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 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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".