²] Count [%] Count [%] <29 [ml/min/1,73m²] 30-44 29 [ml/min/1,73m²] 45-59 29")
![[ml/min/1,73m²] >60 [ml/min/1,73m²] overall prevalence 36.](/docs-images/88/116013168/images/11-1.jpg "1 % MDRD 2004 <29 [ml/min/1,73m²] 30-44 29 [ml/min/1,73m²] 45-59 29 [ml/min/1,73m²] >60 [ml/min/1,73m²] MDRD 2007 Age [years] Age [years] Gergei I et al. submitted 21 G-PCS-0268/GLB.ALI.13.07.22 Cystatin C is more sensitiv in creatinine-blind\" sector Fliser D, Ritz E.")
11 Count [%] Count [%] 01/12/2016 Prevalence of CKD in primary care Germany n = 4339 <29 [ml/min/1,73m²] [ml/min/1,73m²] [ml/min/1,73m²] >60 [ml/min/1,73m²] overall prevalence 29.7% CKD-EPI 2004 <29 [ml/min/1,73m²] CKD-EPI [ml/min/1,73m²] [ml/min/1,73m²] >60 [ml/min/1,73m²] Count [%] Count [%] <29 [ml/min/1,73m²] [ml/min/1,73m²] [ml/min/1,73m²] >60 [ml/min/1,73m²] overall prevalence 36.1 % MDRD 2004 <29 [ml/min/1,73m²] [ml/min/1,73m²] [ml/min/1,73m²] >60 [ml/min/1,73m²] MDRD 2007 Age [years] Age [years] Gergei I et al. submitted 21 G-PCS-0268/GLB.ALI Cystatin C is more sensitiv in creatinine-blind" sector Fliser D, Ritz E. Am J Kidney Dis 2001;37:79 22 G-PCS-0268/GLB.ALI
12 Cystatin C is a better indicator for long-term prognosis than creatinine Woitas R et al Atherosclerosis G-PCS-0268/GLB.ALI The main threat in patients with chronic kidney disease is cardiovascular death rather than progression to dialysis 24 G-PCS-0268/GLB.ALI
13 Major Causes of Atheroslerosis Age Sex Smoking Hypertension Diabetes mellitus LDL Cholesterol Low HDL- Cholesterol Will they act differently in CKD? 25 G-PCS-0268/GLB.ALI Major Causes of Atheroslerosis Age Sex Smoking Hypertension Diabetes mellitus LDL Cholesterol Low HDL- Cholesterol 26 G-PCS-0268/GLB.ALI
14 Hazard Ratio 01/12/2016 HDL-Cholesterol and CHD-risk Age- and sex-adjusted Adjusted to further risk factors n = HDL-Cholesterin (mg/dl) The Emerging Risk Factors Collaboration. JAMA 2009;302: G-PCS-0268/GLB.ALI Biological functions of HDL Eur Heart J 2013;34: G-PCS-0268/GLB.ALI
15 HDL dysfunction Coronary heart disease Chronic kidney disease Diabetes mellitus Anti-phospholipid syndrome Rheumatoid arthritis Morbus Crohn Circulation Feb 26;127(8): ; JAMA Sep 16;302(11):1210-7; Immunity Apr 18;38(4): G-PCS-0268/GLB.ALI HDL, CKD and CVD mortality Ludwigshafen Risk and Cardiovascular Health Study Zewinger S, Speer T et al. J Am Soc Nephrol G-PCS-0268/GLB.ALI
16 SAA displaces Apo AI from HDL Apo A1 SAA Apo A2/C 0 h 1 h Apo A1 SAA Apo A2/C 0.2 h 3 h Coetze GA, et al. J Biol. Chem. 1986; 21: G-PCS-0268/GLB.ALI SAA inactivates HDL Eur Heart J Nov 14;36(43): doi: /eurheartj/ehv352. Epub 2015 Aug 6 32 G-PCS-0268/GLB.ALI
17 Calculation of effective HDL-C from SAA measured HDL-C (mg/dl) SAA 2 6 mg/l Biologically active HDL-C (mg/dl) SAA 4 0 mg/l SAA 6 7 mg/l SAA 16 9 mg/l SAA 50 0 mg/l Eur Heart J Nov 14;36(43): doi: /eurheartj/ehv352. Epub 2015 Aug 6 33 G-PCS-0268/GLB.ALI Many CVD biomarkers (e.g. troponins, natriuretic peptides) are altered in CKD. Would this mean that the need the be interpreted differently in CKD? 34 G-PCS-0268/GLB.ALI
18 NT-proBNP and renal function Ludwigshafen Risk and Cardiovascular Health Study 35 G-PCS-0268/GLB.ALI NT-proBNP and cardiovascular mortality Ludwigshafen Risk and Cardiovascular Health Study Die Deutsche Diabetes Dialyse Studie NT-proBNP (ng/ml) 36 G-PCS-0268/GLB.ALI
19 Conclusions Biomarkers need to be evaluated according to clinical outcomes. There is no single biomarker reflecting all CVD risk conditions. The novel biomarker combination Coropredict allows to identify patients at high risk of CVD death independent of clinical information. Further research has indicated that other, even newer biomarkers hardly would beat the Coropredict combination. Chronic kidney disease strongly impacts on cardiovascular risk. Cardiac biomarkers in part capture the risk conferred by chronic kidney disease, indicating a cardio-renal continuum. 37 G-PCS-0268/GLB.ALI
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