DELIVERABLE 5 - vph-dare.euIT D5.9 1v3 Final.pdf · DARE@IT is an FP7 Project supported by the European Union under grant agreement no. 601055. For more information on the project,

DELIVERABLE 5.9

Role of chronic effects of circadian/lifestyle

alterations on physical variables

Grant agreement no.: 601055 (FP7-ICT-2011-9)

Project acronym: VPH-DARE@IT

Project title: Dementia Research Enabled by IT

Funding Scheme: Collaborative Project

Project co-ordinator: Prof. Alejandro Frangi, University of Sheffield

Tel.: +44 114 22 20153

Fax: +44 114 22 27890

E-mail: [email protected]

Project web site address: http://www.vph-dare.eu

Due date of deliverable Month 42

Actual submission date Month 42

Start date of project April 1st 2013

Project duration 48 months

Work Package & Task WP 5, Task 5.6

Lead beneficiary USFD

Editor T. Lassila

Author(s) T. Lassila, Z. Taylor, A. Frangi

Quality reviewer A. Venneri, C Bludszuweit-Philipp

Project co-funded by the European Union within the Seventh Framework Programme

Dissemination level

PU Public X

PP Restricted to other programme participants (including Commission Services)

RE Restricted to a group specific by the consortium (including Commission

Services)

CO Confidential, only for members of the consortium (including Commission

Services)

mailto:[email protected]

http://www.vph-dare.eu/

FP7-601055: VPH-DARE@IT D5.9 Role of chronic effects of circadian/lifestyle alterations on physical variables 30/10/2016

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Issue Record

Version no. Date Author(s) Reason for modification Status

1.0 15/09/16 Toni Lassila Initial release Initial draft

1.01 13/09/16 Alejandro Frangi,

Zeike Taylor

Consolidated feedback Updated draft

1.02 20/09/16 Annalena Venneri Consolidated feedback Updated draft

1.1 10/10/16 Toni Lassila Modifications according

to feedback from UCL

Updated draft

1.2 20/10/16 Toni Lassila Modifications according

to feedback from UEF

Finalised draft

1.3 30/10/16 PMO Final check Finalised

Copyright Notice

Copyright © 2013 VPH-DARE@IT Consortium Partners. All rights reserved. VPH-

DARE@IT is an FP7 Project supported by the European Union under grant agreement no.

601055. For more information on the project, its partners, and contributors please see

http://www.vph-dare.eu. You are permitted to copy and distribute verbatim copies of this

document, containing this copyright notice, but modifying this document is not allowed. All

contents are reserved by default and may not be disclosed to third parties without the prior

written consent of the VPH-DARE@IT consortium, except as mandated by the grant agreement

with the European Commission, for reviewing and dissemination purposes. All trademarks and

other rights on third party products mentioned in this document are acknowledged and owned

by the respective holders. The information contained in this document represents the views of

VPH-DARE@IT members as of the date of its publication and should not be taken as

representing the view of the European Commission. The VPH-DARE@IT consortium does not

guarantee that any information contained herein is error-free, or up to date, nor makes

warranties, express, implied, or statutory, by publishing this document.

Author(s) for Correspondence

Toni Lassila

T: +44 114 2225398; F: +44 114 2227890; E: [email protected]; W:

http://www.cistib.org

http://www.vph-dare.eu/

http://www.cistib.org/


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Table of Contents

TABLE OF CONTRIBUTIONS ........................................................................................................... 5

TABLE OF ACRONYMS...................................................................................................................... 5

EXECUTIVE SUMMARY .................................................................................................................... 6

1. INTRODUCTION .............................................................................................................................. 7

2. CLINICAL AND AMBULATORY DATA COLLECTION .......................................................... 9

3. MATHEMATICAL MODELLING OF CEREBROVASCULAR FLOW ................................. 11

4. LIFESTYLE-FACTORS AND MILD COGNITIVE IMPAIRMENT ........................................ 14

5. MODEL-PREDICTED CEREBRAL BLOOD FLOW AND MCI STATUS.............................. 17

6. LIFESTYLE FACTORS AND MODEL PREDICTED CBF ....................................................... 21

7. DISCUSSION AND THE PATH TO MODELLING LIFESTYLE EFFECTS .......................... 24

REFERENCES ..................................................................................................................................... 26

Table of Figures

Figure 1 Summary of measurements collected in the Lido Study. Vascular parameters are used

to drive the lumped parameter circulation model and to predict 24-hour CBF, which then enters

the statistical analyses. Lifestyle information and actigraph measurements enter directly in the

statistical analyses. .................................................................................................................. 10 Figure 2 Model personalisation pipeline for prediction of 24-hour cerebral blood flow ....... 13 Figure 3 Meta-analysis for the relative risk of AD incidence when comparing subjects with

high fish consumption to subjects who ate no fish. Variance computed with random-effects

model of DerSimonian and Laird. Estimated pooled effect size was RR=0.60 (95%-CI: 0.51-

0.71). ........................................................................................................................................ 15 Figure 4 Correlation plots between measured ICA-L/ICA-R flow velocity (top), pulsatility

index (middle), and arterial pulse pressure (bottom). Intervals indicate 24-h variability of

predictions. .............................................................................................................................. 19 Figure 5 Model-predicted CBF parameters, groupwise comparison between controls (N=32)

and MCI cases (N=12). All quantities except systolic blood pressure variability were significant

at p<0.05. ................................................................................................................................. 20


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Table of Tables

Table 1: Groupwise means (standard deviations) between normal controls and MCI cases for

the lifestyle factors considered in the Lido Study. Statistical significance in univariate analysis

computed by the Kruskal-Wallis one-way ANOVA -test. ...................................................... 15 Table 2 Groupwise means (standard deviations) between normal controls and MCI cases for

the cerebral blood flow parameters modelled in the Lido Study. Statistical significance in

univariate analysis computed by the Kruskal-Wallis one-way ANOVA –test. ....................... 18 Table 3 Groupwise means (standard deviations) between men and women for the CBF

parameters modelled. Significance in univariate analysis computed by the Kruskal-Wallis one-

way ANOVA –test. ................................................................................................................. 20 Table 4 Groupwise means (standard deviations) between young old/very old subjects for the

CBF parameters modelled. Significance computed by the Kruskal-Wallis one-way ANOVA –

test. .......................................................................................................................................... 20 Table 5 Groupwise means (standard deviations) between controls and MCIs, stratification

between men/women for total perfusion. Significance computed by Kruskal-Wallis one-way

ANOVA –test. ......................................................................................................................... 20 Table 6 Groupwise means (standard deviations) between controls and MCIs, stratification

between young old/very old for APP. Significance computed by Kruskal-Wallis one-way

ANOVA –test. ......................................................................................................................... 20 Table 7 Five strongest correlations between lifestyle factors and total cerebral blood flow .. 22 Table 8 Five strongest correlations between lifestyle factors and total perfusion (in men only)

................................................................................................................................................. 22 Table 9 Five strongest correlations between lifestyle factors and total perfusion (in women

only)......................................................................................................................................... 22 Table 10 Five strongest correlations between lifestyle factors and arterial pulse pressure (in

young old only) ....................................................................................................................... 22 Table 11 Five strongest correlations between lifestyle factors and arterial pulse pressure (in

very old only) .......................................................................................................................... 22 Table 12 Five strongest correlations between lifestyle factors and arterial pulsatility index

