Регулирование сосудистого риска у людей с множественным склерозом во время диагностики в Англии: демографическое исследование.

Introduction

People with Multiple Sclerosis (PwMS) have an increased incidence and prevalence of
vascular comorbidities, including diabetes, hypertension and dyslipidemia, as
compared with people without MS. 1 – 7 This contributes to the 30%
greater risk of macrovascular disease. 1 , 4 Moreover, as the vascular
burden rises over time,
2
it is associated with increased disability worsening,
8
healthcare utilization,
9
and all-cause/vascular mortality. 1 , 3

Given this background, the assessment and management of vascular comorbidities is
vital, particularly in the very early stages. However, there is a paucity of studies
examining vascular risk factor control in PwMS. A Canadian retrospective cohort
study of 971,799 individuals identified using a primary care database between 2014
and 2016, of whom 2926 were PwMS, concluded that MS was not associated with poorer
control of blood pressure and diabetes or difference in the median number of
medications used to treat these conditions.
4
Furthermore, a previous study conducted in Italy reported higher use of
antihypertensive in PwMS than matched controls.
10
However, the findings from these studies may not apply to other health
systems with differing access to and systems of care and with different treatment guidelines.
11

To take this forward, we used a large data set representative of the English
population to assess the (1) the prevalence of vascular risk factors; (2) and the
intensity of management of vascular risk in PwMS, at the time of
diagnosis as compared with a matched control population. A novel aspect
was the incorporation of the validated electronic frailty index (eFI) as a proxy of
MS disability, as previous research has shown that frailty indices are strongly
associated with MS disease duration, disability and fatigue.
12

Methods

Study design

We conducted a population-based cross-sectional study which included PwMS and
matched controls registered with general practices in England, diagnosed between
1 January 1987 and 30 September 2018. The Independent Scientific Advisory
Committee of the CPRD (protocol no. 18_279R) granted ethics approval.

Data source

Data were drawn from the UK Clinical Practice Research Datalink (CPRD) GOLD, one
of the largest databases of electronic medical records globally.
13
The CPRD GOLD holds anonymized routinely collected longitudinal primary
care records from general practices using the same software system
(Vision ® ) who have agreed at practice level to provide data monthly.
13
The database includes information on all patients registered with the
participating practices unless they have individually requested to opt out of
data sharing. The database covers approximately 7% of the UK population; it is
representative with respect to age, sex and ethnicity.
14
As linkage to Hospital Episode Statistics and Office for National
Statistics mortality data is available only for the English data set,
13
we limited the study to individuals registered with English general
practices.

Study population

We adopted a previously described algorithm to identify MS cases. 1 , 15 Briefly,
we identified possible MS cases based on diagnostic and management primary care
codes (Read codes), the International Classification of Diseases (ICD)-X codes
and on prescription of disease-modifying therapies used exclusively to treat MS.
Consistent with Culpepper et al.,
16
to reduce the risk of misclassification, we defined MS cases as those
with ⩾3 MS events recorded in their available clinical history. Date of the
first MS diagnosis was considered the index date.
1

As described elsewhere, additional inclusion criteria for MS cases were as
follows: (1) diagnosis after 1 January 1987, when magnetic resonance imaging
(MRI) was available to support the diagnosis; (2) continuous registration with
the CPRD practice for ⩾1 year before the first MS event to ensure that
information regarding key covariates was available at onset; (3) defined sex
(male or female); (4) valid date of birth; (5) age ⩾18 years at cohort entry;
(6) MS events recorded before the date of death; and (7) validity of patients’
clinical records in terms of continuous follow-up and data recording defined by
the CPRD definition of up-to-standard (UTS).
1
The UTS is deemed as the date at which the practice is considered to have
high-quality data, based on continuity in data and death recording. Individuals
were considered eligible if the clinical information recorded in the year before
the index date and the follow-up were considered UTS.

