Macronutrient intake and physical activity levels in individuals with and without metabolic syndrome: An observational study in an urban population.
Bibliographic record
Abstract
BACKGROUND: We aimed to compare dietary macronutrient intake and physical activity level (PAL) between community-based samples of Iranian adults with metabolic syndrome (MetS+) and without metabolic syndrome (MetS-). METHODS: This cross-sectional study was conducted among 3800 men and women aged 35-65 years. The International Diabetes Federation (IDF) criteria were used to define MetS. A 24-hour recall was used to evaluate dietary intake. The James and Schofield human energy requirements equations were used to calculate PAL and questions were categorized into time spent on activities during work (including housework), during non-work time, and in bed. RESULTS: The mean ± standard deviation (SD)age of the MetS+ and MetS- subjects was, respectively, 48.8 ± 7.8 years (521 men and 1178 women) and 47.6 ± 7.5 years (714 men and 1222 women) (P = 0.930). The mean energy intake was higher in the MetS+ men compared with MetS- men (1977.4 ± 26.6 vs. 1812.7 ± 21.7 Kcal; P < 0.001). Crude and energy-adjusted intake from total fat was lower in MetS+ women compared with MetS- women (both P < 0.010). PALs were lower in MetS+ compared with MetS- participants (P < 0.001). After adjusting for confounders, no significant association was observed between the intake of individual macronutrients and MetS. In contrast, PAL was inversely associated with the incidence of MetS [OR = 0.34 (95% CI: 0.17-0.57); P < 0.001]. CONCLUSION: In the current study, there was an inverse relationship between PAL and the risk of MetS, but no association between individual dietary macronutrients intake and the incidence of MetS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".