Effects of Lifestyle and GLP-1RA based Interventions on Waist Circumference: A Systematic Review and Meta-Analysis

This dataset and code package accompanies the systematic review and meta-analysis:“Effects of Lifestyle and GLP-1RA Based Interventions on Waist Circumference: A Systematic Review and Meta-Analysis” (INPLASY202580071).BackgroundVisceral adiposity, frequently indexed by waist circumference (WC), is a strong predictor of cardiometabolic risk. Lifestyle interventions (diet, exercise, diet+exercise) and glucagon-like peptide-1 receptor agonist therapies (GLP1RAs) are known to reduce body weight, but their comparative impact on WC has not been systematically synthesized.MethodsFollowing PRISMA guidelines, randomized controlled trials (RCTs) published between 2010–2025 were identified through PubMed, Embase, and Cochrane Library. A total of 36 RCTs (49 arms) reporting visceral adipose tissue (VAT) outcomes were screened, with 21 RCTs (27 arms; n=2,839) included in the primary analysis of WC change. Random-effects models were used to pool mean differences (MD) in WC; subgroup analyses and meta-regression examined baseline moderators (age, BMI, WC, body weight, type 2 diabetes prevalence). Prediction intervals (PIs) were calculated for pooled and category-level effects. Publication bias was evaluated via funnel plots and Egger’s regression.Results (summary)Overall, interventions significantly reduced WC (pooled MD −4.36 cm; 95% CI −4.97 to −3.74; PI −6.98 to −1.73). Subgroup analyses confirmed reductions with GLP1RAs, diet, and diet+exercise, while exercise alone was non-significant. Baseline BMI, WC, weight, and quadratic age significantly moderated the effect. Funnel plots suggested small-study effects.Contents of this repositoryMasterDataset.xlsx — tidy dataset combining baseline and intervention characteristics across 27 study arms, analysis-ready.R scripts (00–06) — reproducible code for primary, sensitivity, subgroup, and meta-regression analyses, forest plots, funnel plots, ROB2 templates, and prediction intervals.Python script (05_correlation_python.py) — scatter plot of WC vs. %VAT change (Pearson correlation).README_Figshare.txt — detailed instructions, package versions, and workflow.Software environmentR 4.4.3 (metafor ≥4.8-0, ggplot2, dplyr, cowplot, readxl)Python 3.11.9 (pandas, matplotlib 3.9.2, scipy)RegistrationProtocol registered with INPLASY: INPLASY202580071 (DOI: 10.37766/inplasy2025.8.0071).