The rationale, design and baseline data of FLOW, a kidney outcomes trial with once-weekly semaglutide in people with type 2 diabetes and chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a common complication of type 2 diabetes (T2D). Glucagon-like peptide-1 receptor agonists (GLP-1RAs) improve glycaemic control and lower body weight in people with T2D, and some reduce the risk of cardiovascular (CV) events in those with high CV risk. GLP-1RAs might also have kidney-protective effects. We report the design and baseline data for FLOW (NCT03819153), a trial investigating the effects of semaglutide, a once-weekly (OW) GLP-1RA, on kidney outcomes in participants with CKD and T2D. METHODS: FLOW is a randomised, double-blind, parallel-group, multinational, phase 3b trial. Participants with T2D, estimated glomerular filtration rate (eGFR) ≥50‒≤75 ml/min/1.73 m2 and urine albumin:creatinine ratio (UACR) >300‒<5000 mg/g or eGFR ≥25‒<50 ml/min/1.73 m2 and UACR >100‒<5000 mg/g were randomised 1:1 to OW semaglutide 1.0 mg or matched placebo, with renin-angiotensin-aldosterone system blockade (unless not tolerated/contraindicated). The composite primary endpoint is time to first kidney failure (persistent eGFR <15 ml/min/1.73 m2 or initiation of chronic kidney replacement therapy), persistent ≥50% reduction in eGFR or death from kidney or CV causes. RESULTS: Enrolled participants (N = 3534) had a baseline mean age of 66.6 years [standard deviation (SD) 9.0], haemoglobin A1c of 7.8% (SD 1.3), diabetes duration of 17.4 years (SD 9.3), eGFR of 47.0 ml/min/1.73 m2 (SD 15.2) and median UACR of 568 mg/g (range 2‒11 852). According to Kidney Disease: Improving Global Outcomes guidelines categorisation, 68.2% were at very high risk for CKD progression. CONCLUSION: FLOW will evaluate the effect of semaglutide on kidney outcomes in participants with CKD and T2D, and is expected to be completed in late 2024.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.066 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".