Dexlife Handbook DRAFT

2
DRAFTThis handbook presents biomarkers
and personalised medicine for
predicting diabetes and preparing
individualised treatment regimes
www.dexlife.eu
@dexlife

DRAFT
DIET EXERCISE and LIFE
Executive Summary
DEXLIFE was set up to investigate and prevent the progression from pre-diabetes to type 2 diabetes. The DEXLIFE
project focuses on type 2 diabetes, from its earliest stages in high risk but otherwise healthy individuals through
to established diabetes. We have identified novel diagnostic and predictive biomarkers, based on the core
pathophysiology, that underlie the progression from normal glucose tolerance through to pre-diabetes followed by
type 2 diabetes.
Diabetes is a global pandemic, driven by many factors associated with modern living, not least an even bigger
pandemic of obesity. There is a pressing need for better methods of detection of those at high risk and more
effective methods of selection for prevention and treatment. The current approach to diabetes risk is based on clinical
assessment including risk scoring systems along with some form of glucose measurement. The current approach to
the treatment of type 2 diabetes follows generalised algorithms, applicable to all patients.
DEXLIFE places major focus on a deeper individual phenotype both for people with pre-diabetes and for those with
diabetes. Using new methodologies in metabolomics and lipidomics we have characterised baseline risk, progression
and response to intervention in a range of well characterised European cohorts.
This handbook summarises our research and describes the use of novel biomarker values for preparing individualised
treatment regimes. Our aim is to provide healthcare professionals with support to design personalised regimes based
on the biomarkers profile of the patient.

DRAFT
Handbook Contents
Current practice for dealing with individuals at high risk of diabetes..................................................... 1
Current clinical practice for treating patients with type 2 diabetes......................................................... 3
What is DEXLIFE and why was the project developed?............................................................................. 5
Personalised medicine: Metabolite biomarkers that predict diabetes................................................... 8
Personalised medicine: Molecular lipids that predict diabetes................................................................ 11
Personalised medicine: A ‘self-selected’ lifestyle intervention................................................................. 13
DEXLIFE applications in the clinic....................................................................................................................... 19
Metabolomics analyses - factors to consider ................................................................................................ 20
References................................................................................................................................................................. 21
DEXLIFE partners..................................................................................................................................................... 22
The DEXLIFE project investigated the mechanisms of
prevention of type 2 diabetes by lifestyle intervention in
subjects with pre-diabetes or at high risk for progression.

1
DRAFT
Current practice for dealing with individuals at high risk of diabetes
Identifying those at risk of diabetes
Current tests: Pre-diabetes is defined in various ways based on glucose
measurements including: (i) fasting plasma glucose (FPG: 5.6-6.9 mM); (ii) 2-hour
plasma glucose from an oral glucose tolerance test (OGTT: 7.8-11.0 mM); and (iii)
HbA1c (5.7-6.4%) (1). These categories identify only partially overlapping groups
of subjects (2, 3), and are likely reflect different pathophysiological states during
the progression from normal glucose metabolism to type 2 diabetes.
Different pathophysiology: Elevation of the 2hPG value (Impaired Glucose
Tolerance, IGT) is considered to reflect mainly peripheral insulin resistance
coupled with a β-cell defect, while elevated FPG is considered to be mainly
due to hepatic insulin resistance (4) along with a β-cell defect. HbA1c provides
a measure of average plasma glucose levels and does not reflect any particular
pathophysiology (5).
Constraints and limitations: The OGTT has been used for many years for the
diagnosis of type 2 diabetes and IGT but less in recent years (6) because of
constraints on clinical time, cost, and lack of popularity with both patients and
physicians. Patients at risk for diabetes are more likely to have FPG and HbA1c
measured in routine examinations in current clinical practice than to undergo
an OGTT (7, 8). As a consequence, IGT subjects with normal FPG and HbA1c
values will be missed by this screening approach. This is unsatisfactory since
IGT is a high risk state for type 2 diabetes, with annual progression rates of
5-15% from IGT to diabetes (9). A more convenient method for identifying IGT
subjects other than the OGTT or a method of stratifying risk for subjects with
IGT would be very important progress in the clinical process of diabetes risk
assessment.
Evidence for different patterns of progression to DM
Pathophysiology of diabetes: Type 2 diabetes develops gradually over the
course of many years. The prodromal stage of disease is clinically silent, and
without symptoms. Progression to type 2 diabetes is a major focus of DEXLIFE,
and is thought to result from two major pathologies: (i) gradual loss of insulin
secretion along with (ii) persistent and increasing insulin resistance, with ongoing
demand for compensatory increased insulin secretion. Vulnerability to diabetes
is not simple and may include factors that are not obvious from a simple clinical
risk assessment.
Different phenotypes of diabetes: There is increasing interest in mapping
trajectories of progression prior to the diagnosis of diabetes in carefully
phenotyped cohorts. Diabetes progression has been studied in the Whitehall II
cohort (10). Those diagnosed on the basis of increased FPG or 2 hour glucose
concentrations, or both, were found to have quite distinct cardiometabolic risk
development before diagnosis. Despite gradual weight gain in more than 90%
of subjects, individuals that had high FPG and 2-hr glucose had:
• A greater risk of cardiovascular disease
• Evidence of beta-cell decompensation
• Accelerated glucose concentrations and insulin resistance before diagnosis
Tracking progression to diabetes should reflect pathophysiology: In a cross-
sectional analysis of the ADDITION-PRO study, Færch and colleagues found
that using HbA1c for the diagnosis identified individuals with a mixture of
pathophysiology and that the diversity of pathophysiology captured by the oral
glucose tolerance test could not be detected using HbA1c alone (11). Better
understanding of the individual’s vulnerability is likely to improve both prevention
and treatment and to allow a more stratified or personalised approach.

