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The complementary iceberg tips of diabetes and precision medicine.

February 2016

Authors:

Athabasca University

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.

Secondary complications resulting from chronic elevated blood glucose levels and untreated diabetes.

…

Schematic representation of glucose modulation by the pancreatic hormones insulin and glucagon. (Adapted with permission from the International Diabetes Federation in Brussels, Belgium).

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Figures - uploaded by Jack Yensen

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Content uploaded by Jack Yensen

Content may be subject to copyright.

The Complementary

Iceberg Tips of Diabetes

and Precision Medicine.

by Jack Yensen Ph.D & Stephen Naylor Ph.D

Introduction

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.

PRECISION TOOLS

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

diabetic9.

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

themselves15.

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

mercury)

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.

PRECISION TOOLS

Type 2

4.73%

~15 Subtypes T2

~15 Subtypes T1

Gestational

1.52% Post-partum

resolution - 60%

T1 - 4%

T2 - 36%

Type 1

93.75%

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

SOUTH AND CENTRAL

AMERICA

14.2M

29.6M

48.8M

39.3M

78.3M

140.2M

23.0M

44.3M

60.5M

NORTH AMERICA AND

CARIBBEAN

48.4M

59.8M

71.1M

EUROPE

19.2M

35.4M

72.1M

MIDDLE EAST AND

NORTH AFRICA

7.1M

14.2M

34.2M

AFRICA

SOUTH-EAST

ASIA

43.0M

153.2M

214.8M

WESTERN PACIFIC AND

CHINA

WORLD

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.

SOUTH AND CENTRAL

AMERICA

18.5M

42.2M

73.9M

20.3M

51.8M

67.4M

NORTH AMERICA AND

CARIBBEAN

63.2M

31.7M

36.6M

EUROPE

18.7M

30.2M

56.6M

MIDDLE EAST AND

NORTH AFRICA

21.4M

34.9M

79.0M

AFRICA

WORLD

314M

318M

481M

78.5M

102M

130M

WESTERN PACIFIC AND

CHINA

93.4M

42.2M

73.9M

SOUTH-EAST

ASIA

WORLD

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.

PRECISION TOOLS

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)

Nephropathy

(Kidney)

Peripheral Neuropathy

(Nervous System)

Peripheral Vascular/Neuropathy

(Diabetic Foot)

Peripheral Vascular Disease

(Lower Limbs)

Pregnancy

Complications

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

INSULIN

GLUCAGON

LIVER

Glycogen Glucose

Stimulates glycogen

breakdowns

Stimulates glycogen

formation

Promotes

glucagon

release

Promotes

insulin

release

Raises

Blood

Glucose

Low

Blood

Glucose

Tissue and Cells

(muscle, adipose,others)

PANCREAS

High

Blood

Glucose

Lowers

Blood

Glucose

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

blindness.

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

Intertwined

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

PRECISION TOOLS

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.

T2DM

Fasting

Liver TG

Insulin

Glucose

Lipid Re-esterification

Lipolysis

Glycogen

IMCL

T2DM

Fed

Liver TG CHO

Intake

Glucose

Glycogen

Insulin

IMCL

Glucose

Glucose Transport

Glycogen Synthesis Lipolysis

Fasting

Glycogen

Insulin

Glycogen

Glucose

Lipolysis

Insulin

Glucose

Glycogen

Glucose

CHO

Skeletal

Muscle

Liver

Intake

Fed

Pancreas

(B-cells)

Glycogen

White

Adipose

Tissue

Glucose Transport

Glycogen Synthesis

Lipolysis

T2DM

Fasting

Liver TG

Insulin

Glucose

Lipid Re-esterification

Lipolysis

Glycogen

IMCL

T2DM

Fed

Liver TG CHO

Intake

Glucose

Glycogen

Insulin

IMCL

Glucose

Glucose Transport

Glycogen Synthesis Lipolysis

Fasting

Glycogen

Insulin

Glycogen

Glucose

Lipolysis

Insulin

Glucose

Glycogen

Glucose

CHO

Skeletal

Muscle

Liver

Intake

Fed

Pancreas

(B-cells)

