View PDF - The Journal of Precision Medicine

The Complementary
Iceberg Tips of Diabetes
and Precision Medicine.
by
D
Jack Yensen Ph.D & Stephen Naylor Ph.D
iabetes is perceived as a “simple disease” and manifested in the form of inadequate blood
glucose regulation. Paradoxically, it is now also recognized as a global, pandemic disease,
with ~415 million patients reported to be suffering from diabetes. The disease is projected
Introduction
1
There appears to be a chronic lack of confidence in global healthcare systems . Stakeholder expectations have been fueled by repetitive media
reports of spectacular advances in the diagnosis and treatment of the major diseases that afflict patients. However, those same patients
perceive a lack of delivered value from their healthcare provider. In part this is predicated on the transition of patients into consumers, and
their accompanying expectations, particularly in the developed world. Many of these patient complaints involve diagnostic and prognostic
inaccuracies, poor treatment efficacies for a specific disease indication, and lack of timely access to patient care. The general dissatisfaction is
apparent regardless of the specific healthcare delivery system. The patient could be participating in a single payer, socialized medicine system
2
like the National Health Service of the UK, or a market-driven, predominantly privatized model, such as the patchwork system in the USA .
How did we get to such a critical, and, some might argue, dysfunctional point in the practice of medicine?
21
COGNOSCIENTI
to impact ~642 million people worldwide by 2040. This appears to be the tip of a diabetic
iceberg, since one-in-two adults are actually undiagnosed diabetics, an additional ~318 million people
have impaired glucose tolerance, and are usually described as “pre-diabetic,” and 20-25 % of adults have
metabolic syndrome. The limitations of modern healthcare have been ascribed as causative agents in the
diabetes crisis. The current healthcare system tends to provide a reactive response to patient symptoms,
with a subsequent diagnosis and corresponding treatment of the specific disease. More recently a rapid
improvement in OMIC analyses, bioinformatics and knowledge management tools, as well as the
emergence of big data analytics, and systems biology have led to a better understanding of the profound,
dynamic complexity and variability of individuals and human populations as they undertake their daily
activities. These developments in conjunction with escalating healthcare costs and relatively poor disease
treatment efficacies have fermented a rethink in how we execute current medical practice. This has led
to the emergence of “P-Medicine” which includes personalized and precision medicine. P-medicine is still
in a fledgling and evolutionary phase and there has been considerable debate over its current status and
future trajectory, as well as its ability to affect the runaway crises of pandemic diabetes. Some have argued
that as personalized medicine has morphed into precision medicine (PM) we are just realizing the tip of
the PM iceberg. In this paper we evaluate such claims and address the impact of PM on the diagnosis,
treatment and prognosis of diabetes and the complementarity of the diabetic iceberg tip of despair and
the PM iceberg tip of potential and hope.
The current modus operandi of modern medicine
is based on the determination of an individual’s
symptoms, along with an associated diagnosis
and subsequent response to a specific treatment.
These data for the individual are compared to a
statistically similar and disease-relevant patient
population dataset. There is also a focus on a
specific disease indication as it pertains to compartmentalized tissue and/or organs involving,
in many cases, a highly specialized clinician. The
current healthcare system tends to be reactive,
providing treatment post-onset of the disease,
with limited efforts focused on prediction and
prevention. This reliance on the comparative
In a surprising development, a report published
by the National Research Council (USA- NRC)
in 2011 suggested that much of personalized
medicine had been predicated on single,
7
anecdotal stories involving lone individuals .
The report suggested that such a premise
makes for a weak foundation on which to
make a diagnosis, treatment and prognosis
recommendation to a patient by a clinician.
Another common complaint noted was that
the term “personalized medicine” implies the
prospect of creating a unique treatment for each
personalized/precision medicine, the estimated
adult population with diabetes has risen to a
staggering ~415 million people in 2015, which
represents 8.8% of the global adult population.
This appears to be the tip of an iceberg, since
one-in-two adults actually have undiagnosed
diabetes, and an additional ~318 million people
have impaired glucose tolerance, and are
frequently described as “pre-diabetic” and
individuals with metabolic syndrome have a
five-fold enhanced probability of becoming
9
diabetic .
Based on the statistics the initial tentative
8
analysis of an individual compared to a defined
population tends to neglect and disregard human
individuality, complexity and variability. It
also fails to recognize the systems level inter-
22
6
can develop targeted treatment and prevention plans” .
individual patient . Whilst the actual practitioners conclusion is that to date, the impact of
of personalized medicine have not suggested any personalized and precision medicine on diabetes
such thing, the premise took hold and fueled
has been minimal. Diabetic onset rates have
the disappointment and disillusionment with
increased significantly. In this work we consider
2
it and the ascendancy of PM . The NRC report
the mismatched expectations of personalized/
attempted to define and differentiate PM from
precision medicine advocates versus that of the
personalized medicine. The report stated that
global adult population as the latter struggles
“Precision medicine is the tailoring of medical treatment
with rampant diabetes. We discuss the concept
The current medical system has facilitated the
to the individual characteristics of each patient. It does
of diabetes as a “simple disease,” and the future
escalation of healthcare costs, but has had
not literally mean the creation of drugs or medical devices
role of PM in the diagnosis and treatment of
limited impact on the prediction, prevention,
that are unique to a patient, but rather the ability to
diabetes. Finally, we consider the complementary
accurate diagnosis, and effective treatment of
classify individuals into subpopulations that differ in their
icebergs of diabetes and PM. We regard the
acute and chronic disease. This lack of progress
susceptibility to a particular disease, in the biology and/or former as the tip of despair and the latter the
in concert with a growing awareness of the
prognosis of those diseases they may develop, or in their
connectedness of human molecular biology,
biochemistry, metabolism and physiology in the
3, 4
form of systems, network and pathway biology .
complexity and variability of individual patients
as well as our limited understanding of causal
mechanisms of most diseases has necessitated
a growing need for change. The clamor for
innovation led to the emergent growth of
2
“P-Medicine” . The P-Medicine initiatives that
have been implemented over the past decade
include Personalized, Precision, Preventive,
Predictive, Pharmacotherapeutic, Portable and
Patient Participatory medicine. Historically,
the first roots of the P-Medicine revolution
5
took hold in the early 2000’s . The Personalized
Medicine Coalition was founded in 2004. This
organization represented the interests of the
then fledgling personalized medicine community,
and they defined personalized medicine as “...
an evolving field in which physicians use diagnostic tests
to determine which medical treatments will work best for
each patient. By combining the data from those tests with
an individual’s medical history, circumstances and values,
health care providers
tip of hope and potential.
7
response to a specific treatment” .
Diabetes - the Simple Disease?
P-medicine in the form of personalized and
Diabetes is ostensibly a simple and well
precision medicine is ~twelve years old, and
characterized disease state
continues to be accompanied by enormous
diabetes manifests elevated blood glucose levels.
hope, hype, enthusiasm and expectation.
This is primarily caused either by the individual’s
Therefore, it is useful to evaluate the impact of
body not producing enough insulin or because
personalized/precision medicine on a specific
at the cell/tissue/organ level insulin transport of
disease indication such as diabetes. Diabetes
glucose is compromised. The resulting chronic
is often described and perceived as a “simple
hyperglycemia can result in systemic damage
9
9-13
. An individual with
disease” of mis-regulated blood glucose . One
to the body leading to possible disability and
might assume that armed with the powerful
life-threatening complications. In 2015 it was
new personalized/precision medicine toolkit
estimated that ~5 million people worldwide
of OMIC analytics, and -TIC bioinformatics/
died because of diabetes and the global cost
knowledge management/big data analytics, such
was “between $673 billion and $1.197 trillion
a “simple disease” should be both preventable
in healthcare spending.” It is also important to
and curable! In 2003, the global estimate for
consider that for such a “simple disease,” most
adults (20-79 years of age) suffering from
countries spend 5-20% of their total annual
diabetes was ~194 million, constituting 5.1%
healthcare expenditures on the diagnosis,
of the worldwide adult population . However,
treatment and care of diabetic patients .
10
in the intervening twelve year life-span of
9
Diabetic Types
Diabetes manifests ultimately as elevated blood
glucose levels with concomitant excessive thirst,
frequent urination and blurred vision. However,
causal onset can be due to a number of factors
and this has resulted in the definition of three
main types of diabetes as summarized in Figure 1.
Type 1 diabetes (T1D) - afflicts 4.73% of the
population and is caused by a poorly under9
stood auto-immune response of the body .
