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Reflections Blog

Dopamine, Drug Addiction, & Personalized Medicine

12/2/2021

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​Neuroscience, Personalized Medicine
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What is dopamine?
Dopamine is a neurotransmitter, a chemical that shapes how the brain processes information. It does this by binding to different categories of dopamine receptors which then leads to changes in the intracellular processes of neurons, the cells responsible for transmitting information in and outside the brain. The D1 family of dopamine receptors (D1 & D5) increase intracellular levels of a chemical second messenger, cyclic AMP, which can then affect how a neuron processes other signals it receives. The D2 family of dopamine receptors (D2, D3, & D4) decrease cyclic AMP, which can also shape neural responses. How the dopamine signals interact with other signals in the brain can be quite complex and is beyond the scope of this piece. For more see this review article.

Dopamine signaling plays a role in a variety of critical cognitive processes including motor control, learning, and decision making. It has also been implicated in the addictive nature of drugs of abuse, which I studied in some detail during my Ph.D. and postdoctoral research. 
Positron Emission Tomography and measuring dopamine signaling in the human brain
Positron Emission Tomography (PET) allows scientists to measure dopamine signaling in the living brain. PET has been around since the 1960s and involves imaging the location and amount of a radiotracer (radioactively-tagged compound) in the body. Most PET radiotracers contain C-11, F-18, or O-15 radioactive isotopes. These isotopes release positrons (which are the antiparticle of the electron) which, when they interact with nearby electrons in the body produce an annihilation event leading to 2 gamma ray photons being emitted at 180 degrees. The PET scanner "counts" these gamma ray events and ultimately reconstructs the image that produced the events by projecting the gamma ray counts back into the body part being imaged. These PET images give quantifiable data regarding the amount of tracer that accumulates in a particular area over time.
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Schematic of how a PET scanner measures gamma rays to quantify the level of a radiotracer in particular anatomical areas of the brain. Image by Jens Maus (http://jens-maus.de/); Public Domain, https://commons.wikimedia.org/w/index.php?curid=401252
​Brain PET is a particularly powerful technique in that we can use radiotracers that allow us to investigate brain metabolism, neurotransmitter receptors (dopamine or opioid, among others), neurotransmitter synthesis, and the presence of beta-amyloid plaques (often present in Alzheimer's disease). With these compounds we gain a better understanding of individual differences that may be useful as markers of disease state or risk for developing a particular disease. Common radiotracers for imaging the dopamine system include FDOPA, C-11-Raclopride, F-18-Fallypride, FMT, and others. Several groups have used some of these compounds to better understand the dopamine system's role in drug abuse. 
Do dopamine signaling differences reflect risk for drug addiction?
All drugs of abuse release dopamine in the brain. Dopamine, among other things, links pleasure/wanting with the stimuli its release is paired with. Thus, differences in dopamine signaling in response to drugs of abuse may relate to a greater propensity to re-use drugs found to be rewarding and potentially lead to increased risk for drug addiction.

