96-OR: Liver Fibrosis Scores in Novel Subclusters of Patients at Risk for Type 2 Diabetes
Bibliographic record
Abstract
Metabolic diseases such as type 2 diabetes (T2D) are linked to the development of fatty liver, steatohepatitis and liver fibrosis. We have previously described novel clusters of patients at risk for T2D. Three clusters (3, 5 and 6) either had a high risk to progress to T2D or to develop cardiorenal complications. While high-risk clusters differ in liver fat content, it remains unknown if they also harbor a different risk for hepatic fibrosis. Thus, we determined clinically applicable scores of liver fibrosis in 1115 subjects at risk for T2D from the TUEF/TULIP study, which were categorized into high-risk clusters (n=602). Liver fat content was determined by 1H-magnetic resonance spectroscopy. The AST-to-platelet-ratio index (APRI), fibrosis-4 score (FIB-4) and NAFLD fibrosis score (NFS) were used to determine fibrosis risk. ANCOVA model analysis adjusted for liver fat content was performed. Of the high-risk clusters (3: n= 83, 5: n= 203, 6: n= 316), cluster 6 had the highest BMI 44.1±9.7 kg/m² (vs. cluster 3: 29.7±3.1 kg/m², p<0.001 and cluster 5: 42.8.0±8.7, p=0.32) and cluster 5 had the highest liver fat content (13.4±8.8% vs. cluster 3: 8.0±6.5, p<0.001 and cluster 6: 9.0±6.7, p<0.001). All fibrosis scores correlated with liver fat content (e.g. FIB-4: r= 0.45, p< 0.0001). Although FIB-4, APRI and NFS were generally low, they were highest in cluster 5, but despite highest liver fat content, they did not differ from cluster 3 and 6 (e.g. FIB-4: cluster 5: 0.19±0.45 vs. cluster 3: 0.14±0.01, p=0.59 and cluster 6: 0.12±0.15, p=0.39). Interestingly, cluster 3 with the lowest liver fat content exhibited higher FIB-4 compared to cluster 6 (p=0.02). According to clinically applicable scores, the most insulin resistant cluster 5 has the highest FIB-4 score. The insulin deficient cluster 3 has a relatively high FIB-4 score despite relatively low liver fat content and BMI. Further studies are needed to investigate the clinical relevance of these findings. Disclosure V.Minelli faiao: None. A.Peter: None. A.L.Birkenfeld: None. N.Stefan: Advisory Panel; Pfizer Inc., Research Support; Sanofi, Speaker's Bureau; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Lilly Diabetes, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. R.Jumpertz von schwartzenberg: Other Relationship; Sanofi, Amgen Inc., Lilly, Novo Nordisk. A.Sandforth: None. S.Katzenstein: None. A.Fritsche: Advisory Panel; Novo Nordisk, Lilly, Sanofi, Boehringer-Ingelheim, Speaker's Bureau; AstraZeneca, SYNLAB Holding Deutschland GmbH. L.Fritsche: None. J.Machann: None. F.Schick: None. H.Häring: None. R.Wagner: Advisory Panel; Daiichi Sankyo, Speaker's Bureau; Novo Nordisk, Sanofi.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".