Non-alcoholic fatty liver and fibrosis is associated with cardiovascular structure and function in young adults
Bibliographic record
Abstract
BACKGROUND: Non-alcoholic fatty liver disease shares many risk factors with other metabolic disorders. We sought to establish whether non-alcoholic fatty liver disease may be associated with cardiovascular health independently of other known risk factors. METHODS: In this prospective, population-based cohort of young adults, controlled attenuation parameter-defined liver steatosis, transient elastography-defined liver fibrosis, echocardiography, carotid ultrasonography, and pulse wave analysis were assessed at age 24 years. We examined associations between liver and cardiovascular measures, with and without accounting for demographics, body mass index, alcohol, smoking, blood pressure, lipidemia, glycemia, and inflammation. RESULTS: We included 2047 participants (mean age 24.4 y; 36.2% female): 212 (10.4%) had steatosis, whereas 38 (1.9%) had fibrosis. Steatosis was associated with cardiovascular measures after adjusting for demographics, but with more comprehensive adjustment, steatosis only remained associated with stroke index [β (95% CI) of -1.85 (-3.29, -0.41) mL/m2] and heart rate [2.17 (0.58, 3.75) beats/min]. Fibrosis was associated with several measures of cardiovascular structure and function after full adjustment for risk factors, including left ventricular mass index [2.46 (0.56, 4.37) g/m2.7], E/A ratio [0.32 (0.13, 0.50)], tricuspid annular plane systolic excursion [0.14 (0.01, 0.26) cm], carotid intima-media thickness [0.024 (0.008, 0.040) mm], pulse wave velocity [0.40 (0.06, 0.75) m/s], cardiac index [-0.23 (-0.41, -0.06) L/min⋅m2], and heart rate [-7.23 (-10.16, -4.29) beats/min]. CONCLUSIONS: Steatosis was not associated with measures of cardiovascular structure and function nor with subclinical atherosclerosis after adjusting for known cardiovascular risk factors. Fibrosis, however, was associated with several cardiovascular measures, including indicators of subclinical atherosclerosis, even after full adjustment. Further follow-up will help determine whether cardiovascular health worsens later with steatosis alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".