The association between immunoinflammatory biomarkers NLR, PLR, LMR and nonalcoholic fatty liver disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disorder closely linked to metabolic syndrome. Identifying novel, easily measurable biomarkers could significantly enhance the diagnosis and management of NAFLD in clinical settings. Recent studies suggest that immunoinflammatory biomarkers-specifically, the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR)-may offer diagnostic value for NAFLD. However, the effectiveness of these biomarkers has not been comprehensively assessed in this patient population. This systematic review and meta-analysis aimed to evaluate the association between these immunoinflammatory biomarkers and NAFLD. As of August 8, 2024, databases including PubMed, EMBASE, Cochrane Library, Web of Science, and Scopus were systematically searched to compare NLR, PLR, and LMR levels in NAFLD patients and healthy controls. Study quality was assessed using the Newcastle-Ottawa Scale, and standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated (PROSPERO registry number: CRD42024580812). A total of 20 studies were included in the meta-analysis. Results indicated that NAFLD patients had significantly higher NLR levels (SMD = 0.43; 95% CI 0.28-0.58; p < 0.001) and lower PLR levels (SMD = - 0.29; 95% CI - 0.41 to - 0.17; p < 0.001) compared to controls. However, no significant difference in LMR was observed between NAFLD patients and controls(SMD = 0.08; 95% CI - 0.00 to 0.17; p = 0.051). These findings suggest that NLR and PLR may hold promise as diagnostic markers for NAFLD, while LMR appears to have limited diagnostic utility. Further research is warranted to explore the potential role of these biomarkers in tracking disease progression.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".