2
DRAFT
Predicting rick of diabetes with screening score
Type 2 diabetes is a multifactorial metabolic disorder with a
stronggeneticbasisalongwithwellrecognisedenvironmental
risk factors such as obesity, physical inactivity and poor diet.
Simple risk scores have been shown to be helpful in identifying
those who are at higher risk, as a basis for further screening.
FINDRISC is one of the best known risk scoring systems (12).
FINDRISC can be completed in minutes and that can help to
quantify risk of progression to diabetes (Figure 1). Risk-based
screening is followed by blood glucose testing as outlined
above. The FINDRISC questionnaire can be found
here http://www.dexlife.eu/survey.html
Current practice for dealing with individuals at high risk of diabetes
FINDRISC is the “Finnish diabetes
risk score questionnaire.” It is used
for screening for undiagnosed type 2
diabetes.
Figure 1. FINDRISC questionnaire

3
DRAFT
Clinical Evaluation
Newly diagnosed patients are
managed in different ways and
settings. In many European countries,
patients with type 2 diabetes are
managed mainly by their general
practitioner.
Baseline assessment, lifestyle advice
(diet and physical activity, smoking
cessation),riskfactorandcomplication
screening and initial therapy occur in
general practice or in a shared service
between primary and secondary
care. It is not current practice to sub-
phenotype or to stratify patients with
type 2 diabetes, although clinicians
make assessments based on the
usual clinical variables such as age,
BMI, gender and co-morbidities.
There is a wide range of
current approaches to
diabetes care.
Current clinical practice for treating patients with type 2 diabetes
Figure 2. Current approaches to diabetes care (American Diabetes Association Diab Care 2015;38:S41-S48

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DRAFT
Current clinical practice for treating patients with type 2 diabetes
Management
Metformin is now the established drug of first choice, following an initial period of
lifestyle modification (healthy eating, weight control, increased physical activity,
smoking cessation) and diabetes education (Figure 2) (1). The most recent
ADA guidelines recommend intensifying treatment to dual and triple therapy at
3-monthly intervals, with a wide menu of options for add-on treatments at each
stage. The most intensive regimen comprises metformin with basal insulin and
either mealtime insulin or GLP-1 receptor agonists.
Generalised but not personalised
This is a generalised guideline applicable to any patient and does not
feature stratification or personalisation of options based on individual patient
pathophysiology. The ADA’s most recent recommendation for the introduction
of insulin treatment for type 2 diabetes (Figure 3) is also a general guideline for
introduction and intensification rather than a tailored recommendation.
Figure 3. American Diabetes Association recommendations for insulin treatment in
type 2 diabetes. American Diabetes Association Diab Care 2015;38:S41-S4
Basal insulin
(usually with metformin± other noninsulin agent)
Start: 10 U/day or 0.1-0.2 U/kg/day.
Adjust: 10-15% or 2-4 U once-twice weekly to reach FBG
target.
For hypo: Determine and address cause; ↓dose by 4 U
or 10-20%.
If not
controlled after
FBG targetis reached
(or if dose >0.5 U/kg/day),
treat PPG excursions with
mealtime insulin. (Consider
initial GLP-1-RA
trial.)
Add 1 rapid insulin injection
before largest meal
Start: 4 U, 0.1 U/kg, or 10% basal dose. If A1C
<8%, consider ↓ basal by same amount.
Adjust: ↑ dose by 1-2 U or 10-15% once-twice
weekly until SMBG target reached.
For hypo: Determine and address cause; ↓
corresponding dose by 2-4 U or 10-20%.
If not
controlled,
consider
basal bolus.
Add ≥2 rapid insulin injections
before meals (“basal-bolus”)
Start: 4 U, 0.1 U/kg, or 10% basal dose/meal. If A1C <8%,
consider ↓ basal by same amount.
Adjust: ↑ dose by 1-2 U or 10-15%, once-twice weekly until
SMBG target reached.
For hypo: Determine and address cause; ↓corresponding
dose by 2-4 U or 10-20%.
Change to premixed
insulin twice daily
Start: Divide current basal dose into 2/3 AM, 1/3
PM or 1/2AM, 1/2 PM.
Adjust: ↑ dose by 1-2 U or 10-15% once-twice
weekly until SMBG target reached.
For hypo: Determine and address cause; ↓
corresponding dose by 2-4 U or 10-20%.
Flexibility more flexible less flexible
#
Injections
1
2
3+
Complexity
low
mod.
high
If not
controlled,
consider
basal bolus.

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DRAFT
What is DEXLIFE and why was the project developed?
The methods have been described thoroughly in a recent publication by
Andersen et al. (2014) (13). The main components of the project
are outlined below.
• Novel circulating metabolomic and lipidomic profiles of progression to
diabetes were identified in two cohorts, the METSIM study in Finland and the
Vhi Healthcare cohort in Ireland.
• A small cohort of young people with type 2 diabetes, matched controls and
older people with type 2 diabetes, were used to identify biomarkers of early
onset diabetes.
• A prospective lifestyle intervention was implemented in a high risk group
of individuals to determine if the new candidate biomarkers tracked with
improved glucose tolerance.
• The new candidate biomarkers were validated using the RISC cohort of
healthy European people from all over the continent and other samples from
the Vhi Healthcare cohort that were not included in the original analysis.
Aim
Pre-diabetes The DEXLIFE project was developed to identify novel diagnostic
and predictive biomarkers, based on the core pathophysiology, that underlie the
progression from normal glucose tolerance through to pre-diabetes followed
by type 2 diabetes. In particular, the objectives were to:
1. Identify a ‘metabolic signature’ of circulating and tissue specific biomarkers
that characterise the development of abnormal blood glucose.
2. To better detect the progression toward diabetes in high risk individuals with
different phenotypes.
3. To evaluate the responsiveness of these biomarkers to a lifestyle intervention
known to prevent type 2 diabetes.
Project Overview
The range of cohorts available to DEXLIFE (Figure 4) has made it possible to
map the natural background progression to diabetes in certain groups.
Figure 4. The DEXLIFE cohorts
Cohort
N
total
N
DEXLIFE
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
METSIM
VHI
DCU
YT2
RISC
10,000 1,900
27,000 700
400 400
135 135
1,200 300 Baseline
Baseline
Baseline
Baseline
Baseline
Follow-up
Follow-up
Follow-up
Follow-up
Inter-
vention
Inter-
vention