PET imaging has shown that lower dopamine D2/3 receptors are present in a variety of drug-addicted individuals (alcohol, cocaine, methamphetamine, heroin) when compared to healthy controls. Whether low D2 receptors are a cause or consequence of problematic drug use has been difficult to determine in human studies, however.
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Animal work has suggested that behavioral impulsivity is associated with lower D2 receptor levels in rodents. These researchers also found that high impulsive rats would later go on to self-administer more cocaine than low impulsive rats (Dalley et al., 2007). Thus, D2 receptors may confer a greater propensity to engage in behaviors that are associated with drug addiction risk in humans (impulsivity, novelty seeking). Furthermore, work in non-human primates has shown that low D2 receptor levels predict escalation in cocaine self-administration, which leads to lower D2 receptor levels (Nader et al., 2006). This work suggests that low D2 receptor levels may predispose individuals to escalate drug use and that chronic drug use further changes these receptor levels.
Human PET studies have focused on individuals with a family history of addiction to try to corroborate the animal work linking dopamine D2 receptors with addiction risk. Volkow et al. 2006 have shown that individuals with a family history (FH) of alcoholism show heightened striatal (a region deep in the brain responsible for reward processing, learning, and action initiation) D2 receptor levels compared to subjects without a family history. They argue these high D2 levels may serve as a protective factor that prevented these individuals from becoming alcohol abusers themselves. This finding highlights the complexity of working with human subjects as the animal literature might have suggested the opposite finding (lower D2 in FH individuals). Human motives to use drugs are many and often the environment greatly shapes behavior. It could be argued that FH positive individuals with lower D2 (not observed in Volkow et al) had behavioral profiles (see Dalley et al., 2007; above) that resulted in them already transitioning to alcohol/drug abuse and thus being excluded from the Volkow study. Undoubtedly, there are more variables associated with risk for drug use than low D2 levels and future work may be able to identify what other factors (genetic, environmental, social) interact with D2 levels to predict drug abuse risk.
Genetic factors affecting dopamine signaling
There has also been interest in understanding whether genetic differences may lead to different levels of D2 receptor availability, potentially placing some individuals at greater risk for addictive disorders. I investigated the effect of some common D2 receptor single nucleotide polymorphisms (SNPs) on D2 receptor availability using F-18-Fallypride as part of my postdoctoral research. Many of these SNPs had been previously associated with dopamine receptor differences in relatively small PET studies or been associated with potential increased risk for drug addiction. 
  • Taq1A - A1 allele associated with lower striatal D2 receptor availability (replicated in separate study but not in a third)
  • C957T - C allele associated with lower striatal D2 receptor availability in study of 45 individuals 
  • -141C Ins/Del - inconsistent findings on whether it affects D2 receptor availability
For more see: Genetic variation and dopamine D2 receptor availability: a systematic review and meta-analysis of human in vivo molecular imaging studies
Since the Taq1A SNP was discovered to associate with differences in dopamine signaling first, researchers have used it as a proxy for D2 receptor status (or more loosely as an index of general dopamine functioning). However, given that the Taq1A polymorphism does not occur within the DRD2 gene itself, researchers have speculated that polymorphisms in Taq1A may associate with other SNPs in the DRD2 gene that are the real drivers of expression of the receptor in vivo.

The C957T and -141C Ins/Del polymorphisms are in strong linkage disequilibrium with Taq1A and have themselves been associated with striatal D2/3 receptor availability. Despite the data suggesting that these SNPs are strongly linked, few studies have systematically investigated the effect of C957T, -141C Ins/Del, and Taq1A in isolation and combination on D2/3 receptor availability. Beyond the potential link to drug addiction risk, characterizing the functional effect of these SNPs on D2/3 receptor availability has implications for better understanding the mechanisms through which they exert their demonstrated influence on motivated behaviors including learning and decision making, impulsivity, and reward responsivity. 

