Design: Systematic review and meta-analysis.
Data Sources: Studies published before December 2017 were identified through electronic searches using PubMed, EMBASE and Web of Science.
Eligibility criteria for selecting studies: Prospective cohort, case-control or nested case-control studies reporting risk estimates for total CVD, coronary heart disease (CHD) and stroke by levels of arsenic, lead, cadmium, mercury or copper were included.
Review methods: Information on study characteristics and outcomes were extracted independently by two investigators in accordance with PRISMA and MOOSE guidelines. Relative risks (RRs) were standardised to a common scale and pooled across studies for each marker using random effects meta-analyses.
Main outcome measures: Total CVD, CHD and stroke.
Results: The review identified 37 unique studies comprising 348,259 non-overlapping participants, with 13,033 CHD, 4,205 stroke and 15,274 CVD outcomes in aggregate. Comparing top versus bottom thirds of baseline levels, pooled RRs for arsenic and lead were 1.30 (95% CI: 1.04 to 1.63) and 1.43 (1.16 to 1.76) for CVD; 1.23 (1.04 to 1.45) and 1.85 (1.27 to 2.69) for CHD and 1.15 (0.92 to 1.43) and 1.63 (1.14 to 2.34). RRs for cadmium and copper were 1.33 (1.09 to 1.64) and 1.81 (1.05 to 3.11) for CVD; 1.29 (0.98 to 1.71) and 2.22 (1.31 to 3.74) for CHD; 1.72 (1.29 to 2.28) and 1.29 (0.77 to 2.17) for stroke. Whereas, mercury had no significant association with cardiovascular outcomes. Additionally, there was a linear dose-response association for arsenic, lead and cadmium with CVD outcomes.
Conclusion: Results of this meta-analysis indicate that exposure to arsenic, lead, copper and cadmium is associated with an increased risk of coronary heart disease and cardiovascular disease. By contrast, mercury was not associated with cardiovascular risk. These findings reinforce the (often under-recognised) importance of environmental toxic metals in cardiovascular risk, beyond the roles of conventional behavioural risk factors. Further detailed work, however, to better characterise these associations and to assess causality, are needed.