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all 53 individuals for follow-up assessments; however,
our sample size calculation confirmed that an n of 28
was reasonable to investigate the longitudinal effects
in this study design.
GMFCS distribution was as follows: I = 6; II = 7;
III = 5; IV = 7; V = 3. The mean time interval between
baseline and follow-up assessments was 4.0 ± 1.2
years. Changes in CVD risk factors between baseline
and follow-up are shown in Table I. Between baseline
and follow-up assessments at the group level, 64% of
participants had an increase in waist circumference,
absolute and relative FMD decreased in 85% of parti-
cipants, 93% of participants experienced an increase in
cIMT, carotid distensibility decreased in 59% of parti-
cipants, and cfPWV increased in 70% of participants.
Three participants at follow-up had cfPWV values that
were above the clinical value (i.e. 10 m/s) deemed at
risk for future cardiovascular events, whereas only
one participant was at risk at baseline. With difference
in age in months included as a covariate, repeated
measures analysis of variance revealed statistically
significant differences in mean absolute FMD (0.31
(SD 0.13) vs 0.22 (SD 0.08) mm, p = 0.045 95% CI
0.040, 0.151), relative FMD (9.9 (SD 4.7) vs 7.5 (SD
2.6) %, p = 0.049, 95% CI 0.464, 4.42), and cIMT (0.52
(SD 0.17) vs 0.67 (SD 0.33) mm, p = 0.041, 95% CI
–0.242, –0.074) between baseline and follow-up as-
sessments, respectively.
As a result of the varying time intervals between the
2 sets of assessments between participants, changes
in traditional and non-traditional risk factors were
divided by time for each participant and reported as
Table II. Multiple linear regression analyses
cIMT, n = 27
GMFCS grouping
Age (baseline)
Waist circumference, n = 28
GMFCS grouping
Age (baseline)
SBP, n = 28
GMFCS grouping
Age (baseline)
Distensibility, n = 27
GMFCS grouping
Age (baseline)
cfPWV, n = 23
GMFCS grouping
Age (baseline)
Absolute FMD, n = 27
GMFCS grouping
Age (baseline)
Relative FMD, n = 27
GMFCS grouping
Age (baseline)
Coef. p-value 95% CI
–0.009
0.001 0.507
0.031 –0.035 to 0.018
3.4E–4 to 0.002
1.01
0.015 0.231
0.659 –0.690 to 2.71
–0.054 to 0.0.84
0.292
0.127 0.825
0.066 –2.41 to 2.99
–0.011 to 0.220
2.5E–4
1.3E–5 0.169
0.086 –1.1E–4 to 6.2E–4
–1.9E–6 to 2.7E–5
–0.053
0.009 0.724
0.119 –0.360 to 0.255
–0.003 to 0.022
–0.011
1.6E–4 0.523
0.784 –0.047 to 0.025
–0.001 to 0.001
–0.461
0.006 0.486
0.796 –1.80 to 0.882
–0.039 to 0.050
95% CI: 95% confidence interval; SBP: systolic blood pressure; FMD: flow
mediated dilation; cIMT: carotid artery intima media thickness; GMFCS:
Gross Motor Function Classification System; cfPWV: carotid-femoral pulse
wave velocity.
www.medicaljournals.se/jrm
rates of change for the regression analyses. Multiple
linear regression analysis for rate of change in cIMT
revealed an R-squared of 0.294, p = 0.015, with age
at baseline being a significant predictor of change in
cIMT. From Cooks distance calculation, participant
19 (age = 58 years; GMFCS III; change in cIMT = 1.07
mm) was identified as an outlier with a residual of
0.14. The regression was performed without this data
point, resulting in an R-squared of 0.261, p = 0.031 and
again with age at baseline being a significant predictor
of change in cIMT (Table II). Age at baseline and/or
GMFCS grouping were not significant predictors of
rates of change for waist circumference, SBP, carotid
artery distensibility, cfPWV, and both absolute and
relative FMD (Table II).
DISCUSSION
The objective of this study was to examine the longi-
tudinal changes in traditional and non-traditional risk
factors for CVD in a cohort of individuals with CP, in
order to better understand the development of CVD in
this population. An important finding was that, while
risk factors for CVD increased in at least 50% of parti-
cipants with CP relative to baseline over approximately
a 4-year time period, some non-traditional indices
seemed to have higher sensitivity for detecting signi-
ficant changes over time. Specifically, after controlling
for the varying time intervals between baseline and
follow-up assessments among participants, significant
changes were apparent only for the non-traditional
risk factors absolute FMD, relative FMD, and cIMT.
Importantly, this informs us that non-traditional mea-
sures may detect changes in CVD risk in individuals
with CP when not revealed by tracking traditional risk
factors, which is consistent with the pathophysiology
of the cardiovascular system in general; functional
impairment of the arterial wall may occur at an early
stage of the atherosclerotic process, before clinical
symptoms of CVD are present (17).
Concerning traditional risk factors for CVD, obesity
and hypertension have shown a strong association with
the non-traditional risk factors arterial stiffness and
endothelial dysfunction. In fact, some non-traditional
risk factors are considered negative prognostic factors
of hypertension (18, 19). The increases in waist circum-
ference and significant changes in FMD and cIMT
within this cohort of individuals with CP underscore
the importance of monitoring and managing CVD
risk in this population to prevent the development of
hypertension.
As this was the first longitudinal cohort study to in-
vestigate changes in non-traditional CVD risk variables
in individuals with CP, it was important to understand