Body Muscle-to-Fat Ratio, Rather Than Fat-to-Muscle Ratio, Significantly Correlates With Measured Insulin Resistance in Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: Insulin resistance (IR) assessment is important in treating type 2 diabetes mellitus (T2DM). We thus compared body muscle-to-fat ratio (BMFR) and fat-to-muscle ratio (FMR) values against M/I values as clinical index of IR. Methods: Subject included 118 untreated T2DM patients. Hyperinsulinemic-euglycemic clamp examination was performed to calculate the M/I as index of IR. Body composition was measured by impedance analysis using InBody770. Results: Simple linear regression analyses confirmed correlations between M/I and BMFR (B: 0.756 (P < 0.01), coefficients of determination (R 2 ): 0.572, mean absolute error (MAE): 3.19, and root mean squared error (RMSE): 4.14), and between M/I and FMR (B: -0.601 (P < 0.01), R 2 : 0.362, MAE: 3.97, and RMSE: 5.05). Against the M/I values, BMFR also showed better goodness-of-fit than did FMR. In comparing correlation coefficients, the BMFR absolute B value was significantly larger than that of FMR (P = 0.027). Conclusions: BMFR is more useful than FMR in quantifying IR in patients with T2DM because the correlation between BMFR and the insulin sensitivity index M/I is significantly greater than that between FMR and M/I. J Clin Med Res. 2021;13(7):387-391 doi: https://doi.org/10.14740/jocmr4401
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Clinical study comparing body composition indices against measured insulin resistance in type 2 diabetes.
This clinical study compares body composition measures of insulin resistance, not research.
Clinical correlation of body composition ratios with insulin resistance in T2DM.
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 | 0.003 |
| 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 | 0.001 |
| 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".