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The complementary iceberg tips of diabetes and precision medicine.


Abstract and Figures

Diabetes 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 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.
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The Complementary
Iceberg Tips of Diabetes
and Precision Medicine.
by Jack Yensen Ph.D & Stephen Naylor Ph.D
There appears to be a chronic lack of confidence in global healthcare systems1. 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
like the National Health Service of the UK, or a market-driven, predominantly privatized model, such as the patchwork system in the USA2.
How did we get to such a critical, and, some might argue, dysfunctional point in the practice of medicine?
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
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 com-
partmentalized 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
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-
connectedness of human molecular biology,
biochemistry, metabolism and physiology in the
form of systems, network and pathway biology3, 4.
The current medical system has facilitated the
escalation of healthcare costs, but has had
limited impact on the prediction, prevention,
accurate diagnosis, and effective treatment of
acute and chronic disease. This lack of progress
in concert with a growing awareness of the
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
“P-Medicine”2. 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
took hold in the early 2000’s5. 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
can develop targeted treatment and prevention plans”6.
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,
anecdotal stories involving lone individuals7.
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
individual patient8. Whilst the actual practitioners
of personalized medicine have not suggested any
such thing, the premise took hold and fueled
the disappointment and disillusionment with
it and the ascendancy of PM2. The NRC report
attempted to define and differentiate PM from
personalized medicine. The report stated that
“Precision medicine is the tailoring of medical treatment
to the individual characteristics of each patient. It does
not literally mean the creation of drugs or medical devices
that are unique to a patient, but rather the ability to
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 treatment”7.
P-medicine in the form of personalized and
precision medicine is ~twelve years old, and
continues to be accompanied by enormous
hope, hype, enthusiasm and expectation.
Therefore, it is useful to evaluate the impact of
personalized/precision medicine on a specific
disease indication such as diabetes. Diabetes
is often described and perceived as a “simple
disease” of mis-regulated blood glucose9. One
might assume that armed with the powerful
new personalized/precision medicine toolkit
of OMIC analytics, and -TIC bioinformatics/
knowledge management/big data analytics, such
a “simple disease” should be both preventable
and curable! In 2003, the global estimate for
adults (20-79 years of age) suffering from
diabetes was ~194 million, constituting 5.1%
of the worldwide adult population10. However,
in the intervening twelve year life-span of
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
Based on the statistics the initial tentative
conclusion is that to date, the impact of
personalized and precision medicine on diabetes
has been minimal. Diabetic onset rates have
increased significantly. In this work we consider
the mismatched expectations of personalized/
precision medicine advocates versus that of the
global adult population as the latter struggles
with rampant diabetes. We discuss the concept
of diabetes as a “simple disease,” and the future
role of PM in the diagnosis and treatment of
diabetes. Finally, we consider the complementary
icebergs of diabetes and PM. We regard the
former as the tip of despair and the latter the
tip of hope and potential.
Diabetes - the Simple Disease?
Diabetes is ostensibly a simple and well
characterized disease state9-13. An individual with
diabetes manifests elevated blood glucose levels.
This is primarily caused either by the individual’s
body not producing enough insulin or because
at the cell/tissue/organ level insulin transport of
glucose is compromised. The resulting chronic
hyperglycemia can result in systemic damage
to the body leading to possible disability and
life-threatening complications. In 2015 it was
estimated that ~5 million people worldwide
died because of diabetes and the global cost
was “between $673 billion and $1.197 trillion
in healthcare spending.” It is also important to
consider that for such a “simple disease,” most
countries spend 5-20% of their total annual
healthcare expenditures on the diagnosis,
treatment and care of diabetic patients9.
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 under-
stood auto-immune response of the body9.
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 previ-
ously known as “juvenile diabetes.”
Type 2 diabetes (T2D) - this is the most common
type of diabetes occurring in 93.5% of all
estimated cases of adults9. Causal onset is
predominantly due to lifestyle choices, and
risk factors include physical inactivity, poor
nutrition and excessive body weight in the
form of adipose tissue. This type of diabetes
often goes unrecognized and it is estimated that
one-in-two people are actually undiagnosed9.
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
a 75gram bolus of glucose14.
Gestational diabetes (GD) an elevated blood
glucose level detected in a pregnant mother
is classified as either i) gestational diabetes
mellitus where fpg levels are 92-125mg/dL; or ii)
diabetes mellitus in pregnancy, where fpg levels
are >126mg/dL. The number of expectant
mothers who are diagnosed with gestational
diabetes is relatively small representing only
1.52% of the diabetic population. Gestational
diabetes normally disappears after birth (60%),
but some women can develop T2D (36%), or
less commonly T1D (4%) as highlighted in Figure
1. It is interesting to note that babies born to
mothers, who develop post-partum T2D, also
have a higher probability of developing T2D
Pre-diabetic conditions – a normal, healthy
individual has an fpg level within a 70-100 mg/
dL range. However, individuals with elevated
fpg levels of 100-126 mg/dL plus a 2-hpg of
<140-200 mg/dL are diagnosed with Impaired
Glucose Tolerance (IGT). Similarly, patients
with an fpg of 110-125 mg/dL and 2-hpg of
<140 mg/dL are classified as Impaired Fasting
Glucose (IFG) individuals14. The fundamental
difference is that IGT is defined as a high
blood glucose level after eating; whereas IFG
is defined as high blood glucose after a period
of fasting. People with IGT face a significant
probability of developing diabetes. Approxi-
mately 5-10% of such individuals become
diabetic on an annual basis and overall, up to
70% will eventually develop diabetes16.
However, this trajectory is not inevitable,
and there are numerous reports that lifestyle
change in the form of healthy diet and physical
exercise and/or medication can prevent the
progression to diabetes17.
Metabolic syndrome – is the current term used for
a group of risk factors that considerably raise
the risk for developing T2D, as well as heart
disease and stroke. In order for a patient to be
diagnosed with metabolic syndrome he/she
must have at least three of the following five
risk factors18.
l Abdominal obesity (35 inch waist for
women and 40 inch waist for men)
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
l High fasting blood sugar (>>100mg/dL)
It is estimated that 20-25% of the global adult
population has metabolic syndrome and 85%
of Type 2 diabetics also have metabolic
syndrome. Conversely, individuals with
metabolic syndrome have a fivefold enhanced
probability of developing T2D18. This is yet
another significant component of the diabetic
iceberg scenario!
Figure 1: Global prevalence of Type 1 diabetes,
Type 2 diabetes and Gestational diabetes. Data
derived from IDF Diabetes Atlas9. On the right hand side
is the estimated number of subtypes for Type 1 and Type 2
diabetes. Gestational diabetes data delineates the
percentage of women post partum that develops Type1
versus Type 2 diabetes or resolves the hyperglycemia.
Type 2
~15 Subtypes T2
~15 Subtypes T1
1.52% Post-partum
resolution - 60%
T1 - 4%
T2 - 36%
Type 1
Diabetes Pandemic
In 1994 the International Diabetes Federation
(IDF) estimated that the “global burden” of
individual adults with diabetes” was ~100
million adults, constituting ~2.9% of the world
adult population19. The diabetic population
had increased to ~194 million (5.1%) adults by
200310, and up again to ~415 million (8.8%)
individuals by 20159. Over that same ~20 year
period conventional medicine had also been
confronted with the advent of personalized/
precision medicine (2001-2003)5. The latter
developed under a cloud of scrutiny and the
withering refrain “personalized medicine -
technological innovation and patient
empowerment or exuberant hyperbole”?5 To
date based on the data showing an approximate
doubling of diabetic adults every 10 years both
conventional medicine and personalized/
precision medicine have failed to limit the
impact of this “simple disease.” Indeed, the IDF
described diabetes as “one of the largest global
health emergencies of the 21st century” in their
report published
194M (2003)
415M (2015)
642M (2040)
last year9. The WHO added that, high blood
glucose is the third highest global risk factor
for premature mortality, after high blood
pressure and tobacco use20.
