Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Food and health

Baseline characteristics associated with different BMI trajectories in weight loss trials: a case for better targeting of interventions

Abstract

Background/Objectives:

Dietary weight loss interventions have heterogeneous outcomes in long-term studies, with many participants regaining part or all of the lost weight. Growth mixture modelling is a novel analytic approach that can be used to identify different trajectories of weight change during a trial rather than focussing on the total amount of weight lost.

Subjects/Methods:

Data were pooled from two 12-month dietary weight loss studies where no significant difference was detected between the treatment and control arms, thus allowing analysis independent of treatment. The data set included 231 subjects (74.5% female), with a mean weight loss of 6.40 kg (4.96). Growth mixture models were used to identify participants with similar trajectories of change in body mass index (BMI).

Results:

Three subgroups were identified. A rapid and continuing BMI loss over the study period (rapid, n=53), a rapid initial weight loss in the first 3 months with a slowing rate over the remaining 9 months (maintainers, n=146) and those with an initial loss trajectory, which slowed and began to increase at 9 months (recidivists, n=53). Age (s.d.) and BMI (s.d.) were significantly different between the three groups (rapid 53 years (7), 28.99 kg/m2 (3.30); maintainers 47 years (9), 30.90 kg/m2 (2.95); recidivists 44 years (7), 34.84 kg/m2 (1.92), both P<0.001).

Conclusions:

Older subjects with lower BMIs were more likely to have a rapid and continuing weight loss in a 1-year dietary-based weight loss intervention. Different interventional approaches may be necessary for different ages and baseline BMIs and stratification prior to randomisation may be necessary to prevent confounding in weight loss trials.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2

Similar content being viewed by others

References

  1. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014; 384: 766–781.

    Article  Google Scholar 

  2. MacLean PS, Wing RR, Davidson T, Epstein L, Goodpaster B, Hall KD et al. NIH working group report: innovative research to improve maintenance of weight loss. Obesity 2014; 23: 7–15.

    Article  Google Scholar 

  3. Blackburn G . Effect of degree of weight loss on health benefits. Obesity Res 1995; 3: 211s–216s.

    Article  Google Scholar 

  4. Neiberg RH, Wing RR, Bray GA, Reboussin DM, Rickman AD, Johnson KC et al. Patterns of weight change associated with long-term weight change and cardiovascular disease risk factors in the Look AHEAD Study. Obesity (Silver Spring) 2012; 20: 2048–2056.

    Article  CAS  Google Scholar 

  5. Jung T, Wickrama KAS . An introduction to latent class growth analysis and growth mixture modeling. Soc Personal Psychol Compass 2008; 2: 302–317.

    Article  Google Scholar 

  6. Muthen B, Brown HC, Hunter A, Cook IA, Leuchter AF . General approaches to the analysis of course: applying growth mixture modeling to randomized trials of depression medication. In: Shrout PE (ed.) Causality and Psychopathology: Finding the Determinants of Disorders and Their Cures. Oxford University Press: New York, NY, USA. pp 159–178, 2011.

    Google Scholar 

  7. Muthen B, Muthen LK . Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. Alcohol Clin Exp Res 2000; 24: 882–891.

    Article  CAS  Google Scholar 

  8. Tapsell LC, Batterham MJ, Thorne RL, O'Shea JE, Grafenauer SJ, Probst YC . Weight loss effects from vegetable intake: a 12-month randomised controlled trial. Eur J Clin Nutr 2014; 68: 778–785.

    Article  CAS  Google Scholar 

  9. Tapsell LC, Batterham MJ, Charlton KE, Neale EP, Probst YC, O'Shea JE et al. Foods, nutrients or whole diets: effects of targeting fish and LCn3PUFA consumption in a 12mo weight loss trial. BMC Public Health 2013; 13: 1231.

    Article  Google Scholar 

  10. Baecke JA, Burema J, Frijters JE . A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr 1982; 36: 936–942.

    Article  CAS  Google Scholar 

  11. Bauer DJ, Curran PJ . Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes. Psychol Methods 2003; 8: 338–363.

    Article  Google Scholar 

  12. Rindskopf D . Mixture or homogeneous? Comment on Bauer and Curran. Psychol Methods 2003; 8: 364–368. discussion 384-393.

    Article  Google Scholar 

  13. Muthen B . Statistical and substantive checking in growth mixture modeling: comment on Bauer and Curran. Psychol Methods 2003; 8: 369–377. discussion 384-393.

    Article  Google Scholar 

  14. Franz MJ, VanWormer JJ, Crain AL, Boucher JL, Histon T, Caplan W et al. Weight-loss outcomes: a systematic review and meta-analysis of weight-loss clinical trials with a minimum 1-year follow-up. J Am Diet Assoc 2007; 107: 1755–1767.

