IDnr: 0591521 Eindhoven university of Technology Department of Biomedical Engineering Cardiovascular Biomechanics Supervisor: Dr. ir. K.D. Reesink Committee: Dr. I. Ferreira Prof. dr. J.G.R. de Mey Dr. ir. N.H.L. Kuijpers Prof. dr. ir F.N. van de Vosse

Effect of transient high blood glucose level on brachial artery

flow-mediated dilation: methodological aspects

MSc thesis F.C.G. van Bussel

August 2011 BMTE 11.14

Abstract

Healthy arteries dilate in response to changes in wall shear stress (i.e. due to an increase in blood flow). This response is known as flow-mediated dilation (FMD). Flow-mediated dilation is considered a surrogate for the evaluation of cardiovascular health and is blunted in patients with diabetes, suggesting that glucose metabolism plays an important role in worsening arterial function. Brachial artery flow-mediated dilation can be measured non-invasively by ultrasound and represents the (dys)function of the interaction between endothelial cells and smooth muscle cells. FMD is generally defined as the peak increase in diameter relative to the baseline diameter after a period of induced peripheral hyperemia (i.e. due to cuff inflation). The increased blood flow exerts greater wall shear stress (WSS) releasing nitric oxide from endothelial cells, which causes smooth muscle cells to relax, leading to arterial dilation. Therefore, it has been proposed that when measuring FMD it should be normalized for the change in WSS (FMDn). Measuring FMD is technically challenging with a week-to-week variability of 33-50%. This relatively poor performance is partly due to an insufficient sampling rate of the diameter over time, whereby the peak diameter can be missed.

For these reasons, we have developed a new analysis method to determine changes in diameter and blood flow velocity (i.e. WSS) beat-to-beat over the entire FMD measurement duration, which may improve the quality of the FMD estimate and thus sensitivity. Although studies showed that flow-mediated dilation is decreased after one hour of glucose intake, none investigated whether flow-mediated dilation is affected in the early postprandial phase (30 minutes) after glucose intake.

For these reasons, we have investigated; 1) the time-resolved relationship between blood glucose level and brachial artery flow-mediated dilation in the early and late postprandial phase; 2) the reproducibility of FMD; 3) and 1+2 again for the normalized FMDn.

After two baseline FMD measurements, twenty-two healthy volunteers underwent a standard oral glucose tolerance test. Then brachial artery flow-mediated dilation was measured in the early (after 30 minutes) and late (after 75 minutes) postprandial phases. Volunteers underwent this protocol twice. From the four baseline measurements we calculated intra-class correlation coefficients (ICC) and evaluate agreement between repeated observations by Bland-Altman plots. ICCs were 0.63 and 0.47 for FMD and FMDn, respectively. The inter-session coefficients of variation for FMD and FMDn were 21% and 30%, respectively. We observed a decreased flow-mediated dilation response in the early postprandial phase (P-values were 0.068 and 0.001 for FMD and FMDn respectively) and this remains decreased for FMDn in the late postprandial phase.

In conclusion, this study has three important findings; 1) Flow-mediated dilation response is decreased in healthy volunteers in the early postprandial phase (30 minutes after glucose intake) for both assessments (FMD and FMDn) and remains decreased in the late postprandial phase (75 minutes after glucose intake) for FMDn; 2) the reproducibility of FMD with the new beat-to-beat method over the entire FMD measurement is improved (21%) as compared to the ‘old’ method (33-50%) used in previous studies; 3) FMD normalization (FMDn) for the change in WSS stimulus improves the sensitivity of the assessment of glucose induced changes in flow-mediated dilation.

Samenvatting Slagaders pulseren dagelijks zo’n 90.000 keer. Bloed dat sneller door de slagader stroomt (bijvoorbeeld tijdens inspanning) zorgt voor wrijving op het endotheel, de binnenste laag van een slagader. Door de wrijving wordt er stikstof oxide (NO) afgegeven, waardoor de gladde spiercellen in de vaatwand ontspannen en het bloedvat verwijdt. Hierdoor neemt de wrijving op het endotheel af. Dit proces wordt ‘flow-mediated dilation’ (FMD) genoemd. FMD zegt iets over de vaatconditie en wordt daardoor gezien als een maat voor cardiovasculaire aandoeningen. FMD is verminderd in patiënten met Diabetes Mellitus en dit suggereert dat glucose metabolisme een belangrijke rol speelt in het functioneren van de bloedvaten. Met behulp van echo-ultrageluid wordt de slagader (Brachialis) in de bovenarm in beeld gebracht en kan FMD niet-invasief gemeten worden. FMD is gedefinieerd als de toename in diameter (piek diameter) ten opzichte van de oorspronkelijke diameter nadat een versnelde bloedstroom in de slagader is gecreëerd door toedoen van een opgeblazen onderarm band. Dit leidt tot meer wrijving op het endotheel en tenslotte tot tijdelijke verwijding van de slagader in de bovenarm. De grootte van de wrijving op het endotheel speelt dus een belangrijke rol in hoezeer de slagader verwijdt. Het is daarom logisch om de verwijding van het bloedvat te corrigeren voor de grootte van deze wrijving (FMDn). Het meten van FMD was tot nu toe technisch lastig met een variabiliteit van 33-50% tussen twee herhaalde FMD metingen over één week. Dit komt vooral doordat de verandering van de diameter over tijd niet voldoende vaak gemeten is, waardoor de piek diameter onjuist bepaald kan zijn.

Daarom hebben wij een nieuwe analyse methode ontwikkeld waardoor we in staat zijn om veranderingen in diameter en bloedsnelheid per hartslag over de hele FMD meting te bepalen. Deze nieuwe analyse methode kan bijdragen om de FMD beter te bepalen. Eerdere onderzoeken hebben aangetoond dat FMD vermindert één uur na toediening van een suikerdrankje. Echter, nog niemand heeft gekeken hoe FMD zich gedraagt in een vroegtijdig stadium (na 30 minuten) na toediening van een suikerdrankje.

Het doel van dit onderzoek was 1) om nauwkeurig te meten wat het effect is van een verhoogd bloedsuikerspiegel op FMD in een vroegtijdig stadium (na 30 minuten) en in een later stadium (na 75 minuten), 2) of we na een week hetzelfde effect kunnen meten en 3) hoe bovenstaande doelen zich houden ten opzichte van FMDn.

De FMD meting werd eerst tweemaal uitgevoerd vóór het drinken van het suikerdrankje. Daarna hebben 22 gezonde vrijwilligers een standaard orale glucosetolerantietest ondergaan en werd de FMD nogmaals tweemaal gemeten. Één in een vroegtijdig stadium (30 minuten) en één in een later stadium (75 minuten) na het drinken van een suikerdrankje. Dit protocol werd herhaald na één week. De ‘intraklasse correlatiecoëfficiënt’ (ICC) over FMD metingen voorafgaand aan het suikerdrankje zijn 0.63 voor FMD en 0.47 voor FMDn. De variabiliteit van de FMD meting over een week is 21% en 30% voor FMDn. We observeerden een afname in

vaatverwijding van de bovenarm slagader na 30 minuten na toediening van het suikerdrankje (P=0.068 voor FMD en P=0.001 voor FMDn). Deze afname bleef verminderd voor FMDn na 75 minuten na toediening van het suikerdrankje.

Hieruit kunnen we concluderen dat 1) de vaatverwijding in gezonde vrijwilligers is verminderd na inname van het suikerdrankje in het vroegtijdige stadium (30 minuten) voor FMD en FMDn en dat het verminderd blijft in het late stadium (75 minuten) na inname van het suikerdrankje zoals aangegeven voor FMDn, 2) met de nieuwe ontwikkelde analyse methode de reproduceerbaarheid van de FMD meting is verbeterd (21% versus 33-50%) en 3) de verandering in vaatverwijding door een verhoogd bloedsuikerspiegel makkelijker te detecteren valt als FMD gecorrigeerd wordt voor de grootte van de wrijving (FMDn).

Contents

1.0 Introduction ........................................................................................................9

1.1 Background ................................................................................................. 11

1.2 Hypothesis ................................................................................................... 13

2.0 Methods .......................................................................................................... 14

2.1 Study design ................................................................................................ 14

2.2 Data analysis ............................................................................................... 16

2.3 Statistical analysis ....................................................................................... 21

3.0 Results ............................................................................................................ 25

3.1 Population characteristics ............................................................................ 25

3.2 Reproducibility ............................................................................................. 27

3.3 Effect of transient high blood glucose on flow-mediated dilation ................. 29

4.0 Discussion ....................................................................................................... 32

5.0 Conclusion ...................................................................................................... 38

6.0 Acknowledgement ........................................................................................... 39

7.0 Appendices ..................................................................................................... 41

7.1 A: Pilot study ................................................................................................ 42

7.2 B: Detailed measurement protocol .............................................................. 43

7.3 C: Scanner settings ..................................................................................... 46

7.4 D: Derivation normalized flow-mediated dilation ......................................... 47

7.5 E: Steps video analysis program ‘MyFMD’ .................................................. 51

7.6 F: Excluded measurements ......................................................................... 55

7.7 G: Remaining results glucose ...................................................................... 61

7.8 H: Power calculation baseline diameter ...................................................... 67

7.9 I: Water results ............................................................................................ 68

7.10 J: Changed automatic video analysis program ‘MyFMD’ code .................... 74

8.0 References ...................................................................................................... 77

9

1.0 Introduction Cardiovascular complications play an important role in diabetes mellitus [1]. These problems are mainly caused by vascular dysfunction. Disease-related demands placed on blood vessels are initially countered by the vessel through adaptation to maintain homeostasis. When the blood vessel is unable to adapt, the blood vessel will be damaged and this leads to loss of function. Large conduit arteries become less distensible, which leads to an increased workload on the heart [2-5]. Moreover, these arteries dilate less in response to increased blood flow, reflecting endothelial dysfunction [6, 7]. This response is known as ‘flow-mediated dilation’ (FMD).

Non-invasive assessment of endothelial function by ultrasound (i.e. flow-mediated dilation) has become widely used and is considered a surrogate for the evaluation of cardiovascular health [8]. FMD, which is used by many research groups, is generally defined as the peak diameter change (typically at 60 seconds) after cuff deflation relative to the initial baseline diameter [9]. The blood vessel dilates due to the acute increase in blood flow, which exerts greater wall shear stress (WSS) on the endothelial surface [10]. This dilation response to elevated blood flow is abolished after removal of the endothelial lining [11]. On the basis of this physiological phenomenon, Celermajer et al. [9] developed a noninvasive technique to assess endothelial function and Corretti et al. [12] presented the first guideline for this technique, which is based on measuring the diameter of the blood vessel. The technique employs 5 minute lower arm occlusion to generate hyperemia induced increased brachial artery shear stress, which should result in endothelium dependent vasodilation. Brachial artery flow-mediated dilation is mostly used for the non-invasive assessment of vascular (dys)function, which represents the (dys)function of the interaction between the endothelial cells and the smooth muscle cells. Decreased flow-mediated dilation of the brachial artery has been associated with greater cardiovascular risk [13, 14].

Because the hyperemic shear stress is the main stimulus for flow-mediated dilation, adjustment of FMD for the applied stimulus has been proposed [15]. Many research groups used an estimate of the wall shear stress [16] or an estimate of the shear rate [15, 17] for normalizing FMD. However, there is significant variation in the literature with respect to calculating shear rate: previous studies used an estimate of the shear rate defined as mean blood flow velocity divided by the arterial diameter [15, 18], 8 times the mean blood flow velocity divided by the arterial diameter [19, 20] or 4 times the mean or peak blood flow velocity divided by the arterial diameter [21, 22]. The wall shear stress is defined as blood viscosity times the shear rate [23]. However, the determination of blood viscosity needs invasive blood sampling [16, 24] and this hampers non-invasive assessment of normalized FMD. Furthermore, the whole blood viscosity was determined at baseline only [16], whereas this differs at different shear rates [25]. Another limitation of this method is that it does not take into account the

10

baseline wall shear stress. Therefore, we derived a normalized FMD response, based on the change in wall shear stress with respect to baseline wall shear stress and contain only relative diameter and relative velocity terms. This allows non-invasive measurement of normalized FMD. A major advantage of this method, as compared to previous studies that determined wall shear stress indirectly using blood viscosity [16], is the direct determination of the velocity terms (to quantify the change in wall shear stress stimulus) and diameter simultaneously using dual mode.

At the epidemiological level, an elevated blood glucose level is linked to a blunted flow-mediated dilation response [6, 7, 26, 27]. However, the mechanisms by which an elevated blood glucose level causes vascular dysfunction remains to be unraveled. Current insights contend that the function of vascular endothelium, which hosts an extensive array of physiological pathways, plays a pivotal role in the development of vascular dysfunction. Two important pathways that we will address in the present study are 1) the shear stress sensing [28] and 2) the glucose (i.e. indirectly via an effect of insulin) [29] pathways, which release nitric oxide (NO) into the artery wall causing dilation (see background section).

From basic physiology it is known that glucose increases blood flow in the microcirculation in the early postprandial phase (i.e. resistance arteries dilate in response to insulin-binding to vascular endothelium) [29]. Previous studies show that in large arteries flow-mediated dilation is decreased after one hour of glucose intake [6, 7, 26, 27]. However, it is unknown how flow-mediated dilation is affected in the early phase after glucose intake (30 minutes postprandially). A small pilot study suggests that the flow-mediated dilation response increases in the early postprandial phase (appendix A). Although this study was small, the results might be explained by the fact that an increase in wall shear stress and an increase in blood glucose level lead to vasodilation through smooth muscle cell relaxation (see background section).

Measuring flow-mediated dilation is technically challenging with a week-to-week variability of 33-50% [30-32]. This relatively poor performance is partly due to an insufficient sampling rate of the diameter over time. Recently, we have developed a method to determine changes in diameter and blood flow velocity beat-to-beat and over the entire duration of the FMD measurement, which may improve the quality and sensitivity of the FMD estimate.

Sensitive assessment of the time-dependent effect of an acute, transient, high blood glucose level on FMD may contribute to improved diagnosis of vascular dysfunction in diabetic patients, while it provides insight into the metabolic and biophysical functions of the endothelium. Accordingly to the poor reproducibility, we first want to investigate if we can reproduce the flow-mediated dilation response in healthy male volunteers. Therefore, we investigate the reproducibility of the brachial artery flow-mediated dilation measurements and the time-resolved relationship between an acute, transient, high blood glucose level and the brachial artery flow-mediated dilation response in healthy male volunteers.

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1.1 Background

1.1.1 Arterial properties & functions

The arterial vessel wall is organized in three layers (Figure 1): an inner layer (intima), an intermediate layer (media) and an outer layer (adventitia). The intima contains endothelial cells at the luminal surface and the endothelial cells are in direct contact with the flowing blood. The media is a complex structure of proteins (collagen and elastin matrix), smooth muscle cells (SMC) and mainly determines the elastic properties of the artery. The adventitia is the outermost layer of the blood vessel and is mainly composed of collagen. The collagen serves to anchor the blood vessel to nearby organs and gives stability to the artery.

Figure 1. Structure of an artery. The artery wall contains three layers: an inner layer (intima), an intermediate

layer (media) and an outer layer (adventitia). (taken from www.octc.kctcs.edu)

The elastin fibers in the media bear the majority of tensile load when a low mechanical stretch is exerted on the artery. When a high mechanical stretch is exerted on the artery, the much stiffer collagen fibers limit further deformation of the artery. Structural changes in the elastin-collagen matrix underlie loss of arterial function. Smooth muscle cells are attached to the elastin-collagen matrix and auto-regulate local mechanical stresses by adapting their contractile tone.

