Transcript Using Biomarkers to Measure Alcohol Use

Using Biomarkers to Measure
Alcohol Use
Sarah Cook
London School of Hygiene & Tropical Medicine
Using biomarkers in research on health workshop 20th February 2015
Alcohol Consumption
• Alcohol consumption is associated with many negative effects
including physical, psychological and social problems
• Alcohol research needs valid measures of alcohol consumption
Measurement of
Alcohol Consumption
• Population level data on sales, tax and production
• Self-reported from survey data
• Volume of ethanol
• Drinking pattern
• Drinking behaviours (e.g. drunkenness, hangover)
Self-reported
volume of ethanol
• Quantity- Frequency
Usual amount X Frequency X Drink strength
(number of drinks) (drinking days)
• Graduated- Frequency
• Recent recall
(ethanol content)
Problems with measuring
alcohol Consumption
• Measurement error
• Difficult to remember
• Difficulty in calculating volumes
– What is a “Standard drink”?
– Shared containers
– Variation in strength of drinks
• Social desirability
Alcohol Biomarkers
• Using self-reported data on alcohol use is problematic
• More objective measures of alcohol use are needed
• Alcohol consumption has an effect on a wide range of
biological parameters
Alcohol Biomarkers
• Blood
–
–
–
–
Liver enzymes (GGT, AST, ALT)
Carbohydrate deficient transferrin (CDT)
High Density Lipoprotein cholesterol (HDL)
Mean corpuscular volume (MCV)
• Urine
– 5-Hydroxtryptophol
• Hair
– Ethyl glucuronide
Liver Enzymes
• Gamma glutamyl transferase (GGT)
• Aspartate Transanimase (AST)
• Alanine Transanimase (ALT)
GGT
• Serum GGT is most commonly used alcohol biomarker
• Raised with chronic heavy drinking (on average 80-200g
ethanol/day for several weeks) but this can be influenced by
other factors (age, gender).
• More sensitive to alcohol than other liver enzymes but still
generally low
• Not specific – raised by many other factors
Carbohydrate Deficient
Transferrin (CDT)
• Variant of serum glycoprotein transferrin produced in the liver
• Increased with chronic heavy drinking (60-80g ethanol daily over at least 2
weeks)
• Several factors effect relationship between alcohol and CDT e.g. gender,
smoking, body mass index
• More specific than GGT but similar sensitivity
• Not strongly correlated wit GGT so they could be used in combination.
Limitations of
alcohol biomarkers
• There are lots of potential alcohol biomarkers but no gold standard
measure has been found
• Sensitivity and specificity generally low
• Only raised with heavy, sustained drinking
• Relationship between alcohol intake and biomarker may be influenced by
other factors (interactions)
• Difficult to interpret raised levels in terms of actual volume consumed,
drinking pattern
• Are alcohol biomarkers
useful?
Association between alcohol use and cardiovascular
risk factors in Russian Men: An Example of the Use
of Alcohol Biomarkers
Study Design
• Data from the Izhevsk Family Study
• Cross-sectional survey of men aged 25-60 living in the city of Izhevsk,
Russia (2008-9)
• Men and a proxy completed a questionnaire which included very
detailed questions on alcohol consumption
• Attended a health check which included a blood test
• Sample size N=978 men
Outcome: Cardiovascular
risk factors
• Hypertension (binary)
• Serum lipids
– High density lipoprotein cholesterol (mmol/litres)
– Low Density lipoprotein cholesterol (mmol/litres)
Alcohol measures: selfreported spirit intake
Usual volume of spirits
Maximum volume of
spirits
0.76
1.00
Spirit Intake
0.49
Frequency of drinking
spirits
Alcohol measures- proxy
reported acute dysfunctional
drinking
Frequency of hangover
0.84
0.93
Frequency of excessive drunkenness
0.84
Frequency of sleeping in clothes
