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Does a parsimonious measure of complex body mass index trajectories exist?

International Journal of Obesity (2018) | Download Citation



A single measure that distills complex body mass index (BMI) trajectories into one value could facilitate otherwise complicated analyses. This study creates and assesses the validity of such a measure: average excess BMI.


We use data from Waves I–IV of the National Longitudinal Study of Adolescent to Adult Health (n = 17,669). We calculate average excess BMI by integrating to find the area above a healthy BMI trajectory and below each subject-specific trajectory and divide this value by total study time. To assess validity and utility, we (1) evaluate relationships between average excess BMI from adolescence to adulthood and adult chronic conditions, (2) compare associations and fit to models using subject-specific BMI trajectory parameter estimates as predictors, and (3) compare associations to models using BMI trajectory parameter estimates as outcomes.


Average excess BMI from adolescence to adulthood is associated with increased odds of hypertension (OR = 1.56; 95% CI: 1.47, 1.67), hyperlipidemia (OR = 1.36; 95% CI: 1.26, 1.47), and diabetes (OR = 1.57; 95% CI: 1.47, 1.67). The odds associated with average excess BMI are higher than the odds associated with the BMI intercept, linear, or quadratic slope. Correlations between observed and predicted health outcomes are slightly lower for some models using average excess BMI as the focal predictor compared to those using BMI intercept, linear, and quadratic slope. When using trajectory parameters as outcomes, some co-variates associate with the intercept, linear, and quadratic slope in contradicting directions.


This study supports the utility of average excess BMI as an outcome. The higher an individual's average excess BMI from adolescence to adulthood, the greater their odds of chronic conditions. Future studies investigating longitudinal BMI as an outcome should consider using average excess BMI, whereas studies that conceptualize longitudinal BMI as the predictor should continue using traditional latent growth methods.

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This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain Add Health data files is available on the website: No direct support was received from grant P01-HD31921 for this analysis. Benjamin Sokol provided MATLAB® coding support, with the application of the user-contributed M Files, hatchfill2 © 2016 Takeshi Ikuma, and legendflex © 2015 Kelly Kearney.


This work was supported by the National Institute of Child Health and Human Development (T32-HD07376), and by the National Institute on Drug Abuse of the National Institutes of Health (K01 DA035153).

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  1. Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, USA

    • Rebeccah L. Sokol
    • , Nisha C. Gottfredson
    • , Jennifer M. Poti
    • , Carolyn T. Halpern
    • , Meghan E. Shanahan
    • , Edwin B. Fisher
    •  & Susan T. Ennett


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The authors declare that they have no conflict of interest.

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Correspondence to Rebeccah L. Sokol.

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