Eliciting the mitochondrial unfolded protein response by nicotinamide adenine dinucleotide repletion reverses fatty liver disease in mice
Bibliographic record
Abstract
UNLABELLED: With no approved pharmacological treatment, nonalcoholic fatty liver disease (NAFLD) is now the most common cause of chronic liver disease in Western countries and its worldwide prevalence continues to increase along with the growing obesity epidemic. Here, we show that a high-fat high-sucrose (HFHS) diet, eliciting chronic hepatosteatosis resembling human fatty liver, lowers hepatic nicotinamide adenine dinucleotide (NAD(+) ) levels driving reductions in hepatic mitochondrial content, function, and adenosine triphosphate (ATP) levels, in conjunction with robust increases in hepatic weight, lipid content, and peroxidation in C57BL/6J mice. To assess the effect of NAD(+) repletion on the development of steatosis in mice, nicotinamide riboside, a precursor of NAD(+) biosynthesis, was added to the HFHS diet, either as a preventive strategy or as a therapeutic intervention. We demonstrate that NR prevents and reverts NAFLD by inducing a sirtuin (SIRT)1- and SIRT3-dependent mitochondrial unfolded protein response, triggering an adaptive mitohormetic pathway to increase hepatic β-oxidation and mitochondrial complex content and activity. The cell-autonomous beneficial component of NR treatment was revealed in liver-specific Sirt1 knockout mice (Sirt1(hep-/-) ), whereas apolipoprotein E-deficient mice (Apoe(-/-) ) challenged with a high-fat high-cholesterol diet affirmed the use of NR in other independent models of NAFLD. CONCLUSION: Our data warrant the future evaluation of NAD(+) boosting strategies to manage the development or progression of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".