Thyroid Function and the Risk of Nonalcoholic Fatty Liver Disease: The Rotterdam Study
Bibliographic record
Abstract
CONTEXT: Although thyroid function is associated with several risk factors of nonalcoholic fatty liver disease (NAFLD), its role in NAFLD development remains unclear. OBJECTIVE: We aimed to prospectively investigate the association between variations in thyroid function and NAFLD. DESIGN AND SETTING: The Rotterdam Study, a large population-based, prospective cohort study. PARTICIPANTS AND MAIN OUTCOME MEASURES: Participants with thyroid function measurements at baseline and NAFLD data (ie, at baseline fatty liver index/at follow-up ultrasound) were eligible. Transient elastography was performed to assess the presence of fibrosis in patients with NAFLD, using the liver stiffness measurements more than or equal to 8 kPa as cutoff for clinically relevant fibrosis. The association between thyroid parameters and incident NAFLD was explored by using logistic regression models. RESULTS: A total of 9419 participants (mean age, 64.75 y) were included. The median follow-up time was 10.04 years (interquartile range, 5.70-10.88 y). After adjusting for age, sex, cohort, follow-up time, use of hypolipidemic drugs, and cardiovascular risk factors, higher free T4 levels were associated with a decreased risk of NAFLD (odds ratio, 0.42; 95% confidence interval [CI], 0.28-0.63). In line, higher TSH levels were associated with an increased risk of having clinically relevant fibrosis in NAFLD (odds ratio, 1.49; CI, 1.04-2.15). Compared with euthyroidism, hypothyroidism was associated with a 1.24-fold higher NAFLD risk (CI, 1.01-1.53). Moreover, NAFLD risk decreased gradually from hypothyroidism to hyperthyroidism (P for trend = .003). CONCLUSION: Lower thyroid function is associated with an increased NAFLD risk. These findings may lead to new avenues regarding NAFLD prevention and treatment.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".