Metabolic and renal changes in patients with chronic hepatitis C infection after hepatitis C virus clearance by direct‐acting antivirals
Bibliographic record
Abstract
Abstract Background and Aim The impact of hepatitis C virus (HCV) clearance by direct‐acting antiviral agents (DAAs) on HCV‐related extrahepatic manifestations is not well known. We evaluated the effect of viral clearance on metabolic and renal parameters. Methods In this prospective study, HCV patients who achieved a sustained virologic response (SVR) by DAAs were evaluated before, at the end, and 24 weeks after treatment for glycemic (serum glucose and insulin, HOMA‐IR, HOMA‐β, and HOMA‐S) and lipid (serum cholesterol, triglycerides, low‐density lipoprotein [LDL], high‐density lipoprotein) metabolism and renal function (serum creatinine, estimated glomerular filtration rate [eGFR]). Results A total of 343 consecutive HCV patients were evaluated. At 24 weeks of post‐follow‐up, an increase in body mass index (BMI) was observed ( P < 0.05). Regardless of hepatic fibrosis levels and BMI, a reduction in serum glucose ( P = 0.001), HOMA‐IR ( P < 0.001) and HOMA‐β ( P < 0.001) and an increase in HOMA‐S ( P < 0.001) values were observed at 24 weeks after HCV clearance as compared to pretreatment values; 32.4% of patients with impaired fasting glucose normalized serum glucose values and 44.6% of diabetics showed an improvement in glycemic control. In contrast, serum cholesterol ( P < 0.001) and LDL cholesterol ( P < 0.001) values were increased. Renal function was improved with about 10% reduction of serum creatinine values ( P < 0.02) and an increase of eGFR ( P < 0.001). A baseline eGFR of ≤60 mL/min/1.73 m 2 was a negative predictor of renal function improvement. HCV clearance was an independent factor improving glucose metabolism and renal function. Conclusions Our study shows an occurrence of changes in metabolic and renal parameters in HCV patients with SVR, anticipating possible future clinical scenarios that the clinician must know for proper management.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".