Assessment of plasma lyso-Gb3 for clinical monitoring of treatment response in migalastat-treated patients with Fabry disease
Bibliographic record
Abstract
Purpose To assess the utility of globotriaosylsphingosine (lyso-Gb 3 ) for clinical monitoring of treatment response in patients with Fabry disease receiving migalastat. Methods A post hoc analysis evaluated data from 97 treatment-naive and enzyme replacement therapy (ERT)–experienced patients with migalastat- amenable GLA variants from FACETS (NCT00925301) and ATTRACT (NCT01218659) and subsequent open-label extension studies. The relationship between plasma lyso-Gb 3 and measures of Fabry disease progression (left ventricular mass index [LVMi], estimated glomerular filtration rate [eGFR], and pain) and the relationship between lyso-Gb 3 and incidence of Fabry-associated clinical events (FACEs) were assessed in both groups. The relationship between changes in lyso-Gb 3 and kidney interstitial capillary (KIC) globotriaosylceramide (Gb 3 ) inclusions was assessed in treatment-naive patients. Results No significant correlations were identified between changes in lyso-Gb 3 and changes in LVMi, eGFR, or pain. Neither baseline lyso-Gb 3 levels nor the rate of change in lyso-Gb 3 levels during treatment predicted FACE occurrences in all patients or those receiving migalastat for ≥24 months. Changes in lyso-Gb 3 correlated with changes in KIC Gb 3 inclusions in treatment-naive patients. Conclusions Although used as a pharmacodynamic biomarker in research and clinical studies, plasma lyso-Gb 3 may not be a suitable biomarker for monitoring treatment response in migalastat-treated patients.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".