Flow-mediated dilation reflects the function of the artery to adapt the diameter to an increase in blood flow. Figure 2 shows that in a normal resting situation the amount of blood flowing through an artery is determined by a certain diameter. When the blood flow increases (i.e. through exercise etc) the artery adapts its diameter resulting in vasodilation of the artery.

Artery

Endothelium of tunica Interna (Intima)

Connective tissue (elastic and collagenous fibers)

Tunica media

Tunica externa (adventitia)

12

Figure 2. function of the artery (schematic). An increase in blood flow causes an increase in diameter.

1.1.2 Physiology of flow-mediated dilation

Figure 2 shows that the artery has a certain diameter and blood flow velocity in a normal resting situation. Showing a closer look at the artery, figure 3A shows the normal resting situation at a certain blood flow velocity. The blood flow velocity exerts a frictional force on the endothelial layer. This frictional force is represented by the wall shear stress (WSS). When the blood flow velocity increases, the friction on the endothelial layer and thus also the wall shear stress increases (Figure 3B). This leads to more production of nitric oxide (NO). The vascular smooth muscle cells of the artery respond to NO by relaxation, which in turn leads to dilation and a consequent decrease of the wall shear stress. Taken together, this results in a greater diameter of the artery.

Figure 3. Flow-mediated dilation; resting state with a normal flow (A); increased blood flow velocity (and thus

higher wall shear stress (WSS)) inducing endothelial nitric oxide (NO) release (B); this leads to smooth muscle

cell relaxation and vessel dilation, which results in lower wall shear stress, but has still increased flow (C).

blood flow diameter

WSS

A

elastincollagen

WSS

NONO

NO

WSSWSS

NONO

B

WSSWSS

C

smooth muscle cell

endothelial cell

13

Figure 4. Interaction of endothelial cell and smooth muscle cells; both insulin binding and increased shear stress

promote endothelial NO production, which leads to vasodilation through smooth muscle cell relaxation.

Showing a more detailed look at the endothelial cell, figure 4 shows two pathways leading to NO release: the wall shear stress stimulus and the glucose stimulus. The wall shear stress stimulus [28] and the glucose (i.e. indirectly via an effect of insulin) stimulus [29] leads to more production of NO. When the glucose level increases (i.e. due to an oral glucose tolerance test (OGTT)), insulin increases as well. Then, insulin binds to its receptor and leads via PI3K to more NO production. Increased wall shear stress leads also to more NO production. Both pathways share a common branch (with PI3K phosphorylation as a key process) and lead to more NO production. NO diffuses to the smooth muscle cell and this leads to vasodilation through smooth muscle cell relaxation.

1.2 Hypothesis There is limited evidence in the literature how an acute increase in blood glucose level modulates large artery flow-mediated dilation in the early postprandial phase (after 30 minutes). Our pilot study (Appendix A) and the observed results in the microcirculation [29] may suggest that flow-mediated dilation could be enhanced in the early postprandial phase, which can be explained by the mechanical interaction between endothelial cells and smooth muscle cells (see background section). However, this pilot study was small (n=6). Previous studies show a decreased FMD after glucose intake in the late postprandial phase (60-120 minutes) [6, 7, 26, 27]. Therefore, in this study we investigate if we can reproduce the results of the pilot study or that flow-mediated dilation is decreased in the early postprandial phase, as others show in the late postprandial phase, with the following hypothesis:

In large muscular arteries of healthy men, an acute increase in blood glucose level may enhance flow-mediated dilation in the early postprandial phase, and may again reduce flow-mediated dilation in the late phase.

insulin

L-Arg

NOS

NO

Ins-R

PI3k

SMC tone

shear stress

+

endothelialcell

14

2.0 Methods In this study the time-resolved relationship between blood glucose levels and flow-mediated dilation (FMD) of the brachial artery was investigated. FMD is generally quantified by the peak increase in artery diameter relative to its baseline diameter [9] and will vary with the amplitude of the stimulus (i.e. the change in wall shear stress (WSS) stimulus (∆τw)). Therefore, many research groups support normalization of the FMD response [17, 33]. First, the study population and the study protocol will be discussed. Second, the derivation of the normalized FMD response will be derived and the automatic video analysis will be discussed.

2.1 Study design

2.1.1 Study population

Healthy male volunteers (n=30) were recruited through advertisement at the university. Inclusion criteria for the study were as follows; volunteers’ age ≥ 18 years; fasting blood glucose level below 6.1 mmol/l; normotensive (<140/90 mmHg); no history of diabetes, myocardial infarction or cardiovascular disease; not using any medication and abstinence from exercise, caffeine consumption, smoking, alcohol consumption, vitamin supplements, food and drinks for twelve hours prior to the measurements [28]. Drinking water was permitted until 3 hours prior to the start of the study protocol. All volunteers gave informed consent to participate in the study, which was approved by the medical research ethics committee of Maastricht University Medical Centre (MUMC).

2.1.2 Study protocol

The exact same protocol described below was executed twice in each of the volunteers within approximately 8.5 ± 3.4 (minimum 3 days, maximum 20 days) days. 22 volunteers underwent an oral glucose tolerance test and 8 volunteers received plain water (same volume). This allows studying the inter-session reproducibility of the measurement techniques in 30 volunteers and of an effect (if any) of glucose on flow-mediated dilation in 22 volunteers.

Figure 5 shows the timeline that describes the procedure of one session (Appendix B provides more details of the measurement procedure and timing within one session). In one session 4 flow-mediated dilation measurements were obtained. Measurements were all performed in the morning due to diurnal variation [34], in a climate controlled room (21-23oC) with lights dimmed. First, volunteers were positioned in supine position for 15 minutes to acclimatize. During acclimatization, fasting blood glucose was determined using a point of care (POC) glucosemeter (OneTouch® Ultrasmart®, LifeScan, California, USA). If the fasting blood glucose

15

Figure 5. Measurement procedure which combines a standard oral glucose tolerance test (OGTT) and flow-

mediated dilation (FMD) measurements. After an initial point-of-care glucose measurement (POCgluc), venous

blood samples were drawn to quantify the blood glucose concentration. Effect of glucose on flow-mediated

dilation (FMD) was measured effectively 30 and 75 minutes after glucose intake. At the end a POCgluc was

repeated to determine whether glucose concentration was at the normal level again. Time axis was set on zero at

the start of OGTT. For the consistency FMD (T=-15) and blood sampling (T=-1) were all named T=0 in the rest of

the document.

level was higher than 6.1 mmol/l, the volunteer was excluded from further participation. If the fasting blood glucose level was below 6.1 mmol/l, a blood sampling needle was inserted (Figure 6B) in the antecubital vein of the left arm for blood sampling. A sphygmomanometer cuff (Hokanson TD 312, D.E. Hokanson, Inc., Bellevue, U.S.A.) required for the FMD measurement, was placed on the right forearm. A second sphygmomanometer cuff (Omron 705 IT, Omron Healthcare Europe B.V., Hoofddorp, the Netherlands), required for blood pressure measurement, was placed on the left upper arm. Electrocardiography (ECG) electrodes were placed on the chest to provide a time reference for the echo ultrasound scanner. Peripheral blood pressure was measured continuously on the right middle finger (Penaz-method; BMEye Nexfin system) for pressure monitoring.

After 25 minutes the first blood sample was collected (Figure 5). Immediately after blood sampling, the first baseline brachial artery flow-mediated dilation was measured using continuous ultrasound echography in dual mode (simultaneous B-mode and pulsed wave mode; MyLab™70, Esaote, Genova, Italy) (Appendix C). The linear array ultrasound transducer (7.5 MHz) was fixed using a custom-built probe holder (Figure 6B).

Flow-mediated dilation was measured continuously over 13 minutes. During the first 3 minutes baseline diameter and baseline blood flow velocity were determined. After that, the cuff on the right forearm was inflated to 200 mmHg and left at that pressure

POCgluc

POCgluc

OGTT

10 min

*

* : blood sampling �Time (min)

*

60 12075300

*

**

Session 2

FMD FMD

Session 1�

glu

cose

[mm

ol/l

]

Baseline

*

-15-40-65

16

Figure 6. Research room (A) with acquisition computer (left), finger pressure (Nexfin), tonometer, echo ultrasound scanner. During measurement the brachial artery was scanned with an ultrasound probe on the right upper arm (B), a sphygmomanometer cuff (blue cuff) was placed on the right forearm. Peripheral blood pressure was measured continuously on the right middle finger. Blood was drawn from the antecubital vein of the left arm and blood pressure (grey cuff) was measured on the left arm.

for five minutes in order to cause distal (forearm) hypoxia. After 5 minutes the cuff was rapidly deflated and the arterial diameter and blood flow velocity were measured for another five minutes. FMD was calculated as the change in diameter with respect to the baseline diameter. During FMD measurement, echo images were recorded on DVD.

The above procedure (for the baseline FMD measurement (Figure 5)) was repeated after 10 minutes. This allows establishing an intra-session reproducibility of FMD. Just after the second baseline FMD measurement, a blood sample was collected. Then the volunteers (n=22) consumed a drinkable glucose solution (75 gram of dextrosemonohydrate) following a standard oral glucose tolerance test (OGTT). FMD measurements were repeated, in such a way that FMD measurements effectively took place at 30 and 75 minutes after glucose intake. This enables discrimination of an early and a late postprandial phase. Blood samples were taken at 30, 60, 75 and 120 minutes after consuming the glucose solution. Finally, the blood glucose level was measured, using the point of care glucosemeter, to determine whether the glucose concentration was in the normal range again (<6.1 mmol/l).

2.2 Data analysis

2.2.1 Derivation of the FMD response

Upon cuff-release reactive hyperemia ensues, which increases brachial artery blood flow by a factor of 4-5 [35]. Flow-mediated dilation is generally quantified by the peak increase in artery diameter relative to its baseline diameter [9]. However, this response will vary with the amplitude of the wall shear stress stimulus (WSS) [36]. The wall shear stress is the drag per unit area exerted by the vessel wall on the

A B

17

flowing blood [37]. Previous studies used an estimate of the wall shear stress [16] or an estimate of the shear rate [15, 17] for normalizing FMD. However, there is significant variation in the literature with respect to calculating shear rate: previous studies used an estimate of the shear rate defined as mean blood flow velocity divided by the arterial diameter [15, 18], 8 times the mean blood flow velocity divided by the arterial diameter [19, 20] or 4 times the mean or peak blood flow velocity divided by the arterial diameter [21, 22]. The wall shear stress is defined as blood viscosity times the shear rate [23]. However, the determination of blood viscosity needs invasive blood sampling [16, 24] and this hampers the non-invasive assessment of normalized FMD. Another limitation of this method is that it does not take into account the baseline wall shear stress. Therefore, we derived a normalized FMD response, based on Poiseuille velocity distribution, using only relative diameter and velocity terms. This allows non-invasive and continuous measurement of normalized FMD (please see below for the derivation and appendix D provides more details).

A major advantage of this method, as compared to previous studies that determined wall shear stress indirectly using blood viscosity, which was measured at a later moment at the laboratory [16], is the direct determination of the change in velocity terms (to quantify the change in wall shear stress stimulus) and diameter simultaneously in dual mode.

The derivation is given below (and in more details in Appendix D) :

In clinical studies, wall shear stress (WSS in text, τw in equations) is often calculated from blood viscosity (η) and wall shear rate (γ� ), which is equal to the radial derivative of the velocity distribution V(r) at the wall (r=R) [23]:

τw = η γ� (1)

γ� = �∂ V(r)∂ r �r = R (2)

Figure 7. Blood vessel with radius R and Poiseuille velocity V(r). r and z are the standard cylindrical coordinates.

rz

V(r)

R

18

In steady state, a Poiseuille velocity distribution is assumed with the following assumptions:

- There is fully developed flow (parabolic flow profile) - There is a no-slip condition at the blood vessel wall (V(r) = 0 at r=R)

- The blood vessel is axially symmetric ( ∂V(r)∂r� = 0 at r=0)

The Poiseuille velocity distribution is given by [38]:

V�r = Vmax 1� � rR 2� (3)

with Vmax the maximum velocity and R the radius of the blood vessel, r is the standard cylindrical coordinate (Figure 7).

With the derivative of Poiseuille velocity distribution, which contains the assumption that the blood vessel is axial symmetric and with the diameter D = 2 * R, the wall shear rate from equation 2 is rewritten as follows:

γ� � ��2Vmax � rR � �1

R��r=R

� �2VmaxR

=� 2VmaxD2� =� 4Vmax

D (4)

with Vmax, the maximum blood flow velocity; R, the radius of the blood vessel; D, the diameter of the blood vessel; r is the standard cylindrical coordinate (Figure 7).

From equation 4 and with Vmax = 2 * Vmean [39], the wall shear stress (τw) from equation 1 is rewritten as follows:

τw � � 8ηV�D = η γ� (5)

with η the blood viscosity; V�, the mean blood flow velocity; D, the diameter of the blood vessel.

However, the wall shear stress cannot be measured simultaneously with the change in diameter and therefore we rewrite the wall shear stress to velocity and diameter terms, which can be measured by ultrasound echography in dual mode. A first order Taylor polynomial approximation was used (Appendix D). Then the change in wall shear stress (∆τw), which is induced by a change in velocity (∆V) or a change in diameter (∆D), is given by:

19

∆τw � �8ηD ∆V� � 8ηV�D2∆D (6)

The first term indicates the change in wall shear stress (∆τw) evoked by a change in velocity and the second term reflects the change in wall shear stress evoked by a change in diameter.

FMDn (Dfm,n in equations) is defined as the ratio between these two factors (Appendix D) and is given by:

Dfm,n =

8ηV�∆DD2�

8η∆V�D� =

V�*∆DD*∆V� =

∆DD�

∆V�V��

(7)

with D and V the mean baseline diameter and velocity, respectively, of the brachial artery before occlusion of the forearm, ∆D is the change in diameter of the brachial artery, due to the increased blood flow, with respect to the baseline diameter, ∆V is the change in blood flow velocity, due to increased blood flow after cuff deflation, with respect to baseline blood flow velocity. Equation 7 can be turned into:

Dfm,n �∆D

Dbaseline�

∆V�V�baseline�

(8)

These parameters were automatically determined by a custom-written Matlab program described below.

2.2.2 Video analysis

The video images were processed automatically to determine the velocity and diameter profiles over the entire 13 minutes of the FMD measurement using an existing custom-written Matlab program (“MyFMD”, Prof. A.P.G. Hoeks, Biomedical Engineering, Maastricht). The existing program determined the velocity profile only over the positive flow velocities. Therefore, the baseline velocity was overestimated and thus the normalized FMD response was overestimated. To approach a more physiological flow-mediated dilation response, the negative flow velocities were taken into account (Figure 8). Therefore, the existing program was adapted (Appendix J).

20

Figure 8. The velocity profile was determined in the older version of MyFMD by only taking into account the positive flow (green) velocities (A). To approach a more physiological flow-mediated dilation response, the program was adapted to include also negative flow (green) velocities (B).

The videos existed of >19.000 frames and had a resolution of 720 x 576 pixels. “MyFMD” read 30 frames together as a block and is based on pixel densities. The R- top of the ECG was detected (Appendix E) and was used as trigger to determine the beat-to-beat end-diastolic diameter and blood flow velocity. After processing all the video frames of one FMD recording, “MyFMD” smoothed the raw end-diastolic diameter and the raw velocity curves with a 3th order 35-points Savitzky-Golay filter and then the FMD waveform characteristics were extracted. Figure 9 shows the raw (red) and smoothed (blue) velocity and diameter profiles generated by “MyFMD”.

Baseline diameter and baseline velocity were determined automatically as the median over 120 seconds before cuff inflation. The mean velocity was determined automatically as the mean over 180 seconds post occlusion with respect to baseline velocity. The peak diameter was determined automatically as the maximum over 180 seconds after cuff deflation with respect to baseline diameter (Figure 9).

