Circulating inflammatory markers are associated with MR visible Perivascular Spaces but
not directly with White Matter Hyperintensities
Online-Only Supplementary Material
Measurement of Inflammatory Markers
Bivariate Statistics
Multivariate Statistics using Structural Equation Modelling
1
Measurement of Inflammatory Markers
C-reactive protein (CRP) and interleukin 6 (IL6) were analysed at the University of Glasgow using
high sensitivity ELISA from R&D Systems (Oxford, UK). For IL-6, the minimum detectable dose
(MDD) ranged from 0.016-0.110 pg/mL (mean=0.039 pg/mL). The intra-assay coefficient of
variation (CV) ranged from 6.9% to 7.8%, while the inter-assay CV ranged from 6.6% to 9.6%. For
CRP, the MDD ranged from 0.005-0.022 ng/mL (mean=0.010 ng/mL). The intra-assay CV ranged
from 3.8% to 8.3%, while the inter-assay CV ranged from 6.0% to 7.0%. Fibrinogen was measured
using an automated Clauss assay (TOPS coagulometer, Instu-mentation Laboratory, Warrington,
UK) with a CV of 3.69% [1].
Bivariate Statistics
Supplementary Table 1 shows the descriptive statistics. The total median (and interquartile range )
Fazekas score was, 2.0 (1.0), range 0 to 6 (periventricular median score was 1.0 (1.0), range 0 to 3,
and deep median score was 1.0 (0), range 0 to 3). The median and interquartile range PVS scores
were 0 (0), range 0 to 2 in the hippocampus, 1.0 (1.0), range 0 to 4 in the BG, and 2.0 (1.0), range 0
to 4 in the CS. Inflammatory marker levels are given in Supplementary Table 1.
Supplementary Table 2 presents the bivariate association between markers of Inflammation, WMH
and PVS measures. The main results have been presented in the main text. As expected, Fibrinogen,
CRP and IL-6 were all significantly positively correlated with each other as were all measures of
WMH with each other and all measures of PVS with each other.
Correlations between the covariates and inflammation markers, WMH and PVS, are presented in
Supplementary Table 3. History of hypertension and of stroke correlated consistently with WMH (r
range 0.08 to 0.19, P<0.01). History of smoking correlated consistently with inflammation (r range
0.08 to 0.16, P<0.001). Advancing age (even within the narrow range included) was associated with
declining fibrinogen (r = -0.13, P<0.001), but not CRP or IL-6, and increase in WMH (r=0.14,
P<0.001). Sex was significantly associated with Fazekas deep score (r=0.11, P=0.003) with women
having higher Fazekas scores than men. Other health covariates (diabetes, hypercholesterolaemia
and cardiovascular disease histories) showed no significant correlation with markers of
inflammation, WMH or PVS.
Multivariate Statistics using Structural Equation Modelling
In statistical modelling, latent variables are employed when the parameter of interest is inadequately
measured by a single variable. Latent variables are estimated based on the shared common variance
between a set of measured indicator variables, and this allows the exclusion of measurement errors
when estimating the contribution that each measured variable makes to the latent variable [2, 3].
Structural equation models (SEM), used in this study, simultaneously provides estimates of such
latent variables, and the correlations and uni-directed paths (regression parameters, which may be
thought of like standardised, partial beta weights) between latent variables. The SEM is
conventionally represented by a diagram as in the figures presented here where the rectangular
boxes each represent measured observations, eg CRP or WMH volume. Each circle or ellipse
represents a latent construct, eg ‘inflammation’ or ‘PVS’, which is a weighted combination of the
correlated individual measured variables. Paths in the diagrams that are single-headed arrows
represent hypothesised causal pathways from a predictor variable to the outcome variable, and
2
double headed arrows represent correlations. Solid arrows represent associations that are
statistically significant at p <0.05 while dashed arrows represent associations that are not significant
at p <0.05. Numbers adjacent to paths may be squared to obtain the shared variance between
adjacent variables. The numbers on the arrows pointing from the latent to the measured variables,
called the factor loading or correlation coefficient, represent the strength of the association between
the measured and latent variables, +/- 1 indicate the strongest association. Thick arrows pointing to
the measured variables mean that that error term was accounted for in the modelling and the
numbers adjacent represent the r2 which is the square of the factor loading.
