ArticleEvolution, medicine, and public health2016
Meta-analysis of variance: an illustration comparing the effects of two dietary interventions on variability in weight.
Article in Evolution, medicine, and public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 7 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Meta-analysis of variance in tDCS effects on response inhibition.Scientific reports · 2024Pooled it
- The influence of immune challenges on the mean and variance in reproductive investment: a meta-analysis of the terminal investment hypothesis.BMC biology · 2023Pooled it
- A global meta-analysis reveals higher variation in breeding phenology in urban birds than in their non-urban neighbours.Ecology letters · 2022Pooled it
- Animal pollination increases stability of crop yield across spatial scales.Ecology letters · 2022Pooled it
- Meta-analytic evidence that sexual selection improves population fitness.Nature communications · 2019Pooled it
- Pooled it
- Dietary restriction increases variability in longevity.Biology letters · 2017Pooled it
- Unusual outcome variances as a method to identify potentially problematic clinical trials.PloS one · 2026Article
- The effects of an obesogenic diet on behavior and cognition in zebrafish (Ecology and evolution · 2022Article
- Compensatory recruitment allows amphibian population persistence in anthropogenic habitats.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology.Global change biology · 2022Article
- Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.Human brain mapping · 2022Article
- Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research.PLoS biology · 2021Article
- Free-Living Energy Balance Behaviors Are Associated With Greater Weight Loss During a Weight Loss Program.Frontiers in nutrition · 2021Article
- Predicting Postoperative Lung Function Following Lung Cancer Resection: A Systematic Review and Meta-analysis.EClinicalMedicine · 2019Article
- Dietary macronutrient content, age-specific mortality and lifespan.Proceedings. Biological sciences · 2019Article
- Meta-analysis of variance: an illustration comparing the effects of two dietary interventions on variability in weight.Evolution, medicine, and public health · 2016Article
- A multi-lab study of bilingual infants: Exploring the preference for infant-directed speech.Advances in methods and practices in psychological scienceArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Meta-analysis, which drives evidence-based practice, typically focuses on the average response of subjects to a treatment. For instance in nutritional research the difference in average weight of participants on different diets is typically used to draw conclusions about the relative efficacy of interventions. As a result of their focus on the mean, meta-analyses largely overlook the effects of treatments on inter-subject variability. Recent tools from the study of biological evolution, where inter-individual variability is one of the key ingredients for evolution by natural selection, now allow us to study inter-subject variability using established meta-analytic models. Here we use meta-analysis to study how low carbohydrate (LC) ad libitum diets and calorie restricted diets affect variance in mass. We find that LC ad libitum diets may have a more variable outcome than diets that prescribe a reduced calorie intake. Our results suggest that whilst LC diets are effective in a large proportion of the population, for a subset of individuals, calorie restricted diets may be more effective. There is evidence that LC ad libitum diets rely on appetite suppression to drive weight loss. Extending this hypothesis, we suggest that between-individual variability in protein appetite may drive the trends that we report. A priori identification of an individual's target intake for protein may help define the most effective dietary intervention to prescribe for weight loss.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.