Body Composition Is Altered in Pre-Diabetic Patients With Impaired Fasting Glucose Tolerance: Results From the NHANES Survey
Bibliographic record
Abstract
BACKGROUND: Previous studies suggest that type 2 diabetes mellitus (T2DM) is associated with a loss of muscle mass but the impact of fasting blood glucose (FBG) on body composition remains underappreciated in pre-diabetic subjects. The aim of this study was to determine the influence of FBG on lean mass (LM), fat mass (FM) and their distribution (trunk vs. appendicular), separately in middle-aged men and women. METHODS: One thousand nine hundred and eleven (1,911) men (63.9 ± 11.7 years) and 1,977 women (63.7 ± 12.1 years) from the National Health and Nutrition Examination Survey (1999 - 2004) were divided into four groups: normal glucose tolerance (NGT), low impaired fasting glucose (IFG), high IFG and T2DM. Body composition was obtained from dual X-ray absorptiometry (DXA). RESULTS: Of the patients, 68.7% had NGT, 16.1% low IFG, 9.4% high IFG and 5.8% T2DM. After adjustment for age, body mass index, ethnicity, smoking, alcohol and physical activity (PA), no change in appendicular LM was observed between groups, but significant increases in trunk FM (in both gender) and trunk LM (in women) were found with increased glucose intolerance (T2DM > IFG > NGT), as well as significant decreases in trunk and total LM/FM ratios (T2DM < IFG < NGT) and a significant increase in trunk/appendicular FM ratio (T2DM > IFG > NGT). CONCLUSIONS: Elevated FBG within the normal range is not associated with a significant loss of appendicular LM, but modifications in LM and FM trunk and total distribution with IFG suggest that nutritional and physical lifestyle strategies should be implemented in the pre-diabetic state.
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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".