Effect of <i>PNPLA3</i> polymorphism on diagnostic performance of various noninvasive markers for diagnosing and staging nonalcoholic fatty liver disease
Bibliographic record
Abstract
BACKGROUND AND AIM: Patatin-like phospholipase domain-containing protein 3 (PNPLA3) I148M (rs738409) genotype influences clinical/biochemical characteristics in patients with nonalcoholic fatty liver disease (NAFLD), but whether PNPLA3-I148M (rs738409) genotype also influences the diagnostic performance of noninvasive diagnostic tests for NAFLD is uncertain. Our aim was to investigate the differences in diagnostic performance of noninvasive diagnostic tests for NAFLD according to PNPLA3-I148M (rs738409) genotype. METHODS: Fifty-eight healthy controls and 349 patients with biopsy-proven NAFLD were included. Areas under the receiver operating characteristic curve (AUROCs) were calculated to predict hepatic steatosis (fatty liver index and hepatic steatosis index), nonalcoholic steatohepatitis (cytokeratin-18 M30 and M65), and significant fibrosis (≥F2 fibrosis) (fibrosis-4 and BARD), stratifying by rs738409 genotypes (CC and CG + GG groups). RESULTS: Fatty liver index and hepatic steatosis index showed good diagnostic performance for diagnosing steatosis only in the CG + GG group with AUROCs ranging from 0.819 to 0.832. Cytokeratin-18 M30 (AUROC = 0.688) and M65 (AUROC = 0.678) had suboptimal performance for diagnosing nonalcoholic steatohepatitis in the CG + GG group, whereas both had good performance (AUROC = 0.814 and 0.813, respectively) in the CC group. BARD score showed good performance in the CG + GG group compared with the CC group (AUROC = 0.805 and 0.532, respectively). Fibrosis-4 had suboptimal performance in the CG + GG group and good performance in the CC group (AUROC = 0.662 and 0.801, respectively). CONCLUSIONS: Diagnostic performance of noninvasive tests for NAFLD varied markedly according to PNPLA3 genotypes. Clinicians should be aware that PNPLA3 genotype limits the clinical utility of noninvasive diagnostic tests for diagnosing NAFLD.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".