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Body mass index and cardiorespiratory fitness improve stroke prediction beyond classical cardiovascular risk factors

Session Challenges in improving risk prediction

Speaker Erik Prestgaard

Event : ESC Congress 2018

  • Topic : preventive cardiology
  • Sub-topic : Risk Factors and Prevention – Epidemiology
  • Session type : Abstract Session

Authors : E Prestgaard (Oslo,NO), J Mariampillai (Oslo,NO), K Engeseth (Oslo,NO), J Bodegard (Oslo,NO), J Erikssen (Oslo,NO), K Gjesdal (Oslo,NO), K Liestol (Oslo,NO), S Kjeldsen (Oslo,NO), I Grundvold (Oslo,NO), E Berge (Oslo,NO)

Authors:
E. Prestgaard1 , J. Mariampillai1 , K. Engeseth1 , J. Bodegard1 , J. Erikssen2 , K. Gjesdal2 , K. Liestol3 , S. Kjeldsen1 , I. Grundvold1 , E. Berge1 , 1Oslo University Hospital, Cardiology - Oslo - Norway , 2University of Oslo, Medicine - Oslo - Norway , 3University of Oslo, Informatics - Oslo - Norway ,

Citation:
European Heart Journal ( 2018 ) 39 ( Supplement ), 641

Background: The classical risk factors in the Framingham and European cardiovascular disease risk assessment models are age, gender, systolic blood pressure, total serum cholesterol and cigarette smoking.

Purpose: We aimed to investigate whether the addition of body mass index and cardiorespiratory fitness improved the prediction of stroke in a cohort of healthy middle-aged men followed for 35 years.

Methods: The study enrolled 2014 healthy men, aged 40–59 years, between 1972 and 1975. The baseline examination included fitness level measured with a maximal ergometer exercise test. Participants were followed over 35 years and stroke end-points were collected from follow-up visits, the National Cause of Death Registry and from medical records in all of the nation's hospitals. Participants in the highest quartile of baseline systolic blood pressure, total cholesterol and body mass index were grouped with those in the lowest quartile of fitness and with active smokers. We compared those having ≥1 risk factor with those having none, applying regression analyses and adjusting for age (Model 1). Finally we compared this model with a model that included body mass index and cardiorespiratory fitness (Model 2).

Results: During a median follow-up time of 31.9 years 316 first-time strokes occurred. No participants were lost to follow-up. Those who had 2 or more risk factors at baseline in Model 1 had a significantly higher stroke risk (HR 2.08, CI 1.07–3.70) than those with no risk factors. When including BMI and fitness in the model (Model 2), the participants with 4 or more risk factors had very high risk of stroke (HR 3.21, CI 1.89–5.26) compared to men with no risk factors at baseline (Fig. 1).

Conclusions: Our data suggest that the addition of body mass index and cardiorespiratory fitness improve a conventional stroke prediction model in healthy middle-aged men.

Figure 1

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