Investigating Gene–Gene and Gene–Environment Interactions in the Association Between Overnutrition and Obesity-Related Phenotypes
Bibliographic record
Abstract
Introduction – Animal studies suggested that NFKB1, IKBKB and SOCS3 genes could be involved in the association between overnutrition and obesity. This study aims to investigate interactions involving these genes and macronutrient intakes affecting obesity-related phenotypes. Methods – We used a traditional statistical method, logistic regression, and compared it to alternative statistical method, multifactor dimensionality reduction (MDR) and penalized logistic regression (PLR), to better detect genes/environment interactions in the Toronto Nutrigenomics and Health Study (n=1639) using dichotomized body mass index (BMI) and waist circumference as obesity-related phenotypes. Exposure variables included genotype on 54 single nucleotide polymorphisms (NFKB1: 18, IKBKB: 9, SOCS3: 27), macronutrient (carbohydrates, protein, fat) and alcohol intakes and ethno-cultural background. Results – After correction for multiple testing, no interaction was found using logistic regression. MDR identified interactions between SOCS3 rs6501199 and rs4969172, and IKBKB rs3747811 affecting BMI in the Caucasian population; SOCS3 rs6501199 and NFKB1 rs1609798 affecting WC in the Caucasian population; and SOCS3 rs4436839 and IKBKB rs3747811 affecting WC in the South Asian population. PLR found a main effect of SOCS3 rs12944581 on BMI among the South Asian population. Conclusion – While MDR and PLR had discordant results, some models support results from previous studies. These results emphasize the need to use alternative statistical methods to investigate high-order interactions and suggest that variants in the nutrient-responsive hypothalamic IKKB/NF-kB signaling pathway may be involved in obesity pathogenesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".