Long-Term Efficacy of Migalastat on Renal Function and Outcomes in Patients with Fabry Disease (FD)
Bibliographic record
Abstract
Background: FD, caused by pathogenic GLA variants leading to functional deficiency of α-galactosidase A (α-Gal A), can eventually result in key organ damage. Preserving renal function and preventing Fabry-associated clinical events (FACEs) are important treatment goals. Approved therapies include enzyme replacement therapy (ERT) and the pharmacological chaperone migalastat. Stabilized renal function and FACE occurrence up to 30 mo have been reported in migalastat-treated adults with amenable GLA variants; here, we extend those analyses up to 8.6 yrs. Methods: Integrated data from phase 3 clinical trials (FACETS, NCT00925301; ATTRACT, NCT01218659) and open-label extension studies (NCT01458119; NCT02194985) were used to evaluate the eGFR slope using linear regression in pts treated with migalastat for ≥2 yrs (n=78). Incidences of FACEs (predefined renal, cardiac, and cerebrovascular events) were assessed in all pts (N=97). Analyses were stratified by prior treatment and phenotype. Cox regression modeling was used to identify predictors of FACEs. Results: eGFR remained stable for both ERT-naive and ERT-experienced pts who received migalastat for ≥2 yrs (median [min-max] duration: 5.9 [2.0-8.6]); the mean (SD) annualized rates of change in eGFR (mL/min/1.73 m2) were -1.6 (3.1) and -1.6 (3.6), respectively. In male pts with the classic phenotype (classification based on multiorgan involvement and [ERT-naive only] α-Gal A level at baseline; n=25), mean (SD) rate of change in eGFR was -2.2 (4.4) mL/min/1.73 m2. eGFR was also analyzed by baseline renal function and proteinuria levels. In all migalastat-treated pts (median duration: 5.1 yrs), the incidence of composite FACEs (per 1000 patient-years) was 48.3 (65.3 for classic males) and incidence of renal events was 4.4 (14.5 for classic males). Lower baseline eGFR was a predictor of FACEs in classic males vs all others; however, rate of renal events was too low to analyze predictors. Conclusions: Results demonstrate long-term efficacy of migalastat in stabilizing eGFR in pts with FD, including male pts with the classic phenotype. FACE incidence in pts receiving migalastat compared favorably to historic reports of ERT. The inverse correlation of eGFR with FACEs suggests the importance of early diagnosis and treatment to preserve renal function. Funding: Commercial Support - Amicus Therapeutics, Inc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".