PwMS were randomly matched to up to six people without MS by age, sex and general
practice. Controls had UTS clinical data recorded during the study period and
did not have MS or any other demyelinating disease event recorded (e.g. optic
neuritis, transverse myelitis, acute disseminated encephalomyelitis and central
nervous system demyelination not elsewhere classifiable); this minimized the
possibility of including controls who might develop MS in the future. PwMS were
matched to multiple controls to reduce the variance.
17
We assigned the controls the index date of their matched MS case.

Study variables

We extracted information on study variables at index year. Consistent with
previous research using CPRD data,
18
we defined study variables using comprehensive primary care code lists
(which included both diagnostic and management codes) and ICD-X codes. This
broader approach has been recommended in a previous validation study using CPRD data.
19
Prescribing data were extracted using the British National Formulary
(BNF) codes. Study outcomes included diagnosis of diabetes and hypertension,
body mass index (BMI), systolic and diastolic blood pressure, treatment with
lipid-lowering, oral antidiabetic and antihypertensive treatments ( Supplemental Appendix Tables 1 and 2 ). Use of lipid-lowering
medication was considered as proxy of dyslipidemia because the proportion of
cholesterol levels recorded in this study population was too low.

Study covariates included the following socio-demographic characteristics: age
(continuous), sex, ethnicity (white, non-white) and index of multiple
deprivation (quintiles);
20
vascular risk factors including smoking status (current smoker, former
smoker, non-smoker) and antiplatelet treatment in the index year and year of MS
diagnosis. Consistently with previously adopted methodology,
18
study covariates were determined considering information available in
primary care and hospital data (age, sex, ethnicity and smoking status), as well
as linkage data (index of multiple deprivation).
13
Information on antiplatelet treatment was extracted using BNF codes. We
also included the number of primary care visits preceding the index year, to
account for differences in health care utilization between the MS and matched
cohorts (surveillance bias), and the eFI, a score which identifies people with
frailty by including 36 equally weighted deficit variables using routinely
collected primary care data ( Supplemental Appendix Table 3 ). 21 , 22 The eFI score was
calculated considering the number of deficits identified divided by the total.
Individuals were classified as fit (a score below 0.12), mildly frail (0.12 to
0.24), moderately frail (0.24 to 0.36) or severely frail (0.36 and above).
22

Statistical analysis

To reduce missing data at index year, we used the latest clinical data for each
individual within the 5 years before the start of the study period. 1 , 15 After
checking missing data assumptions, we used multiple imputation by chained
equations (10 copies) to estimate missing data for blood pressure and BMI (49.9%
for blood pressure and 50% for BMI). Variables entered in the regression models
included MS status (yes/no), sex, ethnicity, region, deprivation index, number
of primary care visits in the previous year, smoking status, number of
comorbidities (defined by a previously published list)
23
, treatment with lipid-lowering, oral antidiabetic, antiplatelet,
anticoagulant and antihypertensive therapies in the index year.

Differences in study variables between PwMS and controls at the index year were
assessed using the Chi-square, Student’s t -tests and the
Kruskal–Wallis tests, as appropriate. To compare prevalence in PwMS and matched
controls, we estimated the prevalence ratios (PRs) at baseline employing
multivariable logistic regression models. Similarly, we employed linear
regression models to estimate differences in the means of continuous outcomes
(blood pressure, BMI). Multivariable regression models were adjusted for the
study covariates indicated above. We repeated these analyses after stratifying
by sex to assess effect modification.

Sensitivity analysis

Considering the percentage of missing data for blood pressure and BMI, we
repeated the analyses limited to complete cases to check consistency with
main analyses.

Results are presented as regression coefficients (coeff.), PRs and 95%
confidence intervals (95% CIs), as appropriate. A
p -value < 0.05 was considered statistically significant.
We used Stata 17 MP (StataCorp. 2017, College Station, TX, USA: StataCorp
LLC) to conduct statistical analyses.

Results

Study population

We identified 12,251 PwMS diagnosed between January 1987 and December 2018 and
72,572 matched controls. On average, each MS subject was matched to 5.9 (±0.3)
controls. The average age at index (diagnosis) year was 44.9 years (±13.3), 70%
of the population were female, and 20% of the population lived in deprived
areas. The proportion of smokers was greater in PwMS than matched controls
(37.9% vs 29.4%). On average, 3.8% of PwMS were at least mid-frail, 1.2% more
than matched controls. PwMS had 2.7-fold the number of primary care visits as
controls in the year preceding the index year ( Table 1 ).