This results in damage to the beta cells of the
pancreas, which are responsible for the body’s
insulin production. The disease is diagnosed in
individuals of all age groups, but predominantly
affects children or young adults, and was previously known as “juvenile diabetes.”
Figure 1: Global prevalence of Type 1 diabetes,
Type 2 diabetes (T2D) - this is the most common
Type 2 diabetes and Gestational diabetes. Data
type of diabetes occurring in 93.5% of all
derived from IDF Diabetes Atlas9. On the right hand side
23
is the estimated number of subtypes for Type 1 and Type 2
9
estimated cases of adults . Causal onset is
diabetes. Gestational diabetes data delineates the
percentage of women post partum that develops Type1
risk factors include physical inactivity, poor
versus Type 2 diabetes or resolves the hyperglycemia.
nutrition and excessive body weight in the
form of adipose tissue. This type of diabetes
often goes unrecognized and it is estimated that
9
one-in-two people are actually undiagnosed .
According to the World Health Organization
(WHO), diabetes should be diagnosed if fasting
plasma glucose (fpg) is >126mg/dL, or two-hour
plasma glucose (2-hpg) is >
> 200mg/dL following
14
a 75gram bolus of glucose .
Pre-diabetic conditions – a normal, healthy
Metabolic syndrome – is the current term used for
individual has an fpg level within a 70-100 mg/
a group of risk factors that considerably raise
dL range. However, individuals with elevated
the risk for developing T2D, as well as heart
fpg levels of 100-126 mg/dL plus a 2-hpg of
disease and stroke. In order for a patient to be
<140-200 mg/dL are diagnosed with Impaired
diagnosed with metabolic syndrome he/she
Glucose Tolerance (IGT). Similarly, patients
must have at least three of the following five
with an fpg of 110-125 mg/dL and 2-hpg of
risk factors .
<140 mg/dL are classified as Impaired Fasting
14
Glucose (IFG) individuals . The fundamental
18
l
Abdominal obesity (35 inch waist for
women and 40 inch waist for men)
Gestational diabetes (GD) – an elevated blood
difference is that IGT is defined as a high
glucose level detected in a pregnant mother
blood glucose level after eating; whereas IFG
is classified as either i) gestational diabetes
is defined as high blood glucose after a period
mellitus where fpg levels are 92-125mg/dL; or ii)
of fasting. People with IGT face a significant
diabetes mellitus in pregnancy, where fpg levels
probability of developing diabetes. Approxi-
are >126mg/dL. The number of expectant
mately 5-10% of such individuals become
mothers who are diagnosed with gestational
diabetic on an annual basis and overall, up to
diabetes is relatively small representing only
70% will eventually develop diabetes .
1.52% of the diabetic population. Gestational
However, this trajectory is not inevitable,
It is estimated that 20-25% of the global adult
diabetes normally disappears after birth (60%),
and there are numerous reports that lifestyle
population has metabolic syndrome and 85%
but some women can develop T2D (36%), or
change in the form of healthy diet and physical
of Type 2 diabetics also have metabolic
less commonly T1D (4%) as highlighted in Figure
exercise and/or medication can prevent the
syndrome. Conversely, individuals with
1. It is interesting to note that babies born to
progression to diabetes .
16
17
l
Elevated blood triglyceride levels
(>>150 mg/dL)
l
Low HDL cholesterol (<<40 mg/dL - women and <
< 50 mg/dL - men)
l
High blood pressure (>>130/85 mm of
mercury)
l
High fasting blood sugar (>>100mg/dL)
metabolic syndrome have a fivefold enhanced
18
mothers, who develop post-partum T2D, also
probability of developing T2D . This is yet
have a higher probability of developing T2D
another significant component of the diabetic
15
themselves .
iceberg scenario!
COGNOSCIENTI
predominantly due to lifestyle choices, and
9
Diabetes Pandemic
last year . The WHO added that, high blood
2003, 2015 and projections for 2040 taken from
In 1994 the International Diabetes Federation
glucose is the third highest global risk factor
the 2nd and 7th IDF Diabetes Atlas reports
(IDF) estimated that the “global burden” of
for premature mortality, after high blood
The IDF noted that in 2015 ~415 million people
individual adults with diabetes” was ~100
pressure and tobacco use .
million adults, constituting ~2.9% of the world
The escalating pandemic of diabetes appears
19
adult population . The diabetic population
had increased to ~194 million (5.1%) adults by
10
2003 , and up again to ~415 million (8.8%)
9
individuals by 2015 . Over that same ~20 year
period conventional medicine had also been
confronted with the advent of personalized/
5
precision medicine (2001-2003) . The latter
developed under a cloud of scrutiny and the
withering refrain “personalized medicine technological innovation and patient
5
empowerment or exuberant hyperbole”? To
date based on the data showing an approximate
doubling of diabetic adults every 10 years both
24
20
conventional medicine and personalized/
precision medicine have failed to limit the
impact of this “simple disease.” Indeed, the IDF
to continue unabated. The IDF has noted and
tabulated this phenomenon by continuously
gathering data from 220 countries/territories
around the globe. In addition, they have
described the global presence of diabetes by
grouping the world into seven IDF regions:
Africa (AFR), Europe (EUR), Middle East and
North Africa (MENA), North America and
Caribbean (NAC), South and Central America
(SACA), South-East Asia (SEA) and Western
9,10
Pacific/China (WPC)
. We have utilized
the data published by the IDF in their annual
reports and attempted to capture the enormity
of the global diabetes pandemic and this is
highlighted in Figure 2.
described diabetes as “one of the largest global
This is a composite figure that shows regional
health emergencies of the 21st century” in their
estimates for diabetes for the specific years
report published
9,10
.
worldwide or 8.8% of adults aged 20-79, were
estimated to have diabetes, and that ~75% live
in low- and middle-income countries. They
went on to state that if these trends continue,
by 2040 one adult in ten, will have diabetes.
The largest increases will take place in the
regions where economies are moving from
9
low-income to middle-income levels .
Worryingly enough, this is already reflected
in the current statistics for 2015, where 7 of
the top 10 countries/territories for adults with
diabetes were developing economies. They are
in descending order China (109 million, ranked
1st); India (69.2 million, 2nd); Brazil (14.3
million, 4th); Mexico (11.5 million, 6th);
Indonesia (10.0 million, 7th); Egypt (7.8
9
million, 8th); and Bangladesh (7.1 million, 10th) .
The data showing the global incidence of
diabetes and the projections for 2040 in Figure 2
are ominous. However, we have noted above
Figure 2: Global estimates of the number of adults (20-79 years old) with Type 1, Type 2 and Gestational diabetes. The estimates are delineated by regions as defined by the IDF and are
Africa (AFR), Europe (EUR), Middle East and North Africa (MENA), North America and Caribbean (NAC), South and Central America (SACA), South-East Asia (SEA) and Western Pacific/China
(WPC). Note for each region the top number is for the year 2003, the middle number is for 2015 and the bottom number is a projected estimate for 2040, as shown in the black world
bubble at the bottom of the figure. All data were derived from either the 2nd or 7th IDF Diabetes Atlas editions,9,10.This composite figure was Adapted with Permission from the International
Diabetes Federation in Brussels, Belgium.
that this is the tip of the diabetic iceberg.
cardiovascular disease, but also, in many cases
21
afflicted, representing 13.9% of the adult
It was estimated that in 2015 as many as
remains undetected . In addition, people with
population. Another disturbing facet of the
one-in-two adults were unaware of their
IGT have a 70% probability of developing
data was that approximately half of adults with
16
diabetic condition and this constitutes ~193
diabetes , and the condition is still popularly
IGT were under the age of 50 (~159 million),
million potential patients. The vast majority
referred to as “pre-diabetes.” In 2003, it was
reversing the temporal trend observed for T2D,
of these cases are Type 2 diabetics, and globally
estimated that ~314 million people worldwide,
where onset is more likely to occur after 50
~80% of them live in low-to middle income
9
developing economies . Whilst a person
(8.2%) of adults in the age group 20-79 years
years of age. In addition, the IDF noted that
of age, had IGT . Ominously the developing
“it is important to note that nearly one-third
with T2D can live quiescently for a protracted
economies of the SEA region had the highest
(29.8%) of all those who currently have IGT
period of time, the elevated blood glucose is
number of people with IGT at ~93 million
[2015] are in the 20 to 39 age group and are
silently causing extensive and systemic damage
people as well as the highest prevalence rate
therefore likely to spend many years at high
to the body. The consequences of this non-
of 13.2%. The WPC region that includes China
risk” . By 2040, the number of people with
diagnosis add considerably to future individual
was the next highest with an estimated ~78
IGT is projected to increase to ~482 million,
human suffering as well as contribute to
million IGT patients representing 5.7% of the
or 7.8% of the adult population . Much of this
escalating healthcare costs.
