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\begin{document}

\title{Relationship between Coronary Microvascular Dysfunction and Left
Ventricular Diastolic Function in Patients with Chest Pain and
Unobstructed Coronary Arteries}



\author[1]{Jeng Hwan Lam}%
\author[2]{Jing Xian Quah}%
\author[3]{Thomas Davies}%
\author[4]{Christopher Boos}%
\author[3]{Karen Nel}%
\author[5]{Christopher Anstey}%
\author[1]{Tony Stanton}%
\author[1]{Kim Greaves}%
\affil[1]{Sunshine Coast University Hospital}%
\affil[2]{Flinders Medical Centre}%
\affil[3]{Poole Hospital NHS Foundation Trust}%
\affil[4]{Poole Hospital NHS Foundation Trust }%
\affil[5]{The University of Queensland Faculty of Medicine}%


\vspace{-1em}



  
  \date{June 02, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Diastolic dysfunction (DD) is reported to affect up to 35\% of the adult
general population. The consequence of progressive DD is heart failure
with preserved ejection fraction (HFpEF). Coronary microvascular
dysfunction (CMD) has been suggested as one of the pathologic mechanisms
leading to HFpEF. We investigated whether there was an association
between coronary microvascular function and echocardiographic indices of
left ventricular diastolic function in patients with chest pain and
unobstructed coronary arteries (CPUCA). This retrospective observational
study recruited patients referred to cardiology clinics assessment of
chest pain who subsequently underwent assessment via CT coronary
angiogram (CTA). Coronary microvascular dysfunction was determined by
myocardial blood flow reserve (MBFR; \textless{}2.0) using myocardial
contrast echocardiography. Echocardiographic indices of diastolic
function (septal mitral annular e'; septal mitral annular E/e') were
measured from baseline transthoracic echocardiogram. 149 patients (52\%
men) with a mean age 59.7(9.5) years were recruited. Mean (standard
deviation) MBFR was 2.2 (0.51). 37\% (55/149) had MBFR\textless{}2.0.
Median {[}interquartile range{]} septal mitral annular e' velocity and
septal mitral annular E/e' were 7.6 cm/s {[}6.2, 8.9{]} and 9.5 {[}7.5,
10.8{]} respectively. Univariate regression analysis showed only age was
a significant predictor of increasing septal mitral annular E/e'
(?=+0.20 95\% CI 0.13, +0.28, p\textless{}0.001) but not MBFR.
Multivariable analysis also showed no association between these septal
mitral annular E/e' and MBFR after adjustment for cardiovascular risk
factors. There was no relationship found between echocardiographic
indices of left ventricular diastolic function and coronary
microvascular function.%
\end{abstract}%



\sloppy


\emph{INTRODUCTION}

There is an increasing body of evidence linking coronary microvascular
dysfunction (CMD) with left ventricular (LV) diastolic dysfunction and a
future risk of developing heart failure (HF) with preserved ejection
fraction (HFpEF).\textsuperscript{1--3} This is of clinical significance
as it has been recently shown that patients with CMD-associated
diastolic dysfunction have an up to three-fold increased mortality rate
and up to five-fold risk of developing a major adverse cardiovascular
event (MACE).\textsuperscript{4} Previous studies have also reported
similar changes in the echocardiographic indices of diastolic
dysfunction in patients with CMD including reduction in left ventricular
(LV) early diastolic strain rate, early volumetric filling rate and
elevated LV filling pressures.\textsuperscript{5,6} However, there is a
relatively limited amount of published research in this area regarding
the relationship between CMD and LV diastolic function. A better
understanding could help target future therapies against this condition.
This study sought to investigate whether there was an association
between CMD and LV diastolic function in a group of patients presenting
with CPUCA.

\emph{METHODS}

\textbf{Study design}

This was a single centre, prospective cross-sectional, observational
study carried out at Poole Hospital NHS Foundation Trust, UK. The study
recruited patients between 2011-2013 aged 30-80 years who were referred
to the cardiology clinics with chest pain suggestive of myocardial
ischemia and subsequently had a diagnostic CT coronary angiography (CTA)
demonstrating no evidence of significant coronary stenosis. Exclusion
criteria were known ischemic heart disease (previous acute coronary
syndrome, previous percutaneous coronary intervention or previous
coronary artery bypass graft), valvular heart disease, LV hypertrophy or
LV ejection fraction \textless{}55\%. We obtained information regarding
patient baseline demographics including age, body mass index (BMI),
hypertension, smoking status, dyslipidemia and presence or absence of
diabetes. Smoking status included both current and past smokers. Blood
samples were analyzed for fasting lipid profile (low density lipoprotein
(LDL) and triglyceride (TG)). Given the possible association between
HFpEF and inflammation,\textsuperscript{7} levels of high sensitivity
C-reactive protein (Hs-CRP), a marker of systemic inflammation, were
also measured in our patients.

