Ethnic differences in grains consumption and their contribution to intake of B-vitamins: results of the Multiethnic Cohort Study
Bibliographic record
Abstract
BACKGROUND: Research indicates that a diet rich in whole grains may reduce the risk of prevalent chronic diseases, including cardiovascular disease, diabetes, and some cancers, and that risk for these diseases varies by ethnicity. The objective of the current study was to identify major dietary sources of grains and describe their contribution to B vitamins in five ethnic groups. METHODS: A cross-sectional mail survey was used to collect data from participants in the Multiethnic Cohort Study in Hawaii and Los Angeles County, United States, from 1993 to 1996. Dietary intake data collected using a quantitative food frequency questionnaire was available for 186,916 participants representing five ethnic groups (African American, Latino, Japanese American, Native Hawaiian and Caucasian) aged 45-75 years. The top sources of grain foods were determined, and their contribution to thiamin, riboflavin, niacin, vitamin B6, and folic acid intakes were analyzed. RESULTS: The top source of whole grains was whole wheat/rye bread for all ethnic-sex groups, followed by popcorn and cooked cereals, except for Native Hawaiian men and Japanese Americans, for whom brown/wild rice was the second top source; major contributors of refined grains were white rice and white bread, except for Latinos. Refined grain foods contributed more to grain consumption (27.1-55.6%) than whole grain foods (7.4-30.8%) among all ethnic-sex groups, except African American women. Grain foods made an important contribution to the intakes of thiamin (30.2-45.9%), riboflavin (23.1-29.2%), niacin (27.1-35.8%), vitamin B6 (22.9-27.5%), and folic acid (23.3-27.7%). CONCLUSIONS: This is the first study to document consumption of different grain sources and their contribution to B vitamins in five ethnic groups in the U.S. Findings can be used to assess unhealthful food choices, to guide dietary recommendations, and to help reduce risk of chronic diseases in these populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".