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DRAFT
What is DEXLIFE and why was the project developed?
The project has mapped, as completely as possible, the stages of evolution
of diabetes in the population, from young adults through to middle-aged and
older subjects at various stages of progression of metabolic disease. With this
degree of clinical ‘coverage’ of phenotypes, the partners have a range of clinical
settings from which to derive the cell, tissue and circulating biomarkers needed
to classify and predict clinical endpoints. This includes examination of clinical
phenotypes, metabolome/lipidomics, protein expression, transcriptomics and
epigenetics and genetics at baseline and over the natural progression of type
2 diabetes.
Personalised medicine in DEXLIFE: Personalised medicine relies on using
biological markers to stratify patients into specific sub-groups which may
respond best to specific interventions and therapies. In DEXLIFE, we have taken
a unique approach by stratifying both the biological markers and the response
to lifestyle intervention.
• Biological markers: The success of personalized medicine requires the
development of accurate and reliable diagnostics and on the identification of
predictive biomarkers. For the identification of suitable biomarkers, research
on early disease processes is important in order to identify which factors to
study as potential predictors of disease and of treatment response.
• Lifestyleintervention:Stratifiedphenotypeswillbestrengthenediftheyinclude
responses to lifestyle intervention and are not solely based on biological
progression. Individual responses to dietary and exercise interventions are
varied and can have a major impact on biological biomarkers.
Benefits: Integrating the biological and lifestyle components offers the best
opportunity to stratify subgroups for personalised prevention or treatment
programmes. This approach promises to reduce the burden of disease by
targeting prevention and treatment more effectively. When effective therapy can
be selected for a given patient based on individualized profile, the treatment
can be more effective and/or adverse effects can be avoided. This will in
turn reduce healthcare costs by minimizing costs associated with ineffective
treatment and avoidable adverse events. The goal of this approach is also to
focus healthcare resources on prevention, moving from illness to wellness, and
from treating disease to maintaining health.
What do we mean by ‘personalised medicine?
Definition: Personalised medicine emerged as a medical model using
characterization of individuals’ phenotypes and genotypes (e.g. molecular
profiling,medicalimaging,lifestyledata)fortailoringtherighttherapeuticstrategy
for the right person at the right time, and/or to determine the predisposition to
disease and/or to deliver timely and targeted prevention (Figure 5).
Looking for individual responses: The pressure on European health care
systems is rising due to the increasing costs of non-communicable diseases.
The ‘omics’ technologies allow for comprehensive characterization of individual
phenotypes and genotypes, providing an opportunity for both identifying the
risk of disease early and for tailoring the interventions for disease prevention
or treatment.
DEXLIFE maps the stages of evolution
of diabetes in the population.

7
DRAFT
Systems approach to tackle Diabetes
Figure 5. Personalised medicine relies on systems approach to identify biological markers

8
DRAFT
Personalised medicine: Metabolite biomarkers that predict diabetes
Metabolomics of Diabetes and Prediabetes and
their Sub-phenotypes
DEXLIFE has identified 23 metabolites associated with progression from normal
glucose tolerance to a state of dysglycemia and finally to type 2 diabetes.
What are metabolites? Metabolites are the intermediates and products of
metabolic pathways characteristically seen in textbooks of biochemistry.
Metabolites are small, non-protein molecules having molecular weights of less
than 1,500 Daltons. Several thousand metabolites can be detected in human
plasma although considerably fewer are measured in a typical experiment.
Circulating metabolites may result from a number of sources including
endogenous production, diet, gut microbes, drugs, and personal care products
and their levels can be affected by ethnicity, age, BMI, and sex. Metabolite levels
can change rapidly along with changes in flux through metabolic pathways (e.g.
after a meal) and can reflect the current metabolic status of an individual.
What is metabolomics? Metabolomics refers to the global study of metabolites
in cells, tissues and biofluids (e.g. plasma, urine). Metabolomic analysis can be
carried out in a non-targeted or a targeted manner. The former can be used
to identify candidate biomarkers and the latter to validate/replicate these
biomarkers.
Metabolites of Prediabetes
In total, 23 metabolites associated with progression from normal glucose
tolerance to a state of dysglycemia and finally to type 2 diabetes were identified
- using non-targeted discovery in the two DEXLIFE cohorts, as well as from the
literature (Table 1) (14). A panel of quantitative assays for use in EDTA plasma
samples was developed as a research tool to better understand and utilize
metabolite profiling in prediabetes (15).
Table 1. Quantitative Assay List – Panel of 23 Metabolites identified and validated by
the DEXLIFE project
Metabolite Classification
α-Hydroxybutyric acid (AHB) Methionine/Threonine metabolism
β-Hydroxybutyric acid (BHB) Ketone body
2-Aminoadipic acid (2AAA) Lysine metabolism
3-Hydroxyisobutyric acid (3HIB) Valine metabolism
3-Methyl-2-oxobutyric acid (3MOB) Valine metabolism
3-Methyl-2-oxopentanoic acid (3MOP) Isoleucine metabolism
4-Methyl-2-oxopentanoic acid (4MOP) Leucine metabolism
α-Ketobutyric acid (AKB) Methionine/Threonine metabolism
α-Ketoglutaric acid (AKG) Krebs cycle intermediate
Creatine Creatine metabolism
Glycine Amino acid
Hydroxyisovaleroyl carnitine Leucine metabolism
Isoleucine Branched-chain amino acid
Leucine Branched-chain amino acid
lysoPC(18:2(9Z,12Z)); also known as LGPC Lysophosphatidylcholine
Oleic acid Fatty acid
Phenylalanine Aromatic amino acid
Serine Amino acid
Trigonelline (N’-methylnicotinate) Nicotinate metabolism
Tyrosine Aromatic amino acid
Valine Branched-chain amino acid
Vitamin B5 (pantothenic acid) Vitamin; precursor to CoA
X-12063 Unknown

