Dear Editor,
We are grateful to Danpanichkul et al. [
1] for thoughtful interpretation of our manuscript [
2]. The evolving burden of alcohol-associated liver disease (ALD) and alcohol-attributable cancers demands rigorous epidemiological research to inform public health action. While recent studies, including the analysis by Danpanichkul et al. [
3], have highlighted critical trends—such as rising mortality among women and geographic disparities in the U.S.—the broader research landscape remains constrained by methodological limitations that obscure actionable insights. These gaps span data granularity, mechanistic understanding, and policy translation, ultimately hindering effective intervention design.
Existing studies on alcohol-related diseases face three fundamental constraints. First, policy intervention analyses are largely ecological. While evidence confirms that alcohol taxation reduces consumption (e.g., a 10% tax hike lowers cirrhosis deaths by 8%) and minimum unit pricing curbs mortality (e.g., Scotland’s 10% decline) [
4,
5], most research fails to isolate policy efficacy from confounding socioeconomic factors. Second, ethnicity data aggregation perpetuates inequity. Databases like the Global Burden of Disease (GBD) omit racial/ethnic stratification, masking 2–3-fold higher ALD mortality in Hispanic and Native American populations versus non-Hispanic Whites—disparities linked to genetic susceptibility (PNPLA3 variants), healthcare access barriers, and social determinants [
6]. Third, sex-specific mechanisms are underexplored. Women’s accelerated ALD progression, driven by hormonal modulation of inflammation and lower alcohol-metabolizing enzyme activity, lacks integration into clinical frameworks. Biomarkers like cytokeratin-18 for fibrosis staging or microbiome signatures for progression risk are rarely studied in sex-stratified cohorts.
Danpanichkul et al.’s work, while valuable in quantifying national ALD trends, inherits these broader limitations. The study identifies state-level mortality variations but does not interrogate policy drivers, such as the restrictiveness of alcohol laws (e.g., Alcohol Policy Scale scores) or implementation of screening programs. For instance, high-mortality states like New Mexico or West Virginia could benefit from targeted taxation, yet the analysis stops at correlation. Similarly, the reliance on GBD data obscures ethnic disparities. Hispanic communities exhibit higher ALD prevalence despite lower alcohol consumption, and Asian females face reduced transplant access—neither factor is addressable without disaggregated data. Further, while noting rising female mortality, the study overlooks biopsychosocial mechanisms: stigma-driven treatment delays, estrogen’s role in hepatic inflammation, and metabolic differences in alcohol metabolism [
7,
8]. Finally, the use of Cause of Death Ensemble model for cause-of-death estimation risks undercounting alcohol-related cancers (e.g., misclassified ICD-10 codes in Mexico underestimated deaths by 15–20%) [
9], a concern unaddressed in U.S. validation.
To overcome these gaps, establishing integrated data platforms is paramount. Combining GBD estimates with granular sources such as NHANES and the National Death Index can rectify ethnicity gaps and coding inaccuracies [
10]. This integration allows for a more comprehensive understanding of the disease burden across different populations. Biorepositories like the All of Us Program should be leveraged to study gene-alcohol interactions across diverse populations. For instance, examining how genetic variants such as ADH1B and PNPLA3 influence alcohol metabolism and disease susceptibility in various ethnic groups can provide valuable insights. Linking these data to policy databases like the Alcohol Policy Information System enables quasi-experimental analyses of interventions. By examining how policy changes impact alcohol consumption and related health outcomes, researchers can better evaluate the effectiveness of different measures. This approach not only enhances our understanding of policy effects but also helps in tailoring interventions to specific communities based on their unique demographic and genetic profiles.
Conducting sex-disaggregated mechanistic studies is another crucial area for future research. Prospective cohorts must prioritize sex-stratified biomarker discovery. For example, validating microRNA signatures for early fibrosis in women could lead to more timely diagnoses and interventions. Additionally, quantifying estrogen’s effect on alcohol-induced gut permeability can shed light on the unique physiological responses women have to alcohol consumption. Community-based participatory research can concurrently address social barriers, such as stigma in alcohol use disorder (AUD) treatment seeking [
11]. By involving community members in the research process, studies can better identify and address the social and cultural factors that influence alcohol use and treatment uptake. This participatory approach ensures that the research is relevant to the community’s needs and more likely to yield effective solutions.
Policy-outcome modeling offers a powerful tool for projecting the impacts of real-world policies. Computational simulations, such as agent-based models, can be used to explore various policy scenarios [
12]. For instance, simulating the effects of federal subsidies for primary care AUD screening, in line with US Preventive Services Task Force (USPSTF) guidelines, can help policymakers understand the potential benefits and challenges of such measures. Similarly, modeling the impact of zoning laws that reduce alcohol outlet density in high-risk neighborhoods can provide insights into how environmental changes might influence alcohol consumption patterns. Pairing these simulations with difference-in-differences analyses of natural experiments, such as Massachusetts’ 2018 alcohol tax reform, can further strengthen the evidence base for policy decisions. This combined approach allows researchers to assess both the intended and unintended consequences of policies, ensuring that interventions are both effective and equitable.