In our work, we used F-18-Fallypride, which is a D2/3 receptor tracer with favorable affinity to measure both striatal and extrastriatal dopamine receptors, and assessed the impact of C957T, Taq1A and -141C Ins/Del SNPs on D2/3 receptor availability in a sample of 84 healthy subjects.
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The C allele of the C957T SNP was associated with lower D2/3 receptor availability in the ventral striatum and putamen. No other SNP investigated demonstrated an effect on D2/3 receptor availability. BPnd=binding potential, a measure of D2/3 receptor availability; VS=ventral striatum
We found that the C957T SNP was associated with variation in dopamine D2/3 receptor availability in areas of the striatum often implicated in reward processing. The fact that the C allele was associated with lower dopamine receptor availability suggests it could be a useful genetic measure for at least one biological factor (lower D2 receptor availability) linked with drug addiction. While more work needs to be done to confirm these results, certainly further study of the C957T SNP in the DRD2 gene is warranted. 
Individual differences in dopamine release
Another area of focus regarding dopamine’s role in addiction is understanding differences in dopamine release to potential drugs of abuse. This measure is more closely associated with the biological processes associated with actual drug use, but is collected in a more controlled, laboratory setting. PET psychostimulant challenge studies allow researchers to examine dopamine release in the brains of human subjects. Methylphenidate and d-amphetamine (dAMPH) are often used in these PET studies as both release dopamine in the brain by blocking and/or reversing the dopamine transporter. If PET radiotracers that are displaceable by endogenous dopamine are used, researchers can perform a PET scan after placebo or psychostimulant administration and measure the change in radiotracer signal. The PET signal will go down after a psychostimulant for a tracer that is displaceable as the increased endogenous dopamine released by the drug lowers the binding sites for the tracer in the brain. This change in binding potential of the radiotracer can be used as a measure of dopamine release and has become a useful tool in research into addiction related processes.
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Areas of significant change in D2/3 receptor availability as measured by F-18-Fallypride PET after dAMPH administration when compared to PET data collected on placebo. This change in receptor availability on dAMPH is interpreted as a measure of the level of dopamine release to the dAMPH. Data from 34 healthy young adults. dAMPH=d-amphetamine
Using this PET technique, Casey et al 2014 found that young adults with a multigenerational FH of substance use disorders showed reduced dAMPH-induced dopamine than either healthy controls or subjects that personally used drugs at similar levels to the FH group but without a FH of substance use disorders. This study was particularly informative as the effects of current drug use were also investigated and measured separately from family history. Furthermore, our group and others have demonstrated that dAMPH-induced dopamine release correlates with subjective ratings of the drug, particularly wanting more, in drug naïve individuals. These data confirm animal work linking changes in dopamine signaling after drug use to wanting processes (which has been labeled incentive salience).

Read more about wanting, liking, and drug abuse in a previous blog post.
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The concept of blunted dopamine signaling (lower D2 receptor levels and less dopamine release) as biomarkers of addiction has also been recently reviewed (Trifilieff et al 2017; Leyton, 2017). While more work needs to be done, understanding factors that influence these PET-based biomarkers of dopamine signaling in human subjects has the potential to identify at risk individuals. This risk identification may allow intervention to be attempted earlier in the addiction process or perhaps prevent addiction before it even occurs.
Individual differences in dopamine signaling and the future of personalized medicine
The term “personalized medicine” has gained popularity in recent years. While it may seem like a buzzy term, its potential for improving treatment of a variety of medical conditions is vast. Personalized medicine involves tailoring treatments to individuals based on some aspect of their biology that might affect how they respond to a treatment. For example, you might give one patient with a particular genetic variant a different pharmacological treatment than another if that variant affects how they process (metabolize) or respond to that particular drug. This particular approach of using genetic information to understand response to pharmaceuticals is termed pharmacogenomics (see also).
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The rapid reduction in the cost to sequence the human genome (complete set of an individual’s DNA) as well as proliferation of genotyping services such as 23andMe (which genotype common genetic polymorphisms, or areas in human DNA most likely to vary across individuals) means that genetic data can be readily obtained by anyone who wants it. This technological advance will allow physicians greater information of a patient’s underlying biology and eventually will be merged with growing insights into the effects of genetic variation on drug metabolism, brain signaling, and behavior to make personalized medicine commonplace. In fact, pharmacogenomic data has been added to several drugs by the FDA.

My own work, referenced above, suggests that genetic variation in a gene encoding the dopamine D2 receptor (DRD2) can affect the relative availability of this receptor in the brain as measured with PET (Smith et al., 2017 Translational Psychiatry). Individuals with a particular genetic variant in DRD2 that is associated with less availability of the receptor (C957T CC individuals) may need either a higher dose of a D2 drug or a higher affinity D2 drug to receive a therapeutic benefit.