The escalating pandemic of diabetes appears
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
Pacific/China (WPC)9,10. 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.
This is a composite figure that shows regional
estimates for diabetes for the specific years
2003, 2015 and projections for 2040 taken from
the 2nd and 7th IDF Diabetes Atlas reports9,10.
The IDF noted that in 2015 ~415 million people
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
low-income to middle-income levels9.
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
million, 8th); and Bangladesh (7.1 million, 10th)9.
The data showing the global incidence of
diabetes and the projections for 2040 in Figure 2
are ominous. However, we have noted above
that this is the tip of the diabetic iceberg.
It was estimated that in 2015 as many as
one-in-two adults were unaware of their
diabetic condition and this constitutes ~193
million potential patients. The vast majority
of these cases are Type 2 diabetics, and globally
~80% of them live in low-to middle income
developing economies9. Whilst a person
with T2D can live quiescently for a protracted
period of time, the elevated blood glucose is
silently causing extensive and systemic damage
to the body. The consequences of this non-
diagnosis add considerably to future individual
human suffering as well as contribute to
escalating healthcare costs.
A different and equally problematic situation
is the staggering number of adults estimated to
have IGT. Not surprisingly, individuals that have
IGT share many of the same characteristics as
Type 2 diabetics. IGT afflicts adults of advancing
age, who are overweight and possess insulin
regulatory problems. The disease has been
identified as a significant risk-factor for
cardiovascular disease, but also, in many cases
remains undetected21. In addition, people with
IGT have a 70% probability of developing
diabetes16, and the condition is still popularly
referred to as “pre-diabetes.” In 2003, it was
estimated that ~314 million people worldwide,
(8.2%) of adults in the age group 20-79 years
of age, had IGT10. Ominously the developing
economies of the SEA region had the highest
number of people with IGT at ~93 million
people as well as the highest prevalence rate
of 13.2%. The WPC region that includes China
was the next highest with an estimated ~78
million IGT patients representing 5.7% of the
population10. By 2015, some ~318 million people
worldwide, representing 6.7% of adults, were
estimated to have IGT.
The socioeconomic trend has continued and
the vast majority (69.2%) of those people lived
in low- and middle-income countries. However,
in an interesting reversal of trends the NAC
region was observed to have the highest
prevalence of IGT with ~51.8 million adults
afflicted, representing 13.9% of the adult
population. Another disturbing facet of the
data was that approximately half of adults with
IGT were under the age of 50 (~159 million),
reversing the temporal trend observed for T2D,
where onset is more likely to occur after 50
years of age. In addition, the IDF noted that
“it is important to note that nearly one-third
(29.8%) of all those who currently have IGT
[2015] are in the 20 to 39 age group and are
therefore likely to spend many years at high
risk”9. By 2040, the number of people with
IGT is projected to increase to ~482 million,
or 7.8% of the adult population9. Much of this
data is summarized in Figure 3 and represents
a global pictogram of the estimated IGT adult
populations dating from the period 2003-2040.
The combination of undiagnosed diabetes, the
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.
314M (2003)
318M (2015)
481M (2040)
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.
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 esti-
mates 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.
Nephropathy – caused by damage to the
blood vessels in the kidney leading to
impairment and loss of function.
Neuropathy – this can lead to systemic malfunction
throughout the body leading to problems with
urination, digestion, and erectile dysfunction
in men. In particular, peripheral neuropathy
of the feet leads to infection, ulceration, and
“diabetic foot” which can result in extreme
cases to amputation.
Gingivitis – leads to a variety of oral health
inflammation issues and increased risk of tooth
loss and cardiovascular problems.
Sleep Apnea – it is estimated that ~40% of
individuals with sleep apnea also have diabetes.
Such a simple elevation of blood glucose leads
to systemic, serious and often times life-threat-
ening complications for patients with diabetes.
This is all captured and summarized in Figure 4.
Cerebrovascular Disease
(Brain & Cerebral Circulation)
Retinopathy (Eyes)
Gingivitis (Oral Health)
Coronary Heart Disease
(Heart & Coronary Circulation)
Peripheral Neuropathy
(Nervous System)
Peripheral Vascular/Neuropathy
(Diabetic Foot)
Peripheral Vascular Disease
(Lower Limbs)
auto-immune response resulting in damage to
the beta cells of the pancreas. Over a period
of years, depletion of the beta cell population
occurs and when this reaches ~80-90% then
symptoms of diabetes begin to manifest in the
individual. At this point the pancreas fails to
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
occur due to ketoacidosis11-13.
Type 2 diabetes is the most common form of
the disease (see Figure 1) and is associated with
imperceptible onset and poor diagnostic rates.
Manifestation of T2D can occur through insulin
resistance and/or dysfunctional beta cells
of the pancreas11-13. In the case of insulin
resistance, target tissues such as liver, adipose
and muscle inefficiently respond towards
circulating insulin. This is followed by
uncontrolled glucose production in the liver
and decreased uptake of glucose by muscle
and adipose tissue (see Figure 5). A primary
cause of insulin resistance is obesity22,
although some authors postulate that insulin
resistance causes obesity for many individuals.
However, it is noteworthy that a significant
Glycogen Glucose
Stimulates glycogen
Stimulates glycogen
Tissue and Cells
(muscle, adipose,others)
Stimulates glucose uptake from blood
Figure 4: Secondary complications resulting from chronic
elevated blood glucose levels and untreated diabetes.
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).
Diabetic Complications
The regulation of blood glucose levels is
rigorously controlled in a healthy individual.
However, when the process goes awry, the
immediate consequence of elevated blood
glucose appears to be somewhat inconsequential.
Unfortunately, if this condition is left untreated
it can lead to life-threatening secondary diseases
affecting eyes, heart, kidneys, nerves and the
peripheral circulatory system11-13. Long term
sufferers of diabetes can be afflicted with:
Retinopathy – the network of blood vessels
to the retina of your eye can become blocked
resulting in blurred vision and ultimately
Cardiovascular Disease – is the most common
cause of death for individuals suffering from
diabetes. This includes congestive heart failure,
angina, myocardial infarction and peripheral
artery disease.
Diabetes – Carbohydrates and Lipids
The regulation of blood glucose in a healthy
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
tissue. This complex series of interactions is
summarized in Figure 5. In the case of diabetes,
we have noted above it is perceived as a “simple
disease” involving dysregulation of blood
glucose levels. In reality it is more appropriate
to consider the disease as a heterogeneous
amalgam of syndromes, where causal onset and
progression are still poorly understood. The
disease is ultimately characterized by elevated
blood glucose caused by a relative or absolute
deficiency of the hormone insulin11-13.
The causal onset of diabetes is different for
T1D versus T2D. In the case of T1D there is
ultimately an absolute deficiency of insulin.
This is caused by a poorly understood
percentage of obese individuals with insulin
resistance do not become diabetic, since the
presence of a functional pancreas compensates
by producing increased levels of insulin.
Type 2 diabetes actually develops in insulin
resistant patients when beta cell function
becomes impaired.
The cause of insulin resistance is also controver-
sial. However, compelling arguments have been
advanced suggesting that fat accumulation is
important to facilitate insulin resistance onset.
This adds a layer of additional complexity since
adipose tissue should not simply be regarded as
an energy store, but also is active in secreting a
variety of hormones such as leptin, resistin and
adiponectin22. Finally, it should be noted that
T2D beta cell dysfunction is also different than
for T1D. In the former case the pancreas retains
some beta cell function, but does not secrete
enough insulin to modulate elevated blood
glucose levels.
Elevation of blood glucose levels that occurs
as a result of either Type 1 or Type 2 diabetes
results in a slow, tsunami-like cascade effect.
