Cardiovascular health and atrial fibrillation

Abstract

The American Heart Association (AHA) has set decadelong impact goals since the 90s, aimed on reducing the cardiovascular disease (CVD) burden, with reflections on patient care and cardiovascular research around the globe. The last completed cycle ended in 2020. In that cycle, the objective was ‘by 2020, to improve the cardiovascular health of all Americans by 20% while reducing deaths from CVDs and stroke by 20%’. The main strategy to achieve this goal was aligned with the foundations of primary prevention by Geoffrey Rose, and advocated that interventions should focus on increasing the proportion of individuals free of CVD with ideal (1) diet, (2) physical activity, (3) body mass index (BMI), (4) blood pressure, (5) fasting plasma glucose and (6) total cholesterol, as well as of (7) nonsmokers (never smokers or, alternatively, past smokers with at least 1 year from quitting). This has also resulted in a 7point ideal cardiovascular health (CVH) score, with specific metrics for each risk factor profile. Since then, several articles have used the CVH score, analysing the prevalence of ideal metrics in different populations, or measuring its association with CVD. 4 In the present decade, the AHA has adopted even more ambitious aims. For 2030, the AHA aims an equitable increase in healthadjusted life expectancy (HALE) from 66 to 68 years in the USA and from 64 to 67 years worldwide. This represents two major changes compared with the previous objectives. First, the current impact goals were extended beyond the American population, establishing worldwide aims as well. Second, the AHA adopted HALE as the main followup indicator for these decade goals. HALE is one of the main metrics systematically estimated by the Global Burden of Disease Study, and it is influenced by overall (instead of cardiovascular only) health loss during the lifespan and premature mortality. Atrial fibrillation (AF) is a known major risk factor for stroke, which is, in turn, a main driver for CVD burden. As the AHA points towards the need for more equitable advances in global health, AF will probably be increasingly in the spotlight. AF is still more prevalent in highincome compared with lowincome and middleincome countries (LMICs). However, trends in the past few decades have shown this difference is narrowing, with an increasing proportion of global AFrelated morbimortality concentrated in LMICs. This is aggravated by the worse access to stroke prevention therapy with oral anticoagulants in this setting, increasing stroke risk among these patients. In addition, considering the difficulties on the implementation of secondary prevention of stroke in LMICs, this closes a vicious cycle of higher mortality and morbidity, directly impacting on HALE. It is noteworthy that the articles that analysed CVH using the ideal CVH score in the 2010s had rarely focused on AF. As the fight to increase the prevalence of ideal CVH profiles seems a reasonable effort to prevent healthy life loss, it is important to verify to which extent changes in the prevalence of ideal CVH metrics are supposed to influence AF epidemiology. Lee et al analysed data from >200 000 individuals aged 60 years or older from the Korea National Health Insurance ServiceSenior cohort, who were followed for a median of >7 years. They found that a onepoint increase in ideal CVH score was associated with a 5% lower risk of incident AF in the sample. Although the net effect of increasing ideal CVH scores was towards a reduced risk for incident AF, the contribution of each of the seven ideal CVH metrics was heterogeneous. They found an inverse association between ideal smoking, BMI, physical activity and blood pressure ideal CVH metrics. However, the authors also pointed a higher risk of incident AF in individuals with ideal total cholesterol. While these results reinforce that multiple clinical conditions may be preventable targeting the same behavioural and clinical factors, it also highlights that the ideal CVH score, as initially proposed, may hide important differences in the association between each CVH factor and specific diseases. This seems particularly true for AF. The cholesterol paradox, in which a lower AF prevalence in individuals with higher cholesterol levels is reported, has been described by other authors several years ago. Until now, there is no consensual explanation for the cholesterol paradox in AF. Recently, Anzai et al suggested the cholesterol paradox may be at least partly due to prevalenceincidence (Neyman’s) bias. In Neyman’s bias, the association between a risk factor and disease incidence is weaker than the association between the same risk factor and mortality among those individuals affected by the disease. Applied to the AF cholesterol paradox, it would mean that, although higher cholesterol may increase the odds for developing AF, a higher mortality rate would be observed among individuals with AF and high cholesterol, compared with those with AF but normal cholesterol levels. In this case, a crosssectional analysis would reveal an inverse (paradoxical) association, because of the individuals with AF and higher cholesterol levels who died before study enrolment. Indeed, some analyses that described the cholesterol paradox were crosssectional, and more prone to Neyman’s bias. However, the cohort study by Lee et al adds important information, confirming the existence of a paradoxical association between total cholesterol and AF prospectively. This suggests Neyman’s bias may not be a sufficient explanation to these findings. Stimulating high proportions of ideal CVH profiles in populations is certainly not outdated and remains a strong population strategy to improve global health in the current decade, similarly as proposed by Rose in the past century. The AHA’s ideal CVH score seems to be an easily understandable tool to scan this progress in populations, with a strong association with allcause and CVD mortality. However, the burden of specific conditions (as AF) varies around the globe, with heterogeneous consequences in highincome countries and LMICs. Refining our risk measurement tools, adapting them for specific scenarios and outcomes, have the potential to improve estimates of the impact (and costeffectiveness) of selected interventions in different populations. This is an essential step towards a healthier and more equitable world. Centro de Pesquisa Clínica e Epidemiológica, Hospital Universitário da Universidade de São Paulo, São Paulo, Brazil Departamento de Clínica Médica, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil