Patients with cardiovascular disease (CVD) are a heterogeneous group regarding their body mass index (BMI) levels at the time of diagnosis. To address the heterogeneity of CVD, we examined the trajectories of change in body mass index (BMI) and in other cardio-metabolic risk factors before CVD diagnosis. The study included 6126 participants from the prospective population-based Rotterdam Study, followed over 22 years with clinical examinations every 4 years. Latent class trajectory analysis and mixed-effect models were used to develop trajectories of BMI and other cardio-metabolic risk factors respectively. During follow-up, 1748 participants developed CVD, among whom we identified 3 distinct BMI trajectories. The majority of participants (n = 1534, 87.8 %) had steady BMI levels during follow-up, comprising the “stable weight” group. This group showed decrease in mean high-density lipoprotein (HDL) cholesterol over time. The second group, the “progressive weight gain” group (n = 112, 6.4 %), showed a progressive increase in BMI levels. In this group, mean waist circumference increased, mean HDL cholesterol decreased and mean fasting glucose levels were fluctuating over follow-up. In the third group, the “progressive weight loss” group (n = 102, 5.8 %), BMI levels decreased during follow-up. This group showed a decrease in mean waist circumference and in fasting glucose. In conclusion, the majority of individuals who developed CVD had a stable weight during follow-up, suggesting that BMI alone is not a good indicator for identifying middle-aged and elderly individuals at high risk of CVD. Waist circumference, HDL cholesterol, and glucose trajectories differed between the identified BMI subgroups, further highlighting that CVD is a heterogeneous disease with different pathophysiological pathways.

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European Journal of Epidemiology
Department of Bioinformatics

Dhana, K., van Rosmalen, J., Vistisen, D., Ikram, A., Hofman, A., Franco, O., & Kavousi, M. (2016). Trajectories of body mass index before the diagnosis of cardiovascular disease: a latent class trajectory analysis. European Journal of Epidemiology, 31(6), 583–592. doi:10.1007/s10654-016-0131-0