The implications for this finding go beyond potential treatments or interventions for drug addiction. D2 agonists are commonly used in Parkinson’s Disease patients to preserve motor function and D2 antagonist-like drugs are used in the treatment of Schizophrenia. Understanding the genotype of individuals affected with these conditions, then, could enhance the effectiveness of their D2 drug treatments (by suggesting a physician might want to start with a higher or lower dose of the drug). While studies such as ours linking genetic variation with differences in biology are encouraging, DNA can also be modified by the environment. Researchers have begun studying these epigenetic effects on behavior, with most work occurring in rodents. As we integrate this knowledge, we will begin to better understand the impact gene by environment interactions have on biology and behavior.
Non-genetic factors also influence dopamine signaling
Genetics are not the only variables that could be worth attending to in future treatments. Additionally, dopamine signaling is known to decline with age (see also a previous blog post on this topic). So, doses of dopaminergic drugs that work well on young adults might need to be titrated in older adults. Furthermore, we and others have shown that estradiol levels in naturally cycling women can affect dopaminergic brain functions (assessed by fMRI imaging and a genetic variant (COMT) know to affect dopamine levels in the higher-order, prefrontal areas of the brain). Thus, a dopaminergic medication might be more effective at treating a female patient’s symptoms at certain points of her menstrual cycle but not others. We are only beginning to understand the role of female sex hormones in a variety of biological systems as basic research historically has focused on male model organisms.​
Dopamine signaling complexity and developing future treatments
The role of dopamine in drug addiction is quite complex. In addition, implementing personalized medicine when treating psychiatric or behavioral disorders is challenging as most of these disorders do not have a single, identifiable biological cause. The brain is complex enough and the fact that genetics, sex hormones, age, and environment can all affect one neurotransmitter (dopamine) among the many others involved in brain function speaks to the vast challenge that lies ahead for researchers.

​Our quest to better understand individual differences, however, has the potential to lead to more targeted treatments and therapies for a variety of dopamine-associated disorders including ADHD, Schizophrenia, Parkinson’s Disease, and drug addiction. The development of these personalized treatments will undoubtedly improve healthcare in the 21st Century and beyond but will require further research focused on measuring and categorizing individual differences. 
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Explore more neuroscience-related posts on the blog:
  • ​Declining Dopamine: How aging affects a key modulator of reward processing and decision making
  • Stress & the Brain: How genetics affects whether you are more likely to wilt under pressure
  • Wanting, Liking, & Dopamine's Role in Addiction
  • Now vs Later - How immediate reward selection bias may be a risk factor for addiction 

More scholarly articles on dopamine and its effects:
  • What does dopamine mean?
  • Fifty years of dopamine research
  • Dopamine, behavior, and addiction
  • Dopamine and effort-based decision making
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Using Data Science and Artificial Intelligence to Improve Healthcare

9/30/2019

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Data Analytics & Healthcare
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This article originally appeared on the Health:Further blog on October 14, 2018.
It has been updated with current news and findings.
Data science is a buzzy term not only in the technology sector but in the wider culture as well. It has seeped into the common vernacular and promises increased insights and knowledge extracted from the vast quantity of data being generated every day.

The use of data science in healthcare is growing and artificial intelligence (AI) represents a huge business opportunity in the space. However, the potentially identifiable nature of health records and ethical concerns about how the data should be utilized and by whom makes working in this space a challenge.

The recent publication of work suggesting AI may be as good as clinicians in diagnosing disease further highlights the increased importance this technology will have in 21st Century healthcare.