(MCA) ..................................................................................................................................... 23


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Table of contributions

Contributors Contribution

A. Frangi Provided feedback on scientific rationale

T. Lassila Wrote document

A.Venneri Managed data collection, provided feedback on interpretation of results

Z. Taylor Provided feedback and copy editing (*) in alphabetical order of surname

Table of acronyms

AD Alzheimer’s disease

APP Arterial pulse pressure

ASL-MRI Arterial spin labelling magnetic resonance imaging

CAM Cerebral autoregulation model

CBF Cerebral blood flow

CO Cardiac output

DBP Diastolic blood pressure

HR Heart rate

ICA Internal carotid artery

LPCM Lumped parameter circulation model

LV Left ventricle

MCA Middle cerebral artery

MCI Mild cognitive impairment

MLF Modifiable lifestyle factor

MMSE Mini mental state examination

PI Pulsatility index

RR Relative risk

SBP Systolic blood pressure

SPECT Single Positron Emission Computed Tomography

Executive summary

Task 5.6 of Work Package 5 of VPH-DARE@IT includes modelling the effect of modifiable

lifestyle and environmental factors in mechanistic models of cerebral blood flow and water

transport in the human brain. Such effects are of importance when attempting to make patient-

specific predictions of conversion from mild cognitive impairment to Alzheimer’s disease,

since modifiable lifestyle factors have been proposed as potential modulators of this

pathological process. However, of the multitude of modifiable lifestyle factors that have been

linked to increased/decreased risk of Alzheimer’s disease, few have conclusively been

demonstrated to follow pathways that can easily be incorporated into mechanistic models, and

most factors have so far only been studied using phenomenological models.

In this deliverable, we analyse the preliminary data for N=79 subjects of the cross-sectional

case-control study performed at the IRCCS San Camillo Hospital in Venice, Italy. This multi-

modal dataset is used to drive a patient-specific mechanistic modelling pipeline that provides

predictions of cerebral blood flow over a 24-hour period. Combined with subject-specific

lifestyle information collected by questionnaires, we then investigate the three-way association

between lifestyle, cerebral blood flow, and mild cognitive impairment. The objective is to

identify lifestyle factors that are associated with alterations in cerebral blood flow and

consequent early microvascular changes that are common in Alzheimer’s disease. Several

blood flow parameters are identified that correlate with mild cognitive impairment in the study

cohort, and correlations between these parameters and modifiable lifestyle factors are studied.

In summary, the analysis in this deliverable confirms previously observed associations between

changes in cerebrovascular flow and certain modifiable lifestyle factors, namely fish

consumption, smoking history, and physical exercise. By developing further models that

account for changes in vascular compliance and microvascular endothelial dysfunction, a

strategy is developed for patient-specific modelling of the effect of modifiable lifestyle factors

in concert with natural ageing. This will allow setting up patient-specific what-if scenarios in

the future, for example investigating the subject-specific long-term effects of smoking cessation

at middle-age versus later in life, and the consequent risk of converting from mild cognitive

impairment to Alzheimer’s disease.

1. Introduction

The objective of this deliverable is to investigate the chronic influence of modifiable lifestyle

factors (MLF) and systemic factors in the biophysical, metabolic, biochemical and

biomechanical determinants of Alzheimer’s disease. The connection between late-onset

Alzheimer’s disease (AD) and MLFs was previously reviewed by (Di Marco et al., 2014). No

strong connection between any single MLF and the occurrence risk of late-onset AD was

supported by the clinical evidence reviewed, indicating that a combined modulatory effect of

several MLFs over the course of several years may be involved in the complex pathogenesis of

AD. Many of the reported risk factors for AD (and, indeed, vascular dementia) are the same as

those for cardiovascular disease; hypertension, diabetes, lack of exercise, alcohol intake,

smoking, previous strokes, atrial fibrillation, ApoE ε4, lipids, and dietary choices (Purnell, Gao,

Callahan, & Hendrie, 2009). One way that MLFs may influence the risk of developing mild

cognitive impairment (MCI) and later conversion to AD is then through the vascular pathway.

The vascular hypothesis of AD states that chronic cerebral hypoperfusion (reviewed by (Di

Marco et al., 2015)) plays an important role in the early stages of AD. Changes in cerebral

perfusion manifest already in the state of MCI even before conversion to AD. Imaging of

chronic hypoperfusion has been used to predict the conversion from MCI to AD by SPECT

imaging (Borroni et al., 2005) and to detect functional changes in the prodromal stages of AD

by ASL-MRI (Binnewijzend et al., 2013). However, the connection between these changes and

MLFs is not clear. We therefore set out: (i) to investigate how cerebral perfusion differences in

MCI subjects and cognitively normal controls is associated with their MLFs, (ii) to identify

possible determinants of cerebral blood flow (CBF) patterns that are associated with MCI

status, and (iii) to design a strategy for modelling changes to CBF during ageing.

This deliverable contains a preliminary analysis of the cross-sectional case-control study carried

out at the IRCCS San Camillo Hospital in Venice, Italy (Lido Study), in which lifestyle

information is collected together with circadian physiological data from a cohort of cognitively

impaired subjects and age-matched controls. Section 2 summarises briefly the data collection

procedure used to obtain multi-modal data including clinical ultrasound measurements,

ambulatory blood pressure measurements, and self-reported lifestyle questionnaires.

Section 3 describes the data-driven modelling approach used to derive cerebral blood

predictions for each study subject, and compares the model-predicted 24-hour CBF to the flow

measured with clinical ultrasound at the level of the carotids. Using the subject-specific BP

measurements, a data-driven, personalised modelling pipeline is used to predict 24-hour

variability of CBF. A set of CBF parameters are then defined to investigate the modulatory

effect of lifestyle factors that may take place through cerebrovascular pathways.

In Section 4 we analyse the data collected in the Lido Study and look for associations between

diagnosed MCI status and MLFs. Based on a previously conducted literature review on MLFs

in dementia (Di Marco et al., 2014), different MLFs have been identified as being either

protective or risk factors for AD and/or all-cause-dementia, but no definite link between any

single MLF and risk of conversion from MCI-to-AD has been demonstrated so far.

In Section 5 we investigate associations between MCI status and parameters of CBF, such as

total perfusion, arterial pulse pressure, and the pulsatility index. These associations are stratified

for known effects of age and sex. The objective is to identify mechanistic pathways that can be

used to link modifications in CBF profiles to specific MLFs.

Section 6 investigates correlations between CBF and MLFs. We attempt to find possible

mechanistic pathways through which MLFs may influence the risk of conversion from MCI-

to-AD. This could help determine a strategy for augmenting the mechanistic models of CBF


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developed within WP5 with MLF-influenced effects that could be used to make patient-specific

predictions about the long-term changes in a subject’s CBF profile.

Finally, Section 7 concludes the findings of the study and sets out a plan for incorporating MLFs

into existing mechanistic models through their effects on vascular compliance and

microvascular endothelial function.


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2. Clinical and ambulatory data collection

The cohort for this study was recruited at the IRCCS San Camillo Hospital in Venice, Italy and

included a total of 104 subjects (52 cognitively normal controls, 52 with diagnosed MCI). The

study was approved by the joint ethics committee of the Health Authority Venice 12 and the

IRCCS Fondazione Ospedale San Camillo and all subjects gave informed consent to participate

in the study. Each subject underwent a series of examinations over the course of five days.