However, the automated determination of velocity and diameter values is not flawless and incorrect velocity and diameter values can be introduced. Therefore, all the outcomes of the custom-written automatic video analysis program were checked blindly by an experienced researcher who was unaware of the volunteer and the moment of FMD measurement. Incorrect values were identified and were corrected based on the diameter and velocity profiles during 13 minutes of FMD measurement.

A

B

21

Figure 9. Custom-written automatic video analysis program “MyFMD”. Raw (red) and smoothed (blue) brachial artery diameter curves (A) and flow velocity curves (B) obtained by sequential beat-to-beat video analysis by the custom-written “MyFMD” program. Baseline diameter (Dbaseline) and velocity (Vbaseline) were determined over 120 seconds pre-occlusion. Peak diameter (∆Dpeak) was determined as the maximum diameter value within 180 seconds post-occlusion with respect to baseline diameter. Mean velocity (∆Vmean) was determined as the mean over 180 seconds post-occlussion with respect to baseline velocity (B, gray). ♦, cuff inflation; ●, cuff deflation.

2.3 Statistical analysis Systolic blood pressure, diastolic blood pressure and fasting glucose levels of the baseline characteristics are presented as an average over 4 baseline measurements (2 at each session) (Table1). Analyses were performed with SPSS version 17.0. The statistics used are further explained below:

2.3.1 Coefficient of variation

Intra-session and inter-session coefficients of variation (CV’s) were calculated as follows:

The intra-session coefficient of variation was defined as:

CV � �σintra-sessionx�intra-session� � 100 (9)

A B

22

with the intra-session standard deviation (σ) and mean (x�) over baseline measurement 1 and 2. The intra-session coefficient of variation is presented in Table 2 and was calculated for both sessions.

The inter-session coefficient of variation was defined as:

CV � �σinter-sessionx�inter-session� � 100 (10)

with the inter-session standard deviation (σ) and mean (x�) over two baseline measurements between sessions. The inter-session coefficient of variation is presented in Table 3 and was calculated as follows:

1) baseline measurement 1 and 2 of session 1 and baseline measurement 1 and 2 of session 2

2) baseline measurement 1 of session 1 and baseline measurement 1 of session 2

3) baseline measurement 2 of session 1 and baseline measurement 2 of session 2

2.3.2 Intra class correlation coefficient

The intra-class correlation coefficient (ICC) is an estimate of the proportion of the combined variability of volunteers and error that may be attributed to the variability between volunteers. The ICC was calculated over 4 baseline measurements (2 at each session) and was defined as [40]:

ICC =σB2

�σB2+σW2 � (11)

where σB2 is the between-volunteer variance, and σW

2 is the within-volunteer variance, which were defined as follows:

σB2 � ���xmi � x�m2

n

i=1

M

m=1

(12)

σW2 ����xim � x�i2

M

m=1

n

i=1

(13)

23

with M, the number of total measurements; n, the total number of volunteers included in this study; xim, the observation of volunteer i of measurement m; x�m mean of measurement m; x�i mean of volunteer i.

2.3.3 Paired T-test

Paired T-test was applied to test whether the mean values of intra-session and inter-session baseline measurement differed statistically (Table 2 and 3) and was calculated as follows:

t=d�-0σ √n�

(14)

with t, the test statistic; d�, the average of the difference between measurements; σ, the standard deviation of the differences, and n is the total number of volunteers.

The p-value was determined with SPSS, based on Student’s t-distribution table with t-values and the corresponding degrees of freedom (df = n-1).

2.3.4 Repeated analysis of variance (ANOVA)

To statistically test for changes in the mean glucose level, change in wall shear stress stimulus, flow-mediated dilation and normalized flow-mediated dilation over time, repeated ANOVA was used. Repeated ANOVA was calculated as follows [40]:

sst � ���xim � x�2n

i=1

M

m=1

(15)

with sst, the total sum of squares; M, the number of repeated measurements; n, total number of volunteers included in this study; xim, value of volunteer i of measurement m; x�, mean value over all the measurements.

Then the between (ssb), within (ssw) and ‘individual’ (ssi) sum of squares were calculated as follows:

ssb � n��x�m � x�2m

m=1

(16)

24

ssw � ���xim � x�m2n

i=1

M

m=1

(17)

with M, the number of repeated measurements; n, total number of volunteers included in this study; x�m mean of measurement m; x�, mean value over all the measurements; xim, value of volunteer i of measurement m; x�i, mean value over all the repeated measurements of volunteer i.

A certain proportion (ssi/ssw) can be explained as individual differences and therefore the error sum of squares (sse) was calculated as follows:

with ssw the within sum of square and ssi the individual sum of squares.

Then the mean square was calculated for the sum of squares by dividing the sum of square with the degrees of freedom. After that, the mean squares were divided by the corresponding mean square of the error and an F-value was obtained. This F-value corresponds in the F-distribution table with a p-value and in this way the statistical test is performed.

2.3.5 Bland-Altman plots

Agreement between intra-session and inter-session were investigated by constructing Bland and Altman plots [41]. On the x-axis the mean of two FMD measurements was plotted and on the y-axis the differences between FMD1 and FMD2 was plotted. The middle dotted line represents the mean difference d� and the upper and lower dotted line represents the ‘limits of agreement’, defined as:

d� � 2σd� (20)

with σd� the standard deviation of the mean difference.

ssi � M��x�i � x�2n

i=1

(18)

sse � ssw � ssi (19)

25

3.0 Results

Table 1. Baseline characteristics of volunteers

n=30

Age [years] 24 ± 9

Height [m] 1.81 ± 0.09

Weight [kg] 75 ± 13

Body mass index [kg/m2] 22.8 ± 2.9

Waist circumference [cm] 81.8 ± 8.2

Systolic BP [mmHg] 120 ± 8

Diastolic BP [mmHg] 67 ± 4

Fasting glucose [mmol/l] 4.5 ± 0.3

Data are means ± SD; n represents the number of healthy male volunteers; SD, standard

deviation; BP, blood pressure.

3.1 Population characteristics The baseline characteristics of the male volunteers (n=30; 22 underwent an OGTT, 8 received plain water) enrolled in the study are summarized in Table 1. Systolic and diastolic blood pressure, body mass index, and waist circumference were all within a healthy adults range. Fasting glucose was 4.5 mmol/l (minimum 4.0 mmol/l, maximum 5.0 mmol/l), indicating that volunteers enrolled in the study indeed had normal fasting glucose (<6.1 mmol/l). None of the volunteers were excluded as none of the fasting blood glucose levels was higher than 6.1 mmol/l. Furthermore, at the end of the study every volunteer returned to a normal glucose level (<6.1 mmol/l).

All 30 volunteers completed the measurements of session 1 and session 2. Data of 6 volunteers (4 in the fasting phase and 2 after an OGTT) were discarded due to insufficient quality of the measurement or due to extreme values, likely related to a measurement or quantification error (Appendix F). The reproducibility of the baseline velocity, the baseline diameter, change in wall shear stress stimulus, flow-mediated dilation and normalized flow-mediated dilation of the brachial artery was established for the inter-session in 30 volunteers, for the intra-session in 27 and 29 volunteers in session one and two, respectively, and the intra-class correlation coefficient for baseline velocity, change in wall shear stress stimulus and normalized flow-mediated dilation in 26 volunteers, and for baseline diameter and flow-mediated dilation in 27 volunteers, respectively (intra-session reproducibility, Table 2, inter-session

26

Table 2. INTRA-session coefficients of variation of baseline velocity, baseline diameter, change in wall

shear stress stimulus, flow-mediated dilation and normalized flow-mediated dilation for both sessions

Session 1 Session 2

base1 + base2 2

Intra-session

SD CV [%]

base1 + base2 2

Intra-session

SD CV [%]

Vbase [pixels] 11±5a 2.20 20 12±5c 2.17 19

Dbase [pixels] 55±6b 0.74 1 54±6c 0.79 1

∆WSSstim [%] 1.1 ± 0.6a 0.30 26 1.1 ± 0.5c 0.34 31

FMD [%] 5.2±2.5b 0.89 17 5.4±2.3c 1.08 20

FMDn [-] 0.067±0.050a 0.029 43 0.069±0.047c 0.033 48

Data are means ± SD; Vbase, baseline velocity; Dbase, baseline diameter; ∆WSSstim, change in wall shear stress

stimulus, defined as ∆Vmean /Vbase; FMD, flow-mediated dilation, defined as ∆Dpeak /Dbase; FMDn, normalized

flow-mediated dilation, defined as ∆Dpeak*Vbase / Dbase*∆Vmean; ∆Dpeak, peak diameter with respect to Dbase;

∆Vmean, mean velocity with respect to Vbase; base, baseline measurement; intra-session SD, standard deviation

over both baseline measurements; CV, coefficient of variation, defined as (intra-session SD)/mean(intra-

session). a n=27; b n=28; c n=29

Table 3. INTER-session coefficients of variation of baseline velocity, baseline diameter, change in wall shear

stress stimulus, flow-mediated dilation and normalized flow-mediated dilation

Session 1 & Session 2

Inter-session mean over

base (1+2)

Inter-

session

SD

CV

[%]

Inter-session

mean over

base1

Inter-

session

SD

CV

[%]

Inter-session

mean over

base2

Inter-

session

SD

CV

[%]

Vbase [pixels] 11 ± 5 2.14 19 12 ± 5a 2.76 22 10 ± 5a * 1.93 19

Dbase [pixels] 55 ± 6 1.49 3 55 ± 6b 1.63 3 54 ± 6a 1.42 3

∆WSSstim [%] 1.1 ± 0.5 0.23 21 1.0 ± 0.6a 0.32 31 1.2 ± 0.6a * 0.26 22

FMD [%] 5.2 ± 2.2 1.09 21 5.3 ± 2.4b 1.22 23 5.2 ± 2.3a 1.22 24

FMDn [-] 0.068±0.045 0.02 30 0.080±0.061a 0.033 41 0.057±0.040a * 0.016 29

Data are means ± SD; Vbase, baseline velocity; Dbase, baseline diameter; ∆WSSstim, change in wall shear stress

stimulus, defined as ∆Vmean /Vbase; FMD, flow-mediated dilation, defined as ∆Dpeak /Dbase; FMDn, normalized flow-

mediated dilation, defined as ∆Dpeak*Vbase / Dbase*∆Vmean; ∆Dpeak, peak diameter with respect to Dbase; ∆Vmean, mean

velocity with respect to Vbase; base, baseline measurement; inter-session SD, standard deviation over both baseline

measurements; CV, coefficient of variation, defined as (inter-session SD)/mean(inter-session); ICC, intra-class

correlation coefficient of S1_base1, S1_base2, S2_base1, and S2_base2, with S1, session 1 and S2, session 2.

n=30; a n=28; b n=29. * P<0.05, data significant different from inter-session mean over base1 (paired t-test).

27

reproducibility, Table 3, intra-class correlation coefficient, Table 4). To investigate the time-resolved relationship between blood glucose levels and flow-mediated dilation of the brachial artery, fulldata were available in 22, 21 and 20 individuals (Table 5). Repeated ANOVA analyses were performed for glucose concentrations, change in wall shear stress stimulus, FMD and FMDn in 22, 16, 17 and 16 volunteers, respectively.

3.2 Reproducibility The intra-session coefficients of variations were ≤ 1% for baseline diameter, ≤ 20% for baseline velocity, ≤ 31% for the change in wall shear stress stimulus, ≤ 20% for FMD and ≤ 48% for FMDn and were comparable between both sessions (Table 2). Inter-session coefficients of variations for the average of both baseline measurements per session were ≤ 3% for baseline diameter, ≤ 19% for baseline velocity, ≤ 21% for the change in wall shear stress stimulus, ≤ 21% for FMD and ≤ 30% for FMDn, and similar for the average of only the first baseline measurement and the average of only the second baseline measurement (Table 3). Only the inter-session coefficient of variation for baseline measurement 1 of FMDn is different, 41% versus 30%. The coefficient of variation is higher when FMD is normalized for the change in wall shear stress stimulus.

Reliability, estimated by intra-class correlation coefficients (ICC) based on four baseline measurements (two at both weeks), showed an ICC of 0.91 for the baseline diameter; 0.63 for FMD and 0.49 for WSS. This result thus indicates that FMD and WSS have a worse reliability as compared to diameter. However, after normalization for the change in wall shear stress stimulus (FMDn) the ICC (0.047) was similar as compared to the ICC of the WSS stimulus (0.49) (Table 4). The ICC is lower when FMD is normalized for the change in wall shear stress stimulus.

Table 4. Intra-class correlation coefficients of baseline velocity, baseline diameter, change in wall shear stress stimulus, flow-mediated dilation and normalized flow-mediated dilation

Session 1 & Session 2 ICC

Vbase [pixels] 0.60 Dbase [pixels] 0.91 ∆WSSstim [%] 0.49

FMD [%] 0.63 FMDn [-] 0.47

Data are means ± SD; Vbase, baseline velocity; Dbase, baseline diameter; ∆WSSstim, change in wall shear stress stimulus, defined as ∆Vmean /Vbase; FMD, flow-mediated dilation, defined as ∆Dpeak /Dbase; FMDn, normalized flow-mediated dilation, defined as ∆Dpeak*Vbase / Dbase*∆Vmean; ∆Dpeak, peak diameter with respect to Dbase; ∆Vmean, mean velocity with respect to Vbase; ICC, intra-class correlation coefficient over 4 baseline measurements (2 baseline measurements of session 1 and 2 baseline measurements of session2).

28

Bland-Altman analyses show an intra-session agreement for FMD and for FMDn. For the FMD the mean difference was 0.10% with a standard deviation of the difference of 1.97%. For the FMDn the mean difference was 0.022 with a standard deviation of the difference of 0.060 (Figure 10). The inter-session agreement, investigated by Bland-Altman analyses, showed for the FMD a mean difference of 0.01% with a standard deviation of the difference of 2.20%. For the FMDn the mean difference was 0.002 with a standard deviation of the difference of 0.041 (Figure 11).

Figure 10. Bland-Altman plots for baseline flow-mediated dilation (FMD), and baseline normalized flow-mediated

dilation (FMDn) of session 1 and session 2. The upper and lower dotted lines represent 2 times the standard

deviation (95% confidence interval), the middle dotted line represents the mean difference. The x-axis shows

the average flow-mediated dilation and the y-axis the difference between pairs. FMD, flow-mediated dilation;

FMDn, normalized flow-mediated dilation.

Figure 11. Bland-Altman plots for baseline flow-mediated dilation (FMD), and baseline normalized flow-mediated

dilation (FMDn) between session1 and session 2. The upper and lower dotted lines represent 2 times the

standard deviation (95% confidence interval), the middle dotted line represents the mean difference. The x-axis

shows the average flow-mediated dilation and the y-axis the difference between pairs. FMD(n) S1, (normalized)

flow-mediated dilation of session 1; FMD(n) S2, (normalized) flow-mediated dilation of session 2.