The three measurement models of ‘inflammation’, ‘WMH’ and ‘PVS’ all fitted well (model fit
statistics are presented in figure legends). The factor loadings for the latent variables of
‘inflammation’, ‘WMH’ and ‘PVS’ were moderate to large, indicating that the measurement models
were good for the three latent traits. The standardised path values were all large (shown in Figures
1-3 and supplementary Figures 1 to 3) indicating that the latent variables are robust and account for
between approximately 20% and 88% of variance in the measured variables in all models.
Model fit parameters are shown in the legends: the maximum modification index (Max MI, which
indicates the degree of greatest local strain within the model in terms of a potential reduction in
model chi square); the comparative fit index, (CFI, 0.90 indicates acceptable fit); and the root
mean square error of approximation (RMSEA, <0.06 indicates acceptable fit).
All models were estimated using full information maximum likelihood estimation. Model fit was
evaluated based on commonly adopted cut-off points for the Root Mean Square Error
Approximation (RMSEA), Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), incremental
Fit index (IFI) and the Standardized Root Mean Square of <0.06, >0.90, >0.90, >0.90 and <0.06
respectively [4]. Model modifications were based on the standardized residual covariance < 1.96.
Associations were modelled using a stepwise approach. The first set of models included only the
measures of inflammation markers & PVS measures, inflammation markers & WMH measures,
PVS measures & WMH measures or inflammation markers & PVS measures & WMH measures
(Supplementary Figure 1, Supplementary Table 4). Then, demographic variables were introduced
(Supplementary Figure 2). All health variables were then introduced (Supplementary Figure 3).
Model fit measures and p-values for regression coefficients were inspected to identify covariates
which did not make any significant contributions to the model fit, such covariates were removed to
improve model fit. Finally, the standardized residual covariance matrices were inspected to identify
significant associations which were not included in the models, such associations were then
included to produce the final models (Figures 1 - 3) which produced satisfactory model fit
(Supplementary Table 5). Supplementary Figures 1 to 3 show some of the intermediate results.
All imaging features were defined according to the STandards for ReportIng Vascular Changes on
NEuroimaging (STRIVE) v1 [5].
3
Supplementary Table 1: Descriptive statistics for markers of inflammation, measures of WMH,
PVS, demographic and health conditions
N=634
Measures
Mean(SD)
Markers of Inflammation
CRP
2.94(5.97)
Fibrinogen
3.34(0.60)
IL6
2.00(1.74)
Hippo-PVS
0.25(0.45)
BG-PVS
1.43(0.58)
CS-PVS
2.07(0.74)
WMH (mm3)
12186.18(13289.57)
ICV (mm3)
1442352.06(142201.34)
% WMH in ICV
0.85(0.92)
Fazekas Periventricular scores,
median (IQR)
1.00(1.00)
Fazekas Deep scores, median
(IQR)
1.00(0.00)
Age in years, mean (SD)
72.5(0.72)
% men
53
Measures of PVS
WMH and related measures
Demographic
YES
Health conditions
History of hypertension (%)
48.70
History of diabetes (%)
11
History of stroke (%)
6.9
History of smoking (%) current
or ever smoked
56.1
History of hypercholesterolemia
(%)
41.4
Note. CRP=C-reactive Protein, IL6=Interleukin 6, hippo=Hippocampus, BG=Basal Ganglia,
CS=Centrum Semiovale, PVS=Perivascular Spaces, WMH=White Matter Hyperintenities,
ICV=Intracranial Volume, IQR=Interquartile Range, SD=Standard Deviation
4
Supplementary Table 2: Bivariate Correlations (p values) within and between markers of
Inflammation, WMH and PVS measures; Bonferroni corrected (N=634)
% WMH in ICV
Fazekas Peri
Fazekas Deep
CRP
Fibrinogen
Hippo-PVS
BG-PVS
CS-PVS
% WMH in ICV
Fazekas Peri
CRP
Fibrinogen
IL6
Hippo-PVS
BG-PVS
Inflammation and WMH
CRP
Fibrinogen
0.00(0.97)
0.00(0.92)
0.00(0.99)
0.05 (0.21)
0.01 (0.74)
0.00(0.97)
0.41 (<0.001)
PVS and WMH
% WMH in ICV
Fazekas Peri
0.11(0.03)
0.21(0<0.001)
0.31(0<0.001)
0.32(0<0.001)
0.18(0<0.001)
0.23(0<0.001)
0.72(<0.001)
Inflammation and PVS
PVS hippo
BG-PVS
0.03(0.40)
0.01(0.86)
0.06(0.15)
0.05(0.24)
0.02(0.58)
0.02(0.54)
0.25(<0.001)
Abbreviations: See note Table 1.
