Excessive alcohol use (EAU) is one the leading causes of preventable injury and death in the Unites States. Timely intervention and effective treatment of EAU relies on early detection of excessive drinking, usually through clinical screening. Delayed recognition of early signs and symptoms of EAU would lead to missed opportunities to interrupt the accumulation of alcohol exposure and result in suboptimal care outcomes in alcohol-related health conditions. There is an urgent need for increased vigilance and action to identify and to counsel patients that are at-risk for EAU. Using human transcriptome along with proteomic approaches, we have uncovered markers that are potentially linked to the EAU pathophysiology. We found that activation of monocytes by alcohol (sCd14, sCD163, and urine neopterin) and suppression of CD4+ follicular T cells (sCD40) are strongly associated with levels of alcohol consumption. We believe that with the aid of modern analytical techniques, these novel biomarkers could be used as basis for a new mechanistic-based screening strategy that have significantly improved the diagnostic performance. To test this hypothesis, we will pursue the following aims:
Specific Aim 1. To ascertain the diagnostic potential of these novel serum and urinary markers for EAU;
Specific Aim 2. To construct a screening score that represents the EAU probability of individual subject by using modern semiparametric regression techniques and markers identified in Aim#1;
and Specific Aim 3. To validate the screening model. If successfully implemented, results from this project could revolutionize the practice of EAU screening and diagnosis. The identification of the novel markers also provides a scientific basis to better understand the mechanisms underlying the health sequelae of EAU.
Excessive alcohol use (EAU) is being recognized as an emerging public health concern. Our previous studies have found serum and urine markers that reflect cellular response to alcohol exposure. The current study seeks to use these mechanistically derived biomarkers, in combination with the state-of-the-art statistical analysis, to develop a prediction model for EAU screening.
