Blood Biomarkers to Identify Renal Angiomyolipomas in People With Tuberous Sclerosis Complex
Bibliographic record
Abstract
Background and Objectives: Renal angiomyolipomas (AMLs) affect 80% of people with tuberous sclerosis complex (TSC) during their lifetime. We aimed to determine the diagnostic accuracy of blood biomarkers in identifying the presence and size of renal AMLs in people with TSC. Methods: We collected clinical data and serum samples from individuals followed at 1 TSC clinic (Centre hospitalier de l'Université de Montréal [CHUM] cohort). We also obtained clinical data and plasma samples from participants in the TSC Alliance Natural History Database and Biosample Repository (TSC Alliance cohort). We measured vascular endothelial growth factor D (VEGF-D), kidney injury molecule 1, neutrophil gelatinase-associated lipocalin (NGAL), and cystatin C concentrations in all individuals. We computed receiver operating characteristic curves for each biomarker and determined the optimal thresholds to identify AML vs no AML, and large AML (≥ 3 cm in diameter) vs small/no AML. Results: The CHUM and TSC Alliance cohorts included 41 (23 with AML) and 38 (26 with AML) individuals, respectively. In both cohorts, VEGF-D had the greatest area under the curve, with a sensitivity of at least 0.80 (95% CI 0.49-0.94) and a specificity of at least 0.97 (95% CI 0.83-0.99) in identifying large AML. When VEGF-D and cystatin C were combined, sensitivity increased to 0.96 (95% CI 0.79-0.99) and 1.00 (95% CI 0.72-1.00) for AML presence and size, respectively, in the CHUM cohort. Similar results were observed in a second, independent cohort (the TSC Alliance cohort) when combining VEGF-D and NGAL. Discussion: VEGF-D with either cystatin C or NGAL can accurately screen for large renal AMLs in people with TSC. Smaller renal AMLs can also be screened using these biomarkers, albeit with lower accuracy. Further research is necessary to determine how to implement these biomarkers in clinical practice.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".