The dietary inflammatory index is positively associated with insulin resistance in underweight and healthy weight adults
Bibliographic record
Abstract
The aim of this study was to explore the relationship between dietary inflammatory index (DII) and insulin resistance (IR) in underweight and healthy weight adults. This cross-sectional study involved 3205 participants from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. All dietary data used to calculate the DII were obtained based on the average of two 24-h dietary recall interviews. Participants were divided into an anti-inflammatory diet group and a pro-inflammatory diet group based on DII < 0 and DII ≥ 0, respectively. Fasting blood glucose and fasting insulin data used to calculate IR index (HOMA-IR) were from laboratory data in the NHANES database. According to the linear regression analysis results of DII and HOMA-IR, we found that there was a positive relationship between DII and IR. A positive association between DII and HOMA-IR was seen in the following groups after stratification: by age in 20-39-year olds, by sex in males, by race in Non-Hispanic Whites, by family history of diabetes in those without a family history of diabetes, by education level in those with high school education, by smoking status in current smokers and non-smokers, by hypertension in those with hypertension, by BMI in those with a BMI of 18.5-24.99, by hypertriglyceridemia (HTG) in those without HTG, by poverty impact ratio (PIR) in those with PIR ≤ 1.3 and >1.3, and by physical activity in those with moderate recreational activities. In conclusion, in underweight and healthy weight adults, DII was positively correlated with the risk of IR.
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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 | 0.001 |
| 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".