Original Article | Published:

Lipids and cardiovascular/metabolic health

Comparison of various surrogate obesity indicators as predictors of cardiovascular mortality in four European populations

European Journal of Clinical Nutrition volume 67, pages 12981302 (2013) | Download Citation

Abstract

Background/Objectives:

Body mass index (BMI) is the most commonly used surrogate marker for evaluating the risk of cardiovascular disease (CVD) mortality in relation to general obesity, while abdominal obesity indicators have been proposed to be more informative in risk prediction.

Subject/Methods:

A prospective cohort study consisting of 46 651 Europeans aged 24–99 years was conducted to investigate the relationship between CVD mortality and different obesity indicators including BMI, waist circumference (WC), waist-to-hip ratio (WHR), waist-to-stature ratio (WSR), A Body Shape Index (ABSI) and waist-to-hip-to-height ratio (WHHR). Hazard ratio (HR) was estimated by the Cox proportional hazards model using age as timescale, and compared using paired homogeneity test.

Results:

During a median follow-up of 7.9 years, 3435 participants died, 1409 from CVD. All obesity indicators were positively associated with increased risk of CVD mortality, with HRs (95% confidence intervals) per standard deviation increase of 1.19 (1.12–1.27) for BMI, 1.29 (1.21–1.37) for WC, 1.28 (1.20–1.36) for WHR, 1.35 (1.27–1.44) for WSR, 1.34 (1.26–1.44) for ABSI and 1.34 (1.25–1.42) for WHHR in men and 1.37 (1.24–1.51), 1.49 (1.34–1.65), 1.45 (1.31–1.60), 1.52 (1.37–1.69), 1.32 (1.18–1.48) and 1.45 (1.31–1.61) in women, respectively. The prediction was stronger with abdominal obesity indicators than with BMI or ABSI (P<0.05 for all paired homogeneity tests). WSR appeared to be the strongest predictor among all the indicators, with a linear relationship with CVD mortality in both men and women.

Conclusions:

Abdominal obesity indicators such as WC, WHR, WSR and WHHR, are stronger predictors for CVD mortality than general obesity indicator of BMI.

Introduction

Body mass index (BMI) is currently the most widely used anthropometric measurement to predict health risk related to weight status. However, BMI does not distinguish between muscle and fat accumulation and does not reflect fat distribution, hence is recognized as a crude surrogate for general obesity. Waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-stature ratio (WSR) appears to be a better indicator of abdominal obesity than BMI for the stronger correlation with intra-abdominal fat content and cardiometabolic risk factors.1, 2, 3, 4, 5, 6, 7 A Body Shape Index (ABSI) has been proposed recently to combine WC, weight and height together in one algorithm to predict all-cause mortality,8 as well as waist-to-hip-to-height ratio (WHHR) was shown to be superior to BMI, WC or WSR in predicting cardiovascular disease (CVD) risk.9 But to date, no study has formally compared these obesity indicators regarding their effect sizes for CVD mortality.

In this paper, we made paired comparison among BMI, WC, WHR, WSR, ABSI and WHHR regarding their effect sizes for CVD mortality based on collaborative analysis of the data from 12 European cohorts enrolled in the DECODE (Diabetes Epidemiology: Collaborative analysis Of Diagnostic criteria in Europe) study.

Subjects and methods

Study population

Data were collected from 12 prospective studies in four European countries (Finland, Sweden, Turkey and UK) and the study population comprised 50 093 men and women aged 24–99 years at baseline. Data collection included a self-reported questionnaire on smoking status and leisure-time physical activity, and a medical examination to measure weight, height, WC and hip circumference, as described in detail in previous DECODE publications.10 Participants with missing data on smoking status, leisure-time physical activity, weight, height, WC and hip circumference and on date of loss to follow-up were excluded. Subjects who had emigrated but the dates of the emigration were recorded, were treated as censored cases. A total of 46 651 participants (24 686 men and 21 965 women) were eligible for the final data analysis.

Individual participant data from each cohort was sent to the National Institute for Health and Welfare in Helsinki, Finland for collaborative data analyses. Each study was approved by the local ethics committees, and the analysis plan was approved by the ethics committee of the National Institute for Health and Welfare.

