Cross-sectional analysis of the health profile and dietary intake of a sample of Canadian adults diagnosed with non-alcoholic fatty liver disease
Bibliographic record
Abstract
Background: Dietary intake is an important factor in the development and management of non-alcoholic fatty liver disease (NAFLD); however, optimal dietary composition remains unclear. Moreover, there is minimal evidence on the relationship between dietary intake and markers of liver health in Canadian adults diagnosed with NAFLD. Objective: The aim of this study is to characterize the dietary intake of a sample of Canadian adults diagnosed with NAFLD and examine the correlations with markers of liver health. Design: Forty-two adults recruited from the community and hepatology clinics in Calgary, Canada from 2016 to 2019 completed a 3-day food record. Anthropometrics, blood biomarkers, liver stiffness (FibroScan), and liver fat (magnetic resonance imaging) were measured. Nutrient intake was compared with the data from the 2004 and 2015 Canadian Community Health Surveys. Relationships were assessed using Pearson’s correlation and regression analysis. Results: Relative to Canadian dietary recommendations, participants consumed lower magnesium, fiber, calcium, vitamin D, and vitamin E, and higher cholesterol, saturated fat, total fat, fructose, iron, vitamin B12, selenium, phosphorus, and sodium. Compared with the national average, participants consumed more energy, fiber, sodium, total fat, and saturated fat. Systolic blood pressure (P = 0.012), serum α-2 macroglobulin (P = 0.008), carbohydrate (P = 0.022), total fat (P = 0.029), and saturated fat intakes (P = 0.029) were associated with FibroScan scores. Liver fat was correlated with serum triglycerides (P < 0.001), trunk fat (P = 0.029), added sugar (P = 0.042), phosphorus (P = 0.017), and magnesium intake (P = 0.013). In females, selenium intake was associated with liver fat (P = 0.015) and FibroScan score (P = 0.05), while in males, liver fat was associated with trunk fat (P = 0.004), body weight (P = 0.004), high-density lipoprotein (P < 0.001), and fructose intake (P = 0.037). Regression analysis showed that increasing magnesium intake corresponds to a decrease in liver fat. Conclusion: Despite the higher energy intake of participants, overall nutrient intake is low, suggesting lower diet quality. Associations between select micronutrients and liver health markers warrant further investigation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".