Weight loss, insulin resistance, and study design confound results in a meta-analysis of animal models of fatty liver
Abstract
The classical drug development pipeline necessitates studies using animal models of human disease to gauge future efficacy in humans, however there is a low conversion rate from success in animals to humans. Non-alcoholic fatty liver disease (NAFLD) is a complex chronic disease without any established therapies and a major field of animal research. We performed a meta-analysis with meta-regression of 603 interventional rodent studies (10,364 animals) in NAFLD to assess which variables influenced treatment response. Weight loss and alleviation of insulin resistance were consistently associated with improvement in NAFLD. Multiple drug classes that do not affect weight in humans caused weight loss in animals. Other study design variables, such as age of animals and dietary composition, influenced the magnitude of treatment effect. Publication bias may have increased effect estimates by 37-79%. These findings help to explain the challenge of reproducibility and translation within the field of metabolism.
Data availability
The raw dataset used for analysis, including references to individual studies, are available Figure 1 - Source Data and deposited in the Dryad repository at https://doi.org/10.5061/dryad.pzgmsbcgc.R code used for analysis are available in Supplementary Data.Source data files have been provided for Figures 2-8.
-
Data from: Weight loss, insulin resistance, and study design confound results in a meta-analysis of animal models of fatty liverDryad Digital Repository, 10.5061/dryad.pzgmsbcgc.
Article and author information
Author details
Funding
Wellcome Trust (216329/Z/19/Z)
- Jake P Mann
European Society for Paediatric Research (Young Investigator Start-Up Grant)
- Jake P Mann
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Joel K Elmquist, University of Texas Southwestern Medical Center, United States
Version history
- Received: March 3, 2020
- Accepted: October 15, 2020
- Accepted Manuscript published: October 16, 2020 (version 1)
- Version of Record published: November 6, 2020 (version 2)
Copyright
© 2020, Hunter et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,728
- views
-
- 219
- downloads
-
- 4
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Medicine
Caesarean section scar diverticulum (CSD) is a significant cause of infertility among women who have previously had a Caesarean section, primarily due to persistent inflammatory exudation associated with this condition. Even though abnormal bacterial composition is identified as a critical factor leading to this chronic inflammation, clinical data suggest that a long-term cure is often unattainable with antibiotic treatment alone. In our study, we employed metagenomic analysis and mass spectrometry techniques to investigate the fungal composition in CSD and its interaction with bacteria. We discovered that local fungal abnormalities in CSD can disrupt the stability of the bacterial population and the entire microbial community by altering bacterial abundance via specific metabolites. For instance, Lachnellula suecica reduces the abundance of several Lactobacillus spp., such as Lactobacillus jensenii, by diminishing the production of metabolites like Goyaglycoside A and Janthitrem E. Concurrently, Clavispora lusitaniae and Ophiocordyceps australis can synergistically impact the abundance of Lactobacillus spp. by modulating metabolite abundance. Our findings underscore that abnormal fungal composition and activity are key drivers of local bacterial dysbiosis in CSD.
-
- Medicine
- Neuroscience
Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and to monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking, as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.