The present analysis of the STRATEGY study demonstrates a correlation of CIMT with values of common risk factors within the range of normal and with deduced risk prediction scores even in individuals at low cardiovascular risk lacking overt risk factors. Yet, the correlations were modest. Thus, both, clinical risk factors and CIMT may separately reflect a contribution to atherogenesis. Since prediction of cardio- and cerebrovascular events has been shown likewise for risk factors or risk scores and for the CIMT, based on the results of this study of only partial correlation of risk scores and CIMT it appears reasonable to assume an additive predictive power for the combination of the two approaches in individuals at low cardiovascular risk according to their risk factor profile. The perception of CIMT as an at least in part independent cardiovascular risk factor rather than merely a reflection of the common risk factors is in line with findings in the general population including subjects at clearly increased cardiovascular risk. However, at that stage of overt risk CIMT appears to add little to the risk predicted solely by risk factors [18].
The data of Table 2 illustrate that the cardiovascular risk factors of the studied individuals were on average in the range of normal despite clear gender differences. However, considering the variance, in individual participants some risk factors reached elevated levels though not in the range supposed to require therapy, e.g., the systolic blood pressure reached on average already the range of prehypertension. Thus, almost 50% of men were prehypertensive which bears a substantial cardiovascular risk. Also, a considerable number of men and women had a fasting glucose above the limit of prediabetes, which corresponds to a markedly elevated cardiovascular risk as well. In addition, the standard deviation of the LDL-cholesterol indicates a prevalence of individually clearly elevated cholesterol levels. This may in part explain the somewhat elevated average CIMT in the studied population, considering that in male and female infants the CIMT measures 0.3–0.5 mm [15,16,17].
In line with the presumption that CIMT may increase due to the conventional cardiovascular risk factors, we found highly significant correlations of the basic risk factors systolic blood pressure, fasting glucose and cholesterol with CIMT. Though age reached the highest correlation, it may just or in part be a surrogate of increasing number and severity of risk factors with age. Similarly, weight may merely express the prevalence of components of the metabolic syndrome. In accord, waist circumference as a measure of central obesity reached a higher and significant correlation than weight and BMI. Multivariate analysis yielded a strong significant correlation for age, while among the modifiable risk factors only systolic blood pressure reached significance in line with previous investigations [19, 20]. Similar results were reported for the correlation of age, gender and systolic blood pressure with CIMT [21].
However, since age may confound the effects of the individual risk factors due to their increase with age, a multivariate analysis was performed omitting age. The correlation of blood pressure, LDL- and HDL-cholesterol and fasting glucose with CIMT increased, which was substantial regarding blood pressure and fasting glucose, the latter of which reached significance in addition to blood pressure. In total, this analysis indicates that the sum of the modifiable clinical risk factors has to be taken into account when comparing their effect on cardiovascular risk with that of CIMT.
The individual point estimates of the calculated scores showed a linear relationship with CIMT. However, the coefficients of determination were rather low indicting a vast variation. In other words, more aspects than the considered parameters of conventional risk factors are responsible for the increase of CIMT. Thus, in individuals without overt cardiovascular risk factors CIMT may well add to the information of the risk scores with regard to the prediction of cardiovascular risk, which needs to be shown in large longitudinal studies, though.
The present findings of the STRATEGY study have several limitations that need to be addressed. First, the analysis is confined to primarily quite healthy volunteers characterized by a healthy lifestyle and a low cardiovascular risk profile, while individuals with overt and treated cardiovascular risk factors were excluded. However, this low-risk study cohort has been deliberately chosen as it represents the target population for which risk prediction is important to individualize and initiate necessary measures to prevent cardio- and cerebrovascular events at an early stage.
Second, the sample size of the study is limited, thus, analysis of a larger population may have allowed to detect significant associations between risk factors and CIMT and their differences with regard to gender. Also, determination of CIMT has followed the protocol of the SHIP-study for matters of conformity and to allow comparison. Automated edge detection may provide more precise results.
Third, some risk factors are underrepresented, e.g., smoking, and thus may be underscored. However, smoking is an obvious risk factor and needs to be stopped in any event. This also applies to markedly elevated lipids and plasma glucose in the range of diabetes. Thus, the findings and derived conclusions may not apply to populations at clearly increased vascular risk.
Fourth, in this context it may be borne in mind that there are more parameters that may be worth considering as to cardio- and cerebrovascular risk as further laboratory values like lipoprotein(a), lifestyle factors, sociodemographic determinants and psychological characteristics, which may add significant predictive power. Although plaque thickness may be considered, plaques are rare in individuals at low cardiovascular risk as in this study [22, 23].
In summary, despite these limitations, our data provide relevant information about two screening methods that share some predictive power, but also suggests that a combination of both may add predictive value in individuals at currently low vascular risk according to conventional risk scores. Such approach certainly requires further confirmation in a prospective cohort not only to prove an additional prognostic value, but also benefit as to the prevention of vascular events.