Focus on risk factors and prediction

This edition of the Journal presents the important topic of risk factor stratification and prediction of outcomes in cardiovascular diseases (CVDs). Type 2 diabetes (T2D) is one of the most clinically relevant (CVD) risk factor, recognized as a priority in the latest Guidelines on CVD prevention.1 Accordingly, T2D patients at very high risk are recommended to receive glucagon-like peptide-1 receptor agonists (GLP-1 RAs) or sodium–glucose co-transporter-2 inhibitors to reduce the risk of major adverse cardiovascular (CV) events (MACE). In this issue, Eliasson et al.2 used the 2017 Swedish National Diabetes Register of T2D patient, not receiving CV-protective glucose-lowering medication, to estimate how many people would have been suitable for treatment and the potential therapeutical size effect. They found that 40.5% (129 512) of the subjects had a very high CVD risk and that a history of previous CVD or high level of glycated haemoglobin conferred additional risk. Based on this registry, the use of a drug such as semaglutide would have prevented around 803 CV events each year, confirming the clinical relevance of CVD prevention Guidelines. CVD risk stratification, according to current clinical scores, is still imperfect based on the evidence that about 40% of atherosclerotic events occur in people classified as low- or intermediate-risk. The analysis of millions of single-nucleotide polymorphisms defining polygenic risk score (PRS) has been recently proposed to predict genetic predisposition to CVD.3 In this issue, de La Harpe et al.4 explored the ability of PRS in reclassifying patients risk over current scores [systematic coronary risk evaluation 2 (SCORE2) and the pooled cohort equation (PCE)]. They considered a prospective cohort of 4218 subjects and identified the metaGRS coronary artery disease (CAD) as the most accurate PRS, able to reclassify the 9.6% [95%, confidence interval (CI) 0.3–18.8] and 12.0% (95%, CI 1.5–22.6) of patients with intermediate risk, as assessed by SCORE2 and PCE, respectively. Based on this paper, PRS could be an emerging tool to fill the gap of intermediate CVD risk stratification. The burden of CVD is high, and there is a continuing clinical need to enhance risk classification models. To estimate the 10-year risk of CV morbidity and mortality, several predictive models were created and validated. While being validated and widely used, these models have limitations such as overestimation of CVD risk in specific populations and a lack of data on socioeconomic status, medications, and renal function. Serum uric acid has been investigated as a novel biomarker for primary or secondary CV prevention, because of its link with CVD. A study presented here examined how serum uric acid affects risk classification models such as pooled cohort equations (PCE) model, SCORE2, and SCORE2-older persons.5 The authors evaluated 19 769 asymptomatic self-referred adults aged 40–79 years free of CVD and diabetes who were screened annually in a preventive healthcare setting. The primary endpoint was the composite of death, acute coronary syndrome, or stroke, after excluding subjects diagnosed with metastatic cancer during follow-up. High serum uric acid improved classification accuracy by 13% in PCE and SCORE2 models. In the SCORE2 model, serum uric acid was independently linked with the study endpoint among normal-weight subjects but not overweight persons. Secondary prevention after a CV event is crucial to reduce the risk of recurrence. What is the adherence to current recommendations? Are all the therapeutical targets reached in clinical practice?6 In this issue, van Trier et al.7 gathered data from six large observational studies and used the Secondary Manifestations of Arterial Disease-Reduction of Atherothrombosis for Continued Health (SMART- REACH) lifetime risk prediction model to estimate the 10-year and a lifetime risk of recurrent atherosclerotic CV events. They showed a 10-year risk of 20% (15–27%) and lifetime risk of 54% (47–63%). High percentages of patients, ranging from 30% to 65%, continued to have uncontrolled risk factors, linked to unmet significant risk reduction and the loss of an average of seven-healthy years of life. The study provides additional evidence for further stressing a tight adherence to current recommendations. Is there a link between the gut microbiome and the risk of ischaemic stroke (IS)? Is there a potential new therapeutic frame to prevent IS?8 In this issue, Meng et al.9 contributed to elucidate the link between gut microbiome and IS. The authors presented a two-sample Mendelian randomization (MR) analysis considering 194 bacterial traits from the MiBioGen consortium and three subtypes of IS: cardioembolic stroke, small vessel stroke, and large artery stroke. Any of these IS subtypes resulted associated with the presence of specific bacteria conferring increased or reduced risk