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1. PERSONALIZING FOOD-AS-MEDICINE
RECOMMENDATIONS WITH GENOMIC DATA
How DNA testing can inform diet to prevent chronic disease and
improve outcomes.
By Drea Burbank, MD
An MD with a professional certificate in Preventative Medicine from Stanford University.
Dr. Drea Burbank specializes in the utilization of disruptive technologies to promote healthier happier lives.

2. In the 21st
century “patients” have become health consumers and they are
demanding increased personalization from their digital health products. While
millions of Americans have invested in independent DNA testing from direct-
to-consumer labs, this genomic data has little value outside of the context of
individual health status (i.e. everyday diet and body mass index). Simultaneously,
companies that manufacture digital health products for nutritional interventions
often struggle to provide proof-of-efficacy based on dietary self-report, weight
changes, or subtle long-term effects.
Enterprises, wellness apps, and healthcare teams often coach individuals in
reaching nutritional goals, and/or collect life-data to measure the effectiveness
of such interventions, but very few target those goals based on genomic data.
Without insights from genomic data (i.e. individual predisposition to disease,
metabolic variation, and treatable genetic conditions) nutritional interventions
have limited efficacy.
In this paper, we’ll discuss the emerging market for genomic personalization
of diet-coaching products. We’ll show how artificial intelligence (AI) systems
can provide a “GPS for health”, an individualized preventative lifestyle system
to navigate to to nutritional fitness.
Targeting Nutrition
As the egalitarian philosopher Will Durant said »We are what we repeatedly do.
Excellence, then, is not an act, but a habit.»
However there is an additional caveat to this good advice; we must repeatedly
do excellent things. As anyone who has ever been diagnosed with a gluten
intolerance while routinely eating whole-wheat bread can tell you, repeatedly
following standard nutritional guidelines isn’t always excellent.
Sometimes to eat healthy you need a little bit of personalized direction.

3. The Modern Dietary Marketplace
Food is necessity, culture, art, habit, and social activism. In developed nations
populations are aging, chronic-disease management has become more relevant
than episodic care, and we are living longer but not necessarily healthier. In
response, health sciences have focused on longevity through preventative
medicine, and this has sparked a food-as-medicine movement. Independent
of economics, societies are devoting increased attention towards how food is
procured, prepared, and consumed. Correspondingly, the biosciences are refining
the evidence-base for nutritional goals and healthy diet as the first line of defence
against chronic disease.
Modern adults view health as a reliable financial investment, they are willing to
engage in habit change, and they have become savvy shoppers. Almost everyone
is aware that a “healthy diet” is necessary for health. But, defining exactly what
a healthy diet contains on a day-by-day basis can be difficult for even the best-
educated, disciplined, and motivated practitioner, let alone the general public.
Mainstream health-care spending and
health-consumers have begun to align
with expert-opinion. The public is
aware that the human lifespan has been
significantly extended, the successes
of tobacco control have highlighted
that habit change can prevent disease,
regenerative medicine breakthroughs
are routinely in the news, and books
and movies celebrate longevity as a
reachable goal for the public. With this
awareness, consumers have begun
to focus on products that facilitate
behavioral change (i.e. diet, fitness,
and stress) to extend health over an
increasingly-expanded lifespan.
Experts and Diet
We know food affects health. But we are still learning how. While historically,
nutritionalguidancewasdeliveredinaone-size-fitsallformatsuchasfood-industry-
influenced food pyramids, modern evidence-based guidelines have reversed
this approach. Experts currently recommend that nutritional recommendations
are personalized based on personal variables: sex, age, metabolic biomarkers
circulating in blood, food quality, meal frequency, lifestyle factors (i.e. physical
activity), microbiome profile, and environmental variables (i.e. occupation).
For companies looking for a competitive edge, genomic data is the logical next
step in personalization, more so when combined with direct-to-consumer options
(i.e. health apps and wearables), and longevity research.