population . By 2015, some ~318 million people data is summarized in Figure 3 and represents
A different and equally problematic situation
is the staggering number of adults estimated to
10
9
10
worldwide, representing 6.7% of adults, were
a global pictogram of the estimated IGT adult
estimated to have IGT.
populations dating from the period 2003-2040.
have IGT. Not surprisingly, individuals that have The socioeconomic trend has continued and
IGT share many of the same characteristics as
9
the vast majority (69.2%) of those people lived
Type 2 diabetics. IGT afflicts adults of advancing in low- and middle-income countries. However,
in an interesting reversal of trends the NAC
regulatory problems. The disease has been
region was observed to have the highest
identified as a significant risk-factor for
prevalence of IGT with ~51.8 million adults
incidence of IGT, and manifestation in younger
adults combined with the growing envelopment
of metabolic syndrome represents the hidden
underbody of the diabetic iceberg, and must be
a cause of concern for all of us.
Figure 3: Global estimates of adults (20-79 years old) with Impaired Glucose Tolerance. The estimates represent the same seven regions described in Figure 2, and the three different estimates per region represent 2003, 2015 and 2040. All data were derived from either the 2nd or 7th IDF Diabetes Atlas editions,9,10.This composite figure was Adapted with Permission from
the International Diabetes Federation in Brussels, Belgium.
25
COGNOSCIENTI
age, who are overweight and possess insulin
The combination of undiagnosed diabetes, the
Cerebrovascular Disease
(Brain & Cerebral Circulation)
Retinopathy (Eyes)
Gingivitis (Oral Health)
Coronary Heart Disease
(Heart & Coronary Circulation)
38
30
Nephropathy
(Kidney)
Pregnancy
Complications
Peripheral Neuropathy
(Nervous System)
Peripheral Vascular Disease
(Lower Limbs)
26
Peripheral Vascular/Neuropathy
(Diabetic Foot)
Figure 4: Secondary complications resulting from chronic
elevated blood glucose levels and untreated diabetes.
Diabetic Complications
Neuropathy – this can lead to systemic malfunction
tissue. This complex series of interactions is
The regulation of blood glucose levels is
throughout the body leading to problems with
summarized in Figure 5. In the case of diabetes,
rigorously controlled in a healthy individual.
urination, digestion, and erectile dysfunction
we have noted above it is perceived as a “simple
However, when the process goes awry, the
in men. In particular, peripheral neuropathy
disease” involving dysregulation of blood
immediate consequence of elevated blood
of the feet leads to infection, ulceration, and
glucose levels. In reality it is more appropriate
glucose appears to be somewhat inconsequential.
“diabetic foot” which can result in extreme
to consider the disease as a heterogeneous
Unfortunately, if this condition is left untreated
cases to amputation.
amalgam of syndromes, where causal onset and
it can lead to life-threatening secondary diseases
affecting eyes, heart, kidneys, nerves and the
11-13
peripheral circulatory system
. Long term
sufferers of diabetes can be afflicted with:
Gingivitis – leads to a variety of oral health
inflammation issues and increased risk of tooth
loss and cardiovascular problems.
progression are still poorly understood. The
disease is ultimately characterized by elevated
blood glucose caused by a relative or absolute
11-13
deficiency of the hormone insulin
.
Sleep Apnea – it is estimated that ~40% of
individuals with sleep apnea also have diabetes.
The causal onset of diabetes is different for
resulting in blurred vision and ultimately
Such a simple elevation of blood glucose leads
ultimately an absolute deficiency of insulin.
blindness.
to systemic, serious and often times life-threat- This is caused by a poorly understood autoening complications for patients with diabetes. immune response resulting in damage to the
Retinopathy – the network of blood vessels
to the retina of your eye can become blocked
Cardiovascular Disease – is the most common
cause of death for individuals suffering from
T1D versus T2D. In the case of T1D there is
This is all captured and summarized in Figure 4. beta cells of the pancreas. Over a period of
years, depletion of the beta cell population
angina, myocardial infarction and peripheral
Diabetes – Carbohydrates and Lipids
Intertwined
occurs and when this reaches ~80-90% then
artery disease.
The regulation of blood glucose in a healthy
individual. At this point the pancreas fails to
diabetes. This includes congestive heart failure,
Nephropathy – caused by damage to the
blood vessels in the kidney leading to
impairment and loss of function.
individual involves the complex interplay of the
hormones insulin and glucagon, as well as
glucose. The primary tissue/organs involved are
the pancreas, liver, skeletal muscle and adipose
symptoms of diabetes begin to manifest in the
respond to the ingestion of glucose, negligible
amounts of insulin are produced, and this leads
to elevation of blood glucose. If the patient
does not receive exogenous insulin, death can
presence of a functional pancreas compensates
11-13
occur due to ketoacidosis
.
by producing increased levels of insulin.
Type 2 diabetes actually develops in insulin
Type 2 diabetes is the most common form of
resistant patients when beta cell function
the disease (see Figure 1) and is associated with
becomes impaired.
imperceptible onset and poor diagnostic rates.
Manifestation of T2D can occur through insulin
The cause of insulin resistance is also controver-
resistance and/or dysfunctional beta cells
sial. However, compelling arguments have been
11-13
of the pancreas
. In the case of insulin
advanced suggesting that fat accumulation is
resistance, target tissues such as liver, adipose
important to facilitate insulin resistance onset.
and muscle inefficiently respond towards
This adds a layer of additional complexity since
circulating insulin. This is followed by
adipose tissue should not simply be regarded as
uncontrolled glucose production in the liver
an energy store, but also is active in secreting a
and decreased uptake of glucose by muscle
variety of hormones such as leptin, resistin and
and adipose tissue (see Figure 5). A primary
adiponectin . Finally, it should be noted that
22
22
cause of insulin resistance is obesity ,
T2D beta cell dysfunction is also different than
although some authors postulate that insulin
for T1D. In the former case the pancreas retains
resistance causes obesity for many individuals.
some beta cell function, but does not secrete
However, it is noteworthy that a significant
enough insulin to modulate elevated blood
percentage of obese individuals with insulin
glucose levels.
resistance do not become diabetic, since the
27
High
Blood
Glucose
Promotes
insulin
release
GLUCAGON
LIVER
Glycogen
Glucose
Stimulates glycogen
breakdowns
Stimulates glycogen
formation
PANCREAS
INSULIN
Stimulates glucose uptake from blood
Tissue and Cells
(muscle, adipose,others)
Lowers
Blood
Glucose
Figure 5: Schematic representation
of glucose modulation by the pancreatic
hormones insulin and glucagon. (Adapted
with permission from the International
Diabetes Federation in Brussels, Belgium).
Low
Blood
Glucose
Promotes
glucagon
release
COGNOSCIENTI
Raises
Blood
Glucose
Elevation of blood glucose levels that occurs
as a result of either Type 1 or Type 2 diabetes
CHO
Glycogen
Glycogen CHO
CHO
CHO
Intake
Glycogen
Glycogen Intake
Intake
Intake
Liver
Liver
Liver
Liver
results in a slow, tsunami-like cascade effect.
The resultant impact is a series of metabolic
Glucose
Glucose
Glucose
Glucose
changes that occur primarily in the liver,
muscle, adipose tissue and the circulatory
system. They include:
Hyperglycemia – caused by the increased
Insulin
Insulin
Insulin
Insulin
TG
TG
TG
TG
liver production of glucose in concert with
diminished use of peripheral circulating glucose.
This is driven by the inability of muscle and
adipose tissue to take up glucose (see Figure 6).
Hypertriacylglycerolemia – after food
ingestion not all the fatty acids flooding the
liver can be readily disposed of via oxidation.
