The latest issue of the European Journal of Preventive Cardiology is focused on cardiovascular disease (CVD) risk factors and prediction.  More than two decades now the need for global CVD risk assessment has been highlighted by various scientists, scientific organizations and societies.  During this time, a variety of CVD risk prediction models and related tools, e.g., Framingham Heart Sheet, Pooled Cohort Equations, SCORE2, etc., have been developed to assess overall risk using multiple, but common traditional risk factors, like age, sex, smoking habit, blood pressure and lipids levels. However, the need for refinement of CVD risk prediction models is considered necessary due to the high level of uncertainty in forecast, even when novel risk factors (e.g., polygenic scores, inflammatory markers, renal function, gut microbiome and proteomic profiling, sleep apnea, etc.) were considered.  Recently the interest has focused, again, on a well-known marker, Lipoprotein(a) (Lp(a)). Evidence that elevated Lp(a) levels contribute to CVD development and calcific aortic valve stenosis is now substantial, and novel therapies arise; several investigators have also suggested that Lp(a) levels may explain the residual CVD risk and contribute to better risk classification. 
Lipoprotein(a) and CVD risk
Lipids and lipoprotein particles contribute to atherosclerosis, influence inflammatory processes as underlying pathology, and, ultimately, determine CVD risk. Beyond total, Low- and High- Density Lipoprotein (LDL, HDL)-cholesterol, also other lipid mediators contribute to cardiovascular risk. Lipoprotein(a) (Lp(a)) is one of them. Lp(a) discovery traces back to 1960s, and 1970s, as a complex plasma lipoprotein consisting of LDL-cholesterol and an apolipoprotein B-100 particle, linked to plasminogen-like apolipoprotein-(a) via a disulphide bridge. The Lp(a) and CVD risk hypothesis is related to various cardiometabolic paths, including the accumulation of its particles in human atherosclerotic lesions, and the amplification of plaque area. [4, 5] About 1 billion of people worldwide have been detected with elevated Lp(a) levels (i.e., usually above >150 nmol/L). Several meta-analyses have revealed that Lp(a) elevated levels are associated with increased CVD risk. In a publication by Patel at al.,  in which 460506 middle-aged UK Biobank participants were studied, the relationship of Lp(a) to incident CVD was revealed over a median follow-up of 11.2 years. The relationship between Lp(a) and CVD seemed linear across the Lp(a) distribution, irrespective of participants’ sex and race. A high Lp(a) concentration (defined as ≥150 nmol/L) was present in 12.2% of those without and 20.3% of those with CVD. Moreover, based on UK Biobank data, a formula was proposed to refine the 10-year CVD risk estimate (i.e., Lp(a)-adj 10-year CVD risk = CVD risk × [1.11(Lp(a), per 50 nmol/L)]; however, no information was given as regards the prognostic ability of the proposed formula.  However, CVD risk models and scores do not typically include Lp(a) levels as a predictor variable. Recently, a new risk calculator was introduced under the 2022 Consensus Statement which considers Lp(a) together with traditional cardiovascular risk factors; it remains to see in the future whether this tool confers to a better risk prediction and classification. Despite the consistency in data setting Lp(a) as an independent CVD risk factor for primary prevention, evidence of increased CVD risk in patients under guideline-recommended treatments remain uncertain, where meta-analyses on secondary prevention show heterogeneity in their findings. Moreover, a controversy remains regarding the shape of the Lp(a) – CVD risk curve and the extent to which Lp(a) confers to improvements in CVD risk prediction. [5, 7, 8]
In conclusion, Lp(a) concentrations are associated with incident CVD, mainly in primary prevention context. Currently, guidelines and consensus statements recommend Lp(a) measurement in all adults at least once during their lifetime. However, whether Lp(a) levels may confer to a considerable improvement in CVD risk prediction remains a topic that should be further studied.