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Author Spotlight: A Pharmacodissection Approach to Uncover Mechanisms in Cardiovascular Disease Risk Populations
Published on: July 21, 2023
Ethnic Differences in Cardiovascular Disease Risk Factors: A Systematic Review of North American Evidence
Danijela Gasevic1, Emily S Ross2, Scott A Lear3
1Centre for Population Health Sciences, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom.
Background:
Canada is often referred to as a 'land of immigrants,' and the high level of immigration has resulted in significant ethnic diversity in Canada.
Methods:
We performed a systematic review of the literature published from 2000 onward to summarize the evidence on ethnic differences in cardiovascular disease (CVD) risk factors; by comparing the presence of CVD risk factors of Arab, black, Chinese, Hispanic, indigenous, and Filipino ethnic groups with that of CVD risk factors in the white ethnic group.
Results:
One hundred ten studies met the inclusion criteria for this review. Evidence consistently reported greater prevalence of hypertension in black individuals, greater prevalence of diabetes, overall and abdominal obesity and smoking in indigenous people, greater prevalence of diabetes in Hispanic individuals, and lower prevalence of overall obesity and smoking in Chinese individuals compared with their white counterparts. Although inconsistent, most evidence also indicated higher diastolic blood pressure in black individuals, higher hypertension prevalence in indigenous people, higher prevalence of obesity and diabetes in black individuals, and lower prevalence of smoking in Filipino and Hispanic individuals compared with white individuals. The evidence on ethnic differences in CVD risk factors in Arab, Chinese, and Filipino individuals compared with white individuals is limited.
Conclusions:
We observed significant ethnic differences in CVD risk factors. However, because most studies were of cross-sectional design and many of them explored the ethnic differences in CVD risk factors without adjustment for potential confounders, more robust designs are needed to get a better insight into where the true differences lie, what factors they are attributed to, and whether they persist or change over time.

