Associations Between Added Sugars Intake from Various Food and Beverage Sources and Diet Quality Among the U.S. Population
Bibliographic record
Abstract
Background: A diet high in added sugars has been linked to poor diet quality; however, little is known about specific sources of added sugars and their association with diet quality. Objective: This study examined associations between added sugars intake from specific food and beverage sources and diet quality, as indicated by the Healthy Eating Index (HEI) 2020 score, among the U.S. population. Methods: Data from eight consecutive 2-year cycles of the National Health and Nutrition Examination Survey (2003–2004 through 2017–2018) were pooled, and regression analysis was conducted to examine associations between total HEI-2020 score or HEI-2020 component scores and added sugars intake (% kcal) from key contributors: soft drinks, fruit drinks and coffee and tea; ready-to-eat cereals; flavored milk; sweet bakery products; and snack/meal bars. Results: A higher added sugars intake from soft drinks, fruit drinks and coffee and tea was associated with lower diet quality (lower total HEI score and lower scores on most of the HEI components) among both children and adults (p < 0.0001). In contrast, higher added sugars intakes from flavored milk (p < 0.0001) and snack/meals bars (p < 0.001) among children, and from sweet bakery products (p < 0.0001) among adults, were associated with higher diet quality. For all these associations, changes in the total HEI score across quintiles of added sugars intake were very small, ranging from 50.2 to 52.8 for children and 55.4 to 57.5 for adults, depending on the added sugars source. Conclusions: The nature of the relationship between added sugars intake and diet quality depends on the source of added sugars. While the small differences in diet quality may be of limited practical significance, our results suggest that the consideration of the different roles of various added sugars sources in the diet is warranted when developing dietary guidance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".