-6

-4

-2

0

2

4

6

0 2 4 6 8 10 12

FM

D1

-F

MD

2

FMD1 + FMD22

FMD1 vs FMD2 of session 1 ( ) & 2 ( )

-0.20-0.15-0.10-0.050.000.050.100.150.200.250.30

0.00 0.05 0.10 0.15 0.20 0.25

FM

Dn1

-F

MD

n2

FMDn1 + FMDn22

FMDn1 vs FMDn2 of session 1 ( ) & 2 ( )

-6

-4

-2

0

2

4

6

0 2 4 6 8 10 12

FM

DS

1 -

FM

DS

2

FMDS1 + FMDS2

2

FMD1+2 of session 1 vs FMD1+2 of session 2

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.00 0.05 0.10 0.15 0.20

FM

Dn

S1 -

FM

Dn

S2

FMDn S1 + FMDn S2

2

FMDn1+2 of session 1 vs FMDn1+2 of session 2

▲ ■ ▲ ■

29

3.3 Effect of transient high blood glucose on flow-mediated dilation Table 5 shows the mean and standard deviations of the glucose concentrations, the change in wall shear stress stimulus, FMD and FMDn for each time moment per session. Compared with the fasting time moments, the concentration of glucose as stable at T= -40 and T= 0, increased significantly between T= 0 and T= 30 (Figure 12A) and decreased at the later stage between T= 75 and T= 120 (Figure 12A). This pattern was similar for both sessions (Table 5 and Figure 12A). The change in wall shear stress stimulus increased significantly in the early hyperglycemia phase between T= 0 and T= 30, similarly in both sessions (Table 5 and Figure 12B). The

Figure 12. Mean blood glucose levels (A), change in wall shear stress (∆WSS) stimulus (B), FMD (C) and FMDn (D) over the entire measurement of session 1 and session 2. The dotted line represents the borderline between session 1 and session 2. FMD, flow-mediated dilation; FMDn, normalized flow-mediate dilation; S1_BS#, number of blood sampling of session 1; S2_BS#, number of blood sampling of session 2; S1_M#, number of measurements of session 1; S2_M#, number of measurements of session 2; BS1-3 and BS5 corresponds with M1-4, respectively. *p < 0.05 between subsequent blood sampling or measurement.

Glu

co

se [

mm

ol/l]

7.0

6.5

6.0

5.5

5.0

4.5

4.0

Blood Sampling

S2_BS6

S2_BS5

S2_BS4

S2_BS3

S2_BS2

S2_BS1

S1_BS6

S1_BS5

S1_BS4

S1_BS3

S1_BS2

S1_BS1

Glucose p<0.001

Oral glucose tolerance test of session 1 & session 2

* * * *

A

# measurementS2_M4S2_M3S2_M2S2_M1S1_M4S1_M3S1_M2S1_M1

Sti

mu

lus

[%

]2.20

2.00

1.80

1.60

1.40

1.20

1.00

0.80

glucose p<0.001

WSS stimulus of session 1 & session 2

*

*

*

B

FM

D [

%]

6.5

6.0

5.5

5.0

4.5

# measurementS2_M4S2_M3S2_M2S2_M1S1_M4S1_M3S1_M2S1_M1

glucose p=0.068

FMD session 1 & session 2

*

*

C

# measurementS2_M4S2_M3S2_M2S2_M1S1_M4S1_M3S1_M2S1_M1

FM

Dn

[-]

0.080

0.060

0.040

0.020

glucose p=0.001

FMDn of session 1 & session 2

*

*

*

*

D

30

Tab

le 5. Blo

od

glu

cose levels, ch

ang

e in w

all shear stress stim

ulu

s and

flow

-med

iated d

ilation

, befo

re (T = -40 &

T = 0) an

d after an

oral g

luco

se toleran

ce test (T = 30 &

T = 75)

S

ession 1

Session 2

T

= -40 T

= 0 T

= 30 T

= 60 T

= 75 T

= 120

T = -40

T = 0

T = 30

T = 60

T = 75

T = 120

Glucose

[mm

ol/l]

4.5±0.4

4.4±0.3

6.9±1.2

6.5±1.9

6.1±1.7

4.8±1.3

4.5±

0.3 4.6±

0.3 6.6±

1.0 6.1±

1.6 5.9±

1.3 4.5±

1.1

∆W

SS

stim

[%]

0.94±0.33

a 1.14±

0.47b

1.60±0.86

b

1.71±0.69

1.03±0.56

1.17±0.59

b 1.84±

0.77

1.90±0.88

b

FM

D [%

] 5.8±

3.0b

5.6±2.7

b 4.9±

2.5b

5.6±

3.5

5.8±

2.7 6.0±

2.6b

5.2±3.5

5.7±

3.2b

FM

Dn [-]

0.079±0.060

a 0.057±

0.034b

0.039±0.025

b

0.037±0.026

0.076±0.058

0.068±0.051

b 0.035±

0.030

0.038±0.033

b

Data are m

eans ± S

D; ∆

WS

Sstim , change in w

all shear stress stimulus; defined as ∆

Vm

ean / Vbase ; F

MD

, flow-m

ediated dilation; defined as ∆D

peak /Dbase ; F

MD

n , normalized flow

-mediated dilation;

defined as ∆D

peak *Vbase / D

base *∆V

mean ; D

base , baseline diameter; ∆

Dpeak , peak diam

eter with respect to D

base ; Vbase , baseline velocity; ∆

Vm

ean , mean velocity w

ith respect to Vbase ; T

, time m

oment

in minutes, w

ith T =

-40 & T

= -1, fasting time m

oment w

ith respect to the start of an oral glucose tolerance test; T =

30, T = 60, T

= 75 and T = 120, tim

e mom

ent after an oral glucose tolerance

test.

n=22; a n=20; b n=21.

31

pattern of the change in wall shear stress stimulus between session 1 and session 2 is comparable.

FMD decreased between T= 0 and T= 30 for both sessions (p-value is 0.053 and 0.010 for each session, respectively) (Table 5, Figure 12C). FMD increased between T= 30 and T= 75 (i.e. FMD measurement 3 and 4) for both sessions (p-value is 0.018 and 0.062 for each session, respectively) (Table 5, Figure 12C). The pattern was similar at both sessions.

Normalization for the change in wall shear stress stimulus (FMDn) shows a comparable pattern with a significant decrease in the early hyperglycemia phase between T= 0 and T= 30 (p-value is 0.016 and 0.001 for each session, respectively) and an increase between T= 30 and T= 75 (i.e. FMDn measurement 3 and 4) (Table 5, Figure 12D), only statistically significant (p-value is 0.033) in session 1 (Figure 12D). For both visits, this increase is less pronounced as compared to FMD. The pattern was similar at both sessions.

32

4.0 Discussion In this study we have investigated the time-resolved relationship between blood glucose level and brachial artery flow-mediated dilation, using continuous and simultaneous measurements of the blood flow velocity and diameter of the brachial artery. This study has three important findings; 1) the reproducibility of the above method is better in comparison to the reproducibility in other studies; 2) FMD normalized (FMDn) for the change in wall shear stress stimulus (∆WSS) improves the sensitivity of the assessment of glucose induced changes in flow-mediated dilation; 3) Flow-mediated dilation response is decreased in healthy volunteers in the early postprandial phase (30 minutes after glucose intake) for both assessments (FMD and FMDn) and remains decreased in the late postprandial phase (75 minutes after glucose intake) for FMDn.

Measuring of flow-mediated dilation is technically challenging and thus the reproducibility of the technique is generally low. Reported week to week variability of FMD, which is generally defined as the peak increase in diameter relative to the baseline diameter, is 2-3% in absolute terms around a mean FMD of 6% corresponding with a relative variability of 33-50% [30-32]. This relatively poor performance is partly due to insufficient sampling of the diameter over time. In the present study, we used a method of analysis that enables us to determine changes in diameter and flow velocity beat-to-beat (i.e. continuous) over the entire duration of the FMD measurement. The traditional approach of FMD calculations, which is used by many research groups [13, 27, 42-44], is based on the “peak” diameter change at 60 seconds after cuff release, relative to the baseline diameter [9]. A previous study measured the time course of flow-mediated dilation in volunteers and found a time to peak diameter difference (50 seconds versus 80 seconds and 83 seconds) between different groups (young versus fitness matched older and older untrained volunteers) [45]. This suggests that when using the traditional FMD approach of 60 seconds post-occlusion, the true peak diameter could easily be missed and will lead to its underestimation. Our newly developed method of analysis determines the diameter during 5 minutes post-occlusion and this allows to determine the ‘true’ peak diameter. In our study the peak diameter was determined over a time window of 3 minutes (this time window can be manually adjusted).

The inter-session reproducibility of FMD, when using newly developed method of analysis, is improved (21% relative, Table 3) as compared to other reports in the literature (33-50%). However, the reproducibility of FMD is still poor relative to other measurements (i.e. diameter measurement or blood glucose levels, which are determined in the laboratory), probably due to biological variability and/or technical factors. For the baseline diameter, the inter-session coefficient of variation is 3% and the intra-session coefficient of variation is 1%, with a reliability (ICC) of 0.91 (Table 2-4). This indicates good reliability of the baseline diameter between and within

33

sessions. Bland-Altman analyses show an inter-session agreement for FMD over both baseline measurements a mean difference of 0.01% with a standard deviation of the difference of 2.20% (Figure 11). Two out of 30 FMD measurements were extreme values. The mean difference is 0.01%, indicating that FMD of session 1 is approximately equal to FMD of session 2. However, the standard deviation of the difference is large in comparison with the mean difference. This indicates that there are large differences between FMD measurements of both sessions. The good reproducibility of the baseline diameter (3%) did not result in a good reproducibility of FMD. The poor reproducibility of FMD is most likely attributed to biological variations and/or technical factors (i.e. brachial artery movements due to deflation of the cuff or movements of the volunteer) (Appendix F) during brachial artery cuff inflation/deflation. Normalizing the FMD response to the corresponding change in wall shear stress stimulus deteriorates the coefficient of variation (21% to 30%, Table 3) and the ICC (0.63 to 0.47, Table 4). A possible explanation for the differences in CV and ICC between FMD and FMDn is the fact that FMDn includes an estimate of the change in wall shear stress stimulus, adding more biological information but also error to this measure. If the additional, wall shear stress stimulus related measurement error is not (completely) related to the error of the FMD measurement, this might have led to the greater coefficient of variation and lesser ICC. This may explain the deterioration of the reproducibility of the normalized FMDn as compared to FMD.

The derivation for the normalized FMD includes diameter and velocity terms only. These were measured simultaneously and continuously over the entire 13 minutes. This is a major advantage of our method in comparison with others. In previous studies peak diameter was measured over a few cardiac cycles only at arbitrary time points (i.e. 45, 60, 90, 180 seconds) after cuff release [9, 46-48]. Our diameter measurement is based over around 800 cardiac cycles instead of a few (4 to 12) cardiac cycles. This allows a better characterization of the true peak diameter, because with our method the diameter curve over the entire FMD measurement was obtained. Therefore, we were better able to determine the true peak diameter.

The FMD response is linearly proportional to the wall shear stress stimulus and thus will vary when the wall shear stress changes [36]. Therefore, previous studies used an estimate of the wall shear stress [16] or an estimate of the shear rate [15, 17], which is an estimate of wall shear stress without blood viscosity, to normalize the FMD. However, there is significant variation in the literature with respect to calculating shear rate: previous studies used an estimate of the shear rate defined as mean blood flow velocity divided by the arterial diameter [15, 18], 8 times the mean blood flow velocity divided by the arterial diameter [19, 20] or 4 times the mean or peak blood flow velocity divided by the arterial diameter [21, 22]. The wall shear stress is defined as blood viscosity times the shear rate [23]. Determining wall shear stress using blood viscosity has drawbacks. In these studies [16, 24] whole blood viscosity was determined through invasive blood sampling, which hampers non-

34

invasive assessment of normalized FMD. Furthermore, the whole blood viscosity was determined at baseline only [16]. This assumes that whole blood viscosity remains constant throughout the measurement protocol and, more importantly, is a valid estimate of local blood viscosity at different levels of peak flow velocity. However, whole blood viscosity differs at different shear rates [25]. The latter assumption might not optimally reflect true biology as flow (velocity and profile) determines where particles (cells, platelets, proteins) are situated in the blood when it flows through a blood vessel (greater particles are situated at the center of high blood flow, whereas the smaller particles are situated near the wall of the blood vessel). These particles determine blood viscosity and the blood viscosity at the wall gives the best estimate of the wall shear stress. Despite the above limitations, previous studies took whole blood viscosity into account to estimate the wall shear stress and at high blood flow velocity the wall shear stress is overestimated. Another limitation of this method is that it does not take into account the baseline wall shear stress.

In the present study, we circumvent the different approaches for the stimulus (wall shear stress or shear rate), which is used to normalize FMD, and the limitations of measuring wall shear stress using blood viscosity through a new derivation of FMDn. The major advantage of the new derivation is that no viscosity term is left as the viscosity terms in the numerator and in the denominator cancel each other out. Applying this new derivation allows estimation of a change in wall shear stress stimulus based on velocity and diameter terms only and, in addition, there will no uncertainty anymore about the determination of the wall shear stress stimulus. Importantly, this enables to estimate the change in wall shear stress stimulus simultaneously when the diameter (for FMD) is determined.

To summarize, our newly developed method of analysis allows a continuous measurement of diameter and change in wall shear stress stimulus, both simultaneously, throughout the full FMD protocol, without the drawbacks of determining viscosity and the different approaches for the wall shear stress stimulus.

Figure 12 shows that normalization for the change in wall shear stress stimulus (FMDn) improves sensitivity of FMD assessment as normalization allowed a better discrimination of glucose induced changes in flow-mediated dilation as compared to non-normalized FMD (P=0.001 for FMDn versus P=0.068 for FMD). However, the reproducibility for the normalized FMD is deteriorated as compared to FMD. Nevertheless, these results suggest that the change in wall shear stress stimulus adds important biological information to the estimation of the normalized flow-mediated dilation response (FMDn) after a glucose intake as compared to the non-normalized flow-mediated dilation response. This might suggest that when investigating the effect of (dietary) interventions on flow-mediated dilation, normalization for the change in wall shear stress stimulus increases the sensitivity of FMD-measurement.

35

We observe that in young healthy adults the flow-mediated dilation response decreased in the early postprandial state (30 minutes after glucose intake). Importantly, we reproduce this result in this population within approximately a week. Figure 12C shows that FMD decreased in both sessions after 30 minutes of an OGTT. Furthermore, figure 12D shows a significant decrease of FMDn in the early postprandial state at both sessions. Figure 12A and figure 12B show a significant increase of the glucose concentration and the change in wall shear stress stimulus in both sessions after 30 minutes. This suggests that glucose influences the wall shear stress stimulus. In previous studies, an effect of glucose on FMD was mostly investigated in the late postprandial phase (1 hour and/or 2 hours after glucose loading) [6, 7, 26] and/or in older individuals with impaired glucose tolerance or diabetes mellitus [6, 46, 49, 50]. We have investigated young healthy volunteers and we show that glucose might have an early effect on FMD, as suggested by others [27, 51]. But we did not at this stage included subjects with impaired glucose metabolism. Previous studies investigating healthy volunteers showed a significant decrease in flow-mediated dilation after one hour of glucose intake [7, 27]. We, however, did not observe a significant decrease in flow-mediated dilation after 75 minutes of glucose intake. Our results suggest that FMD has returned to the baseline FMD value and this suggest the importance to measure the diameter continuously to determine the true peak diameter.

Figure 12D shows a decrease in FMDn between both baseline measurements, only significant in the first session. This can be explained by the following reasons:

1) The variability of flow-mediated dilation measurements were high (see reproducibility) and in combination of the relatively small number of volunteers included in this study we cannot exclude the possibility that this finding might have been introduced because of change. Importantly, we did not observe any significant decrease in FMD between both baseline measures (Figure 12C) and the decrease in FMDn was only statistically significant in session 1. This latter observation depends on the significant increase in baseline wall shear stress stimulus (Figure 12B).

2) The time between the two subsequent baseline measurements is around 10 minutes and this time interval may be too short. Harris et al. investigated that repetitive reactive hyperemia has no effect on the FMD measurements within a time interval of 30 minutes [52]. Therefore, they recommended a time interval of 30 minutes between repeated FMD measurements. However, they did not investigate shorter time intervals. The decrease of flow-mediated dilation over such a short time interval could be explained by the biological variation of the blood flow velocity (Table 3 shows a significant change in mean value of the baseline velocity, change in wall shear stress stimulus and FMDn between baseline measurement 1 and 2) or by reduced NO bioavailability, maybe there is not enough NO available after 10 minutes and then the smooth muscle cells could not dilate maximally. However, this is only a speculation and should be investigated further.