4. Futurists predict that the human lifespan will be 100 years by 2060 and this is
without taking into account disruptive new technologies. 1
Indeed, public interest in healthy aging is soaring. In 2014 the Pew Research
Foundation reported that globally, the US is one of the few countries where
a large portion of the public believes individuals are responsible for their own
aging. As a case in point, the popular science books by Dan Buettner featuring
Blue Zones (sub-regions where humans routinely live significantly longer than
their counterparts with a common set of dietary habits) hit the New York Times
bestseller list in 2015 and spawned an ongoing series.
Consumers and Longevity
A major concern with an aging population is sustainable healthcare because
chronic conditions are also on the rise. Conditions which are significantly modified
by diet, such as type 2 diabetes, accounted for an estimated 86% of the US
$2.7 trillion annual health care expenditure in 2010. These diseases are fiscally
unsustainable, and increased attention is being devoted to “reengineering” the
US health system towards prevention rather than episodic care. 2
Consequently,
behavioral-health coaching has become a focus of 21st
century health systems
and consumers alike.
Preventative medicine brings novel problems. Like the weather, behavioral
change has nonlinear predictive models 3
. Using diet to extend health has cause-
effect relationships that are more difficult to quantify than expensive episodic
treatments occuring at the end of life. 2
Diet is significantly influence by social
determinants of health, factors such as education to read food labels, urban-
planning to avoid “food deserts”, and economic disparity in food quality. These
social justice issues are generally considered to be outside the purview of clinical
care but their impact on diet cannot be ignored 4
.
Consumers and Diet

5. As anyone who has ever munched pizza in front of the television can tell you,
technological change contributes to poor diet. However, like any other tool,
technology can also be beneficial and experts believe that direct-to-consumer
options like health apps and wearables may be a preventative medicine solution,
because they offer inexpensive methods to deliver dietary advice where it is
most needed, outside the hospital and into the restaurant, grocery store, and
kitchen.
Health apps and wearables can also measure passive life-data which contribute
to diet (i.e. location, sleep, and activity levels) and/or health goals which might
change dietary needs (plans for a conception, training for a marathon, or dieting).
Not only are direct-to-consumer
options potentially more effective for
dietary coaching, they are also popular
with consumers. In 2015, there were
over 165,000 mobile health apps for
download from the U.S. Apple iTunes
store and Google Play, with greater
than 2
/3 focused on behavioral health
(i.e. diet, fitness, and stress) 5
.
A 2016 study reported consumer use
of wearables and mobile health apps
had nearly doubled in the past two
years, from 16% to 33% 6
.
Genomic Personalization of Dietary Advice
Health consumers want insights from genomic data, which means direct-to-
consumer diet-coaching products need to personalize to retain a competitive
edge.
Personalized health is a consumer health trend. In the 2018 Fast Company
ranking for “most-innovative” health companies 4 out of 10 involved genetic
testing.
Simultaneously DNA sequencing technology is becoming affordable. The first
genome mapping cost $3 billion USD in 2000, today it costs roughly $1,000, and
it’s projected to bottom in the next five years to take less than an hour and cost
less than an x-ray 7
.
The market for DNA testing is booming. An estimated 2 million Americans have
had their genomes sequenced in early direct-to-consumer DNA offerings for
ancestry testing. The US government recently followed suit with a 2015 initiative
to sequence 1 million Americans DNA for precision medicine research. In fact
many health-care providers are already sequencing DNA routinely although
admittedly in specialized use-cases like chemotherapy drugs and in diagnosis of
rare genetic conditions.

