Resting energy expenditure of women with and without polycystic ovary syndrome: a systematic review and meta-analysis
This repository contains all the code and data for a project conducting a systematic review and meta-analysis of differences in resting energy expenditure between women with, and without, Polycystic Ovary Syndrome (PCOS).
Preprint, please cite as: Kirwan, R., Peele, L., Nuckols, G., Kohlhoff, G., Cabré, H., Olenick, A., and Steele, J. (2025). Resting energy expenditure of women with and without polycystic ovary syndrome: a systematic review and meta-analysis. medrxiv DOI: https://doi.org/10.64898/2025.12.03.25341536.
Context: Polycystic ovary syndrome (PCOS) is common in reproductive-age women, who often have higher BMI classification. This is assumed to stem from lower resting energy expenditure (REE), influencing lifestyle intervention guidelines. However, evidence for reduced REE in women with PCOS compared with those without is inconsistent. Objective: To systematically search and meta-analyse the existing literature to estimate and describe the difference in REE between women with and without PCOS. Data Sources: A systematic search was conducted using PubMed, Medline and Web of Science databases of published research from January 1990 to January 2025. Study Selection: Studies that measured REE in women living with PCOS, both with and without control arms of women without PCOS, were included. Data Extraction: Bibliometric, demographic, and REE data was extracted by one investigator and checked in triplicate. Data Synthesis: Thirteen studies were included in a Bayesian arm-based multiple condition comparison (i.e., network) type meta-analysis model with informative priors to compare both mean REE, and between person variation in REE, between women with and without PCOS. Mean REE differed between groups by 30 kcal/day [95% quantile interval: -47 to 113 kcal/day] and the contrast ratio for between person standard deviations was 0.98 [95% quantile interval: 0.71 to 1.33]. Conclusions: These findings indicate that REE does not meaningfully differ between women with and without PCOS. Group-level differences in resting energy expenditure are small, insignificant, or not physiologically relevant.
This repository contains the necessary files and code to reproduce the analyses, figures, and the manuscript.
To reproduce the analyses, you will need to have R (https://cran.r-project.org/) and RStudio (https://www.rstudio.com/products/rstudio/download/#download) installed on your computer.
To help with reproducibility, this project uses the renv R package (see https://rstudio.github.io/renv/articles/renv.html). With renv, the state of this R project can be easily loaded as renv keeps track of the required R packages (including version), and (if known) the external source from which packages were retrieved (e.g., CRAN, Github). With renv, packages are installed to a project specific library rather than your user or system library. The renv package must be installed on your machine before being able to benefit from its features. The package can be installed using the following command:
install.packages("renv")Once you have renv installed, you can get a copy of this repository on your machine by clicking the green Code button then choose Download zip. Save to your machine and extract. After extraction, double click the pcos_ree_meta.Rproj file in the root directory. This will automatically open RStudio. This will ensure all paths work on your system as the working directory will be set to the location of the .Rproj file. Upon opening, RStudio will recognize the renv files and you will be informed that the project library is out of sync with the lockfile. At shown in the console pane of RStudio, running renv::restore() will install the packages recorded in the lockfile. This could take some time depending on your machine and internet connection.
This project also uses a function based analysis pipeline using
targets. Instead of script based pipelines the targets package makes use of functions applied to targets specified within the pipeline. The targets can be viewed in the _targets.R file, and any user defined functions are available in R/functions.r.
You can view the existing targets pipeline by downloading the targets_pipeline.html file and opening it in your browser.
Useful console functions:
tar_edit()opens the make filetar_make()to run targetstar_visnetwork()to view pipeline
The grateful package was used to create citations to all software and packages used in the analysis. The grateful report can be viewed by downloading the grateful-report.pdf file.
Shield:
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