The resultant impact is a series of metabolic
changes that occur primarily in the liver,
muscle, adipose tissue and the circulatory
system. They include:
Hyperglycemia – caused by the increased
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
triacylglycerol which is then packaged and
secreted as very-low-density-lipoproteins
((VLDL). Ultimately this leads to elevated
plasma chylomicrons and VLDL levels (Figure 6).
Insulin levels – these levels are either
non-existent (Type 1) or extremely low (Type
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.
Liver TG
Lipid Re-esterification
Liver TG CHO
Glucose Transport
Glycogen Synthesis Lipolysis
Glucose Transport
Glycogen Synthesis
Liver TG
Lipid Re-esterification
Liver TG CHO
Glucose Transport
Glycogen Synthesis Lipolysis
Glucose Transport
Glycogen Synthesis
Liver TG
Lipid Re-esterification
Liver TG CHO
Glucose Transport
Glycogen Synthesis Lipolysis
Glucose Transport
Glycogen Synthesis
Liver TG
Lipid Re-esterification
Liver TG CHO
Glucose Transport
Glycogen Synthesis Lipolysis
Glucose Transport
Glycogen Synthesis
Role of Lipids We have discussed above the
poorly understood causality of pancreatic beta
cell dysfunction. Recently, however Taylor and
colleagues from Newcastle University demon-
strated that fat accumulation in the pancreas is
a major causative factor of the under-produc-
tion of insulin by the beta cells23. In a small,
but pivotal clinical trial, nine obese people
diagnosed with T2D and a nine person control
cohort were measured for weight, triglyceride
levels in the pancreas (using functional NMR),
and insulin response. These measurements
were determined eight weeks before and eight
weeks post bariatric surgery. Prior to surgery
all T2D patients had significantly higher triglyc-
erides in their pancreas compared to controls.
After surgery both the T2D and control groups
had lost similar amounts of weight, as well as
comparable losses of systemic adipose tissue.
However, pancreatic triacylglycerol normalized
in the T2D cohort, whilst it did not change in
the control cohort. In addition, insulin produc-
tion 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
and limited insulin production23.
In the case of insulin resistance Shulman
has argued that “Insulin resistance is a complex
metabolic disorder that defies a single etiological
pathway. Accumulation of ectopic lipid metabolites,
activation of the unfolded protein response pathway and
innate immune pathways have all been implicated
in the pathogenesis of insulin resistance. However, these
pathways are also closely linked to changes in fatty acid
uptake, lipogenesis, and energy expenditure that
can impact ectopic lipid deposition. Ultimately,
accumulation of specific lipid metabolites (diacylglycerols
and/or ceramides) in liver and skeletal muscle may be a
common pathway leading to impaired insulin signaling
and insulin resistance”22. This is all highlighted and
summarized in Figure 6 A-D.
Lipids are insidiously associated with insulin
resistance. It has however been unclear whether
circulating lipids or tissue specific lipids result
in insulin resistance. After food ingestion
dietary carbohydrate (CHO) increases plasma
glucose and results in insulin secretion from
the pancreatic beta cells (Figure 6A). In the
skeletal muscle, insulin increases glucose
transport, allowing glucose entry and glycogen
synthesis. In the liver, insulin promotes
glycogen synthesis as well as de novo lipogenesis
and also inhibiting gluconeogenesis. In the
adipose tissue, insulin suppresses lipolysis and
promotes lipogenesis22.
In individuals who are fasting, insulin
production and release is decreased. The loss
of insulin mediation leads to an increase of
hepatic gluconeogenesis and promotes
glycogenolysis. Hepatic lipid production
diminishes while adipose lipolysis increases
(Figure 6B). In contrast for T2D, ectopic lipid
accumulation impairs insulin signaling. With
accumulation of intracellular lipid, insulin
mediated skeletal muscle glucose uptake is
also impaired. As a result, glucose is diverted
to the liver. In the liver, increased liver lipids
also impair the ability of insulin to regulate
gluconeogenesis and activate glycogen synthesis.
In contrast, lipogenesis remains unaffected,
and together with the increase delivery of
dietary glucose, leads to increased lipogenesis
and worsening. Impaired insulin action in the
adipose tissue allows for increased lipolysis
which promotes re-esterification of lipids in
other tissues (e.g. liver) and further exacer-
bates insulin resistance. This is coupled with a
decline in pancreatic beta cells, and hyperglyce-
mia develops (Figures 6C and 6D)22. It is
evident from Shulman’s work and others that
lipid deposition in specific organs and tissue
plays a critical role in inducing insulin
resistance and appears to be organ/tissue specific.
Role of Carbohydrates We have inferred above
that elevated blood glucose levels are an effect
of diabetes. However, it is unclear as to the role
of carbohydrates in the mechanistic causal
onset of the disease. In part this is due to the
misunderstanding of the intimate relationship
of glucose with insulin and their combined
roles in insulin resistance. The concept of
“insulin dependent” glucose uptake by muscle
and other tissue has been propagated since the
1950s. Indeed, insulin does stimulate the
translocation of the glucose transporter Glut-4
from the cell cytoplasm of muscle tissue to the
cell membrane. This facilitates glucose uptake
in an insulin dependent mechanism as
compared to the basal state of the cell without
insulin present24. However, there are numerous
other glucose transporters such as Glut-1,
which is responsible for basal glucose uptake
in muscle by an insulin-independent
A study in 1983 indicated that in post-absorptive
human subjects 75-85% of glucose uptake is
noninsulin-mediated and provided additional
evidence that insulin may modestly increase
glucose uptake merely by providing additional
transport sites26. A subsequent study in 2001
concluded that there is a sufficient population
of glucose transporters in cell membranes at
all times to ensure enough glucose uptake to
satisfy the cell’s respiration, even in the
absence of insulin. Insulin can and does
increase the number of these transporters in
some cells but glucose uptake is never truly
insulin dependent – in fact, even in uncon-
trolled diabetic hyperglycemia, whole body
glucose uptake is inevitably increased24.
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 increas-
es glucose uptake into tissues, particularly
muscle. In fact, this is not the case. It has been
shown that insulin concentrations that are
within the normal physiological range lowers
blood glucose through inhibiting hepatic
glucose production without stimulating
peripheral glucose uptake24.
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.
Contrary to most textbooks and previous
teaching, glucose uptake is actually increased
in uncontrolled diabetes and decreased by
insulin administration! The explanation for
this is that because, even in the face of insulin
deficiency, there are plenty of glucose transporters
in the cell membranes. The factor determining
glucose uptake under these conditions is
the concentration gradient across the cell
membrane; this is highest in uncontrolled
diabetes and falls as insulin lowers blood
glucose concentration primarily (at physiological
insulin concentrations) through reducing
hepatic glucose production24. In other words,
glycogen release of glucose by the liver is the
causative reason that blood glucose rises, and
insulin when present, mediates this action by
inhibiting glucose release by the liver27.
It is often believed that experimental and
observational studies suggest that dietary
glucose intake is associated with the develop-
ment of T2D. However, when one considers
this issue in isolation and independent of
obesity related issues, it is unclear whether
alterations in glucose intake can account for
differences in diabetes prevalence among
populations. In that light, Sabu and colleagues
recently investigated this very matter and
noted that numerous countries had high
diabetes prevalence but low obesity rates28.
The list included a diverse range of socioeco-
nomic and culturally different countries that
included France, Romania, Bangladesh,
Philippines and Georgia. They also noted
that these trends were also dys-synchronous
within countries. For example, in Sri Lanka
the diabetes prevalence rate rose from 3% in
the year 2000 to 11% in 2010, while its obesity
rate remained at 0.1% during that time period.
Conversely, diabetes prevalence in New
Zealand declined from 8% in 2000 to 5%
in 2010 while obesity rates in the country
rose from 23% to 34% during that decade28.