6. More contemprorary wellness offerings add value to sequencing technology by
interpreting genomic data in context and offering targeted guidance for diet.
For instance, BIOHM is a recent startup that will sequence the genome of all the
bacteria living in your gut, report ratios of harmful-beneficial bacteria, then offer
personalized nutrition counseling and a probiotic to help you correct imbalances.
Although DNA sequencing at this point is still a largely static measure, what is
really evolving is the meaning of that sequence and this is where health apps
would be wise to invest.
Next Steps in Genomic Data for Diet
While consumer-demand for diet-coaching is rising, these products are still
struggling to find relevance. In 2013, 12% of health apps accounted for 90% of
consumer downloads, and 40% have less than 5k downloads 5
.
We’ve established that there is market for connecting genomic data to nutritional
advice, and that the cost of DNA testing is bottoming. The question then becomes
is there added value in connecting genomic data to nutritional advice?
Critiques of Genomic Data and Diet-Coaching Products
In the past physicians have been critical of DNA testing, as it’s global clinical value
has been debated. It’s true that our DNA sequence only tells us our potential.
A DNA test is a static record, but DNA itself and the human body is a complex
system, dynamic and constantly responding to stress, lifestyle factors, and age.
When researchers factor in proteomics, incomplete reference datasets, or as-yet-
unexplored genetic variation the links between DNA sequences and pathology
become more complicated and less predictable 7
.
Its a basic principle of DNA that our environment, everyday health
habits and our aspirations are essential contexts for evaluating our
genetic potential, and this is exactly where health apps are best
positioned to add value.

7. In 2014 the FDA announced its intention to regulate digital products that “may
be intended for use in the diagnosis of disease or other conditions, or in the cure,
mitigation, treatment, or prevention of disease” However the 21st
Century Cures
Act (Cures Act) excluded products intended “for maintaining or encouraging
a healthy lifestyle” while continuing to regulate products for the “mitigation,
prevention, or treatment of a disease or condition”.
• Type 2 diabetes needs different dietary
advice 14
.
• Celiacs disease is an allergy to gluten
found in “healthy” grains like wheat,
barley, and some oats which are
commonly recommended in standard
diets15
.
• Hemochromatosis is an inability to rid
the body of excess iron which can cause
chronic disease if untreated exacerbated
by iron-containing dietary supplements
16
.
• Seasonal mood disorders can cause
circadian-rhythm related depression that
may respond better to additional dietary
vitamin D17
.
Best Value for Genomic Data and Diet-Coaching Products
The human genome was first sequenced in 2000 and we are only beginning to
interpret genetic code. The data on DNA and longevity is conflicting, and the
debate continues.8,9
But although genetics might not affect living longer, genetics
do affect living more comfortably. DNA testing is important in healthy-living
interventions because the evidence shows our static genetic code influences
the four highest-value wellness targets which both prevent chronic disease and
interrelate to diet: exercise, diet, smoking, and alcohol consumption 10
:
• Genetics influence predisposition to exercise participation 9
• Genetics shape the gut microbiome and affect diet and metabolics 11
• Genetics affect susceptibility to nicotine 12
• Genetics affect alcohol overuse 13
Genetic tests can also screen for conditions likely to impede standard nutritional
recommendations or at least render them ineffective, for instance:
Diet-coaching products would be well served to connect to genomic data for
two reasons, static genomic data (DNA testing) can provide personalized targets
for diet-coaching products, and dynamic genomic data (ie. proteomics, or
epigenetic processing) can provide proof of efficacy for diet-coaching products
targeting long-range or subtle conditions (i.e. cognitive decline or metabolic
syndrome) .
Static genomic data to target dietary advice
This has left many nutritional app developers confused as to the
level of proof they must demonstrate to market their software and
scrambling to validate prevention claims that are difficult to prove
even in well-recognized interventions like plant-based diets.