During one of these days, the subject wore a portable Holter device (Cardioline walk200b,

Cardioline S.p.A.) that recorded 24-hour ambulatory measurements of systolic/diastolic blood

pressure and heart rate (SBP/DBP/HR) at 15 minute intervals. For the duration of this period,

the subjects were told to act as they would normally in their everyday lives. Data for N=79

subjects were fully processed and available for analysis at the time of the writing of this

deliverable, with a further 25 cases undergoing processing after data collection.

The clinical part of the examination included carotid ultrasound (Siemens Acuson X300PE,

Siemens Medical Solutions) and cardiac ultrasound (Siemens Acuson SC2000, Siemens

Medical Solutions) examinations, which were used to measure internal carotid artery (ICA)

flow velocities and cardiac left ventricle volumetric indices (ejection fraction and end-diastolic

volume). The ICA velocity measurements were recorded for both left and right carotids and

their temporally averaged waveforms were digitised from the DICOM images. The recorded

ICA flow velocities established the baseline flow for the purposes of cerebral autoregulation

modelling. They were also used for validating the model-predicted CBF quantities.

Activity monitoring was performed by a wrist-portable actigraph device (MotionWatch 8,

CamNTech Ltd), which recorded both raw activity counts and ambient light measurements.

Based on the raw activity counts, a logistic regression model (Lötjönen et al., 2003) was used

to identify periods of sleep/wakefulness and measure the total amount of sleep. For periods of

wakefulness, thresholding of activity counts was applied (Freedson, Melanson, & Sirard, 1998)

to identify periods of sedentariness and light/moderate/vigorous exercise. The total numbers of

hours in each activity type were averaged over the 5-day measurement period to obtain daily

averages that were taken to be representative of the subjects’ normal daily activities.

Each subject underwent a series of neurocognitive tests, which were used to determine their

MCI status and level of cognitive decline. The 30-point Mini Mental State Examination

(MMSE) score was used as a gold-standard measure of neurocognitive performance. The

subjects also filled a lifestyle questionnaire previously used in the CAIDE study (Ngandu et al.,

2006). Together with the sleep and physical activity –related factors obtained by actigraph

measurements, a total of 56 modifiable lifestyle –related factors were recorded for each subject

and transformed from categorical variables to continuous variables when necessary.

The entire data collection, modelling, and analyses pathway used in this deliverable is

graphically illustrated in Figure 1.


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Figure 1 Summary of measurements collected in the Lido Study. Vascular parameters are used to drive

the lumped parameter circulation model and to predict 24-hour CBF, which then enters the statistical

analyses. Lifestyle information and actigraph measurements enter directly in the statistical analyses.


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3. Mathematical modelling of cerebrovascular flow

While the carotid ultrasound examination provided a view of instantaneous blood flow into the

brain, it could not measure the circadian variability of CBF without constantly disrupting the

subject’s normal daily activities. We therefore constructed a computational model that took the

24-hour SBP/DBP/HR measurements as input data, and generated continuous waveforms of

blood pressure (BP), cardiac output (CO), and CBF as outputs.

A lumped parameter circulation model (LPCM) was developed based on a model (Ursino,

1998) and used to simulate circulatory flow, systemic BP, and CO. The model contained a four-

chamber model of the heart, plus separate compartments for the systemic arteries and veins,

pulmonary arteries and veins, and splanchnic arteries/veins. A carotid baroreflex controller

varied the heart rate and contractility in response to observed BP changes in the systemic arterial

compartment. The LPCM had a total of 26 equations and 84 model parameters, the reference

values of which in (Ursino, 1998) were provided for young, healthy adults and did not conform

to our study population consisting of middle-aged to very old subjects. Therefore, the LPCM

parameters needed to be personalised for each subject based on their measured cardiovascular

profiles.

In the first stage of model personalisation, we introduced a set of five parameters that were used

for model personalisation. These parameters were selected following a literature review of age,

gender, and lifestyle-related changes in the systemic arteries (McDonnell et al., 2013; Tanaka

et al., 2000; van Empel, Kaye, & Borlaug, 2014). These parameters were: (i) total blood volume

(𝑉𝑡𝑜𝑡), (ii) systemic arterial compliance (𝐶𝑠𝑎), (iii) systemic arterial resistance (𝑅𝑠𝑎), (iv)

systolic interval length (𝐿𝑠𝑖), and (v) cardiac chamber volume (𝑉𝑐𝑐). The parameter values used

in the fitting ranged between 80-120% (for 𝑉𝑡𝑜𝑡) and 60-140% (for others) of the reference

values (Ursino, 1998). From the simulation we extracted five outputs that were matched with

24-hour ambulatory measurements of diastolic blood pressure (𝑃𝐷), systolic blood pressure

(𝑃𝑆), LV ejection fraction (𝐸𝑓), and LV end-diastolic volume (𝑉𝑙𝑣,𝑒𝑑). The LPCM was run for

50 s of simulation time until a periodic steady-state was reached, and CBF values were recorded

from the last heartbeat.

In order to accelerate the parameter fitting of the LPCM, we constructed a surrogate model for

its input-output response. This surrogate model used as predictors the five model parameters,

𝒙 = (𝑉𝑡𝑜𝑡, 𝐶𝑠𝑎, 𝑅𝑠𝑎 , 𝐿𝑠𝑖 , 𝑉𝑐𝑐),

as linear and quadratic factors, and explained the four observed variables

𝒚 = (𝑃𝐷 , 𝑃𝑆, 𝐸𝑓 , 𝑉𝑙𝑣,𝑒𝑑).

The surrogate models therefore had the form:

𝑃�̃�(𝒙) = ∑ 𝛽𝑖,1𝐷 𝑥𝑖 + 𝛽𝑖,2

𝐷 𝑥𝑖2, 𝑃�̃�(𝒙) = ∑ 𝛽𝑖,1

𝑆 𝑥𝑖 + 𝛽𝑖,2𝑆 𝑥𝑖

2,

5

𝑖=1

5

𝑖=1

𝐸�̃�(𝒙) = ∑ 𝛽𝑖,1𝐸 𝑥𝑖 + 𝛽𝑖,2

𝐸 𝑥𝑖2, 𝑉𝑙𝑣,𝑒�̃�(𝒙) = ∑ 𝛽𝑖,1

𝑉 𝑥𝑖 + 𝛽𝑖,2𝑉 𝑥𝑖

2.

5

𝑖=1

5

𝑖=1

The response surfaces for each of the dependant variables were built by sampling the model

parameter space using a central composite design with a total number of 27 output evaluations


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(the centre point was included 9 times to reduce bias), followed by multivariate regression to

identify the coefficients 𝛽𝑖,∙𝐷, 𝛽𝑖,∙

𝑆 , 𝛽𝑖,∙𝐸 , 𝛽𝑖,∙

𝑉 .

Once the surrogate model was generated, it was used to infer the values of the model parameters

x through a nonlinear fitting procedure. As the 24-hour Holter measurements included HR

values, we used them directly as an input by running the LPCM in the open-loop configuration.

