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Record W4403150528 · doi:10.1210/jendso/bvae163.619

7622 The Effects of Fibroblast Growth Factor-21 Analogues in Patients with Metabolic Dysfunction Associated Steatohepatitis (MASH): A Systematic Review and Meta-Analysis of Randomized Controlled Trials

2024· review· en· W4403150528 on OpenAlexaff
José Carlos Cardoso, Renan Yuji Ura Sudo, Mariana C. Souza, Felipe Dircêu Dantas Leite Pessôa, Francine Moraes, Natália Mulinari Turin de Oliveira, Francinny Alves Kelly, Lílian Maria Lopes, P G Lima, M S Barros, F F Bandeira

Bibliographic record

VenueJournal of the Endocrine Society · 2024
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsSteatohepatitisMeta-analysisRandomized controlled trialMedicineInternal medicineFGF21GastroenterologyFibroblast growth factorEndocrinologyFatty liverDisease

Abstract

fetched live from OpenAlex

Abstract Disclosure: J.C. Cardoso: None. R.Y. Ura Sudo: None. M.C. Souza: None. F.D. Pessôa: None. F.A. Moraes: None. N.M. Oliveira: None. F.A. Kelly: None. L.M. Lopes: None. P.G. Lima: None. M.S. Barros: None. F.F. Bandeira: None. Fibroblast growth factor 21 (FGF-21), has been proposed to improve liver histologic features, hepatic steatosis and markers of inflammation and fibrosis, thus emerging as a treatment avenue to metabolic dysfunction associated steatohepatitis (MASH). However, the potential treatment effects of FGF-21 analogues remain unclear. Therefore we aimed to perform a meta analysis exploring the effects on the liver of FGF-21 analogues when compared to placebo. We systematically searched different databases for randomized controlled trials (RCT): Pubmed, Web of Science, Cochrane - from inception to January 2024. Statistical analysis was performed in R software 4.3.1. A random-effects model was employed to compute mean differences (MD) and risk ratios (RR) with 95% confidence intervals (CI) for continuous and binary endpoints, respectively. A p-value of <0.05 was considered statistically significant. Heterogeneity was examined with the Cochran Q test, prediction interval and I² statistics. The results were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement guideline. The protocol was registered in PROSPERO (CRD42023493083). A total of 12 RCTs with 1235 patients were included, of whom 852 (69%) were randomized to FGF-21 analogues therapy. The mean age was 54 years and the mean body mass index (Kg/m²) was 39.1. Over a mean follow-up time of 31.667 weeks, the analysis showed us that while the ≥ 50% reduction in hepatic fat fraction (HFF) (RR 6.53; 95% CI 3.13 to 13.63; p < 0.001; I² = 0%) and the fibrosis improvement in ≥1 stage without worsening of MASH (RR 1.77; 95% CI 1.12 to 2.79; P=0.0145; I² =29%) were significantly higher in patients treated with FGF-21 analogues compared with placebo, the higher MASH resolution without worsening of fibrosis (RR 2.48; 95% CI 0.87 to 7.08; P=0.090;I²=60%) in FGF-21 group wasn’t statistically significant. The FGF-21 group also presented significantly mean decreases in ELF score (MD -0.45; 95% CI -0.64 to -0.25; p < 0.001; I² =34%), liver stiffness (MD -2.10 KPa; 95% CI -3.53 to -0.68; p = 0.004; I²=94%), GGT (MD -16.33 U/L; 95% CI -29.92 to -2.75; p=0.018; I²=81%), ALP (MD -7.95 U/L; 95% CI -11.53 to -4.37; p<0.001; I²=0%), HFF (MD -40.26%; 95% CI -63.01 to -17.51; p < 0.001; I²=97%), ALT (MD -19.31%; 95% CI -28.84 to -9.77; p<0.001; I²=63%), AST (MD -19.32%; 95% CI -28.53 to -10.11; p<0.001; I² =59%), Pro-C3 (MD -13.65%; 95% CI -21.93 to -5.38; p=0.001; I²=81%). In conclusion, the use of FGF-21 analogues significantly improves liver histologic features, inflammation and fibrosis, however there were nonsignificant improvement in the composite outcome of MASH resolution without worsening of fibrosis. Presentation: 6/3/2024

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0210.035
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.026
GPT teacher head0.310
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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
Published2024
Admission routes1
Has abstractyes

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