Ashby D. A statistical investigation of the relationship of serum biochemistry and haematology to
alcohol consumption.
Thesis: PhD. 1984 University of London.
Abstract: In a clinical survey of 7735 men, alcohol intake was assessed using an administered
questionnaire. From blood samples given by each man, 25 biochemical and haematological
determinations were made. Many of these showed a relationship with alcohol consumption, the
strongest of which was for gamma-glutamyl-transpeptidase (GGT). Initially the biochemistry of the
heaviest drinkers (e.g. more that three pints daily) was compared with occasional drinkers. Using
binary logistic regression, the best five-variable model included GGT, HDL-Cholesterol, urate, mean
corpuscular haemoglobin and blood lead. Analysis was extended to include all men, grouped into
eight ordered categories based on frequency and quantity of alcohol consumption. The relationship
between alcohol consumption and the five biochemical variables was explored using an ordered
polychotomous model. The goodness-of-fit of the model was examined. There was some lack of fit
for the non-drinkers (who included ex-drinkers) and for the heavy drinkers, not all of whom reflected
their drinking in their biochemistry. Three methods of allocating individuals to groups explored. A
method based on fixed proportions allocated to each group was most informative. Some alcohol
groups were biochemically similar, so amalgamations of adjacent groups were considered. A threegroup model comprising light, medium and heavy drinkers gave an efficient condensation of the
model. The relationship of other personal factors to alcohol consumption and biochemistry was
investigated to identify potential confounding. Smoking and body mass were most important.
Analysis-of-covariance was used to adjust for these for each biochemical variable. The modelling of
interactions between ordered categorical variables such as alcohol and smoking was explored. In
future analysis, the linear combination of biochemical variables resulting from the ordered
polychotomous model may be interpreted as an individual's response to alcohol consumption. In
general, techniques for the analysis of ordered categorical data need wider recognition, and the
ordered polychotomous model provides a powerful tool.