Cardiogenetics (Jun 2021)

The Multi-Ethnic New Zealand Study of Acute Coronary Syndromes (MENZACS): Design and Methodology

  • Malcolm. E. Legget,
  • Vicky. A. Cameron,
  • Katrina. K. Poppe,
  • Sara Aish,
  • Nikki Earle,
  • Yeunhyang Choi,
  • Kathryn. E. Bradbury,
  • Clare Wall,
  • Ralph Stewart,
  • Andrew Kerr,
  • Wil Harrison,
  • Gerry Devlin,
  • Richard Troughton,
  • A. Mark Richards,
  • Graeme Porter,
  • Patrick Gladding,
  • Anna Rolleston,
  • Robert N. Doughty

DOI
https://doi.org/10.3390/cardiogenetics11020010
Journal volume & issue
Vol. 11, no. 2
pp. 84 – 97

Abstract

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Background. Each year, approximately 5000 New Zealanders are admitted to hospital with first-time acute coronary syndrome (ACS). The Multi-Ethnic New Zealand Study of Acute Coronary Syndromes (MENZACS) is a prospective longitudinal cohort study embedded within the All New Zealand Acute Coronary Syndrome Quality Improvement (ANZACS-QI) registry in six hospitals. The objective of MENZACS is to examine the relationship between clinical, genomic, and cardiometabolic markers in relation to presentation and outcomes post-ACS. Methods. Patients with first-time ACS are enrolled and study-specific research data is collected alongside the ANZACS-QI registry. The research blood samples are stored for future genetic/biomarker assays. Dietary information is collected with a food frequency questionnaire and information about physical activity, smoking, and stress is also collected via questionnaire. Detailed family history, ancestry, and ethnicity data are recorded on all participants. Results. During the period between 2015 and 2019, there were 2015 patients enrolled. The mean age was 61 years, with 60% of patients aged Conclusion. MENZACS represents a cohort with optimal contemporary management and will be a significant epidemiological bioresource for the study of environmental and genetic factors contributing to ACS in New Zealand’s multi-ethnic environment. The study will utilise clinical, nutritional, lifestyle, genomic, and biomarker analyses to explore factors influencing the progression of coronary disease and develop risk prediction models for health outcomes.

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