# Combined Conventional Blood Biomarkers as Discriminators of Excessive Alcohol Consumption in Men: A Large-Scale Cross-Sectional Study

## TL;DR

This study finds that combining blood markers like MCV, GGT, and HDL-C can help identify men who consume excessive alcohol, potentially aiding in hypertension prevention.

## Contribution

The study introduces a novel alcohol consumption index (ACI) combining three biomarkers to better estimate excessive alcohol intake in men.

## Key findings

- The alcohol consumption index (ACI) combining MCV, GGT, and HDL-C showed the strongest correlation with excessive alcohol intake.
- ACI had an area under the ROC curve of 0.819, with a cutoff of 194,863 for identifying excessive alcohol consumption.
- The positive predictive value of ACI was 69.2%, indicating moderate effectiveness in identifying heavy drinkers.

## Abstract

Background/Objectives: Blood biomarkers for estimating alcohol consumption are useful for preventing alcohol-related harms. Although there are conventional blood biomarkers of heavy alcohol drinkers, it remains to be clarified whether their combination is useful for estimation of excessive alcohol consumption from the viewpoint of preventing hypertension. Methods: Participants included 8172 men aged from 31 to 70 years who had undergone health checkups. Overall, participants were classified into three groups of nondrinkers, occasional drinkers, and regular drinkers by frequency; regular drinkers were further classified into four groups of light (<22 g/day), moderate (≥22 and <44 g/day), heavy (≥44 and <66 g/day), and very heavy drinkers (≥66 g/day) according to the amount of average daily alcohol consumption. The relationships of blood biomarkers (mean corpuscular volume [MCV], gamma glutamyl transferase [GGT], and HDL cholesterol [HDL-C]) and their products with alcohol consumption were investigated by using correlation analysis and receiver-operating characteristics (ROC) analysis. Results: Seven variables of blood biomarkers and their products were significantly correlated with frequency and amount of alcohol consumption, and the degrees of the correlations were stronger in the following order: HDL-C alone < product of MCV and HDL-C < MCV alone < GGT alone < product of MCV and GGT < product of GGT and HDL-C < product of MCV, HDL-C and GGT. In the ROC analysis, the area under the ROC curve for the relationship between the product of MCV, HDL-C, and GGT (named the alcohol consumption index [ACI]) and excessive alcohol intake (22 g/day or more) was 0.819 (95% confidence interval: 0.809–0.830), and the cutoff of this index was 194,863 with a sensitivity and specificity of 0.745 and 0.751, respectively. The positive predictive value was 69.2%. Conclusions: Among the three conventional blood biomarkers and their combinations, ACI demonstrated the strongest associations with alcohol consumption and excessive alcohol intake in men. Although the combined biomarkers are unlikely to be useful as a diagnostic tool, there is a possibility of future application by integrating ACI with recent biomarkers including carbohydrate-deficient transferrin for estimation of alcohol consumption.