Tremor Induced by Cyclosporine, Tacrolimus, Sirolimus, or Everolimus: A Review of the Literature
Bibliographic record
Abstract
Calcineurin inhibitors such as cyclosporine and tacrolimus are immunosuppressant drugs that are known to induce tremors. Non-calcineurin inhibitors such as sirolimus and everolimus have also reportedly been accompanied by tremors, albeit less likely. However, the prevalence rates reported in the literature are notably wide, and the risk profiles for these drug-induced tremors are less understood. We searched PubMed to extract data on the risk of tremors with these drugs when prescribed for various transplant and non-transplant indications. We ascertained whether the risk of drug-induced tremor is influenced by the underlying diagnosis, dosing formulations, drug concentrations, and blood monitoring. We extracted data on treatment strategies and outcomes for tremors. Articles were primarily screened based on English language publications, abstracts, and studies with n ≥ 5, which included case series, retrospective studies, case-controlled studies, and prospective studies. We found 81 eligible studies comprising 33 cyclosporine, 43 tacrolimus, 6 sirolimus, and 1 everolimus that discussed tremor as an adverse event. In the pooled analysis of studies with n > 100, the incidence of tremor was 17% with cyclosporine, 21.5% with tacrolimus, and 7.8% with sirolimus and everolimus together. Regarding the underlying diagnosis, tremor was more frequently reported in kidney transplant (cyclosporine 28%, tacrolimus 30.1%) and bone marrow transplant (cyclosporine 40%, tacrolimus 41.9%) patients compared with liver transplant (cyclosporine 9%, tacrolimus 11.5%) and nontransplant indications (cyclosporine 21.5%, tacrolimus 11.3%). Most studies did not report whether the risk of tremors correlated with drug concentrations in the blood. The prevalence of tremors when using the twice-daily formulation of tacrolimus was nearly the same as the once-daily formulation (17% vs 18%). Data on individual-level risk factors for tremors were lacking. Except for three studies that found some benefit to maintaining magnesium levels, there were minimal data on treatments and outcomes. A large body of data supports a substantive and wide prevalence of tremor resulting from tacrolimus use followed by cyclosporine, especially in patients receiving a kidney transplant. However, there is little reporting on the patient-related risk factors for tremor, risk relationship with drug concentrations, treatment strategies, and outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".