Identification of six cardiovascular risk biomarkers in the young population: A promising tool for early prevention
Graphical abstract
Introduction
Cardiovascular disease (CVD) is the leading cause of premature death worldwide despite improvements in outcomes. Cardiovascular (CV) risk is the result of multiple and interacting factors, and different algorithms are available to estimate CV risk in apparently healthy persons in the short-medium term (5–10 years), mainly based on age, gender, race, cholesterol levels, blood pressure, smoking habits and existence of diabetes. The Systematic Coronary Risk Evaluation (SCORE), recommended by the European Society of Cardiology (ESC) prevention guidelines [1], estimates the 10-year risk of a fatal atherosclerotic event, including coronary artery disease (CAD), stroke and abdominal aortic aneurysm. Therefore, CV risk of mortality rather than total CV risk (fatal and nonfatal) is assessed. The ESC guidelines suggest that the risk of total CVD may be calculated from the risk of CVD mortality using a fixed multiplier (3 × ). Nevertheless, the use of a fixed multiplier to estimate the 10-year total CVD risk from CVD mortality risk is controversial [2,3]. On the other hand, American guidelines define the 10-year risk as the risk of developing a first atherosclerotic CV event as nonfatal myocardial infarction or coronary heart disease death or fatal or nonfatal stroke over a 10-year period [4]. In any case, estimations in the short-medium-term have limitations since the majority of patients with low CV risk over the next 10 years show high risk at long-term if the estimation is calculated along their likely remaining lifetime (Lifetime Risk calculation or LTR QRISK) [[5], [6], [7], [8]]. As age is one of the most contributing factors in these algorithms, CV risk of young population is particularly underestimated. As result, few young individuals reach treatment thresholds for intervention and, consequently, efficient prevention strategies are delayed. This limitation collides with the fact that the atherosclerotic process begins early in life and optimum prevention strategies should start in young population. In agreement, the prevalence and progression of subclinical atherosclerosis in individuals of ≤50 years with low 10-year CV risk but high LTR is greater than in individuals with low 10-year and low LTR [9].
Despite the knowledge of main risk factors and the enormous efforts dedicated to improve prevention, the asymptomatic and silent course of atherosclerosis hampers an accurate and individualized CV risk evaluation. Additional strategies and novel tools are needed to add further knowledge of molecular subjacent mechanisms taking place in atherosclerosis development. Omics technologies allow the identification of significant changes in proteins or metabolites abundance in CVD without pre-selection of molecular targets to be investigated, meaning that no-bias is introduced in the study [10,11]. Our group previously identified proteins and metabolites in urine, showing an altered response in acute coronary syndrome and reflecting patient's recovery [12]. Significant alterations directly occurring at arterial tissue were also identified with reflection in plasma [13,14]. Specific molecular fingerprints in urine and plasma were previously identified in hypertensive patients developing albuminuria as those of higher CV risk [[15], [16], [17], [18], [19], [20], [21]]. In those sense, omics not only allow identification of novel markers to improve early diagnosis but also to monitor patient's prognosis [22]. Following these approaches, here we aimed to identify novel urinary targets linked to CV risk in the young population (30–50 years), with added value to current estimations based on LTR. When ordinary CV risk factors are elevated in young people, they predict a significant increase in cardio-renal damage at later stages in life. On the other hand, subclinical atherosclerosis is already present in the middle-age population and, in a small percentage of young people, CV events or even death can occur [23]. However, in the great majority of this population, CV risk factors are within normal levels. In this work, we recruited young subjects who were classified in three groups to represente the general population at that age concerning CV risk.
Section snippets
Patient selection and urine collection
Urine samples were collected from 81 subjects aged between 30 and 50 years, who were classified according to their CV risk in 3 groups: “control” group (C) (n = 32) of healthy subjects with no medication; “factor” group (F) (n = 24), including individuals with glomerular filtration rate <100 or albuminuria, and at least one of: arterial hypertension or patients treated with anti-hypertensive medications, hyperglycaemia (glucose in blood >110 mg/dL) and/or metabolic syndrome; and “CV event”
Results
Characteristics of the study population are listed in Table 1, corresponding to the 81 recruited subjects classified as control (C), CV risk factor group (F) or those who had suffered a CV acute event (E). All subjects were aged between 30 and 50 years. As expected, the main differences observed between groups are those resulting from their CV risk status, e.g. hypertension, glycaemia, lipid profile or pharmacological treatment.
Discussion
The need to overcome current limitations of CV risk available estimations, particularly in the young population, prompts the identification of novel indicators with added value to existing algorithms. In this sense, the ultimate goal to reduce CVD mortality is to introduce novel and easily quantifiable molecular targets in the clinic, which can serve to monitor the general population at early stages of atherosclerosis development [23]. The actual proteomic strategies offer the possibility to
Conflict of interests
The authors declared they do not have anything to disclose regarding conflict of interest with respect to this manuscript.
Author contributions
PJM, MBM, JAL and MA performed the experiments. PJM, MBM, JAL, MML, ASH, JV participated in data analysis/interpretation, figures and literature search. MC and EV participated in clinical data collection. GRH, FV, LMR, MGB and GAL designed the study and contributed to data interpretation, and manuscript drafting.
Financial support
ISCIII co-supported by FEDER grants (PI14/01650, PI14/01917, PI14/01841, PI16/01334, IF08/3667-1, FI12/00126, CPII15/00027, CP15/00129, PT13/0001/0013, PI17/01093, PI17/01193, PRB3 (IPT17/0019 ISCIIIS-GEFI/ERDF, REDinREN (RD12/0021/0001, RD16/0009)), Fundación SENEFRO, Fundación Íñigo Álvarez de Toledo and Fundación Conchita Rábago de Jiménez Díaz. Results are lined up with the Spanish initiative on the Human Proteome Project.
Acknowledgements
The authors acknowledge Lucía Guerrero and Maria Cruz Casal (Hospital 12 de Octubre) for their participation in samples collection and patients' classification.
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