Glycogen

White

Adipose

Tissue

Glucose Transport

Glycogen Synthesis

Lipolysis

T2DM

Fasting

Liver TG

Insulin

Glucose

Lipid Re-esterification

Lipolysis

Glycogen

IMCL

T2DM

Fed

Liver TG CHO

Intake

Glucose

Glycogen

Insulin

IMCL

Glucose

Glucose Transport

Glycogen Synthesis Lipolysis

Fasting

Glycogen

Insulin

Glycogen

Glucose

Lipolysis

Insulin

Glucose

Glycogen

Glucose

CHO

Skeletal

Muscle

Liver

Intake

Fed

Pancreas

(B-cells)

Glycogen

White

Adipose

Tissue

Glucose Transport

Glycogen Synthesis

Lipolysis

T2DM

Fasting

Liver TG

Insulin

Glucose

Lipid Re-esterification

Lipolysis

Glycogen

IMCL

T2DM

Fed

Liver TG CHO

Intake

Glucose

Glycogen

Insulin

IMCL

Glucose

Glucose Transport

Glycogen Synthesis Lipolysis

Fasting

Glycogen

Insulin

Glycogen

Glucose

Lipolysis

Insulin

Glucose

Glycogen

Glucose

CHO

Skeletal

Muscle

Liver

Intake

Fed

Pancreas

(B-cells)

Glycogen

White

Adipose

Tissue

Glucose Transport

Glycogen Synthesis

Lipolysis

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

mechanism25.

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.

PRECISION TOOLS

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 “..is 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,

HHEX, KCNQ1, SLC30A8, HLA-B, INS-IGF,

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

PRECISION TOOLS

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. (www.curehunter.com), Therametrics

(www.therametrics.com) and BioVista’s

(www.biovista.com) 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

diseases

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

Index

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

production

Bile acid sequestrants Colesevelam binds intestinal bile acids, may decrease hepatic glucose 1*

increasing hepatic bile acid production; may increase

production incretin levels

Dopamine-2 agonists 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

Alogliptin

Insulins Rapid-acting activates insulin receptors increases glucose disposal 3

Short-acting decreases hepatic glucose

Intermediate-acting production

Basal analogs

Meglitinides Repaglinide shuts ATPK channels on β- increases insulin secretion 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

Gliclazide

Glimepiride

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

diabetes.

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

PRECISION TOOLS

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

comparison.

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

http://www.nice.org.uk/guidance/ng28/

evidence/full-guideline-2185320349) for

the treatment of T2DM took 6 years to

develop. The recent 2015 updated guidelines

from the Canadian Diabetes Association

(http://guidelines.diabetes.ca/executivesummary)

and the January 2016 Consensus statement

by the American Association of Clinical

Endocrinologists and American College of

Endocrinology on the comprehensive T2D

management algorithm: 2016 executive

summary compare favorably with CureHunter

recommendations.

PRECISION TOOLS

Related Diseases: Related Drugs/ Related Therapies: CureHunter

602 Bio-Agents: 227 Effectiveness Index

2408

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

2408

PRECISION TOOLS

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.

Conclusions

What have we learned from this journey across

the current bleak iceberg terrain of diabetes

and PM? The concept and perception that

diabetes is a “simple disease” has fueled and

driven much of our limited understanding of

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.

doi:10.1155/2014/371475.

EA combined with the oral insulin sensitizer

rosiglitazone improved insulin sensitivity in

rats and humans with type II diabetes mellitus

(DM).

Zhang X, Liu Y, Xiong D, Xie C. Insulin

combined with Chinese medicine improves

glycemic outcome through multiple pathways

in patients with type 2 diabetes mellitus.

J Diabetes Investig. 2015;6(6):708-715.

doi:10.1111/jdi.12352.

