FTO genotype, dietary protein intake, and body weight in a multiethnic population of young adults: a cross-sectional study
Bibliographic record
Abstract
Variation in the fat mass and obesity-associated gene ( FTO ) has been associated with susceptibility to obesity, but the association appears to be modified by diet. We investigated whether dietary protein intake modifies the association between FTO variant rs1558902 and body mass index (BMI) and waist circumference in young adults ( n = 1491) from the cross-sectional Toronto Nutrigenomics and Health Study. Lifestyle, genetic, anthropometric, and biochemical data were collected and diet was assessed using a Toronto-modified Willett Food Frequency Questionnaire. General linear models stratified by ethnicity and adjusted for age, sex, and total energy intake were used to examine the association between FTO genotypes and measures of body weight, and whether protein intake modified any of the associations. East Asians who were homozygous for the rs1558902 risk allele (A) had a greater BMI ( p = 0.004) and waist circumference ( p = 0.03) than T allele carriers. This association was not observed in individuals of Caucasian or South Asian ancestry. Among East Asians, a significant FTO-protein interaction was observed for BMI ( p = 0.01) and waist circumference ( p = 0.007). Those with low protein intake (≤ 18% total energy intake) who were homozygous for the rs1558902 risk allele (A) had significantly higher BMI ( p < 0.0001) and waist circumference ( p = 0.0006) compared to carriers of the T allele. These associations were absent in the high protein intake group (> 18% total energy intake). Compared to Caucasians and South Asians, East Asians consumed a significantly higher ratio of animal-to-plant protein ( p < 0.05). These findings suggest that high dietary protein intake may protect against the effects of risk variants in the FTO gene on BMI and waist circumference.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".