Association between Dietary Macronutrient Intake and Symptoms in Uninvestigated Dyspepsia: Evidence from a Population-Based, Cross-Sectional Study
Bibliographic record
Abstract
(1) Background: Limited evidence from laboratory-based studies suggests that specific dietary macronutrients, particularly fat, can induce dyspeptic symptoms. Through a population-based study, we investigated the relationship between dietary macronutrients and dyspeptic symptoms and sought to determine macronutrient intake thresholds to predict or prevent dyspepsia and reduce symptoms in patients with dyspepsia. (2) Methods: A total of 4763 Iranian people were enrolled in this population-based, cross-sectional study. Uninvestigated dyspepsia (UD) and its symptoms, including postprandial fullness, early satiation, and epigastric pain, were evaluated using a modified Persian version of the Rome III criteria. The dietary intakes of participants were evaluated using a validated food−frequency questionnaire. Receiver operating characteristic (ROC) curve analysis was used to calculate threshold intakes of dietary macronutrients to prevent UD in the general population. The analysis was then repeated in those with UD to calculate intake thresholds for reducing UD symptoms. (3) Results: Early satiation occurred in 6.3% (n = 302), postprandial fullness in 8.0% (n = 384) and epigastric pain in 7.8% (n = 371) of participants. The prevalence of UD was 15.2%. Compared with individuals without UD, those with UD had a lower intake of carbohydrates (48.2% vs. 49.1%) and a higher intake of fats (38.3% vs. 37.4%), while protein and energy intakes did not differ. Higher dietary fat and protein intakes were associated with a higher prevalence of postprandial fullness and epigastric pain, respectively. Macronutrient intakes to predict UD in the general population were <49% of energy from carbohydrates, >14.7% from protein, and >37.7% from fats. Carbohydrate, protein, and fat intakes to prevent symptoms among those with UD were calculated to be >48.2%, <14.6%, and <38.6%, respectively. (4) Conclusion: Higher carbohydrate intake and lower fat or protein intakes were associated with a lower likelihood of UD. Prospective studies carefully manipulating dietary macronutrient composition are warranted to investigate the value of dietary changes to improve symptoms in people with UD.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".