Fructose and non-fructose sugar intakes in the US population and their associations with indicators of metabolic syndrome
Bibliographic record
Abstract
BACKGROUND: Relationships of sugar intakes with indicators of metabolic syndrome are important concerns for public health and safety. For individuals, dietary intake data for fructose and other sugars are limited. METHOD: Descriptive statistics. The data from 25,506 subjects, aged 12-80 yr, contained in the NHANES 1999-2006 databases were analyzed for sugar intakes and health parameters. RESULTS: Dietary fructose was almost always consumed with other sugars. On average, fructose provided 37% of total simple sugar intake and 9% of energy intake. In more than 97% of individuals studied, fructose caloric contribution was lower than that of non-fructose sugars. Fructose and non-fructose sugar intakes had no positive association with blood concentrations of TG, HDL cholesterol, glycohemoglobin, uric acid, blood pressure, waist circumference, and BMI in the adults studied (aged 19 to 80 yr, n=17,749). CONCLUSION: Daily fructose intakes with the American diet averaged approximately 37% of total sugars and 9% of daily energy. Fructose was rarely consumed solely or in excess over non-fructose sugars. Fructose and non-fructose sugar ordinary consumption was not positively associated with indicators of metabolic syndrome, uric acid and BMI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".