I spoke with data scientists from 3 different healthcare companies on how their groups are using data to improve the quality and efficiency of healthcare.
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axialHealthcare, Nashville, TN

Lindsey Clark, Ph.D., is Director of Data Science and Analytics at axialHealthcare in Nashville, TN. Since joining the company in 2015, Clark has watched axialHealthcare grow rapidly from 8 to more than 100 employees. Focusing on pain management and opioid care, axialHealthcare leverages medical, behavioral, and pharmacy claims data to drive improved patient care and financial savings for health insurers through technology-enabled capabilities.
In essence, axialHealthcare's goal is to understand which treatments are both effective and safe in the treatment of pain. Axial is also focused on determining if treatment approaches beyond potentially addictive opioids are viable for particular patients.

A big question at axialHealthcare is, “what does safe and effective pain management look like?” The answer seeks to ensure that 1) opioids are prescribed judiciously given their propensity for causing dependence and addiction and 2) that other pain reduction therapies are considered when warranted.
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Clark emphasized the unique challenges of working with health information, including the critical need for data security and privacy as well as navigating the complex United States health system. The focus for most of U.S. healthcare is on what is reimbursable by either health insurers or Medicare/Medicaid (federal & state payers). Thus, every company trying to improve cost efficiency in healthcare must think about how their recommendations fit into the payers’ current reimbursement framework.

axialHealthcare has organized its Research and Development (R&D) group into data science and statistics/communication branches that communicate closely but have different functions. These teams work with the product team whose job is to think about the value they can extract from data insights and models to benefit customers. The R&D groups also provide support to the company’s clinical outreach team comprised of licensed clinical pharmacists and engagement specialists who work to change provider behavior and improve patient outcomes, which ultimately reduces costs on the healthcare system and protects insurers from spending on ineffective treatments.
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axialHealthcare synthesizes a variety of data to improve patient care and drive savings for payers.
Most of the company’s data comes from insurance claims, but some is also gleaned from patient behavioral and electronic health record data. Although the team is always focused on new data-derived models of improved care and cost savings, it is critical for the data science team to align their projects with what the market needs.
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“Insurers, our main clients, are very focused on short-term costs and so it’s often critical that our company frame the work in a way that indicates both short-term and long-term costs can be improved through data insights. Selling clients on the long-term cost savings can be difficult, especially if the short-term effects are increased costs to insurers,” according to Clark.

This point illustrates the challenge of providing solutions that are good for the business of healthcare and also for the health of patients.

Framing information in a way where payers can see the long-term savings generated from costly approaches in the short-term is critical to enacting meaningful, effective interventions.
Ultimately, the company hopes to collect its own data for two reasons: 1) access to data can be a challenge and 2) variables that may be of interest to the data science team aren’t always available in the data collected by a third party. Nevertheless, axialHealthcare’s current approach has proven effective for patients and payers.

Artificial Intelligence (AI) in Healthcare

At the 2018 Health:Further Festival held in Nashville, TN, I talked with two companies working to use artificial intelligence (AI) to improve healthcare.

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Change Healthcare AI group, San Francisco, CA

Change Healthcare is the largest independent healthcare information technology company in the U.S., handling approximately 60% of medical claims. Alex Ermolaev is part of a growing AI team there. The goal of Change Healthcare’s AI group is to improve efficiency and add value on top of existing data management and analytics solutions.

See Alex's video on AI in Healthcare below.
Change Healthcare uses large, aggregated and de-identified claims, medical history, and treatment plan data from their databases to provide insights on how to increase the effectiveness of healthcare, particularly how to provide treatment that is more efficacious and economical. While Change has mostly claims data, Ermolaev said most of it is very extensive, often up to 400 pages per claim, so that meaning and insights can be extracted from the various doctor notes and other details from healthcare providers. Text from claims can be read and analyzed using natural language processing approaches to identify relevant information in the record.
Ermolaev, formerly at Nvidia, mentioned that most AI models can achieve very high accuracy (>95%) as long as the following 3 factors are available: 1) large amounts of data, 2) bigger/more complex models, 3) more computing power.