8. While DNA testing has become relatively standardized, a critique is that it is
static while diet is constantly changing. In contrast, there are newer genomic
measurements that measure dynamic gene function such as epigenetics or
proteomics.
Dynamic genomic data to measure efficacy of dietary interventions
A 2013 study showed a 6-month exercise intervention had genome-
wide effects on dynamic gene-data (i.e. DNA methylation), with
significant variation in people with static genetic markers for
metabolic abnormalities (i.e. diabetes and obesity) 19
.
Proteomics is a field studying the dynamic content of proteins in a cell, which
is an indirect measure of gene expression. This is another dynamic measure
that might be an excellent biomarker for diet-coaching products seeking to
demonstrate the impact of complex interventions like diet 20 which is notoriously
unreliable on self-report 21
.
A 2013 study showed that when diabetic pigs were fed either a
healthy diet (high unsaturated fat Mediterranean diet) or a unhealthy
diet (saturated fat/cholesterol/sugar diet) they developed
significantly different proteomes. However healthy pigs showed
little difference in their proteins between the two diets 22
.
While dynamic measure of genomic data are still evolving, if they follow the
same trends as DNA testing, they are likely to become affordable enough to
reach consumers in the near future and their utility should not be ignored for
nutritional interventions.
A “GPS for Health”
Given that genomic data can expand the value of diet-coaching products
competing for relevance, how can it best be integrated?
Epigenetics is a field studying how DNA is expressed. For instance next-
generation sequencing (NGS) can measure DNA methylation or inactivated gene
expression in response to environmental cues. This may be a more sensitive and
specific measure of the health benefits of diet-coaching products than clinical
measures (i.e. bloodwork or disease development) 18
.

9. The majority of diet-coaching products
do not have the resources to stay
abreast of the ever-expanding scientific
literature linking genomic data to diet,
and from there, to long-term health-
status. Currently, the entire body of
medical literature is estimated to double
every 3-4 years. Even if we were to
ignore the newer fields of proteomics
and epigenetics, our DNA sequence
is still not fully interpreted. Of the
more than 100 million genetic variants
currently identified, only 0.3% have been
interpreted, of which still have uncertain
significance 7
.
• Collecting and collating life-data from consumer products (ie smartphone
apps and wearables).
• Interpreting static and dynamic genomic-data in the context of this life-
data.
• Constantly updating this interpretation from the evolving scientific
literature on longevity, nutrition, and behavior change.
• Delivering personalized wellness coaching back to consumer products in
actionable directives.
Fortunately AI technology has co-evolved with genomic research. AI is best
positioned for the continuous analytics required to coordinate these four distinct,
constantly evolving, and interrelated information streams.
We propose a “GPS for health” where AI systems stay abreast of genomic
research findings then connect static or dynamic genomic-data from health
consumers to dynamic and individualized life-data from consumer wellness apps
and/or wearables. 23
.

10. For wellness companies specializing in nutrition, personalized products mean a
competitive edge.
• Consumers are interested in living longer, they want to make sure these
years are healthy, and they are willing to invest in dietary coaching products
that work.
• Genomic data is inexpensive, personalized, and capable of targeting
nutritional interventions to make them more effective.
• Artificial intelligence systems can connect genomic data to life-data from
health apps and wearables, then update with expanding research to create a
“GPS-for-health” an individualized, real-time, constantly updated map to help
consumers navigate to the best diet for them.
The Bottom Line
The end result is a constantly updated and timely nutritional recommendation
delivered back to consumers applications directing them towards the “true
north” of a healthier and happier 21st
century life.
The future of healthcare is a transition from expert-driven directives – with
poor predictive power and large gaps in the fundamental understanding of
human biology – into a data-driven predictive science where individuals take
responsibility for, and directly manage their health.

