Role and Significance of In-Vitro-Diagnostics in the Healthcare Systems of the Future Prevention of Cardiovasular Diseases Michael Walter Charité University Medicine, Berlin & Unfallkrankenhaus Berlin
Prevention of Cardiovasular Diseases: Significance 2004 Diabetes mellitus 2004 1988-2000 200 300 Myocardial Infarction 2010 (Prognose) 2003 280 340 Colon Cancer 2000 1996 67 52 Breast Cancer Prostate Cancer Cervical Cancer 2003 1990 45 49 2000 41 1990 26 6,6 7,3 2000 1990 Incidence / year (x1000), Germany 0 50 100 150 200 250 300 350 400 Percentage Obesity 2040 (Prognose) 50% 2004 20% 0% 10% 20% 30% 40% 50% 60% Felix Burda Foundation
Prevention of Cardiovasular Diseases: General Principles Primary prevention: asymptomatic individuals Population-based strategy: educating a healthy life style in the general population High-risk patient strategy: identification and treatment of high risk patients Secondary prevention: symptomatic patients with established disease Treatment and counseling to prevent a progression of disease
Prevention of Cardiovasular Diseases: High-Risk Patient Strategy Step 1: a prospective study Measurement Time Event Y/N Framingham function Sheffield Tables Canadian Tables PROCAM Risk Score UKPDS Risk Engine SCORE Risk Charts Step 2: a mathematical model (Cox-/Weibull-/Neural Network-Algorithm..)
PROCAM-Study: Ranking of Risk Factors R 1. Age 0.2418 2. LDL cholesterol 0.1935 3. Smoking 0.1552 4. HDL cholesterol -0.1003 5. Systolic blood pressure 0.0975 6. Diabetes/Glucose 120mg/dl 0.0781 7. Family history of MI 0.0477 8. Triglycerides 0.0426 PROCAM = Prospective CArdiovascular Münster Study Assmann G, et al. Circulation, 105: 310-315; 2002
High-risk Patient Strategy: PROCAM-Study Age 35-39 0 40-44 6 45-49 11 50-54 16 55-59 21 60-65 26 Triglycerides (mg/dl) <100 0 100-149 2 150-199 3 >199 4 LDL-Cholesterol (mg/dl) <100 0 100-129 5 130-159 10 160-189 14 >189 20 HDL-Cholesterol (mg/dl) <35 11 35-44 8 45-54 5 >54 0 Sy Blood pressure (mm Hg) <120 0 120-129 2 130-139 3 140-159 5 >=160 8 Diabetes mellitus MI Family History Smoking No 0 0 0 Yes 6 4 8
High-Risk Patient Strategy: PROCAM-Study Myocardial Infarctions (per 1000) in 10 years PROCAM Score 500 400 300 200 100 0 455 289 143 63 29 5 7 0-21 22-29 30-40 41-47 48-56 57-65 >65 MI risk in 10 years (%) <1 1-2 2-5 5-10 10-20 20-40 40 Prevalence (%) 12.7 18.1 29.8 16.8 15.0 6.1 1.6 Assmann G, et al. Circulation, 105: 310-315; 2002
High-risk Patient Strategy: Challenges Only 50% of the risk of atherosclerosis can be explained by the established risk factors. Current knowledge is incompletely implemented.
PROCAM: Continuum of Patients at Risk for MI < 10% Risk of myocardial infarction 10-20% > 20% 22.3% 27.7% 50.0% MI risk in 10 years 33.9% 12.8% 3.2% proportion of all MI 58.2% 28.5% 13.4% No. of MI s/ year in Germany 18,000 8,700 4,100 Prevalence Age 51-65 years 241 fatal and nonfatal myocardial infarctions in 2,207 men
PROCAM: Continuum of Patients at Risk for MI > 20% 1.5% MI risk in 10 years 37.5% proportion of all MI 17.6% No. of MI s/year in Germany 1,300 10-20% 5.3% 17.3% 26.1% 1,800 < 10% Risk of myocardial infarction 93.2% 2.1% 56.4% 4,000 Prevalence Age 35-50 years 165 fatal and nonfatal myocardial infarctions in 4,945 men
The Competition : High-Risk versus Population Approach Aggressive pharmacological treatment in individuals with a 10-year event risk of >20% would reduce major cardiovascular disease (CVD) by 34%. A 10% population-wide reduction in long-term mean blood cholesterol and blood pressure could reduce major cardiovascular disease by 45%. Emberson et al. Eur Heart J. 2004;25:484-491.
Options To Improve Risk Prediction I A better understanding of the process of atherosclerosis. A better understanding of the interaction of risk factors. Identification of new risk factors/indicators and methods. Decades Decades. Minutes Circulation. 2001;104:365.
