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Obesity as one of the primary risk factors for cardiovascular disease (CVD), a risk factor that originates early in life. MD‐Paedigree will develop computational models with high predictive power to better understand the mechanism of CVD development.

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Clinical Concept:

The World Health Report 2002 revealed that, in developed countries, approximately one third of all coronary heart diseases and ischaemic strokes and almost 60% of hypertensive diseases can be directly attributed to obesity. These figures confirm obesity as one of the primary risk factors for cardiovascular disease (CVD), a risk factor that originates early in life. As autopsy studies have shown, the levels of lipids, blood pressure, and obesity in the young are directly associated with the extent of early atherosclerosis of the aorta and coronary arteries. For this reason, it is of particular concern that there has been a significant increase in childhood and adolescent obesity over the last decade. In the United States, 32% of children and adolescents are now at or above the eightyfifth percentile of the 2000 BMI‐for‐age growth charts, but also in the United Kingdom, the prevalence of obesity in children is approaching one third.

One of the challenges concerning the study of childhood obesity and its influence on CVD risk is the required time span for longitudinal studies: cardiovascular events occur mostly later in adulthood, which means that longitudinal studies have to comprise several decades. Nonetheless, cross‐sectional studies are able to show correlation between childhood obesity and established surrogate markers for CVD, such as atherosclerosis and cardiac hypertrophy. The Strong Heart Study, which analysed data from over 450 adolescents, demonstrated that in patients with obesity and/or metabolic syndrome a significantly higher prevalence of left ventricular hypertrophy and left atrial dilation paired with impairment in both systolic and diastolic function is observed.

MD-Paedigree Goals:

MD‐Paedigree will integrate the variety of known biomarkers for CVD risk assessment into one common framework, enhance body fat distribution biomarker measurement, and analyse

interdependencies between the biomarkers. In addition, MD‐Paedigree will develop computational models with high predictive power to better understand the mechanism of CVD development. These models will also allow the simulation of interventions to make personalised predictions for the optimal therapy.

 

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