A comparison of models with weight, height, and BMI as predictors of mortality

INTRODUCTION: Body mass index (BMI) is a composite variable of weight and height, often used as a predictor of health outcomes, including mortality. The main purpose of combining weight and height in one variable is to obtain a measure of obesity independent of height. It is however unclear how accurate BMI is as a predictor of mortality compared with models including both weight and height or a weight × height interaction as predictors. METHODS: The current study used conscription data on weight, height, and BMI of Swedish men (N = 48,904) in 1969/70 as well as linked data on mortality (3442 deaths) between 1969 and 2008. Cox proportional hazard models including combinations of weight, height, and BMI at conscription as predictors of subsequent all‐cause and cause‐specific mortality were fitted to data. RESULTS: An increase by one standard deviation on weight and BMI were associated with an increase in hazard for all‐cause mortality by 5.4% and 11.5%, respectively, while an increase by one standard deviation on height was associated with a decrease in hazard for all‐cause mortality by 9.4%. The best‐fitting model indicated lowest predicted all‐cause mortality for those who weighed 60.5 kg at conscription, regardless of height. Further analyses of cause‐specific mortality suggest that this weight seems to be a compromise between lower optimal weights to avoid cancer and CVD mortality and a higher optimal weight to not die by suicide. CONCLUSIONS: According to the present findings, there are several ways to make better use of measured weight and height than to calculate BMI when predicting mortality.