Hemoglobin and Cholesterol Affect Apparent Tacrolimus Clearance in Pediatric Transplant Recipients—A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Tacrolimus has a narrow therapeutic index with substantial inter- and intra-patient variability, requiring therapeutic drug monitoring (TDM). Influences beyond genetic and developmental factors need to be better understood. Recent studies among adult patients suggest that hemoglobin affects the apparent clearance (CL/F) of tacrolimus, whereas this and other potential factors in children are under-investigated. METHODS: After ethics approval, we performed a single-center retrospective cohort study of pediatric renal transplant recipients between January 1, 2004, and June 30, 2018. Patients without tacrolimus therapy or those with concomitant sirolimus were excluded. Apparent clearance (CL/F) was predicted for this analysis using a regression equation derived from 12-point pharmacokinetic (PK) profiles. The equation allowed for the estimation of the area under the curve (AUC) from trough levels, which were then used to calculate CL/F. Data were collected from electronic health records, and univariate and multivariate mixed-effect regression analyses were performed to evaluate the impact of hemoglobin, albumin, cholesterol, and HDL on CL/F. RESULTS: Thirty-three patients were included. The median age at transplantation was 10 years, 52% were female, and the median tacrolimus AUC was 133 ng•h/mL. CL/F correlated with hemoglobin (n = 1257, r = -0.3767, p < 0.0001), HDL-cholesterol (n = 236, r = -0.3973, p < 0.0001), and total cholesterol (n = 373, r = -0.1821, p = 0.0004). In multivariate mixed-effect regression, hemoglobin and cholesterol remained significant predictors of CL/F. CONCLUSIONS: The present study suggests a moderate impact of hemoglobin and cholesterol on tacrolimus CL/F. Lower hemoglobin appears to increase CL/F, while higher cholesterol reduces it. These findings highlight the potential value of integrating biochemical parameters into dosing strategies to optimize TDM in pediatric kidney transplant recipients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".