The modern food landscape, marked by a rising prevalence of highly refined, ultra-processed, and highly palatable foods, combined with genetic and environmental susceptibilities, is widely considered a key factor driving obesity at the population level. Gaining insight into the physiological and behavioral mechanisms that shape food preferences and choices is crucial for understanding obesity's development and informing prevention strategies. One factor influencing habitual eating patterns, which may impact body weight, is flavor-nutrient learning. Research suggests that post-oral signaling is diminished in both animals and humans with obesity, potentially affecting flavor-nutrient learning. By analyzing pooled data from two similar preliminary studies, we found that markers of glycemic control—specifically fasting glucose and HbA1C—rather than BMI, were negatively correlated with changes in flavor liking in our flavor-nutrient learning task. These findings contribute to the expanding body of research on flavor-nutrient learning and underscore the variability in individual responses to these paradigms. Obesity is increasingly recognized as a complex and heterogeneous condition with diverse underlying mechanisms. Together, our findings and existing evidence emphasize the importance of further investigating how phenotypic factors interact to shape food preferences and eating behaviors.
These data are presented in a manuscript under revision at Physiology and Behavior.
Publisher
University Libraries, Virginia TechCorresponding Author Name
Alexandra G. DiFeliceantonioFiles/Folders in Dataset and Description
File 0_clean_blood_data.R - clean the blood glucose and insulin data. Take the raw values and calculate AUCs, slopes, and maxes
File 0_clean_cart_data.R - clean the cart data. Take the raw values, calculate the resting averages, and then calculate AUCs, slopes, and maxes
File 0_clean_outcomes.R - clean the outcomes data to have only 1 row. Calculate the difference in differences of the liking and wanting outcomes
File 1_figure1.R - Create the data containing panels from Figure 1. Additionally, run the paired t-tests to see if there are differences in any of the baseline measures
File 1_figure2.R - Create the 2 panels from Figure 2. Additionally, run paired t-tests to test whether there are differences in differences (for liking) or just a difference (for wanting)
File 1_figure3.R - Create the 6 panels from Figure 3. Run correlations between the liking DnD for each of the 6 panels (outputs in an R matrix)
File 1_table1.R - Create the demographics table
File 2_sup_figure1.R - Create the 4 panels for supplemental Figure 1. Additionally, run the linear mixed effects model to assess whether there is a difference in fasting time across conditions
File 2_sup_figure2.R - Create the 4 panels for the Supplemental Figure 2. No statistics are run here
File 2_sup_tables.R - Create the two supplemental tables which contain correlation values between the liking and wanting outcomes with each of 8 AUC and slope measures (4 each -- Glucose, Insulin, MR, RQ)
Folder data
Folder derived - data files created by step 0_ R files
File blood_summary.csv
File cart_data_summaries.csv
File cart_over_time.csv - this file contains 1 row per person per condition per MINUTE observed after consuming the beverage in the cart session
File outcome_differences_by_condition.csv
Folder - raw
File blood.csv - raw blood glucose and insulin data, long format with approximately 7 rows per person per condition
File cart.csv - raw cart data, long format with one row per minute per person per condition
File compliance.csv - data containing information about consumption of drinks at home, long format with 4 rows per condition per person
File demographics.csv - demographic data and anthropomorphic measures, one row per person
File is_ratings.csv - data from the internal state checks, long format two rows per blood session, three rows per cart session, and two rows for the post session (for each person for each of the two conditions
File outcomes.csv - liking, wanting, intensity, familiarity ratings, two rows per person (one for each session)
