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Energy Density of Unappetizing Foods Predicts Weight Gain
- 1. Increasing energy density of unappetizing foods is associated with lower weight gain in adolescents Jennifer R. Sadler Department of Nutrition University of North Carolina, Chapel Hill
- 2. How do food preferences relate to weight change?
- 7. Does the relationship between energy density and preference predict weight change over time in adolescents? Food Preference Weight Change
- 8. Does the relationship between energy density and preference predict weight change over time in adolescents? Food Preference Weight Change Hypothesis High ED foods Weight Gain Low ED foods
- 9. Sample • 133 adolescents recruited in Oregon
- 10. Sample • 133 adolescents recruited in Oregon • Baseline assessment (age 15): – BMI – Demographics – Dietary Restraint via DEBQ1 – Image Rate Paradigm2 1. (Van Strien et al. 1986) 2. (Burger et al. 2009)
- 11. IMAGE RATE
- 12. How appetizing is this food? Disgusting Extremely Appealing
- 13. How appetizing is this food? Disgusting Extremely Appealing
- 14. IMAGE RATE • 103 food images • Varied energy density • Representative food groups/types • Mix of natural & empty backgrounds
- 15. Sample • 133 adolescents recruited in Oregon • Baseline (age 15): – BMI – Image Rate Paradigm • 3 years of follow-up anthropometrics – 117 completed 1 or more years of follow-up
- 16. Analysis • Per participant, selected food image groups – Appetizing: 32 highest rated images – Unappetizing: 32 lowest rated images • For each image group, calculated average palatability rating and average energy density
- 17. Analysis • Calculated BMI change as ∆BMI/years • Used linear regression to test if BMI change (slope) was predicted by image rate results
- 18. Analysis • Calculated BMI change as ∆BMI/years at year 4 • Used linear regression to test if BMI change (slope) was predicted by image rate results Model 1 ∆BMI = avg. rating of appetizing foods + avg. rating of unappetizing foods
- 19. Analysis • Calculated BMI change as ∆BMI/years at year 4 • Used linear regression to test if BMI change (slope) was predicted by image rate results Model 2 ∆BMI = avg. ED of appetizing foods + avg. ED of unappetizing foods
- 20. Analysis • Controlled for self-reported hunger (pre Image Rate paradigm), dietary restraint, sex, and baseline BMI
- 21. Analysis • Controlled for self-reported hunger (pre Image Rate paradigm), dietary restraint, sex, and baseline BMI • Analysis completed in RStudio (v. 1.1.453).
- 22. Analysis • Controlled for self-reported hunger (pre Image Rate paradigm), dietary restraint, sex, and baseline BMI • Analysis completed in RStudio (v. 1.1.453). • Model significance thresholded at Bonferroni-corrected p < 0.025. Variable significance thresholded at Bonferroni- corrected p < 0.007.
- 23. Sample Characteristics (n=117) Sex 55% Female Baseline Age 15.0 ± 0.9 years Baseline BMI 21.2 ± 2.3 kg/m2 Race/Ethnicity 76% Non-Hispanic White Self-Reported Hunger (0- 100) 11.0 ± 4.0 Dietary Restraint 16.4 ± 6.3 Pubertal Development Stage (0-4) 2.6 ± 0.4 ∆BMI (per year) 0.5 ± 0.7
- 24. Image Rate Results Ratings (-100 – 100) All images 15.6 ± 17.8 Appetizing foods 71.4 ± 18.4 Unappetizing foods -41.6 ± 25.2 Energy Density (1.0 – 2.8) Appetizing foods 2.0 ± 0.4 kcal/g Unappetizing foods 1.9 ± 0.4 kcal/g
- 25. Significant difference in ratingsDensity 0 0.005 0.01 0.015 Average Rating -100 -50 0 50 100 Appetizing Unappetizing
- 26. No significant difference in energy density Appetizing Unappetizing 1.0 0.75 0.5 0.25 0 1.0 1.5 2.0 2.5 3.0 Density Average Energy Density (kcal/g)
- 27. Most common appetizing food images
- 28. Most common appetizing food images Low ED High ED
- 29. Most common unappetizing food images
- 30. Most common unappetizing food images High ED Low ED
- 31. Model 1: Ratings of appetizing and unappetizing foods does not predict change in BMI 4 2 0 -2 ∆BMI -100 1000 average rating of foods Appetizing Unappetizing
- 32. Model 1: Ratings of appetizing and unappetizing foods does not predict change in BMI 4 2 0 -2 ∆BMI -100 1000 average rating of foods Ratings of food images does not significantly predict BMI change (Model: p = 0.15) Appetizing Unappetizing