The results from our study showed that the

combination therapy of SQF and insulin

signiﬁcantly improved the clinical outcome

of type 2 diabetes mellitus compared with

insulin monotherapy.

Chobit’ko VG, Zakharova NB, Rubin VI.

[Relation between the changes in thiamine

metabolism and energy processes in 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

elderly.

Selected Studies

Milionis H. Combining a statin with a ﬁbrate

versus ﬁbrate monotherapy: efﬁcacious but

safe? Expert Opin Drug Saf.

2014;13(3):267-269. doi:

10.1517/14740338.2014.887679.

Combination Therapy Description

Fibrate (agonist of peroxisome 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

toxicity.

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.

PRECISION TOOLS

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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.

Acknowledgements

We would like to thank Mr. Andrew Jackson

(ﬂaircreativedesign.com) and Mr. Damian

Doherty (Editor-Journal of Precision Medicine)

for their considerable help and input on the

ﬁgures and content contained in this article.

In addition, we express grateful appreciation

to Mr. Judge Schonfeld, Founder and CEO of

CureHunter Inc, for access to the CHI software

and his unﬂagging support of our efforts.

The complementary iceberg tips of diabetes and precision medicine.

Data

March 2016

Jack Yensen

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Investigation of the effects Of thrombin and electrical pulse stimulation on metabolic function in cultured human skeletal muscle cells

Thesis

Full-text available

Sep 2020

Ali Al-bayati

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.

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Oct 2016

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.

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May 2016

Ahmed H. Alhasan, Mohammed T. Al-Nazzawi , Mohammed A. Aljun

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.

KİŞİSELLEŞTİRİLMİŞ TIP VE GENOM ARAŞTIRMALARININ SAĞLIK ÇIKTILARI BAĞLAMINDA DEĞERLENDİRİLMESİ / EVALUATION OF PERSONALIZED MEDICINE AND GENOME RESEARCH IN THE CONTEXT OF HEALTH OUTCOMES

Article

Full-text available

Dec 2019

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.

Curative and Preventive Treatment for Cardiovascular Disease (CVD) Targeting Multiple Etiology

Article

Jan 2021

Josefino Ballesteros Tunac

An Analysis of Comorbidities’ Role in Diabetes Mellitus and Its Data-Intensive Technology-Based Prediction to Reduce Risk and Diagnostic Costs

Chapter

Jan 2020

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.

A first attempt to develop a diabetes prediction method based on different global datasets

Conference Paper

Jan 2016

Weight Loss Decreases Excess Pancreatic Triacylglycerol Specifically in Type 2 Diabetes

Article

Full-text available

Dec 2015
DIABETES CARE

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.

Gut microbiota and diet in patients with different glucose tolerance

Article

Full-text available

Nov 2015

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.

Identification of type 2 diabetes subgroups through topological analysis of patient similarity

Article

Full-text available

Oct 2015

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.

Behavioral Programs for Type 2 Diabetes Mellitus: A Systematic Review and Network Meta-analysis for Effect Moderation

Article

Full-text available

Sep 2015
ANN INTERN MED

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).

Precision Medicine, Personalized Medicine, Omics and Big Data: Concepts and Relationships

Article

Full-text available

Jan 2015

Xiaohua Zhang

Diabetes mellitus: The epidemic of the century

Article

Full-text available

Jun 2015

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.

A Systematic Review of Interventions Addressing Adherence to Anti-Diabetic Medications in Patients with Type 2 Diabetes—Components of Interventions

Article

Full-text available

Jun 2015
PLOS ONE

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.

NanoTech Firm Takes Passage to India

Article

May 2009

Peter Gwynne

Personalised medicine: Technological innovation and patient empowerment or exuberant hyperbole?

Article

Jun 2007

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.

Personalized Nutrition by Prediction of Glycemic Responses

Article

Nov 2015

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.

The complementary iceberg tips of diabetes and precision medicine.

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