In this configuration the vagal parasympathetic regulation of HR was disabled in the LPCM

and the value of HR was directly prescribed. For each time point tk during the 24-hour period,

we took the values of 𝑃𝐷𝑘 = 𝑃𝐷(𝑡𝑘) and 𝑃𝑆

𝑘 = 𝑃𝑆(𝑡𝑘), and solved a nonlinear multi-objective

optimisation problem to find the vector xk, such that

min𝑥𝑘

{𝑤1 |𝑃𝐷

𝑘 − 𝑃�̃�(𝑥𝑘)

𝑃𝐷∗ |

2

+ 𝑤2 |𝑃𝑆

𝑘 − 𝑃�̃�(𝑥𝑘)

𝑃𝑆∗ |

2

+ 𝑤3 |𝐸𝑓

𝑘 − 𝐸�̃�(𝑥𝑘)

𝐸𝑓∗ |

2

+ 𝑤4 |𝑉𝑙𝑣,𝑒𝑑

𝑘 − 𝑉𝑙𝑣,𝑒�̃�(𝑥𝑘)

𝑉𝑙𝑣,𝑒𝑑∗ |

2

+ 𝜈 ∑|𝑥𝑖𝑘 − 1|

2𝑖

𝑖=1

} , min𝑥𝑘

{𝑤1 |𝑃𝐷

𝑘 − 𝑃�̃�(𝑥𝑘)

𝑃𝐷∗ |

2

+ 𝑤2 |𝑃𝑆

𝑘 − 𝑃�̃�(𝑥𝑘)

𝑃𝑆∗ |

2

+ 𝑤3 |𝐸𝑓

𝑘 − 𝐸�̃�(𝑥𝑘)

𝐸𝑓∗ |

2

+ 𝑤4 |𝑉𝑙𝑣,𝑒𝑑

𝑘 − 𝑉𝑙𝑣,𝑒�̃�(𝑥𝑘)

𝑉𝑙𝑣,𝑒𝑑∗ |

2

+ 𝜈 ∑|𝑥𝑖𝑘 − 1|

2𝑖

𝑖=1

}

where the reference values 𝑃𝐷∗ = 60 mmHg, 𝑃𝑆

∗ = 120 mmHg, 𝐸𝑓∗ = 50%, and 𝑉𝑙𝑣,𝑒𝑑

∗ = 120 ml

were used to scale the quantities, and the values 𝑤1 = 𝑤2 = 10 and 𝑤3 = 𝑤4 = 1 were used to

give more weight to fitting the SBP/DBP values over the other two quantities. A penalisation

term with ν = 1 was added to avoid parameter overfitting. Cardiac ultrasound images taken at

the clinic were used to estimate 𝐸𝑓 and 𝑉𝑙𝑣,𝑒𝑑 for each subject. Consistent underestimation bias

in the 𝐸𝑓 and 𝑉𝑙𝑣,𝑒𝑑 was corrected for (Malm, Frigstad, Sagberg, Larsson, & Skjaerpe, 2004).

Once the optimal LPCM parameter vector xk for each measurement time point tk during the 24-

hour period was found using the surrogate model, the same parameter values were used to run

the full-order LPCM for 50 s of simulation time, after which the waveforms for systemic BP

and CO were then extracted from the last heartbeat.

The outputs of the LPCM (CO/BP in the systemic circulation) then acted as inputs to a specific

model for predicting cerebral artery flow under the effect of the cerebral autoregulation system.

The cerebral autoregulation model (CAM) was based on a two-component viscoelastic model

(Mader, Olufsen, & Mahdi, 2014) that was used in this work to derive middle cerebral artery

(MCA) flow velocity waveforms based on arterial pressure waveforms simulated during the

24-hour period. The CAM has been previously calibrated based on orthostatic stress test both

on young adult and elderly subjects. It takes as input arterial blood pressure 𝑃𝑠𝑎 waveforms, and

outputs blood flow velocity 𝑣MCA waveforms in the MCA, which act as a surrogate for

quantifying the amount of cerebral perfusion in our study.

Since the CAM is a feedback control model that attempts to maintain minimum flow velocity

across a range of cerebral perfusion pressure, we had to define the baseline (end-diastolic) flow

𝑣𝑏𝑎𝑠 that the controller attempted to maintain. In order to find the value of this baseline flow,

we relied on clinical ultrasound measurements of ICA flow velocity for each subject. These

needed to be translated into MCA flow velocities (the controlled quantity in the CAM).

Experimental evidence suggests that the MCA flow velocity has a linear relationship to the ICA

flow velocity, where the proportionality constant increases significantly with age in women but

not significantly in men (Krejza et al., 2005). This justified writing a simple formula for the

MCA flow velocity 𝑣MCA as a function of the ICA flow velocity 𝑣ICA:

𝑣MCA = 𝛾𝑣ICA,


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where 𝛾 = 2 (for men) and 𝛾 = 1.67 + 0.005 × AGE (for women). The baseline CBF velocity

𝑣ICA were taken as the end-diastolic (minimum) vales of the carotid ICA waveforms during the

cardiac cycle. The resting state baseline flow velocity was expected to vary only moderately

across different operating conditions (HR and BP) due to the effects of the cerebral

autoregulation system. Once the CAM parameters were set, the model was run for 10 heartbeats

using as input each BP waveform extracted from the LPCM. Finally, the MCA flow velocity

waveform was extracted from the last heartbeat. Together with the previously described outputs

from the LPCM, the output of the joint model comprised CO, BP, and CBF waveforms for each

of the measurement periods during the 24-hour period.

Since CBF correlates with brain size (Vernooij et al., 2008), a more informative indicator of

cerebral hypoperfusion can be obtained by dividing total CBF with total brain volume. For this

purpose, T1-weighted MR images obtained from a subset of the study cohort were used to

segment the brain and estimate the total brain volume. These volume estimates 𝑉𝑡𝑜𝑡were

derived from the GIF parcellation of these images (Cardoso et al., 2015), which was available

for N=58 subjects at the time of writing, and were used to divide the combined CBF estimate

QICA,tot and to obtain an estimate for the total brain perfusion:

PERFtot =QICA,tot

𝑉𝑡𝑜𝑡.

The full model personalisation pipeline is illustrated in Figure 2.

Figure 2 Model personalisation pipeline for prediction of 24-hour cerebral blood flow


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4. Lifestyle-factors and mild cognitive impairment

To explore possible associations between diagnosed MCI status and MLFs observed in the Lido

Study cohort, a lifestyle questionnaire was used to track several MLFs related to recreational

activities, physical exercise, diet, and social status. A subset of 56 of these factors were

transformed from categorical variables to continuous variables for statistical analysis. Summary

statistics for the chosen subset of lifestyle factors in univariate analysis of groupwise differences

(MCI vs. controls) are presented in Table 1 for N=79 subjects.

Finding: No lifestyle factor reached significance in univariate analysis of MCI vs. normals

after correcting for multiple comparisons (Bonferroni). Only (i) shorter daily exposure to

sunlight, (ii) shorter duration of daily light exercise, and (iii) lower fish consumption were

significantly (p<0.05) associated with MCI before correcting for multiple comparisons.

The strongest association with MCI found in univariate analysis was decreased duration of

direct exposure to sunlight. The effect of sunlight exposure in conversion to AD / all-case-

dementia has not been directly studied in longitudinal control studies. Nevertheless, increased

sunlight exposure may increase the dietary intake of vitamin-D, which has been indicated as

being a protective factor against AD, especially in women (Annweiler et al., 2012). However,

it is unlikely that any such link would act through changes in the cerebrovascular system, and

therefore the effects are not easily included in the mechanistic models considered here.