These excess fatty acids are converted to
Glucose
Glucose
Glucose
Glucose
FA
FA
FA
FA
FA
FA
FA
FA
Lipolysis
Lipolysis
Lipolysis
Lipolysis
Lipolysis
Lipolysis
Lipolysis
Lipolysis
Glycogen
Glycogen
Glycogen
Glycogen
Adipose
Adipose
White
White
Tissue
Tissue
Adipose
Adipose
Tissue
Tissue
Fed
Fed
Fed
Insulin
Insulin
Insulin
Insulin
TG
TG
TG
TG
TG
TG
TG
TG
White
White
Glycogen
Glycogen
Glycogen
Glycogen
Skeletal
Skeletal
Muscle
Muscle
Skeletal
Skeletal
Muscle
Muscle
Glucose
Glucose
Glucose
Glucose
Pancreas
Pancreas
(B-cells)
(B-cells)
Pancreas
Pancreas
(B-cells)
(B-cells)
Glucose
GlucoseTransport
Transport
Glycogen
Glycogen
Synthesis
Synthesis
Glucose
Glucose
Transport
Transport
Glycogen
Glycogen Synthesis
Synthesis
triacylglycerol which is then packaged and
28
Glycogen
Glycogen
Glycogen
Glycogen
Fasting
Fasting
Fasting
B
A
secreted as very-low-density-lipoproteins
TG
TG
TG
TG
((VLDL). Ultimately this leads to elevated
plasma chylomicrons and VLDL levels (Figure 6).
CHO
CHO
CHO
CHO
Intake
Intake
Intake
Intake
Liver
Liver TG
TG
Liver
Liver TG
TG
Insulin levels – these levels are either
non-existent (Type 1) or extremely low (Type
Glucose
Glucose
Glucose
Glucose
2) which results in glucagon dominating the
glucose cycle shown in figure 5.
Ketoacidosis – this typically occurs only in
Type 1 diabetics. It results from an increased
mobilization of fatty acids from adipose tissue,
combined with accelerated hepatic synthesis
of the two organic acids, 3-hydroxybutyrate
and acetoacetate which build up in the
circulatory system.
TG
TG
TG
TG
Glucose
Glucose
Glucose
Glucose
Insulin
Insulin
Insulin
Insulin
FA
FA
FA
FA
Glucose
GlucoseTransport
Transport
Glycogen
Glycogen
Synthesis
Synthesis
Glucose
Glucose
Transport
Transport
Glycogen
Glycogen Synthesis
Synthesis
IMCL
IMCL
IMCL
IMCL
Lipid
LipidRe-esterification
Re-esterification
Lipid
Lipid Re-esterification
Re-esterification
Insulin
Insulin
Insulin
Insulin
Glucose
Glucose
Glucose
Glucose
Glycogen
Glycogen
Glycogen
Glycogen
Liver
Liver TG
TG
Liver
Liver TG
TG
FA
FA
FA
FA
Lipolysis
Lipolysis
Lipolysis
Lipolysis
T2DM
T2DM
T2DM
Fed
Fed
Fed
C
TG
TG
TG
TG
Lipolysis
Lipolysis
Lipolysis
Lipolysis
TG
TG
TG
TG
Glycogen
Glycogen
Glycogen
Glycogen
IMCL
IMCL
IMCL
IMCL
D
T2DM
T2DM
T2DM
Fasting
Fasting
Fasting
Figure 6: Overview of insulin action on glucose, glycogen and lipid flux.
A. Healthy individual after food ingestion
B Healthy individual after several hours without food
C. Type 2 diabetic after food ingestion
D. Type 2 diabetic after several hours without food.
These figures were taken from Samuel and Shulman’s work22 and were Adapted with Permission.
Role of Lipids – We have discussed above the
Lipids are insidiously associated with insulin
onset of the disease. In part this is due to the
poorly understood causality of pancreatic beta
resistance. It has however been unclear whether
misunderstanding of the intimate relationship
cell dysfunction. Recently, however Taylor and
circulating lipids or tissue specific lipids result
of glucose with insulin and their combined
colleagues from Newcastle University demon-
in insulin resistance. After food ingestion
roles in insulin resistance. The concept of
strated that fat accumulation in the pancreas is
dietary carbohydrate (CHO) increases plasma
“insulin dependent” glucose uptake by muscle
a major causative factor of the under-produc-
glucose and results in insulin secretion from
and other tissue has been propagated since the
tion of insulin by the beta cells . In a small,
the pancreatic beta cells (Figure 6A). In the
1950s. Indeed, insulin does stimulate the
but pivotal clinical trial, nine obese people
skeletal muscle, insulin increases glucose
translocation of the glucose transporter Glut-4
diagnosed with T2D and a nine person control
transport, facilitating glucose entry and
from the cell cytoplasm of muscle tissue to the
cohort were measured for weight, triglyceride
glycogen synthesis. In the liver, insulin pro-
cell membrane. This facilitates glucose uptake
levels in the pancreas (using functional NMR),
motes glycogen synthesis as well as de novo lipo-
in an insulin dependent mechanism as
and insulin response. These measurements
genesis and also inhibits gluconeogenesis. In
compared to the basal state of the cell without
were determined eight weeks before and eight
the adipose tissue, insulin suppresses lipolysis
insulin present . However, there are numerous
weeks post bariatric surgery. Prior to surgery
and promotes lipogenesis .
23
all T2D patients had significantly higher triglycerides in their pancreas compared to controls.
After surgery both the T2D and control groups
had lost similar amounts of weight, as well as
22
24
other glucose transporters such as Glut-1,
which is responsible for basal glucose uptake
In individuals who are fasting, insulin
production and release is decreased. The loss
of insulin mediation leads to an increase of
in muscle by an insulin-independent
25
mechanism .
A study in 1983 indicated that in post-absorptive
glycogenolysis. Hepatic lipid production
human subjects 75-85% of glucose uptake is
diminishes while adipose lipolysis increases
noninsulin-mediated and provided additional
(Figure 6B). In contrast for T2D patients
evidence that insulin may modestly increase
ectopic lipid accumulation impairs insulin
glucose uptake merely by providing additional
signaling. With accumulation of intracellular
transport sites . A subsequent study in 2001
lipid, insulin mediated skeletal muscle glucose
concluded that there is a sufficient population
uptake is also impaired. As a result, glucose
of glucose transporters in cell membranes at
is diverted to the liver. In the liver, increased
all times to ensure enough glucose uptake to
liver lipids also impair the ability of insulin to
satisfy the cell’s respiration, even in the
regulate gluconeogenesis and activate glycogen
absence of insulin. Insulin can and does
synthesis. In contrast, initial lipogenesis in
increase the number of these transporters in
In the case of insulin resistance Shulman
the liver remains unaffected, but together with
some cells but glucose uptake is never truly
has argued that “Insulin resistance is a complex
the incremental delivery of dietary glucose,
insulin dependent – in fact, even in uncon-
metabolic disorder that defies a single etiological
leads to increased fatty liver. Impaired insulin
trolled diabetic hyperglycemia, whole body
pathway. Accumulation of ectopic lipid metabolites,
action in the adipose tissue allows for increased
glucose uptake is inevitably increased .
activation of the unfolded protein response pathway and
lipolysis which promotes re-esterification of
innate immune pathways have all been implicated
lipids in other tissues (e.g. liver) and further
in the pathogenesis of insulin resistance. However, these
exacerbates insulin resistance. This is coupled
pathways are also closely linked to changes in fatty acid
with a decline in pancreatic beta cells, and
uptake, lipogenesis, and energy expenditure that
hyperglycemia develops (Figures 6C and 6D) .
can impact ectopic lipid deposition. Ultimately,
It is evident from Shulman’s work and others
accumulation of specific lipid metabolites (diacylglycerols
that lipid deposition in specific organs and
and/or ceramides) in liver and skeletal muscle may be a
tissue plays a critical role in inducing insulin
common pathway leading to impaired insulin signaling
resistance and appears to be organ/tissue specific.
However, pancreatic triacylglycerol normalized
in the T2D cohort, whilst it did not change in
the control cohort. In addition, insulin production by pancreatic beta cells also normalized
(i.e. increased) in the T2D group but remained
unchanged in the control group. This clinical
trial was small, but nevertheless provided some
compelling evidence that “fatty pancreas” is a
major causative factor in beta cell dysfunction
23
and limited insulin production .
22
22
and insulin resistance” . This is all highlighted and
summarized in Figure 6 A-D.
