FFM Index, FM Index and PBF in Subjects with Normal, Overweight, and Obese BMI in Saudi Arabia Female Population
Bibliographic record
Abstract
Aims: To assess Fat Free Mass Index, Fat Mass Index and Percent Body Fat in subjects with normal, overweight, and obese BMI and to examine if FFMI and FMI as compared to BMI have higher predictability in identification of high risk groups as defined by metabolic measurements among female college students and employees in Hail, Northern part of Saudi Arabia. Methods: Sample of 514 female college students and employees were enrolled and body composition was measured by using bioelectrical impendence technique. FFMI and FMI are calculated using the standard formula. Blood pressure (BP) and pulse were measured using automatic BP reader in a resting sitting position. Random blood glucose was tested using strip method (One touch, Simple). Results: Around 11 percent of study subjects were underweight while 25 percent were overweight and another 22 percent were obese. Only 42 percent of study population had normal weight. Except for height there were significant differences for weight, BMI, FM, FFM and %BF across age groups. Weight, FM, FFM shows a linear trend till the age 40 yrs after which an inverse trend begins. BMI continues to show linear trend across all ages. Mean FFMI was around 14 kg/m2 (range 5th – 95th percentile: 12.5 – 17.8 kg/m2) and was modestly but significantly higher (P < 0.001) in the higher age group. Similarly, Mean FMI was 8.4 kg/m2 (range 5th – 95th percentile: 3.8 – 18.3 kg/m2) and significantly higher (P < 0.001) in the higher age group. In Regression models for SBP, BMI and %BF explain 18.7 % of variance; while for DBP, WC and %BF explain 11.2 % of variance. For blood glucose, it is FFMI, FMI and Visceral fat which explain maximum variance. Conclusion: BMI alone cannot provide information about the respective contribution of FFM or fat mass to body weight. This study presents FFMI and BFMI values that correspond to low, normal, overweight, and obese BMIs. FFMI and BFMI provide information about body compartments, regardless of height.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".