"Man Is as Old as His Arteries" Taken Literally: In Search of the Best Metric.

The famous quotation in the title, attributed to Thomas Sydenham (1624–1689)—the English Hippocrates—is often the very first appetizer for interested colleagues, shown on the first slide of a presentation. A quick PubMed research using “arterial age” or “vascular age” yields roughly 1000 results, a Google search on “arterial aging” gives 650 000 results within half a second, only topped by 328 billion of hits for “biological age. ” The common perception that an individual’s general health status can be dramatically different from the majority of people at the same chronological age, actually in both directions, combined with the common knowledge that cardiovascular diseases, namely ischemic heart disease and stroke, are the leading causes of death worldwide, seems to make vascular age (VA) intuitively understood even by lay people. Notwithstanding the importance of such widely held concepts and beliefs, scientific approaches—and there are many—are different. In the context of high blood pressure (BP), the concept of early VA (EVA), presented roughly 10 years ago in this journal, has been widely adopted since. At present, the topic is extensively addressed in the European Cooperation in Science & Technology Action VascAgeNet (www.vascagenet.eu). But how to transform the concept into clinical practice (Figure)? VA can be calculated from existing risk scores (Systematic Coronary Risk Evaluation, Framingham), using classical cardiovascular risk factors. In this context, an individual’s heart/VA is calculated as the age of a person with the same predicted risk but with all other risk factor levels in normal ranges. Next, structural and functional arterial properties can be used as a proxy of VA: atherosclerosis (plaque burden or intima-media thickness, frequently assessed at the carotid artery; coronary artery calcium) is commonly investigated in daily routine, less often so is arteriosclerosis (arterial stiffness, eg, carotid-femoral pulse wave velocity [cfPWV]), very rarely endothelial function. Following the concept that arterial damage reflects the net result of all harmful influences on the arterial wall over the life course, based on a given genetic background, a dedicated measure of atherosclerosis (eg, coronary artery calcium) or arteriosclerosis (most often cfPWV) alone may quantify an individual’s VA. Based on cfPWV, for healthy VA (HVA), the age quintile–specific tenth percentile of the population or less than tenth percentile of a young (<30 years) population with normal BP has been used; for EVA, a cfPWV value above the age quintile– specific 90th percentile. Based on coronary artery calcium, population-based regression models, but also outcome-based approaches (assigns a VA that has the same expected coronary heart disease risk as the observed coronary artery calcium), have been reported. Against this background, Bruno et al investigated the prognostic role of the difference between chronological age and VA (called EVA and supernormal VA [SUPERNOVA]), based on cfPWV and classical risk factors. VA was calculated from the cross-sectional Reference Values for Arterial Stiffness Collaboration Database (derivation cohort), using a complex multivariable regression model including cardiovascular risk factors (sex, smoking, height, heart rate, systolic and diastolic BP, total cholesterol, and glycemia; the last 3 factors stratified by drug treatment) plus a measure of arteriosclerosis (cfPWV), together explaining 27.8% of the age variance. Systolic (20.9%) and diastolic (19.6%) BP were the variables with a greater relative importance, followed by sex (14.7%) and cfPWV (11.0%). Variables showing nonlinear associations with age, such as cfPWV or BP, were transformed using splines in the regression model. The 10th and 90th percentile of the age difference distribution were used as cutoffs to define VA categories: EVA (age difference, <−5.7 years), normal VA in between, and SUPERNOVA (age difference, > 6.2 years). All categories had the same VA but differed by chronological age, which was 61 (EVA), 67.6 (normal VA), and 76 (SUPERNOVA) years, respectively. Classical cardiovascular risk factors were, by and large, similar across the groups. CfPWV was higher in the (chronologically younger) EVA group by 1 m/s. In 2642 individuals from the Malmö Diet and Cancer Study cohort (validation cohort), the prognostic value was investigated. After 6.6 years of follow-up and cardiovascular events having occurred in 265 individuals, compared with normal VA, SUPERNOVA individuals had a lower risk (hazard ratio, 0.59), whereas EVA individuals had a higher risk (hazard ratio, 2.70) of cardiovascular events, in particular coronary events, even after adjustments for chronological age, sex, and Atherosclerotic Vascular Disease score. Adding SUPERNOVA/EVA categories significantly increased the C statistics of the model and improved the reclassification rate of the model by 28%. In additional analysis, HVA, The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association. From the Cardiology Department, Klinikum Wels-Grieskirchen, Austria (T.W.); and Center for Health and Bioresources, Austrian Institute of Technology, Vienna (C.C.M.). Correspondence to Thomas Weber, Cardiology Department, Klinikum Wels-Grieskirchen, Grieskirchnerstrasse 42, 4600 Wels, Austria. Email thomas.weber3@liwest.at “Man Is as Old as His Arteries” Taken Literally In Search of the Best Metric