\textbf{CT-Coronary Angiogram}

CT coronary angiogram (CTA) was performed using a 64-channel CT scanner
(GE Lightspeed VCT, GE Medical Systems). Reconstructed CTA images were
analyzed on a dedicated 3-dimensional workstation (CardIQ Xpress, GE
Medical Systems) with curved multiplanar reformation and short-axis
cross-sectional viewing techniques. The mean heart rate during the scan
was 64 beats/min. Calcium scoring and helical scan data was performed on
all patients using prospective ECG triggering at 75\% of the R-R
interval. Following this, a 20ml bolus of contrast (Niopram 370\selectlanguage{ngerman}®) at
6mls/sec was injected and the timing of peak contrast enhancement in the
aortic arch was used to determine the timing of scan acquisition. The
contrast-enhanced scan used 80mls contrast injected at 6 ml/s, followed
by a 50ml at 6mls/sec saline flush, during a single expiration breath
hold. The CTA scan parameters were: collimator 20~mm, slice thickness
0.625~mm; gantry rotation 350~ms; helical acquisition using a pitch of
0.16; tube current 455--515~mA with ECG tube current modulation; tube
voltage range 100--140~kV; rotation time, 350~ms. The estimated
radiation dose per patient was 3.3~mSv. Unobstructed coronary artery
disease was defined as a quantitatively measured luminal
stenosis\textless{}50\%. All patients were in sinus rhythm.

\textbf{Baseline Echocardiography and Diastolic Function Assessment}

All patients underwent resting transthoracic echocardiography (TTE) for
analysis of LV function, assessment of LV diastolic function and
exclusion of existing valvular pathologies using standard
echocardiographic views. Early diastolic transmitral filling velocity
(E) was measured at the mitral valve leaflets tips using pulsed-wave
doppler while spectral tissue doppler imaging was used to measure early
diastolic relaxation velocities (e') at the septal mitral annulus from
the apical four-chamber window as previously
described.\textsuperscript{8} LV diastolic function was measured using
septal mitral annular E/e' ratio (the ratio between early mitral inflow
velocity and mitral annular early diastolic velocity). Elevated LV
filling pressures were defined as septal mitral annular E/e' ratio \selectlanguage{english}[?]
15.\textsuperscript{8}

\textbf{Coronary Microvascular Function assessment}

Noninvasive quantification of coronary microvascular function was
performed using myocardial contrast echocardiography
(MCE).\textsuperscript{9} This has previously been shown to correlate
with PET derived myocardial blood flow.\textsuperscript{10} Briefly, MCE
was performed using ultrasound machine iE33 (Phillips Medical Systems)
and SonoVue (Bracco Research SA) as the contrast infusion given at
constant infusion. Real time echocardiographic images were recorded
within 3-4 minutes in the apical 4, 2, and 3-chamber views with
low-power settings at mechanical index of 0.1. SonoVue was at an
infusion rate of 60 ml/h via peripheral intravenous access with VueJect
infusion syringe pump (Bracco Research, SA). Thereafter, rate was
maintained between 48 and 60 ml/h to maximise image quality. Machine
settings were held constant throughout each participants study post
optimisation. Flash-impulse imaging using high mechanical index of 1.0
was performed to achieve complete myocardial bubble destruction, after
which 10 end-systolic frames were recorded in each apical view.
Dipyridamole was infused at 0.56 mg/kg over a 4-minute period after
acquisition of resting images. Post stress images were recorded within 3
to 4 minutes after an interval of 2 minutes. Quantitative analysis was
performed offline using QLab V7.0 (Philips Medical Systems) blinded to
patient demographic and CTCA data. Quantitative assessment of myocardial
perfusion was performed for 10 consecutive end-systolic frames after
microbubble destruction. A region of interest was placed over the
thickness of the myocardium. Plots of peak myocardial contrast intensity
(linearly related to myocardial blood volume A cm\textsuperscript{3})
versus pulsing intervals (representing time) were automatically
constructed to fit the mono-exponential growth function: y=A (1 -
e\textsuperscript{-Bt}) where B is the instantaneous initial slope of
the resulting curve and represents myocardial blood velocity
(sec\textsuperscript{-1}) and the product of A and B yields a reliable
measure of myocardial blood flow (MBF)
(cm\textsuperscript{3}.sec\textsuperscript{-1}).