The authors ultimately concluded that every
150 kcal/person/day increase in sugar availability
(in the form of both glucose and fructose) was
associated with increased diabetes prevalence
after testing for potential selection biases and
controlling for all other variables. The impact
of glucose/fructose on diabetes was independ-
ent of sedentary behavior and alcohol use, and
the effect was modified but not confounded by
obesity or overweight. Duration and degree
of sugar exposure correlated significantly with
diabetes prevalence in a dose-dependent
manner, while declines in sugar exposure
correlated with significant subsequent
declines in diabetes rates independently of
other socioeconomic, dietary and obesity
prevalence changes. This is one of the first
studies to indicate a direct correlation between
carbohydrate intake and diabetes onset28.
Human Microbiome Twist
The discussions so far clearly indicate that
our understanding of the “simple disease” of
diabetes is far from complete. The complex
intertwining of carbohydrate and lipid metab-
olism is just now being unraveled. However, a
new factor has recently been interjected in the
form of the intestinal human microbiome29.
The microbiome refers to ~100-300 trillion
microbes, composed of ~10,000 different
species that constitute 1-3% of our body
weight and contain an estimated 8 million
protein-coding genes. These microorganisms
play an intimate and undetermined role in the
health and pathobiology of the human host2.
Recently our knowledge of the microbiome in
relation to the function of the human digest
system has increased immensely due to the
development of new analytical methods such
as high-throughput metagenomic sequenc-
ing. This has enabled researchers to identify
possible effects of the microbiome on human
metabolism, including its potential role in
metabolic disorders like obesity and T2D29.
Recently the potential role of the gut microbiome
in these metabolic disorders has been identified.
For example, obesity is associated with changes
in the composition of the intestinal microbiota,
and the obese microbiome seems to be more
efficient in harnessing energy from the diet.
There is hope that such differences in gut
microbiota composition might function as
early diagnostic markers for the development
of T2D in high-risk patients.
However, for the present time, it is unclear
as to the exact role our ~100-300 trillion
microbial tenants play in modulating the
biochemical elements associated with
diabetes. Do they hold the key to innovative
new diagnostic and therapeutic protocols, or
are these microbes an exponential additive to
the indeterminate nature of diabetes?
Recent studies suggest that the human micro-
biome will add several new dimensions of
complexity to consider in the management and
treatment of diabetes and related disorders.
For example, non-caloric artificial sweeteners
(NCAS) have been in use for over a century.
They have provided a vehicle for ingestion of
“sweet” foods without the accompanying high
calorific content of the sugar/carbohydrate. In
the past 20 years NCAS have been introduced
into a wide variety of cereals, sodas and
desserts and marketed as alternative food and
beverage sources for individuals suffering from
IGT and diabetes. Most NCAS pass through the
human gastrointestinal tract without being
digested and thus encounter the human
microbiome, with a surprising and confounding
outcome for diabetic patients!
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
composition of gut microorganisms30. 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
a high-fat diet. Although saccharin, sucralose,
and aspartame are three different compounds,
“the effects were quite similar to each
other”30. When the sweetener-fed mice were
given antibiotics to clear their gut of bacteria,
their blood sugar levels dropped back down
to normal. To gather more evidence of the
relationship between artificial sweeteners,
gut bacteria, and blood sugar levels, the
researchers transferred feces from mice that
drank artificially sweetened water into mice
that never had. Somewhat surprisingly, blood
sugar levels rose in the recipients
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
those who did not30. People who used artifi-
cial sweeteners also tended to have higher fpg
levels and IGT. Finally, the researchers recruit-
ed seven volunteers, five men and two women,
who normally didn’t eat or drink products
with artificial sweeteners and followed them
for a week, tracking their blood sugar levels.
The volunteers were given the FDA’s maximum
acceptable daily intake of saccharin from day
two through day seven. By the end of the
week, blood sugar levels had risen in four
of the seven people. Transfers of feces from
people whose blood sugar rose increased blood
sugar in mice, more evidence that the artificial
sweetener had changed the gut bacteria. The
authors concluded that NCAS were extensively
introduced into our diets with the intention of
reducing caloric intake and normalizing blood
glucose levels without compromising the
human ‘sweet tooth’. The findings suggested
that “NCAS may have directly contributed to enhancing
the exact [diabetic] epidemic that they themselves were
intended to fight”30.
Diabetes Complexity and Precision Medicine
We have suggested elsewhere that the percep-
tion of diabetes as a “simple disease” has led
to misunderstanding, poor diagnosis, ineffi-
cient treatments and a global prognoses of the
disease that is now the “diabetic iceberg tip
of despair.” The primary focus has been on
the treatment of the effect of elevated blood
glucose levels, and not a therapeutic paradigm
that considers human systems level complexi-
ty, variability and causality of diabetes. Recall
that PM “ the tailoring of medical treatment to the
individual characteristics of each patient”7. Does a
reconsideration of the causal mechanisms of
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?
Earlier, diabetes was described as a heterogeneous
mix of syndromes, each having a characteristic
and differentiating polygenic etiology. The
cardinal features are chronic hyperglycemia
along with disturbances in the metabolism of
carbohydrates, lipids and proteins (Figures
5 and 6). These disturbances are linked to
deficiencies in insulin production and/or
insulin resistance. This globally impaired
metabolism, attended by varying degrees of
hyperglycemia and lack of glycemic balance,
is linked to inflammation and wide-ranging
impact on all body systems11-13. This systemic
impact on the diabetic patient leads to
significant disease variability within individuals.
However, it is also important to consider the
individual susceptibility to diabetes. For
example more than 50 loci have been
implicated as determinants of T1D risk using
the genome wide association studies (GWAS)
approach31. However, it is likely that only the
HLA genes, INS and PTPN22 loci have signif-
icant effects on disease risk. It is not clear
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.
In T2D GWAS studies across diverse popula-
tions 70 loci associated with T2D have been
identified, although it is not known how many
of these loci have a significant effect on the
risk or expression of the disease. Significant
association of single nucleotide polymor-
phisms (SNPs) in the CDKAL1, CDKN2A/B,
TCF7L2 and CAPN10 genes might be associated
with equivalent clinical subtypes32. If this
were the case, it might explain why sub-
populations of individuals with T2D responded
differentially to drug monotherapy across
different categories of anti-diabetic drugs.
From retrospective analysis of electronic
medical records, support for at least 3 distinct
subtypes from a clinical population of T2D
exists33. The first subtype was associated with
diabetic nephropathy and diabetic retinopathy,
while subtype 2 was associated with cancer
malignancy and cardiovascular diseases. The
third subtype was also associated with cardio-
vascular diseases, plus HIV infections, allergies
and neurological diseases. Genetic association
analysis of the subtypes found 1279 SNPs that
mapped to 425 unique genes. In the second
subtype 1227 SNPs mapped to 322 genes and
the third showed 1338 SNPs mapping to 437
genes. These 3 subtypes may substantiate the
lower end of the range of subtypes for T2D
(Figure 1). We suggest a speculative upper range
of ~15 for both T1D and T2D as being about
20% of the gene loci in either case (one might
argue that this is reasonable based on genetic
association analysis of oncologic diseases). This
would represent a reasonable estimate of the
proportion of specific gene loci likely to have
significant (detectable) impact on either risk or
expression of disease components. As genomic
studies gain sensitivity it may be necessary to
identify and link more subtypes.
Across all of diabetes, it seems that there are
subtypes that may respond differentially to
various single and combined therapies. In GD,
there are clearly, at least 3 subtypes31, whereas
in T1D and T2D there are grounds to suggest
somewhere between 3 and 15 subtypes of
treatment significance33-35. This means that in
approaching the treatment of diabetes, there
could be anywhere from 9-33 or more treatable
subtypes, where each subtype had treatment-
sensitive 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 sin-
gle or combined therapies. One of the goals of
precision medicine is to be able to determine
the exact subtype sensitivity to therapies. This
idea will be further developed below.
