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Record W4403150514 · doi:10.1210/jendso/bvae163.620

7675 The Effects of Fibroblast Growth Factor-21 Analogues in Patients with Metabolic Syndrome: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

2024· review· en· W4403150514 on OpenAlexaff
Jorge Henrique Cavalcanti Orestes Cardoso, Renan Yuji Ura Sudo, Maria Eduarda Cavalcanti Souza, Felipe Dircêu Dantas Leite Pessôa, Francisco Cézar Aquino de Moraes, Natalia Menezes Nunes de Oliveira, Francinny Alves Kelly, Lucca Moreira Lopes, Pedro Lucas Gomes Lima, Maria Luísa Siegloch Barros, Francisco Farias Bandeira

Bibliographic record

VenueJournal of the Endocrine Society · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFibroblast Growth Factor Research
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMeta-analysisRandomized controlled trialFGF21MedicineMetabolic syndromeInternal medicineFibroblast growth factor

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), a potent insulin sensitizer, has been shown to improve lipid profile, glucose metabolism and energy expenditure. The FGF-21 analogues emerged as a promising treatment avenue for critical components of metabolic syndrome, such as metabolic dysfunction associated steatohepatitis, hypertriglyceridemia, diabetes, and obesity. However, their potential treatment effects remain unclear. We systematically searched Pubmed, Web of Science, and Cochrane databases, from inception to January 2024, for randomized controlled trials (RCT) comparing the effects of FGF-21 analogues on glycemic control and lipid profile against placebo. 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 prospectively registered in PROSPERO under CRD42023493083 identification number. A total of 15 RCTs with 1427 patients were included, of whom 1038 (72.7%) were randomized to FGF-21 analogues therapy. The mean age was 55 years and the mean body mass index (Kg/m^2) was 35.2. Over a mean follow-up time of 20.8 weeks, the FGF-21 group presented a significant decrease in body weight (MD -0.75%; 95% CI -1.25 to -0.24; p=0.004; I²=0%), serum trigliceryde (MD -22.76%; 95% CI -30.48 to -15.04; p<0.001; I²=62%), LDL-C (MD -5.46%; 95% CI -9.73 to -1.19; p=0.012; I²=33%), nonHDL-C (MD -10.65%; 95% CI -14.13 to -7.17; p<0.001; I²=0%) and a significant increase in HDL-C (MD 10.50%; 95% CI 6.70 to 14.30; p<0.001; I²=67%) and adiponectin (MD 18.80%; 95% CI 10.26 to 27.33; p<0.001; I²=88%). Despite these favorable outcomes over placebo, the FGF-21 analogues caused nonsignificant decreases in HOMA-IR (MD -1.38; 95% CI -4.69 to 1.92; p=0.412; I²=85%), HbA1c (MD -0.52%; 95% CI -1.45 to 0.41; p=0.275; I²=99%), plasma glucose (MD -0.45 mg/dL; 95% CI -3.99 to 3.08; P = 0.802; I²=15%), plasma insulin (MD -7.24%; 95% CI -19.16 to 4.68; p=0.234; I²=0%), C peptide (MD -0.37 nmol/L; 95% CI -0.86 to 0.10; P = 0.126; I²=78%) and apolipoprotein B (MD -2.36%; 95% CI -26.45 to 21.72; p=0.848, I²=93%). In conclusion, the use of FGF-21 analogues led to significant improvements in the lipid profile without changes in glycemic control. 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)0.027
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0220.032
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 head0.025
GPT teacher head0.327
Teacher spread0.303 · 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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