because of drunkenness
Frequency of failing family or personal
obligations because of drinking
0.85
Acute alcoholrelated dysfunction
Alcohol measures- alcohol
biomarkers
• Log Gamma-glutamyl transferase
(GGT)
• Log Carbohydrate Deficient
Transferrin (CDT)
Alcohol and
Hypertension
Spirit intake
Acute alcohol-related dysfunction (proxy-report)
1.5
Log odds ratio (95% CI) for hypertension
Log odds ratio (95% CI) for hypertension
1.5
1
0.5
0
-0.5
-1
-1.5
1
0.5
0
-0.5
-1
-1.5
Fifth of Factor score
Fifths of Factor score
CDT
2.5
2
1.5
1
0.5
0
1st (ref)
-0.5
2nd
3rd
Fifth of GGT
4th
5th
Log odds ratio (95% CI) for hypertension
Log odds ratio (95% CI) for hypertension
GGT
2
1.5
1
0.5
0
1st (ref)
2nd
3rd
4th
5th
-0.5
-1
Fifth of CDT
Adjusted for age, education, level of amenities, marital status, employment status, smoking, physical activity and
body mass index
Alcohol and Hypertension
Alcohol Use Measure
Odds ratio* (95% CI)
Spirit intake
1.27 (1.08, 1.49)
Acute alcohol-related dysfunction
1.33 (1.14, 1.56)
GGT
2.26 (1.79, 2.87)
CDT
1.74 (1.43, 2.13)
*Adjusted for age, education, level of amenities, marital status, employment status,
smoking, physical activity and body mass index
Alcohol use and
HDL-C
Spirit intake
Acute Alcohol-related dysfunction (Proxy-report)
0.5
Mean difference (95% CI) in HDL (mmol/L)
Mean difference (95% CI) in HDL (mmol/L)
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
Fifths of factor score
Fifth of Factor Score
-0.4
CDT
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
1st (ref)
2nd
3rd
Fifth of GGT
4th
5th
Mean difference (95% CI) in HDL (mmol/L)
Mean difference (95% CI) in HDL (mmol/L)
GGT
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
1st (ref)
2nd
3rd
4th
Fifth of Factor Score
5th
Alcohol and HDL
Alcohol Use Measure
Coefficient* (95% CI)
Spirit intake
0.18 (0.15, 0.20)
Acute alcohol-related dysfunction
0.16 (0.13, 0.19)
GGT
0.20 (0.18, 0.23)
CDT
0.31 (0.29, 0.33)
*Adjusted for age, education, level of amenities, marital status, employment
status, smoking, physical activity and body mass index
Alcohol Use and
LDL-C
Acute Alcohol-related Dysfunction (Proxy-report)
Spirit Intake
0.4
Mean difference (95% CI) in LDL (mmol/L)
Mean difference (95% CI) in LDL (mmol/L)
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.5
Fifth of Predicted Factor Score
Fifth of Predicted Factor Score
CDT
0.4
0.3
0.2
0.1
0
1st (ref)
2nd
3rd
-0.1
-0.2
-0.3
Fifth of GGT
4th
5th
Mean Difference (95% CI) in LDL (mmol/L)
Mean Difference (95% CI) in LDL )mmol/L)
GGT
0.5
0.4
0.3
0.2
0.1
0
-0.1
1st (ref)
2nd
3rd
-0.2
-0.3
-0.4
-0.5
Fifth of CDT
4th
5th
Alcohol and LDL-C
Alcohol Use Measure
Coefficient* (95% CI)
Spirit intake
-0.11 (-0.17, -0.05)
Acute alcohol-related dysfunction
-0.12 (-0.19, -0.06)
GGT
0.04 (-0.02, 0.11)
CDT
-0.16 (-0.23, -0.08)
*Adjusted for age, education, level of amenities, marital status, employment status,
smoking, physical activity and body mass index
Why does GGT look
different?
• GGT is raised for many reasons (not just alcohol)
• These include age, obesity, smoking, use of certain medications, non
alcoholic liver disease and diabetes
• GGT associated with cardiovascular disease even in non-drinkers
• Is it an appropriate biomarker for the question?
Conclusions
• Alcohol biomarkers are not a simple solution to the issues of how to
measure alcohol use
• BUT they can be useful alongside data on alcohol use from other
sources to improve overall picture/provide validation
• Caution is needed when your biomarker is raised for many reasons
as you may see associations not due to the factor of interest
• The best way to use biomarkers in alcohol research is still a question
to explore
Acknowledgements
• David Leon
• Bianca De Stavola