Treatment, in all cases, has the targets of
glycemic control, both in the acute and long
term sense, and the prevention and reduction
of complications, including disturbances in
lipid and protein metabolism and pathology
in other body systems. Contemporary practi-
tioners have a wide array of evidence-based
treatment options and clinical decision support
systems for choosing among these options.
These options include:
l Drug mono-therapy
l Combination drug therapies
l Non drug therapies
l Combination therapy of drug therapies and
non-drug therapies
l Lifestyle factor modifications
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
apply, for example clinical decision support
systems for option management in the
treatment of diabetes. These include CureHunter
Inc. (, Therametrics
( and BioVista’s
( platforms. For illustration,
we will highlight how CureHunter provides
PM decision support at a treatment level and
how easy it is to use the platfrom to provide
given drug, biomarker, or active biological
agent. The platform consists of five modules:
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
3. Array Module- all data is arrayed into a
drug-disease-outcome relationships
database where each relation is an
empirically weighted contributor to
clinical efficacy
4. Analysis Module- using Network Graph
Theory and a suite of algorithms to
determine the most centric and similar
components of clinical efficacy for all
5. Prediction Module- Answer System
analytics automation layer with graphic
user interface (GUI) for real time output
of new indications with high probability
of clinical success prediction
The CureHunter platform facilitates the capture
and automated analysis of all of published
biomedical knowledge (in the USNLM) demon-
strating a functional role in the clinical efficacy
potential of over 250,000 active biological
molecules, markers, mechanisms, and drugs
operating in over 11,600 disease states. The
following table (Table 2) illustrates the typical
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
ɑ-glucosidase inhibitors Acarbose inhibits intestinal slows carbohydrate 3
Miglitol ɑ-glucosidase digestion/ absorption
Amylin mimetics Pramlintide activates amylin receptors decreases glucagon secretion 1*
slows gastric emptying
increases satiety feelings
Biguanides Metformin activates AMP-kinase decreases hepatic glucose 4.5
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 Bromocriptine activates dopaminergic modulates hypothalamic 1*
receptors regulation of metabolism
DPP-4 inhibitors Sitagliptin inhibits DPP-4 activity, increases insulin secretion 2
Vildagliptin increasing postprandial decreases glucagon secretion
Saxagliptin active incretin (GLP-1, GIP)
Linagliptin concentrations
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 4*
Nateglinide cell plasma membranes
SGLT2 inhibitors Canagliflozin inhibits SGLT2 in the Blocks glucose reabsorption 1*
Dapagliflozin proximal nephron by the kidney, increasing
Empagliflozin glucosuria
Sulfonylureas Glyburide/glibenclamide shuts ATPK channels on - increases insulin secretion 3
Glipizide cell plasma membranes
Thiazolidinediones (TZDs) Pioglitazone activates nuclear increases insulin sensitivity 3
Rosiglitazone transcription factor PPAR-β 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.
Based upon Inzucchi et al. 201557 and CureHunter output.
* Denotes Effectiveness Index value based on much more limited data and therefore lower confidence level scores.
cost-effectiveness support too. Pharmacoeco-
nomics are pivotal to the involvement and
decision making of the diabetic patient in joint
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
categories of common glucose lowering agents
have a variety of mechanisms and actions and
include direct insulin administration, and the
popular SGLT2 proximal nephron inhibitors
and this information is all captured and
summarized in Table 1.
We have suggested that T2D involves distur-
bances not just in carbohydrate metabolism
but also in lipid and protein metabolism,
inflammatory pathways, and microbiome and
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.
In the search for further subtypes, it may be
helpful to recognize that drug class therapy
failure may help to characterize subtype-
sensitivity profiles. For example, in GD the
commonest management includes prenatal
care, nutrition therapy and occasional use
of insulin and other drug therapies. Prenatal
care and nutrition therapy are highly effective
therapies for glycemic control in gestational
In the era of PM how can such tools be utilized
to address such complex issues. As one
example CureHunter Inc. utilizes an Integrated
Systems Biology platform which effectively
synthesizes all the data and knowledge
available from literature sources and clinical
trials to produce a clinical outcome database.
This database is structured for autonomous
prediction of new disease indications for any
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
meta-analysis output to the point of care
(POC) provider is to support decision making
about drug and related therapies with a higher
net benefit to the patient populations being
served. Any provider is able to drill down into
the articles behind any effectiveness index
for any drug or related therapy with one click
access in order to satisfy themselves about the
provenance of the engine’s recommendations.
In the subsequent table (Table 3) the typical
output is shown for a query about T2D using
the CureHunter engine. It shows the top
ranked related therapies for treatment based
upon an analysis of all relevant articles. Just
as with drug therapies, 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
The CureHunter recommendations for the
treatment of T2D cover drug monotherapy,
and comparison between CureHunter output
measured in milliseconds with the recommen-
dations of typical national agencies for
diabetes. For example, the recent (NICE, 2015
evidence/full-guideline-2185320349) for
the treatment of T2DM took 6 years to
develop. The recent 2015 updated guidelines
from the Canadian Diabetes Association
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
Related Diseases: Related Drugs/ Related Therapies: CureHunter
602 Bio-Agents: 227 Effectiveness Index
Related/Drug/ Outcome Trial/Study All Context
Important Statements Statements Statements
Bio-Agent (IBA)
Insulin 273 795 5426 3
Metformin 106 244 938 4.5
Acarbose 34 78 179 3
Pioglitazone 32 108 326 2
Sitagliptin 29 45 157 3
Glyburide 27 56 239 3
Rosiglitazone 26 61 256 2
Glucagon-like peptide 1 25 34 357 3
Exenatide 18 38 182 2
Glargine 16 48 136 3
Glipizide 14 27 95 3
Alpha-glucosidases 14 23 82 2
Thiazolidinediones 14 12 192 3
Troglitazone 13 27 109 4
Miglitol 5 11 32 1
Pramlinitide 1 3 16 1
Covesevalam 7 5 40 1
Bromocriptine 1 2 17 1
Table 2:
Type 2 Diabetes mellitus drug therapy
The first column indicates the related drug or bio-agent. 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.
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.
Related/Therapy Outcome Trial/Study All Context
Procedure Statements Statements Statements
Gastric bypass 53 24 147 3
Bariatric surgery 53 16 157 4
Gastrectomy 15 19 83 3
Caloric restriction 7 6 45 4
Diet therapy 5 7 41 1
Aftercare 3 13 40 3
Resistance training 3 8 32 4
Renal dialysis 2 14 68 1
Angioplasty 2 14 24 3
Transplants 2 5 50 5
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
Electroacupuncture 2 0 4 1
Related Diseases: Related Drugs/ Related Therapies: CureHunter
602 Bio-Agents: 227 Effectiveness Index
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
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
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 anti-
diabetic 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 subtype-
sensitive therapies to specific subtypes, one of
the essential features of precision treatments.
There are numerous studies supporting the
use of combination therapies both for drug
combinations and combinations of drug ther-
apy with non drug approaches36,37. In some
cases, positive treatment outcomes may not
be directly attributable to specific components
of the combination therapy, across or within
subtypes. For treatment failure, however, it is
possible to assert that the specific combination
was ineffective across or within subtypes.
Alternative Approach
Earlier, we suggested that approaches of preci-
sion medicine (treatment) to diabetes need to
be reasonable and rational, where reasonable
includes the practical and the pragmatic38-41.
Tricorder technology refers to a form of
precision nanomedicine using microfluidics
with a lab-on-a-chip, where small samples of a
person’s body fluid can be analyzed at low cost
within a few minutes. Tricorder technology
is able to diagnose at least 15 conditions and
monitor vital signs for 72 hours. As tricord-
er technology evolves, it will be applied to
the problem of identifying subtypes that are
sensitive to specific treatment approaches42,43.
