Diet-Dependent Acid Load—The Missing Link Between an Animal Protein–Rich Diet and Nonalcoholic Fatty Liver Disease?
Bibliographic record
Abstract
OBJECTIVE: Our group recently showed that animal protein was independently associated with nonalcoholic fatty liver disease (NAFLD). We hypothesize that this may be explained by a high diet-dependent acid load [dietary acid load (DAL)]. METHODS: This cross-sectional study is embedded in a prospective population-based cohort. We estimated DAL proxies via food-frequency questionnaires using potential renal acid load (PRAL; using dietary protein, phosphorus, potassium, calcium, and magnesium intake), net endogenous acid production (NEAP; using protein and potassium intake), and the animal protein-to-potassium ratio (A:P). We defined NAFLD using ultrasound after excluding secondary steatogenic causes. We used logistic regression models-adjusted for sociodemographic, lifestyle, and metabolic traits-on categorized [quartile (Q)1 to 4] and continuous DAL proxies (allowing for nonlinearity) and NAFLD. RESULTS: We included 3882 participants, of which 1337 had NAFLD. All DAL proxies were higher, meaning more acidic, in individuals with NAFLD (PRAL, -2.9 vs -5.5 mEq/d; NEAP, 37.0 vs 35.1 mEq/d; and A:P, 13.3 vs 12.4; all P < 0.001). The highest Q of DAL proxies was associated with NAFLD independent of sociodemographic and lifestyle confounders, but significance dissipated after correction for metabolic confounders and multiple testing. However, the P value for nonlinearity was significant in all DAL proxies (P < 0.001). Natural cubic splines performed better with than without DAL proxies in the fully adjusted model (all P ≤ 0.038). The highest probability of NAFLD was found for an acidic diet. CONCLUSIONS: This study showed an independent nonlinear association between an acidic diet and NAFLD. Further studies with acid-base biomarkers are needed, but our findings might provide a mechanistic explanation for the harmful association between an animal protein-rich diet and NAFLD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".