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DRAFT
Validation of metabolite panel: The panel of quantitative assays was used to
validate the metabolites in 1,620 diabetes-free subjects from the RISC cohort
and the Vhi Healthcare cohort (16) not used in the original discovery phase.
Conclusion: A set of 23 metabolites that predict progression to type 2 diabetes
have been identified and validated by the DEXLIFE project. In most cases the
metabolite changes were present in pre-diabetes indicating they could be used
as early indicators of diabetes risk.
Insulin Sensitivity: AHB and the other metabolites found to associate with
iIGT above had previously been shown to associate with insulin sensitivity as
measured by the hyperinsulinemic euglycemic clamp in the RISC study. This
linkage may be a reflection of the peripheral insulin resistance associated
with IGT given that the clamp is measuring peripheral insulin sensitivity. These
findings were utilized previously to develop a test for insulin resistance called
Quantose IR (17). This test estimates the glucose disposal from the clamp using
fasting values of AHB, LGPC, oleic acid and insulin.
On the other hand, a different metabolic profile is associated with the HOMA-
IR estimate of insulin sensitivity. In this case the metabolites most strongly
associated with HOMA-IR were the three branched-chain amino acids (valine,
leucine, isoleucine) and the aromatic amino acid tyrosine. Interestingly, these
same 4 amino acids were also among the most strongly correlated with BMI. The
different metabolic profiles associated with HOMA-IR and the hyperinsulinemic
euglycemic clamp suggests that these methods are measuring different aspects
of insulin sensitivity.
Stratification of Prediabetes Sub-phenotypes
with Metabolite Profiles
Isolated Impaired Fasting Glucose and Isolated Impaired Glucose Tolerance:
The non-overlapping prediabetic states of isolated IFG (iIFG) and isolated
impaired glucose tolerance (iIGT) were examined. These states have different
metabolic profiles (e.g. hepatic insulin resistance in iIFG vs peripheral insulin
resistance in iIGT) so they might be expected to have different metabolite
profiles as well. In the analysis, associations of metabolites for normal fasting
glucose and glucose tolerance versus iIFT or iIGT were made (unpublished
data). This exercise identified AHB, LGPC, oleic acid, and serine as selective
biomarkers of IGT (Table 2). On the other hand, 2 branched-chain amino acid
catabolites, 3MOP and 4MOP, were shown to be selective biomarkers of iIFG.
23 metabolites that predict progression
to type 2 diabetes have been identified
and validated by DEXLIFE
Table 2. Metabolites Associated with Sub-phenotypes of Prediabetes
Condition Metabolites
isolated IFG 3MOB, 4MOP
isolated IGT AHB, LGPC, oleic acid, serine
Insulin resistance
Hyperinsulinemic-euglycemic clamp
HOMA-IR
AHB, LGPC, oleic acid, glycine
valine, isoleucine, leucine, tyrosine
IGT (Quantose IGT test) FPG, AHB, LGPC, oleic acid, serine, 4MOP,
β-hydroxybutyric acid, pantothenic acid

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DRAFT
A number of models were generated and one, in particular,
including FPG, AHB, LGPC, oleic acid, serine, 4MOP,
β-hydroxybutyric acid, and pantothenic acid had AUCs of 0.82
and 0.83 in RISC and Vhi respectively. This IGT test is called
Quantose IGT and the score is called QIGT
. This test estimates
the probability that a subject is IGT. The higher the score,
the more likely the subject is IGT. The test has a sensitivity/
specificity of 78%/72% and may serve as a convenient
alternative to the OGTT to identify subjects at risk of being
IGT. The test is currently available at Metabolon, Inc. (Durham,
NC USA, metabolon.com) and will be commercialized soon in
Europe.
Development of an IGT Test
The 23 metabolite data was used to develop a novel IGT test
byidentifyingmetaboliteswhoseplasmalevelscorrelatedwith
the 2 hour plasma glucose (2hPG) from the OGTT and those
whichhadpredictivevalueinidentifyingsubjectswithIGT(17).In
general the best metabolites were those previously identified
as being biomarkers of insulin resistance as measured by the
clamp and this may be a reflection of the peripheral insulin
resistance associated with IGT. The variables with the highest
correlation with 2hPG are fasting plasma glucose, AHB, LPGC,
and oleic acid. These four metabolites had higher correlations
than did anthropometric and metabolic parameters such as
insulin, age, BMI, and HOMA-IR. NC USA, metabolon.com)
and will be commercialized soon in Europe.
Metabolon’s Quantose IGTTM
is an laboratory-developed test that reflects the degree
of impaired glucose tolerance. Impaired glucose tolerance is a core metabolic defect in
dysglycemia and is a known risk factor for prediabetes. Quantose IGT may be used as an
alternative to an oral glucose tolerance test (OGTT) or to identify patients who may be
candidates for an OGTT.

