Resting metabolic rate in obese diabetic and obese non-diabetic subjects and its relation to glycaemic control
Bibliographic record
Abstract
BACKGROUND: Both obesity and type II diabetes mellitus are associated with insulin resistance and abnormal metabolic reactions. This study was conducted to evaluate resting metabolic rate in obese diabetic patients and to assess its relation to glycaemic control. RESULTS: This is a case control study conducted in Gabir AbuEliz centre in Khartoum, Sudan. A random sample of 40 obese diabetic patients (cases) and 40 obese non-diabetic subjects (controls) were interviewed and examined clinically to exclude presence of acute or chronic medical illness. Haemoglobin A1c was measured for each participant using the "NycoCard Haemoglobin A1c test" (Axis -Shield/ Norway). Fasting blood sugar was measured using one touch(R) glucometer (LifeScan Canada Ltd). The PowerLab 8/35 with a gas analyzer (AD Instruments, Castle Hill Australia) was used for measurement of VO2, VCO2 and Respiratory exchange ratio (RER). Resting metabolic rate was calculated using the Weir equation. VO2 (mean+/-SD) ml/min was significantly higher among cases (209.9+/-42.7) compared to the controls (192.4+/-28.1), (P = 0.034). Similarly, VCO2 (mean+/-SD) ml/min was higher among cases (191.4+/-35.0) than controls (178.3+/-22.5), (P = 0.05). Resting metabolic rate "RMR" (mean+/-SD) kcal/day was higher in obese diabetic patients (1480.7 +/- 274.2) than obese non-diabetic subjects (1362.4+/- 184.8), (P = 0.027). Participants with high glycated haemoglobin had higher RMR than those with normal glycated haemoglobin (P = 0.016). CONCLUSION: It is concluded that resting metabolic rate is significantly higher in obese diabetic patients compared to obese non-diabetics, especially in those with poor glycaemic control.
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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 | 0.000 | 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".