Coronary heart disease (CHD) is the leading cause of death in the Western W
orld. For effective treatment and prevention strategies to be put in plate,
the major risk factors associated with this disease must be identified. Da
ta show that almost 300 variables are statistically associated with CHD, Ho
wever, evidence suggests that the vast majority of coronary events can be e
xplained on the basis of blood pressure, lipids, smoking, and diabetes. Lab
oratory, experimental, and epidemiologic data identify dyslipidemia as a pi
votal 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 cholester
ol, such as China and Japan, the incidence of CHD remains low even when smo
king and hypertension are highly prevalent.
Observational data have clearly established that CHD risk factors tend to c
luster in individuals, The impact of coexisting risk factors is greater tha
n 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 f
indings,in the past most treatment algorithms have viewed risk factors sepa
rately and have recommended discrete treatment targets, More recent guideli
nes 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 factor
s 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 CH
D risk evaluation and preventive management appears logical based on eviden
ce from animal-experimental, observational, and clinical trial evidence. Am
J Hypertens 1999; 22:92S-95S (C) 1999 American Journal of Hypertension, Lt
d.