Coronary heart disease (CHD) is the leading cause of death in the Western World. For effective treatment and prevention strategies to be put in place, the major risk factors associated with this disease must be identified. Data show that almost 300 variables are statistically associated with CHD. However, evidence suggests that the vast majority of coronary events can be explained on the basis of blood pressure, lipids, smoking, and diabetes. Laboratory, experimental, and epidemiologic data identify dyslipidemia as a pivotal CHD risk factor, in the absence of which other risk factors cease to produce any important increase in absolute risk of events. For example, in populations with relatively low levels of low-density lipoprotein cholesterol, such as China and Japan, the incidence of CHD remains low even when smoking and hypertension are highly prevalent. Observational data have clearly established that CHD risk factors tend to cluster in individuals. The impact of coexisting risk factors is greater than additive, and indeed is usually multiplicative. The implications of such an interactive effect are that relatively normal levels of two or more risk factors in coexistence may have a profound impact on risk. Despite these findings, in the past most treatment algorithms have viewed risk factors separately and have recommended discrete treatment targets. More recent guidelines have taken a broader view and provide simple, yet accurate, methods of evaluating absolute risk based on the consideration of several risk factors. Coronary heart disease is clearly a multifactorial disease with risk factors that tend to cluster and interact in an individual to determine the level of coronary risk. The current trend towards a more holistic approach in CHD risk evaluation and preventive management appears logical based on evidence from animal-experimental, observational, and clinical trial evidence.