n a paper published in The Gerontologist a few years ago, my colleagues and I applied a joint generalized growth mixture-proportional hazards survival model and found that weight gain among older adults has little detrimental effects on mortality while weight loss is a powerful predictor of mortality.

Recently,  working with brilliant collaborators Snehalata Huzurbazar and Mark Greenwood, we used a powerful nonparametric approach to model BMI trajectories.  In a paper published in Journal of Aging and Health, we applied hierarchical clustering of body weight functions estimated via the PACE algorithm to determine typical weight trajectories and their health correlates.  This method is completely novel – its components have recently been developed in statistical journals but it has never been applied to any substantive questions, whether in population health or elsewhere.

We found three types of weight trajectories: stable overweight, obese declining, and overweight gaining.   Losing weight among older adults was associated with substantially worse health outcomes than either being overweight or even further gaining weight.   Looking forward, I plan to expand this flexible functional approach to understanding aspects of body weight trajectories such as socioeconomic and health correlates.