of events. The authors demonstrated the causal effect between bacterial abundance and IS, opening new potential therapeutical targets for preventing IS. Across the entire world, coronary heart disease (CHD) is one of the main causes of death. Targeting residual inflammatory risk may be an effective secondary prevention strategy because inflammation is a major factor in the development and progression of this condition. In this context, a number of inflammatory biomarkers have each been linked to CHD mortality as well as disease progression. During the last decade, advancements in technology have led to novel proteomic strategies to identify biomarker patterns.10,11 By taking into account a population of 432 myocardial infarction (MI) patients and 323 controls, Valdes-Marquez et al. explored the connection between circulating inflammatory plasma proteins and MI.12 The plasma concentrations of 62 out of the 92 proteins under study were higher than the lower limit of detection in at least 50% of the samples. After adjusting for covariates and correcting for multiple testing, 15 of these proteins were found to be substantially linked to MI. Five of these 15 proteins—CDCP1, CD6, IL1-8R1, IL-6, and CXCL1—were each independently linked to an increased risk of MI of up to three times higher. Although the main causes of CHD (such as obesity, inactivity, and hypertension) are widely known, less is known about how social stressors affect MI.13 Studies have proven for more than a century that unemployment is either a direct or indirect risk factor for poor health. It is now known that unemployment is associated with a wide range of risk factors and a lack of resources, which may raise the risk of CVD. Data on the relationship between employment position at the time of the event and future prognosis, including death and recurrent MI, in younger patients with MI are scarce, despite some evidence suggesting that job stress and job insecurity may impart a worse long-term prognosis after MI. In the present issue, the authors from Denmark looked at the national cohort of patients who were admitted with MI and alive at discharge to determine the relationship between job position before admission with MI and the risk of all-cause death and first-time recurrent MI hospitalization. 16 060 patients were included in the study. They discovered that among these patients of working age not being part of the workforce was associated with a higher long-term rate of all-cause mortality and recurrent MI.14 Changes in modifiable risk factors, such as blood pressure, glucose, lipids, body mass index (BMI), and lifestyle, could increase future CVD risks. To reduce the burden of CVD, the American Heart Association developed and updated the concept of cardiovascular health (CVH) in 2022, which was defined as the simultaneous presence of four health behaviours (diet, physical activity, smoking, and sleep) and four health factors (BMI, non-high-density lipoprotein cholesterol, blood glucose, and blood pressure). Life’s Essential 8 (LE8), a novel CVH construct, was scaled from 0 to 100 to increase the sensitivity of scoring to interindividual variances for the quantification of CVH. The primary goal of Xing and colleagues study was to look into the relationship between baseline and long-term CVH (time-varying and time-updated) evaluated by the new LE8 metrics and the risk of early CVD and all-cause death in Chinese young adults aged 18–40 years.15 A total of 16 011 CVD-free participants aged 18–40 years were enrolled in the Kailuan cohort study. The CVH score was categorized into low, moderate, and high. Among young adults aged 40 years, baseline and longitudinal low CVH status were related to an increased risk of CVD and all-cause mortality. Of the eight LE8 metrics, poorer health component scores were related to a higher risk of CVD and all-cause mortality, emphasizing the importance of keeping health behaviours and factors earlier in life. Among the eight metrics of LE8, lower scores of health factors were associated with a higher risk of CVD and all-cause. Traditional observational studies have suggested that sleep apnoea (SA) is a modifiable risk factor for CVDs such as hypertension, CAD, atrial fibrillation, heart failure, and stroke. Snoring has been linked to an increased risk of CVDs and has been identified as a sign of the early stages of obstructive SA. However, establishing causation in observational research is challenging due to residual confounding from unknown or unmeasured factors and reverse causality (i.e. the scenario whereby CVDs increase the risk for SA). In the study of Wang and colleagues, the authors used the bi-directional, two-sample MR study to assess the existence and direction of the causal relationship between SA or snoring and CVDs. In brief, they showed that SA and snoring increased the risk of hypertension and CAD, and these associations may partly be driven by the BMI.16