- 33. Model 2: Energy density of unappetizing foods significantly predicts change in BMI 4 2 0 -2 ∆BMI 1.5 2.0 2.5 average energy density of foods 3.01.0 Appetizing Unappetizing
- 34. Model 2: Energy density of unappetizing foods significantly predicts change in BMI 4 2 0 -2 ∆BMI 1.5 2.0 2.5 average energy density of foods 3.01.0 Appetizing Unappetizing Overall model significantly predicts change in BMI p = 0.0036
- 35. Model 2: Energy density of unappetizing foods significantly predicts change in BMI 4 2 0 -2 ∆BMI 1.5 2.0 2.5 average energy density of foods 3.01.0 Appetizing Unappetizing Overall model significantly predicts change in BMI p = 0.0036 ED of appetizing food images does not significantly predict BMI change (p = 0.07)
- 36. Model 2: Energy density of unappetizing foods significantly predicts change in BMI 4 2 0 -2 ∆BMI 1.5 2.0 2.5 average energy density of foods 3.01.0 Appetizing Unappetizing Overall model significantly predicts change in BMI p = 0.0036 ED of appetizing food images does not significantly predict BMI change (p = 0.07) ED of unappetizing food images significantly predicts BMI change (p = 0.0005)
- 37. Model 2: Energy density of unappetizing foods significantly predicts change in BMI 4 2 0 -2 ∆BMI 1.5 2.0 2.5 average energy density of unappetizing images Slope = -0.8 Controlled for hunger, dietary restraint, sex and baseline BMI
- 38. Conclusions • Energy density of food images rated as unappetizing predicts three year weight change in adolescents
- 39. Conclusions • Energy density of food images rated as unappetizing predicts three year weight change in adolescents • Effect is controlled for… – Baseline BMI – Hunger – Dietary Restraint – Sex
- 40. Conclusions • Energy density of food images rated as unappetizing predicts three year weight change in adolescents • Effect is controlled for… – Baseline BMI – Hunger – Dietary Restraint – Sex • Ratings of ED foods did not predict change in BMI
- 41. Conclusions • Dislike of high ED foods protects against weight gain – Preference Food Choice
- 42. Conclusions • Dislike of high ED foods protects against weight gain – Preference Food Choice • Dislike as a target for intervention
- 43. Conclusions • Dislike of high ED foods protects against weight gain – Preference Food Choice • Dislike as a target for intervention1 1. (Legget et al, 2013)
- 44. Conclusions • Dislike of high ED foods protects against weight gain – Preference Food Choice • Dislike as a target for intervention • Limitation: Preferences change over time
- 45. Conclusions • Dislike of high ED foods protects against weight gain – Preference Food Choice • Dislike as a target for intervention • Limitation: Preferences change over time • Valence of preference matters
- 46. niblunc.org jen_sadler@unc.edu @jennyrsadler Dr. Eric Stice Dr. Kyle Burger Dr. Grace Shearrer Funding: R01DK092468 Connect with me:
- 47. References • Drewnowski, A. (1998). Energy density, palatability, and satiety: implications for weight control. Nutrition reviews, 56(12), 347-353. • Potter, C., Griggs, R. L., Ferriday, D., Rogers, P. J., & Brunstrom, J. M. (2017). Individual variability in preference for energy-dense foods fails to predict child BMI percentile. Physiology & behavior, 176, 3-8. • Nederkoorn, C., Houben, K., Hofmann, W., Roefs, A., & Jansen, A. (2010). Control yourself or just eat what you like? Weight gain over a year is predicted by an interactive effect of response inhibition and implicit preference for snack foods. Health Psychology, 29(4), 389. • Dietz, W. H. (1994). Critical periods in childhood for the development of obesity. The American journal of clinical nutrition, 59(5), 955-959. • Van Strien, T., Frijters, J. E., Bergers, G. P., & Defares, P. B. (1986). The Dutch Eating Behavior Questionnaire (DEBQ) for assessment of restrained, emotional, and external eating behavior. International journal of eating disorders, 5(2), 295-315. • Drewnowski, A. (1997). Taste preferences and food intake. Annual review of nutrition, 17(1), 237- 253. • Legget, K. T., Cornier, M. A., Rojas, D. C., Lawful, B., & Tregellas, J. R. (2015). Harnessing the power of disgust: a randomized trial to reduce high-calorie food appeal through implicit priming. The American journal of clinical nutrition, 102(2), 249-255.