“The main limit to using AI in healthcare is the lack of large enough data sets,” according to Ermolaev. This shortage of data is not unexpected given the sensitivity of personal health information and the vast privacy protections in place. Thus, companies with access to the data have a great advantage when competing in the AI healthcare space.
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As AI and predictive analytics grow in healthcare, Ermolaev believes we are moving from evidence-based healthcare, where treatment decisions are based on what’s been proven effective for the population in general, to intelligence-based care where a particular patient’s medical history informs more personalized treatment.
I was surprised to hear Ermolaev mention that genetic information (frequently promoted in academic circles as the key to precision medicine) is often not required to develop personalized insights. This highlights the fact that currently available medical history, behavioral, and symptom data is often adequate in creating dramatically more effective personalized treatment plans.

UPDATE:
Change Healthcare announced its Claims Life Cycle AI in February 2019, which seeks to reduce the number of denials for medical insurance claims processed by healthcare providers (see infographic).
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Droice Labs, Brooklyn, NY

While at Health:Further, I also met with representatives from Droice Labs on Entrepreneur Alley at the 2018 Health:Further Festival, a showcase area where over 70 startup companies could meet with conference attendees.

Droice Labs brings the power of artificial intelligence to hospitals. The company’s technology provides personalized predictions of how a given treatment (e.g., a drug or a medical device) will perform for a given patient. This software solution is based on a combination of the latest medical research and learning algorithms, which together analyze how a treatment has performed on similar patients in the past by aggregating data from millions of patient records and treatment plans. This allows doctors to consider all of their options in real-time and choose the right treatment.
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Droice labs uses a variety of patient datapoints to better understand individual differences in response to medical treatment.
Droice Labs has approximately 30 employees and works with providers, payers, pharma, and government clients to build applications that augment processes and improve workflows. The goal of this work is to improve the quality of care for patients while also decreasing the burden on physicians with the ultimate outcome of increasing the efficiency of the healthcare system.

The company has been around for just over 2.5 years. The founders of Droice have extensive backgrounds in technology and AI and take a “deep dive” approach to their projects, trying to understand the causation behind their insights and results. They then communicate these findings transparently to their clients.
The relatively small company has a very collaborative culture with employees from a variety of backgrounds—tech, healthcare (including clinicians), scientific research—that bring different but complementary perspectives to their work. “The company is structured to be very horizontal, an organizational setup that fosters the sharing of ideas among all individuals in the team,” according to Droice Labs Co-founder & CEO Mayur Saxena, Ph.D.

Speaking to M. Saxena and Co-founder & Chief Product Officer Harshit Saxena (no relation), it is clear Droice Labs has a growth-focused, startup-like culture with a hunger from employees to continue to innovate and do more. The company has a mission that appeals to young workers that want to work for a values-driven company. “One measure of success at Droice Labs is how many people we were able to impact by our work today,” says M. Saxena. “Do the insights we develop increase the well-being of an extra 1,000 people? How can we improve things to increase that impact?”
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Both of the founders agree with a point mentioned on the main kickoff stage at the Health:Further Festival: healthcare touches everyone. When asked what got them into using their analytic skills in the healthcare space instead of a traditional technology company, M. Saxena emphasized the common human experience of healthcare: “my thought was, if I am going to consume it, I might as well work to improve it.” He went on to say that healthcare is full of great people that work tirelessly to improve human life and that the industry needs technology to enable healthcare workers to do their jobs more easily and effectively.

At companies like Droice Labs, Change Healthcare, and axialHealthcare, the approaches may differ but the goal is the same: to improve healthcare in the 21st Century through data and insights.

Read More
Artificial intelligence in healthcare: Past, present, and future
Artificial intelligence in healthcare
Applied data science in patient-centric healthcare


Additional Resources

Want to get into data science?
The Insight Data Science Fellowship program offers a fabulous training opportunity with demonstrated success in job placement afterwards.

