Association between MTTP genotype (-493G/T) polymorphism and hepatic steatosis in hepatitis C: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Hepatitis C has been associated with the development of hepatic steatosis, which increases the risk of liver cancer. The microsomal triglyceride transporter protein (MTTP), is a lipid transport protein that mediates lipid metabolism and CD1d antigen presentation. The study aimed to explore the association between MTTP genotype (-493G/T) polymorphism and hepatic steatosis in hepatitis C. Methods The database “Pubmed, Cochrane library, CNKI, Web of science, Embase and CBM” were retrieved to identify the literature. The quality of the selected literature was evaluated using the “the Newcastle–Ottawa Scale” (NOS). Relevant data was extracted and analyzed using the Stata software. Heterogeneity was expressed by “Cochran's Q and I2”, with I2 ≥ 50% or P < 0.05 indicating high heterogeneity. A random-effects model and subgroup analysis were conducted to identify the sources of heterogeneity. We also used “Funnel plots”, “Egger’s tests” and “Begg’s tests” to evaluate biases in the literature. Results The study found a significant and positive association between liver steatosis and the HCV genotype 3 with a dominant model of the MTTP genotype (-493G/T) (OR = 11.57, 95%CI: 4.467–29.962, P < 0.001). In contrast, no correlation was found between hepatic steatosis and either the recessive, homozygous or heterozygous models (OR = 1.142, P = 0.5; OR = 1.581, P = 0.081; OR = 1.029, P = 0.86). There was no significant publication biases, as measured by the Funnel plot, and the Egger’s and Begg's tests. Finally, sensitivity analysis showed the obtained results are stable. Conclusions Dominant mutations in the T allele of the MTTP genotype (-493G/T) increase susceptibility to hepatic steatosis in patients presenting with the HCV genotype 3.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".