Reply: More on nonalcoholic/nonmetabolic dysfunction–associated steatohepatitis
Bibliographic record
Abstract
We appreciate the letter from Drs Vajro and Mandato, highlighting the complexities of this classification of steatohepatitis disorders. They bring up an important concept relating to genetic diseases and consanguinity that may vary globally. We did not address this issue and bringing attention to this for a global audience is helpful. They correctly highlight additional specific rare diagnoses within the genetic and metabolic category that can be associated with steatohepatitis presentations. Enveloped into metabolic and genetic causes including mitochondrial hepatopathy, we did not specifically highlight muscular dystrophies as the associated steatosis is most likely related to the metabolic derangements including insulin resistance, hypertriglyceridemia, and hormonal imbalances that result in the steatosis and less likely the direct effect of muscular dystrophy. Given the pituitary and adrenal hormonal imbalances, these patients do present with more advanced fibrosis, which should have been acknowledged in the article. Most of these patients would already have a diagnosis of muscular dystrophy making a creatinine kinase level unlikely helpful. Importantly though, they remind us that the included diseases within genetic fatty acid and lipid storage diseases were not exhaustive. We are grateful for the attention to the example of the neutral lipid storage disease, a rare (<100 reported cases) disease associated with hepatic steatosis and lipid droplets in white blood cells, skin, and muscles among other organs. This entity would be diagnosed by clinical presentation (skin changes, muscular weakness, etc) combined with the identification of lipids in tissues, and certainly, adding a creatinine kinase level to blood testing if this was a clinical consideration would be appropriate in both age groups. It is challenging to include all possible genetic disorders linked with steatosis and we are grateful to have this chance to remind readers to look more deeply into rare genetic causes when atypical presentations arise. Lean metabolic associated steatotic liver disease is a vexing issue that has more questions than answers and was not addressed directly in the review. While the new definitions of metabolic associated steatotic liver disease do not overtly subdivide patients based on differing cardiometabolic criteria (eg, BMI ≥25 kg/m2 or BMI <25 kg/m2), previous literature has identified lean individuals with metabolic associated steatotic liver disease as having different rates of disease progression, associated conditions, and cardiometabolic risk. There remain areas of unmet need in this population that require further, ongoing study and investigation. In the case of lean celiac disease, the referenced study also hypothesized similar relationships between the intestinal microbiome and nutrient imbalances to their finding of increased steatosis in treated celiac disease (both lean and obese). Readers are directed to the additional reference for this interesting topic. Lastly, we welcome the reminder that our discussion of toxins associated with steatosis was also not exhaustive and missed the inclusion of bisphenol A and phthalates (and undoubtedly more to surface), allowing us another opportunity to direct the reader to look for these (and other) important potential contributors to steatosis. We are happy to have this review stimulate such thoughtful and helpful insights by the Hepatology readership.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.029 | 0.036 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".