MétaCan
Menu
Back to cohort
Record W4416976958 · doi:10.52403/ijhsr.20251122

Association Between Thyroid Dysfunction and Non-Alcoholic Fatty Liver Disease: A Systematic Review

2025· article· W4416976958 on OpenAlexaboutno aff
Tanya .

Bibliographic record

VenueInternational Journal of Health Sciences and Research · 2025
Typearticle
Language
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsSubclinical infectionFatty liverSteatosisThyroidThyroid functionThyroid dysfunctionHormoneCochrane Library

Abstract

fetched live from OpenAlex

Background: Non-alcoholic fatty liver disease (NAFLD) is a leading metabolic liver disorder with growing global prevalence. Thyroid hormones regulate lipid metabolism and hepatic energy balance. This review assessed the association between thyroid dysfunction and NAFLD. Methods: A systematic search was performed in PubMed, Embase, Scopus, Web of Science, and Cochrane Library up to 2025. Studies evaluating thyroid hormones (TSH, FT3, FT4) in adults with NAFLD were included. Two reviewers independently extracted data and evaluated quality using the Newcastle–Ottawa Scale. Results were summarized descriptively and quantitatively. Results: Forty studies met inclusion criteria, with 25 rated high quality (NOS≥7). Most showed significantly higher TSH and lower FT3/FT4 in NAFLD than in controls. Subclinical hypothyroidism increased NAFLD risk by 1.5–2.3-fold. Low-normal thyroid function correlated with greater hepatic steatosis and fibrosis. Mean NOS score was 7.4±0.8. Conclusion: Thyroid dysfunction, particularly subclinical hypothyroidism, is consistently linked with NAFLD presence and severity. Routine thyroid function testing in NAFLD may improve early detection and management. Key words: NAFLD; Thyroid dysfunction; Hypothyroidism; Subclinical hypothyroidism; Liver fibrosis; Metabolic disease

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.530
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.447
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Health Sciences and ResearchSame topicThyroid Disorders and TreatmentsFrench-language works237,207