Liver steatosis and nonalcoholic fatty liver disease with fibrosis are predictors of frailty in people living with HIV
Bibliographic record
Abstract
OBJECTIVE: The aim was to investigate the contribution of liver steatosis and significant fibrosis alone and in association [nonalcoholic fatty liver disease (NAFLD) with fibrosis] to frailty as a measure of biological age in people living with HIV (PLWH). DESIGN: This was a cross-sectional study of consecutive patients attending Modena HIV Metabolic Clinic in 2018-2019. METHODS: Patients with hazardous alcohol intake and viral hepatitis coinfection were excluded. Liver steatosis was diagnosed by controlled attenuation parameter (CAP), while liver fibrosis was diagnosed by liver stiffness measurement (LSM). NAFLD was defined as presence of liver steatosis (CAP ≥248 dB/m), while significant liver fibrosis or cirrhosis (stage ≥F2) as LSM at least 7.1 kPa. Frailty was assessed using a 36-Item frailty index. Logistic regression was used to explore predictors of frailty using steatosis and fibrosis as covariates. RESULTS: We analysed 707 PLWH (mean age 53.5 years, 76.2% men, median CD4 cell count 700 cells/μl, 98.7% with undetectable HIV RNA). NAFLD with fibrosis was present in 10.2%; 18.9 and 3.9% of patients were classified as frail and most-frail, respectively. Univariate analysis demonstrated that neurocognitive impairment [odds ratio (OR) = 5.1, 1.6-15], vitamin D insufficiency (OR = 1.94, 1.2-3.2), obesity (OR = 8.1, 4.4-14.6), diabetes (OR = 3.2, 1.9-5.6), metabolic syndrome (OR = 2.41, 1.47-3.95) and osteoporosis (OR = 0.37, 0.16-0.76) were significantly associated with NAFLD with fibrosis. Predictors of frailty index included steatosis (OR = 2.1, 1.3-3.5), fibrosis (OR = 2, 1-3.7), NAFLD with fibrosis (OR = 9.2, 5.2-16.8), diabetes (OR = 1.7, 1-2.7) and multimorbidity (OR = 2.5, 1.5-4). CONCLUSION: Liver steatosis and NAFLD with fibrosis were associated with frailty. NAFLD with fibrosis exceeded multimorbidity in the prediction of frailty, suggesting the former as an indicator of metabolic age in PLWH.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".