Laboratory inflammation-nutrition index predicts mortality in MASLD/MetALD: evidence from a national cohort

Dear Editor,

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a term recently introduced through international expert consensus to replace “nonalcoholic fatty liver disease (NAFLD),” emphasizing the coexistence of hepatic steatosis and metabolic abnormalities^[1]. The term metabolic dysfunction-associated alcohol-related liver disease (MetALD) is used to describe fatty liver in individuals with moderate to heavy alcohol intake combined with metabolic risk factors. These entities often coexist with other causes such as viral hepatitis, autoimmune liver disease, or drug-induced liver injury, collectively forming a clinically heterogeneous population referred to as MASLD/MetALD and mixed-etiology fatty liver disease^[2,3]. These conditions are widely prevalent in individuals with obesity, hypertension, and diabetes, and have become leading causes of cirrhosis, hepatocellular carcinoma, and liver transplantation globally, with a rising trend in incidence^[4,5].

Despite the growing disease burden, there remains a lack of simple and effective clinical tools for early risk identification and prognostic evaluation in the MASLD/MetALD population. Against this background, the recent study by Li et al^[6] introduces the advanced lung cancer inflammation index (ALI) to investigate its association with all-cause mortality in MASLD/MetALD and mixed-etiology fatty liver disease, offering meaningful insights.

ALI is a composite indicator reflecting systemic inflammation and nutritional status, calculated as ALI = BMI × serum albumin/NLR (neutrophil-to-lymphocyte ratio)^[7]. Originally developed as a prognostic tool in lung cancer, ALI has demonstrated predictive value in gastrointestinal malignancies, cardiovascular disease, and chronic inflammatory conditions^[7–10]. By integrating metabolic reserves (BMI), nutritional status (albumin), and systemic inflammation (NLR), ALI is particularly aligned with the pathophysiological features of MASLD/MetALD as chronic metabolic-inflammatory diseases.

The present study draws on data from the nationally representative NHANES database (2007–2018), enrolling 4502 individuals diagnosed with MASLD/MetALD or mixed-etiology steatotic liver disease. Using multivariable Cox proportional hazards models and Kaplan–Meier survival analysis, the authors systematically assessed the association between ALI and all-cause mortality. The findings showed that compared with individuals in the lowest ALI tertile, those with medium and high ALI had significantly lower risks of all-cause mortality – by 42% and 49%, respectively. These associations remained consistent across various demographic and clinical subgroups, with no significant interactions detected. These results suggest that ALI may serve as an independent predictor of mortality risk in this population, offering potential utility for clinical stratification.

We believe this study holds several important implications:

• 1. High clinical applicability: The three components of ALI are derived from routine laboratory assessments, facilitating rapid implementation in primary care settings or epidemiologic surveys for large-scale screening and individualized management.

• 2. Methodological rigor: The study design appropriately accounts for the complex sampling structure of NHANES, employs three levels of model adjustment to control confounding, and performs subgroup and sensitivity analyses to ensure robustness.

• 3. Filling a research gap: This is the first study to evaluate the prognostic value of ALI in patients with MASLD/MetALD and mixed-etiology steatotic liver disease, thereby expanding the index’s application scope and offering new avenues for metabolic liver disease risk stratification.

Nevertheless, several aspects merit further investigation. First, ALI was measured only at baseline, limiting its ability to capture temporal changes in inflammation and nutritional status. Second, the identification of MASLD/MetALD was based primarily on the fatty liver index (FLI) and metabolic parameters, lacking imaging or histological confirmation. Third, the use of all-cause mortality as the endpoint precludes cause-specific analysis (e.g., cardiovascular, hepatic, neoplastic mortality), thus limiting its interpretability for clinical decision-making. Lastly, as NHANES data represent the U.S. population, external validation in non-Western settings is warranted.

We therefore propose several future directions to build upon this research:

• 1. Longitudinal studies evaluating dynamic changes in ALI over time and their association with disease progression and treatment response;

• 2. Integrating liver-specific scores (e.g., FIB-4, NFS) with ALI to develop comprehensive models for identifying high-risk MASLD/MetALD patients;

• 3. Refining mortality endpoints by categorizing deaths into cardiovascular, hepatic, or malignancy-related causes to assess ALI’s predictive value across distinct pathways;

• 4. Cross-regional and multi-ethnic validation in East Asian, South Asian, and European populations to enhance generalizability and external validity.

In summary, this study lays an important foundation for the clinical application of ALI in the context of metabolic liver disease. It also contributes empirical evidence to our understanding of the interplay among inflammation, nutrition, and metabolism. We highly value the authors’ forward-looking work and look forward to future mechanistic and interventional studies to strengthen the clinical translation of ALI in this field.

Ethical approval

This correspondence did not require ethical approval.

Consent

Not applicable.

Funding

The authors confirm that this work did not receive any funding related to its research, authorship, or publication.

Author contributions

J.Z.: found the question and wrote the draft. Y.L.: revised the manuscript. All authors have approved the letter.

Conflicts of interest disclosure

The authors declare that they have no competing interests related to this work.

Guarantor

Jiamin Zou and Yunluan Li.

Research registration unique identifying number (UIN)

Not applicable.

Provenance and peer review

This paper was not invited.

Data availability statement

All data analyzed or generated during this study are included in the article.

Declaration and use of AI

No artificial intelligence tools were used in the design, execution, or writing of this study, and our article is compliant with the TITAN Guidelines 2025 (11).