If tricorder technology is combined with a
network biology approach plus retrospective
analysis of large populations with known sub-
types looking for the most successful treatment
outcomes, the resulting trivalent convergence
should become a powerful strategy for reduc-
ing the global burden of diabetes, particularly
for T2D33-35,42. As trivalent convergence meth-
odology identifies and characterizes new T2D
subtypes, we see precision treatments evolving
along the following lines:
l Drug mono-therapy
l Combination drug therapies36,37
l Non drug therapies44
l Combination therapy of drug therapies and
non-drug therapies
l Lifestyle factor modifications45-48
l Nutritional adjustment - addition or removal
of nutrients49-52
l Microbiome adjustments53-56
Compelling evidence exists to support the
existence of T2D subtypes of differential
sensitivity to different drug monotherapies,
varying combination drug therapies and several
related therapies. This points to T2D being a
constellation of subtypes where differentiating
characteristics include single or combined dys-
functions of many aspects of metabolism, not
just limited to carbohydrate metabolism.
It would seem that achieving the goals of PM
for T2D could be accelerated by using a com-
bination of clinical decision support systems
along with trivalent convergence methodol-
ogy and using a broad spectrum approach to
treatment that included drug monotherapy,
combination drug therapies, related non
drug therapies, combination therapy of drug
therapies and non-drug therapies, lifestyle
factor modifications, nutritional adjustment,
and microbiome adjustment. Beyond T2D we
argue that this multi-pronged approach would
satisfy the goals of PM for many other chronic
conditions and diseases and simultaneous-
ly involve patients in the decision-making
process, affording them more autonomy and
responsibility for self-care along with choices
in cost-effective treatments. This path will,
by its very nature, make significant inroads to
the huge proportion of national GDP currently
spent on treatments in health care.
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
disease onset and therapeutic treatment over
the past half century. The impact that conven-
tional medicine, and fledgling “P-Medicine” in
the form of PM, has had on diabetes has been
exceedingly limited and disappointing. The
resultant outcome has been a global pandemic
that is staggering in its proportion, and yet
reflects just the tip of the iceberg of diabetic
despair. It is clear that diabetes results from
a complex interplay of both carbohydrate
and lipid metabolism mediated by a suite of
hormones such as insulin and glucagon. But
the clandestine role of lipids manifested in
the form of fatty liver and fatty pancreas in
diabetes onset and progression is just now
being spotlighted.
Does this new insight afford innovative and
novel approaches to augment the current tired
and somewhat one-dimensional treatment
regimes that provide a palliative to elevated
blood sugar? At this time, it is unclear. There is
a dire human health and socioeconomic need
for innovation and creation of new approaches
to the prevention, diagnosis, treatment and
improved prognosis of this insidious disease.
The advent of PM has been accompanied by
the usual hyperbole and enthusiastic promise
of a “brave new world” in human healthcare,
with the past failures of personalized medicine
conveniently forgotten. Do the spectra of the
twin icebergs of diabetes and PM afford a
complementary opportunity? The old cliché
“only time will tell” is sorely overused, and
woefully inadequate given the current crises
of pandemic diabetes, and massive under-
diagnosis, accompanied by IGT and metabolic
syndrome. It is imperative that PM affords new
insights and paradigms into the diagnosis,
management and treatment of diabetes
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 character-
istics of each patient and…..classify individ-
uals 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
treatment”7. Maybe this is just what we need
and what the doctor should order!
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.
EA combined with the oral insulin sensitizer
rosiglitazone improved insulin sensitivity in
rats and humans with type II diabetes mellitus
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.
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 erythro-
cytes 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 exhib-
ited 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 incu-
bation 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 diabe-
tes 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
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:
Combination Therapy Description
Fibrate (agonist of peroxisome proliferator-
activated receptor-ɑ, PPR-ɑ) monotherapy is
effective for the treatment of hypertriglyceri-
demia, 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 combi-
nation 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
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.
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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), patho-
physiology, 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.
We would like to thank Mr. Andrew Jackson
( 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.
... In an exceptional way for a non-infectious disease, in 2007 the Centres for Disease Control and Prevention in USA categorised the increase in diabetes incidence as an "epidemic" (Home et al., 2008). According to the International Diabetes Federation (IDF), disease prevalence is expected to reach 642 million persons in 2040 compared to 415 million people who have diabetes worldwide in 2015 (Yensen and Naylor, 2016). ...
... Gestational diabetes is a clinical condition of elevated blood glucose levels above the normal values that is firstly recognised during pregnancy. It is established as a consequence of hormonal and inflammatory changes of pregnancy that lead to insulin resistance (Yensen and Naylor, 2016), ...
... The normal consequences of gestational diabetes are as follow: 60% of cases of gestational diabetes return to normal non-diabetic state after termination of pregnancy, 36% develop T2D and only 4% convert to T1D. Therefore, gestational diabetes is regarded as an added risk factor for developing diabetes in the future (Yensen and Naylor, 2016). ...
Full-text available
Hyper-coagulability (elevated thrombin) is a feature of T2D. There is an emerging evidence of a correlation between the genetic basis of hypercoagulation and T2D. We hypothesized that thrombin affects insulin activity and/or exercise responses in human skeletal muscle cells which could link the hypercoagulability and insulin resistance in T2D. Furthermore, we hypothesized that the metabolic benefits of exercise are decreased in cultured muscle cells from T2D patients. Cultured human myotubes were used aiming to explore the effects of thrombin on insulin signalling and glucose uptake as well as the effect of thrombin on metabolic function in the presence and absence of EPS as an in vitro model of exercise. Furthermore, to explore the effects of EPS on metabolic function in muscle cell cultures derived from T2D and non-diabetic control subjects. The findings of the first three chapters of this thesis demonstrated that thrombin was shown to have multiple metabolic effects represented by a decrease in insulin stimulated IRS1 and Akt activation which was mediated through PKCα, but thrombin had no effect on the parallel insulin-stimulated aPKC and AS160 pathway. Thrombin directly increased glucose uptake through an AMPK mediated mechanism. Furthermore, the increase in AMPK activity, elevation of glucose uptake and the rise in cytokine release in response to EPS (above basal values) that were noted with non-thrombin treated myotubes was lost upon thrombin treatment. The key findings of the last chapter there were, AMPK activation and glucose uptake increased in response to EPS in control myotubes, and EPS enhanced the effect of insulin on glucose uptake and distal insulin signalling pathway (AS160). In diabetic myotubes, EPS did not increase AMPK activation and glucose uptake, nor enhanced the action of insulin. Thus, hypercoagulation associated with diabetes could be involved in multiple metabolic effects in skeletal muscle including insulin signalling, exercise signalling, proinflammatory pathways, and glucose uptake. There is an intrinsic defect in diabetic myotubes represented by defective AMPK, glucose uptake and distal insulin signalling in response to EPS that are consistent with the changes observed in vivo.
... The arena of clinical trials for new drugs is one form of medical knowledge generation where crowdsourcing via patient-focused social media platforms has been employed for some years as an alternative to the expensive traditional format of the standard clinical trial. The accumulation of big data that is afforded by the new digital media technologies is positioned as an innovative way forward for health care, supposedly providing better, more informed and more economically efficient medical treatment (Yensen and Naylor, 2016). ...
Full-text available
Medication development plays a prominent role in the fight against chronic illness such as hypertension, diabetes mellitus, asthma, etc. Without proper testing and methods for management of drug data, the disease management would fail. Providers rely on pharmaceutical companies to provide research data in widespread formats and pharmaceutical companies rely on hospitals for electronic medical record data (EHR) and for pharmacy refill records from insurance companies. Big Data Analytics (BDA) provides an excellent basis to examine and manage terabytes of data that comprises drug data and can manage all aspects of drug development. This survey paper examines the current literature to determine what is current practice in the area of Big Data analytics and medication management.
