Genomics: Insight

Research Question: What is the impact of environmental factors and genomics on Alcohol Use Disorder?

Alcohol Use Disorder and Its Societal Impact

Alcohol Use Disorder (AUD) is a chronic relapsing condition referring to impaired control over alcohol use and continued intake despite negative consequences.¹ 27.9 million people over the age of 12 in the U.S. had AUD in 2024, of whom 775,000 were adolescents.² AUD is one of the most common substance-related disorders. Studies have shown that 36.0% of men and 22.7% of women have met criteria for AUD at some point, with 17.6% of men and 10.4% of women meeting criteria in the past year.^3 4

Alcohol contributes to approximately 178,000 deaths in the U.S. and about 3 million deaths globally per year, translating to about 4.7% to 5.9% of all global deaths.¹ On an economic level, AUD costs the U.S. an estimated $249 billion yearly, with $27 billion in healthcare costs, and has heritability estimates of 50% to 60%,⁵ showing a highly heritable condition with major societal significance.

Environmental Variables Associated with AUD

The development of AUD is frequently associated with environmental factors that encourage consumption and increase alcohol craving.⁶ Variables such as personal location, events, times of week and day, exposure to individuals like family, coworkers, and partners, and emotional states such as stress or boredom often influence alcohol consumption multidimensionally. One research article delved into the online Reddit forum r/StopDrinking, using an algorithmic AI to search and analyze posts related to alcohol craving; Table 1 displays the percentage of posts referencing certain factors.⁷ The experiment featured 25,000 posters, predominantly from North America.  and found a significant amount of craving-related posts centered on specific environmental stimuli and situations, as Table 1 depicts. ⁷ 

Table 1. Environmental Factors Associated With Alcohol Craving

Of these variables, emotional stimulus and time are the most consistently correlated with alcohol craving. 45% and 40% of posts reference stress and boredom, respectively, and 44% of posts that referenced craving alcohol occurred during the evening or night; the hours between 13:00 and 24:00 averaged around 925 posts each. Undoubtedly, combining factors like emotion, location, and time increases the likelihood of alcohol craving and, therefore, consumption. 

Underdiagnosis of Alcohol Use Disorder

The research and analysis of craving-related posts investigated the association of environmental variables with alcohol craving and consumption.⁷ The data lacks reference to gender, age, race, specific location, or possible AUD diagnosis. AUD is widely underdiagnosed, as found in a study where out of 172 patients in a psychiatric hospital’s acute care unit, twenty-two reported daily consumption, but forty percent of these patients had neither been diagnosed with AUD nor had their alcohol consumption been noted in medical records.⁸ Therefore, the data on environmental impact on alcohol craving, whether or not a subject is diagnosed with AUD, is still relevant.

Genetic Risk Factors for Alcohol Use Disorder

Genetic factors explain between 40% and 60% of the risk for Alcohol Use Disorder (AUD), and over 476 differentially expressed genes are associated with the disorder, including 25 in both the prefrontal cortex and the nucleus accumbens.⁹ AUD is a polygenic trait influenced by many genes that each contribute small effects, instead of one single gene causing AUD by itself.¹⁰ In addition, AUD is heterogeneous, meaning that it is not biologically the same in all individuals, as people can develop the disorder in a variety of ways. Some genes involved in AUD risk show stronger evidence of genetic risk, while others are more highly responsive to the environment and are altered through epigenetic changes. Genome-wide association studies (GWAS) have identified inherited variants associated with susceptibility to AUD, while gene expression studies have identified genes whose activity changes in the brains of individuals with AUD. These associations are often measured using odds ratios, and values greater than 1 indicate increased risk.