11
DRAFT
Personalised medicine: Molecular lipids that predict diabetes
to develop and validate a lipid-based model that predicts
diabetes. Ultra high performance liquid chromatography
coupled to quadrupole time-of-flight mass spectrometry
global lipidomic profiling was applied to measure a total of
280 molecular lipids. Lipidomic profiles from 107 men who
developed T2D during the follow-up period and 216 controls
were used to build the predictive model, which was validated
in a group of 640 men.
Forward stepwise logistic regression was used to build a
model comprising five lipids (Table 3), which independently
predicted progression of type 2 diabetes in five year follow-
up (Figure 6). In the validation series, the model based on
lipids alone was superior to FINDRISC score (AUClipids
=0.71
vs. AUCFINDRISC
=0.66; P<0.01). Lipids also improved the model
based on fasting plasma glucose alone (AUCFPG+lipids
=0.84 vs.
AUCFPG
=0.82; P<0.01).
The lipid-based risk score is an independent predictor of type
2 diabetes. The score can be used to complement the other
risk scores, such as based on FINDRISK or fasting plasma
glucose. The ongoing and future studies will establish if the
lipid-based risk score can identify specific responders or
non-responders to specific interventions aimed at diabetes
prevention.
Progression to type 2 diabetes - molecular lipids
DEXLIFE has identified and validated lipidomic profiles
associated with progression to type 2 diabetes and has
developed and validated a lipid-based model that predicts
diabetes. Lipids are a diverse group of metabolites that have
manykeybiologicalfunctions,actingasstructuralcomponents
of cell membranes, energy storage sources and intermediates
in signaling pathways. Due to their importance lipids are under
tight homeostatic control and exhibit spatial and dynamic
complexityatmultiplelevels.Itisthusnotsurprisingthataltered
lipid metabolism plays important roles in the pathogenesis of
most of the common diseases including in diabetes.
Lipidomics emerged as a discipline closely related to
metabolomics, which is dedicated to global study of lipidomes,
including pathways and networks of lipids in biological
systems. Advanced analytical techniques, particularly mass
spectrometry, have played the crucial role in the recent
progress of lipidomics.
DEXLIFE applied global lipid profiling at baseline and at
5-year follow-up plasma samples of well phenotyped male
participants from the longitudinal study METSIM (METabolic
Syndrome In Men) to identify and validate lipidomic profiles
associated with progression to type 2 diabetes, and

12
DRAFT
Personalised medicine: Molecular lipids that predict diabetes
Table 3. Panel of five lipids used in the risk score to predict type 2 diabetes
Lipid Classification Baseline levels in diabetes progres-
sors as compared to normoglycemic
controls
lysoPC(18:2)* lysophosphatidylcholine diminished
PC(36:4) phosphatidylcholine diminished
PC(O-42:6) ether-phosphatidylcholine diminished
TG(50:1) triacylglycerol elevated
TG(54:5) triacylglycerol elevated
*This lipid was also identified as biomarker of diabetes in metabolomics assay (Table 1).
Figure 6. The lipid-based risk score

13
DRAFT
How did it work?
We had a total of 335 subjects complete the study, which had a
retention rate greater than 90%. In the end, we had 256 people
thatcompletedthe12weeklifestyleinterventionand79control
participants who were given national recommendations for
physical activity and dietary intake. We focused on two key
components to modify lifestyle and achieve our aim.
• Exercise: We set a target of 4-hrs of exercise per week and
encouraged participants to join group classes that were
specially designed in the University sports complex. We
also developed a special web diary that allowed us track
all of the exercise sessions, even for those that could not
come to the sports complex.
• Diet: Our aim was to develop a self-selected healthy
eating food plan based on a 3-day estimated food diary.
The dietician assisted in identifying food choices that were
unhealthy, offering alternative healthy options and advice
on reducing intake by 600 kcal per day. Each participant
was contacted every four weeks to ensure that they were
following their dietary plan.
Why did we include a lifestyle intervention in DEXLIFE?
How many of the biomarkers that predicted progression to
diabetes also track in the opposite direction when diabetes
risk is reduced? The DEXLIFE lifestyle intervention has helped
us to identify the biomarkers most strongly associated with
changes in glucose tolerance. Our lifestyle intervention,
comprising targeted changes in physical activity and diet, has
been shown to significantly reduce the incidence of diabetes.
What did the programme hope to achieve?
The aim of the programme was to decrease the risk of
progressing to diabetes by improving fitness levels, changing
dietary intake and decreasing body fat levels. In so doing
we hypothesised that blood glucose levels would decrease
and that some of the biomarkers that predict progression to
diabetes would move in the expected direction of change.
Our lifestyle intervention has helped to
identify the biomarkers most strongly
associated with changes in glucose tolerance.
Personalised medicine: A ‘self-selected’ lifestyle intervention

14
DRAFT
What did we measure before and after the intervention?
• Bodysizeandshape:Wemeasuredchangesinbodyweight,
shape and composition. We measured the circumference
of the waist and hip using standard protcols. Using a DEXA
scanner we were able to detect changes in the amount
of fat and muscle in the body and we also measured the
thickness of fat in the abdomen using ultrasound.
• Physical fitness: Participants walked on a treadmill until
they could no longer keep going, using a standard exercise
stress test protocol (Figure 7). They had ECG, blood
pressure and oxygen consumption measured during the
test. A higher amount of oxygen consumed is associated
with better aerobic fitness. Prior to the test they also had
heart rate variability measured using the Vagus system.
• Glucose tolerance: After an overnight fast, participants
were given a standard 75g oral glucose tolerance test.
Blood samples were taken before glucose ingestion and at
30-, 60-, 90-, 120- and 180-mins post.
• Questionnaires: Each participant completed a series of
questionnairestoassessqualityoflifeandvariousindicators
of health and wellbeing.
What was different about this approach?
The intervention focused on providing choices for participants
so they could select the best option for them.
• There were over 50 exercise classes available each
week, at all times of the day, and to suit all activities. The
group classes were specially designed for chronic disease
management and based on a circuit of exercise stations that
worked the upper and lower body. Other options included
yoga, pilates and spinning classes or participants could
exercise themselves using the gym equipment.
• The dietary intervention was minimally invasive and focused
on offering alternatives to unheathly food choices that were
healthier and had a lower calorie content.
• The online diary was used to record exercise sessions and
to monitor body weight. This was regularly monitored and
participants were contacted if they had not entered a recent
exercise session or body weight had not changed.


15
DRAFTMain Results
Physical characteristics: After completing the 12-week
programme the intervention, but not control, group had a
significant reduction in body weight, BMI, waist circumference,
subcutaneous fat thickness, and whole body fat percentage
(Table 4). There was no difference in the visceral fat thickness
in either the control of intervention groups. The intervention
group also significantly improved their aerobic fitness while
the control group did not change from baseline.