MBFR was measured as previously defined as the ratio of
post-dipyridamole (stress) MBF to baseline MBF, dividing the stress MBF
by the baseline MBF for the same segment.\textsuperscript{9} A
16-segment model was used excluding the basal segments in view of
contrast attenuation and analyzing the 10 remaining mid- and apical
cardiac segments. A segment was excluded if there was artefact,
inadequate microbubble destruction, attenuation or a wide variation in
contrast intensity. Segmental MBFR was calculated by dividing peak MBF
with resting MBF of the same segment. MBFR was the average MBFR of all
segments. CMD was defined as MBFR\textless{}2.0.\textsuperscript{9}

\textbf{Statistical analysis}

Normality of continuous variables was quantified using the Shapiro-Wilk
test and was reported as the mean (standard deviation, SD) and the
median {[}inter-quartile range, IQR{]} for parametric and non-parametric
data respectively. Two group comparisons of continuous data for those
with and without CMD was performed using a non-paired t-test and
Wilcoxon rank-sum tests respectively. Comparisons between the MBFR
\textless{} 2.0 group and the MBFR \selectlanguage{english}[?] 2.0 group were undertaken using a
Fisher's Exact Test.

In univariate and multivariate analysis, septal mitral annular E/e' was
used as the main outcome variable while known influencers of septal
mitral annular E/e' (age, gender, diabetes, hypertension, smoking
status, Body Mass Index (BMI), fasting TG, fasting LDL, MBFR and Hs-CRP)
were used as the input variables. For multivariate analysis, an initial
fully saturated model was constructed using all the known predictor
variables. Non-significant predictors were sequentially deleted, and the
model re-run. Likelihood ratio testing was employed to quantify
significant differences, or lack thereof, between model iterations. The
final models only contained the significant predictors. Results are
reported as regression slope (\selectlanguage{greek}β\selectlanguage{english}) and its level of significance. Where
required, inferential analysis was performed using standard regression
techniques except in the case of a dichotomous outcome variable, where
logistic regression was employed. Predictor variables were tested for
co-linearity using a standard Pearson's correlation. Variables with
correlation coefficients (R) greater than \selectlanguage{ngerman}±0.80 were considered
co-linear with one or other excluded largely on empirical grounds. Where
appropriate, dummy variables were used when examining the significance
of categorial predictors and all two-way interactions were tested for
significance. Finally, all statistical models were tested for adequacy
using appropriate regression diagnostics with regression residuals
tested for normality using the Shapiro-Wilk test and for homoscedascity
using the Breusch-Pagan test.

Statistical analysis was performed using a propriety statistical package
(STATA version 15.1) with the level of significance set at p \textless{}
0.05.

\textbf{Ethics}

This study complied with the Declaration of Helsinki and was approved by
the local research ethics committee (H0102/78). All patients provided a
written informed consent.

\emph{RESULTS}

\textbf{Baseline participant characteristics}

The baseline characteristics for all patients are as listed in Table I.
We recruited 183 patients of whom 34 studies had to be subsequently
excluded from the analysis due to poor echocardiographic image quality
(Figure I). No significant differences in patient demographics were
found between those included in the analysis and those excluded from the
study. The mean age of the study population was 59.7(9.5) years of whom
52\% were male, 11\% were diabetics, 56\% had dyslipidemia and 39\% had
hypertension. The measured median septal mitral annular e' was 7.6 cm/s
{[}6.2, 8.9{]} while the measured median septal mitral annular E/e' was
9.5 {[}7.5, 10.8{]}. In addition, the measured mean Hs-CRP was 2.6
{[}0.7, 3.0{]}. Elevated LV filling pressures (septal mitral annular
E/e' ratio \selectlanguage{english}[?] 15) were present in a total of 10 (6.7\%) patients. Five
out of the 10 patients with elevated LV filling pressures had evidence
of CMD. Only age was the significant predictor for elevated LV filling
pressures (Table II) with an odds ratio of 1.07 (95\% CI {[}1.03,
1.13{]}, p=0.001).

