Waist Circumference to Height Ratio: Better Correlation with Fat Mass Than Other Anthropometric Indices During Dietary Weight Loss in Different Rates
Bibliographic record
Abstract
BACKGROUND: The first-line strategy for the treatment of obesity is weight loss (WL) through decreasing calorie intake. However, a diet that is capable of attenuating fat free mass decline following WL is preferred. Furthermore, it is required to choose proper measurements and appropriate obesity-assessment indices to monitor weight and body composition during WL program. METHODS: A total of 68 adults with overweight and/or obesity underwent a WL program (rapid and slow WL). Dependent variables include: weight, resting metabolic rate (RMR), body composition, and related measurements such as waist circumference (WC), waist to height ratio (WHtR), body adiposity index (BAI), a body shape index (ABSI), fat mass to lean body mass (FM/LBM), and percentage body fat (PBF). RESULTS: Obesity measurements decreased in both groups (all P < 0.05) while LBM and RMR decreased more in the rapid WL group (P < 0.05). After age and sex adjustment, a robust correlation was observed between FM/LBM and PBF (r = 0.918), LBM% and PBF (r = -0.949), LBM% and FM/LBM (r = -0.904), WHtR and WC (r = 0.986), and BAI% and HC (r = 0.986) (P < 0.001 for all correlations). FM has the highest correlation with WHtR among other indices (r = 0.706). CONCLUSIONS: Compared to fast WL, our data support that slow and gradual WL is more effective to improve body composition and obesity-assessment indices. The robust relationship was observed between FM and WHtR, among other indices such as BMI or ABSI. Therefore, in order to evaluate FM, where the FM is not measurable, the WHtR might be the reasonable index.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".