Definition of covariates

Smoking status at baseline was classified based on responses to the questionnaire into three categories of never smokers, former smokers and current smokers. Reading, watching TV, house works, sewing and walking <1 km daily that do not require moving much and do not physically tax was defined as leisure-time physically inactive, and all other higher levels of physical activity were defined as physically active. Height and weight were measured without shoes and with light clothing. WC was measured midway between the lower rib margin and iliac crest. Hip circumference was measured at the level of widest circumference over the greater trochanters. BMI was calculated as body weight in kilograms divided by the square of height in meters. WHR, WSR or WHHR was calculated as WC divided by hip circumference, height or both in meters. The calculation of ABSI was based on WC adjusted for weight and height, which was defined as follows: ABSI=WC × weight−2/3 × height5/6.8

Definition of fatal events

Median follow-up time varied between 2.5 and 21.8 years between different cohorts. Vital status and causes of death were recorded in all of the studies included. CVD mortality was defined according to the International Classification of Disease codes 401–448 (9th revision) or I10–I79 (10th revision).

Statistical analyses

Pearson’s partial correlation coefficients between anthropometric indicators were computed, adjusting for baseline age and cohort. The Cox proportional hazards model was used to estimate the multivariate hazard ratios (HRs) and confidence intervals (CIs) of CVD mortality for each anthropometric measurement adjusted for baseline smoking status, leisure-time physical activity and cohort with attained age as timescale. All analyses were performed separately for men and women. HRs per standard deviation increase of each obesity indicator in relation to CVD mortality has been formally compared by paired homogeneity test which is a Wald test of the linear hypothesis of the Cox model regression coefficients, performed to test the null hypothesis of equality of the effect sizes. To further explore the relationship between mortality and obesity indicators, Akaike’s information criterion was used to judge the best-fitting models.11 The ‘best’ model is the one with the lowest Akaike’s information criterion. Similar analyses were repeated among participants excluding of the first five years of follow-up or stratified by attained ages (<55, 55–75, >75 years old). Furthermore, similar analyses were performed within each BMI category classified based on the World Health Organization definition of normal weight (BMI value of 18.5–24.9 kg/m2), overweight (BMI value of 25.0–29.9 kg/m2) and obesity (BMI value of 30.0 kg/m2).

Data analyses were performed in Stata Intercooled 11.2 (StataCorp, College Station, TX, USA).

Results

Over a median follow-up of 7.9 years, 2381 men and 1055 women died, 1071 men (45.0%) and 339 women (32.1%) from CVD (Tables 1 and 2). When controlling for baseline age and cohort, most of the obesity indicators exhibited significant correlations with each other (Pearson’s partial correlation coefficients 0.47–0.96, except for weak positive correlation between ABSI and BMI, 0.15 and 0.11 for men and women, respectively, data not shown).

Table 1: Baseline characteristics and the data of follow-up of the survey in men
Table 2: Baseline characteristics and the data of follow-up of the survey in women

All obesity indicators were positively associated with increased risk of CVD mortality, with HRs (95% confidence intervals) per standard deviation increase of 1.19 (1.12–1.27) for BMI, 1.29 (1.21–1.37) for WC, 1.28 (1.20–1.36) for WHR, 1.35 (1.27–1.44) for WSR, 1.34 (1.26–1.44) for ABSI and 1.34 (1.25–1.42) for WHHR in men and 1.37 (1.24–1.51), 1.49 (1.34–1.65), 1.45 (1.31–1.60), 1.52 (1.37–1.69), 1.32 (1.18–1.48) and 1.45 (1.31–1.61) in women, respectively (Table 3). Paired homogeneity tests showed the prediction was stronger with abdominal obesity indicators than with BMI and ABSI, and the WSR was the strongest predictor among all. The main results remained even among individuals excluding the first 5 years of follow-up (Table 3). Moreover, increased risk of CVD mortality in relation to abdominal obesity indicators was independent of BMI levels (Table 4).

Table 3: Paired homogeneity test for prediction of cardiovascular disease mortality according to one standard deviation increase in obesity indicatorsa
Table 4: Hazard ratios and their 95% confidence intervals for one standard deviation increase of abdominal obesity indicators in relation to cardiovascular disease mortality within BMI subgroupa

Stratified data analyses by attained age or leisure-time physical activity did not change the observation that the prediction was stronger with the abdominal obesity indicators than with BMI or ABSI (Supplementary Table 1).