\textbf{Characteristics of patients with coronary microvascular
dysfunction}

CMD was present in 55 (37\%) patients with Table I showing baseline
characteristics of patients subdivided into those with and without CMD.
There were more patients with diabetes, hypertension, and higher LDL
levels in those with CMD compared to those without. However, there were
no significant differences between septal mitral annular e' (cm/s) or
septal mitral annular E/e' ratio in both groups. Hs-CRP levels were also
similar in both groups.

\textbf{Relationship between coronary microvascular dysfunction and left
ventricular diastolic function}

In a univariate regression analysis using MBFR as a continuous
independent variable and septal mitral annular E/e' as a continuous
outcome variable (Table III), no significant association was found
between MBFR and septal mitral annular E/e' (\selectlanguage{greek}β\selectlanguage{english}=-0.24 95\% CI -1.53,
+1.05, p=0.71). Increasing age was the only significant predictor for
increasing septal mitral annular E/e' (\selectlanguage{greek}β\selectlanguage{english}=+0.19 95\% CI +0.12, +0.25,
p=\textless{}0.001). Multivariable regression analysis in a model using
known influencers of LV diastolic function (age, sex, diabetes,
hypertension, smoking status, BMI, fasting TG, fasting LDL, MBFR and
Hs-CRP) showed only age was the significant predictor of increasing
septal mitral E/e' (\selectlanguage{greek}β\selectlanguage{english}=+0.20 95\% CI +0.13, +0.28, p\textless{}0.001,
r\selectlanguage{ngerman}²=0.21) (Table IV).

Sensitivity analysis of the model above was performed instead defining
CMD as a binary independent variable (MBFR\textless{}2.0) and septal
mitral annular E/e' as a continuous outcome variable. No significant
association was found between patients with evidence of CMD with
increasing septal mitral annular E/e' in the univariate (\selectlanguage{greek}β\selectlanguage{english}=-0.57, p=0.8)
or multivariable regression analysis.

\emph{DISCUSSION}

This study evaluated the association between MBFR, a measure of coronary
microvascular function and LV diastolic function, measured via septal
mitral annular E/e' in a group of patients presenting with chest pain
and unobstructed coronary arteries. Our study showed that there was no
association found between MBFR and LV diastolic function in both
univariate and multivariate regression analysis after taking traditional
cardiovascular risk factors into account.

Contrary to the findings from Taqueti,\textsuperscript{4} we did not
find any correlation between septal mitral annular E/e' and MBFR. In
their study, they included patients with a similar profile (patients
presenting with chest pain and no evidence of flow limiting coronary
artery disease (CAD) on PET) yet found that impaired coronary flow
reserve (CFR) in these patients was independently associated with
diastolic dysfunction.\textsuperscript{4} In addition, they also used a
similar echocardiographic definition for LV diastolic dysfunction to our
study (E/e' septal\textgreater{}15) and demonstrated a significant
direct relationship between CFR and septal e' and an inverse
relationship between CFR and E/e' septal. Their findings were clinically
important as those patients with evidence of both CMD and elevated
septal E/e' ratio had a five-fold increased risk for HFpEF
hospitalisation after a median follow up of 4
years.\textsuperscript{4}In our study, only 5 out of the 55 (9\%)
patients with CMD had evidence of raised LV filling pressures. It is not
clear from the study by Taqueti how many patients with CMD also had
echocardiographic evidence of elevated LV filling pressures (septal
mitral annular E/e' ratio [?] 15), although the published median and
quartiles for E/e' were 13.0 (9.3-16.1) in patients with CMD vs 10.8
(8.7-12.8) in patients without CMD.\textsuperscript{4}

A larger prospective multicenter study by Shah enrolled 202 patients
with a confirmed diagnosis of HFpEF and measured the prevalence of CMD
via left anterior descending artery Doppler flow signals at rest and
during adenosine infusion.\textsuperscript{11} CMD was present in nearly
75\% of patients and associated with abnormal longitudinal myocardial
function assessed by TTE. However, the main limitation of their study
was the lack of exclusion of macrovascular CAD which would have led to a
lower measurement of CFR. \textsuperscript{11,12}

There are several potential reasons that might explain the discrepancy
between the findings in our study and previous studies. Firstly, in the
study by Taqueti, their patient cohort included a greater proportion of
comorbidities known to affect both coronary microvascular function and
myocardial stiffness.\textsuperscript{4} The difference between their
study population and ours were the higher numbers of the patients who
were elderly (median age of 66 vs 59.7), proportions female (64.7\% vs
48\%), hypertensive (75.6\% vs 39\%) and had diabetes mellitus (32.8\%
vs 11\%).\textsuperscript{4} Additionally, it is possible that despite
the presence of CMD, our patient cohort with its lower prevalence of
co-morbidities, may be earlier on the spectrum of myocardial stiffness
and not yet manifesting detectable diastolic
dysfunction.\textsuperscript{11,12}

