“Progression” of a risk factor on cardiovascular outcome: A valuable point, a questionable interpretation

Cardiovascular disease (CVD) risk factors (RFs), such as systolic blood pressure and serum lipids, play pivotal roles in clinical practice and research. Since RFs causally influence the natural course of CVDs, they have been the targets of clinical treatment via lifestyle modification and/or pharmacological agent(s). Since RFs have been shown to predict future risk of CVDs, they have been incorporated in risk prediction models such as the SCORE and the Framingham CVD score, and the use of such prediction models is recommended by the major clinical guidelines. The predicting property of some RFs, however, seem to be lessened in recent years. In line with this contemporary concern, a group of researchers from the PROG-IMT consortium has tested an interesting hypothesis: average measurements of risk factors assessed at a separate time point ((RF1þRF2)/2), but not ‘‘progression’’ between the measures (defined as the difference (RF2–RF1)), predicts future CVD risk. The authors selected four RF measures to examine the hypothesis: systolic blood pressure, concentrations of total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. By inserting the average and the difference of a RF in one model, the authors showed that the former had a positive and expected association with CVD risk whereas the latter had a null to minimum association. The consortium used a large-sized sample (n1⁄4 34,072), pooling data from multiple cohort studies with the average time between the two RF measurements ranging from 2.2–6.6 years, and the durations of follow-up after the second RF assessment varying from 3.9–14.2 years. The authors concluded that the results ‘‘suggest that measuring risk factor progression based on two measurements only a few years apart has a very low signal to noise ratio which does not allow for appropriate individual risk prediction. ’’ The authors raised a valuable and relevant point for the prediction of CVD risk in modern era. Clearly, the results should not be interpreted as evidence of lack of causal effect of those RFs on CVDs. Rather, one should seek potential reasons why the ‘‘progression’’ did not predict a risk in recent years. First, as indicated by the authors and others, recent treatment for a risk factor such as statin has become so potent and widespread in many populations that measured RF tends to reflect more ‘‘treated’’ values, which is likely to weaken its predictive property as compared with ‘‘non-treated’’ values in the past. Secondly, the second measurement (RF2) may not be so different from the first measurement (RF1) owing to a relatively stable lifestyle and/or biological condition over the two time-points. This would make the regression to the mean phenomena come into play for the difference (RF2–RF1), leading to a low signal-to-noise ratio. In that case, it is no surprise that the average ((RF1þRF2)/2) value has a stronger relation with risk than the ‘‘progression’’ (RF2–RF1) because the latter has little information beyond RF1 (i.e. RF2 takes a similar value to RF1). For effective risk prediction, therefore, ‘‘measurements taken over a longer time span . may be necessary’’ per the authors which I totally agree with. From the methodological viewpoint, the authors additionally argued that in assessing the association of ‘‘progression’’ with disease risk, one must adjust for the average but not for baseline value alone (i.e. RF1) because, per the authors, the latter adjustment model leads to a biased estimate, thus, to be avoided. I do not entirely agree with this argument because, in my opinion, both models can be misleading for the reasons I detail below. Two models discussed in the paper can be formulated from a simpler model, as follows: