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Clinical Chemistry 47: 681-685, 2001;
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(Clinical Chemistry. 2001;47:681-685.)
© 2001 American Association for Clinical Chemistry, Inc.

Articles

Improved Diagnostic Classification of Alcohol Abusers by Combining Carbohydrate-deficient Transferrin and {gamma}-Glutamyltransferase

Pekka Sillanaukee1,2,3,a and Ulf Olsson4

1 Pharmacia & Upjohn Diagnostics AB, Alcohol Related Diseases, 751 82 Uppsala, Sweden.

2 University of Tampere, Medical School and Tampere University Hospital, Department of Clinical Chemistry, 33014 Tampere, Finland.

3 Karolinska Institute, Medical School, 171 77 Stockholm, Sweden.

4 Swedish University of Agricultural Sciences, Department of Biometry and Informatics, 750 07 Uppsala, Sweden.
a Address correspondence to this author at: Oy Finnish Immunotechnology Ltd., Lenkkeilijänkatu 8, 33 520 Tampere, Finland. Fax 358-3-31387050; e-mail Pekka.Sillanaukee{at}finnish-immunotech.com.


   Abstract
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 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Background: Biochemical markers can provide objective evidence of high alcohol consumption. However, currently available markers have limitations in their diagnostic performance.

Methods: The diagnostic values of the most frequently used markers [carbohydrate-deficient transferrin (CDT), {gamma}-glutamyltransferase (GGT), aspartate aminotransferase, alanine aminotransferase, and mean corpuscular volume] were studied in an analysis of six different clinical studies (n = 1412) on alcohol abusers and social drinkers. The purpose of the analyses was to determine whether a combination of markers would improve the diagnosis of subjects.

Results: Discrimination between alcohol abusers and social drinkers, as measured by the areas under nonparametric ROC plots, was significantly better (P <0.001) for the new combined marker [{gamma}-CDT = 0.8 · ln(GGT) + 1.3 · ln(CDT)] than for any of the separate markers or combination of CDT or GGT with other markers. The cutoff values for {gamma}-CDT (6.5) can be taken to be the same among males and females.

Conclusions: The combined variable {gamma}-CDT is a powerful tool to discriminate alcohol abusers from social drinkers and is recommended for clinical use.


   Introduction
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 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Alcohol abuse is associated with many health and social problems and brings high economic costs. It is estimated that at least 20% of adults who visit a physician have had an alcohol problem at one time (1), and 12–30% of patients admitted to an inpatient service (2) and 9–20% of patients admitted to a primary healthcare service screened positive for alcohol abuse (2)(3). Despite the high prevalence of alcohol-related problems among medical patients, usually fewer than one-half of the patients with alcohol problems are identified (2), and treatment is offered in even fewer cases (4). In management of alcohol problems, a crucial requirement is an effective and accurate procedure that will enable clinicians to identify alcohol abuse (5) and to monitor progress in treatment. Clinical laboratory procedures frequently are used to corroborate results of physicians’ interviews and clinical examinations. Biological laboratory markers of alcohol abuse can provide objective evidence of excessive drinking, especially in patients who deny their drinking problems. Generally, the clinical utility of biological markers of high alcohol consumption is in detecting and monitoring excessive alcohol consumption as well as in differential diagnosis between alcohol- and nonalcohol-related disease.

Unfortunately, an optimal marker of excessive drinking has not been found, either for men or for women. {gamma}-Glutamyltransferase (GGT),1 the most widely used marker, has a sensitivity of 34–85% (6)(7)(8) but a relatively low specificity. Increased GGT concentrations are also caused, e.g., by nonalcoholic liver disease, most hepatobiliary disorders, obesity, diabetes mellitus, hypertriglyceridemia, and the use of liver microsome-inducing drugs (6)(8)(9)(10). Carbohydrate-deficient transferrin (CDT) is a new marker for high alcohol consumption with a reported sensitivity of 31–91% and a high specificity (6)(7)(11)(12). False positives have been reported in patients with inborn errors of glycoprotein metabolism, the genetic D-variant of transferrin, severe nonalcoholic liver diseases such as primary biliary cirrhosis, and diseases with high total transferrin (12)(13)(14)(15). In addition to GGT and CDT, aspartate aminotransferase (ASAT) and alanine aminotransferase (ALAT) as well as erythrocyte mean corpuscular volume (MCV) are frequently used as markers for alcohol abuse (6).

