<i>FTO</i> Genotype, Dietary Protein Intake and Body Weight in a Population of Young Adults
Bibliographic record
Abstract
Background Variation in the fat mass and obesity‐associated gene ( FTO ) has been shown to be associated with susceptibility to obesity. There may be a link between patterns of dietary intake, variation in FTO and obesity, but the relationship remains unclear. Previous studies have shown that dietary protein might modify the association between FTO genotype and body weight and composition. Objective The objective was to determine whether protein intake modifies the association between FTO variant rs1558902 and BMI/waist circumference. Methods Participants (n=1,491) were from the Toronto Nutrigenomics and Health Study, a cross sectional examination of young adults. 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 rs1558902 and measures of body weight, and whether protein intake modified any observed associations. Results East Asians with protein intake below the median (<17% total energy intake) who were homozygous for the risk allele (A) of rs1558902 had significantly higher BMI (AA= 25.0 kg/m 2 , AT/TT=21.5kg/m 2 ; p< 0.0001) and waist circumference (AA=78.9 cm, AT/TT=70.6cm; p=0.0006) compared to carriers of the T allele. These associations were absent in the high protein intake group (>17% total energy intake) and there was a significant gene‐diet interaction for both BMI (p=0.01) and waist circumference (p=0.007). No significant interactions were observed among the Caucasian or South Asian groups. Conclusion These findings suggest that dietary protein intake might modify the effect of FTO variants on measures of body weight in certain populations. Support or Funding Information Research support from the Advanced Foods and Materials Network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".