Discussion

Abdominal obesity indicators of WC, WHR, WSR and WHHR, are stronger predictors for CVD mortality than BMI and ABSI, and the prediction is independent of BMI levels. WSR appeared to be the strongest predictor among all.

Studies have reported that relative risks for CVD mortality corresponding to one standard deviation increment in abdominal obesity indicators were higher than that of BMI,8, 12, 13, 14, 15 but none of these studies performed a formal paired statistical test. In our study, results from both paired homogeneity tests and Akaike’s information criterion comparison revealed a stronger effect size with abdominal obesity indicators of WC, WHR, WSR and WHHR than with BMI or ABSI regarding the CVD mortality in both men and women. Abdominal fat is known to produce inflammatory cytokines, which have been associated with increased CVD risk.16 The stronger associations between CVD mortality and WC, WHR, WSR and WHHR, compared with BMI, might be partly explained by the higher degree of inflammation in the abdominal obesity individuals. In addition, body fat distribution, especially visceral adipose tissue accumulation, has been found to have a key role in the development of cardiovascular and metabolic disease.17 To minimize the potential effect of reverse causation,18 we also performed a sensitivity analyses by excluding the first five years of follow-up. The results were not substantially altered. The main findings remained significant even when stratified analysis was performed by attained age groups. It appears sound to recommend the use of abdominal obesity indicators, including WC, WHR, WSR and WHHR, to assess risk of CVD mortality instead of BMI in Europeans.

Physical activity has been known as an independent risk factor of CVD mortality19, 20, 21, 22, 23, 24 and to weaken, but not eliminate the highlighted risk associated with excess weight.23, 24, 25, 26 A stratified data analysis by level of leisure-time physical activity did not change the observations. In addition, smoking is associated with both a lower body weight and an increased risk of mortality and is interrelated with obesity in relation to mortality.23, 27, 28, 29 A recent study showed that smoking cessation resulted in substantial increase in WC,30 whether the high WC or the lag effect of smoking has contributed to the increased CVD mortality needs to be investigated with repeat measures of WC. In addition, other CVD risk factors, for example, high total cholesterol, hypertension or diabetes, were not included in our analyses to avoid over-adjustment bias,31, 32 for part of obesity−CVD mortality is mediated via these factors.33

Our study was based on data from large European population- or occupational-based studies, with sufficient power to investigate the association between anthropometric measurements and risk of mortality. However, data on changes in anthropometric measurements before the baseline and during the follow-up are not available, which precludes the possibility to study ‘reverse causation’ and the effect of quitting smoking. Nevertheless, the results were not altered much after excluding the first 5 years of follow-up. As this is a collaborative data analysis, certain lifestyle variable such as physical activity has been recorded differently in different studies. In spite of the efforts that have been made to ‘harmonize’ the variables and to adjust for studies in data analysis, discrepancies exist. In addition, sagittal abdominal diameter, an anthropometric index of visceral obesity associated with sudden death or coronary heart disease mortality,34, 35 is not available in our study. Its performance cannot be compared directly with others.

In conclusion, abdominal obesity indicators of WC, WHR, WSR and WHHR are stronger predictors for CVD mortality than BMI and ABSI, and independent of the BMI levels.

References

  1. 1.

    , , , , , et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 1994; 73: 460–468.

  2. 2.

    , , , , . A single threshold value of waist girth identifies normal-weight and overweight subjects with excess visceral adipose tissue. Am J Clin Nutr 1996; 64: 685–693.

  3. 3.

    , , , , . The prediction of abdominal visceral fat level from body composition and anthropometry: ROC analysis. Int J Obes Relat Metab Disord 1999; 23: 801–809.

  4. 4.

    , , , , , et al. Hypertriglyceridemic waist: a marker of the atherogenic metabolic triad (hyperinsulinemia; hyperapolipoprotein B; small, dense LDL) in men? Circulation 2000; 102: 179–184.

  5. 5.

    , , . A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev 2010; 23: 247–269.

  6. 6.

