Lower Transferrin Saturation (TSAT) Index Is Associated with an Anemia-Independent Risk of Increased Mortality in Non-Dialysis (ND) CKD Patients
Bibliographic record
Abstract
Background: Iron Deficiency (ID), defined by a TSAT index <20 %, is present in approximately half of ND-CKD patients, varying little by CKD stage. Distinct from approaches in conditions such as heart failure, the importance of iron reserves and the basis for iron therapy in CKD has focused primarily on supporting effective erythropoiesis. A comprehensive approach and design to estimate the impact of ID, independently from hemoglobin (Hb) levels, on mortality risk has not been explored in ND-CKD until the present. Methods: 5144 patients from Brazil (N=294), France (N=2227), the US (N=494), and Germany (N=2129) enrolled in the Chronic Kidney Disease Outcomes and Practice Patterns Study (CKDopps) from 2013-2019 with available TSAT were included in the analysis. We categorized patients by first available TSAT at enrollment. Hb measurements at same time as TSAT were used. Cox models were used to estimate hazard ratios (HR) of TSAT on mortality, censored at start of dialysis or kidney transplantation. Models were progressively adjusted for confounders, including demographics, comorbidities, inflammation surrogates, treatment with erythropoietin stimulating-agents and Hb. Results: Sample characteristics were: 59% male; 45% diabetes; and mean (SD) age 69 (13) years, eGFR 28 (11) mL/min, Hb 12 (2) g/dL, TSAT 24 (2) %, ferritin 196 (214) ng/dL. TSAT levels below 25% were progressively associated with higher mortality risk, while patients with TSAT greater than 45% tended to have higher risks for mortality (Figure). Conclusions: ID, as measured by the TSAT index, is associated with higher risk of all-cause mortality in ND-CKD patients, even after extensive adjustments for clinical, demographic and biochemical confounders, including Hb levels. Interventional studies evaluating the impact of iron supplementation and alternative targets on clinical outcomes in ND-CKD patients are needed to better inform ID management strategies. Funding: Commercial Support - Vifor Pharma
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".