Was the DEXLIFE programme a success?
YES, the lifestyle intervention achieved the objectives. In
comparison to a control group that were provided standard
dietary and physical activity recommendations, the lifestyle
intervention group attained significant improvements in
physiological and psychological variables. Several of the
key findings relating to the success of the programme are
summarised below. They are intended to provide assistance
to clinicians, researchers and healthcare providers that wish
to set up and deliver a similar programme.
Figure 7. Testing the physical fitness of participants in the lifestyle intervention.

16
DRAFTExercise programme
The average number of exercise sessions completed was 44.3 out an expected
48 sessions over the 12 week intervention. Approximately half of these sessions
were completed in DCU gym (on average 22.8 sessions). The average time
spent on exercise was 44.2 hrs over the 12 weeks by the group.
There was a positive association between the amount of time spent exercising
and the changes in body composition and fitness (Table 6). It was also evident
that the ‘self-selected’ options available in DCU Sport had significantly greater
improvements in health outcomes than those performed at home.
Main Results
Glucose tolerance: Fasting blood glucose and 2-hr blood glucose, following
the OGTT, were significantly lower in the intervention group following the
intervention programme. There were no differences in the control group.
Quality of life: The overall quality of life score significantly improved in
the intervention group following the 12-week intervention. The significant
improvements in physical functioning, general health and vitality seem to
contribute most to the change in quality of life. The control group had no change
in quality of life or any of the individual components (Table 5).
Table 4. Descriptive results for intervention and control groups at baseline and follow-up (3 months).
Intervention
PRE POST
Control
PRE POST p-value
Body Composition
Weight (kg) 90.9±18.2 85.9±17.2 91.8±18.1 91.4±19.1 <0.001
BMI (kg/m2
) 31.3±5.1 29.7±4.9 31.5±4.9 31.2±5.0 <0.001
Waist (cm) 104.7±12.5 98.6±12.4 106.5±12.4 104.0±12.9 <0.001
Fat (%) 38.4±8.7 36.1±9.0 38.7±8.6 38.0±8.6 <0.001
Subcutaneous Fat (cm) 2.94±1.21 2.51±1.07 3.28±1.43 2.95±1.26 0.086
Visceral fat (cm) 7.37±2.26 6.20±2.05 7.31±2.33 7.36±2.60 <0.001
Clinical measurement*
FPG baseline (mmol/l) 5.85±0.88 5.64±0.68 5.68±0.61 5.65±0.66 0.140
2h plasma glucose (mmol/l) 6.68±2.27 6.43±2.12 6.56±1.91 6.55±2.01 0.108
Total Cholesterol (mmol/L) 5.25±1.56 5.13±1.39 5.14±1.43 5.18±1.29 0.414
Triglycerides (mmol/L) 1.44±0.78 1.25±0.68 1.50±0.90 1.50±0.77 0.008
Fitness
VO2max (ml/kg/min) 28.54±8.2 31.21±9.94 28.41±7.37 29.00±7.98 0.034
Data presented as mean±SD. * results for blood parameters are for a sub-group as not all have been analysed

17
DRAFT
What happened to the biomarkers?
The 23 metabolite panel was measured
pre- and post-intervention for 170 of the
subjects that completed the 12-week
intervention. In addition to the clinical
and physiological changes there was a
significant improvement in the Quantose
IR (MQ
) and Quantose IGT (QIGT
) scores.
Did the metabolites change following
the intervention? Several of the
23 metabolites measured changed
significantly with the intervention (Table
7) and with the expected direction of
change based on their values in normal
and dysglycemic subjects with the
exception of 3-hydroxybutyric acid. This
metabolite, a ketone body, increased
with the intervention and may be
serving as marker of increased fatty acid
oxidation accompanying weight loss.
Interestingly, none of the branched-chain
amino acids were changed as a result of
the intervention. This is in contrast to an
earlier report of a decrease in response
to weight loss (18).
Table 6. Relationship between amount / location of exercise and health outcomes.
*significant improvements
Total minutes of exercise Rho P value
Changes in Body Fat 0.36 0.003*
Change in VO2 MAX (L/kg/min) 0.27 0.02*
Change in Weight 0.36 0.001*
Total minutes of sessions IN gym NOT gym
Changes in Body Fat 0.41 (.001)* 0.06 (.631)
Change in VO2 MAX (L/kg/min) 0.29 (.011)* 0.13 (.232)
Change in Weight 0.34 (.002)* 0.19 (.093)
Table 7. Key Metabolites Changed by the Intervention
Metabolite Fold Change P value
Tyrosine 0.95 8.7E-06
Alpha-ketoglutaric acid 0.93 1.2E-05
3-methyl-2-oxopentanoic acid (3MOP) 0.96 4.7E-05
Serine 1.06 1.1E-04
3-Methyl-2-oxobutyric acid (3MOB) 0.97 1.5E-03
Creatine 0.96 1.6E-03
Phenylalanine 0.98 5.9E-03
3-Hydroxybutyric acid 1.98 1.7E-02
LGPC 1.10 2.0E-02
Glycine 1.03 3.2E-02
2-Aminoadipic acid 1.01 4.1E-02

18
DRAFT
Metabolite changes and glucose tolerance. Table 8 summarizes the ability of metabolites to track with FPG, 2hPG, and
body weight. Change in tyrosine correlates (0.34) best with change in FPG and with change in body weight. No metabolite
correlated particularly well with change in 2hPG, the best being change in alpha-ketobutyric acid (0.21). The best overall
biomarker for the diet and exercise intervention is tyrosine when both its performance and practicality are taken into
consideration. Tyrosine can be measured easily as it is included in standard amino acid clinical panels.
Table 8. Pearson Correlations for Change in Metabolite Concentration Versus Change in Glucose
δ-Metabolite δ-FPG δ-2hPG δ-Weight
Tyrosine 0.34 0 0.4
Alpha-ketoglutaric acid 0.22 0.13 0.1
3-Methyl-2-oxobutyric acid -0.03 0.16 -0.02
Phenylalanine 0.17 -0.01 0.26
2-Aminoadipic acid 0.29 0.03 0.14
Leucine 0.23 0.05 0.27
Isoleucine 0.25 0.08 0.15
alpha-ketobutyric acid 0.07 0.21 -0.2
These metabolite changes reflect a single time point (12 weeks) after initiation of a diet and exercise lifestyle modification.
As such, more research is needed to understand metabolite changes in both shorter and longer timeframes for this
intervention. In addition, metabolite changes with exercise only and diet only interventions need to be investigated as well
as responses to drug therapy (e.g. metformin).