Diagnosis of alcohol abuse using a combination of different routine laboratory markers may be valuable. Combinations of more than one marker of alcohol abuse are known to give better sensitivity but usually reduced specificity in diagnosing alcohol abuse. Discriminant analysis is a statistical tool that may be applied to a set of markers and may improve the classification power above that of any single variable, thus improving the final discrimination [see, e.g., Refs. (16)(17)(18)(19)(20)].

The primary aims of the analyses presented in this report were (a) to find laboratory variables and a combination of them that can discriminate between well-characterized subjects who are "alcohol abusers" and "social drinkers", and (b) to demonstrate that the model is clinically easy to accept.


   Materials and Methods
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 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
subjects
Six data sets based on clinical studies done in Germany (studies 1 and 4), Spain (study 2), France (study 3), Finland (study 5), and Japan (study 6) and performed originally by the Medical and Regulatory Affairs division of Pharmacia & Upjohn Diagnostics AB were used for analysis of the present study. Subjects with alcohol consumption <60 g/day were classified as social drinkers (controls). Subjects with alcohol consumption >60 g/day were defined as alcohol abusers (21). In addition to the social drinkers and the alcohol abusers, the data sets also contained data on patients with alcoholic or nonalcoholic liver disease, and alcoholics with cirrhosis. Those groups were not included in the present study.

Study 1 included 79 patients (20 females and 59 males) who consumed high amounts of alcohol (>60 g/day) for at least 1 week prior to blood sampling and 108 healthy subjects (60 females and 48 males) who consumed low amounts of alcohol.

Study 2 included 55 alcoholics (8 females and 47 males) who had abstained <3 days prior to sampling. All of them had drunk >80 g/day during the 2 months prior to sampling. Controls were 34 social drinkers (16 females and 18 males) who had consumed <15 g of alcohol/day.

Study 3 included 51 actively drinking alcoholics (23 females and 28 males; alcohol consumption >80 g/day). A group of 62 blood donors (32 females and 30 males) who had consumed <40 g of alcohol/day were used as controls.

Study 4 included 202 (46 females and 156 males) alcohol-dependent inpatients (as diagnosed according to DSM-III-R) and 94 (45 females and 49 males) healthy controls, of whom 83 (42 females and 41 males) consumed 0–20 g of alcohol/day and 11 (3 females and 8 males) consumed 20–60 g/day.

Study 5 included 59 alcoholics (12 females and 47 males) who consumed >60 g of alcohol/day for at least 1 week prior to blood sampling and 299 healthy university students (194 females ad 105 males) who consumed <30 g of alcohol/day as controls.

Study 6 included 26 male alcoholics who consumed >80 g of alcohol/day. The controls were 343 healthy volunteers (165 females and 178 males) who consumed <20 g of alcohol/day.

methods
Serum CDT concentrations were analyzed by a double antibody assay (CDTectTM; AXIS-SHIELD, Oslo, Norway). GGT, ASAT, and ALAT were analyzed in serum samples, and MCV was analyzed in whole blood by routine clinical chemistry methods.

statistical methods
Discriminant analysis [see, e.g., Ref. (22)] was used to find a diagnostic rule based on the biochemical markers that could differentiate as well as possible between alcohol abusers and social drinkers. A stepwise selection procedure was applied to the calibration data set to derive the best combination of markers and to eliminate redundant information provided for different markers. Scrutiny of the data indicated that the variances might be different in the different groups. Therefore, the discriminant analysis was done allowing for variance heterogeneity (22).