36

Prior to the present investigation we executed a pilot study. In contrast to the present investigation, in this pilot study (Appendix A) flow-mediated dilation increased in an early postprandial phase after glucose intake. However, this pilot study was only executed in 6 volunteers and the second FMD measurement was timed between 30 and 45 minutes after glucose intake, depending on the glucose concentration, which was determined during the protocol with a POC glucosemeter. From the observation in this pilot study we hypothesized that an acute increase in blood glucose level may enhance FMD in the early postprandial state and that the observed increase in FMD was attributed to an increase in wall shear stress and an increase in insulin due to the glucose load. Both pathways [53-55] lead to an increase in nitric oxide (NO) production, which is an important vasodilator by causing smooth muscle cell relaxation [56-59]. In the present investigation we find an opposite result as compared to the pilot study; a decrease in flow-mediated dilation in the early postprandial phase. When this result is also attributed to the two pathways hypothesized above, it could be that the baseline diameter in early postprandial phase is greater in comparison with the fasting baseline diameter and that then the maximum dilation of the artery will be less. We have not found any significant difference between the fasting baseline diameter and the early postprandial baseline diameter measurement. To investigate whether this phenomenon plays a role, we first should perform a power calculation (Appendix H) whether our group size is big enough to consider this phenomenon in our current research data or that we cannot conclude this phenomenon with our current research data. From the outcome of the power calculation we were able to found a difference in baseline diameter in our current research data, if there was any (Appendix H).

Thus the baseline diameter appears not greater in the early postprandial phase and therefore we rejected our hypothesis that an acute increase in blood glucose level may enhance flow-mediated dilation in the early postprandial phase. However, there should be other mechanisms which explain the decrease in flow-mediated dilation response in the early postprandial phase. Many studies have revealed that acute increase in blood glucose level is associated with an increase in oxidative stress and oxidative stress impairs NO bioavailability or contributes to endothelial cell injury [6, 55, 60-63].

Our study had several limitations. First, our study was limited due to a limited number of volunteers (i.e. 16/17 volunteers) to investigate the relationship between acute hyperglycemia and FMD. This is a result of discarded data. Six measurements (3% of 240 measurements) were excluded due to insufficient quality of the measurement or due to extreme values, which were checked blindly by an experienced researcher who was unaware (blinded) of the volunteer and the moment of FMD measurement. Repeated ANOVA can only handle full data (i.e. when 1 measurement out of 8 was discarded, this volunteer could not be analyzed with repeated ANOVA). Second, the custom-written automatic video analysis program might estimate incorrect velocities and diameter values, because the program cannot handle drift in the diameter or

37

velocity curves (Appendix F). To correct for drift, the results were checked by an experienced researcher who was unaware (blinded) of the volunteer and the moment of FMD measurement. However, this is time consuming. The correct values were identified and were corrected based on the diameter and velocity profiles during 13 minutes of FMD measurement (Appendix F). 10% of the baseline diameters, 3% of the peak diameters and 44% of the baseline velocities over 240 measurements were corrected. Third, in this study we were not able to discriminate whether only the change in wall shear stress stimulus or only the glucose stimulus or both stimuli influenced the flow-mediated dilation respons because our study was not designed to discriminate between those stimuli. Fourth, B-mode and PW-doppler were recorded on a DVD. Therefore, the FMD resolution is (not totally) independent of the transducer resolution and depends on the resolution of the video images. The videos had a resolution of 720 x 576 pixels and a spatial resolution of 0.072 mm. The transducer had a spatial resolution of 0.60 mm (by a center frequency of 4 MHz). In this case the resolution of the videos does not hamper the analysis. Fifth, although we carefully monitored the placement of the transducer within each session to reduce the variations of different transducer placements, placement between two sessions could differ. Nevertheless, coefficients of variations were similar within and between sessions. Sixth, FMD measurement of the brachial artery is challenging, because of the day-to-day variation, all the technical factors involved, the fact that the brachial artery is small, and because every small (unconscious) movement of the volunteer might result in a loss of the visualized brachial artery on the DVD during the FMD measurement. Nevertheless, it was possible to learn to measure the brachial artery flow-mediated dilation and to get some interesting results after such a short training period.

We had investigated the time-resolved relationship between glucose levels and flow-mediated dilation. However, 8 volunteers received plain water, as mentioned in the methods, to investigate whether the observed FMD response after intake of the glucose solution is not caused by ingestion only and the idea was to use this group as a control group. Afterwards, this group was clearly underpowered, because if we expect that water has the same effect of as glucose, we need at least the same number of volunteers as calculated from the power calculation of the glucose group (n=20) to show such an effect. If water has a smaller effect than glucose, we would even need a greater number of volunteers for this control group. Nevertheless, we present the results of the water group in appendix I, although these results should be interpreted with caution.

38

5.0 Conclusion In this study we have investigated the time-resolved relationship between blood glucose level and brachial artery flow-mediated dilation (FMD), using continuous beat-to-beat and simultaneous measurements of the blood flow velocity and diameter of the brachial artery. With this method we can conclude that:

1) The reproducibility of flow-mediated dilation measurements is improved.

2) FMD normalization (FMDn) for the change in wall shear stress stimulus

improves the sensitivity of the assessment of glucose induced changes in flow-mediated dilation.

3) Flow-mediated dilation response is decreased in healthy volunteers in the early postprandial phase (30 minutes after glucose intake) for both assessments (FMD and FMDn) and remains decreased in the late postprandial phase (75 minutes after glucose intake) as indicated by FMDn.

39

6.0 Acknowledgement

Hereby I would like to take the opportunity to thank some people for their support during my graduation project.

First of all, I would like to thank Koen Reesink for his enthusiastic guidance and support and for always being around to address questions and solve any problems during my graduation project and during other projects. Koen, thanks for giving me the opportunity to do my graduation project under your supervision. I had a great year, having a lot of fun while learning a lot about research. Also I would like to thank Professor Arnold Hoeks for valuable discussions about how to measure arteries, and for his help with his automatic video analyses program. Furthermore, I would like to thank Jos Op’t Roodt who taught me all the practical skills of arterial measurements (ultrasound echography and tonometry). Also I would like to thank Professor Casper Schalkwijk for his cooperation and valuable discussions about the multiple functions of the endothelium that can be measured with different biomarkers (with FMD as an important functional biomarker). Special thanks go to my brother Bas, who shared a lot of ‘measuring’ time together with me in the lab and supported me throughout my graduation project.

It was nice cooperating with them.

40

41

7.0 Appendices

1. A: Pilot study 2. B: Detailed measurement protocol 3. C: Scanner settings 4. D: Derivation normalized flow-mediated dilation 5. E: Steps video analysis program ‘MyFMD’ 6. F: Excluded measurements 7. G: Remaining results glucose 8. H: Power calculation baseline diameter 9. I: Water results 10. J: Changed automatic video analysis program ‘MyFMD’ code

42

7.1 A: Pilot study

Hemodynamics and brachial artery parameters (n=6)

Units Fasting 30 minutes after glucose

intake

p-value (student t-test)

Systolic BP

mmHg 120 ± 11 116 ± 12 <0.044

Diastolic BP

mmHg 67 ± 7 62 ± 5 <0.013

Blood glucose level

mmol/l 4.5 ± 0.2 7.5 ± 0.9* <0.001

∆Vmean / Vbaseline

% 76.4 ± 19.3 64.3 ± 22 <0.357

FMDd= ∆Dpeak/Dbaseline

% 3.2 ± 2.6 7.4 ± 4.9* <0.011

∆Dpeak/Dbaseline FMDn=

∆Vmean/Vbaseline [-] 0.047 ± 0.042 0.133 ± 0.047* <0.001

Data as mean ± standard deviation. ∆Vmean: mean velocity with respect to baseline

mean velocity (Vbaseline); ∆Dpeak: diameter peak value with respect to baseline

diameter (Dbaseline); FMD(n): (normalized) flow mediated dilation. No significant

difference in baseline diameter between fasting and glucose loaded conditions (not

showed in table). *p=0.03, data significant different from fasting, Wilcoxon

matched-pairs signed rank test.

Methods

• 6 healthy volunteers (Age 28±3 yrs, BMI 25.6±4.9

• kg/m2)

• Point of care glucosemeter (OneTouch® UltraSmart®)

• Duplex mode (simultaneous B mode and pulsed wave

• mode) ultrasound

• ECG & two sphygmomanometer cuffs

• Brachial artery FMD: 3 minutes baseline, 5 minutes cuff

• inflation (50 mmHg above systolic blood pressure), 5

• minutes after cuff deflation during fasting conditions

• Potable solution of 75 gram dextrosemonohydrate

• Brachial artery FMD measurement repeated 30 minutes

• after glucose intake

• Blood flow velocity & diastolic diameter measurement by

• sequential beat-to-beat video analysis

Discussion

• Normalization for the stimulus (FMDn) improves

• sensitivity of FMD assessment.

• Both insulin binding and increased shear stress promote

• endothelial NO production, which leads to vasodilation

• through smooth muscle cell (SMC) relaxation.

• Involvement of more endothelial cells and/or more

• insulin receptors could explain the observed increase in

• FMD in the early postprandial phase.

BackgroundInsulin induces microcirculatory vasodilation by

stimulating endothelial nitric oxide production. We

investigate in healthy volunteers whether glucose

intake modulates large artery flow mediated dilation

(FMD) in the early postprandial phase.

Early postprandial increase in blood glucose doubles flow

mediated dilation in young healthy subjectsF.C.G. van Bussel, T.S. van Domburg, J. Op ‘t Roodt, E.P. Klein, A.P.G. Hoeks, K.D. Reesink

Biomedical Engineering, Cardiovascular Research Institute Maastricht,

Maastricht University, Maastricht, and Eindhoven University of Technology, Eindhoven, the Netherlands

f.c.g.v.bussel@student.tue.nl

Conclusions

Glucose loading leads to an increased brachial artery

FMD response in the early postprandial phase,

possibly due the synergistic effect of insulin and

shear stress induced NO production.

Smoothed brachial artery flow velocity and diameter curves obtained by sequential beat-to-beat video analysis. FMD is based on mean velocity over 3 minutes (∆Vmean) plus change in diameter (∆Dpeak).

Video analysis: Determination of mean blood velocity (yellow envelope) from Doppler sonogram (left) and diameter with wall identification (blue) in B-mode (right).

insulin

L-Arg

eNOS

NO

Ins-R

+GLUT

SMC tone

glucoseshear stress

+

endothelialcell

PI3k

insulin

L-Arg

eNOS

NO

Ins-R

+GLUT

SMC tone

glucoseshear stress

++

endothelialcell

PI3k

Results

Insulin induced NO

FMD induced changes in NO

NO

Dia

mete

r

∆NO

∆Diam

Insulin induced NO

FMD induced changes in NO

NONO

Dia

mete

rD

iam

ete

r

∆NO

∆Diam

43

7.2 B: Detailed measurement protocol See flow chart below. Detailed description:

� Volunteers are positioned in supine position for 15 minutes to acclimatize first.

� During acclimatization fasting blood glucose level is determined using a point of

care (POC) glucosemeter (OneTouch® UltraSmart®, LifeScan, California, USA).

� When the fasting blood glucose level is higher than 6.1 mmol/l, the participant will

not continue the study and will be excluded from further participation.

� When the fasting blood glucose level is below 6.1 mmol/l, a blood sampling

needle is inserted in the antecubital vein of the left arm for blood sampling.

� A sphygmomanometer cuff, required for FMD measurement, is placed on the

right forearm. ECG electrodes are placed on the chest to provide a time reference

for the echo scanner. Peripheral blood pressure is continuously measured on the

right middle finger (Penaz-method; BMEye Nexfin system) for pressure

monitoring and (off-line) calibration of the tonometric brachial artery blood

pressure waveform.

The following three procedures form a unit, which is repeatedly performed in the

protocol:

• Tonometry in combination with simultaneous diameter measurement enables

accurate quantification of vessel distensibility. Right-arm brachial artery blood

pressure and diameter waveforms are simultaneously measured in triplicate,

using a tonometer device (Sphygmocor®, AtCor Medical, NSW, Aus) and a

clinical ultrasound scanner (MyLab™70, Esaote, Genova, It). Acquisitions are

synchronized beat-to-beat.

• Blood samples are collected for the quantification of plasma glucose, insulin,

and soluble biomarkers of endothelial function (asymmetric dimethylarginine

(ADMA), endothelin-1 (ET-1), nitric oxide (NO), (pro)-von Willebrand factor (vWf))

and soluble biomarkers of inflammation of the endothelium (soluble endothelial

selectin (sE-selectin), soluble vascular cell adhesion molecule-1 (sVCAM-1),

soluble intercellular adhesion molecule-1 (sICAM-1) and soluble thrombomodulin

(sTM)).

44

Figure 13. Flow scheme of the study, abbreviations: FMD (flow mediated dilation), OGTT (oral glucose tolerance test). #Glucose measurement: using point-of-care device. *Venous blood sampling for plasma glucose, insulin and soluble biomarkers of endothelial function measurement.

Time (min)

15

61

baseline

after 10 min

*Blood sampling1 min

FMD measurement (right)13 min

15 min rest for acclimatization

# Glucose measurement0 min

Excluded from study

> 6.1 mmol/l < 6.1 mmol/l

Blood pressure measurement

(Cuff & Nexfin left arm)+

Local pressure –diameter

measurement (triplo)

(Tonometer right forearm)9 min

133

88

OGTT

75g glucose solution 1 min

+ 15 min +60 min

+ 21 min

Blood pressure measurement

(Cuff & Nexfin left arm)+

Local pressure –diameter

measurement (triplo)

(Tonometer right forearm)9 min

*Blood sampling1 min

FMD measurement (right)13 min

Blood pressure measurement

(Cuff & Nexfin leftr arm)+

Local pressure –diameter

measurement (triplo)

(Tonometer right forearm)9 min

FMD measurement (right)13 min

*Blood sampling1 min

184

*Blood sampling1 min

#/*Blood sampling1 min

146

+ 37 min

*Blood sampling1 min

45

• Brachial artery FMD is measured using continuous ultrasound echography in

dual mode (combination of B and PW mode; MyLab™70 system) and recording

of echo images on DVD. After a 3 minute reference period, the cuff on the right

forearm is inflated to 200 mmHg for five minutes, causing distal (forearm)

hypoxia. Upon cuff-release reactive hyperemia ensues, which increases brachial

artery blood flow by a factor of 4 - 5. The FMD response is then quantified as the

change in diastolic diameter relative to reference diameter, and normalized to

stimulus, i.e. the relative increase in blood flow velocity after hyperemia.

After baseline measurements (Figure 13), volunteers undergo an oral glucose

tolerance test and thus consume a standard drinkable glucose solution (75 grams of

dextrosemonohydrate).

Measurements are repeated, such that FMD measurements effectively take place at

30 and 75 minutes after glucose intake, enabling discrimination of an early and a late

postprandial phase.

Blood samples for plasma glucose, insulin and biomarker measurment are taken at

30, 60, 75 and 120 minutes after consuming the glucose solution.

46

7.3 C: Scanner settings

Figure 14. Duplex mode ultrasound (MyLab TM70 systeem). Left B-mode image and right PW-mode. Study settings of the ultrasound scanner, were given below:

1. Start up the ultrasound scanner ‘MyLab .70’ (Esaote Europe, Maastricht, the Netherlands).

2. Select the ‘vascular’ application 3. Select ‘CAROTID’ under preset 4. Switch on the scanner the button ‘Frequency’ until RES-H appears on the

screen 5. Set the depth to 30 millimeters 6. Press the button ‘PW’ and select ‘dual mode’ under ‘Format’ and select ‘min’

under ‘Sweep’. 7. Put the doppler angle parallel to the blood vessel wall (around 700) and put the

size of the sample volume on 2. 8. Turn the button ‘B-gain’ in such a way that the B-mode gain is satisfied to

discriminate the posterior and anterior vessel wall (for the present study we used approximately between 30% and 40%).