... The greatest increase in prevalence is, however, expected to occur in Asia and Africa, where most patients will probably be found by 2030 [21]. About 415 million people have diabetes in the world and more than 35.4 million people in the Middle East and North Africa (MENA) Region; by 2040 this will rise to 72.1 million [22]. There were 3.4 million cases of diabetes in Saudi Arabia in 2015 [23]. ...
Aim: To screen for diabetes among students of Jeddah University in the KSA. Methodology: A cross sectional descriptive study involved n= 42 students of Jeddah University in the KSA who are 18 to 21 years of age. Random sampling methods used, data were collected by measuring random blood glucose level (RBG) of all participants by using glucometer (Bioniam GM300), for four months from 2 April 2014 to 12 of August 2014. Results: 4.76% students had a high level of blood sugar, which classified as prediabetes, the most of students showed an ideal level of random blood glucose to be 95.25% in which RBS level below 140 mg/dL. Conclusion: Male students of Jeddah are free from diabetes mellitus.
... Hassas tıp, diyabet(Yensen, Naylor 2016) ve alzheimer hastalığı(Waring, Naylor 2016) gibi farklı alanlarda günlük uygulamada, hastaların tedavisini sorgulamada veya uygulamaya koymaya başlarken kullanılmaktadır. Özellikle onkoloji topluluğu, hassas tıbbı, çeşitli kanserlerin tanı ve tedavisinde sık kullanmaktadır(Buchman et al. 2016). ...
Full-text available
Genel tıp anlayışında hastalıklar ve salgınlar üzerine odaklanılmış, tedavilerin bir sınırı olabileceği düşünülerek halkı hastalıktan korumak adına, "önce zarar verme" ilkesi benimsenmiştir. İlerleyen zamanlarda ise "hastalık yoktur hasta vardır" anlayışı benimsenmeye başlanmıştır. Günümüzde ise "hastalık yoktur, hasta vardır" anlayışında bilim ve teknolojinin gelişimiyle somut adımlar atılmış ve oldukça ilerleme kaydedilmiştir. 21. yy.'dan günümüze ivme kazanan bu anlayış mevcut tıp bilgisinde ve eğitiminde de gelişmeleri ve yeni kavramları beraberinde getirmiştir. Kişiselleştirilmiş tıp veya hassas tıp olarak geçen kavramlar bunlardan bazılarıdır. İnsan Genom Projesi öncülüğünde genom araştırmaları kişiselleştirilmiş tıbbın ön plana çıkmasında oldukça etkili olmuştur. Kişiselleştirilmiş tıp genel olarak bir kişinin sağlık risklerini etkileyen genetik, fenotipik ve çevresel faktörleri belirlemeye çalışmaktadır. Kişiselleştirilmiş tıp, daha etkin, yan etkilere daha az eğilimli ve mevcut tedavilerin çoğundan potansiyel olarak daha maliyet etkili, her bir hastanın kendine has özelliklerine uyarlanmış tedaviler sunmayı vaat etmektedir. Kişiselleştirilmiş tıbbın amaçları, insanlar sağlıklıyken veya hastalığın ilk aşamalarındayken önleyici sağlık stratejileri ve ilaç tedavilerini optimize etmek için hastalığın moleküler temelinden yararlanmaktır. Bu faktörler her insan için farklı olduğundan, hastalığın doğası, başlangıcı, seyri ve ilaca veya diğer müdahalelere nasıl tepki verebileceği bireyseldir. Kişiselleştirilmiş tıbbın sağlık hizmeti sağlayıcıları ve hastaları tarafından kullanılması için, bu bulguların hassas teşhis testlerine ve hedefli tedavilere çevrilmesi gerekmektedir. Genel hedef her bireyin tıbbi bakımını ve sonuçlarını optimize etmek olduğundan, tedaviler, ilaç tipleri ve dozajlar ve/veya önleme stratejileri kişiden kişiye farklılık gösterebilir, bu da sağlık hizmetlerinin benzersiz şekilde özelleştirilmesine neden olmuştur. Bu çalışmada günümüzde sıkça kullanılan fakat Türkçe kaynağın fazla bulunmadığı kişiselleştirilmiş tıp kavramı ve bileşenleri açıklanmıştır.
Present disease management system focuses on identifying single disease and its curable methods. This approach is not suitable for patients with diabetes in present and in future. Diabetes must be concerned with healthcare providers (health care constituents) with new and evolving attributes of diabetes risk factors (like gut micro-biota, Hct, Plt, Hgb, and MPV) through data-intensive technology (Big Data), which has capability to ensure the accuracy delivery in the line of diabetes patients’ care prediction. Having an understanding of diabetes-related comorbid conditions is crucial when dealing with diabetes because comorbidities are known to significantly increase the risk of getting serious, and ultimately, the cost for the medication will increase. Comorbidity is the incidence of additional persistent conditions in the same patient with a prominent disease and occurs frequently among patients with diabetes. The main purpose of study is to identify the impact of data-driven comorbidity effects in diabetes patients by predicting their risk status through data-intensive technology (Big Data), as uncovered problem domain in computer application. Hence, this effort would open up more impacts to enhance the research potentials on the killer disease, diabetes mellitus.
Full-text available
Objective: This study determined whether the decrease in pancreatic triacylglycerol during weight loss in type 2 diabetes mellitus (T2DM) is simply reflective of whole-body fat or specific to diabetes and associated with the simultaneous recovery of insulin secretory function. Research design and methods: Individuals listed for gastric bypass surgery who had T2DM or normal glucose tolerance (NGT) matched for age, weight, and sex were studied before and 8 weeks after surgery. Pancreas and liver triacylglycerol were quantified using in-phase, out-of-phase MRI. Also measured were the first-phase insulin response to a stepped intravenous glucose infusion, hepatic insulin sensitivity, and glycemic and incretin responses to a semisolid test meal. Results: Weight loss after surgery was similar (NGT: 12.8 ± 0.8% and T2DM: 13.6 ± 0.7%) as was the change in fat mass (56.7 ± 3.3 to 45.4 ± 2.3 vs. 56.6 ± 2.4 to 43.0 ± 2.4 kg). Pancreatic triacylglycerol did not change in NGT (5.1 ± 0.2 to 5.5 ± 0.4%) but decreased in the group with T2DM (6.6 ± 0.5 to 5.4 ± 0.4%; P = 0.007). First-phase insulin response to a stepped intravenous glucose infusion did not change in NGT (0.24 [0.13-0.46] to 0.23 [0.19-0.37] nmol ⋅ min(-1) ⋅ m(-2)) but normalized in T2DM (0.08 [-0.01 to -0.10] to 0.22 [0.07-0.30]) nmol ⋅ min(-1) ⋅ m(-2) at week 8 (P = 0.005). No differential effect of incretin secretion was observed after gastric bypass, with more rapid glucose absorption bringing about equivalently enhanced glucagon-like peptide 1 secretion in the two groups. Conclusions: The fall in intrapancreatic triacylglycerol in T2DM, which occurs during weight loss, is associated with the condition itself rather than decreased total body fat.
Full-text available
Type 2 diabetes (T2D) is a serious disease. The gut microbiota has recently been identified as a new potential risk factor in addition to well-known diabetes risk factors. To investigate the gut microbiota composition in association with the dietary patterns in patients with different glucose tolerance we analyzed 92 patients: with normal glucose tolerance (n=48), prediabetes (preD, n=24), T2D (n=20). Metagenomic analysis was performed using 16SrRNA sequencing. The diet has been studied by a frequency method with a quantitative evaluation of food intake using a computer program. Microbiota in the samples was predominantly represented by Firmicutes, in a less degree by Bacteroidetes. Blautia was a dominanting genus in all samples. The representation of Blautia, Serratia was lower in preD than in T2D patients, and even lower in those with normal glucose tolerance. After the clustering of the samples into groups according to the percentage of protein, fat, carbohydrates in the diet, the representation of the Bacteroides turned to be lower and Prevotella abundance turned to be higher in carbohydrate cluster. There were more patients with insulin resistance, T2D in the fat-protein cluster. Using the Calinski-Harabasz index identified the samples with more similar diets. It was discovered that half of the patients with a high-fat diet had normal tolerance, the others had T2D. The regression analysis showed that these T2D patients also had a higher representation of Blautia. Our study provides the further evidence concerning the structural modulation of the gut microbiota in the T2DM pathogenesis depending on the dietary patterns.
