Effects of Veverimer on Serum Bicarbonate and Physical Function in Patients with Diabetes and CKD: Subgroup Analysis from a Randomized Trial
Bibliographic record
Abstract
Background: Metabolic acidosis (MA) is a complication of CKD and has deleterious effects on kidney function, bone (demineralization), and muscle (protein catabolism). Patients (pts) with diabetes and CKD are prone to functional limitations that adversely affect their quality of life. Veverimer, an investigational non-absorbed polymer that binds gastrointestinal hydrochloric acid, is being developed to treat MA in CKD. Methods: TRCA-301E is a multicenter, Phase 3, randomized, blinded, placebocontrolled trial in 196 pts with CKD (eGFR 20-40 ml/min/1.73 m2) and MA (serum bicarbonate 12-20 mEq/L) who were treated for up to 1 yr with veverimer or placebo, with dose titration targeted to achieve a normal serum bicarbonate. Results: Compared with placebo, veverimer significantly increased serum bicarbonate and significantly improved physical function as reported on the Kidney Disease and Quality of Life-Physical Function Domain (KDQOL-PFD) and the 5-times repeated chair stand test (RCS) with a safety profile that was similar to placebo (Wesson, The Lancet 2019). In the subgroup with diabetes (n=70, veverimer; n=57, placebo), mean age was 63years, mean baseline eGFR was 28.5 mL/min/1.73 m2, and mean serum bicarbonate was 17.3 mEq/L; 10% were on background oral alkali. In the veverimer group, at Week 52, mean serum bicarbonate increased by 4.39 mEq/L (P<0.05 vs. placebo) and significantly more pts had a ≥4mEq/L increase or normalization of serum bicarbonate (64% v 38%, P< 0.01). Pt-reported limitations of physical function (KDQOLPFD) (e.g. walking several blocks, climbing a flight of stairs) significantly improved in the veverimer group (+12.5 v +0.3, P<0.001) as did objective physical performance on the RCS at Week 52 (P<0.0001). There was no significant effect of the presence or absence of diabetes on the effect of veverimer on improvement in either measure of physical function (rank-based ANCOVA, P≥0.6). Conclusions: Few interventions for CKD have improved QOL or physical functioning. Our study demonstrates that veverimer is an effective treatment for diabetic pts with MA in CKD. Treatment with veverimer significantly improved how these pts felt and functioned. Funding: Commercial Support - Tricida, Inc.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".