Integration of Sweet Taste and Metabolism Determines Carbohydrate Reward

Abstract

Post-ingestive signals related to nutrient metabolism are thought to be the primary drivers of reinforcement potency of energy sources. Here, in a series of neuroimaging and indirect calorimetry human studies, we examine the relative roles of caloric load and perceived sweetness in driving metabolic, perceptual, and brain responses to sugared beverages. Whereas caloric load was manipulated using the tasteless carbohydrate maltodextrin, sweetness levels were manipulated using the non-nutritive sweetener sucralose. By formulating beverages that contain different amounts of maltodextrin+sucralose, we demonstrate a non-linear association between caloric load, metabolic response, and reinforcement potency, which is driven in part by the extent to which sweetness is proportional to caloric load. In particular, we show that (1) lower-calorie beverages can produce greater metabolic response and condition greater brain response and liking than higher-calorie beverages and (2) when sweetness is proportional to caloric load, greater metabolic responses are observed. These results demonstrate a non-linear association between caloric load and reward and describe an unanticipated role for sweet taste in regulating carbohydrate metabolism, revealing a novel mechanism by which sugar-sweetened beverages influence physiological responses to carbohydrate ingestion.

Keywords: dopamine; energy expenditure; fMRI; gustation; gut-brain axis; metabolism; nucleus accumbens; obesity; sugar.

Figure 1. Design

Pre-conditioning session: Subjects rated ten non-caloric versions of the flavored beverages. If five were rated similarly and slightly liked subjects performed a triangle test to rule out oral detection of maltodextrin (Figure 2A). Exposure sessions: Qualifying subjects were scheduled for 5 exposure days (1 for each of 5 beverages differing in maltodextrin load as shown). At each session two bottles of the same beverage were consumed in the lab, one in the evening and three the next day at home for a total of 6 exposures per beverage. Post-conditioning session: Subjects again rated the 10 non-caloric versions of the flavored beverages. fMRI scan session: Brain response to the non-caloric versions of the five beverages was assessed using previously validated fMRI protocols and flavor delivery methods. For further details please refer to the STARS methods.

Figure 2. Experiment 1 perceptual results

(A) Triangle Test. Y-axis = number of correct responses. X-axis = subjects. Six correct responses were required for reliable detection (critical value). All qualifying subjects performed at chance. (B) Liking ratings: Y-axis = liking rating on the labeled hedonic scale. X-axis = conditioned stimuli (CS), corresponding to the 5 beverages before (dotted) and after (solid) pairing with the caloric load indicated in superscript. Error bars (+/− SEM). See also Table S1.

Figure 3. Experiment 1 fMRI results

Neural response in the amygdala (Amy, A) we observed decreased response to CS^37.5 compared to all other stimuli. Neural response in Nacc (B), was greater to the CS⁷⁵ compared to all other stimuli. Brain sections show significant clusters (corrected for multiple comparisons) of voxels, with the bargraphs showing average parameter estimate (PE) in arbitrary units (+/− SEM) of the peak voxel in the cluster. For illustrative purposes we performed post-hoc t-test using Bonferonni correction to determine which CSs specifically were significantly different from CS⁷⁵ or CS ^37.5. Color bar indicates T-values of voxels. See also Figure S1 and Table S2.

Figure 4. Experiment 2

(A) Change in the average REE for 5 minutes preceding, and for 5 minutes post beverage consumption (25–30 min post consumption; y-axis) for each beverage (x-axis). (B) Line graphs present the data from 10 – 30 minutes post beverage consumption expressed as a percent of the mean REE for all beverages 10 minutes prior to consumption. A repeated-measures ANOVA revealed a main effect of beverage (F = 19.653, p<.001), with post-hoc t-test showing change in REE greater following consumption of the 112.5kcal beverage relative to both the 0kcal and 150kcal beverages at each time point. In this and following graphs asterisk stands for significant post-hoc t-test at α = .05 corrected with Bonferonni adjustment for multiple comparisons.

Figure 5. Experiment 3

A) Change in liking (y – axis) pre vs. post-conditioning for each beverage (x-axis). Replicating study 1, planned comparisons reveal an increase in liking for CS^112.5, but not CS¹⁵⁰ (p = .03 and p = .83). (B) Change in REE across time (as in Figure 4B) depicting the main effect of beverage (F=5.994, p=0.011), with post-hoc t-tests showing change in REE greater following consumption of the 112.5kcal beverage relative to the 0kcal beverage at each time point and relative to the 150kcal at four timepoints. (C) The average change in REE as described in Figure 4A. Replicating study 2, there is greater change following the 112.5-kcal compared to the 0-kcal and 150-kcal beverages. (D) Change in blood glucose (y-axis) after consumption of each beverage (x-axis) depicting the effect of beverage, with greater change after 150- and 112.5-kcal beverages versus the non-caloric beverage. (E) Brain section shows significant clusters (corrected for multiple comparisons) of voxels. See also Table S3.

Figure 6. Experiment 4

(A) The average change in REE as described in Figure 4A for the 75 Kcal and 150 Kcal beverages with matched and mismatched calories and sweetness. (B) Change in REE across time. (C) The average change in REE for the matched and mismatched 112.5 Kcal beverage. (D) Change in REE across time.