NC State's Institute for Advanced Analytics offers a well-respected Master of Science in Analytics degree with excellent career outcomes for graduates.
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NIH All of Us Research Program: A new standard in participant engagement and partnership

8/15/2019

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Personalized Medicine
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This article originally ran on the Health:Further Blog on July 2, 2018.
It has been updated with new content and findings. 


Precision medicine is a phrase often used in healthcare but not well understood, especially by the general public. What type of precision are we talking about? Precision medicine is an emerging approach to disease treatment and prevention that considers differences in people’s lifestyles, environments and biological makeup, including genes. When the Precision Medicine Initiative launched in 2015 it was promoted as a great leap forward in understanding the various factors that contribute to human health and disease in the United States (U.S.). Read the Precision Medicine Working Group Report here.

The project has since been branded the All of Us Research Program and that title could not be more fitting. The goal of the program is to recruit 1 million individuals who will volunteer to provide their biological and health information in the form of medical records, genetic samples, and lifestyle data that is both self-reported and obtained from wearables (FitBit-like devices).

All of Us Seeks to Represent & Engage a Diverse Population
The All of Us program participants are to reflect the diversity of individuals living in the U.S. and are being treated as partners in the study. This concept of partnership speaks to the idea of participant engagement, an area
Consuelo H. Wilkins, MD, MSCI, Executive Director of the Meharry-Vanderbilt Alliance and leader of the All of Us Research Program Engagement Core, knows quite a bit about.  Engagement differs from recruitment in that its goal is to involve participants in the design, implementation, and oversight of the research, not just to enroll them in a study. Engagement is also a 2-way line of communication where research plans are transformed based on the perspectives of all those involved in it: researchers and participants.
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Leveraging Community Input
Another aspect of how the community provides useful feedback on the
All of Us program is in the form of community engagement studios, modeled on a similar program at Vanderbilt University. These engagement groups were held across the country and allowed individuals from the community to voice concerns or share their thoughts on various practices the program was planning to implement to recruit and interact with potential study participants. More than 70 studios were held in preparation for the nationwide rollout of All of Us on May 6, 2018.

Community Engagement Continues
The Engagement Core works closely with community partners across the U.S. to help host the community engagement studios in a manner that makes community members feel welcome. For example, the community organizations are asked for guidance on everything from the time of day the studio should be held to the location and what type of food should be ordered. There was one particularly memorable instance in Chicago where the Engagement Core staff had to bring a large amount of cash to purchase food at a cash-only restaurant recommended by an Asian community group. While a seemingly small thing, acts such as this engender trust between
All of Us and its community partners because the partners can see that their opinions are being used to guide not just food but research and policy choices made by the national program. Changes coming out of these community engagement studio sessions have included making the language in participant materials easier to understand (use of less jargon and complex terms) as well as providing a small amount of monetary compensation to All of Us participants. Many potential participants saw the compensation, even if it was small, as evidence they and their time was being valued. Such changes emerging out of these studios can have a major impact on study participation (see, for example) and the Engagement Core expects it to be the case for the All of Us
program as well.
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Participant Engagement is Critical to the Success of All of Us
So far,
All of Us research participants have been “all in” to contribute to the program. Alecia Fair, DrPH, Research Assistant Professor with the Meharry-Vanderbilt Alliance, has been in the public health and health promotion/education field since 1992 and has never seen the degree of investment participants in the All of Us pilot program
have displayed since it began in 2016.

