Association between dietary fat intake, liver fat, and insulin sensitivity in sedentary, abdominally obese, older men
Bibliographic record
Abstract
Whether dietary fat influences the interaction between liver fat, visceral adipose tissue (VAT), and metabolic risk is unclear. Thus, we sought to determine the independent associations between 1 and 10 d dietary fat intake, liver fat, and VAT on insulin sensitivity using a cross-sectional design. Liver fat score (LFS) was assessed by computed tomography and VAT by magnetic resonance imaging in 42 abdominally obese older men. Insulin sensitivity was measured by the hyperinsulinemic-euglycemic clamp technique. Diet composition was determined from self-recorded diet records for 1 and 10 d preceding the assessment of LFS. LFS was positively associated with the 10 d average fat and alcohol intake, but not with any 1 d average dietary variables. VAT (r = -0.36) and LFS (r = -0.32) were both negatively correlated with insulin sensitivity (p < 0.05). When LFS and VAT were entered in the same model, only VAT was an independent predictor of insulin sensitivity (p < 0.05). Control for the average 10 d alcohol consumption and fat intake improved the association between insulin sensitivity and LFS (from r = -0.32, p > 0.10 to r = -0.49, p < 0.05), but not VAT. In fact, after control for the 10 d dietary variables, both LFS and VAT were independent predictors of insulin sensitivity (p < 0.05). This was not true for any of the 1 d dietary intake variables. In conclusion, liver fat is associated with dietary fat intake and alcohol consumption over 10 d, but not 1 d. Furthermore, dietary habits may influence the relationship between liver fat and insulin sensitivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".