The second strongest association with MCI was for decreased duration of daily light exercise,

both self-reported and measured by actigraphy. Physical inactivity has been consistently

indicated as a risk factor for late-onset AD, especially in men (Bruijn et al., 2013; Paillard-

Borg, Fratiglioni, Winblad, & Wang, 2009; Ravaglia et al., 2008; Taaffe et al., 2008; Tiukinhoy

& Rochester, 2006). Physical exercise can affect cardiovascular health in many ways, some of

which enter through parameters that are explicitly included in the LPCM, such as vascular

compliance and cardiac ejection fraction. Cerebrovascular changes induced by physical

exercise are therefore perhaps the most suitable vectors through which long-term effects of

MLFs may be included in mechanistic models.

The link between fish consumption and dementia has been studied previously by (Devore et al.,

2009; Kalmijn et al., 1997; Larrieu, Letenneur, Helmer, Dartigues, & Barberger-Gateau, 2003;

Morris M, Evans DA, Bienias JL, & et al, 2003), who have indicated a possible reduction in

AD incidence risk for subjects who consumed large amounts of fish weekly. Based on a

random-effects meta-analysis of these studies, a pooled relative risk for AD can be estimated

at RR=0.6 (95%-CI: 0.51-0.71), see Figure 3, when comparing subjects who ate high amounts

of fish1 versus subjects who ate no fish. However, the reported effect size decreased in later

studies, indicating the possibility that improvements in study methodology have eliminated a

spurious finding in earlier studies with limited power. It is also possible that effects of dietary

choices in our study were confounded by the cohort being drawn from a small population living

on an island off the coast of Venice. This may have resulted in a more homogeneous diet being

observed across the study cohort than would be observed in a mainland population. The

observed differences due to fish consumption may therefore be larger in populations where

adherence to a Mediterranean diet is not the societal norm, as it likely was in our study cohort.

In conclusion, even for the few MLFs that reached significance in univariate analysis there exist

few simple mechanistic links that could be introduced in existing models to explain how these

MLFs act to directly moderate the risk of conversion to late onset AD. Rather than attempting

1 The definition of high fish consumption varied between studies – the definition used by each study was

adopted separately and a random-effects model was used to account for between-studies variability.


- 15 -

to directly model the link between MCI status and MLFs, our strategy will therefore be to

include the effect of MLFs through changes in CBF, which shall be addressed next.

Figure 3 Meta-analysis for the relative risk of AD incidence when comparing subjects with high fish

consumption to subjects who ate no fish. Variance computed with random-effects model of

DerSimonian and Laird. Estimated pooled effect size was RR=0.60 (95%-CI: 0.51-0.71).

Table 1: Groupwise means (standard deviations) between normal controls and MCI cases for the

lifestyle factors considered in the Lido Study. Statistical significance in univariate analysis computed

by the Kruskal-Wallis one-way ANOVA -test.

Lifestyle factor Controls

(N=49)

MCI (N=31) p-value

Direct sunlight exposure, actigraphy (min/day) 30.46(39.25) 14.92(19.15) 0.036

Light exercise, actigraphy (min/day) 32.40(21.18) 21.97(16.97) 0.036

Fish as main course (servings/week) 1.94(1.04) 1.43(0.99) 0.040

Organisational activities, self-reported

(times/week)

1.23(2.21) 0.71(1.91) 0.054

Leisure time exercise, self-reported (min/session) 30.46(26.27) 18.39(23.31) 0.056

Vegetables (portions/day) 2.27(1.48) 1.73(1.31) 0.075

Mixed grain bread (slices/day) 0.53(1.31) 0.13(0.50) 0.092

Leisure time exercise, self-reported (times/week) 1.79(1.69) 1.24(1.60) 0.109

Patisseries/ice cream/puddings/chocolate

(portions/day)

0.53(0.54) 0.73(0.66)

0.136

Milk, 2-3% fat (glasses/day) 0.39(0.73) 0.26(0.86) 0.140

Fruits and berries (portions/day) 2.76(1.62) 2.25(1.53) 0.167

Sleep, actigraphy (hours/day) 8.40(1.31) 8.77(1.35) 0.173

Sugar (teaspoons/day) 0.89(0.73) 1.08(0.68) 0.195

Daily leisure time, self-reported (min/session) 38.42(14.60) 43.47(12.26) 0.198

Cigarettes smoked (per day) 8.27(9.76) 4.72(5.00) 0.208

Muesli (dl/day) 0.00(0.00) 0.03(0.18) 0.209

Porridge (dl/day) 0.00(0.00) 0.10(0.54) 0.209


- 16 -

Water consumption (glasses/day) 4.01(2.82) 4.50(2.43) 0.220

Wine (glasses/week) 5.74(7.55) 4.19(5.33) 0.236

Sweet bread (slices/day) 0.10(0.59) 0.00(0.00) 0.258

Sugar-free juice (glasses/week) 0.08(0.45) 0.00(0.00) 0.258

Vegetarian as main course (servings/week) 7.18(4.35) 6.13(3.95) 0.263

Sedentary awake, actigraphy (hours/day) 6.88(1.37) 7.38(1.45) 0.264

Alcohol use (times/week) 4.03(3.10) 3.50(3.27) 0.278

Spirits (restaurant measures/week) 0.31(0.82) 0.16(0.58) 0.294

Meals or snacks per day 3.09(1.15) 3.35(1.09) 0.334

White bread (slices/day) 2.00(2.32) 2.42(2.35) 0.340

Eggs consumed (qty/week) 1.76(1.45) 1.45(0.96) 0.419

Sweetened juice (glasses/week) 0.29(2.00) 0.00(0.00) 0.426

Light cream (dl/day) 0.02(0.14) 0.00(0.00) 0.426

Milk, <1% fat (dl/day) 0.37(1.13) 0.39(0.76) 0.490

Smoking regularly (years) 24.88(12.54) 24.15(15.92) 0.508

Functional exercise summer, self-reported

(min/session)

41.14(18.96) 37.79(21.15) 0.537

Drunk from alcohol (times/year) 7.53(52.13) 1.23(4.75) 0.552

Meat as main course (servings/week) 1.73(1.62) 1.91(1.62) 0.554

Other stimulating activities, self-reported

(times/week)

3.66(2.73) 3.44(3.00) 0.568

Rye bread (slices/day) 0.35(0.88) 0.48(1.52) 0.606

Ever smoked regularly (yes/no) 0.46(0.50) 0.52(0.51) 0.622

Sugary soft drinks (glasses/week) 0.51(1.75) 0.97(2.99) 0.672

Sausage as main course (servings/week) 0.09(0.28) 0.13(0.34) 0.685

Coffee (cups/week) 8.84(7.19) 9.45(8.33) 0.788

Strong cider (bottles/week) 0.54(3.01) 0.26(0.82) 0.801

Sugar-free soft drinks (glasses/week) 0.10(0.51) 0.03(0.18) 0.822

Currently smoking (yes/no) 0.12(0.33) 0.13(0.34) 0.931

Functional exercise winter, self-reported

(min/session)

35.82(21.09) 36.37(21.91) 0.936

Ever smoked (yes/no) 0.43(0.50) 0.42(0.50) 0.936

Milk, 1-2% fat (dl/day) 1.00(2.28) 0.94(1.59) 0.944

Tea (cups/week) 3.27(4.98) 2.42(3.51) 0.949

Fruit juice (glasses/week) 0.76(2.46) 1.71(6.44) 0.954

Beer (bottles/week) 0.45(1.04) 0.48(1.09) 0.985

Poultry as main course (servings/week) 1.50(1.09) 1.51(1.21) 0.992

Reading and writing, self-reported (times/week) 5.79(2.30) 5.90(2.13) 1.000


- 17 -

5. Model-predicted cerebral blood flow and MCI status

The objective of this Section is to investigate possible indicators of CBF that can be associated

with MCI status. Chronic cerebral hypoperfusion has been identified as a possible initiator of

neurodegenerative processes leading to late-onset Alzheimer’s disease (AD), and may trigger

a vicious cascade of events in the microvascular endothelial layer (Di Marco et al., 2015).

