Review for 10.1186/s12876-025-04212-3 (2025)

Title: Association of Alpha-1 Acid Glycoprotein with Hepatic Steatosis and Liver Fibrosis
Among Women: A Population-Based Study
Version: 1 Date: 04 May 2025
Reviewer's report:
This study investigates the association of serum alpha-1-acid glycoprotein (AGP), an
inflammatory biomarker, with hepatic steatosis and liver fibrosis among adult women aged 18–
49 years using NHANES data (2017–2023). Controlled attenuation parameter (CAP) and liver
stiffness measurement (LSM) obtained via transient elastography were used as surrogates for
steatosis and fibrosis, respectively. The authors reported a significant positive linear association
between Ln AGP and CAP in multivariate regression models, suggesting a role for AGP in
hepatic fat accumulation. In contrast, no consistent association was seen with LSM, though a
nonlinear L-shaped threshold effect was observed.
This is a well-conceived study addressing a clinically relevant question in the realm of
nonalcoholic fatty liver disease (NAFLD) and systemic inflammation. However, multiple
methodological, analytical, and interpretive concerns merit revision before the manuscript can
be considered for publication.
Major Concerns
1. Causal Inference and Study Design Limitations
• The cross-sectional design inherently limits the ability to establish causal relationships.
Although the authors acknowledge this, the discussion often implies directional associations
(e.g., “AGP is associated with…”) that could be misleading.
• No discussion is provided regarding temporality or reverse causality — especially whether
AGP could be elevated secondary to hepatic steatosis, not causally involved.
2. Choice of Population (Adult Women Only)
• The exclusive inclusion of women aged 18–49 years is not justified. Is it due to data availability,
hormonal/metabolic considerations, or an a priori hypothesis? The narrow demographic window
severely limits generalizability.
• The findings may not extend to men, older adults, or adolescents, particularly given known
sex-based differences in both hepatic fat distribution and inflammatory profiles.
3. Measurement of Exposure and Outcomes
• AGP is assessed in logarithmic form (Ln AGP), but the biological interpretability of log-
transformed changes (e.g., “11.93 dB/m per unit Ln AGP”) is not discussed. Readers may benefit
from a back-transformed interpretation (e.g., per SD or % increase in AGP).
• CAP and LSM, although validated, are surrogate measures. No attempt is made to validate
them in this specific NHANES subsample or discuss false positives/negatives.
4. Nonlinear Threshold Analysis Interpretation
• The L-shaped association between Ln AGP and LSM is a central but poorly elaborated finding.
• How plausible is a negative association below the -0.04 g/L threshold, followed by a positive
slope?
• The biological rationale for this inversion point is not clearly articulated.
• Is the threshold statistically derived, or hypothesis-driven?
• This complex modeling requires more detailed explanation, including assumptions, diagnostic
checks (e.g., residuals), and whether spline or GAM models were tested as alternatives.
5. Covariate Adjustment Strategy
• Model 3 includes extensive covariates (age, race, BMI, smoking, cholesterol, HDL-C, HbA1c,
waist circumference, diabetes, CRP, PIR), which is commendable but may risk over-adjustment
or multicollinearity.
• Were variance inflation factors (VIFs) assessed?
• Some variables (e.g., CRP) may lie on the causal path between inflammation and hepatic
outcomes, leading to biased estimates.
• There is no mention of potential confounders such as hormonal status, medications (especially
anti-inflammatories, statins), or nutritional status, all of which may affect AGP levels.
6. Sample Selection and Bias
• From over 26,000 eligible participants, only 2,608 were analyzed. The reasons for exclusion
(missing AGP, LSM/CAP data, viral hepatitis, heavy drinking, age <18) are appropriate, but the
drastic reduction (only ~10% of the original cohort) could introduce bias.
• A flowchart is provided (Figure 1), but no comparison is made between included and excluded
populations to assess selection bias.
Minor Concerns
1. Figure and Table Presentation
• Figures 2 and 3 (smooth curve fitting plots) are informative but lack clinical interpretability.
• Y-axis units (e.g., CAP in dB/m, LSM in kPa) should be labeled with normal/pathological
thresholds for context.
• Confidence bands are not annotated.
• Table 3 (association between AGP and CAP) and Table 5 (AGP and LSM) report model
coefficients but do not explain clinical relevance (e.g., does an 11.93 dB/m increase represent
clinically meaningful steatosis?).
2. Subgroup Analysis Interpretation
• The interaction between AGP and BMI is noted (CAP analysis), but the rationale is not
explored. Could visceral adiposity influence both inflammation and steatosis?
• In LSM analyses, significant interactions were observed for smoking, diabetes, and race, yet
the underlying reasons for these differential effects are not explored.
• For example, why would Mexican American women exhibit a negative association between
AGP and LSM?
3. Language and Terminology
• Multiple instances of awkward phrasing were noted:
• “The cohort was entirely female” # Better: “The cohort comprised adult women aged 18–49
years.”
• “No noteworthy association identified…” # Use “no statistically significant association was
observed.”
• “Constructively observed an L-shaped association…” # “Identified a statistically significant
nonlinear (L-shaped) relationship…”
• Consistency is needed between AGP and “Ln AGP”; specify units when possible.
4. Ethical Oversight and Consent
• Ethical approval is claimed from NCHS and data is public, but given the secondary use of
sensitive health data (e.g., liver fibrosis markers), a sentence clarifying de-identification and
public access should be added for transparency.
Limitations
• Medication Use: AGP is sensitive to medications, particularly steroids, NSAIDs, and
statins. NHANES may collect medication data, and this omission likely introduces residual
confounding.
• Genetic Factors: Known genetic variants (e.g., PNPLA3, TM6SF2) affect hepatic fat and
fibrosis risk and may correlate with inflammation.
• Diurnal and Acute Variation in AGP: AGP is an acute-phase protein and may vary with recent
illness, infection, or stress — none of which were adjusted for or excluded.
• Ethnicity-Specific AGP Thresholds: Given the high variability in AGP concentrations across
ethnicities, the lack of ethnicity-specific stratified analyses weakens conclusions.
Language and References
• The manuscript is generally well-referenced and up to date.
• Reference formatting and integration into the discussion are appropriate, but citations should
be trimmed or consolidated in some over-referenced sentences.
• Minor inconsistencies (e.g., spacing before semicolons, mixing “et al.” with long author lists)
should be checked.
• A full language edit by a native English-speaking editor is strongly advised to improve fluency
and professionalism.