Understanding Gene-Lifestyle Interaction in Obesity: The Role of Mediation versus Moderation
Bibliographic record
Abstract
BACKGROUND: Obesity results from complex interactions between genetic susceptibility to weight gain and poor eating and lifestyle behaviors. The approach that has been traditionally used in genetics to investigate gene-environment/lifestyle interaction in obesity is based on the concept of moderation or effect modification. Another approach called mediation analysis can be used to investigate gene-environment interaction in obesity. The objective of this review article is to explain the differences between the concepts of moderation and mediation and summarize the studies that have used mediation analysis to support the role of eating or lifestyle behaviors as putative mediators of genetic susceptibility to obesity. SUMMARY: Moderation is used to determine whether the effect of an exposure (genes associated with obesity) on an outcome (obesity phenotype) differs in magnitude and/or direction across the spectrum of environmental exposure. Mediation analysis is used to assess the extent to which the effect of the exposure on the outcome is explained by a given set of hypothesized mediators with the aim of understanding how the exposure could lead to the outcome. In comparison with moderation, relatively few studies used mediation analyses to investigate gene-environment interaction in obesity. Most studies found evidence that traits related to appetite or eating behaviors partly mediated genetic susceptibility to obesity in either children or adults. KEY MESSAGES: Moderation and mediation represent two complementary approaches to investigate gene-environment interaction in obesity and address different research questions pertaining to the cause-effect relationship between genetic susceptibility to obesity and various obesity outcomes. More studies relying on mediation are needed to better understand the role of eating and lifestyle habits in mediating genetic susceptibility to obesity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".