5
IL6
0.02(0.54)
0.04(0.30)
0.02(0.57)
0.41(<0.001)
0.37(<0.001)
Fazekas Deep
0.16(0<0.001)
0.31(0<0.001)
0.19(0<0.001)
0.65(<0.001)
0.52(<0.001)
CS-PVS
0.10(0.10)
0.03(0.42)
0.09(0.22)
0.27(<0.001)
0.40(<0.001)
Supplementary Table 3: Pearson bivariate and biserial (*) correlations (p values) between covariate risk factors, markers of inflammation,
WMH and PVS measures
% WMH in
ICV
Fazekas Peri
Fazekas
Deep
CRP
Fibrinogen
IL6
Hippo-PVS
BG-PVS
CS-PVS
Sex
Age
Smoking*
Hypertension*
Diabetes*
Hyperchole
sterolaemia*
Cardiovascular
Disease*
Stroke*
0.01(0.85)
0.01(0.85)
0.14(<0.001)
0.03(0.48)
0.08(0.04)
0.06(0.12)
0.13(0.001)
0.10(0.008)
0.03(0.45)
0.02(0.65)
0.07(0.05)
0.05(0.16)
0.00(0.91)
0.00(0.92)
0.08(0.03)
0.10(0.01)
0.11(<0.01)
0.02(0.55)
0.08(0.03)
-0.04(0.21)
0.03(0.38)
-0.02(0.68)
0.00(0.99)
0.08(0.04)
0.04(0.24)
-0.13(<0.001)
0.04(0.24)
-0.18(<0.001)
0.02(0.60)
0.02(0.56)
0.03(0.43)
0.12(<0.001)
0.16(<0.001)
0.16(<0.001)
-0.05(0.21)
-0.03(0.51)
-0.01(0.72)
0.19(<0.001)
0.10(0.01)
0.04(0.24)
0.06(0.08)
0.02(0.61)
0.11(0.01)
0.03(0.41)
0.00(0.95)
0.02(0.53)
0.02(0.65)
0.10(<0.01)
-0.04(0.28)
-0.03(0.39)
-0.04(0.30)
0.03(0.43)
0.01(0.76)
0.02(0.49)
0.00(0.95)
0.03(0.41)
0.05(0.24)
-0.05(0.19)
0.01(0.73)
-0.03(0.43)
0.01(0.75)
0.02(0.56)
-0.01(0.72)
-0.01(0.88)
-0.05(0.19)
0.08(0.04)
0.09(0.01)
0.13(<0.001)
0.09(0.01)
0.02(0.56)
0.01(0.76)
-0.03(0.49)
Abbreviations: See note Table 1.