Whether prolonged reduction in CBF causes neuronal damage, or whether neuronal damage

and resulting lower metabolic demand causes a reduction in CBF is still debated (Zonneveld et

al., 2015). Increase of the regional oxygen extraction fraction in the parietal cortex of pre-

clinical AD patients has been put forward as evidence that chronic hypoperfusion plays a causal

role in cognitive decline and is not simply a consequence of reduced metabolic demand (Love

& Miners, 2015).

What is known is that total CBF is strongly correlated with brain volume, whereas total brain

perfusion (total CBF/brain volume) is known to remain relatively constant in normal aging and

to be higher for women than for men (Vernooij et al., 2008). Besides total CBF and total

perfusion, other CBF parameters have also been suggested as biomarkers for neurocognitive

decline and conversion from MCI to AD. These include arterial pulse pressure (Nation, D.A.,

Edmonds, E.C., Bangen, K.J., & et al, 2015) defined as

APP = 𝑃𝑆 − 𝑃𝐷 ,

flow pulsatility index (Roher et al., 2011) defined as

PI =𝑣max − 𝑣min

𝑣mean ,

and visit-to-visit systolic blood pressure variability (Lattanzi, Luzzi, Provinciali, & Silvestrini,

2014), which in this study was replaced with (an estimator for) the coefficient of variation for

SBP (CoV) over the 24-hour model-predicted SBP-values 𝑃𝑆𝑖:

CoVSBP =√∑ (𝑃𝑆

𝑖 −1𝑀

∑ 𝑃𝑆𝑘

𝑘 )𝑖

2

∑ 𝑃𝑆𝑘

𝑘

.

Using the patient-specific modelling pipeline described in Section 3, a 24-hour circadian

CBF/BP profile was generated for N=55 subjects. For the remaining subjects the pipeline could

not be run due to either missing or low-quality carotid ultrasound imaging, failed Holter BP

extraction, or missing cardiac ultrasound imaging. Out of these, GIF parcellation of brain partial

volumes was available for N=44 subjects, who were used for the analysis of this section in order

to have patient-specific measurements of total brain perfusion. A correlation plot between the

model-predicted CBF/PI/APP values and clinically measured quantities is presented in Figure

4. It demonstrates that, in general, the correlation between ultrasound measured ICA flow

velocity and model prediction was very strong, whereas slightly weaker correlation was

observed for APP and PI. The APP values recorded were within the variability bounds predicted

by the model and followed the same trend as the mean. For the PI we observed a consistent

over-estimation of the flow pulsatility by the CAM.

Once the model-predicted CBF parameters were deemed to correlate reasonably well with the

clinical measurements, we explored their association with MCI status. A groupwise comparison

(MCI vs. controls) of these CBF parameters is shown in Table 2 and graphically illustrated in

Figure 5. It was found that reduced total CBF, reduced cerebral perfusion, increased arterial

pulse pressure, and increased pulsatility index were all significantly associated (p < 0.05) with

MCI status. Only the SBP CoV was found not significant in univariate analysis. While


- 18 -

associations between changes in CBF and MCI status do not necessarily predict that MCI

subjects with reduced CBF will convert to AD later in life, these results reproduce some of the

earlier findings regarding the general trend of cerebral perfusion changes related to AD.

To eliminate the possibility that correlations between age/sex and CBF might confound the

results, separate groupwise comparisons were made between CBF in men vs. women (Table

3), and CBF in young old vs. very old subjects (Table 4), defined around a cut-off of 70-years-

old. It can be observed that, within the study cohort, the total brain perfusion was higher in

women than men (p = 0.099), and arterial pulse pressure was higher in the very old -group (p =

0.080), though neither measure reached statistical significance. Therefore, we further

investigated whether a stratification of the total perfusion by sex and APP by age, respectively,

changed the results of the MCI vs. normal groupwise comparison. These results are given in

Table 5 and Table 6.

Finding: Cerebrovascular parameters predicted by the model such as lower CBF, lower brain

perfusion, higher arterial pulse pressure, and higher pulsatility index were significantly

associated (p < 0.05) with MCI status in the Lido Study cohort.

The association of lower brain perfusion and MCI was significant (p = 0.023) in women but not

in men (p = 0.343) after stratifying between the sexes.

The association of higher APP and MCI was significant (p = 0.005) in the very old group (age

> 70) but not in young old (p = 1.000) after stratifying between young old vs. very old subjects.

The recent study of (Nation, D.A. et al., 2015) also found an association between elevated APP

and CSF biomarkers for AD, such as P-tau-positive and amyloid-β42. These associations were

stronger in very old subjects (defined as 80+ years old) when compared to young old subjects,

mirroring the findings here. The mechanistic link between elevated APP and AD is still unclear.

One vascular hypothesis of AD states that elevated APP (but not elevated SBP on its own) over

time induces microbleeds in the cerebral vasculature, and that this is one of the mechanisms

that initiates the cascade of AD-related pathologies (Stone, Johnstone, Mitrofanis, & O’Rourke,

2015). A reduction in vascular compliance occurring at a later age would explain why sporadic

AD only occurs at a sufficiently advanced age. While interesting, this could also mean that APP

may only be a suitable biomarker for AD risk in very old subjects, and consequently may not

have much diagnostic utility on middle-aged MCI patients.

The association of MCI and diminished total brain perfusion in women but not in men suggests

that differences in cerebral perfusion between the sexes should be taken into account when

evaluating cerebral perfusion as a vascular risk factor for the development of AD. There exist

conflicting views regarding gender differences in dementia and the higher prevalence of AD in

women (Hebert, Scherr, McCann, Beckett, & Evans, 2001). Some of the effect may be due to

the longer life expectancy of women. It is also known that hypoperfusion in AD is localised in

certain regions, and consequently total perfusion differences may not necessarily be informative

as biomarkers. This study should therefore be further extended to looking also at perfusion-

MRI maps of chosen subjects with concurrent MCI and reduced cerebral perfusion. This could

be used to investigate, whether an association between diminished total perfusion and regional

hypoperfusion can be found in the regions most typically affected by AD.

Table 2 Groupwise means (standard deviations) between normal controls and MCI cases for the

cerebral blood flow parameters modelled in the Lido Study. Statistical significance in univariate

analysis computed by the Kruskal-Wallis one-way ANOVA –test.

CBF parameter Controls (N=32) MCI (N=12) p-value

Total CBF (ml/min) 917.5(265.6) 725.2(288.6) 0.027*

Total brain perfusion (ml/min/100ml tissue) 64.1(17.7) 51.6(18.7) 0.016*


- 19 -

Systolic BP variability (%) 8.52(2.47) 7.63(1.83) 0.316

Arterial pulse pressure (mmHg) 54.14(7.19) 60.07(7.09) 0.014*

Pulsatility index, MCA 1.31(0.32) 1.64(0.49) 0.029*

Figure 4 Correlation plots between measured ICA-L/ICA-R flow velocity (top), pulsatility index

(middle), and arterial pulse pressure (bottom). Intervals indicate 24-h variability of predictions.