26
24
The same author also addressed the issue of
insulin and glucose transport in diabetics. He
concluded that when insulin is administered
to people with diabetes who are fasting, blood
glucose concentration falls. It is generally
assumed that this is because insulin increases glucose uptake into tissues, particularly
muscle. In fact, this is not the case. It has been
shown that insulin concentrations that are
Role of Carbohydrates – We have inferred above
within the normal physiological range lowers
that elevated blood glucose levels are an effect
blood glucose through inhibiting hepatic
of diabetes. However, it is unclear as to the role
glucose production without stimulating
of carbohydrates in the mechanistic causal
peripheral glucose uptake .
24
29
COGNOSCIENTI
hepatic gluconeogenesis and promotes
comparable losses of systemic adipose tissue.
Contrary to most textbooks and previous
(in the form of both glucose and fructose) was
For example, obesity is associated with changes
teaching, glucose uptake is actually increased
associated with increased diabetes prevalence
in the composition of the intestinal microbiota,
in uncontrolled diabetes and decreased by
after testing for potential selection biases and
and the obese microbiome seems to be more
insulin administration! The explanation for
controlling for all other variables. The impact
efficient in harnessing energy from the diet.
this is that because, even in the face of insulin
of glucose/fructose on diabetes was independ-
There is hope that such differences in gut
deficiency, there are plenty of glucose transporters
ent of sedentary behavior and alcohol use, and
microbiota composition might function as
in the cell membranes. The factor determining
the effect was modified but not confounded by
early diagnostic markers for the development
glucose uptake under these conditions is
obesity or overweight. Duration and degree
of T2D in high-risk patients. However, for
the concentration gradient across the cell
of sugar exposure correlated significantly with
the present time, it is unclear as to the exact
membrane; this is highest in uncontrolled
diabetes prevalence in a dose-dependent
role our ~100-300 trillion microbial tenants
diabetes and falls as insulin lowers blood
manner, while declines in sugar exposure
play in modulating the biochemical elements
glucose concentration primarily (at physiological
correlated with significant subsequent
associated with diabetes. Do they hold the key
insulin concentrations) through reducing
declines in diabetes rates independently of
to innovative new diagnostic and therapeutic
hepatic glucose production . In other words,
other socioeconomic, dietary and obesity
protocols, or are these microbes an exponential
glycogen release of glucose by the liver is the
prevalence changes. This is one of the first
additive to the indeterminate nature of diabetes?
causative reason that blood glucose rises, and
studies to indicate a direct correlation between
insulin when present, mediates this action by
carbohydrate intake and diabetes onset .
24
28
biome will add several new dimensions of
27
inhibiting glucose release by the liver .
30
Recent studies suggest that the human micro-
Human Microbiome Twist
complexity to consider in the management and
It is often believed that experimental and
The discussions so far clearly indicate that
treatment of diabetes and related disorders.
observational studies suggest that dietary
our understanding of the “simple disease” of
For example, non-caloric artificial sweeteners
glucose intake is associated with the develop-
diabetes is far from complete. The complex
(NCAS) have been in use for over a century.
ment of T2D. However, when one considers
intertwining of carbohydrate and lipid metab-
They have provided a vehicle for ingestion of
this issue in isolation and independent of
olism is just now being unraveled. However, a
“sweet” foods without the accompanying high
obesity related issues, it is unclear whether
new factor has recently been interjected in the
calorific content of the sugar/carbohydrate. In
alterations in glucose intake can account for
form of the intestinal human microbiome .
the past 20 years NCAS have been introduced
differences in diabetes prevalence among
The microbiome refers to ~100-300 trillion
into a wide variety of cereals, sodas and
populations. In that light, Sabu and colleagues
microbes, composed of ~10,000 different
desserts and marketed as alternative food and
recently investigated this very matter and
species that constitute 1-3% of our body
beverage sources for individuals suffering from
noted that numerous countries had high
weight and contain an estimated 8 million
IGT and diabetes. Most NCAS pass through the
diabetes prevalence but low obesity rates .
protein-coding genes. These microorganisms
human gastrointestinal tract without being
The list included a diverse range of socioeco-
play an intimate and undetermined role in the
digested and thus encounter the human
28
29
2
nomic and culturally different countries that
health and pathobiology of the human host .
microbiome, with a surprising and confounding
included France, Romania, Bangladesh,
Recently our knowledge of the microbiome in
outcome for diabetic patients!
Philippines and Georgia. They also noted
relation to the function of the human digest
that these trends were also dys-synchronous
system has increased immensely due to the
within countries. For example, in Sri Lanka
development of new analytical methods such
the diabetes prevalence rate rose from 3% in
as high-throughput metagenomic sequenc-
the year 2000 to 11% in 2010, while its obesity
ing. This has enabled researchers to identify
rate remained at 0.1% during that time period.
possible effects of the microbiome on human
Conversely, diabetes prevalence in New
metabolism, including its potential role in
Zealand declined from 8% in 2000 to 5%
metabolic disorders like obesity and T2D .
in 2010 while obesity rates in the country
Recently the potential role of the gut microbiome
28
rose from 23% to 34% during that decade .
The authors ultimately concluded that every
150 kcal/person/day increase in sugar availability
29
in these metabolic disorders has been identified.
In a recently widely reported study, Segal and
Elinav found that the NCAS saccharin (brand
name- Sweet‘N Low), sucralose (brand
name- Splenda) and aspartame (brand
names- NutraSweet and Equal) raised blood
sugar levels by dramatically changing the
30
composition of gut microorganisms . They
added saccharin, sucralose, or aspartame to the
drinking water of mice and found that their
blood sugar levels were higher than those of
mice who drank sugar water -- no matter
whether the animals were on a normal diet or
Diabetes Complexity and Precision Medicine
In T2D GWAS studies across diverse popula-
a high-fat diet. Although saccharin, sucralose,
We have suggested elsewhere that the percep-
tions 70 loci associated with T2D have been
and aspartame are three different compounds,
tion of diabetes as a “simple disease” has led
identified, although it is not known how many
“the effects were quite similar to each
to misunderstanding, poor diagnosis, ineffi-
of these loci have a significant effect on the
other” . When the sweetener-fed mice were
cient treatments and a global prognoses of the
risk or expression of the disease. Significant
given antibiotics to clear their gut of bacteria,
disease that is now the “diabetic iceberg tip
association of single nucleotide polymor-
their blood sugar levels dropped back down
of despair.” The primary focus has been on
phisms (SNPs) in the CDKAL1, CDKN2A/B,
to normal. To gather more evidence of the
the treatment of the effect of elevated blood
HHEX, KCNQ1, SLC30A8, HLA-B, INS-IGF,
relationship between artificial sweeteners,
glucose levels, and not a therapeutic paradigm
TCF7L2 and CAPN10 genes might be associated
gut bacteria, and blood sugar levels, the
that considers human systems level complexi-
with equivalent clinical subtypes . If this
researchers transferred feces from mice that
ty, variability and causality of diabetes. Recall
were the case, it might explain why sub-
drank artificially sweetened water into mice
that PM “..is the tailoring of medical treatment to the
populations of individuals with T2D responded
that never had. Somewhat surprisingly, blood
individual characteristics of each patient” . Does a
differentially to drug monotherapy across
sugar levels rose in the recipients
reconsideration of the causal mechanisms of
different categories of anti-diabetic drugs.
30
The study was extended to include 400 human
subjects where it was determined that the
bacteria in the guts of those who ate and
drank artificial sweeteners were different from
30
7
diabetes and a move away from “one size fits
all” provide a foundation for the application of
the PM toolkit now available in the clinicians’
tool bag?
32
From retrospective analysis of electronic
medical records, support for at least 3 distinct
subtypes from a clinical population of T2D
33
exists . The first subtype was associated with
Earlier, diabetes was described as a heterogeneous
diabetic nephropathy and diabetic retinopathy,
cial sweeteners also tended to have higher fpg
mix of syndromes, each having a characteristic
while subtype 2 was associated with cancer
levels and IGT. Finally, the researchers recruit-
and differentiating polygenic etiology. The
malignancy and cardiovascular diseases. The
ed seven volunteers, five men and two women,
cardinal features are chronic hyperglycemia
third subtype was also associated with cardio-
who normally didn’t eat or drink products
along with disturbances in the metabolism of
vascular diseases, plus HIV infections, allergies
with artificial sweeteners and followed them
carbohydrates, lipids and proteins (Figures
and neurological diseases. Genetic association
for a week, tracking their blood sugar levels.
