Analysis of Metabolic Parameters as Predictors of Risk in the RENAAL Study
Bibliographic record
Abstract
OBJECTIVE: Metabolic factors such as glycemic control, hyperlipidemia, and hyperkalemia are important considerations in the treatment of patients with type 2 diabetes and nephropathy. In the RENAAL (Reduction of End Points in Type 2 Diabetes With the Angiotensin II Antagonist Losartan) study, losartan reduced renal outcomes in the patient population. This post hoc analysis of the RENAAL study reports the effects of losartan on selected metabolic parameters and assesses the relationship between baseline values of metabolic parameters and the primary composite end point or end-stage renal disease (ESRD). RESEARCH DESIGN AND METHODS: Glycemic control (HbA(1c)) and serum lipid, uric acid, and potassium levels were compared between the losartan and placebo groups over time, and baseline levels were correlated with the risk of reaching the primary composite end point (doubling of serum creatinine, ESRD, or death) or ESRD alone. RESULTS: Losartan did not adversely affect glycemic control or serum lipid levels. Losartan-treated patients had lower total (227.4 vs. 195.4 mg/dl) and LDL (142.2 vs. 111.7 mg/dl) cholesterol. Losartan was associated with a mean increase of up to 0.3 mEq/l in serum potassium levels; however, the rate of hyperkalemia-related discontinuation was similar between the placebo and losartan groups. Univariate analysis revealed that baseline total and LDL cholesterol and triglyceride levels were associated with increased risk of developing the primary composite end point. Similarly, total and LDL cholesterol were also associated with increased risk of developing ESRD. CONCLUSIONS: Overall, losartan was well tolerated by patients with type 2 diabetes and nephropathy and was associated with a favorable effect on the metabolic profile of this population.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".