A Comparison of Sugar Intake between Individuals with High and Low Trait Anxiety: Results from the NutriNet-Santé Study
Bibliographic record
Abstract
(1) Background: Dietary carbohydrates are likely correlated with mental health in general, and with anxiety in particular. Our aim was to investigate the cross-sectional relationship between trait anxiety and carbohydrate (especially sugar) intake in a large sample derived from the general French population. (2) Methods: The analyses included 20,231 non-diabetic adults enrolled in the NutriNet-Santé e-cohort, who had completed the trait anxiety subscale of the Spielberger State-Trait Anxiety Inventory (T-STAI, 2013–2016) and who were subsequently divided into high and low trait anxiety groups (T-STAI cut-off of 40 points). Sugar-rich food and macronutrient intake was calculated from ≥3 self-administered 24-h dietary records. The association between trait anxiety and carbohydrate intake was evaluated by ANCOVA according to age category (<45 and ≥45 years). (3) Results: In the full sample, 7942 (39.3%) individuals fell into the high trait anxiety category. They were more likely to be women (82.2% versus 69.2%; p < 0.0001) and younger (mean age 51.6 versus 55.1 years; p < 0.0001) compared to the low trait anxiety group. In fully-adjusted models, high-anxiety individuals aged under 45 years had significantly higher mean consumption of added simple sugars (43.9 versus 42.3 g/d; p < 0.0007), whereas those aged over 45 years with high trait anxiety had significantly lower mean consumption of fruit (214.0 versus 219.5 g/d; p < 0.02) compared to their low-anxiety counterparts. (4) Conclusions: This cross-sectional study revealed modest age-specific associations between anxiety status and sugar intake among adults. Prospective studies with representative samples are needed to explore potential bi-directionality of the observed associations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".