1. Phenotyping Using Longitudinal VA Electronic Health Data
The Veterans Aging Cohort Study (VACS) Experience
PI: Amy C. Justice MD, PhD
Lead PI, MVP Beta Testing Project on Multi-substance Use
Chair, Mental Health and Substance Working Group, MVP

2. What is VACS Veterans Aging Cohort Study (VACS)
All HIV+ in national VA demographically and temporally matched to 2 uninfected subjects
-50 K HIV+, 100K uninfected subjects
~20 years of cleaned, annotated, longitudinal EHR data
-Supplemented with
Medicare and Medicaid files
National Death Index Cause of Death data
Substudies:
Longitudinal patient survey data 7,000+ subjects
DNA, plasma, and pellets on 2,250 subjects

3. National Team

4. EHR Phenotypes We Have Validated
Substance use and psychiatric disease
Mental illness: PTSD, schizophrenia, bipolar, depression
Substance use: Harmful alcohol use, smoking
Medical diseases
HIV, HCV, and HBV infection
Metabolic syndrome: myocardial infarction, congestive heart failure, and diabetes
Bacterial pneumonia
Decompensated liver cirrhosis
SEER reportable cancers
Severity of illness/Frailty
Fragility fractures, medically significant falls
VACS Index for mortality, hospitalization, and other clinical outcomes
Prescription drug exposures
Current and total medications, fill/refill adherence
Antiretroviral regimens
Opioids with conversion to morphine equivalents
Total medication counts

5. Early MVP Collaboration of Substance Use Beta Testing Grant: Phenotyping Harmful Alcohol Use for Genetic Discovery under review
Amy C. Justice, Kathleen A. McGinnis, Jan Tate, Ke Xu, William C. Becker, Hongyu Zhao, Joel Gelernter, and Henry R. Kranzler

6. Background: Alcohol Genetics
50% of dependence is heritable, suggesting many potential targets for drug development
Consistent risk loci have been identified
Genes encoding metabolizing enzymes
ADH1B (more common) and ADH1C
But, these account for a small percentage of heritability
Large scale studies are needed to identify other loci
But, complex behavioral phenotypes (DSM Dx) are difficult to measure efficiently
We use EHR quantity/frequency screening data (AUDIT–C) to develop a more feasible phenotype

7. Data Available VA Electronic Health Record Data:
AUDIT-C & diagnoses of alcohol use disorder
laboratory data
medications
Criterion standard: one measure of a direct alcohol biomarker (PEth)
In initial analyses, AUDIT-C demonstrated superior associations with PEth, so we focused on AUDIT-C
Genetic Biomarker: Polymorphism in ADH1B (Arg369Cys)
Much more common in African Americans

8. Approach
AUDIT-C (self-reported alcohol quantity and frequency data) entered in EHR since 2007
Available on 1851 subjects, median of 7 measures
Created three AUDIT-C measures:
Closest (to DNA sampling) AUDIT-C
Highest AUDIT-C
AUDIT-C trajectories using joint trajectory modeling
Criterion validated against direct alcohol biomarker (phosphatidylethanol or PEth)
Final validation: association with ADH1B variant among African Americans (AA) n=1503

9. Highest AUDIT-C
Closest AUDIT-C 1 2 3 4
AUDIT-C = 0
495
181
158
118
AUDIT-C = 1
149
142 67
AUDIT-C = 2 or 3
182
127
AUDIT-C = 4+
232
Agreement: 57.2%
AUDIT-C Highest, higher: 42.8%
Agreement: 61.5%
AUDIT-C Trajectory, higher: 20.0%
AUDIT-C Trajectory lower: 18.4%
AUDIT-C Trajectory
Closest AUDIT-C 1 2 3 4
AUDIT-C = 0
660
216 70 6
AUDIT-C = 1 43
248 60 7
AUDIT-C = 2 or 3
133
158 12
AUDIT-C = 4+ 31
128 73
AUDIT-C Trajectory
Highest AUDIT-C 1 2 3 4
AUDIT-C = 0
495
AUDIT-C = 1
156
173
AUDIT-C = 2 or 3 51
337 94
AUDIT-C = 4+ 7
118
321 98
Agreement: 46.5%
AUDIT-C Trajectory, higher: NA
AUDIT-C Trajectory lower: 53.5%

10. ---Phosphatidylethanol (PEth)---
 ---AUDIT-C--- N
Medianng/mL
%>4
%>20
%>40
%>100
Closest
1 (score = 0)
952 2 33 13 10 5
2 (score = 1)
358 4 51 21 12 7
3 (score = 2-3)
309 17 72 48 34 15
4 (score 4+)
232 49 83 66 55 38
Highest
595 24 8 6 3
330 35 9
482 23 16
544 32 78 58 45 28
Trajectories
1 (little to no use)
709 27
2 (light use)
628 22
3 (moderate use)
416 25 76 54 40
4 (heavy use) 98
106 91 79 67 53