    Article  Google Scholar 

  15. de Vos BC, Runhaar J, Verkleij SP, van Middelkoop M, Bierma-Zeinstra SM . Latent class growth analysis successfully identified subgroups of participants during a weight loss intervention trial. J Clin Epidemiol 2014; 67: 947–951.

    Article  Google Scholar 

  16. Espeland MA, Bray GA, Neiberg R, Rejeski WJ, Knowler WC, Lang W et al. Describing patterns of weight changes using principal components analysis: results from the Action for Health in Diabetes (Look AHEAD) research group. Ann Epidemiol 2009; 19: 701–710.

    Article  Google Scholar 

  17. Wadden TA, Bantle JP, Blackburn GL, Bolin P, Brancati FL, Bray GA et al. Eight-year weight losses with an intensive lifestyle intervention: the look AHEAD study. Obesity (Silver Spring) 2014; 22: 5–13.

    Article  Google Scholar 

  18. Kaiser KA, Gadbury GL . Estimating the range of obesity treatment response variability in humans: methods and illustrations. Human Hered 2013; 75: 127–135.

    Article  CAS  Google Scholar 

  19. Poulson RS, Gadbury GL, Allison DB . Treatment heterogeneity and individual qualitative interaction. Am Stat 2012; 66: 16–24.

    Article  Google Scholar 

  20. Deram S, Villares SM . Genetic variants influencing effectiveness of weight loss strategies. Arq Bras Endocrinol Metabol 2009; 53: 129–138.

    Article  Google Scholar 

  21. Singer JD, Willett JB . Modeling change using covariance structure analysis. In: Applied Longitudinal Data Analysis. Oxford University Press: New York, NY, USA, pp 266–302, 2003.

    Chapter  Google Scholar 

  22. Fitzmaurice G, Davidan M, Verbeke G, Molenberghs G . Longitudinal Data Analysis. Chapman & Hall/CRC: Boca Raton, FL, USA, 2009.

    Google Scholar 

  23. Stull DE, Houghton K . Identifying differential responders and their characteristics in clinical trials: innovative methods for analyzing longitudinal data. Value Health 2013; 16: 164–176.

    Article  Google Scholar 

  24. Rosenbaum M, Hirsch J, Gallagher DA, Leibel RL . Long-term persistence of adaptive thermogenesis in subjects who have maintained a reduced body weight. Am J Clin Nutr 2008; 88: 906–912.

    Article  CAS  Google Scholar 

  25. Rosenbaum M, Leibel RL . Adaptive responses to weight loss. In: Kushner RF, Bessesen DH (eds). Treatment of the Obese Patient. Springer: New York, NY, USA, pp 97–115, 2014.

    Google Scholar 

  26. Sumithran P, Prendergast LA, Delbridge E, Purcell K, Shulkes A, Kriketos A et al. Long-term persistence of hormonal adaptations to weight loss. N Engl J Med 2011; 365: 1597–1604.

    Article  CAS  Google Scholar 

  27. Erez G, Tirosh A, Rudich A, Meiner V, Schwarzfuchs D, Sharon N et al. Phenotypic and genetic variation in leptin as determinants of weight regain. Int J Obes 2011; 35: 785–792.

    Article  CAS  Google Scholar 

  28. Jenkins AB, Campbell LV . Future management of human obesity: understanding the meaning of genetic susceptibility. Adv Genomics Genet 2014; 4: 219–232.

    Article  Google Scholar 

  29. Thomas DM, Ivanescu AE, Martin CK, Heymsfield SB, Marshall K, Bodrato VE et al. Predicting successful long-term weight loss from short-term weight-loss outcomes: new insights from a dynamic energy balance model (The POUNDS Lost study). Am J Clin Nutr 2015; 101: 449–454.

    Article  CAS  Google Scholar 

  30. Thomas DM, Martin CK, Heymsfield S, Redman LM, Schoeller DA, Levine JA . A simple model predicting individual weight change in humans. J Biol Dyn 2011; 5: 579–599.

    Article  Google Scholar 

Download references

Acknowledgements

Funding for the studies was provided by the Australian National Health and Medical Research Council (Project Grant Number 514631) and by Horticulture Australia Limited using the vegetable levy and matched funding from the Australian Government.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M Batterham.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Batterham, M., Tapsell, L. & Charlton, K. Baseline characteristics associated with different BMI trajectories in weight loss trials: a case for better targeting of interventions. Eur J Clin Nutr 70, 207–211 (2016). https://doi.org/10.1038/ejcn.2015.45

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ejcn.2015.45

This article is cited by

Search

Quick links