6
Supplementary Table 4: Association between measures of PVS and WMH using structural
WMH
HIP
BG
CS
WMH V
Faz Peri
Faz Deep
PVS
PVS
WMH
WMH
PVS
PVS
WMH
WMH
WMH
PVS
PVS
PVS
PVS
WMH
WMH
WMH
Age
Sex
Sex
Age
High BP
Stroke
High BP
Stroke
Smoking
Model 1
0.47 (<0.0001)
0.53 (<0.0001)
0.36 (<0.0001)
0.74 (<0.0001)
0.92 (<0.0001)
0.78 (<0.0001)
0.72 (<0.0001)
Model 2
0.47 (<0.0001)
0.38 (<0.0001)
0.73 (<0.0001)
0.53 (<0.0001)
0.92 (<0.0001)
0.77 (<0.0001)
0.72 (<0.0001)
-0.03 (0.531)
-0.02 (0.676)
0.08 (0.043)
0.14(<0.0001)
Mode 3
0.47 (<0.0001)
0.37 (<0.0001)
0.74 (<0.0001)
0.53 (<0.0001)
0.92 (<0.0001)
0.77 (<0.0001)
0.72 (<0.0001)
Model 4
0.47 (<0.0001)
0.38 (<0.0001)
0.73 (<0.0001)
0.53 (<0.0001)
0.92 (<0.0001)
0.77 (<0.0001)
0.72 (<0.0001)
0.13 (<0.0001)
0.11 (0.038)
0.00 (0.955)
0.09 (0.019)
0.08 (0.051)
0.09 (0.024)
0.13 (0.003)
0.11 (0.037)
0.00 (0.999)
0.10 (0.019)
0.08 (0.055)
0.09 (0.023)
Note. Models predicted ‘WMH’ from ‘PVS’. Model 1 is the basic model that contained only
‘WMH’ and ‘PVS’. In model 2, age and sex were introduced. In model 3 all health variables
(history of cardiovascular disease, diabetes, hypertension, smoking, hypercholesterolemia and
stroke) were introduced but only those that passed the inclusion criteria test were retained.
Model 4 is the modified version of model 3 to improve the model fit. This was done using the
standardized residual covariance. Arrows point from the predictors to outcome variables.
7
Supplementary Table 5: Model fit statistics for association between WMH and EPVS
IFI
TLI
CFI
RMSEA
SRMR
Model 1
0.972
0.947
0.972
0.078
0.0347
Model 2
0.938
0.898
0.938
0.081
0.0468
Model 3
0.94
0.915
0.939
0.074
0.0436
Model 4
0.968
0.952
0.968
0.044
0.0316
Note. Models predicted ‘WMH’ from ‘PVS’. Model 1 is the basic model that contained only
‘WMH’ and ‘PVS’. In model 2, age and sex were introduced. In model 3 included health
variables. Model 4, the final model (Figure 1) is the modified version of model 3 to improve
the model fit. This was done using the standardized residual covariance. RMSEA = Root
Mean Square Error Approximation, TLI = Tucker-Lewis Index, CFI = Comparative Fit
Index, IFI = incremental Fit index (IFI) and SRMS = Standardized Root Mean Square. A
model fits well if RMSEA <0.06, TLI >0.90, CFI >0.90, IFI >0.90 and SRMS<0.06 [4].
8
Supplementary Figure 1: SEM of the association between measures of PVS and WMH using the basic model that included only the measures
of PVS and measures of WMH. Model fit: See Supplementary Table 5.
Note. ‘PVS’=latent variable derived from measures of PVS, ‘WMH’=latent variable derived from measures of WMH. Models included only
covariates that were significantly associated with WMH load or measures of PVS. BG=measured basal ganglia PVS, HIP=measured
hippocampus PVS, CS=measured centrum semiovale PVS, high BP=history of hypertension, WMH v=% of WMH in ICV, Faz=Fazekas,
Peri=Perivascular. Solid or thick arrows represent associations that are statistically significant at p < 0.05 while dashed arrows represent
associations that are not significant at p < 0.05.
9
Supplementary Figure 2: SEM of the association between measures of PVS and WMH. Model included age and Sex. Dotted lines are
associations that are not significant.
Note. Abbreviations: See Figure 1.Model fit: See Supplementary Table 5
10
Supplementary Figure 3: SEM of the association between measures of PVS and WMH. Model included age and Sex and all health covariates
(history of cardiovascular disease, diabetes, hypertension, smoking, hypercholesterolemia and stroke). Health variables that showed no
significant association with WMH or PVS and that reduced model fit were removed from the model.
Note. Abbreviations: See Figure 1.Model fit: See Supplementary Table 5
11
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