Neutrophil Percentage-to-Albumin Ratio and Neutrophil-to-Albumin Ratio as novel biomarkers for non-alcoholic fatty liver disease: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a major global health concern, with rising prevalence linked to obesity, insulin resistance, and metabolic syndrome. Timely and accurate identification of individuals at risk is crucial for improving outcomes. Recently, systemic inflammatory and nutritional markers such as the Neutrophil Percentage-to-Albumin Ratio (NPAR) and the Neutrophil-to-Albumin Ratio (NAR) have emerged as promising non-invasive biomarkers for NAFLD. Both ratios reflect inflammation and hepatic nutritional status, offering potential utility in predicting disease presence and progression. This systematic review and meta-analysis aimed to evaluate the diagnostic value of NPAR and NAR in patients with NAFLD. METHODS: A comprehensive search was performed across databases including PubMed, Embase, Scopus, and Web of Science from inception to December 28, 2024. Data extraction was carried out using a standardized form, and the methodological quality of included studies was assessed using the Newcastle-Ottawa Scale. Statistical analyses were performed using STATA version 18, employing a random-effects model. RESULTS: The meta-analysis demonstrated that both the Neutrophil Percentage-to-Albumin Ratio (NPAR) and the Neutrophil-to-Albumin Ratio (NAR) were significantly higher in patients with NAFLD compared to healthy individuals. NPAR showed a standardized mean difference (SMD) of 0.28 (95% CI: 0.22-0.35, P < 0.01), while NAR had a higher effect size with an SMD of 0.69 (95% CI: 0.44-0.93, P < 0.01). The pooled diagnostic performance of NPAR yielded a sensitivity of 69.5% (95% CI: 56.3-82.6%), specificity of 63.1% (95% CI: 46.6-70.0%), and an area under the curve (AUC) of 76.05% (95% CI: 66.3-85.7%). For NAR, the pooled sensitivity was 65.0% (95% CI: 49.0-82.0%), specificity was 63.0% (95% CI: 47.0-79.0%), and AUC was 69.0% (95% CI: 48.0-89.0%). CONCLUSION: In conclusion, both NPAR and NAR were found to be elevated in individuals with NAFLD, supporting their potential as non-invasive and accessible biomarkers. These ratios reflect key aspects of systemic inflammation and nutritional status, offering clinical value in early detection and risk stratification. However, given the limited number of studies available-particularly for NAR-further research is needed to confirm these findings, establish standardized thresholds, and assess their performance across diverse populations and clinical settings. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| 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".