9. Turn the ‘PW-gain’ (pulse wave gain) to 54% 10. Press the ‘PRF/baseline’ button and move up the doppler baseline

(approximately to 1/3 of the doppler screen, Figure 14) 11. Press the ‘Physo’ button and choose ‘ECG’. An ECG signal appears in the

bottom of the screen. Turn the ‘position’ button and place the ECG signal to the top of the screen (Figure 14)

Other settings are the default scanner settings under the CAROTID preset.

47

7.4 D: Derivation normalized flow-mediated dilation

Figure 15. Blood vessel with radius R and Poiseuille velocity V(r). r and z are the standard cylindrical

coordinates.

From physiology it is known that the blood vessel responds to changes in blood flow velocity. When the blood flow velocity increases, the friction on the endothelial layer and thus also the wall shear stress increases. From a physiological point of view the response, caused by a change in wall shear stress due to changes in blood flow velocity, should be normalized by these changes in blood flow velocity. Therefore, the normalized flow-mediated dilation (FMDn in text, Dfm,n in equations) can physiologically given by:

Dfm,n = ∆D

Dbaseline�

∆V�V�baseline� (1)

The derivation is given below:

In clinical studies, wall shear stress (τw) is often calculated from blood viscosity (η) and wall shear rate (γ� ), which is equal to the radial derivative of the velocity distribution V(r) at the wall (r=R) (Figure 15) [23]:

τw = η γ� (2)

γ� = �∂ V(r)∂ r �r = R (3)

We assume a Poiseuille velocity distribution (equation 4). In steady state, this Poiseuille profile has a parabolic shape (fully developed [64]) with the maximum velocity (Vmax) at the center of the blood vessel (r = 0).

V�r = - 1

4µ

dp

dz�R2- r2� (4)

V�0 = Vmax = - 1

4µ

dp

dzR2 (5)

rz

V(r)

R

48

(5) - 1

4µ

dp

dz� Vmax

R2 (6)

Now, we can rewrite (4) with (6) as follows:

V�r = VmaxR2 �R2- r2� � Vmax �1 - r2

R2 � Vmax !1- � r

R "# (7)

The Poiseuille profile has the following assumption: there is a no-slip condition (i.e. V(r) = 0) at the blood vessel wall (i.e. r = R) and the blood vessels is axially

symmetric (i.e. ∂V(r)

∂r = 0 at r = 0). From equation (7) follows that V(R) = 0 if r = R, a

state by the first assumption. Equation (8) is the derivative of equation (7) to r:

∂V(r)

∂r� -2Vmax � rR * �1R (8)

From equation (8) follows that ∂V(r)

∂r = 0 if r=0, a state corresponding to the second

assumption. Now, we can derive the wall shear rate (γ� ) from equation (3) and equation (8), which is given below:

γ� � � 2Vmax

R (9)

with Vmax = 2Vmean [39] and R = 0.5*D, equation (9) and equation (2) are written as follows:

γ� =� 2Vmax

R=� 2Vmax

D2� =� 4Vmax

D=� 8V�

D (10)

τw =� 8ηV�D

(11)

The wall shear stress depends on the mean velocity (V�) and diameter (D). From equation (11) follows:

τw(V�,D) =� 8ηV�D

(12)

The change in wall shear stress (∆τw), which is induced by a change in velocity (∆V) or a change in diameter (∆D), is approximated by a first order Taylor polynomial expression. τw is the force which balances the force on the wall that occurs during velocity changes and is therefore opposed to the blood flow direction.

49

The general equation for the Taylor polynomial is defined as follows [65]:

Pn�x , y=∑ ∑ 1

j!�m-j�!mj=0

nm=0 D1

jD2m-j f�a , b�x-a�j�y-b�m-j

(13)

with n =1 (first order) and the assumptions that ∆D and ∆V are not too large (linear approximation), the Taylor polynomial of the change in wall shear stress can be written as follows:

τw�v , d � τw�V� , D � D1τw�V� , D�v � V� � D2τw�V� , D�d � D (14) with D1 the derivative of the τw (equation (12)) to V� and D2 the derivative of τw (equation (12)) to D, which are given below:

∂τw∂V� =� 8η

D (15)

∂τw∂D=8ηV�D2 (16)

We can rewrite equation (14) with equation (15 and 16) as follows:

τ% �v , d = τw (V�,D)� 8η

D�v� V�� � 8ηV�

D2�d � D (17)

Rewriting equation (17) leads to the change in wall shear stress (∆τw) given in equation (18):

∆τw = � 8η

D∆V� � 8ηV�

D2 ∆D (18)

The first term at the right hand side indicates the change in wall shear stress evoked by a change in blood flow velocity and the second term reflects the change in wall shear stress evoked by a change in diameter.

Now, we can define the normalized flow-mediated dilation (Dfm,n) with the use of equation (18). If Dfm,n is 100%, the change in diameter (∆D) will be zero and thus ∆τw is determined by the Dfm,n term and the change in velocity (∆V). From this we can rewrite equation (18) as:

∆τw = � 8η

D∆V� * Dfm,n � 8ηV�

D2 ∆D (19)

50

If Dfm,n is 0%, the change in wall shear stress (∆τw) is 0 and from equation (19) follows the normalized flow mediated dilation (Dfm,n):

0 = � 8η

D∆V� * Dfm,n � 8ηV�

D2 ∆D (20)

8η

D∆V� * Dfm,n � 8ηV�

D2 ∆D (21)

Dfm,n =

8ηV�∆DD2�

8η∆V�D� =

V�*∆DD*∆V� =

∆DD�

∆V�V�� (22)

with D and V the mean baseline diameter and velocity, respectively, of the brachial artery before occlusion of the forearm. ∆D is the change in diameter of the brachial artery, due to the increased blood flow after reactive hyperemia, with respect to the baseline diameter. ∆V is the change in blood flow velocity, due to increased blood flow after cuff deflation (reactive hyperemia), with respect to baseline blood flow velocity. Equation (22) can be turned into:

Dfm,n �∆D

Dbaseline�

∆V�V�baseline� (23)

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7.5 E: Steps video analysis program ‘MyFMD’ The matlab program ‘MyFMD’ is developed for the Ultrasound FMD measurements. During FMD measurement, echo images were recorded on DVD using a DVD recorder. After recording the DVD-video movies were converted to AVI files with the program Studio-10. These converted files can be processed with the matlab program ‘MyFMD’.

Run MyFMD and a window will appear with the following two options:

1. Process beat-to-beat B-mode/Doppler (avi) images 2. Suppress registration artefacts (smoothing) and wave analysis

Analyze for the first time, choose option 1. Then select the avi-file.

The next window appears. Select manually one R-top to indicate the line of the R-tops:

Figure 16. MyFMD window: Select the R-top.

After indicating the R-top, the following window appears:

Figure 17. MyFMD window: Drag the rectangle to cover the artery.

Indicate R-top threshold position (transsecting red tops)

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Drag the rectangle to cover a representative lumen of the artery and click on the ‘Accept’ button.

Now the following window appears:

Figure 18. MyFMD window: Select the anterior and posterior artery wall.

Indicate the anterior and posterior wall and select twice (left and right) on both walls. After indicating the anterior and posterior wall, identify the zero velocity level in the next window:

Figure 19. MyFMD window: Identify the zero velocity level

Then the program processes all the video frames. This is shown in the next window:

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Figure 20. MyFMD window: Determination of blood flow velocity (white line) and artery wall diameter (blue lines).

When the tracking of the anterior and posterior wall is lost and the anterior wall disappears, the program beeps for attention. This allows the operator to correct this loss of the wall. Press the button ‘Adjust ROI’ and repeat the above steps, starting again by choosing a representative region of interest of the artery by dragging the rectangle.

At the end of the processing the smoothed diameter and velocity curves were given:

Figure 21. MyFMD window: Raw (red) and smoothed (blue) blood flow velocity (left) and brachial artery diameter (right) curves.

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Then the operator identifies the start points of cuff inflation and cuff deflation. The above window is printed and ‘MyFMD’ is programmed to generate an Excel-file with data for every heart beat.

The last step (identification of cuff inflation and deflation) can be repeated (at any moment of choice) by running ‘MyFMD’ again and choosing option 2: ‘Suppress registration artefacts (smoothing) and wave analysis’ at the first window.

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7.6 F: Excluded measurements Figure 22 shows a graphical representation of the raw dataset of FMD and FMDn measurements after analyzing the data with ‘MyFMD’ (custom-written automatic video analysis program). Figure 22A shows some outliers (black dots and stars). Figure 22B shows outliers for FMDn, with one extreme outlier. After watching the corresponding FMD-video again, we saw obviously that the vessel was moving during recording. Here, a negative flow velocity was dominant, probably due to a change in the Doppler angle. Therefore we decided to discard the normalized FMD, baseline and mean velocity values of the corresponding volunteer. Figure 23 shows the boxplots of FMDn measurements without the extreme outlier.

Figure 22. Boxplot of raw data (n=22) of flow-mediated dilation (FMD) (A) and normalized flow-mediated dilation (FMDn) (B) measurements after analyzing with ‘MyFMD’. The dark line in the middle of the boxes represents the median; the bottom and top of the box represents the 25% and 75% percentile respectively; outliers are shown as black dots and extreme outliers as stars. The first 4 boxplots represents the measurements of session 1 and the last 4 boxplots represents the measurements of session 2.

Figure 23. Boxplots of FMDn measurements after exclusion of one extreme outlier.

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After running the custom-written automatic video analysis program (‘MyFMD’) all outcomes were checked by an experienced researcher, who was unaware (blinded) of the volunteer and the moment of FMD measurement. Data were then excluded or corrected based on the corresponding diameter and velocity profiles during the entire FMD measurement as mention in the methods section of the main manuscript.

As mentioned in the results section of the main manuscript, data of 6 volunteers were discarded due to extreme values (i.e. boxplot above) or due to insufficient quality of the measurement as judged by the experienced researcher. The reasons of exclusion due to insufficient quality of measurement are given below and illustrated by three different diameter and velocity profiles and the reason of exclusion due to insufficient quality of measurement were given below:

First, figure 24 shows an example were the blood vessel was lost at the moment of cuff deflation due to movements of the volunteer. The movement occurred simultaneously with the flow-mediated dilation response and therefore we were not able to detect the true peak diameter.

Figure 24. Excluded raw (red) and smoothed (blue) brachial artery diameter curves (A) and flow velocity curves (B), which were obtained by sequential beat-to-beat video analysis program “MyFMD”, of one volunteer. The reason of exclusion was that the blood vessel was lost due to movements (A, T = 480 seconds) of the volunteer at the moment of the flow-mediated dilation response.

Second, figure 25 shows an example were there was no clear response immediately after cuff deflation and were the diameter curve gradually increased after cuff deflation. Therefore this volunteers FMD measurement was discarded. After watching the video again, we observed that just after cuff deflation the blood vessel was lost, which was also suggested by the blue and red peak at approximately 480 seconds. This was for such a short period of time that we were able to detect the response. However, there was no clear response as mention above.

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Figure 25. Excluded raw (red) and smoothed (blue) brachial artery diameter curves (A) and flow velocity curves (B), which were obtained by sequential beat-to-beat video analysis program “MyFMD”, of one volunteer. This volunteer was excluded because there no obvious response after cuff deflation (A, T=480 seconds). Third, figure 26 shows an example were the diameter measurement was very unstable. Therefore, this volunteers FMD measurement was discarded.

The custom-written automatic video analysis program is still under development and cannot handle drift in the diameter or velocity curves. Therefore, all the outcomes of

Figure 26. Excluded raw (red) and smoothed (blue) brachial artery diameter curves (A) and flow velocity curves (B), which were obtained by sequential beat-to-beat video analysis program “MyFMD”, of one volunteer. This volunteer was excluded because of unstable diameter measurement.

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the custom-written automatic video analysis program ‘MyFMD’ were checked on drift. The corrected baseline and peak diameter and baseline velocity, in number and percentages of corrected values, are shown in table 6. For each FMD, we measured the diameter and blood flow velocity until 5 minutes after cuff deflation and used this information to check whether the diameter and blood flow velocity returned back to baseline. Some examples of the velocity and diameter curves are given below to indicate the reason of correcting the velocity and/or diameter values:

Below, we show three more general corrections that we performed on the data acquired by the ‘MyFMD’ program.

First, figure 27 shows a good example why we corrected the baseline velocity. Before cuff inflation (blue star) the baseline velocity increased and therefore the automatic program overestimated this baseline velocity. At the end of the FMD measurement (after cuff deflation) the baseline velocity fluctuated less than in the first 3 minutes of the FMD measurement and returned to a lower velocity value. Therefore we use the

Figure 27. Raw (red) and smoothed (blue) brachial artery diameter curves (A) and flow velocity curves (B), which were obtained by sequential beat-to-beat video analysis program “MyFMD”, of one volunteer. Baseline velocity (B, 60-180 seconds) was overestimated and therefore corrected to the mean value over the last 2 minutes (660-780 seconds) of the FMD measurement.

Table 6. Numbers and percentage of corrected measurements (baseline and peak diameter and baseline velocity) Session 1 Session 2 Total Baseline Diameter 15/120 10/120 25/240 10% Peak Diameter 2/120 5/120 7/240 3% Baseline velocity 52/120 54/120 106/240 44% Discarded measurements 4/120 2/120 6/240 3%

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Figure 28. Raw (red) and smoothed (blue) brachial artery diameter curves (A) and flow velocity curves (B), which were obtained by sequential beat-to-beat video analysis program “MyFMD”, of one volunteer. The diameter is corrected because of an underestimation of the baseline diameter due to a constant drift of the diameter.

last recording part of the blood flow velocity profile to correct the overestimated baseline velocity.

Second, figure 28 shows an example that we corrected the baseline diameter, because the automatic program underestimated the baseline diameter due to a constant drift of the diameter.

Third, figure 29 shows an example were the diameter drifted and both baselines of

Figure 29. Raw (red) and smoothed (blue) brachial artery diameter curves (A) and flow velocity curves (B), which were obtained by sequential beat-to-beat video analysis program “MyFMD”, of one volunteer. The diameter drifted. The baseline diameter and baseline velocity were underestimated and therefore corrected to the mean value over the last 2 minutes (660-780 seconds) of the FMD measurement.

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diameter and velocity were corrected. The baseline diameter was underestimated due to drift in the diameter. Both baselines of the diameter and velocity were underestimated with respect to the corresponding diameter and velocity responses.

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7.7 G: Remaining results glucose Here, we show the results separately for each moment for both and each sessions in contrast with the results shown in the ‘Results’ section of the main manuscript.

Figure 30 shows Bland-Altman plots of the intra-session agreement for FMD and for FMDn. For the two baseline FMDs of session 1 the mean difference was -0.04% with a standard deviation of the difference of 1.80% (Figure 30A). For the two baseline FMDs of session 2 the mean difference was 0.23% with a standard deviation of the difference of 2.16% (Figure 30B). For the two baseline measurements of FMDn of session 1 the mean difference was 0.030 with a standard deviation of the difference of 0.050 (Figure 30C). For the two baseline measurements of FMDn of session 2 the mean difference was 0.021with a standard deviation of the difference of 0.064 (Figure 30D).