11. ADH1B Minor Allele Frequency Among African Americans by AUDIT-C Measures

12. Odds of the ADH1B Minor Allele as a Function of AUDIT-C Measures Among African Americans (n=1,503)*N OR
95% CI P
Closest AUDIT-C
Overall p = 0.3
1 (score= 0)
792 1
N/A
2 (score =1)
274
0.83
0.62, 1.11
0.22
3 (score=2-3)
255
0.78
0.58, 1.05
0.11
4 (score 4+)
182
0.85
0.61, 1.20
0.35
Highest AUDIT-C Overall p=0.08
399
254
0.56, 1.08
0.14
387
0.72
0.54, 0.97
0.029
463
0.55, 0.95
0.021
AUDIT-C Trajectories
Overall p = 0.009
1 (little to no use)
591
2 (light use)
496
0.82
0.64, 1.05
0.12
3 (moderate use)
340
0.79
0.60, 1.05
0.10
4 (heavy use) 76
0.41
0.23, 0.73
0.003

13. Conclusions/Next Steps
Longitudinal AUDIT-C measures are promising
We plan to
Validate this analysis in MVP
Use longitudinal AUDIT-C measures for genetic discovery in MVP
Repeat this approach for smoking and opioid use to enable discovery of risk loci for multi-substance use

14. Lessons Learned Phenotyping in general:
Be clear about what you are studying (process vs. biology)
Think about missing data
Get advice from high volume clinicians and content experts
Validate, validate, validate!
When phenotyping for genetic discovery also:
Learn language and methods (starting this process!)
Think about gene-enviornment-aging interactions
Emphasize specificity over sensitivity
Collaborate closely with genomic experts!

15. Acknowledgements Consortium PI : AC Justice*
Scientific Collaborator (NIAAA): K Bryant
Affiliated PIs: S Braithwaite, K Crothers*, R Dubrow *, DA Fiellin*, M Freiberg*, V LoRe*
Participating VA Medical Centers: Atlanta (D. Rimland*, V Marconi), Baltimore (M Sajadi, R Titanji), Bronx (S Brown, Y Ponomarenko), Dallas (R Bedimo), Houston (M Rodriguez-Barradas, N Masozera), Los Angeles (M Goetz, D Leaf), Manhattan-Brooklyn (M Simberkoff, D Blumenthal, H Leaf, J Leung), Pittsburgh (A Butt, K Kraemer, M Freiberg, E Hoffman), and Washington DC (C Gibert, R Peck)
Core and Workgroup Chairs: C Brandt, J Edelman, N Gandhi, J Lim, K McGinnis, KA Oursler, C Parikh, J Tate, E Wang, J Womack
Staff: H Bathulapalli, T Bohan, J Ciarleglio, A Consorte, P Cunningham, L Erickson, C Frank, K Gordon, J Huston, F Kidwai-Khan, G Koerbel, F Levin, L Piscitelli, C Rogina, S Shahrir, M Skanderson
Major Collaborators: VA Public Health Strategic Healthcare Group, VA Pharmacy Benefits Management, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Yale Center for Interdisciplinary Research on AIDS (CIRA), Center for Health Equity Research and Promotion (CHERP), ART-CC, NA-ACCORD, HIV-Causal
Cross Cohort Collaborators: Richard Moore (NA-ACCORD), Jonathan Sterne (ART-CC), Brian Agan (DoD)
Major Funding by: National Institutes of Health: AHRQ (R01-HS018372), NIAAA (U24-AA020794, U01-AA020790, U01-AA020795, U01-AA020799, U24-AA022001, U24 AA022007), NHLBI (R01-HL095136; R01-HL090342) , NIAID (U01-A ), NIMH (P30-MH062294), NIDA (R01DA035616), NCI (R01 CA173754) and the Veterans Health Administration Office of Research and Development (VA REA , VA IRR Merit Award) and Office of Academic Affiliations (Medical Informatics Fellowship)
*Indicates individual is also the Chair of a Core or Workgroup

16. Acknowledgements Continued
COMpAAAS/Veterans Aging Cohort Study, a CHAART Cooperative Agreement, supported by the National Institutes of Health: National Institute on Alcohol Abuse and Alcoholism (U24-AA020794, U01-AA020790, U01-AA020795, U01-AA020799) and in kind by the US Department of Veterans Affairs.  In addition to grant support from NIAAA, we gratefully acknowledge the scientific contributions of Dr. Kendall Bryant, our scientific collaborator.
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