Thyroid Function and the Risk of Fibrosis of the Liver, Heart, and Lung in Humans: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Fibrotic diseases have an unclear etiology and poor prognosis. Fluctuations in thyroid function may play a role in the development of fibrosis, but evidence is fragmented and inconclusive. This systematic review and meta-analysis aimed to investigate the association of thyroid function with fibrotic diseases of the liver, heart, and lung in humans. Methods: We searched PubMed, Medline Ovid, Embase Ovid, and Web-of-Science for studies published from inception to 14 June 2019, to identify observational studies that investigated the association of thyroid function with fibrosis of the liver, heart, and lung in humans. Study quality was evaluated by the Newcastle–Ottawa Scale. The Mantel–Haenszel method was used to pool the odds ratios (ORs) of studies investigating the association of hypothyroidism with liver fibrosis. Results: Of the 2196 identified articles, 18 studies were included in the systematic review, of which 11 studies reported on liver fibrosis, 4 on myocardial fibrosis, and 3 on pulmonary fibrosis. The population sample size ranged from 36 to 7259 subjects, with median mean age 51 years (range, 36–69) and median percentage of women 53 (range, 17–100). The risk of bias of studies was low to moderate to high. Higher serum thyrotropin and lower thyroid hormone levels were generally associated with higher likelihood of fibrosis. Compared with euthyroidism, overt and subclinical hypothyroidism was associated with a higher likelihood of fibrosis in the liver (six of seven studies), heart (three of three studies), and lung (three of three studies). Based on the results of the seven studies included in the meta-analysis, overt and subclinical hypothyroidism was associated with an increased risk of liver fibrosis (pooled OR, 2.81; 95% confidence interval [CI], 1.74–4.53; heterogeneity, I 2 31.4%; pooled OR, 2.12; CI, 1.45–3.12; heterogeneity, I 2 0%; respectively), without evidence of publication bias. Conclusions: This study suggests that low thyroid function is associated with increased likelihood of chronic fibrotic diseases of the liver, heart, and lung. However, the evidence is mainly based on cross-sectional data. Prospective studies and randomized clinical trials are needed to investigate the potential efficacy of thyroid hormone and its analogs on the occurrence and progression of fibrosis.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".