Figure 31 shows Bland-Altman plots of the inter-session agreement for FMD and for FMDn. For the first baseline FMD the mean difference between sessions was -0.11% with a standard deviation of the difference of 2.46% (Figure 31A). For the second baseline FMD the mean difference between sessions was -0.14% with a standard

Figure 30. Bland-Altman plots for two baseline flow-mediated dilation (FMD) measurements, and two baseline measurements of normalized flow-mediated dilation (FMDn) of session 1 and session 2. The upper and lower dotted lines represent 2 times the standard deviation (95% confidence interval), the middle dotted line represents the mean difference. The x-axis shows the average flow-mediated dilation and the y-axis the difference between pairs. FMD, flow-mediated dilation; FMDn, normalized flow-mediated dilation.

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Figure 31. Bland-Altman plots for the first and second baseline flow-mediated dilation (FMD), and the first and second baseline normalized flow-mediated dilation (FMDn) between session1 and session 2. The upper and lower dotted lines represent 2 times the standard deviation (95% confidence interval), the middle dotted line represents the mean difference. The x-axis shows the average flow-mediated dilation and the y-axis the difference between pairs. FMD(n) S1, (normalized) flow-mediated dilation of session 1; FMD(n) S2, (normalized) flow-mediated dilation of session 2.

deviation of the difference of 2.47% (Figure 31B). For the first baseline FMDn the mean difference between sessions was 0.003 with a standard deviation of the difference of 0.066 (Figure 31C). For the second baseline FMDn the mean difference between sessions was -0.004 with a standard deviation of the difference of 0.033 (Figure 31D).

Figure 32 shows the mean glucose concentration over time for each session. In each session (Figure 32A and B) the concentration of glucose increased significantly (p-value < 0.01) between blood sampling 2 (BS) (T= 0) and BS3 (T= 30). The concentration of glucose decreased significantly (p-value < 0.01) at the later stage between BS5 (T= 75) and BS6 (T= 120) compared with the fasting time moments BS1 (T= -40) and BS2 (T= 0). This pattern was similar for each session (Figure 32A and B).

Figure 33 shows the mean change in wall shear stress (∆WSS) stimulus over time for each session. The change in wall shear stress stimulus increased significantly (p-value is 0.016) in the fasting phase of session 1 and increased significantly (p-value <0.01) in the early hyperglycemia phase between measurement 2 (M2) (T= 0) and M3 (T= 30). The pattern of the change in wall shear stress stimulus between session 1 and session 2 is comparable.

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Figure 32. Mean blood glucose levels over time of session 1 (A) and session 2 (B). BS#, number of blood samples, corresponding with time T= -40, 0, 30, 60, 75 and 120 minutes respectively; *p < 0.05 between subsequent blood samples; n=22.

Figure 34 shows the mean flow-mediated dilation (FMD) over time for each session. FMD decreased between M2 (T= 0) and M3 (T= 30) for each session and was almost statistically significant (p-value is 0.051) for session 1 (Figure 34A). FMD increased between M3 (T= 30) and M4 (T= 75) for each session. The overall p-values were > 0.05 indicating that there were no statistically significant differences in mean FMDs over time. Per session analyses were, however, less powerful as compared to the combined analyses of the two sessions as presented in the main manuscript. Importantly, the pattern was similar at each session. Taken together, this suggest that there is a lower mean FMD at T=30 (measurement 3), but that the statistical power of single session analyses is too low to reveal a statistical significant result.

Figure 33. Mean change in wall shear stress (∆WSS) over time of session 1 (S1) (A) and session 2 (S2) (B). M#, number of measurement, corresponding with time T= -40, 0, 30 and 75 minutes respectively; *p < 0.05 between subsequent measurement; n=18 (A); n=20 (B).

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Figure 34. Mean flow-mediated dilation (FMD) over time of session 1 (S1) (A) and session 2 (S2) (B). M#, number of measurement, corresponding with time T= -40, 0, 30 and 75 minutes respectively; *p < 0.05 between subsequent measurement; n=19 (A), n=20 (B).

Figure 35 shows the mean normalized flow-mediated dilation (FMDn) over time for each session. FMDn decreased from the first baseline FMDn measurement (M1) (T= -40) to M3 (T= 30) and then remained constant for each session. The decrease in the fasting phase (between M1 and M2), was only statistically significant (p-value is 0.024) in session 1 (Figure 35A). Importantly, FMDn decreased in the early hyperglycemia phase between M2 (T= 0) and M3 (T= 30) for both sessions, only statistically significant (p-value is 0.01) in session 2 (Figure 35B). Normalization for the change in wall shear stress improves sensitivity of FMD assessment of glucose induced changes in FMD. This improved sensitivity is indirectly shown by the differences in the overall p-values of the repeated ANOVA analyses; the overall p-value is 0.237 for FMD and the overall p-value is 0.011 for FMDn in session 1; and the overall p-value is 0.786 for FMD and the overall p-value < 0.01 for FMDn in session 2.

Figure 35. Mean normalized flow-mediated dilation (FMDn) over time of session 1 (S1) (A) and session 2 (S2) (B). M#, number of measurement, corresponding with time T= -40, 0, 30 and 75 minutes respectively; *p < 0.05 between subsequent measurement; n=18 (A), n=20 (B).

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Figure 36. Mean baseline diameter over time of session 1 (S1) (A), session 2 (S2) (B) and both sessions (C). The dotted line represents the borderline between session 1 and session 2. M#, number of measurement, corresponding with time T= -40, 0, 30 and 75 minutes respectively; *p < 0.05 between subsequent measurement; n=20 (A, B), n=18 (C).

Figure 36 shows the mean baseline diameter over time for session 1, for session 2 and for session 1 and 2 combined. The mean baseline diameter does not differ significantly (overall p-values are 0.356, 0.080 and 0.410 for session 1, for session 2 and both sessions, respectively). The pattern was similar for each session and suggests an increased mean baseline diameter between M3 and M4 for both sessions (p-value is 0.019 and 0.039) (Figure 36C).

Figure 37 shows the correlation between glucose and flow-mediated dilation (Figure 37A) and normalized flow-mediated dilation (Figure 37B). Both plots show a negative correlation between glucose and FMD and FMDn, respectively. Only the negative correlation between glucose and FMDn is statistically significant (p-value <0.01, for FMD the p-value is 0.902).

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Figure 37. Correlation plots of the blood glucose levels versus flow-mediated dilation (A) and normalized flow-mediated dilation (B); n=22 (per volunteer 8 corresponding glucose samples and FMD measurement)

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7.8 H: Power calculation baseline diameter We consider a difference of 2% in baseline diameter relevant. From table 2 the intra-session reproducibility of the baseline diameter is 1%. The estimated standardized difference, therefore, is 2 (2%/1%). Selecting a statistical power (1-β) of 0.9 and a two-tailed significance level (α) of 0.05, and using the nomogram in [66] the required number of volunteers is 12. Thus, if there was a difference of 2% in baseline diameter we were able to show this difference, statistically significantly in the present investigation.

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7.9 I: Water results 8 volunteers received plain water to investigate whether the observed FMD response after intake of the glucose solution is not caused by ingestion only. Before enrollment of volunteers, we defined that every fourth volunteer would receive water instead of glucose. To end up with 8 water volunteers, we defined that the last volunteer of the study would also receive water. The rest of the protocol was exactly similar as compared to the glucose group. At the start of the study, the idea was to use this group as a control group. However, if we expect that water has the same effect of as glucose, we need at least the same number of volunteers as calculated from the power calculation of the glucose group (n=20) to show such an effect. If water has a smaller effect than glucose, we would even need a greater number of volunteers for this control group. During the study we have learned that inviting the same volunteers, who received an OGTT twice, for a third (and possibly) fourth session, would have generated a more powerful study design to address the question whether ingestion of water only had an effect on FMD. Nevertheless, we present the results of the water group below, although these results should be interpreted with caution as this control group was clearly underpowered.

Figure 38. Mean blood glucose level over time of session 1 (S1) (A), session 2 (S2) (B) and both sessions (C). The dotted line represents the borderline between session 1 and session 2. BS#, number of blood sampling, corresponding with time T= -40, 0, 30, 60, 75 and 120 minutes respectively; *p < 0.05 between subsequent blood sampling; n=7 (A, C), n=8 (B).

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Figure 38 shows the mean blood glucose concentration over time for each and both sessions. The pattern was similar in both sessions (Figure 38A, B and C). Figure 38 shows an increase in blood glucose levels at the end of both sessions. The observed pattern suggest that volunteers increase the glucose concentrations at the end of the protocol. This might be explained by the fact that after a fasting period glucagon, released by the pancreas in the fasting state, increases blood glucose levels. However, the glucose levels in this group were significantly lower (4.7 mmol) as compared to the glucose group (6.0 mmol/l) (P<0.001).

Figure 39 shows the mean change in wall shear stress (∆WSS) stimulus over time for each and both sessions. The pattern was not similar between the sessions (Figure 39). This is illustrated by the differences in measurement 3 (M3) (T=30) and M4 (T=75) between session 1 and 2. In session 1 the change in wall shear stress stimulus decreased, whereas it increased in session 2. However, the change in wall shear stress increased in the early hyperglycemia phase between M2 (T= 0) and M3

Figure 39. Mean change in wall shear stress (∆WSS) over time of session 1 (S1) (A), session 2 (S2) (B) and both sessions (C). The dotted line represents the borderline between session 1 and session 2. M#, number of measurement, corresponding with time T= -40, 0, 30 and 75 minutes respectively; *p < 0.05 between subsequent measurement; n=8 (A, B, and C).

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S2_M4S2_M3S2_M2S2_M1

WS

S s

tim

ulu

s [

%]

2.20

2.00

1.80

1.60

1.40

1.20

1.00

water p=0.007∆WSS stimulus of session 2B

# measurement

S2_M4S2_M3S2_M2S2_M1S1_M4S1_M3S1_M2S1_M1

WS

S s

tim

ulu

s [

%]

2.20

2.00

1.80

1.60

1.40

1.20

1.00

water p=0.004∆WSS stimulus of session 1 & session 2 xC

*

70

(T=30) in session 1 and 2 (statistically significant only in session 1, p-value is 0.022 (Figure 39A and C)).

Figure 40 shows the mean flow-mediated dilation (FMD) over time for each and both sessions. The pattern was not similar between the sessions (Figure 40). In session 1 it seems that the mean FMD did not change after water intake, moreover it seems to fluctuate around the same FMD response level (Figure 40A). Therefore these results should be interpreted with caution.

Figure 41 shows the mean normalized flow-mediated dilation (FMDn) over time for each and both sessions. The pattern seems to be similar between both sessions (Figure 41). FMDn decreased in the fasting phase (between M1 (T= -40) and M2 (T= 0)) for both sessions. The overall p-value > 0.05.

Figure 42 shows the mean baseline diameter for each and both sessions. The pattern was not similar between the sessions. Therefore these results should be interpreted with caution.

Figure 40. Mean flow-mediated dilation (FMD) over time of session 1 (S1) (A), session 2 (S2) (B) and both sessions (C). The dotted line represents the borderline between session 1 and session 2. M#, number of measurement, corresponding with time T= -40, 0, 30 and 75 minutes respectively; *p < 0.05 between subsequent measurement; n=8 (A, B, and C).

# measurement

S1_M4S1_M3S1_M2S1_M1

FM

D [

%]

4.2

4.0

3.8

3.6

3.4

3.2

3.0

water p=0.965FMD of session 1A

# measurement

S2_M4S2_M3S2_M2S2_M1

FM

D [

%]

4.2

4.0

3.8

3.6

3.4

3.2

3.0

water p=0.564FMD of session 2B

# measurement

S2_M4S2_M3S2_M2S2_M1S1_M4S1_M3S1_M2S1_M1

FM

D [

%]

4.2

4.0

3.8

3.6

3.4

3.2

3.0

water p=0.958FMD of session 1 & session 2C

71

To illustrate the differences and similarities between the FMD and FMDn patterns after glucose and water intake, we plotted the mean levels for each group in figure 43. To clearly illustrate the differences and similarities of glucose versus water, we only show the patterns starting from measurement 2 (M2) (these results below were similar when patterns were analyzed from measurement 1 (data not shown)).

With regard to flow-mediated dilation response, there was no significant difference between the pattern after glucose and after water intake. However, the mean FMD values were all lower for the group who received water (Figure 43A). We do not have and explanation for this result. Volunteers with water or glucose were selected in a similar way. Furthurmore, we do not have any clue that the water volunteers were measured differently as compared to the glucose volunteers. As the number of volunteers is small this result migth have been introduced because of chance, and thus should be interpreted with caution.

With regard to the normalized flow-mediated dilation response, we provide (marginal) evidence that the FMDn respons after water intake was not similar as compared to

Figure 41. Mean normalized flow-mediated dilation (FMDn) over time of session 1 (S1) (A), session 2 (S2) (B) and both sessions (C). The dotted line represents the borderline between session 1 and session 2. M#, number of measurement, corresponding with time T= -40, 0, 30 and 75 minutes respectively; *p < 0.05 between subsequent measurement; n=8 (A, B, and C).

# measurement

S1_M4S1_M3S1_M2S1_M1

FM

Dn

[-]

0.080

0.060

0.040

0.020

water p=0.067FMDn of session 1A

# measurement

S2_M4S2_M3S2_M2S2_M1

FM

Dn

[-]

0.080

0.060

0.040

0.020

water p=0.162FMDn of session 2B

# measurement

S2_M4S2_M3S2_M2S2_M1S1_M4S1_M3S1_M2S1_M1

FM

Dn

[-]

0.080

0.060

0.040

0.020

water p=0.128FMDn of session 1 & session 2C

72

Figure 42. Mean baseline diameter over time of session 1 (S1) (A), session 2 (S2) (B) and both sessions (C). The dotted line represents the borderline between session 1 and session 2. M#, number of measurement, corresponding with time T= -40, 0, 30 and 75 minutes respectively; *p < 0.05 between subsequent measurement; n=8 (A, B, and C).

glucose intake (Figure 43B). In session 2, we observed a decrease in FMDn between baseline measurement 2 (T= 0) and measurement 3 (T= 30) in the glucose group, whereas in the water group there was an increase. Furthermore, we observed and increase in FMDn between measurement 3 (T= 30) and measurement 4 (T= 75) in the glucose group, whereas in the water group there was a decrease. These changes were statistically significantly different between the glucose and water group. However, in session 1 we did not observe a different pattern. Again these results should be interpreted with caution because of the limited number of volunteers.

# measurement

S1_M4S1_M3S1_M2S1_M1

Dia

mete

r [m

m]

4,05

4,00

3,95

water p=0.049Diameter of session 1

*

A

# measurement

S2_M4S2_M3S2_M2S2_M1

Dia

mete

r [m

m]

4,05

4,00

3,95

water p=0.556Diameter of session 2B

*

# measurement

S2_M4S2_M3S2_M2S2_M1S1_M4S1_M3S1_M2S1_M1

Dia

mete

r [m

m]

4,05

4,00

3,95

water p=0.181Diameter of session 1 & session 2C

**

73

Figure 43. Flow-mediated dilation (FMD) (A) and normalized flow-mediated dilation (FMDn) (B) patterns after glucose and water intake over the entire measurement of session 1 (S1) and session 2 (S2). The dotted line represents the borderline between session 1 and session 2. M#, number of measurement, corresponding with time T= 0, 30 and 75 minutes respectively; *p < 0.05 between the interaction of blood glucose levels and FMDn measurements; n=18 (glucose); n=8 (water).

