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Author Notes:

E-mail: majid.ezzati@imperial.ac.uk

Conceived and designed the experiments: ME GD GMS.

Analyzed the data: GMS.

Contributed reagents/materials/analysis tools: FF GAS MW DW SK GW QQ SL ED SV CML MKA.

Wrote the paper: ME GMS GD DM.

Analyzed cohort data: APCSC DECODE ERFC PSC.

See publication for list of acknowledgements.

Competing Interests: The authors have declared that no competing interests exist.


Research Funding:

ME is supported by a strategic award from the UK Medical Research Council and by the National Institute for Health Research Comprehensive Biomedical Research Centre at Imperial College Healthcare National Health Service Trust.

GMS is supported a T32 training grant in Academic Nutrition (Grant Number DK007703) from the National Institute of Diabetes and Digestive and Kidney Diseases.

The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, writing of the report, or decision to submit manuscript.

GAS is a staff member of the World Health Organization and alone is responsible for the views expressed in this publication, which do not necessarily represent the decisions, policy, or views of the World Health Organization.


  • Science & Technology
  • Multidisciplinary Sciences
  • Science & Technology - Other Topics

The Age-Specific Quantitative Effects of Metabolic Risk Factors on Cardiovascular Diseases and Diabetes: A Pooled Analysis

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Journal Title:



Volume 8, Number 7


, Pages e65174-e65174

Type of Work:

Article | Final Publisher PDF


Background: The effects of systolic blood pressure (SBP), serum total cholesterol (TC), fasting plasma glucose (FPG), and body mass index (BMI) on the risk of cardiovascular diseases (CVD) have been established in epidemiological studies, but consistent estimates of effect sizes by age and sex are not available. Methods: We reviewed large cohort pooling projects, evaluating effects of baseline or usual exposure to metabolic risks on ischemic heart disease (IHD), hypertensive heart disease (HHD), stroke, diabetes, and, as relevant selected other CVDs, after adjusting for important confounders. We pooled all data to estimate relative risks (RRs) for each risk factor and examined effect modification by age or other factors, using random effects models. Results: Across all risk factors, an average of 123 cohorts provided data on 1.4 million individuals and 52,000 CVD events. Each metabolic risk factor was robustly related to CVD. At the baseline age of 55-64 years, the RR for 10 mmHg higher SBP was largest for HHD (2.16; 95% CI 2.09-2.24), followed by effects on both stroke subtypes (1.66; 1.39-1.98 for hemorrhagic stroke and 1.63; 1.57-1.69 for ischemic stroke). In the same age group, RRs for 1 mmol/L higher TC were 1.44 (1.29-1.61) for IHD and 1.20 (1.15-1.25) for ischemic stroke. The RRs for 5 kg/m 2 higher BMI for ages 55-64 ranged from 2.32 (2.04-2.63) for diabetes, to 1.44 (1.40-1.48) for IHD. For 1 mmol/L higher FPG, RRs in this age group were 1.18 (1.08-1.29) for IHD and 1.14 (1.01-1.29) for total stroke. For all risk factors, proportional effects declined with age, were generally consistent by sex, and differed by region in only a few age groups for certain risk factor-disease pairs. Conclusion: Our results provide robust, comparable and precise estimates of the effects of major metabolic risk factors on CVD and diabetes by age group.

Copyright information:

© 2013 Singh et al.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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