Variables were tested both nontransformed and in logarithmic form. Standard diagnostic methods for linear statistical models (23) suggested that CDT, GGT, ASAT, and ALAT should be analyzed in logarithmic form. The natural logarithm was used. The SAS procedures Stepdisc and Discrim (24) were used for the analyses.

Two of the data sets (studies 1 and 2) were used for calibration, i.e., for building predictive statistical models, and the remaining four data sets (studies 3–6) were used as validation data sets to confirm the findings from the calibration data sets.

For comparisons between different classification criteria, nonparametric ROC plots [see, e.g., Ref. (25)] were prepared. The ROC plots were prepared through a program written by one of the authors (U.O.), using the SAS language (24) and SAS/Graph plots. As summaries of the ROC plots, the area under the curve was calculated for each criterion. The area under the ROC plot can be interpreted as the probability that a randomly selected alcohol abuser has a value for a marker higher than that of a randomly selected person in the control group. Significance tests comparing the areas under nonparametric ROC plots for different markers were done using the method of DeLong et al. (26).

Results were expressed as the mean (SD) for all variables.


   Results
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 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
The mean (SD) values for the different biochemical markers are shown in Table 1 .


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  		Table 1. Mean (SD) values for the different biochemical markers for alcohol abusers and social drinkers (control group), subdivided by sex.

stepwise analysis
The calibration analysis used data from studies 1 and 2, which included a total of 276 individuals, 134 alcohol abusers, and 142 social drinkers. The purpose of this analysis was to find those variables among the markers CDT, GGT, ASAT, ALAT, and MCV that best differentiated between the group with high alcohol consumption and a control group. Because there may be sex differences and differences among the studies, dummy binary variables for sex and study were included.

The stepwise analysis revealed that the natural logarithm transformations of CDT [ln(CDT)] and GGT [ln(GGT)] were the best predictor variables for differentiating between alcoholics and social drinkers; these variables accounted for r2 = 77.8% of all variation. The next variable, [ln(ALAT)], although formally significant, added only 2.8% explained variation. To keep the model simple, we chose to retain only ln(GGT) and ln(CDT) in the model. Neither sex nor the study effect was significant.

predictive model
The stepwise procedure did not supply coefficients for the discriminant function. Therefore, the calibration data were analyzed using discriminant analysis to obtain a predictive model. This analysis gave the following discriminant function: y = 0.8 · ln(GGT) + 1.3 · ln(CDT).

cutoff limit
On the basis of earlier studies (6), recommended cutoff limits in different study populations have been suggested to be as follows: for males, 20 units/L for CDT [ln(CDT) = 3.00] and 40 U/L for GGT [ln(GGT) = 3.69]; for females, 26 units/L for CDT [ln(CDT) = 3.26] and 30 U/L for GGT [ln(GGT) = 3.40]. In our control groups, this corresponds to percentiles 92.4 (CDT, males), 98.5 (GGT, males), 94.7 (CDT, females), and 96.1 (GGT, females), respectively.

As a tentative criterion for a cutoff limit for {gamma}-CDT we chose the 95th percentile for the control groups in our data. This would give the limit as 6.5 for females and 6.6 for males. Because the difference between sexes was small (a t-test comparing {gamma}-CDT between males and females gave P = 0.72), we suggest using the same tentative limit, 6.5, for both sexes. This recommendation may have to be modified once data for larger samples from a general population become available.

evaluation of validation data sets
Sensitivity and specificity.
Table 2 shows the sensitivity and specificity for all data sets for the markers CDT, GGT, and {gamma}-CDT = 0.8 · ln(GGT) + 1.3 · ln(CDT), using the cutoff limits discussed above.


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  		Table 2. Specificity and sensitivity for CDT, GGT, and {gamma}-CDT in the calibration data set and in the validation data set for males and females.

