Extent of regression dilution for established and novel coronary risk factors: results from the British Regional Heart Study

Abstract

Background: Imprecision in measurement of risk factors leads to underestimation of associations with disease outcomes (through regression dilution bias). We examine the extent of this bias for established and novel risk factors for coronary heart disease (CHD) and consider the consequences for CHD prevention.

Design: Prospective cardiovascular study of middle-aged British men followed up over 20 years.

Methods: Repeated measurements of blood lipids, blood pressure and insulin were available at intervals of 1 week, 4, 16 and 20 years; repeated measurements of homocysteine and haemostatic factors were available over 1 week and 4 years.

Results: The use of single baseline measures of both established and novel risk factors in analysis results in marked underestimation of risk associations, increasing over time. The use of a single baseline measurement of total cholesterol results in a 47% (95% confidence interval 44 to 50%) underestimation of its association with CHD risk during the third decade of follow-up; for diastolic blood pressure the corresponding underestimation is 76% (95% confidence interval 73 to 78%). Ignoring the consequences of regression dilution can also lead to error in the assessment of other risk markers, even those measured precisely.

Conclusions: The importance of risk factors for CHD can be greatly underestimated by using a single baseline measure in prospective study analyses. Studies that wish to estimate associations between disease risk and usual exposure levels need to take regression dilution effects into account. Failure to do so can lead to serious misinterpretation of the importance of CHD risk factors.