Network analysis of cognitive function, glycemic-lipid profiles, and hepatic-renal function in individuals with...

Area of Science:

• Clinical Medicine
• Neuroscience
• Biochemistry

Background:

• Harmful drinking habits significantly impact individual health.
• A gap exists in network analysis studies examining clinical indicators in alcohol-dependent populations.
• Understanding these relationships is crucial for targeted health interventions.

Purpose of the Study:

• To investigate the interrelationships among drinking characteristics, cognitive functions, liver and kidney functions, and glucose and lipid levels in alcohol drinkers.
• To apply network analysis to identify central and bridge symptoms within these clinical indicators.
• To provide insights for clinical practice regarding alcohol consumption's health effects.

Main Methods:

• A stratified random sampling survey of 1,432 male employees was conducted in 2016.
• Alcohol use was assessed using the Alcohol Dependence Scale (ADS) and Alcohol Use Disorders Identification Test (AUDIT).
• Cognitive functions were evaluated using HVLT, BVMT, DSCT, and DST; biochemical indicators including glucose, lipids, and hepatic-renal function were measured.

Main Results:

• Network analysis identified Total Cholesterol (TC), Aspartate Aminotransferase (AST), AST/ALT ratio, and Alanine Aminotransferase (ALT) as having the highest strength centrality.
• TC and AST exhibited the highest expected influence centrality.
• The Digit Symbol Coding Test (DSCT) demonstrated high betweenness centrality.

Conclusions:

• Correlations are evident between cognitive function, glycemic/lipid profiles, and hepatic-renal function in individuals with diverse alcohol consumption patterns.
• Lipid and liver function indicators emerged as the most central factors in the network model.
• Clinicians may target these central abnormal indicators for interventions to improve the quality of life for alcohol drinkers.