    , , , , , et al. Assessing adiposity: a scientific statement from the American Heart Association. Circulation 2011; 124: 1996–2019.

  7. 7.

    , , . Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev 2012; 13: 275–286.

  8. 8.

    , . A new body shape index predicts mortality hazard independently of body mass index. PLoS One 2012; 7: e39504.

  9. 9.

    , , , , , et al. Novel and established anthropometric measures and the prediction of incident cardiovascular disease: a cohort study. Int J Obes (Lond). e-pub ahead of print March 28 2013 doi:10.1038/ijo.2013.46.

  10. 10.

    DECODE Study Group, the European Diabetes Epidemiology Group. Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria. Arch Intern Med 2001; 161: 397–405.

  11. 11.

    . A new look at the statistical model identification. IEEE Trans Autom Control 1974; 19: 716–723.

  12. 12.

    , , . Waist-hip ratio is the dominant risk factor predicting cardiovascular death in Australia. Med J Aust 2003; 179: 580–585.

  13. 13.

    , , , , , et al. Comparison of the associations of body mass index and measures of central adiposity and fat mass with coronary heart disease, diabetes, and all-cause mortality: a study using data from 4 UK cohorts. Am J Clin Nutr 2010; 91: 547–556.

  14. 14.

    , , , , . Body configuration as a predictor of mortality: comparison of five anthropometric measures in a 12 year follow-up of the Norwegian HUNT 2 study. PLoS One 2011; 6: e26621.

  15. 15.

    , , , , , et al. Body mass index versus waist circumference as predictors of mortality in Canadian adults. Int J Obes (Lond) 2012; 36: 1450–1454.

  16. 16.

    , , , , , et al. Inflammatory markers and onset of cardiovascular events: results from the Health ABC study. Circulation 2003; 108: 2317–2322.

  17. 17.

    . Is visceral obesity the cause of the metabolic syndrome? Ann Med 2006; 38: 52–63.

  18. 18.

    , , . Guidelines for healthy weight. N Engl J Med 1999; 341: 427–434.

  19. 19.

    , , , , , . Body mass index, physical inactivity and low level of physical fitness as determinants of all-cause and cardiovascular disease mortality – 16 y follow-up of middle-aged and elderly men and women. Int J Obes Relat Metab Disord 2000; 24: 1465–1474.

  20. 20.

    , , , . Body mass index and mortality: the influence of physical activity and smoking. Med Sci Sports Exerc 2002; 34: 1065–1070.

  21. 21.

    , , , . Fitness and fatness as predictors of mortality from all causes and from cardiovascular disease in men and women in the lipid research clinics study. Am J Epidemiol 2002; 156: 832–841.

  22. 22.

    , , , . Caerphilly study. What level of physical activity protects against premature cardiovascular death? The Caerphilly study. Heart 2003; 89: 502–506.

  23. 23.

    , , , , , . The effects of physical activity and body mass index on cardiovascular, cancer and all-cause mortality among 47 212 middle-aged Finnish men and women. Int J Obes (Lond) 2005; 29: 894–902.

  24. 24.

    , , , , , . Combined effects of obesity and physical activity in predicting mortality among men. J Intern Med 2008; 264: 442–451.

  25. 25.

    , , . Cardiorespiratory fitness, body composition, and all-cause and cardiovascular disease mortality in men. Am J Clin Nutr 1999; 69: 373–380.

  26. 26.

    , . Effects of physical inactivity and obesity on morbidity and mortality: current evidence and research issues. Med Sci Sports Exerc 1999; 31 (11 Suppl), S646–S662.

  27. 27.

    , , , , , . A comparison of adiposity measures as predictors of all-cause mortality: the Melbourne Collaborative Cohort Study. Obesity (Silver Spring) 2007; 15: 994–1003.

  28. 28.

    , , , , , et al. The combined relations of adiposity and smoking on mortality. Am J Clin Nutr 2008; 88: 1206–1212.

  29. 29.

    , , , , , et al. Relationship between body mass index and mortality among Europeans. Eur J Clin Nutr 2012; 66: 156–165.

  30. 30.

    , . Waist circumference and weight following smoking cessation in a general population: the Inter99 study. Prev Med 2007; 44: 290–295.

  31. 31.