Advancing causal inference methods is essential for clarifying alcohol’s role in cancer development. Mendelian randomization can be employed to investigate the causal relationship between alcohol consumption and specific cancer subtypes. For example, using ADH1B variants as instrumental variables for hepatocellular carcinoma risk can help establish whether alcohol consumption directly increases the risk of this cancer [
13]. Counterfactual models can then be used to quantify the number of avertable deaths under different policy scenarios [
14]. By understanding the causal pathways through which alcohol affects health outcomes, researchers can better identify high-risk populations and design targeted interventions. This approach not only enhances our scientific understanding but also provides a stronger foundation for public health policies aimed at reducing alcohol-related harm.
The accelerating burden of ALD and alcohol-attributable cancers—especially among women, ethnic minorities, and underserved regions—demands a methodological renaissance. Moving beyond descriptive epidemiology requires synthesizing disaggregated data, elucidating sex-specific pathways, and embedding policy evaluation into study designs. By adopting hybrid data systems, targeted biomarker research, and causal inference frameworks, future studies can transform observational trends into precision public health action. As alcohol-related mortality surges, this evolution is not merely academic but an ethical necessity for equitable disease prevention.
FOOTNOTES
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Conflicts of Interest
The author has no conflicts to disclose.
Abbreviations
alcohol-associated liver disease
REFERENCES
- 1. Danpanichkul P, Diaz LA, Arab JP, Singal AG, Yang JD. Correspondence to letter to the editor on “Sex disparities in alcoholassociated liver disease and subtype differences in alcoholattributable cancers in the United States”. Clin Mol Hepatol 2026;32:e246-e248.
- 2. Liu D. Letter to the editor on “Sex disparities in alcohol-associated liver disease and subtype differences in alcohol-attributable cancers in the United States”. Clin Mol Hepatol 2026;32:e153-e154.
- 3. Danpanichkul P, Pang Y, Mahendru T, Tothanarungroj P, Díaz LA, Arab JP, et al. Sex disparities in alcohol-associated liver disease and subtype differences in alcohol-attributable cancers in the United States. Clin Mol Hepatol 2025;31:1058-1070.
- 4. van den Berg M, van Baal PH, Tariq L, Schuit AJ, de Wit GA, Hoogenveen RT. The cost-effectiveness of increasing alcohol taxes: a modelling study. BMC Med 2008;6:36.
- 5. Maharaj T, Angus C, Fitzgerald N, Allen K, Stewart S, MacHale S, et al. Impact of minimum unit pricing on alcohol-related hospital outcomes: systematic review. BMJ Open 2023;13:e065220.
- 6. Anouti A, Seif El Dahan K, Rich NE, Louissaint J, Lee WM, Lieber SR, et al. Racial and ethnic disparities in alcohol-associated liver disease in the United States: A systematic review and meta-analysis. Hepatol Commun 2024;8:e0409.
- 7. Mellinger JL, Fernandez A, Shedden K, Winder GS, Fontana RJ, Volk ML, et al. Gender disparities in alcohol use disorder treatment among privately insured patients with alcohol-associated cirrhosis. Alcohol Clin Exp Res 2019;43:334-341.
- 8. White AM. Gender differences in the epidemiology of alcohol use and related harms in the United States. Alcohol Res 2020;40:01.
- 9. de Carvalho MH, Álvarez-Hernández G, Denman C, Harlow SD. Validity of underlying cause of death statistics in Hermosillo, Mexico. Salud Publica Mex 2011;53:312-319.
- 10. Haas A, Elliott MN, Dembosky JW, Adams JL, Wilson-Frederick SM, Mallett JS, et al. Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity. Health Serv Res 2019;54:13-23.
- 11. Camacho-Ruiz JA, Galvez-Sánchez CM, Galli F, Limiñana Gras RM. Patterns and challenges in help-seeking for addiction among men: A systematic review. J Clin Med 2024;13:6086.
- 12. Atkinson JA, Knowles D, Wiggers J, Livingston M, Room R, Prodan A, et al. Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms. Int J Public Health 2018;63:537-546.
- 13. Shih S, Huang YT, Yang HI. A multiple mediator analysis approach to quantify the effects of the ADH1B and ALDH2 genes on hepatocellular carcinoma risk. Genet Epidemiol 2018;42:394-404.
- 14. Alston L, Peterson KL, Jacobs JP, Allender S, Nichols M. Quantifying the role of modifiable risk factors in the differences in cardiovascular disease mortality rates between metropolitan and rural populations in Australia: a macrosimulation modelling study. BMJ Open 2017;7:e018307.
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