5 and 6). These disturbances are linked to
analysis of the subtypes found 1279 SNPs that
The volunteers were given the FDA’s maximum
deficiencies in insulin production and/or
mapped to 425 unique genes. In the second
acceptable daily intake of saccharin from day
insulin resistance. This globally impaired
subtype 1227 SNPs mapped to 322 genes and
two through day seven. By the end of the
metabolism, attended by varying degrees of
the third showed 1338 SNPs mapping to 437
week, blood sugar levels had risen in four
hyperglycemia and lack of glycemic balance,
genes. These 3 subtypes may substantiate the
of the seven people. Transfers of feces from
is linked to inflammation and wide-ranging
lower end of the range of subtypes for T2D
11-13
people whose blood sugar rose increased blood impact on all body systems
. This systemic
(Figure 1). We suggest a speculative upper range
sugar in mice, more evidence that the artificial
impact on the diabetic patient leads to
of ~15 for both T1D and T2D as being about
sweetener had changed the gut bacteria. The
significant disease variability within individuals.
20% of the gene loci in either case (one might
authors concluded that NCAS were extensively
However, it is also important to consider the
argue that this is reasonable based on genetic
introduced into our diets with the intention of
individual susceptibility to diabetes. For
association analysis of oncologic diseases). This
reducing caloric intake and normalizing blood
example more than 50 loci have been
would represent a reasonable estimate of the
glucose levels without compromising the
implicated as determinants of T1D risk using
proportion of specific gene loci likely to have
human ‘sweet tooth’. The findings suggested
the genome wide association studies (GWAS)
significant (detectable) impact on either risk or
31
that “NCAS may have directly contributed to enhancing
approach . However, it is likely that only the
expression of disease components. As genomic
the exact [diabetic] epidemic that they themselves were
HLA genes, INS and PTPN22 loci have signif-
studies gain sensitivity it may be necessary to
icant effects on disease risk. It is not clear
identify and link more subtypes.
30
intended to fight” .
whether these 3 loci have any equivalency
relationship to the 3 clinical subtypes of type
1, namely autoimmune, non-autoimmune
non-fulminant and non-autoimmune fulminant.
Across all of diabetes, it seems that there are
subtypes that may respond differentially to
various single and combined therapies. In GD,
31
there are clearly, at least 3 subtypes , whereas
31
COGNOSCIENTI
those who did not . People who used artifi-
in T1D and T2D there are grounds to suggest
cost-effectiveness support too. Pharmacoeco-
given drug, biomarker, or active biological
somewhere between 3 and 15 subtypes of
nomics are pivotal to the involvement and
agent. The platform consists of five modules:
33-35
treatment significance
. This means that in
approaching the treatment of diabetes, there
could be anywhere from 9-33 or more treatable
subtypes, where each subtype had treatmentsensitive characteristics. The existence of these
subtypes could explain much of the variance in
contemporary treatment outcomes, since each
subtype might be sensitive only to specific single or combined therapies. One of the goals of
patient-caregiver dyads.
Drug Therapy Treatment
We have argued the current therapies are
focused on only the effect of diabetes. In order
to highlight that point we have assembled the
marketed therapeutic agents used to lower
systemic glucose levels. The following
1. Controlled Source Knowledge Module- US National Library of Medicine archive (USNLM) from 1809-current
2. Data Acquisition Module- high precision clinical efficacy variable text mining capability using a purpose built semantically intelligent Natural Language Processor
categories of common glucose lowering agents
3. Array Module- all data is arrayed into a have a variety of mechanisms and actions and
drug-disease-outcome relationships include direct insulin administration, and the
database where each relation is an
popular SGLT2 proximal nephron inhibitors
empirically weighted contributor to
Treatment, in all cases, has the targets of
and this information is all captured and
clinical efficacy
glycemic control, both in the acute and long
summarized in Table 1.
precision medicine is to be able to determine
the exact subtype sensitivity to therapies. This
idea will be further developed below.
term sense, and the prevention and reduction
of complications, including disturbances in
32
decision making of the diabetic patient in joint
lipid and protein metabolism and pathology
in other body systems. Contemporary practitioners have a wide array of evidence-based
treatment options and clinical decision support
systems for choosing among these options.
These options include:
4. Analysis Module- using Network Graph We have suggested that T2D involves distur-
Theory and a suite of algorithms to
bances not just in carbohydrate metabolism
determine the most centric and similar
but also in lipid and protein metabolism,
components of clinical efficacy for all
inflammatory pathways, and microbiome and
diseases
gut nutrient composition Therefore it would
appear prudent to evaluate other types of
therapies that may have mechanisms and
actions very different from glucose lowering.
5. Prediction Module- Answer System
analytics automation layer with graphic
user interface (GUI) for real time output
of new indications with high probability
l
Drug mono-therapy
In the search for further subtypes, it may be
l
Combination drug therapies
helpful to recognize that drug class therapy
l
Non drug therapies
failure may help to characterize subtype-
The CureHunter platform facilitates the capture
l
Combination therapy of drug therapies and sensitivity profiles. For example, in GD the
and automated analysis of all of published
commonest management includes prenatal
biomedical knowledge (in the USNLM) demon-
care, nutrition therapy and occasional use
strating a functional role in the clinical efficacy
of insulin and other drug therapies. Prenatal
potential of over 250,000 active biological
care and nutrition therapy are highly effective
molecules, markers, mechanisms, and drugs
therapies for glycemic control in gestational
operating in over 11,600 disease states. The
diabetes.
following table (Table 2) illustrates the typical
non-drug therapies
Lifestyle factor modifications
l
l
Nutritional adjustment - addition or
removal of nutrients
l
Microbiome adjustments
Along with clinical experience, there are a
variety of available PM tools that one can
In the era of PM how can such tools be utilized
apply, for example clinical decision support
to address such complex issues. As one
systems for option management in the
example CureHunter Inc. utilizes an Integrated
treatment of diabetes. These include CureHunter
Systems Biology platform which effectively
Inc. (www.curehunter.com), Therametrics
synthesizes all the data and knowledge
(www.therametrics.com) and BioVista’s
available from literature sources and clinical
(www.biovista.com) platforms. For illustration,
trials to produce a clinical outcome database.
we will highlight how CureHunter provides
This database is structured for autonomous
PM decision support at a treatment level and
prediction of new disease indications for any
how easy it is to use the platfrom to provide
of clinical success prediction
output for a query about T2D using the
CureHunter engine. It shows the top ranked
drug monotherapies for the treatment of T2D
based upon an analysis of all relevant articles
indexed by PubMed (USNLM). Article relevance
is determined using an artificial intelligence
approach that combines and weights each
article based upon its level of evidence and
the numbers of articles containing outcome
statements, clinical trial or study statements
Class
Example
Mechanism(s)
Primary action(s)
CureHunter Effectiveness Index
ɑ-glucosidase inhibitors
3
Acarbose
inhibits intestinal
slows carbohydrate
Miglitol
ɑ-glucosidase
digestion/ absorption
Amylin mimetics
decreases glucagon secretion 1*
Pramlintide
activates amylin receptors
slows gastric emptying
increases satiety feelings
Biguanides
decreases hepatic glucose
Metformin
activates AMP-kinase
4.5
production
Bile acid sequestrants
Colesevelam
binds intestinal bile acids,
may decrease hepatic glucose 1*
increasing hepatic bile acid production; may increase
production
incretin levels
Dopamine-2 agonists
activates dopaminergic
modulates hypothalamic
receptors
regulation of metabolism
DPP-4 inhibitors
Sitagliptin
inhibits DPP-4 activity,
increases insulin secretion
Vildagliptin
increasing postprandial
decreases glucagon secretion
Saxagliptin
active incretin (GLP-1, GIP)
Linagliptinconcentrations
Bromocriptine
1*
2
Alogliptin
Insulins
Rapid-acting
activates insulin receptors
increases glucose disposal
3
Short-acting
decreases hepatic glucose
Intermediate-acting
production
Basal analogs
Meglitinides
Repaglinide
shuts ATPK channels on β- increases insulin secretion
Nateglinide
cell plasma membranes
SGLT2 inhibitors
Canagliflozin
inhibits SGLT2 in the
Blocks glucose reabsorption
Dapagliflozin
proximal nephron
by the kidney, increasing
Empagliflozin
glucosuria
Sulfonylureas
Glyburide/glibenclamide
shuts ATPK channels on -
increases insulin secretion
3
Glipizide
cell plasma membranes
increases insulin sensitivity
3
4*
1*
Gliclazide
Glimepiride
Thiazolidinediones (TZDs) Pioglitazone
activates nuclear transcription factor PPAR-β
Rosiglitazone
2
Table 1:
Classes of marketed therapeutic drugs used for treatment of diabetic patients, showing examples of each class, mechanisms of action, principal actions, and CureHunter effectiveness index.
