Systematic review with meta‐analysis: Steatosis severity and subclinical atherosclerosis in metabolic dysfunction‐associated steatotic liver disease
Bibliographic record
Abstract
Summary Background Metabolic dysfunction‐associated steatotic liver disease (MASLD) is a prevalent liver condition recognised as an independent risk factor for cardiovascular disease (CVD). However, there is ongoing debate regarding the effective strategy for cardiovascular risk assessment in MASLD. Aim To investigate the relationship between liver imaging, specifically focusing on the severity of steatosis and subclinical atherosclerosis. Methods We conducted a thorough search across four databases, from 1950 to April 2023, to identify eligible studies employing imaging to explore the relationship between different degrees of steatosis and subclinical atherosclerosis among MASLD. Additionally, we conducted a quality assessment using the Newcastle Ottawa Scale, performed a meta‐analysis employing the DerSimonian‐Liard random‐effects model, and conducted subgroup analyses for validation. Results In total, 19 studies, encompassing 147,411 middle‐aged individuals without previous CVD (74.94% male; mean age 45.53 years [SD 10.69]; mean BMI 24.3 kg/m2 [SD 3.35]), were included. The pooled odds ratio for subclinical atherosclerosis was 1.27 (95% CI: 1.13–1.41, I2 = 76.68%) in mild steatosis and significantly increased to 1.68 (95% CI: 1.41–2.00, I2 = 89.02%) in moderate to severe steatosis. Sensitivity analysis, focusing on high‐quality studies, consistently supported this finding and the results remained robust across subgroup analyses. Furthermore, meta‐regression revealed that a higher mean AST and ALT, alongside a lower mean HDL, were significant moderators of this association. Conclusions Even mild steatosis is associated with CVD risk, and steatosis severity further intensifies this association. These findings suggest that liver fat quantification enhances CVD risk stratification in patients with MASLD.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".