- 48. 1.5 0 -1 ∆BMI 1.5 2.0 2.5 average energy density of unappetizing images Male Female Significant interaction of sex on relationship between energy density of unappetizing food images and BMI change
Editor's Notes
- Thank you!
- People inherent prefer highly energy dense foods, which theoretically contributes to repeated overconsumption and weight gain. (Drewnowski, A. (1998). Energy density, palatability, and satiety: implications for weight control. Nutrition reviews, 56(12), 347-353.)
- However, tests of this relationship have produced mixed results. Potter et al found no relation between preference for high ED foods and child BMI-percentile in children 3-14 years old. While Nederkoorm found that preference for snack foods interacted with inhibition to predict prospective weight gain in adults.
- However, when teasing apart this relationship, its important to consider that preference has valence. Typically, studies focus on foods that individuals like. But what about foods that they dislike?
- This question is of upmost importance during adolescents, since adolescents gain autonomy in their food choices, allowing their preferences to more strongly impact their weight.
- We hypothesized that preference for high
- Our data came from participants in the Chocolate study, which was a study lead by Eric Stice at the Oregon Research Institute.
- Our data came from participants in the Chocolate study, which was a study lead by Eric Stice at the Oregon Research Institute. Participants were 15 years old at baseline. They completed a baseline assessment
- Our data came from participants in the Chocolate study, which was a study lead by Eric Stice at the Oregon Research Institute.
- BMI change was calculated by regressing BMI at each year of follow-up against time to calculate a BMI slope. We used two linear regression models to test if Body mass variability is represented by distinct functional connectivity patterns
- BMI change was calculated by regressing BMI at each year of follow-up against time to calculate a BMI slope. We used two linear regression models to test if Body mass variability is represented by distinct functional connectivity patterns
- Ran two models and each model had seven variables with controls
- Ran two models and each model had seven variables with controls
- Ran two models and each model had seven variables with controls
- A little over half of the sample were women. The sample was predominantly 15 years old and healthy weight at baseline. Most of sample did not report high dietary restraint, and most of the sample was undergoing puberty. Over the three years of follow-up, on average participants gained half a BMI unit per year.
- Star indicates significant difference between female and male participants
- If we look at the images that most commonly were rated as “appetizing” some clear highly ED foods are found, like chocolates, cookies, and french fries. BUT there also is a consistent preference for low ED fruits.
- If we look at the images that most commonly were rated as “appetizing” some clear highly ED foods are found, like chocolates, cookies, and french fries. BUT there also is a consistent preference for low ED fruits.
- A similar trend is found in the images most commonly rated as unappetizing – there are high ED food items, like trail mix and chicken wings, and there are low ED items like grilled vegetable skewers and wild rice.
- A similar trend is found in the images most commonly rated as unappetizing – there are high ED food items, like trail mix and chicken wings, and there are low ED items like grilled vegetable skewers and wild rice.
- Test interactions
- Test interactions
- Test interactions
- Looking at our two variables of interest, we found that the ED of appetizing food images did not significantly predict change in BMI.
- However, the ED of unappetizing food images DID predict change in BMI. This relationship was highly significant.
- Here I’ve plotted the relationship between energy density of unappetizing food images and change in BMI alone. The slope of this line is negative .8, so with a 1 kcal per gram increase in the average energy density of unappetizing food images, we would expect a 0.8 decrease in BMI change per year. Over our three years of follow-up, this would protect against a 2.4 unit increase in BMI.
- So in a sample of adolescents, followed for three years, we found that the energy density of food images rated as unappetizing predicted BMI, and the relationship was negative such that increasing the energy density of unappetizing food images was associated with lower weight gain over three years.
- This effect is seen controlling for baseline BMI, self-reported hunger before rating the images, dietary restraint, and biological sex.
- We found that ratings of these images did not predict change in BMI either.
- This may be acting through food choice, but the study did not directly assess food choice, so we can’t be sure.
- Group at CU NOT CSU
- One thing