Nutritional and Lifestyle Therapy for NAFLD in People with HIV
Bibliographic record
Abstract
HIV infection and nonalcoholic fatty liver disease (NAFLD) are two major epidemics affecting millions of people worldwide. As people with HIV (PWH) age, there is an increased prevalence of metabolic comorbidities, along with unique HIV factors, such as HIV chronic inflammation and life-long exposure to antiretroviral therapy, which leads to a high prevalence of NAFLD. An unhealthy lifestyle, with a high dietary intake of refined carbohydrates, saturated fatty acids, fructose added beverages, and processed red meat, as well as physical inactivity, are known to trigger and promote the progression of NAFLD to nonalcoholic steatohepatitis, liver fibrosis, and hepatocellular carcinoma. Furthermore, with no currently approved pharmacotherapy and a lack of clinical trials that are inclusive of HIV, nutritional and lifestyle approaches still represent the most recommended treatments for PWH with NAFLD. While sharing common features with the general population, NAFLD in PWH displays its own peculiarities that may also reflect different impacts of nutrition and exercise on its onset and treatment. Therefore, in this narrative review, we aimed to explore the role of nutrients in the development of NAFLD in PWH. In addition, we discussed the nutritional and lifestyle approaches to managing NAFLD in the setting of HIV, with insights into the role of gut microbiota and lean 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".