Changes in serum uric acid levels as a predictor of future decline in renal function in older adults with type 2 diabetes
Bibliographic record
Abstract
ABSTRACT: Given that factors affecting renal function remain unknown, this study aimed to identify key predictors of estimated glomerular filtration rate (eGFR) deterioration, which is a representative of renal function decline in older adults with type 2 diabetes (T2DM). In an exploratory prospective observational study, we enrolled 268 Japanese people with T2DM aged ≥20 years who were followed up at Shinshu University Hospital. Among those, 112 eligible individuals aged ≥65 years were included in the present study. Factors associated with 3-year changes in eGFR (ΔeGFR) and eGFR deterioration (ΔeGFR < 0) were identified using bivariate and multivariable analyses. Regarding baseline values of the subjects, the mean age was 73.5 years, mean blood pressure was 131/74 mm Hg, mean hemoglobin A1c was 7.1%, mean eGFR was 62.0 mL/min/1.73 m2, mean urinary albumin excretion was 222.6 mg/gCre, and mean serum uric acid (UA) was 5.5 mg/mL. In bivariate analysis, the 3-year change in UA (ΔUA) levels was significantly correlated with ΔeGFR (r = -0.491, P < .001), but the baseline UA was not (r = 0.073, P = .444). Multiple linear regression analysis revealed that ΔUA was a significant negative predictor of ΔeGFR in the model that included sex, age, body mass index, serum albumin, and ΔUA as explanatory variables. Moreover, multiple logistic regression analysis demonstrated that ΔUA had a positive association with ΔeGFR <0 (odds ratio 2.374; 95% confidence interval 1.294-4.357). Thus, future renal function decline can be predicted by ΔUA but not by baseline UA in older adults with T2DM. Further research is needed to determine whether lowering the serum UA level can prevent eGFR decline.
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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 | 0.000 | 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".