Individuals have strong motivations for volunteering as participants. Many have loved ones who are sick or died prematurely from disease and hope their contribution to the program as a participant, and also as a voice of participants, will make a difference in the health of others. Generating a high level of individual involvement, investment, and trust in participants is critical as they can volunteer as much or as little information as they want to the program. In order for deeper insights and knowledge to be gleaned from the program, participants will need to be willing to share information from their electronic medical record, genetics, wearable devices, and self-reported daily lifestyle choices.
The data collected in All of Us is not just for researchers. One important goal of the program is that all data collected from any participant will be provided back to the participant through useful insights. The Participant Technology Systems Center (PTSC) for All of Us is being administered by Vibrent Health, a  digital health technology company headquartered in Fairfax, Virginia.
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The PTSC is responsible for innovating, developing and monitoring all participant-facing apps and technology systems used by All of Us (available across a variety of platforms: PC, iOS, Android). These systems enable study enrollment and data collection and, on the back end, data storage, organization, analysis, and curation—the full lifecycle of the participant’s involvement with the study. Vibrent Health provides a large-scale, cost-effective, mission-critical system to the program that needs around-the-clock support, a service they can provide that would be difficult for an academic institution to match.
The All of Us system has been designed by Vibrent to provide an overview of the study to potential participants as well as offer an interactive, informed consent process that allows for the opt-in or out of a variety of data collection processes. Importantly, the information is provided in the form of videos and text to make participant engagement and comprehension of the various data types the program seeks to collect clear. Knowledge of participants’ understanding of how their data will be collected, secured, and used is assessed via comprehension quizzes they must pass before being allowed to sign the consent forms.

Vibrent Health CEO Praduman Jain (PJ), spoke to the importance of returning value back to
All of Us participants, a value that is above and beyond their own health data. Using the company’s Research Platform, Vibrent will develop useful insights from the large and diverse sets of data provided by the All of Us participants, ultimately enabling researchers and clinicians to more precisely predict, prevent, and treat a variety of health conditions.
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Integrating information on participants' environment, lifestyle, genomics, & behavior will be critical to understanding the complexities of human health.
Currently, as the research initiative gears up, Vibrent is in the data collection phase but hopes to soon amass the breadth and depth of data needed to develop meaningful insights through machine learning and other predictive analytics. The security and privacy of the participant information collected by Vibrent Health’s platform is of the utmost importance and various layers of encryption and de-identification of data are in place. In the end, Vibrent expects to develop novel and powerful predictive tools from this work that can be applied to the broader healthcare system in the U.S.

Only through the critical buy-in of participants will a program as ambitious as All of Us succeed. Now over 15 months since the launch of the program, All of Us has recruited nearly 25% of its targeted 1 million participants: 243,000+ at last count (as of August 13, 2019). And you can help them reach their goal (see link to participate)!

In the end, the All of Us program hopes to set a
precedent for how long-term, longitudinal, health and lifestyle research can take place in the 21st Century. No longer will participants in this type of research be passive subjects (often distrusting what is being done with their data) who provide (or don’t, or not truthfully) information and samples from which they never learn the insights obtained. Rather, participants will be partners in the research process, knowing their opinions and ideas matter and that their data is leading to new insights in which they are being informed.
In doing all this,
All of Us leadership expects participants will feel more engaged and empowered: willing to provide an unprecedented amount of health data with the knowledge that it will lead to discoveries that truly benefit All of Us.
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UPDATES:
As of August 13, 2019: 180,000+ individuals have begun enrollment in the program with an excellent ethnic distribution: ~21% African American, ~18% Hispanic/Latino, & 47% Caucasian
80,000+ electronic health records and 188,000+ biosamples are currently in the system

The All of Us Research Hub has an online Data Browser to view publicly-available data:

https://databrowser.researchallofus.org/


For a Deeper Dive:
About the All of Use Research Program

New England Journal of Medicine special report on the project (published 8/15/19)

Want to take part?
Enroll in All of Us here:
https://www.joinallofus.org/en
Have questions about the program? See FAQs here.
Want to engage your community in the program? See resources here.

Read more about Engagement and the All of Us Program Here: 
https://catalyst.nejm.org/precision-medicine-initiative-everyone/


Vibrent Health’s Role in All of Us: 
https://www.vibrenthealth.com/knowledge-center/2018/05/vibrent-health-power-technology-history-making-us-research-program/
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    A neuroscientist by training, I now work to improve the career readiness of graduate students and postdoctoral scholars.

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