Associations of water intake and Intra-Meal fluid consumption with obesity, insulin resistance, and predictors of cardiovascular diseases among Iranian women
Bibliographic record
Abstract
To evaluate the associations of water intake and Intra-Meal Fluid Consumption with obesity, insulin resistance, and predictors of cardiovascular diseases. A cross-sectional study was conducted with 371 women aged 20-50 years in Iran. Physical activity, biochemical, and anthropometric measurements were collected. Dietary intake was collected using a 168-item food frequency questionnaire. Water intake was assessed through three non-consecutive 24-hour dietary recalls, and participants were classified into water intake tertiles, T1 (< 1.5 L/day), T2 (1.5-2.0 L/day), and T3 (> 2 L/day) based on recommended intake levels from the Institute of Medicine. Triglyceride and glucose (TyG) index, lipid accumulation product (LAP) index, Castelli risk indices 1 and 2 (CRI-I and CRI-II), atherogenic index of plasma (AIP), and hypertriglyceridemic waist phenotype were used as predictors of cardiovascular diseases. Women in the second and third tertiles of water intake had significantly lower body weight, BMI, waist circumference, fasting blood glucose, and triglyceride levels compared to those in the lowest tertile (p < 0.05). Higher water intake was also associated with lower odds of TyG index (OR:0.51; 95%CI:0.32,0.81; p = 0.005), LAP index (OR:0.35; 95%CI:0.22,0.56; p < 0.001), CRI-I (OR:0.57, 95%CI:0.33,0.96; p = 0.031), AIP (OR:0.57; 95%CI: 0.36,0.91; p = 0.017), and hypertriglyceridemic waist phenotype (OR:0.20; 95%CI:0.12,0.34; p < 0.001). Higher water intake may be associated with lower odds of obesity, fasting blood glucose, and predictors of cardiovascular disease in Iranian women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".