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The term “biomarker”, which means “biological marker”, refers to “any substance, structure, or process that can be measured in the human body” and “influences or predicts the incidence of outcome or disease” according to the definition of the World Health Organization [1]. Osteocalcin (OC), produced by osteoblasts, is traditionally considered a marker of bone formation, involved in the process of mineralization [2]. However, OC has attracted attention for its relationship with glucose metabolism and fat mass and its association with vascular calcification and atherosclerosis [2, 3]. The manuscript of Xu et al. [4] focuses on the role of OC for the identification of carotid intima-media thickness (C-IMT) over the atherosclerotic cardiovascular disease (ASCVD) risk score in a Chinese population (n = 618) of middle-aged and elderly men. Body mass index and glycated hemoglobin were identified as independent predictors for reduced OC levels, which confirms the active role of OC in cardiometabolic risk [4]. There is a higher recognition rate of elevated C-IMT when both low OC and moderate-to-high ASCVD risk are considered in comparison to considering a single parameter alone [4]. In particular, OC is independently and inversely associated with elevated C-IMT after being adjusted for the 10-year ASCVD risk score, and this relationship is especially evident in subjects with a moderate-to-high ASCVD risk [4]. In this context, a recent review and meta-analysis including 46 studies points out 26 positive, 17 negative and 29 neutral associations between markers of calcification or atherosclerosis and OC in blood, presence of OCpositive cells or OC histological staining. Authors did not conclusively identify a definitive association, although a consistent positive correlation was evident between the presence of OC-positive cells and histological staining with calcification and atherosclerosis [3]. Interestingly, variations related to ethnic differences appear important, as 37% of Asian studies reported negative relationships between OC and calcification or atherosclerosis, compared to 6% of European studies [3]. Such ethnic heterogeneity may be explained by the variation in body composition and genetic-related differences, as well as diet and sociodemographic factors. More generally, difficulties in the interpretation of the OC and atherosclerosis relationship may also be due to the measurement of total OC rather than its undercarboxylated and carboxylated forms, to the variety of studied patient cohorts (including kidney disease, diabetes or glucose intolerance, postmenopausal women and hypertension) and to the lack of studies primarily aiming to assess OC significance in atherosclerosis. In the cardiovascular (CV) field, there are a few multifactorial integrated prediction algorithms for the estimation of CV risk (e.g. the Framingham Heart Study Risk Score), which stratify patients into low-, intermediateand high-risk categories [5]. Nonetheless, although much of CV risk may be attributed to traditional risk factors (e.g. smoking habits, diet or blood pressure) included in classical risk scores, overall CV risk is not entirely explained by traditional risk determinants [5]. Specifically, some events may also occur in subjects defined at low or intermediate risk because they present none or only one of the CV risk factors included in the traditional scores. Conversely, many high-risk individuals will not experience a CV event, even in the long term. These groups may be better classified according to a more adequate risk threshold in order to identify subclinical atherosclerosis signs at the earliest possible stage and, therefore, prioritize those who would need more urgent preventive strategies. Thus, the results discussed in the manuscript of Xu et al. support the interest of combining newly proposed CV risk biomarkers with conventional algorithms. Recently, several different biomarkers have been proposed, such as genetic, circulating and imaging biomarkers [6–9]. Genetic biomarkers reflect the potential of heritable profiling for CV disease, providing a tool for early individual diagnosis and prediction [6]. Interestingly, although association between candidate genes and atherosclerosis rarely gave consistent results in the past, recent data coming from both genomewide association studies and whole-genome sequencing

New biomarkers and traditional cardiovascular risk scores: any crystal ball for current effective advice and future exact prediction?