OBJECTIVE: To describe differences in the 22 y mortality risk associated with body mass index (BMI), body fat or fatfree mass, in order to examine if the differential health consequences of fat and fat-free mass may be responsible for elevated mortality rates at both high and low BMI. DESIGN: Prospective cohort study, a 22 y follow-up. SETTING: General community. The study of men born in 1913, Gothenburg. SUBJECTS: 787 men aged 60 y. MAIN OUTCOME MEASURES: Number and time of total deaths from 1973 to 1995. RESULTS: The risk of dying was a linear function of percentage fat and fat-free mass, and increased from a relative risk of 1.00 in men belonging to the lowest ®fth to 1.4 (95% con®dence interval 1.11 ± 1.99) in men in the highest ®fth of percentage fat mass. For BMI the lowest risk was observed for men belonging to the middle ®fth of BMI. When the relative risk was set at 1.00 for subjects belonging to the middle ®fth of BMI the risk associated with the low BMI ®fth was 1.3 (95% con®dence interval 0.94 ± 1.68) and that with the highest ®fth was 1.5 (95% con®dence interval 1.09 ± 1.96). Analyses including both body fat and fat-free mass showed that total mortality was a linear increasing function of high fat and low fat-free mass. CONCLUSION: The apparent U-shaped association between BMI and total mortality may be the result of compound risk functions from body fat and fat-free mass.

In this study in 437 women born in 1948 selected from five European towns we show that several anthropometric measurements are consistently and significantly associated with a metabolic risk profile in premenopausal women of 38 years of age. Among the circumferences, breast and waist circumference were, after adjustment for body mass index positively correlated with diastolic blood pressure, serum total cholesterol, HDL-cholesterol (negative associations), serum triglycerides, and serum insulin. The optimal level for measuring waist circumference was determined as being midway between the lower rib margin and the superior anterior iliac crest. Circumference ratios generally did show less consistent and similar or lower correlations with diastolic blood pressure, serum cholesterol and serum insulin. Waist/thigh circumference ratio showed the strongest partial associations with HDL-cholesterol and serum triglycerides among all anthropometric variables studied. The authors conclude that, independently of the degree of fatness, indicators of truncal fat distribution (in the region of the breast as well as the abdomen) are related to an unfavourable risk profile in European premenopausal women. The study gives a rationale for selecting the most informative anthropometric measurements to be added to height and weight in epidemiological studies.

The authors studied 512 European men all born in 1950 from six different towns in the period October 1988 to May 1989. Anthropometric measurements were taken, including weight, height, and circumferences (waist, hip, thigh). Educational level, activity scores and information on smoking habits were obtained from a questionnaire. Higher educational level was associated with lower body mass index, waist/hip ratio, and waist/thigh ratio. The sports activity score was negatively related to waist/hip ratio (beta +/- standard error of the mean (SEM): -0.009 +/- 0.003) and waist/thigh ratio (-0.041 +/- 0.007), and this could be attributed to a negative relation with waist circumference and a positive relation to thigh circumference. Smoking habits were not related to body mass index but heavy smokers had larger waist circumferences (difference +/- SEM: 1.4 +/- 0.5 cm) as well as higher waist/hip ratios (difference +/- SEM: 0.014 +/- 0.005) and waist/thigh ratios (0.043 +/- 0.013) compared with men who never smoked. These associations between activity scores and smoking habits and fat distribution remained after adjustment for each other and for body mass index and educational level. The authors conclude that physical activity and smoking are independently related to indicators of fat distribution and may be potential confounders in the relations between fat distribution, risk factors, and disease.

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