Discordance Between Cystatin C-Based eGFR and Creatinine-Based eGFR and Cardiovascular Risk in the FLOW Trial
Bibliographic record
Abstract
Background: Superiority of estimated glomerular filtration rate (eGFR)cystatin C (eGFRcyst) over eGFRcreatinine (eGFRcreat) in cardiovascular risk prediction is established but incompletely understood. It has been hypothesized that low eGFRcyst may be reflecting selective glomerular hypofiltration, characterized by reduced clearance of middle-sized, potentially atherogenic proteins. We analyzed associations of eGFRcyst/eGFRcreat with major adverse cardiovascular events (MACE) and all-cause mortality in participants of the FLOW trial. Methods: GFR was estimated using the CKD-EPI 2009 (eGFRcreat) or 2012 (eGFRcyst) equations. Time to first event was studied using Cox regression. Baseline eGFRcyst/eGFRcreat <0.7 vs ≥0.7 was a fixed factor and adjusted for eGFRcyst and/or eGFRcreat. Results: Of the 3463 participants, 1116 (32%) had eGFRcyst/eGFRcreat <0.7 at baseline (median 0.77) (Figure). Median follow-up was 3.4 years. eGFRcyst/eGFRcreat <0.7 was associated with increased risk of MACE and all-cause mortality (unadjusted hazard ratios [95% confidence interval] of 1.58 [1.31−1.90] and 1.87 [1.57−2.23], respectively). The association was preserved after adjustment for baseline eGFRcyst and/or eGFRcreat, with slight attenuation of effect. Conclusion: In FLOW, eGFRcyst/eGFRcreat <0.7 was associated with substantially increased risk for MACE and all-cause mortality in participants with type 2 diabetes and chronic kidney disease, independent of eGFRcyst. Further research is needed to elucidate potential treatment targets. Funding: Commercial Support - Novo Nordisk A/S
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".