11. 1. Olshansky, S. J. et al. A potential decline in life expectancy in the United States in the 21st
century. N. Engl. J. Med.
352, 1138–1145 (2005).
2. Fani Marvasti, F. & Stafford, R. S. From sick care to health care--reengineering prevention into the U.S. system. N.
Engl. J. Med. 367, 889–891 (2012).
3. Resnicow, K. & Page, S. E. Embracing chaos and complexity: a quantum change for public health. Am. J. Public
Health 98, 1382–1389 (2008).
4. Marmot, M. Social determinants of health inequalities. Lancet 365, 1099–1104 (2005).
5. Aitken, M. & Gauntlett, C. Patient apps for improved healthcare: from novelty to mainstream. Parsippany, NJ: IMS
Institute for Healthcare Informatics (2013).
6. Accenture Federal Services. Conceptualizing a Data Infrastructure for the Capture, Use, and Sharing of Patient-
Generated Health Data in Care Delivery and Research through 2024. HealthIT.gov (2016). at <https://www.healthit.
gov/sites/default/files/Draft_White_Paper_PGHD_Policy_Framework.pdf>
7. Abraham, C. Cracks in the code: Why mapping your DNA may be less reliable than you think. The Globe and Mail
(2018). at <https://www.theglobeandmail.com/technology/science/genetic-testing/article37829424/>
8. vB Hjelmborg, J. et al. Genetic influence on human lifespan and longevity. Hum. Genet. 119, 312–321 (2006).
9. Karvinen, S. et al. Physical activity in adulthood: genes and mortality. Sci. Rep. 5, 18259 (2015).
10. Prevention Centers for Disease Control and. Chronic diseases: The leading causes of death and disability in the
United States. Chronic Disease Overview (2015).
11. Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).
12. Davies, G. E. & Soundy, T. J. The genetics of smoking and nicotine addiction. S. D. Med. Spec No, 43–49 (2009).
13. Mayfield, R. D., Harris, R. A. & Schuckit, M. A. Genetic factors influencing alcohol dependence. Br. J. Pharmacol. 154,
275–287 (2008).
14. Lyssenko, V. & Laakso, M. Genetic screening for the risk of type 2 diabetes: worthless or valuable? Diabetes Care
36 Suppl 2, S120-6 (2013).
15. Cecilio, L. A. & Bonatto, M. W. The prevalence of HLA DQ2 and DQ8 in patients with celiac disease, in family and in
general population. Arq. Bras. Cir. Dig. 28, 183–185 (2015).
16. Pietrangelo, A. Hereditary hemochromatosis: pathogenesis, diagnosis, and treatment. Gastroenterology 139,
393–408, 408.e1 (2010).
17. Zhang, L. et al. A PERIOD3 variant causes a circadian phenotype and is associated with a seasonal mood trait. Proc
Natl Acad Sci USA 113, E1536-44 (2016).
18. Meaburn, E. & Schulz, R. Next generation sequencing in epigenetics: insights and challenges. Semin. Cell Dev. Biol.
23, 192–199 (2012).
19. Rönn, T. et al. A six months exercise intervention influences the genome-wide DNA methylation pattern in human
adipose tissue. PLoS Genet. 9, e1003572 (2013).
20. Wang, J., Li, D., Dangott, L. J. & Wu, G. Proteomics and its role in nutrition research. The Journal of nutrition 136,
1759–1762 (2006).
21. Natarajan, L. et al. Measurement error of dietary self-report in intervention trials. Am. J. Epidemiol. 172, 819–827
(2010).
22. te Pas, M. F. W., Koopmans, S.-J., Kruijt, L., Calus, M. P. L. & Smits, M. A. Plasma proteome profiles associated with
diet-induced metabolic syndrome and the early onset of metabolic syndrome in a pig model. PLoS ONE 8, e73087
(2013).
23. Haghi, M., Thurow, K. & Stoll, R. Wearable devices in medical internet of things: scientific research and
commercially available devices. Healthc. Inform. Res. 23, 4–15 (2017).
Bibliography
GENOMIC INTELLIGENCE
Suisse Life Science’s Genomic Intelligence technology uncovers specific health & wellness learnings from DNA and
connects them to living data from consumer devices -after intelligent, personalized analysis of the cause-and-effect
relationships from the most validated scientific literature available to make DNA actionable for lasting, behavioral
population health intervention and personalized insights.
Our goal is to transform health from an expert-driven field – with poor predictive power and large gaps in its fundamental
understanding of human biology – into a data-driven predictive science that makes it easy for individuals to take a more
active role in managing their health.
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