    . On the relative nature of overadjustment and unnecessary adjustment. Epidemiology 2009; 20: 496–499.

  32. 32.

    , , . Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology 2009; 20: 488–495.

  33. 33.

    , , , , . Body weight, cardiovascular risk factors, and coronary mortality. 15-year follow-up of middle-aged men and women in eastern Finland. Circulation 1996; 93: 1372–1379.

  34. 34.

    , , , . The sagittal waist diameter and mortality in men: the Baltimore longitudinal study on aging. Int J Obes Relat Metab Disord 1994; 18: 61–67.

  35. 35.

    , , , . Sagittal abdominal diameter and risk of sudden death in asymptomatic middle-aged men: the Paris Prospective Study I. Circulation 2004; 110: 2781–2785.

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Acknowledgements

This study was supported by grants from Academy Finland (1129197, 136895 and 141005).

Author information

Author notes

Affiliations

  1. Department of Public Health, Hjelt Institute, University of Helsinki, Helsinki, Finland

    • X Song
    •  & Q Qiao
  2. Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland

    • X Song
    • , P Jousilahti
    • , T Laatikainen
    • , J Tuomilehto
    •  & Q Qiao
  3. Department of Internal Medicine and Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre, Maastricht, The Netherlands

    • C D A Stehouwer
  4. Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden

    • S Söderberg
  5. Baker IDI Heart and Diabetes Institute, Melbourne, Australia

    • S Söderberg
  6. Department of Cardiology, Turkish Society of Cardiology Cerrahpaşa Medical Faculty, Istanbul, Turkey

    • A Onat
  7. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland

    • T Laatikainen
  8. Hospital District of North Karelia, Joensuu, Finland

    • T Laatikainen
  9. Department of Primary Care and Population Sciences, Royal Free and University College Medical School, London, UK

    • J S Yudkin
    •  & R Morris
  10. Unit for Cardiovascular Epidemiology, The Gertner Institute, Sheba Medical Center, Tel Hashomer, Israel

    • R Dankner
  11. Division of Epidemiology and Prevention, School of Public Health, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

    • R Dankner
  12. Center for Vascular Prevention, Danube University Krems, Krems, Austria

    • J Tuomilehto
  13. King Abdulaziz University, Jeddah, Saudi Arabia

    • J Tuomilehto
  14. R&D AstraZeneca AB, Mölndal, Sweden

    • Q Qiao

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Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to Q Qiao.

Supplementary information

Word documents

  1. 1.

    Supplementary Table

Appendices

Appendix

Studies and investigators in this collaborative study are as follows:

Finland National FINRISK 1987, 1992 and 1997 Cohorts: J. Tuomilehto1,2,3, P. Jousilahti2, J. Lindström2, 1. Department of Public Health, University of Helsinki, Helsinki; 2. Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki; 3. South Ostrobothnia Central Hospital, Seinäjoki, Finland.

National FINRISK 2002 Study: J. Tuomilehto1,2, T. Laatikainen2,3,4, M. Peltonen2, J. Lindström2, 1. Department of Public Health, University of Helsinki, Helsinki; 2. Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki; 3. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; 4. Hospital District of North Karelia, Joensuu, Finland.

Sweden Northern Sweden MONICA Survey: S. Söderberg1,2, M. Eliasson1, 1. Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden; 2. Baker IDI Heart and Diabetes Institute, Melbourne, Australia.

The Uppsala Longitudinal Study of Adult Men (ULSAM): B. Zethelius, Department of Public Health/Geriatrics, Uppsala University Hospital, Uppsala.

Turkey Turkish Adult Risk Factor Study (TARFS): A Onat1,2. 1Turkish Society of Cardiology, Istanbul; 2Department of Cardiology, Cerrahpasa Medical Faculty, Istanbul University, Istanbul.

United Kingdom Whitehall II Study: M.G.Marmot1, A.G. Tabák1,2, M. Kivimäki1,3, E.J. Brunner1, D.R. Witte1,4, 1. Department of Epidemiology and Public Health, University College London, London, UK; 2. Semmelweis University Faculty of Medicine, 1st Department of Medicine, Budapest, Hungary; 3. Finnish Institute of Occupational Health, Helsinki, Finland; 4. Steno Diabetes Center, Gentofte, Denmark.

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