Thrifty-Eating Behavior Phenotype at the Food Court – Programming Goes Beyond Food Preferences
Bibliographic record
Abstract
Introduction: Prenatal growth impairment leads to higher preference for palatable foods in comparison to normal prenatal growth subjects, which can contribute to increased body fat mass and a higher risk for developing chronic diseases in small-for-gestational-age (SGA) individuals throughout life. This study aimed to investigate the effect of SGA on feeding behavior in children and adolescents, as well as resting-state connectivity between areas related to reward, self-control, and value determination, such as orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DL-PFC), amygdala and dorsal striatum (DS). Methods: Caregivers and their offspring were recruited from two independent cohorts in Brazil (PROTAIA) and Canada (MAVAN). Both cohorts included anthropometric measurements, food choice tasks, and resting-state functional magnetic resonance imaging (fMRI) data. Results: In the Brazilian sample (17 ± 0.28 years, n=70), 21.4% of adolescents were classified as SGA. They exhibited lower monetary-related expenditure to buy a snack compared to controls in the food choice test. Decreased functional connectivity (n=40) between left OFC and left DL-PFC; and between right OFC and: left amygdala, right DS, and left DS were observed in the Brazilian SGA participants. Canadian SGA participants (14.9%) had non-significant differences in comparison with controls in a food choice task at 4 years old ( ± 0.01, n=315). At a follow-up brain scan visit (10.21 ± 0.140 years, n=49), SGA participants (28.6%) exhibited higher connectivity between the left OFC and left DL-PFC, also higher connectivity between the left OFC and right DL-PFC. We did not observe significant anthropometric neither nutrients' intake differences between groups in both samples. Conclusions: Resting-state fMRI results showed that SGA individuals had altered connectivity between areas involved in encoding the subjective value for available goods and decision-making in both samples, which can pose them in disadvantage when facing food options daily. Over the years, the cumulative exposure to particular food cues together with the altered behavior towards food, such as food purchasing, as seen in the adolescent cohort, can play a role in the long-term risk for developing chronic non-communicable diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".