ROC plots.
Nonparametric ROC plots were prepared separately for males and females. The area under the curve was calculated, along with a test comparing the areas for the different markers. These results are given in Table 3 . The areas under the nonparametric ROC plots were largest for {gamma}-CDT for both sexes (P <0.0001).


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  		Table 3. Comparison of sensitivity and specificity for the different markers, and areas under ROC curves for a joint analysis of all data sets.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
The identification of alcohol abusers by use of biochemical laboratory markers offers methodologic challenges. A new marker should have a sensitivity high enough to differentiate between a broad range of alcohol consumption and should not have disturbances based on gender. Moreover, such a method should be easy to adapt to clinical practice. The accumulation of research findings suggests that combinations of laboratory tests may be useful in the detection of high alcohol consumption (18).

A statistical method such as discriminant analysis uses biochemical markers from defined groups of subjects, and produces weighting factors for tests and derives a discriminant score that minimizes within-group differences and maximizes between-group differences. The use of such an analysis, a model combining five markers (ASAT, MCV, HDL-cholesterol/total cholesterol, triglycerides and log alkaline phosphatase) gave a sensitivity of 91% and a specificity of 96% in discriminating alcoholics from social drinkers. Corresponding values were 72% and 73%, respectively, in discriminating heavy drinkers (18). Shaper et al. (27) found that a combination of GGT, HDL-cholesterol, urate, mean corpuscular hemoglobin, and lead had a discriminant value of 41% for heavy drinkers compared with 17% for the best single test, i.e., GGT. A more complex model including 10 laboratory measurements (chloride, sodium, ratio of direct to total bilirubin concentration, blood urea nitrogen, HDL, monocyte count, phosphorus, platelets, ASAT, and mean corpuscular hemoglobin) correctly identified 98% of heavy drinkers and 95% of light drinkers (28). In spite of their better performance compared with single markers, these combinations include a large number of laboratory measurements.

The aim of this study was to investigate whether diagnosis of excessive alcohol consumption could be improved by combining a few laboratory markers of alcohol abuse into a joint diagnostic model. The diagnostic model was formulated using data from two studies (studies 1 and 2). It was found that a model that included only two markers (CDT and GGT), here called {gamma}-CDT, performed well in these two studies to differentiate between alcohol abusers and social drinkers. {gamma}-CDT was defined as:

The model was tested on data from four independent studies. Diagnostic efficiency was evaluated in two different ways: using nonparametric ROC plots and by calculating sensitivity, specificity, and the total error rate using clinically accepted cutpoints for CDT and GGT. The reference and cutoff values for {gamma}-CDT were the same (6.5) for males and females. The area under nonparametric ROC plots was largest for {gamma}-CDT in both genders. The total error rate among males and females was lowest for {gamma}-CDT.

In the present study, the average sensitivity and specificity for males were, respectively, 75% and 93% for {gamma}-CDT, 58% and 94% for CDT, and 55% and 90% for GGT. The average sensitivity and specificity for females were, respectively, 68% and 96% for {gamma}-CDT, 40% and 94% for CDT, and 52% and 96% for GGT. This indicates a significant improvement in classification using only two markers.

The sensitivity and specificity values are quite similar for GGT and CDT. Many previous studies reported similar sensitivities for GGT and CDT and higher specificities for CDT (29). However, among subjects with liver disease, the specificity of the two markers is similar.

In conclusion, the new marker {gamma}-CDT clearly outperformed both CDT and GGT for differentiating between alcohol abusers and social drinkers. {gamma}-CDT seems to be a valuable addition to the tools for diagnosing alcohol abuse. Because of its relative simplicity, it should be easy to adopt into routine use. Thus, {gamma}-CDT is recommended for the detection of alcohol abuse in settings with a high prevalence of alcohol abuse.


   Footnotes
 
1 Nonstandard abbreviations: GGT, {gamma}-glutamyltransferase; CDT, carbohydrate-deficient transferrin; ASAT, aspartate aminotransferase; ALAT, alanine aminotransferase; and MCV, mean corpuscular volume.


   References
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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