Mosaicism of Podocyte Involvement Is Related to Podocyte Injury in Females with Fabry Disease
Bibliographic record
Abstract
BACKGROUND: Fabry disease. an X-linked deficiency of α-galactosidase A coded by the GLA gene, leads to intracellular globotriaosylceramide (GL-3) accumulation. Although less common than in males, chronic kidney disease, occurs in ∼ 15% of females. Recent studies highlight the importance of podocyte injury in Fabry nephropathy development and progression. We hypothesized that the greater the % of podocytes with active wild-type GLA gene (due to X-inactivation of the mutant copy) the less is the overall podocyte injury. METHODS: Kidney biopsies from 12 treatment-naive females with Fabry disease, ages 15 (8-63), median [range], years were studied by electron microscopy and compared with 4 treatment-naive male patients. RESULTS: In females, 51 (13-100)% of podocytes (PC) per glomerulus had no GL-3 inclusions, this consistent with a non-Fabry podocyte phenotype (NFPC). In PC with GL-3 inclusions [Fabry podocyte phenotype (FPC)], GL-3 volume density per podocyte was virtually identical in females and males, consistent with little or no cross-correction between FPC and NFPC. %NFPC per glomerulus (%NFPC/glom) correlated with age in females (r = 0.65, p = 0.02), suggesting a survival disadvantage for FPC over time. Age-adjusted %NFPC/glom was inversely related to foot process width (FPW) (r = -0.75, p = 0.007), an indicator of PC injury. GL-3 volume density in FPC in females correlated directly with FPW. CONCLUSIONS: These findings support important relationships between podocyte mosaicism and podocyte injury in female Fabry patients. Kidney biopsy, by providing information about podocyte mosaicism, may help to stratify females with Fabry disease for kidney disease risk and to guide treatment decisions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".