The relationship of insulin resistance and body fat in chronic kidney disease patients.
Bibliographic record
Abstract
Background: Insulin resistance has been associated with type 2 diabetes, hypertension, central obesity, and dyslipidemia, all of which are important risk factors for progression of chronic kidney disease (CKD). A greater degree of insulin resistance may predispose to renal injury by worsening renal hemodynamics through the elevation of glomerular filtration fraction. However, there are sparse data on the relationship between insulin resistance, glomerular filtration rate (GFR), and total body fat or phase angle in CKD without diabetes. Methods: We examined 84 non‐diabetes CKD patients according to the K/DOQI definitions; only 79 patients were enrolled into the study (GFR between 15 and 90 ml/min/1.73 m 2 ). The value of insulin resistance was obtained by homeostasis model assessment (HOMA). Bioelectrical impedance analysis was performed to determine the percentage of total body fat or phase angle. GFR was calculated by the average of creatinine and urea clearances. Results: The correlation analysis showed that HOMA‐insulin resistance was positively correlated with phase angle (r = 0.35, P < 0.01), percentage of total body fat (r = 0.27, P < 0.01), body mass index (r = 0.48, P < 0.01) and serum triglyceride levels (r = 0.32, P < 0.01), but not significantly correlated with gender (r = −0.07, P > 0.05), age (r = 0.05, P > 0.05), GFR (r = −0.006, P > 0.05), and mean arterial blood pressure (r = 0.11, P > 0.05). Conclusion: In non‐diabetic chronic kidney disease patients, the major risk factor for insulin resistance is the amount of total body fat. The insulin level is not dependent on the GFR in these patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".