Dietary Sugar Research in Preschoolers: Methodological, Genetic, and Cardiometabolic Considerations
Bibliographic record
Abstract
Excess dietary sugar intake increases the risk of unhealthy weight gain, an important cardiometabolic risk factor in children. To further our understanding of this relationship, we performed a narrative review using two approaches. First, research examining dietary sugar intake, its associations with cardiometabolic health, impact of genetics on sweet taste perception and intake, and how genetics moderates the association of dietary sugar intake and cardiometabolic risk factors in preschool-aged children 1.5-5 years old is reviewed. Second, methodological considerations for collecting and analyzing dietary intake of sugar, genetic information, and markers of cardiometabolic health among young children are provided. Our key recommendations include the following for researchers: (1) Further longitudinal research on sugar intake and cardiometabolic risk factors is warranted to inform policy decisions and guidelines for healthy eating in preschool-aged children. (2) Consistency in sugar definitions is needed across research studies to aid with comparisons of results. (3) Select dietary collection tools specific to each study's aim and sugar definition(s). (4) Limit subjectivity of dietary assessment tools as this impacts interpretation of study results. (5) Choose non-invasive biomarkers of cardiometabolic disease until the strengths and limitations of available biomarkers in preschool-aged children are clarified. (6) Select approaches that account for the polygenic nature of cardiometabolic disease such as genome risk scores and genome wide association studies to assess how genetics moderates the relationship between dietary sugar intake and cardiometabolic risk. This review highlights potential recommendations that will support a research environment to help inform policy decisions and healthy eating policies to reduce cardiometabolic risk in young children.
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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 | 0.254 | 0.476 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".