Notably, approximately 25\% of patients who met the criteria for HFpEF
in the study by Shah did not have evidence of
CMD.\textsuperscript{11}This is interesting as it could be explained by
the heterogenous nature of HFpEF driven by non-cardiac specific causes
of fluid overload, and that besides endothelial dysfunction, there may
be other factors causing HFpEF syndrome in these
patients.\textsuperscript{11} This implies that the presence of CMD does
not necessarily equate to the reduction of diastolic function, hence the
absence of correlation between CMD and LV diastolic function in our
study.

The main question of the causal mechanistic links of whether 1) CMD
leads to LV diastolic dysfunction and subsequently HFpEF or 2) LV
diastolic dysfunction observed in patients with HFpEF leads to CMD,
remains unclear. As alluded to, it is possible that our patient cohort
is at an early stage of CMD, prior to the development of diffuse
myocardial fibrosis and subsequent increase in LV stiffness via
recurrent micro-infarctions secondary to coronary microvascular
ischaemia.\textsuperscript{13} This hypothesis is supported by the
finding of an exacerbated diastolic dysfunction when there was a
detectable troponin in Taqueti's study.\textsuperscript{4,13} Limited by
the retrospective nature of this study, it would be interesting to
observe prospectively how many of our patients with CMD eventually
developed evidence of LV diastolic dysfunction or HFpEF.

Previous studies have also postulated that CMD is a consequence of a
systemic pro-inflammatory state which occurs in medical conditions such
as diabetes or obesity.\textsuperscript{1,6,7,14} Higher levels of
Hs-CRP have also been observed in patients with HFpEF than in patients
with heart failure with reduced ejection fraction (HFrEF), supporting a
link between inflammation and HFpEF.\textsuperscript{7} However, in our
study, we did not find an association between reduced MBFR and raised
Hs-CRP. This potentially is a result of a lower prevalence of medical
co-morbidities in the patients recruited in our study.

\textbf{Study limitations}

There are several limitations to our study. Firstly, this was a single
center study which increases the potential for selection bias. There
were also several patients excluded due to inadequate echocardiographic
image quality which could have introduced an additional bias. However,
no significant differences in patients baseline demographics were found
between those included and excluded from this study. The utilization of
only septal e' and septal mitral annular E/e' ratio is also a limitation
to the assessment of LV diastolic function, as this is susceptible to
changes in LV loading conditions. Nonetheless, these were the same
echocardiographic parameters used in previous studies. Ideally, a more
recent criteria to measure diastolic function should be applied but we
were limited to the data available.

\textbf{Conclusions}

In conclusion, this study did not find an association between CMD and
echocardiographic indices of LV diastolic function in patients
presenting with chest pain and unobstructed coronary arteries.