19
DRAFT
Using DEXLIFE personalised medicine to manage patients
diagnosed with diabetes
Theapplicationofmetabolicandlipidbiomarkersindiabetestreatment
will need further study. Taking the approach from DEXLIFE, the next
step is to test the responses of the leading biomarkers in patients
following the introduction of various targeted therapies.
The current approach to the medical treatment of type 2 diabetes, as
shown in Figure 2, consists of a wide menu of options after metformin,
increasing from dual to triple therapy and up to combined oral and
injected therapy. What will be possible now is to inform these
therapeutic choices at each step of the way with new phenotypic
information (regarding metabolites and lipids), and to test patient
responses to treatment early in the course of care to help define
individual response or non-response to treatment (including the early
response to diet and exercise).
This will support a new era of more personalised treatment of type
2 diabetes, based on the individual metabolic phenotype. Such a
scenario is possible and follows a natural extension of the DEXLIFE
project. It will, however, require further study in settings where the
new biomarkers are available in the clinic for all patients at baseline
and during carefully selected time points during treatment. This is the
logical ambition and aim for the next phase of the research conducted
by the DEXLIFE consortium.
As the results of the DEXLIFE project become available, the potential
will develop for the application of these new biomarkers in clinical
practice. This will require further translational research and further
development of diagnostic instrumentation allowing analysis of blood
and other samples at an acceptable cost for clinical practice. Findings
from DEXLIFE could be tested and implemented in two main settings:
• The community, for prevention of progression from pre-diabetes
(or high risk) to diabetes.
• General practice and specialist clinic practice, to support the
treatment of patients with type 2 diabetes.
Using DEXLIFE personalised medicine to prevent diabetes
Pre-diabetes is an important clinical state in which type 2 diabetes can
be prevented. This is an important and growing issue as the global
obesity and diabetes pandemic continue to accelerate. The current
range and the upcoming new biomarkers from DEXLIFE provide a
completely new aid to determine vulnerability to progression to type 2
diabetes. Thus,followingsimpleriskbasedscreeningwitharisk-score,
the addition of a simple blood test for a targeted set of metabolomic
and lipidomic markers will allow a better method to define even in
simple terms ‘high risk’ or ‘low risk’ for progression to diabetes. The
new knowledge being gained in relation to the biomarkers that track
with lifestyle response will provide the clinician with an additional tool
to assess and follow progress in response to a chosen approach with
exercise and diet. Vulnerability to diabetes is complex and multi-
factorial. These new biomarkers provide the clinician with a relatively
simple tool to more accurately define biological vulnerability in the
early stages of progression, when prevention can be effective.
DEXLIFE Applications in the Clinic

20
DRAFT
Summary of sampling conditions is given in Table 9. The sample
type should be the same within a study, i.e., either serum or a
specific type of plasma, and in cases where that is not possible, the
differences of concentrations of individual compounds should be
checked and accounted for before proceeding to data analysis and
biological interpretation. Most of the major metabolites are relatively
stable during the preprocessing of the serum and plasma samples,
i.e., short time storage (20-30 minutes) before centrifugation at
room temperature does not have a very significant effect on their
concentrations. However, extended storage at room temperature
does result in alterations of metabolite concentrations, and some
specific metabolites, e.g. lysoPCs, are not even stable when stored
at -80 ºC for extended periods of time. Several studies have shown
that concentrations of most metabolites are stable at least after one
freeze-thaw cycle (23-25), but repeated freeze-thawing resulted at
reduced level of most lipid metabolites in both plasma and serum.
Factors affecting metabolomics analyses - sample type, sample
pre-processing, transport and storage: Metabolite levels and thus
also the results may be influenced by the sampling conditions and
by the chosen methodology, including also by the instrumentation.
Critical and essential points include also sample transportation,
number of freeze–thaw cycles, preparation and handling during the
analytical process. Below is a summary of key factors one should
consider when preparing samples for metabolomics analyses.
Blood-derived serum and plasma are the most common sample
matrices used in human metabolic studies. The difference of the
metabolite profiles in serum and various types of plasma has been
investigated and the results show that the metabolite profiles do vary
betweenthesampletypes.However,thereisnoclearconclusionwhich
matrix is optimal for metabolomics. Some studies also suggest that
several lipids (e.g., phosphatidylcholines, phosphatidylethanolamines,
sphingomyelins and triacylglycerols) are found in lower levels in
citrate plasma than in EDTA plasma (19, 20). While one recent study
showed that reliability of metabolite measurements was higher in
serum as compared to plasma samples (21), another study suggested
that plasma was a preferable matrix for lipidomic analyses (22). Some
additives in plasma have a large effect on the metabolic analyses,
for example, citrate plasma is not suitable for the analysis of citric
acid cycle metabolites. Some metabolites are present in higher
concentrations in serum, for example, lactic acid is present in higher
concentrations in serum in comparison with EDTA plasma.
Key factors to consider when preparing samples for Metabolomics analyses
Table 9. Summary of the most critical analytical factors affecting the lipid analysis
Sample type Serum Plasma
Sample processing Clotting time, typically
30 min preferably at
+4 C, centrifugation
at 1000 g for 15 min,
preferably at + 4ºC
Centrifugation at 10000 g for 15 min,
preferably at + 4ºC
EDTA, citrate and heparin suitable for
lipidomics
Citrate plasma not well suited for
analysis of polar metabolites
Storage Most metabolites are stable for short term storage (<24 h) at
+ 4ºC or at least 6 months at -80 ºC
Freeze-thaw cycles Most metabolites stable in 2 cycles

