Mental Health, Genomic Medicine
and Patient Care for our Veterans
Gerry Higgins, M.D., Ph.D.; Facilitator
Chair, Genomics Advisory Panel, OSHERA
TOPICS
1. Mental Health Services – Challenges:
→ Suicide
→ Post-Traumatic Stress
→ Treatment-resistant depression
→ Poly-pharmacy and comorbidity
2. The Importance of the Million Veteran Program
and Genomic Medicine at the VA
3. Integration of Genome Data into the VA’s EHR:
→ Why it is important for the individual Veteran
→ Update, challenges and risks
Rank order, Top Health Care Challenges in the Veterans
Health Administration;
Survey of 67 VA Clinicians, All Specialties at 24 Sites:1
1. Treatment-Resistant Depression
2. Treatment-Resistant Anxiety
3. Traumatic Injury
4. Addiction / Alcoholism
5. Posttraumatic stress
6. Cardiovascular Disease
7. Oncology
8. Gastroenterology
9. Dementia
10. Sleep Disorders
1.
Conducted by Liberty Mutual Patient Safety Institute under contract with the VHA.
Challenges - Suicide
→“Suicide rates among Veterans treated in the VA System are
about 50% higher than in the general population, and the
rate of suicide among active duty service personnel has
recently exceeded the rate in the general population.”
→From DoD Suicide Event Report Program (2008-2010):
Previous diagnosis of Mood Disorder including Bipolar
Previous diagnosis of Anxiety Disorder
Previous diagnosis of Major Depressive Disorder
Previous diagnosis of Posttraumatic Stress
22%
19%
14%
6%
Outpatient behavioral health care visit within past 30 days
21%
Outpatient health care consultation visit within past 30 days
Taking or previously taken psychotropic medications?
44%
23%
Challenges - PTS
→ Using the strictest PCL criterion for OIF/OEF Veterans
PRE-DEPLOYMENT
Grouped: 3.0%
(95% CI: 2.9-3.1)
POST-DEPLOYMENT
Support Unit
5.0% (95% CI: 4.9-5.2)
Operational Unit
19.6% (95% CI: 19.1-20.2)
Percentage diagnosed
→ Time-course using DSM-5 ‘Experiencing’ criteria
35
30
25
20
15
Unintentional Exposure to trauma
Intentional Exposure to Trauma
Israeli War Veterans
10
5
0
1
3
6
12
10
20
month months months months years years*
*5.2% after 20 years
Challenges – Treatment-Resistant Depression
→Major depressive disorder (MDD) strikes 14% - 17% of
Americans during their lifetime.
→ >30% of individuals with MDD do not achieve remission after 4
long antidepressant trials.
→ 22% of Americans view MDD as a ‘personal weakness.’
→ MDD costs the U.S. economy an estimated $150 B each year.
→Co-morbidities:
CARDIO
MDD
SUICIDE
PTS
Poly-Pharmacy
Goals of VA Genomic Medicine Program
• Collect and link genetic information with VA Electronic
Health Record and thereby:
o
o
o
Discover genetic predispositions, causes and mechanisms of disease
Better define treatments
 Pharmacogenomic & interventional customization
Via research, advance understanding in all these areas
• Establish how genetic information will be used in clinical
medicine
o
o
Translational research to link genotype to phenotype
Complex, adult, multi-gene diseases possibly with strong
environmental influences
• The Million Veteran Program
o
o
‘Whole genome sequencing & analysis’ to provide largest study of
disease associations and human genetics
Will help all studies examining how genome variants lead to
disease and help understand gene x environment interactions
Breakthroughs in Epigenomics may lead to
understanding of how combat stress leads to PTS
Epigenomics: A genomic approach to studying environmental
effects, primarily DNA methylation, on gene function.
Repetitive combat
stress causes
Epigenomic changes
to genes in the
stress response
Breakthroughs in Epigenomics may lead to
understanding of how combat stress leads to PTS
Combat stress
→
Prolonged stress
response
Hypermethylation of
glucocorticoid receptor
PTS
Breakthroughs in Epigenomics may lead to
understanding of how combat stress leads to PTS
→ Patients with PTSD have increased plasma levels of cortisol, show a
dysfunctional cortisol rhythm, and an exaggerated stress response.
Plasma
Cortisol
Architecture for Pharmacogenomic Decision Support1:
Infrastructure Overview of Required Elements
Pharmacogenomic
Decision Support
1.Computer processable
medical knowledge
2.Computer-interpretable
patient data
3.Generation of patientspecific advice using
knowledge and patient
data
CDS
Pre-Requisites
Resources
1.Centrally managed
repositories of medical
knowledge
2. Standardization of CDS
information for genomic
medicine
3. Standard approach for
representation and locating
patient data
→ Rules Engine
→ MLM
→ Machine Learning component such as
Support Vector Machine (SVM)
→ Remote web access input
1Based
on: Kawamoto K et al. A national clinical decision support infrastructure to enable the widespread
and consistent practice of genomic and personalized medicine. BMC Medical Informatics and Decision
Making. 2009; 9:17 doi:10.1186/1472-6947-9-17.
Architecture for Advanced Pharmacogenomic Decision Support:
What’s Missing? Drug – Gene Interactions (DGIs)
→ Here are some reasons that the DGI domain has not received the focus it
deserves:
A. Drug-drug interactions are difficult to model, except in a generic
manner, and once the algorithm has to handle many drug pairs (i.e., >2)
it becomes an impossible combinatorial problem – especially as polypharmacy seems to be on the rise. To add a patient's variable
metabolizer phenotype into the math, and the problem becomes
intractable.
B. The advent of next generation sequencing (NGS) has changed
everything. Every vendor is very concerned that maintenance and
upgrades will require tremendous resources to keep up with the very
rapidly world of ‘omics.
C. Physicians are already upset about medication alert fatigue, and
since they were never thoroughly educated in genomics or
pharmacogenomics, vendors are uncertain about how to proceed. In
addition, both professional medical organizations and payors are still
somewhat skeptical about gene testing.
Preemptive Genotyping
Store pharmacogenomic
data in EHR
High Risk Patients in
Medical Home
Obtain known actionable
pharmacogenomic variants
for each patient using
multiplexing / NGS panels
Trigger
EHR system
automatically
provides physician
with
optimal therapeutic
regimen for patient
Advanced
pharmacogenomic
decision support
Find possible risk
variant
Search for
pharmacogenomic
data in patient’s
EHR
Inpatient
Gets New
Prescription
Possible integration of omic data directly into
the VistA electronic health record
1. How could this be achieved?
2. Since epigenomic data, which has been highly
replicated, shows a correlation between
hyper-methylation of the glucocorticoid
receptor and PTS (as well as MMD and BPD) –
how can we integrate these data?
3. What concerns do Veterans have about
protection of personal privacy in this rapidly
moving domain?
END OF PART ONE:
Questions?
Discussion?