57
Based upon Inzucchi et al. 2015 and CureHunter output.
* Denotes Effectiveness Index value based on much more limited data and therefore lower confidence level scores.
33
COGNOSCIENTI
and articles with clear context of study
statements. The engine provides an effectiveness
index on a scale of 1-10, with 10 being the most
effective. The indices shown in the table have
been rounded for ease of comparison. The
importance of this real time computed
Related Diseases: Related Drugs/
Related Therapies: CureHunter
meta-analysis output to the point of care
602
Bio-Agents:
227
Effectiveness Index
(POC) provider is to support decision making
2408
about drug and related therapies with a higher
net benefit to the patient populations being
Related/Drug/
Outcome
served. Any provider is able to drill down into
Important
Statements StatementsStatements
the articles behind any effectiveness index
Trial/Study
All Context
Bio-Agent (IBA)
for any drug or related therapy with one click
access in order to satisfy themselves about the
Insulin
273
provenance of the engine’s recommendations.
Metformin
106 244 9384.5
Acarbose
34
In the subsequent table (Table 3) the typical
78
54263
1793
Pioglitazone 32
108 3262
the CureHunter engine. It shows the top
Sitagliptin
29
45
1573
ranked related therapies for treatment based
Glyburide
27
56
2393
upon an analysis of all relevant articles. Just
Rosiglitazone26
61
2562
as with drug therapies, the engine provides an
Glucagon-like peptide 1
25
34
357
effectiveness index on a scale of 1-10, with 10
Exenatide
18
38
1822
Glargine
16
48
1363
Glipizide
14 27 953
output is shown for a query about T2D using
34
795
being the most effective. The indices shown
in the table have been rounded for ease of
comparison.
The CureHunter recommendations for the
treatment of T2D cover drug monotherapy,
and comparison between CureHunter output
measured in milliseconds with the recommen-
3
Alpha-glucosidases14
23
82
2
Thiazolidinediones14
12
192
3
Troglitazone 13
27
1094
Miglitol
11 321
5
dations of typical national agencies for
Pramlinitide 1
3
161
diabetes. For example, the recent (NICE, 2015
Covesevalam7
5
40 1
http://www.nice.org.uk/guidance/ng28/
Bromocriptine1
2
17 1
evidence/full-guideline-2185320349) for
the treatment of T2DM took 6 years to
Table 2:
Type 2 Diabetes mellitus drug therapy
develop. The recent 2015 updated guidelines
The first column indicates the related drug or bio-agent. The second column shows the number of supporting studies
from the Canadian Diabetes Association
that contain outcome statements. The third column shows the number of supporting studies with clinical trial or study
(http://guidelines.diabetes.ca/executivesummary)
statements, the fourth column shows the number of supporting studies that contain context statements, and the fifth
and the January 2016 Consensus statement
by the American Association of Clinical
Endocrinologists and American College of
Endocrinology on the comprehensive T2D
management algorithm: 2016 executive
summary compare favorably with CureHunter
recommendations.
columns shows the CureHunter effectiveness index. Users are able to scrutinize all or any of the studies that support a
recommended therapy. Studies that are included must meet stringent evidentiary standards that are encoded in the data
model and software that parses all articles published by the US National Library of Medicine by a given date. This table
shows the capture for a late December, 2015 update.
L I V E
Related Diseases: Related Drugs/
Related Therapies: CureHunter
602
Bio-Agents:
227
Effectiveness Index
W E B I N A R
Incorporating
genomic data into
clinical practice
REGISTER NOW
2408
Trial/Study
March 29, 2016 – 2:30pm EST
Related/Therapy
Outcome
All Context
Procedure
Statements StatementsStatements
Gastric bypass
53
24
147
3
Bariatric surgery
53
16
157
4
Gastrectomy15 19 83 3
7
6
45
4
Diet therapy
5
7
41
1
Aftercare
3
13 403
Resistance training
3
8
32
4
Renal dialysis
2
14
68
1
Angioplasty 2
14 243
Transplants 2
5
505
Carbohydrate restricted diet
2
4
13
7
Root planning
2
2
6
1
Stem cell transplantation
2
2
5
1
Biliopancreatic diversion
2
1
11
6
0
4
1
Electroacupuncture2
Table 3:
Type 2 Diabetes mellitus related therapies
The first column indicates the related therapy or procedure. The second column shows the number of supporting studies
that contain outcome statements. The third column shows the number of supporting studies with clinical trial or study
statements, the fourth column shows the number of supporting studies that contain context statements, and the fifth
columns shows the CureHunter effectiveness index. Users are able to scrutinize all or any of the studies that support a
recommended therapy. Studies that are included must meet stringent evidentiary standards that are encoded in the data
model and software that parses all articles published by the US National Library of Medicine by a given date. This table
shows the capture for a late December, 2015 update.
David Roth, MD
Chair of Laboratory and
Pathology Medicine,
Director of Precision Medicine,
Penn Medicine
Brian Wells
Associate Vice President
Technology and Health
Computing, Penn Medicine
Since it launched in 2013, the Center for
Personalized Diagnostics at the University of
Pennsylvania Health System has sequenced
more than 4,000 clinical tumor samples to
help provide actionable genomic data for
the treatment of a variety of cancers. In this
session, participants will learn from UPHS
leaders of the program how other research
centers and health systems can build from
the ground up a genomic testing, and data
collection function into their clinical
operation to provide more precise,
individualized care of cancer patients. Learn
how to build the IT infrastructure needed to
capture, store and analyze genomic data and
how to “break down the walls” that exist
between research and clinical care to deliver
that genomic and diagnostic data across
the care continuum, including directly to
clinicians via the patient’s electronic health
record (EHR).
Regsiter at
www.thejournalofprecisionmedicine.com
Sponsored by
Organized by
35
COGNOSCIENTI
Caloric restriction
Real time meta-analyses of combination therapies
linked to specific patient subpopulations are
also available from CureHunter online to
support further evidence-based steps toward
personalized medicine. Combination therapies
may consist of drug combinations or drug with
non-drug therapies. For example, the POC
Selected Studies
Milionis H. Combining a statin with a fibrate
versus fibrate monotherapy: efficacious but
safe? Expert Opin Drug Saf.
2014;13(3):267-269. doi:
10.1517/14740338.2014.887679.
Combination Therapy Description
Fibrate (agonist of peroxisome proliferatoractivated receptor-ɑ, PPR-ɑ) monotherapy is
effective for the treatment of hypertriglyceridemia, while the combination of a fibrate
with a statin is an option in the management
of patients with combined dyslipidemia and
diabetes mellitus who present with atherogenic
dyslipidemia (low high-density lipoprotein
(HDL) cholesterol and elevated triglyceride
levels). There is evidence that the combination treatment is efficacious towards a
global improvement of the lipid abnormalities
with a safety profile similar to that of fibrate
monotherapy with regard to liver and muscle
toxicity.
Lin R-T, Tzeng C-Y, Lee Y-C, et al.
Acupoint-specific, frequency-dependent,
and improved insulin sensitivity hypoglycemic
effect of electroacupuncture applied
to drug-combined therapy studied by a
randomized control clinical trial. Evid Based
Complement Alternat Med. 2014;2014:371475.
doi:10.1155/2014/371475.
EA combined with the oral insulin sensitizer
rosiglitazone improved insulin sensitivity in
rats and humans with type II diabetes mellitus
(DM).
Zhang X, Liu Y, Xiong D, Xie C. Insulin
combined with Chinese medicine improves
glycemic outcome through multiple pathways
in patients with type 2 diabetes mellitus.
J Diabetes Investig. 2015;6(6):708-715.
doi:10.1111/jdi.12352.
The results from our study showed that the
combination therapy of SQF and insulin
significantly improved the clinical outcome
of type 2 diabetes mellitus compared with
insulin monotherapy.
Chobit’ko VG, Zakharova NB, Rubin VI.
[Relation between the changes in thiamine
metabolism and energy processes in erythrocytes of patients with diabetes mellitus and
approaches to their correction with drugs].
Vopr Med Khim. 1986;32(3):118-121.
Insulin and cocarboxylase as well as insulin
combined with thiamin and adenine exhibited the best effect on the patterns studied;
these drugs normalized the vitamin B1
metabolism and improved the parameters of
energy metabolism within 120 min of incubation in erythrocytes isolated from blood of
patients with diabetes mellitus.