\textbf{Table I:} Baseline participant demographics grouped according to
the presence (MBFR \textless{}2.0) or absence (MBFR \textgreater{}2.0)
of coronary microvascular dysfunction\textbf{.} Values are given as mean
(SD), median {[}IQR{]} or n (\%). P-value denotes differences between
groups.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Variable\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Overall (n=149)\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
MBFR \textless{}2.0 (n=55)\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
MBFR [?]2.0 (n=94)\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
P-value\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Age (years)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
59.7 (9.5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
61 (8.6)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
59 (9.8)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.15\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Male sex\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
78 (52\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
29 (53\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
49 (52\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.99\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Diabetes\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
16 (11\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
11 (20\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5 (5\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.02\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
BMI (kg/m\selectlanguage{ngerman}²)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
27.3 (3.6)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
28.4 (3.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
26.9 (3.7)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.052\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Smoking history\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
70 (47\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
30 (55\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
40 (43\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.18\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Hypertension\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
58 (39\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
28 (51\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
30 (32\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.025\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Dyslipidemia\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
84 (56\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
34 (62\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
50 (53\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.41\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Total cholesterol (mmol/L)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5.2 (1.1)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5.0 (1.2)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5.4 (1.0)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.07\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Triglycerides (mg/dL)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.6 (1.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.7 (1.1)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.6 (1.4)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.66\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Hs-CRP (units)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.4 {[}0.7, 3.0{]}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.1 {[}0.8, 3.6{]}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.3 {[}0.7, 3.0{]}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.13\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Septal mitral annular e' (cm/s)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
66 {[}56, 78{]}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
70 {[}58, 83{]}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
65 {[}56, 77{]}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.32\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Septal mitral annular E/e' ratio\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
13.7 {[}11.2, 16.1{]}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
13.9 {[}11.1, 15.6{]}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
13.6 {[}11.2, 16.5{]}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.71\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table II:} Multivariate regression modelling of demographic
variables, lipid profile, coronary microvascular function and
inflammatory markers against septal mitral annular E/e' [?] 15 as a
dichotomous outcome variable.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Variable} & \textbf{Odds ratio} & \textbf{95\%CI} &
\textbf{P-value}\tabularnewline
\midrule
\endhead
\textbf{MBFR} & 1.19 & 0.55, 2.57 & 0.67\tabularnewline
\textbf{Age (years)} & 1.08 & 1.03, 1.13 & 0.002\tabularnewline
\textbf{Male sex} & 1.16 & 0.52, 2.58 & 0.72\tabularnewline
\textbf{Diabetes} & 1.78 & 0.51, 6.23 & 0.36\tabularnewline
\textbf{Hypertension} & 0.89 & 0.39, 2.05 & 0.79\tabularnewline
\selectlanguage{ngerman}\textbf{BMI (kg/m²)} & 1.10 & 0.97, 1.26 & 0.13\tabularnewline
\textbf{LDL (mmol/L)} & 1.23 & 0.79, 1.92 & 0.36\tabularnewline
\textbf{Hs-CRP (units)} & 0.98 & 0.87, 1.10 & 0.76\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table III:} Univariate regression modelling of demographic
variables, lipid profile, coronary microvascular function and
inflammatory markers against septal mitral annular E/e' as a continuous
outcome variable.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Variable} & \selectlanguage{greek}\textbf{Univariate model β} & \textbf{95\% CI} &
\textbf{P-value}\tabularnewline
\midrule
\endhead
\textbf{MBFR} & -0.24 & -1.53, +1.05 & 0.71\tabularnewline
\textbf{Age (years)} & +0.19 & +0.12, +0.25 &
\textless{}0.001\tabularnewline
\textbf{Male sex} & -0.77 & -2.01, +0.53 & 0.24\tabularnewline
\textbf{Diabetes} & +0.85 & -1.18, +2.89 & 0.41\tabularnewline
\textbf{Hypertension} & +0.38 & -0.95, +1.71 & 0.58\tabularnewline
\selectlanguage{ngerman}\textbf{BMI (kg/m²)} & +0.07 & -0.12, +0.26 & 0.46\tabularnewline
\textbf{LDL (mmol/L)} & +0.47 & -0.23, +1.18 & 0.19\tabularnewline
\textbf{Hs-CRP (units)} & -0.07 & -0.24, +0.09 & 0.36\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table IV:} Multivariable regression analysis in a model using
known influencers of LV diastolic function.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Variable & \selectlanguage{greek}\textbf{β (95\% CI)}\selectlanguage{english} & \textbf{p-value}\tabularnewline
\midrule
\endhead
\textbf{Age} & +0.20 (+0.13, +0.28) & \textless{}0.001\tabularnewline
\textbf{Sex} & -0.28 (-1.68, +1.12) & 0.69\tabularnewline
\textbf{Diabetes} & +0.43 (-1.96, +2.82) & 0.72\tabularnewline
\textbf{Hypertension} & -0.06 (-1.55, +1.44) & 0.94\tabularnewline
\textbf{Smoking status} & +0.27 (-0.90, +1.44) & 0.65\tabularnewline
\textbf{BMI} & +0.08 (-0.14, +0.30) & 0.47\tabularnewline
\textbf{Triglycerides} & +0.52 (-0.57, +1.61) & 0.35\tabularnewline
\textbf{LDL} & +0.41 (-0.38, +1.21) & 0.31\tabularnewline
\textbf{MBFR} & +0.30 (-1.11, +1.71) & 0.67\tabularnewline
\textbf{Hs-CRP} & -0.10 (-0.26, +0.06) & 0.22\tabularnewline
\bottomrule
\end{longtable}

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doi:10.1161/CIRCULATIONAHA.110.992297\selectlanguage{english}
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\end{center}
\end{figure}

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