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Type 2 diabetes (T2D) is a heterogeneous complex disease affecting more than 29 million Americans alone with a rising prevalence trending toward steady increases in the coming decades. Thus, there is a pressing clinical need to improve early prevention and clinical management of T2D and its complications. Clinicians have understood that patients who carry the T2D diagnosis have a variety of phenotypes and susceptibilities to diabetes-related complications. We used a precision medicine approach to characterize the complexity of T2D patient populations based on high-dimensional electronic medical records (EMRs) and genotype data from 11,210 individuals. We successfully identified three distinct subgroups of T2D from topology-based patient-patient networks. Subtype 1 was characterized by T2D complications diabetic nephropathy and diabetic retinopathy; subtype 2 was enriched for cancer malignancy and cardiovascular diseases; and subtype 3 was associated most strongly with cardiovascular diseases, neurological diseases, allergies, and HIV infections. We performed a genetic association analysis of the emergent T2D subtypes to identify subtype-specific genetic markers and identified 1279, 1227, and 1338 single-nucleotide polymorphisms (SNPs) that mapped to 425, 322, and 437 unique genes specific to subtypes 1, 2, and 3, respectively. By assessing the human disease-SNP association for each subtype, the enriched phenotypes and biological functions at the gene level for each subtype matched with the disease comorbidities and clinical differences that we identified through EMRs. Our approach demonstrates the utility of applying the precision medicine paradigm in T2D and the promise of extending the approach to the study of other complex, multifactorial diseases.
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Background: Behavioral programs may improve outcomes for individuals with type 2 diabetes, but there is a large diversity of behavioral interventions and uncertainty about how to optimize the effectiveness of these programs. Purpose: To identify factors moderating the effectiveness of behavioral programs for adults with type 2 diabetes. Data sources: 6 databases (1993 to January 2015), conference proceedings (2011-2014), and reference lists. Study selection: Duplicate screening and selection of 132 randomized, controlled trials evaluating behavioral programs compared with usual care, active controls, or other behavioral programs. Data extraction: One reviewer extracted and another verified data. Two reviewers independently assessed risk of bias. Data synthesis: Behavioral programs were grouped on the basis of program content and delivery methods. A Bayesian network meta-analysis showed that most lifestyle and diabetes self-management education and support programs (usually offering ≥11 contact hours) led to clinically important improvements in glycemic control (≥0.4% reduction in hemoglobin [Hb] A1c), whereas most diabetes self-management education programs without added support-especially those offering 10 or fewer contact hours-provided little benefit. Programs with higher effect sizes were more often delivered in person than via technology. Lifestyle programs led to the greatest reductions in body mass index. Reductions in HbA1c seemed to be greater for participants with a baseline HbA1c level of 7.0% or greater, adults younger than 65 years, and minority persons (subgroups with ≥75% nonwhite participants). Limitations: All trials had medium or high risk of bias. Subgroup analyses were indirect, and therefore exploratory. Most outcomes were reported immediately after the interventions. Conclusion: Diabetes self-management education offering 10 or fewer hours of contact with delivery personnel provided little benefit. Behavioral programs seem to benefit persons with suboptimal or poor glycemic control more than those with good control. Primary funding source: Agency for Healthcare Research and Quality. (PROSPERO registration number: CRD42014010515).
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The epidemic nature of diabetes mellitus in different regions is reviewed. The Middle East and North Africa region has the highest prevalence of diabetes in adults (10.9%) whereas, the Western Pacific region has the highest number of adults diagnosed with diabetes and has countries with the highest prevalence of diabetes (37.5%). Different classes of diabetes mellitus, type 1, type 2, gestational diabetes and other types of diabetes mellitus are compared in terms of diagnostic criteria, etiology and genetics. The molecular genetics of diabetes received extensive attention in recent years by many prominent investigators and research groups in the biomedical field. A large array of mutations and single nucleotide polymorphisms in genes that play a role in the various steps and pathways involved in glucose metabolism and the development, control and function of pancreatic cells at various levels are reviewed. The major advances in the molecular understanding of diabetes in relation to the different types of diabetes in comparison to the previous understanding in this field are briefly reviewed here. Despite the accumulation of extensive data at the molecular and cellular levels, the mechanism of diabetes development and complications are still not fully understood. Definitely, more extensive research is needed in this field that will eventually reflect on the ultimate objective to improve diagnoses, therapy and minimize the chance of chronic complications development.
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Background: Poor adherence to anti-diabetic medications contributes to suboptimal glycaemic control in patients with type 2 diabetes (T2D). A range of interventions have been developed to promote anti-diabetic medication adherence. However, there has been very little focus on the characteristics of these interventions and how effectively they address factors that predict non-adherence. In this systematic review we assessed the characteristics of interventions that aimed to promote adherence to anti-diabetic medications. Method: Using appropriate search terms in Medline, Embase, CINAHL, International Pharmaceutical Abstracts (IPA), PUBmed, and PsychINFO (years 2000-2013), we identified 52 studies which met the inclusion criteria. Results: Forty-nine studies consisted of patient-level interventions, two provider-level interventions, and one consisted of both. Interventions were classified as educational (n = 7), behavioural (n = 3), affective, economic (n = 3) or multifaceted (a combination of the above; n = 40). One study consisted of two interventions. The review found that multifaceted interventions, addressing several non-adherence factors, were comparatively more effective in improving medication adherence and glycaemic target in patients with T2D than single strategies. However, interventions with similar components and those addressing similar non-adherence factors demonstrated mixed results, making it difficult to conclude on effective intervention strategies to promote adherence. Educational strategies have remained the most popular intervention strategy, followed by behavioural, with affective components becoming more common in recent years. Most of the interventions addressed patient-related (n = 35), condition-related (n = 31), and therapy-related (n = 20) factors as defined by the World Health Organization, while fewer addressed health care system (n = 5) and socio-economic-related factors (n = 13). Conclusion: There is a noticeable shift in the literature from using single to multifaceted intervention strategies addressing a range of factors impacting adherence to medications. However, research limitations, such as limited use of standardized methods and tools to measure adherence, lack of individually tailored adherence promoting strategies and variability in the interventions developed, reduce the ability to generalize the findings of the studies reviewed. Furthermore, this review highlights the need to develop multifaceted interventions which can be tailored to the individual patient's needs over the duration of their diabetes management.
The current paradigm of modern healthcare focuses on patient symptoms, subsequent diagnosis and corresponding treatment of the specific disease(s). Escalating healthcare costs and a trial and error approach to diagnosing and treating disease have fermented a rethink in how we carry out such practices. This has led in part to the advent and development of personalised medicine, which encompasses elements of preventive, predictive and pharmacogenomics/pharmacotherapeutic medicine and focuses on methodologies and data output tailored to a person's unique molecular, biochemical, physiological and pathobiological profile. Personalised medicine is still in a fledgling and evolutionary phase and there has been much debate over its current status and future prospects. However, there are already examples of personalised medicine that have been utilised for the benefit of the patient. In addition there are numerous efforts to develop new and innovative tools, technologies and services to satisfy the growing demand from both patients and physicians. Here we describe the concept of personalised medicine, its current state of practice and impact on the pharmaceutical sector, as well as suggestions to future direction. In part the development and growth of personalised medicine will be fuelled by the informed and knowledgeable consumer as well as the thoughtful physician struggling with the complexity of disease diagnosis, onset, progression and treatment.
Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.