21
DRAFT
14 Roberts LD, Koulman A, Griffin JL, Towards metabolic biomarkers of insulin resistance
and type 2 diabetes: progress from the metabolome. The Lancet Diabetes & Endocrinology.
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Lawton K, Elverson R, Carr B, Sinnott M, Ferrannini E, A Novel Test for IGT Utilizing Metabolite
Markers Fof Glucose Tolerance. Journal of Diabetes Science and Technology, 2015; 9(1):69
16 Sinnott M, Kinsley BT, Jackson AD, Walsh C, O’Grady T, Nolan JJ, Gaffney P, Boran G,
Kelleher C, Carr B. Fasting Plasma Glucose as Initial Screening for Diabetes and Prediabetes
in Irish Adults: The Diabetes Mellitus and Vascular Health Initiative (DMVhi). PLoS One. 2015
Apr 15;10(4):e0122704
17 Cobb J, Gall W, Adam K-P, Nakhle P, Button E, Hathorn J, Lawton K, Milburn M, Perichon
R, Mitchell M, Natali A, Ferrannini E. A novel fasting blood test for insulin resistance and
prediabetes. Journal of Diabetes Science and Technology. 2013; 7(1):100
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Wenner BR, Bain JR, Laferrère B, Gorroochurn P, Teixeira J, Brantley PJ, Stevens VJ, Hollis
JF, Appel LJ, Lien LF, Batch B, Newgard CB, Svetkey LP. Branched-chain amino acid levels
are associated with improvement in insulin resistance with weight loss. Diabetologia. 2012;
55(2):321
19 Jørgenrud B, Jäntti S, Mattila I, Pöhö P, Rønningen KS, Yki-Järvinen H, et al. The
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20 Gonzalez-Covarrubias V, Dane A, Hankemeier T, Vreeken RJ. The influence of citrate,
EDTA, and heparin anticoagulants to human plasma LC–MS lipidomic profiling. Metabolomics.
2013;9(2):337-48.
21 BreierM,WahlS,PrehnC,FugmannM,FerrariU,WeiseM,etal.Targetedmetabolomics
identifies reliable and stable metabolites in human serum and plasma samples. PLoS One.
2014;9(2):e89728.
22 Ishikawa M, Maekawa K, Saito K, Senoo Y, Urata M, Murayama M, et al. Plasma and
serum lipidomics of healthy white adults shows characteristic profiles by subjects’ gender
and age. PLoS One. 2014;9(3):e91806.
23 Zivkovic AM, Wiest MM, Nguyen UT, Davis R, Watkins SM, German JB. Effects of
sample handling and storage on quantitative lipid analysis in human serum. Metabolomics.
2009;5(4):507-16.
24 Taylor LA, Arends J, Hodina AK, Unger C, Massing U. Plasma lyso-phosphatidylcholine
concentration is decreased in cancer patients with weight loss and activated inflammatory
status. Lipids Health Dis. 2007;6:17.
25 OnoratoJM,ShipkovaP,MinnichA,AubryAF,EasterJ,TymiakA.Challengesinaccurate
quantitation of lysophosphatidic acids in human biofluids. J Lipid Res. 2014;55(8):1784-96.
1 American Diabetes Association:Standards of Medical Care in Diabetes- 2015.
Diabetes Care, 38, suppl 1 S8-S16 (2015)
2 Cederberg H, Saukkonen T, Laakso M et al. Postchallenge glucose, A1C, and fasting
glucose as predictors of type 2 diabtes and cardiovascular disease: a 10-year prospective
cohort study. Diabetes Care 33:2077-2083 (2010)
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between5.7and6.4%asamarkerforidentifyingpre-diabetes,insulinsensitivityandsecretion,
and cardiovascular risk factors: the Insulin Resistance Atherosclerosis Study (IRAS). Diabetes
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4 Ferrannini E, Gastaldelli A, Iozzo P. Pathophysiology of pre-diabetes. Med Clin N Am
95:327-339 (2011)
5 Borg R, Kuenen JC, Carstensen B, et al. Associations between features of glucose
exposure and A1C: the A1C-Derived Average Glucose (ADAG) study. Diabetes 59:1585-1590
(2010)
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diabetes risk in clinical practice to target and maximize diabetes prevention interventions.
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haemoglobin A1C and fasting plasma glucose for incident diabetes and implications for
screening. Eur J Epidemiol 26:779-787 (2011)
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other prediabetes definitions to type 2 diabetes: a meta-analysis. Diabetologia 56:1489-
1493 (2013)
9 Gillies CL, Abrams KR, Lambert PC, et al. Pharmacological and lifestyle interventions
to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic
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of three subtypes of type 2 diabetes: a post-hoc analysis of the longitudinal Whitehall II
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References

22
DRAFT
Project Partners
Steno Diabetes Center
Denmark
www.steno.dk
Centre for preventive Medicine
Dublin City University, Ireland
www.preventivemedicine.ie
Voluntary Health Insurance Board
Ireland
www.vhi.ie
Fundacio privada institut de recerca biomedica IRB
Spain
www.irbbarcelona.org
Fundació Institut d’Investigació Biomèdica de Bellvitge
Spain
www.idibell.cat
University of Eastern Finland
Finland
www.uef.fi/coe/etusivu
Metabolon
USA
www.metabolon.com
University of Newcastle Upon Tyne
UK
www.ncl.ac.uk/crf
Pintail Ltd
Ireland
www.pintail.eu

DRAFT
The DEXLIFE project (Mechanisms of prevention of type 2 diabetes by lifestyle
intervention in subjects with pre-diabetes or at high-risk for progression) was
supported by the 7th Framework programme (Grant agreement no: 279228).

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