# measurement

S2_M4S2_M3S2_M2S1_M4S1_M3S1_M2

FM

D [

%]

7.0

6.0

5.0

4.0

3.0

waterglucose

FMD session 1 & session 2A

p=0.840

# measurement

S2_M4S2_M3S2_M2S1_M4S1_M3S1_M2

FM

Dn

[-]

0.080

0.060

0.040

0.020

waterglucose

FMDn session 1 & session 2Bp=0.155

*

*

74

7.10 J: Changed automatic video analysis program ‘MyFMD’ code The original automatic video analysis code was modified to take also the negative flow velocities into account. The original and adapted program codes are given below:

Original program code:

function env=imDproc(im)

[pv,ph]=size(im); pval=(max(im,[],2)); thr=max(pval)/4; ind=pv-50;

while (ind>1)&&(pval(ind)>thr)

ind=ind-1; end

ind=ind-40; % systolic top of sonogram if ind<1

ind=1; end

% convert to binary (b/w) image; use only relevant part for threshold Im2=im2bw(im(ind:end,:),graythresh(im(ind:end,:))); data=double(im2); [pv, ph]=size(data); env=zeros(1,ph);

for xp=1:ph

for yp=1:pv-1 % only search between systolic peak and baseline if data(yp, xp)>0.5 break end end env(xp)=yp+ind-1;

end end

Changed program code:

function env=imDproc(im) global vfmd image_2d=im; gem=zeros(10,1);

75

for i=1:10 gm=mean(image_2d(i,:)); gem(i)= gm; end thr=(sum(gem))/length(gem); % threshold = mean threshold + offset thresh=thr +10; % Store 5 points around the zero line storage=zeros(5,3); yp=vfmd.yDzero; xp=vfmd.xDzero; for q=(-2):2 value=image_2d((yp+q),xp); storage((q+3),1)=(yp+q); storage((q+3),2)=xp; storage((q+3),3)=value; end % change pixelvalue of the zeroline and one row above and beneath % the zeroline im=image_2d; line_index=find(storage(:,3)> (max(storage(:,3))-1)); yvalue=storage(line_index,1); im((yvalue-1),:)=0; im((yvalue),:)=(thresh+1); im((yvalue+1),:)=0; % changed imagedata image_2d2=im; %dimension image [ny, nx] = size(image_2d2); env=zeros(1,nx); for k=1:nx col=image_2d2(:,k); index=find(col>thresh); prod= sum(index .* (double(col(index))));%[]; env(k) = prod/sum(col(index)); end

end

76

Beside the previous changed code there were additional changes. First of all, an extension is made in the function set_BDroi(ima) due to the changed function imDproc(im):

imagesc(imDop); colormap gray; title('Identify zero velocity level'); [xp,yp]=ginput(1); vfmd.xDzero=round(xp); vfmd.yDzero=round(yp); close all

There are also made some small changes in the original program code to improve the analysis. Specifically, to make sure that the same R-top is not detected twice. The program code below is added to the function [new_Rtop, new_R]=frameR(old_Rtop,Rline):

new_R = [1,new_Rtop]; dif=diff(new_R); ind=find(dif>10); new_Rtop=new_Rtop(ind);

77

8.0 References

1. Ryden, L., et al., Guidelines on diabetes, pre-diabetes, and cardiovascular diseases: executive summary. The Task Force on Diabetes and Cardiovascular Diseases of the European Society of Cardiology (ESC) and of the European Association for the Study of Diabetes (EASD). Eur Heart J, 2007. 28(1): p. 88-136.

2. Lakatta, E.G. and D. Levy, Arterial and cardiac aging: major shareholders in cardiovascular disease enterprises: Part II: the aging heart in health: links to heart disease. Circulation, 2003. 107(2): p. 346-54.

3. Rothwell, P.M., et al., Population-based study of event-rate, incidence, case fatality, and mortality for all acute vascular events in all arterial territories (Oxford Vascular Study). Lancet, 2005. 366(9499): p. 1773-83.

4. Mattace-Raso, F.U., et al., Arterial stiffness and risk of coronary heart disease and stroke: the Rotterdam Study. Circulation, 2006. 113(5): p. 657-63.

5. Reneman, R.S., et al., Age-related changes in carotid artery wall properties in men. Ultrasound Med Biol, 1986. 12(6): p. 465-71.

6. Kawano, H., et al., Hyperglycemia rapidly suppresses flow-mediated endothelium-dependent vasodilation of brachial artery. J Am Coll Cardiol, 1999. 34(1): p. 146-54.

7. Title, L.M., et al., Oral glucose loading acutely attenuates endothelium-dependent vasodilation in healthy adults without diabetes: an effect prevented by vitamins C and E. J Am Coll Cardiol, 2000. 36(7): p. 2185-91.

8. Deanfield, J.E., J.P. Halcox, and T.J. Rabelink, Endothelial function and dysfunction: testing and clinical relevance. Circulation, 2007. 115(10): p. 1285-95.

9. Celermajer, D.S., et al., Non-invasive detection of endothelial dysfunction in children and adults at risk of atherosclerosis. Lancet, 1992. 340(8828): p. 1111-5.

10. Padilla, J., et al., Adjusting flow-mediated dilation for shear stress stimulus allows demonstration of endothelial dysfunction in a population with moderate cardiovascular risk. J Vasc Res, 2009. 46(6): p. 592-600.

11. Pohl, U., et al., Crucial role of endothelium in the vasodilator response to increased flow in vivo. Hypertension, 1986. 8(1): p. 37-44.

12. Corretti, M.C., et al., Guidelines for the ultrasound assessment of endothelial-dependent flow-mediated vasodilation of the brachial artery: a report of the International Brachial Artery Reactivity Task Force. J Am Coll Cardiol, 2002. 39(2): p. 257-65.

13. Benjamin, E.J., et al., Clinical correlates and heritability of flow-mediated dilation in the community: the Framingham Heart Study. Circulation, 2004. 109(5): p. 613-9.

14. Witte, D.R., et al., Is the association between flow-mediated dilation and cardiovascular risk limited to low-risk populations? J Am Coll Cardiol, 2005. 45(12): p. 1987-93.

15. Pyke, K.E. and M.E. Tschakovsky, Peak vs. total reactive hyperemia: which determines the magnitude of flow-mediated dilation? J Appl Physiol, 2007. 102(4): p. 1510-9.

16. Padilla, J., et al., Normalization of flow-mediated dilation to shear stress area under the curve eliminates the impact of variable hyperemic stimulus. Cardiovasc Ultrasound, 2008. 6: p. 44.

17. Pyke, K.E., E.M. Dwyer, and M.E. Tschakovsky, Impact of controlling shear rate on flow-mediated dilation responses in the brachial artery of humans. J Appl Physiol, 2004. 97(2): p. 499-508.

18. Meendering, J.R., et al., Estrogen, medroxyprogesterone acetate, endothelial function, and biomarkers of cardiovascular risk in young women. Am J Physiol Heart Circ Physiol, 2008. 294(4): p. H1630-7.

78

19. Dyson, K.S., J.K. Shoemaker, and R.L. Hughson, Effect of acute sympathetic nervous system activation on flow-mediated dilation of brachial artery. Am J Physiol Heart Circ Physiol, 2006. 290(4): p. H1446-53.

20. Betik, A.C., V.B. Luckham, and R.L. Hughson, Flow-mediated dilation in human brachial artery after different circulatory occlusion conditions. Am J Physiol Heart Circ Physiol, 2004. 286(1): p. H442-8.

21. Nishiyama, S.K., et al., Limb-specific differences in flow-mediated dilation: the role of shear rate. J Appl Physiol, 2007. 103(3): p. 843-51.

22. Thijssen, D.H., et al., The impact of baseline diameter on flow-mediated dilation differs in young and older humans. Am J Physiol Heart Circ Physiol, 2008. 295(4): p. H1594-8.

23. Hoeks, A.P. and R.S. Reneman, Flow patterns and arterial wall dynamics, in Cerebrovascular ultrasound - Theory, practice and future developments, M.G. Hennerici and S.P. Meairs, Editors. 2001, Cambridge University Press. p. 77-87.

24. Rim, S.J., et al., Decrease in coronary blood flow reserve during hyperlipidemia is secondary to an increase in blood viscosity. Circulation, 2001. 104(22): p. 2704-9.

25. Windberger, U., et al., Whole blood viscosity, plasma viscosity and erythrocyte aggregation in nine mammalian species: reference values and comparison of data. Exp Physiol, 2003. 88(3): p. 431-40.

26. Watanabe, K., et al., Oral glucose loading attenuates endothelial function in normal individual. Eur J Clin Invest, 2010.

27. Akbari, C.M., et al., Endothelium-dependent vasodilatation is impaired in both microcirculation and macrocirculation during acute hyperglycemia. J Vasc Surg, 1998. 28(4): p. 687-94.

28. Harris, R.A., et al., Ultrasound assessment of flow-mediated dilation. Hypertension, 2010. 55(5): p. 1075-85.

29. Steinberg, H.O. and A.D. Baron, Vascular function, insulin resistance and fatty acids. Diabetologia, 2002. 45(5): p. 623-34.

30. Welsch, M.A., J.D. Allen, and J.P. Geaghan, Stability and reproducibility of brachial artery flow-mediated dilation. Med Sci Sports Exerc, 2002. 34(6): p. 960-5.

31. Sorensen, K.E., et al., Non-invasive measurement of human endothelium dependent arterial responses: accuracy and reproducibility. Br Heart J, 1995. 74(3): p. 247-53.

32. Donald, A.E., et al., Methodological approaches to optimize reproducibility and power in clinical studies of flow-mediated dilation. J Am Coll Cardiol, 2008. 51(20): p. 1959-64.

33. Parker, B.A., T.L. Trehearn, and J.R. Meendering, Pick Your Poiseuille: Normalizing the Shear Stimulus In Studies of Flow-Mediated Dilation. J Appl Physiol, 2008.

34. Etsuda, H., et al., Morning attenuation of endothelium-dependent, flow-mediated dilation in healthy young men: possible connection to morning peak of cardiac events? Clin Cardiol, 1999. 22(6): p. 417-21.

35. Olamaei, N., et al., Characterization and reproducibility of forearm arterial flow during reactive hyperemia. Physiol Meas, 2010. 31(6): p. 763-773.

36. Silber, H.A., et al., The relationship between vascular wall shear stress and flow-mediated dilation: endothelial function assessed by phase-contrast magnetic resonance angiography. J Am Coll Cardiol, 2001. 38(7): p. 1859-65.

37. Hoeks, A.P. and R.S. Reneman, Do Doppler Systems Color Arteries Red?, in Blood Flow - Modelling and Diagnostics, T.A. Kowalewski, Editor. 2005: Warsaw. p. 243-271.

38. van de Vosse, F.N. and G.J.F. van Heijst, Wrijvingsgedomineerde stromingen, in Stroming en Diffusie - college aantekeningen 3D030 2006, Eindhoven University of Technology: Eindhoven. p. 57-72.

39. van de Vosse, F.N. and A.C.B. Bogaerds, Internal flows, in Fluid Biomechanics 8W270. 2008, Eindhoven University of Technology: Eindhoven.

40. Twisk, J.W.R., ed. Inleiding in de toegepaste biostatistiek. 2007, Elsevier gezondheidszorg.

79

41. Bland, J.M. and D.G. Altman, Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1986. 1(8476): p. 307-10.

42. Gokce, N., et al., Effects of race and hypertension on flow-mediated and nitroglycerin-mediated dilation of the brachial artery. Hypertension, 2001. 38(6): p. 1349-54.

43. Lambert, J., et al., Endothelium-dependent and -independent vasodilation of large arteries in normoalbuminuric insulin-dependent diabetes mellitus. Arterioscler Thromb Vasc Biol, 1996. 16(5): p. 705-11.

44. Levine, G.N., et al., Ascorbic acid reverses endothelial vasomotor dysfunction in patients with coronary artery disease. Circulation, 1996. 93(6): p. 1107-13.

45. Black, M.A., et al., Importance of measuring the time course of flow-mediated dilatation in humans. Hypertension, 2008. 51(2): p. 203-10.

46. Henry, R.M., et al., Type 2 diabetes is associated with impaired endothelium-dependent, flow-mediated dilation, but impaired glucose metabolism is not; The Hoorn Study. Atherosclerosis, 2004. 174(1): p. 49-56.

47. Hijmering, M.L., et al., Variability of flow mediated dilation: consequences for clinical application. Atherosclerosis, 2001. 157(2): p. 369-73.

48. Henareh, L., T. Jogestrand, and S. Agewall, Glucose intolerance is associated with C-reactive protein and intima-media anatomy of the common carotid artery in patients with coronary heart disease. Diabet Med, 2005. 22(9): p. 1212-7.

49. Henry, R.M., et al., Left ventricular mass increases with deteriorating glucose tolerance, especially in women: independence of increased arterial stiffness or decreased flow-mediated dilation: the Hoorn study. Diabetes Care, 2004. 27(2): p. 522-9.

50. Quinn, C.E., et al., Effect of pioglitazone on endothelial function in impaired glucose tolerance. Diabetes Obes Metab, 2010. 12(8): p. 709-15.

51. Kim, S.H., et al., Effects of acute hyperglycemia on endothelium-dependent vasodilation in patients with diabetes mellitus or impaired glucose metabolism. Endothelium, 2003. 10(2): p. 65-70.

52. Harris, R.A., et al., Variability of flow-mediated dilation measurements with repetitive reactive hyperemia. Vasc Med, 2006. 11(1): p. 1-6.

53. Malek, A.M., S. Izumo, and S.L. Alper, Modulation by pathophysiological stimuli of the shear stress-induced up-regulation of endothelial nitric oxide synthase expression in endothelial cells. Neurosurgery, 1999. 45(2): p. 334-44; discussion 344-5.

54. Kim, J.A., et al., Reciprocal relationships between insulin resistance and endothelial dysfunction: molecular and pathophysiological mechanisms. Circulation, 2006. 113(15): p. 1888-904.

55. Tabit, C.E., et al., Endothelial dysfunction in diabetes mellitus: molecular mechanisms and clinical implications. Rev Endocr Metab Disord, 2010. 11(1): p. 61-74.

56. Furchgott, R.F. and J.V. Zawadzki, The obligatory role of endothelial cells in the relaxation of arterial smooth muscle by acetylcholine. Nature, 1980. 288(5789): p. 373-6.

57. Wallace, S.M., et al., Isolated systolic hypertension is characterized by increased aortic stiffness and endothelial dysfunction. Hypertension, 2007. 50(1): p. 228-33.

58. Wilkinson, I.B., et al., Nitric oxide regulates local arterial distensibility in vivo. Circulation, 2002. 105(2): p. 213-7.

59. Joannides, R., et al., Nitric oxide is responsible for flow-dependent dilatation of human peripheral conduit arteries in vivo. Circulation, 1995. 91(5): p. 1314-9.

60. Ceriello, A., et al., Antioxidant defences are reduced during the oral glucose tolerance test in normal and non-insulin-dependent diabetic subjects. Eur J Clin Invest, 1998. 28(4): p. 329-33.

61. Du, X., K. Stocklauser-Farber, and P. Rosen, Generation of reactive oxygen intermediates, activation of NF-kappaB, and induction of apoptosis in human endothelial cells by glucose: role of nitric oxide synthase? Free Radic Biol Med, 1999. 27(7-8): p. 752-63.

80

62. Arcaro, G., et al., Insulin causes endothelial dysfunction in humans: sites and mechanisms. Circulation, 2002. 105(5): p. 576-82.

63. Xiang, G.D., et al., The antioxidant alpha-lipoic acid improves endothelial dysfunction induced by acute hyperglycaemia during OGTT in impaired glucose tolerance. Clin Endocrinol (Oxf), 2008. 68(5): p. 716-23.

64. Reneman, R.S., T. Arts, and A.P. Hoeks, Wall shear stress--an important determinant of endothelial cell function and structure--in the arterial system in vivo. Discrepancies with theory. J Vasc Res, 2006. 43(3): p. 251-69.

65. Adams, R.A., Taylor Series and Approximations, in Calculus - A Complete Course, P.E.C. Inc., Editor. 2003: Toronto, Ontario. p. 774-782.

66. Altman, D.G., Statistics and ethics in medical research: III How large a sample? Br Med J, 1980. 281(6251): p. 1336-8.