Although South Asian populations have high cardiovascular disease (CVD) burden in the world, their patterns of individual CVD risk factors have not been fully studied. None of the available algorithms/scores to assess CVD risk have originated from these populations. To explore the relevance of CVD risk scores for these populations, literature search and qualitative synthesis of available evidence were performed. South Asians usually have higher levels of both “classical” and nontraditional CVD risk factors and experience these at a younger age. There are marked variations in risk profiles between South Asian populations. More than 100 risk algorithms are currently available, with varying risk factors. However, no available algorithm has included all important risk factors that underlie CVD in these populations. The future challenge is either to appropriately calibrate current risk algorithms or ideally to develop new risk algorithms that include variables that provide an accurate estimate of CVD risk. 1. Introduction Cardiovascular diseases (CVD) related to atherosclerosis, including ischaemic heart disease, stroke, and peripheral vascular disease, are the leading causes of death in most of high-, low-, and middle-income countries [1]. Although low- and middle-income countries account for more than 80% of global deaths and 85% of global disability from CVD, with a few notable exceptions, knowledge and understanding of CVD risk factors are largely derived from data obtained in developed countries [2]. Atherosclerosis and its complications develop over the entire life course as a result of combined influences of lifestyle-related factors, environmental triggers, and genetic susceptibility [3]. Clinically overt CVD is usually preceded by the presence of one or more risk factors and subclinical atherosclerosis [3], and the relationship between risk factors and CVD outcomes represents a continuum. As the individual risk varies greatly, methods that estimate the risk of future CVD events have been developed and applied. Such algorithms recognise the continuous relationship between levels of biomedical and lifestyle variables and future CVD events, weigh risk factors according to their importance, and allow for potential synergistic effects of abnormalities in individual risk factors. Such scores not only stratify individual risk and identify those most likely to benefit from an intervention, but also inform the most cost-effective allocation of preventive therapies. A risk scoring method should include appropriate risk factors, be accurate, and be ideally
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