- 20 -

Figure 5 Model-predicted CBF parameters, groupwise comparison between controls (N=32) and MCI

cases (N=12). All quantities except systolic blood pressure variability were significant at p<0.05.

Table 3 Groupwise means (standard deviations) between men and women for the CBF parameters

modelled. Significance in univariate analysis computed by the Kruskal-Wallis one-way ANOVA –test.

CBF parameter Men (N=17) Women (N=27) p-value

Total CBF (ml/min) 835.0(295.3) 883.9(277.8) 0.433

Total brain perfusion (ml/min/100ml tissue) 55.3(18.4) 64.1(18.3) 0.099


Arterial pulse pressure (mmHg) 53.95(9.66) 56.90(5.82) 0.379

Pulsatility index, MCA 1.43(0.48) 1.38(0.34) 0.971

Table 4 Groupwise means (standard deviations) between young old/very old subjects for the CBF

parameters modelled. Significance computed by the Kruskal-Wallis one-way ANOVA –test.

CBF parameter Young old

(N=21)

Very old

(N=22)

p-value

Total CBF (ml/min) 878.7(292.0) 845.6(283.5) 0.842




Pulsatility index, MCA 1.34(0.35) 1.47(0.43) 0.391

Table 5 Groupwise means (standard deviations) between controls and MCIs, stratification between

men/women for total perfusion. Significance computed by Kruskal-Wallis one-way ANOVA –test.

Men Controls (N=12) MCI (N=5) p-value


Women Controls (N=20) MCI (N=7) p-value

Total brain perfusion (ml/min/100ml tissue) 68.6(16.1) 51.2(19.4) 0.023*

Table 6 Groupwise means (standard deviations) between controls and MCIs, stratification between

young old/very old for APP. Significance computed by Kruskal-Wallis one-way ANOVA –test.

Young old Controls (N=18) MCI (N=3) p-value


Very old Controls (N=14) MCI (N=9) p-value

Arterial pulse pressure (mmHg) 54.7(6.8) 62.3(5.3) 0.005*


- 21 -

6. Lifestyle factors and model predicted CBF

The objective of this section is to investigate the link between MLFs and CBF parameters

associated with MCI status in Section 5. A subset of 56 MLFs were analysed. Pearson’s

correlation coefficients were computed between CBF parameters and each MLF. Total

perfusion was studied separately for men/women, and for APP a stratification between young

old and very old subjects was performed. Correlations between MLFs and SBP CoV were not

investigated as no association with MCI status for this parameter was found in Section 5.

Total CBF is reduced in AD patients (Zonneveld et al., 2015), but the causality of this

connection is debated. No particularly strong correlations were found in this study between any

of the reported MLFs and total CBF, see

Table 7. As discussed previously, total perfusion is potentially a more informative CBF

parameter than total CBF. The best five correlations between reported MLFs and total perfusion

are shown in Table 8 for men only and in Table 9 for women only. It was found that increased

fish consumption was correlated with higher brain perfusion, but only in women. For men no

statistically significant correlations were found. This finding appears to provide a consistent

link between fish consumption, MCI status, and brain perfusion. It is possible that the study

population didn’t contain enough men to successfully identify the effect in that sub-population.

An association between elevated APP and MCI status was identified in Section 5, and offers a

possible mechanistic pathway to AD through the cerebrovascular system. The best five

correlations between MLFs and APP are shown in Table 10 for young old subjects, and in

Table 11 for very old subjects. A strong correlation was observed between long-term smoking

history and increased APP, but only in young old subjects. This is consistent with the known

decrease of arterial compliance in smokers (Mahmud & Feely, 2003). One hypothesis is that

smoking in middle-age may predispose a person to increased APP, which combined with

natural aging leads later in life to cerebral microvascular complications and late-onset AD.

However, the relative effects of past vs. current smoking remain unclear, and the subject’s past

smoking history should be incorporate more carefully in such analyses.

Increased PI has been associated with the clinical diagnosis of presumptive AD (Roher et al.,

2011). The best five correlations between MLFs and PI found in our study are shown in Table

12. Both increased regularity of getting drunk and increased sausage consumption were strongly

correlated with increased PI. It is unclear how these MLFs may act in a mechanistic way to

increase the arterial PI. Only one subject out of 79 reported excessive alcohol usage, so that the

results related may also be skewed by a single outlier. Taken together with the somewhat poor

correlation between observed and model-predicted PI reported in Section 5, these particular

results should be approached with caution.

Finding: Few direct correlations between individual MLFs and CBF parameters could be

identified. The most important ones were:

(i) Moderate correlation (ρ=0.46) was observed between higher total perfusion and fish

consumption, but only in women.

(ii) Strong correlation (ρ=0.68) was observed between elevated APP and smoking history,

but only in young old subjects.

(iii) Moderate correlation (ρ=0.48) was observed between elevated PI and regularity of

drunkenness.


- 22 -

(iv) Moderate correlation (ρ=0.43) was observed between elevated PI and sausage

consumption.

Table 7 Five strongest correlations between lifestyle factors and total cerebral blood flow

Total CBF vs. MLFs Correlation

coefficient ρ

95% confidence

interval

p-value

Sweet bread (slices/day) 0.32 [0.01, 0.58] 0.041*

Meat as main course (servings/week) 0.32 [0.01, 0.57] 0.046*

Strong cider (bottles/week) -0.29 [-0.55, 0.03] 0.072

White bread (slices/day) 0.27 [-0.04, 0.54] 0.086

Milk, <1% fat (dl/day) -0.24 [-0.51, 0.08] 0.135

Table 8 Five strongest correlations between lifestyle factors and total perfusion (in men only)

Total perfusion vs. MLFs (men) Correlation

coefficient ρ

95% confidence

interval

p-value

Poultry as main course (servings/week) -0.51 [-0.82, 0.03] 0.064

Fish as main course (servings/week) -0.48 [-0.81, 0.07] 0.083

Rye bread (slices/day) 0.45 [-0.11, 0.79] 0.106

Meals or snacks per day 0.43 [-0.12, 0.78] 0.120

White bread (slices/day) 0.43 [-0.13, 0.78] 0.121

Table 9 Five strongest correlations between lifestyle factors and total perfusion (in women only)

Total perfusion vs. MLFs (women) Correlation

coefficient ρ

95% confidence

interval

p-value

Fish as main course (servings/week) 0.46 [0.09, 0.72] 0.017*

Ever smoked regularly (yes/no) 0.39 [0.00, 0.67] 0.049*

Sweet bread (slices/day) 0.37 [-0.02, 0.66] 0.060

Cigarettes smoked (per day) -0.36 [-0.66, 0.03] 0.071

Meat as main course (servings/week) 0.34 [-0.05, 0.64] 0.089

Table 10 Five strongest correlations between lifestyle factors and arterial pulse pressure (in young old

only)