A Low HDL Cholesterol as Coronary Risk Factor Risk per 1,000 in 10 years 200 150 high global risk 100 50 low global risk 0 25 30 35 40 45 50 55 60 65 mg/dl mmol/l 0,65 0,78 0,91 1,03 1,16 1,29 1,42 1,55 1,68 PROCAM-Study HDL cholesterol
Defective Reverse Cholesterol Transport in Familial HDL Deficiency (Tangier Disease) Reverser Cholesterintransport HDL CE X ABCA1 HO X Galle SR-B1 ABCG5/8 CE ER GOLGI HDL ABCG1 Hepatozyt Periphere Zelle Rust et al. Nature Genetics 1998;20:96, Nature Genetics 1999;22:352 Walter et al. Arterioscler Thromb Vasc Biol. 1995;15:1975
Synergistic Effect of LDL- and HDL- Cholesterol Incidence of coronary events per 1000 in 10 years HDL-C (mg/dl) LDL-C (mg/dl) PROCAM-Study
GRIPS: Synergistic Effect of LDL-Cholesterol MI-10-year risk [%] (40-60 year-old persons) Myocardial Infarctions (per 1000) in 10 years Familial MI history plus smoking Familial MI history Smoking Glucose intolerance Hypertension No other risk factor Secondary Prevention LDL-Cholesterol (mg/dl) GRIPS, Göttingen Risk, Incidence and Prevalence Study Cremer et al. Atherosclerosis. 1997 Mar 21;129:221-30.
Algorithms are Superior to Individual Risk Factors Sensitivity (%) 100 80 60 40 20 0 0 20 40 60 100 - Specificity (%) R Voss et al.int J Epidemiol. 2002;31:1253-62 PROCAM Neural Networks (90.2%*) PROCAM Algorithm Cox (82.7%*) PROCAM Algorithm Weibull (82.4%*) Age (74.3%*) Body Mass Index (57.5%*) Men (PROCAM-Study)
Options To Improve Risk Prediction II Lifestyle Pharmaka MSCT MRT PET Algorithms Neural Network Lp(a) CRP Homocysteine Phytosterols Metabolic syndrome Genes of clinical relevance
Risk Prediction: Genotype + Phenotype 500 000 SNP s Neural Network AA BB AB HYPERT.TAB FAM AP DIAB.MELLI SMOKER URIC.AC DIAST.BP B.GLUCOSE BMI SYST.BP LDL HDL LNTG TG AGE 0 ( 0-1 ) 1 ( 0-1 ) 0 ( 0-1 ) 0 ( 0-3 ) 1 ( 0-1 ) 70 ( 18-156 ) 82 ( 50-145 ) 98 ( 57-391 ) 254 ( 152-448 ) 108 ( 85-225 ) 178 ( 25-408 ) 44 ( 14-142 ) 4.96 ( 2.64-6.04 ) 142 ( 14-399 ) 46 ( 30-69 ) input layer b bias b hidden layer psp: 0.68 act: 0.66 psp: -6.64 act: 0 psp: 2.89 act: 0.95 psp: -5.18 act: 0.01 true: 0 pred.prob.: 0.0971 pred.class: 0 (th: 0.2 ) psp: -2 2 act: 0.1 output layer nnet.obj nodes: 15,4,1 weights: 84 decay: 0.15 skip: T entropy: T softmax: F censored: F R Voss et al., unpublished
Phytosterols as Cardiovascular Risk Factors* before treatment *Kannenberg F et al. Nutr Metab Cardiovasc Dis. 2006;16:13-21.. after treatment
Telomere Length as a Biomarker of Aging Telomere repeat, (TTAGGG)n Cell Divisions Cell divisions plus Stress Telomere Length Age (healthy blood donors) Walter M et al., unpublished, www.agingandlipids.de
High Cardiovascular Risk in a Subgroup of Patients with Abdominal Obesity.
High-risk patient strategy: Challenges Only 50% of the risk of atherosclerosis can be explained by the established risk factors. Current knowledge is incompletely implemented.
LDL-Cholesterol: The Lower the Better 30 25 Statin Placebo 4S MI Risk (%) 20 15 10 5 4S LIPID LIPID CARE CARE HPS HPS TNT (10 mg of atorvastatin) TNT (80 mg of atorvastatin) 0 70 90 110 130 150 170 190 210 LDL Cholesterol (mg/dl)
Treatment Targets (LDL < 100 mg/dl) are Reached in a Minority of High Risk Patients! Reality Only 43,8% of the hospital patients and 32,2% of medical practice patients reach the LDL-cholesterol target <100 mg/dl. However: 95,8% of the hospital physicians and 94,4% of the physicians in private practice feel that their patients are treated according to current guidelines. Feeling Gitt AK, Eur J Cardiovasc Prev.Rehab. 2009;16:438-444 (2-L-Cardio Registry)
Deadly Myocardial Infarctions in Germany (2001) Deaths Male : Women 75000 ~1 : 2 60000 45000 ~2 : 1 30000 15000 0 Statistisches Jahrbuch 2003 46-75 >75 Alter (Jahre)
Summary Cardiovascular risk prevention includes population-wide and high risk strategies and is only partially implemented. The current risk scores do not provide reliable cardiovascular risk estimates in young patients, in patients with intermediate risk and in certain subgroups of patients (metabolic syndrome). Only the combination of novel in vitro diagnostics (the identification of new risk indicators), the use of intelligent mathematical models, a better understanding of the interrelationships among risk factors and other methods (imaging etc.) can significantly improve risk prediction.