ABO Genotype, ‘Blood-Type’ Diet and Cardiometabolic Risk Factors
Bibliographic record
Abstract
BACKGROUND: The 'Blood-Type' diet advises individuals to eat according to their ABO blood group to improve their health and decrease risk of chronic diseases such as cardiovascular disease. However, the association between blood type-based dietary patterns and health outcomes has not been examined. The objective of this study was to determine the association between 'blood-type' diets and biomarkers of cardiometabolic health and whether an individual's ABO genotype modifies any associations. METHODS: Subjects (n = 1,455) were participants of the Toronto Nutrigenomics and Health study. Dietary intake was assessed using a one-month, 196-item food frequency questionnaire and a diet score was calculated to determine relative adherence to each of the four 'Blood-Type' diets. ABO blood group was determined by genotyping rs8176719 and rs8176746 in the ABO gene. ANCOVA, with age, sex, ethnicity, and energy intake as covariates, was used to compare cardiometabolic biomarkers across tertiles of each 'Blood-Type' diet score. RESULTS: Adherence to the Type-A diet was associated with lower BMI, waist circumference, blood pressure, serum cholesterol, triglycerides, insulin, HOMA-IR and HOMA-Beta (P<0.05). Adherence to the Type-AB diet was also associated with lower levels of these biomarkers (P<0.05), except for BMI and waist circumference. Adherence to the Type-O diet was associated with lower triglycerides (P<0.0001). Matching the 'Blood-Type' diets with the corresponding blood group did not change the effect size of any of these associations. No significant association was found for the Type-B diet. CONCLUSIONS: Adherence to certain 'Blood-Type' diets is associated with favorable effects on some cardiometabolic risk factors, but these associations were independent of an individual's ABO genotype, so the findings do not support the 'Blood-Type' diet hypothesis.
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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) | 0.003 | 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".