Differential gene expression in individuals with AUD reflects genetic changes associated with chronic alcohol exposure. However, many of these changes are consequences of prolonged alcohol consumption rather than inherited risk factors that cause an increased risk of AUD. This distinction highlights the connection between genomic susceptibility and environmental exposure. While some genes are associated with an increased likelihood of having AUD, many genes involved in AUD development are at risk of being altered due to environmental changes. Alcohol consumption itself alters gene expression, exacerbating risk in addition to unrelated environmental factors.¹¹ 

Several polymorphisms have been identified by genetic association studies that influence the risk of developing AUD. Examples of these genetic variants and their roles in AUD susceptibility are summarized in Table 2.

Table 2. Genetic Variants Associated with Increased Risk of AUD.¹²

These genetic variations may increase vulnerability to developing AUD by affecting alcohol metabolism, dopamine signaling, and stress regulation, which contribute to addictive behaviors.¹² While these variants do not guarantee AUD, they may increase the risk, especially when altered by environmental influences,  allowing researchers to identify and predict risk.

The Role of Machine Learning in Understanding AUD Risk

Since AUD emerges from a complicated intersection between genetics and environmental exposure, researchers today are increasingly relying on machine learning (ML) models to assist in analyzing datasets of high dimensions to find patterns that traditional approaches fail to capture. Below is evidence from various ML applications that showcases a few key uses of ML associated with AUD among the diverse applications.

Since 2005, a rapidly expanding literature has evaluated whether environmental factors such as socio-cultural context and adversity interact with genetic influences on drinking behaviors.

Predicting Early Alcohol Use

Niklason et. al. applied the ABCD study, the largest long-term U.S. study of brain development and child health, and analyzed it with machines to predict alcohol sipping among children ages 9 to 10. The environmental ML model yielded an Area Under the Curve (AUC) of 0.76. The AUC value indicated how accurately the model distinguished between children reporting early alcohol sipping and children who did not. Values ranged from 0.5, random guessing, to 1.0, perfect classification. 0.76 indicated that the model correctly identified the two groups significantly better than random guessing. Correlations were found between early alcohol sipping and many environmental variables, such as maternal prenatal alcohol use and socioeconomic status, with ease of access to alcohol at home as the highest contributor, accounting for 8.2% of the predictive model. Health and psychological models performed worse than the environmental model, revealing the dominance of environmental variables.

Monitoring Relapse Risk

A study by Wyant fine-tuned three ML models to predict alcohol relapse at hourly intervals on Ecological Momentary Assessment (EMA) data, which is real-time data collected from people routinely.¹⁴ The median Area Under the Receiver Operating Characteristic (auROC), which is how well the model identified between relapse and non-relapse outcomes, reached between 0.89 and 0.93, indicating strong accuracy. Temporal factors such as craving, stress, and arousal impacted predictions greatly.

Predicting Treatment Dropout

Machine learning is also strong in finding patterns in treatment retention for AUD. Collin et. al. used algorithms–support-vector classification (SVC), multi-layer perceptron (MLP), and logistic regression–to predict premature treatment. For the SVC model, which reflects accuracy of the model in the range of 0.0 (no precision) to 1.0 (full precision), reached 0.824 with a slim margin of error of 0.002.¹⁵ Using another ML method, Shapley Additive Explanation (SHAP), it discovered prior opioid substitution therapy and concurrent drug use are key predictors of dropout from alcohol recovery programs.

Conclusion

Alcohol use disorder is linked to environmental variables that encourage consumption, and machine learning helped detect commonalities between different variables, such as family and outside influence, and genetic risk factors, mainly in the prefrontal cortex. This topic is important because alcohol consumption is prevalent in the United States, and Alcohol Use Disorder is also prevalent, although it lacks attention and diagnosis; we wanted to research how machine learning’s role in analyzing influential environmental factors can improve the issue of alcohol overconsumption. The research reviewed evinces that AUD develops through genetic susceptibility and environmental exposure. We found that machine learning is useful in finding parts of the environment that influence AUD in the form of alcohol craving, as well as predicting and monitoring patients' relapse and treatment while under different environmental conditions. 

References

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