Arterial pulse pressure vs. MLFs (young old) Correlation

coefficient ρ

95% confidence

interval

p-value

Smoking regularly (years) 0.68 [0.32, 0.87] 0.002**

Light exercise, actigraphy (min/day) 0.48 [0.01, 0.77] 0.046*

Cigarettes smoked (per day) -0.43 [-0.75, 0.05] 0.077

Fruit juice (glasses/week) 0.41 [-0.07, 0.74] 0.088

Light cream (dl/day) 0.41 [-0.07, 0.74] 0.088

Table 11 Five strongest correlations between lifestyle factors and arterial pulse pressure (in very old

only)

Arterial pulse pressure vs. MLFs (very old) Correlation

coefficient ρ

95% confidence

interval

p-value

Mixed grain bread (slices/day) -0.42 [-0.71, 0.01] 0.054

Light exercise, actigraphy (min/day) -0.40 [-0.70, 0.02] 0.063

Patisseries/ice cream/puddings/chocolate

(portions/day)

0.39 [-0.04, 0.70] 0.073

Strong cider (bottles/week) 0.38 [-0.05, 0.69] 0.081

Organisational activities, self-reported

(times/week)

0.36 [-0.07, 0.68] 0.098


- 23 -

Table 12 Five strongest correlations between lifestyle factors and arterial pulsatility index (MCA)

Pulsatility index vs. MLFs Correlation

coefficient ρ

95% confidence

interval

p-value

Drunk from alcohol (times/year) 0.48 [0.20, 0.69] 0.002**

Sausage as main course (servings/week) 0.43 [0.14, 0.66] 0.005**

Meat as main course (servings/week) -0.38 [-0.62, -0.08] 0.015*

Poultry as main course (servings/week) 0.26 [-0.05, 0.53] 0.102

Milk, <1% fat (dl/day) 0.26 [-0.06, 0.53] 0.109


- 24 -

7. Discussion and the path to modelling lifestyle effects

The modulatory effects of MLFs on late-onset AD are somewhat controversial. Despite

numerous studies looking at individual lifestyle factors claiming to identify potential

protective/risk factors of AD, the effect sizes reported vary from study to study and may also

depend on the choice of confounders used (Di Marco et al., 2014). It is therefore unlikely that

any single lifestyle factor in isolation has a strong enough modulatory effect to alter the

conversion risk from MCI to AD. Nevertheless, some consistent findings arose from our limited

analysis of the Lido Study data comprising of N=79 subjects (N=49 controls and N=31 MCIs).

We looked at the associations between MLFs and MCI status, the associations between CBF

parameters and MCI status, and the associations between CBF parameters and MLFs. The CBF

parameters that were associated with MCI status were: (i) reduced total CBF, (ii) reduced total

perfusion (in women only), (iii) increased APP (in very old subjects only), and (iv) increased

PI. All of these CBF parameters have been previously linked to increased risk of conversion

from MCI to AD in longitudinal studies. The links between MLFs and CBF parameters/MCI

status were less clear. Lower fish consumption was associated with MCI status and reduced

total perfusion (in women only). Past history of smoking (measured in years) was correlated

with increased APP in the young old. Frequency of drunkenness and sausage consumption were

correlated with increased PI. The implications of such correlations, however, need to be

analysed before any strategy for modelling their effect on brain health can be attempted.

Fish consumption may affect brain vascular health through the anti-inflammatory activity of

omega-3 fatty acids that protect against microvascular endothelial inflammation (Lourida et al.,

2013) and improve endothelial function (Goodfellow, Bellamy, Ramsey, Jones, & Lewis,

2000). This may provide a link with AD through endothelial (dys)function and the resulting

disruption of neurovascular coupling. In our preliminary analysis, a three-way association was

found between lower fish consumption, MCI status, and decreased cerebral perfusion. The

precise strategy for mechanistic modelling of the effect of omega-3 fatty acids at the level of

the microvascular endothelium in the brain is, however, unclear at this time. In the current

mechanistic models developed in WP5 of VPH-DARE@IT, the microvascular endothelial

function in regulating cerebral perfusion is not explicitly modelled.

Smoking reduces vascular compliance and consequently increases arterial pulse pressure. This

effect was found also in our study but was only present in young old subjects. One vascular

hypothesis of AD links increased APP to pulse-induced damage of the cerebral microvessels

that worsens with aging as arterial stiffness naturally increases (Stone et al., 2015). If correct,

such a mechanism could predispose individuals with long-term smoking history to higher risk

of AD due to increased pulse-induced microvascular damage to their brains. Such mechanisms

could then be incorporated into the mechanistic models developed in this WP by introducing a

subject-specific model for arterial compliance, with age and MLFs such as smoking history

acting as modulatory variables.

The link between alcohol usage and AD risk remains somewhat controversial (Di Marco et al.,

2014). Moderate alcohol usage has been identified as a possible protective factor against AD in

some studies (Deng et al., 2006; Huang, Qiu, Winblad, & Fratiglioni, 2002; Larrieu et al., 2003;

Luchsinger, Tang, Siddiqui, Shea, & Mayeux, 2004) but not in others (Peters et al., 2009) . In

our study population, no effect of alcohol usage on either MCI status or CBF was identified,

apart from a correlation between regularity of drunkenness and increased pulsatility index. It is

therefore difficult to suggest an immediate mechanistic link that would model the effect that

moderate alcohol usage could have on cerebrovascular health and risk of AD.

Physical exercise has been reported as a possible protective factor against AD (Di Marco et al.,

2014). In our analysis no significant associations were found between CBF and physical

exercise (or lack thereof), and only weak associations were found between MCI status and lack


- 25 -

of physical exercise. It is possible that self-reported frequency and duration of physical activity

are not accurate measures, and that day-averaged actigraph measurements do not accurately

capture the long-term pattern of activity for a given subject. The effect of physical exercise on

vascular compliance is known to be age-dependent (Tanaka et al., 2000), where it was shown

that differences in vascular compliance between sedentary and physically active subjects may

only manifest at older age. Thus the effect of physical activity could potentially be incorporated

in mechanistic models in the same way as smoking history, namely with a subject-specific

model for arterial compliance with a modulatory variable representing physical activity.

There are several limitations to the study presented here. A relatively small cohort was used,

where only a subset of subjects (N=44) had full MLF + CBF data available due to difficulties

in obtaining the multi-modality measurements necessary to run the entire modelling pipeline.

The cross-sectional Lido Study does not yet have longitudinal follow-up imaging, which meant

that only currently diagnosed MCI status could be tested and no associations with MLFs/CBF

and conversion-to-AD could be made. Genetic data was not available at the time of the writing

so that no ApoE ε4 stratification could be made in the association studies. We characterised CBF

by looking only at total CBF and perfusion estimated at the level of the carotids, and did not

look at focal hypoperfusion in the regions of relevance to AD. Obesity has been proposed as a

risk factor for AD (Xu et al., 2011), but body-mass index was not directly controlled for in any

of the analyses.

In conclusion, MLFs can play a role in the development of MCI and conversion from MCI to

AD. In order to study their potential effects in a subject-specific setting, they should ideally be

considered as an additive combination of sufficiently many protective/risk factors that all

influence the cerebrovascular system at the same time. This would allow the mechanistic

modelling of microvascular changes that accumulate over time, in concert with natural ageing,

and that lead to microvascular dysfunction and breakdown of neurovascular coupling. By using

models of the cerebrovascular system that incorporate subject-specific models of vascular

compliance and, possibly, mechanisms for microvascular endothelial dysfunction, the effect of

chosen key MLFs may be modelled in a mechanistic way.

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