A Comparison of the Predictive Value of 12 Body Composition Markers for Metabolic Dysfunction-Associated Steatotic Liver Disease, At-Risk Metabolic Dysfunction-Associated Steatohepatitis, and Increased Liver Stiffness in a General Population Setting
Bibliographic record
Abstract
INTRODUCTION: Adipose tissue is a key mediator of metabolic dysfunction-associated steatotic liver disease (MASLD) development and progression into metabolic dysfunction-associated steatohepatitis (MASH) and fibrosis. Since direct comparisons of body composition parameters are lacking, we here investigate 12 different body composition parameters. METHODS: Adult participants from National Health and Nutrition Examination Survey 2017-2023 with liver health data were included. Exclusion criteria were age older than 80 years, excessive alcohol (>60 g/d), viral hepatitis, and missing anthropometrics. MASLD was defined as controlled attenuation parameter ≥275 dB/m with metabolic dysfunction, MASH as FibroScan-aspartate aminotransferase ≥0.35, and increased liver stiffness measurement (LSM) as ≥8 kPa. Predictive performance of 12 body composition parameters was assessed using area under the curve analysis. Predicted probabilities of outcomes were visualized for standardized parameters, and nonlinearity was assessed through restricted cubic splines. RESULTS: Among 11,579 participants (age 51 [35-63], 47% male), 41% had MASLD, 6.5% at-risk MASH, and 9.9% increased LSM. Waist circumference (WC) and not BMI or waist-to-height ratio obtained the highest area under the curve for MASLD (0.82), at-risk MASH (0.73), and increased LSM (0.75) outperforming or equaling all other indices across subgroups. Associations between WC and MASLD were nonlinear, with slight risk saturation beyond 100 cm; at-risk MASH was linearly associated across the entire spectrum; and increased LSM risk rose only after WC >100 cm. DISCUSSION: In the general population, MASLD and MASH risk increased even when WC < 100 cm, while increased LSM risk was increasing only >100 cm. Although relatively minor differences, WC consistently demonstrated the highest predictive value for MASLD, at-risk MASH, and increased LSM and therefore most suited for MASLD diagnosis, management, and risk stratification.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".