Johnson JL, Wolf SL, Kabadi UM. Efficacy of
insulin and sulfonylurea combination therapy
in type II diabetes. A meta-analysis of the
randomized placebo-controlled trials. Arch
Intern Med. 1996;156(3):259-264.
Combination therapy with insulin and
sulfonylurea may be a more appropriate and
a suitable option to insulin monotherapy in
subjects with non-insulin-dependent diabetes in whom primary or secondary failure to
sulfonylurea developed. It may also be a more
cost-effective way of long-term management
in this group of subjects, especially in the
elderly.
provider can access instant decision support
for choosing combination therapies where
specific patients are not responding to prior
treatment with monotherapy alone. The example
that follows (Table 4) shows a small subset of
the articles selected and analyzed for a query
about a combination approach to T2D.
Treatment Failure
When people are diagnosed with T2D, the
normative practitioner response is to try drug
monotherapy with metformin, unless it is
36
contraindicated. If the response is not adequate,
other first line agents may be used singly or in
combination. Outcome failure on any of these
approaches may be viewed as a consequence
of combinations of phenotype and genotype,
representing subtypes that do not respond to
specific single or combination approaches.
If we consider the main categories of antidiabetic agents, it may be that specific
categories do not have effective mechanisms
and receptor interactions for people in a given
specific subtype. It will be rewarding to start
mapping categories of agents that are effective
or non-effective for specific known subtypes.
This would constitute a reasonable and rational
approach to precision treatment. An example
of a two pronged approach to this would be
the convergence of a network biology approach
looking to determine subtypes based upon
theoretically supported responses to specific
therapies combined with retrospective analysis
of large populations with known subtypes
looking for the most successful treatment
outcomes in known subtype groups. This
would allow a mapping of specific subtypesensitive therapies to specific subtypes, one of
the essential features of precision treatments.
Table 4:
The first column shows a subset of CureHunter selected studies for a
specific combination therapy and the second column shows a brief
description for that combination.
There are numerous studies supporting the
Compelling evidence exists to support the
a complex interplay of both carbohydrate
use of combination therapies both for drug
existence of T2D subtypes of differential
and lipid metabolism mediated by a suite of
combinations and combinations of drug ther-
sensitivity to different drug monotherapies,
hormones such as insulin and glucagon. But
36,37
apy with non drug approaches
varying combination drug therapies and several
the clandestine role of lipids manifested in
cases, positive treatment outcomes may not
. In some
related therapies. This points to T2D being a
the form of fatty liver and fatty pancreas in
be directly attributable to specific components
constellation of subtypes where differentiating
diabetes onset and progression is just now
of the combination therapy, across or within
characteristics include single or combined
being spotlighted.
subtypes. For treatment failure, however, it is
dysfunctions of many aspects of metabolism,
possible to assert that the specific combination
not just limited to carbohydrate metabolism.
was ineffective across or within subtypes.
Does this new insight afford innovative and
novel approaches to augment the current tired
and somewhat one-dimensional treatment
Alternative Approach
for T2D could be accelerated by using a com-
regimes that provide a palliative to elevated
Earlier, we suggested that approaches of preci-
bination of clinical decision support systems
blood sugar? At this time, it is unclear. There is
sion medicine (treatment) to diabetes need to
along with trivalent convergence methodol-
a dire human health and socioeconomic need
be reasonable and rational, where reasonable
ogy and using a broad spectrum approach to
for innovation and creation of new approaches
treatment that included drug monotherapy,
to the prevention, diagnosis, treatment and
Tricorder technology refers to a form of
combination drug therapies, related non
improved prognosis of this insidious disease.
precision nanomedicine using microfluidics
drug therapies, combination therapy of drug
The advent of PM has been accompanied by
with a lab-on-a-chip, where small samples of a
therapies and non-drug therapies, lifestyle
the usual hyperbole and enthusiastic promise
person’s body fluid can be analyzed at low cost
factor modifications, nutritional adjustment,
of a “brave new world” in human healthcare,
within a few minutes. Tricorder technology
and microbiome adjustment. Beyond T2D we
with the past failures of personalized medicine
is able to diagnose at least 15 conditions and
argue that this multi-pronged approach would
conveniently forgotten. Do the spectra of the
monitor vital signs for 72 hours. As tricord-
satisfy the goals of PM for many other chronic
twin icebergs of diabetes and PM afford a
er technology evolves, it will be applied to
conditions and diseases and simultaneous-
complementary opportunity? The old cliché
the problem of identifying subtypes that are
38-41
includes the practical and the pragmatic
.
ly involve patients in the decision-making
“only time will tell” is sorely overused, and
sensitive to specific treatment approaches
process, affording them more autonomy and
woefully inadequate given the current crises
If tricorder technology is combined with a
responsibility for self-care along with choices
of pandemic diabetes, and massive under-
network biology approach plus retrospective
in cost-effective treatments. This path will,
diagnosis, accompanied by IGT and metabolic
analysis of large populations with known sub-
by its very nature, make significant inroads to
syndrome. It is imperative that PM affords new
42,43
.
types looking for the most successful treatment the huge proportion of national GDP currently
insights and paradigms into the diagnosis,
outcomes, the resulting trivalent convergence
management and treatment of diabetes
should become a powerful strategy for reducing the global burden of diabetes, particularly
33-35,42
for T2D
. As trivalent convergence meth-
odology identifies and characterizes new T2D
subtypes, we see precision treatments evolving
along the following lines:
spent on treatments in health care.
Conclusions
What have we learned from this journey across
the current bleak iceberg terrain of diabetes
and PM? The concept and perception that
diabetes is a “simple disease” has fueled and
driven much of our limited understanding of
l
Drug mono-therapy
disease onset and therapeutic treatment over
l
Combination drug therapies
l
Non drug therapies
tional medicine, and fledgling “P-Medicine” in
l
Combination therapy of drug therapies and the form of PM, has had on diabetes has been
36,37
44
non-drug therapies
the past half century. The impact that conven-
exceedingly limited and disappointing. The
45-48
l
Lifestyle factor modifications
l
Nutritional adjustment - addition or removal that is staggering in its proportion, and yet
of nutrients
l
49-52
resultant outcome has been a global pandemic
reflects just the tip of the iceberg of diabetic
53-56
Microbiome adjustments
despair. It is clear that diabetes results from
predicated on prediction and prevention as
part of the vanguard approach to stave off the
next engulfing wave of global diabetic patients.
In a world where we love to dumb things
down, it may be worthwhile to consider that
such an approach has gotten us here. Recall
that Precision Medicine “is the tailoring of
medical treatment to the individual characteristics of each patient and…..classify individuals into subpopulations that differ in their
susceptibility to a particular disease, in the
biology and/or prognosis of those diseases they
may develop, or in their response to a specific
7
treatment” . Maybe this is just what we need
and what the doctor should order!
37
COGNOSCIENTI
It would seem that achieving the goals of PM
Acknowledgements
We would like to thank Mr. Andrew Jackson
(flaircreativedesign.com) and Mr. Damian
Doherty (Editor-Journal of Precision Medicine)
for their considerable help and input on the
figures and content contained in this article.
In addition, we express grateful appreciation
to Mr. Judge Schonfeld, Founder and CEO of
CureHunter Inc, for access to the CHI software
and his unflagging support of our efforts.
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Jack Yensen, RN., Ph.D. is an educator, writer and
consultant in e-health and e-learning. He has held many
academic and advisory positions throughout North
America in universities and corporations in the fields
of health informatics (former Director of Education,
Canadian Nursing Informatics Association), pathophysiology, pharmacology, research methodology and
advanced nursing. His particular interest in diabetes
was stimulated by serving as a National Publications
Committee member, Canadian Diabetes Association.
Stephen Naylor., Ph.D. is the current Founder,
Chairman and CEO of MaiHealth Inc, a systems/network
biology level diagnostics company in the health/wellness
and precision medicine sector. He was also the Founder,
CEO and Chairman of Predictive Physiology & Medicine
(PPM) Inc, one of the world’s first personalized medicine
companies. He serves as an Advisory Board Member
of CureHunter Inc. In the past he has held professorial
chairs in Biochemistry & Molecular Biology; Pharmacology;
Clinical Pharmacology and Biomedical Engineering, all
at Mayo Clinic (Rochester, MN USA) from 1990-2001.
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