Relation between Serum Uric Acid and Anthropometric Measures in Diabetic Nephropathy Patients
Bibliographic record
Abstract
Background: Diabetic nephropathy is a major challenge of diabetes mellitus, leading to significant morbidityand mortality. Raised serum uric acid (SUA) tier is a potential cause of deterioration of kidney disease, such asdiabetic nephropathy.Anthropometric measuresfor instance Body MassIndex (BMI) and waist circumference(WC) are crucial markers of adiposity, which is intimately linked to both progression of diabetic nephropathyand hyperuricemia. Objective: To evaluate the relation between SUAand anthropometric measures in diabetic nephropathy.Methods: Across-sectional study was executed involving 150 patients by convenience sampling diagnosedwith diabetic nephropathy at the Diabetic Clinic of Tertiary care Hospital in Lahore. SUAlevels were measuredusing enzymatic methods, while anthropometric measures, including BMI and waist circumference, wererecorded. The relation between SUA and these anthropometric measures was analyzed using Spearman correlation coefficient. Results: The median (IQR) SUA in diabetic nephropathy patients was 6.9 (5.4-8.6) mg/dl and in healthygroup was 5.2 (4.6-6.2) mg/dl. In diabetic nephropathy group, a significant direct relation of SUAwas foundwith BMI (rho = 0.296, p < 0.001) and also with waist circumference (rho = 0.435, p < 0.001). In healthy group,significant directrelationwasfoundwithwaist circumference only (rho=0.212, p=0.001).SUA,Waist circumference and BMI were higher considerably in diabetic nephropathy when measured against the control. Conclusion: The study demonstrates a significant relationship between elevated SUA levels and adverseanthropometric measures in diabetic nephropathy patients. These findings suggest that managing obesitythrough lifestyle modifications and pharmacotherapy could play a critical role in controlling SUA andpotentially slowing the progression of diabetic nephropathy.
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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 | 0.002 |
| 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) | 0.002 | 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".