Fiber consumption and metabolic syndrome in adults: Results from NHANES 1999‐2004
Bibliographic record
Abstract
The objective of this study was to examine the effect of increasing fiber and whole grain (WG) consumption on the odds of having metabolic syndrome (MetS) in a recent, nationally representative sample of US adults 19 to 51 years (n=7,039) and 51 + years (n=6,237) using a secondary analysis of NHANES 1999‐2004 data. . Participants were divided into four fiber consumption groups: <10g/d, 10 to <15, 15to <20, =20g/day. For a separate analysis of WG, participants were also divided into four WG consumption groups: <0.6 (control), 0.6 to <1.5, 1.5 to <3.0 and = 3.0 servings. MetS was defined using the ATPIII definition. Least‐square means + SE were calculated. For adults 19‐50 years, mean fiber intake was 14.97 g + 0.37; ORs with MetS were: 0.958 (CI = 0.755 to 1.216), 0.800 (CI = 0.603 to 1.063), and 0.807 (CI = 0.608 to 1.072) for the three fiber groups, respectively; p for trend with MetS was 0.08. For adults 51 + years, mean fiber intake was 15.65 g + 0.26; ORs with MetS were: 1.31 (CI = 1.05 to 1.64), 0.89 (CI = 0.71 to 1.14), and 0.60 (1.47 to 0.77) for the three WG groups, respectively; p for trend was 0.0010. However, whole grain intake was not associated with reduced ORs of MeS in both age groups. These data suggest that fiber consumption may have a positive impact on MeS. Supported by USDA & Kellogg's Corporate Citizenship Fund.
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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 | 0.001 |
| 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".