Weight or waist: which is better for predicting cardiovascular diseases?

1. Weight or waist: which is better for predicting cardiovascular diseases? A review. By Hsin-Jen Chen

2. Outline • Introduction • Cross-sectional evidences • Prospective evidences • Discussion

3. Introduction What is this review for?

4. Obesity as a CVD risk factor • Obesity: excess fat accumulation, which could lead to adverse health outcomes. • Measuring total body fat • Underwater weighing • Image methods: DEXA • Bio-electrical impedance • Body mass index • Measuring fat distribution (esp. at abdomen) • Image methods: CT, MRI… • Waist circumference, waist-hip ratio…

5. Total fat vs. abdominal fat • Facts • General obesity (BMI) has been considered as a risk factor for many diseases. • Two types of fat accumulations are correlated. • ATP III adopts abdominal rather than general obesity in the definition of metabolic syndrome.

6. Take stroke for instance Subjects: Korean Song et al., 2004. Stroke

7. Total fat vs. abdominal fat • Facts • General obesity (BMI) has been considered as a risk factor for many diseases. • Two types of fat accumulations are correlated. • ATP III adopts abdominal rather than general obesity in the definition of metabolic syndrome.

8. Iwao et al., 2001, Obes Res

9. Total fat vs. abdominal fat • Facts • General obesity (BMI) has been considered as a risk factor for many diseases. • Two types of fat accumulations are correlated. • ATP III adopts abdominal rather than general obesity in the definition of metabolic syndrome.

10. NCEP ATP III, 2001, JAMA

11. Total fat vs. abdominal fat • Facts • General obesity (BMI) has been considered as a risk factor for many diseases. • Two types of fat accumulations are correlated. • ATP III adopts abdominal rather than general obesity in the definition of metabolic syndrome. • Questions • Do elevated abdominal fat imposes CVD risk independently from total fat? • Which is better for evaluating CVD risk?

12. Cross-sectional evidences

13. Associations between BMI, waist, and CVD • Step 1: • Comparing the correlation coefficients

14. Study: NHANES III • Subjects: US white men and women (n=9019) • Age: 20~90 Zhu et al., 2002, Am J Clin Nutr

15. Associations between BMI, waist, and CVD • Step 1: • Comparing the correlation coefficients • Step 2: • Taking age into account

16. Study: Baltimore Longitudinal Study of Aging (BLSA) • Subjects: US white men and women (n=1941) • Age: 17~100 (dichotomized at 65 y/o) Iwao et al., 2001, Obes Res

17. Study: The Australian Diabetes, Obesity and Lifestyle Study • Subjects: Australian residents in non-rural areas (mostly white, n=11247) • Age: >=25 y/o Dalton et al., 2003, J Intern Med. Next page

18. Before and after age-adjustment: Dalton et al., 2003, J Intern Med.

19. Associations between BMI, waist, and CVD • Step 1: • Comparing the correlation coefficients • Step 2: • Taking age into account • Step 3: • Controlling BMI and WC/WHR simultaneously

20. Study: Baltimore Longitudinal Study of Aging (BLSA) Iwao et al., 2001, Obes Res

21. Study: Canada Heart Health Surveys • Subjects: Canadian men and women (n=7981) • Age: 20~75 y/o Ardern et al., 2003, Obes Res

22. Summary • Focusing on the associations between fatness (BMI, WC, WHR) and other CVD risk factors • Data showing the cross-sectional relation between fatness and presence of CVD are rare. • Body compositions in subjects with overt diseases would have been changed • Age can explain much of the association between WC and CVD risk factors.

23. Prospective evidences

25. Weight or waist predicts better? • Jointly comparison • Estimating the effects of BMI and WC (or WHR) on health outcomes in the same model

26. All causes mortality

27. All causes mortality Sex specific lines (50~64 y/o) Dashed lines: unadjusted RR Solid lines: mutual adjusted RR

28. CHD mortality

29. CHD mortality

30. CHD mortality

31. CHD mortality

32. CHD incidence

33. CHD incidence Age-adjusted incidence rates

34. CHD incidence

35. CHD incidence

36. CHD incidence Age-adjusted incidence rates

37. CHD incidence Model 2: further adjusted BMI Model 3: further adjusted WHR

38. CHD incidence

39. CHD incidence

40. CHD incidence

41. CHD incidence Quartiles Men BMI: 24.7, 26.9, 29.7 WHR: 0.93, 0.96, 1.00 Women BMI: 23.3, 26.5, 31.0 WHR: 0.84, 0.89, 0.95

42. CHD incidence

43. CHD incidence Shanghai Women’s Health Study

44. Other CVD health outcomes

45. Other CVD health outcomes

46. Summary: CVD events

47. Summary: other outcomes

48. Discussion

49. What we can learn from epi data? • Weight and waist predict differently on different outcomes • Mortality • From all cause and CVD other than CHD • From CHD • Morbidity (CHD) • The effects of weight and waist depend on the sex and age • Elderly: WHR • Younger: BMI • Women: waist/WHR • Men: BMI

50. Prospective considerations • Age and sex would be better stratified in future research regarding obesity and CVDs. • Should the definition of metabolic syndrome take this discrepancy into account? • Could these evidences be translated into age-sex specific advices on CVD prevention? • Dilemma between concision and precision.