Table 1. Characteristics of the study population. Male Female Overall MS subjects Control subjects p -value MS subjects Control subjects p -value MS subjects Control subjects p -value
N
3685 21,931 8566 50,640 12,251 72,572 Follow-up time (years) 9.9 (6.1) 11.4 (6.5) <0.001 10.4 (6.3) 11.5 (6.5) <0.001 10.3 (6.3) 11.5 (6.5) <0.001 Female (%) 69.9 69.8 0.752 Age (years) 46.3 (13.3) 46.3 (13.3) 0.852 44.3 (13.3) 44.3 (13.3) 0.907 44.9 (13.3) 44.9 (13.3) 0.727 Ethnicity – white (%) 92.3 93.5 0.013 91.5 94.1 <0.001 93.9 91.2 <0.001 Smoking status (%)  Non-smoker 41.5 53.8 49.5 60.0 47.1 58.1  Ex-smoker 17.5 14.7 <0.001 13.9 11.6 <0.001 15 12.5 <0.001  Current smoker 41.1 31.6 36.6 28.5 37.9 29.4 eFI ratio 0.02 (0.04) 0.01 (0.03) <0.001 0.03 (0.04) 0.02 (0.04) <0.001 0.03 (0.04) 0.02 (0.04) <0.001  Fit 97.2 98.3 <0.001 95.8 97.1 <0.001 96.2 97.4 <0.001  Mid frailty 2.8 1.6 4.0 2.8 3.7 2.5  Moderate frailty 0.0 0.0 0.2 0.1 0.1 0.1  Severe frailty 0.0 0.0 0.0 0.0 0.0 0.0 Number of primary care visits in previous year 6.9 (10.3) 2.2 (5.0) <0.001 8.2 (11.5) 3.2 (6.1) <0.001 7.8 (11.2) 2.9 (5.9) <0.001 Index of multiple deprivation (IMD; %)  1Q – least deprived 13.7 13.7 14.6 14.6 14.4 14.4  2Q 18.5 18.4 1.000 18.5 18.5 1.000 18.5 18.5 1.000  3Q 17.6 17.6 17.9 17.9 17.8 17.8  4Q 20.2 20.3 18.8 18.8 19.2 19.2  5Q – most deprived 20.6 20.6 20.3 20.3 20.4 20.4  Missing data 9.3 9.4 9.8 9.9 9.7 9.7 Individuals were classified as fit, if the eFI score was below 0.12;
mildly frail, if the score was between 0.12 and 0.24; moderately
frail, if the score was between 0.24 and 0.36; severely frail, if
the score was 0.36 and above. Differences between groups were
assessed employing Chi-square test, Student’s
t -test and the Kruskal–Wallis test, as
appropriate.

Characteristics of the study population.

Individuals were classified as fit, if the eFI score was below 0.12;
mildly frail, if the score was between 0.12 and 0.24; moderately
frail, if the score was between 0.24 and 0.36; severely frail, if
the score was 0.36 and above. Differences between groups were
assessed employing Chi-square test, Student’s
t -test and the Kruskal–Wallis test, as
appropriate.

Differences in diagnoses and medication usage

Type 2 diabetes

7.2% of PwMS had type 2 diabetes, compared to 5.0% of matched controls. After
controlling for confounders, PwMS still had a 30% increased prevalence of
type 2 diabetes at baseline, as compared with matched controls (PR = 1.30,
95% CI = 1.19, 1.42; Figure 1 ). The PR between PwMS and matched controls was greater
in men (PR = 1.35; 95% CI = 1.16, 1.57) than in women (PR = 1.29, 95%
CI = 1.15, 1.45), although the difference was not significant
( p  = 0.665). Among subjects with type 2 diabetes at
index year ( n  = 4511), PwMS had a 56% lower prevalence of
antidiabetic usage compared with controls (PR = 0.44, 95% CI = 0.33, 0.58).
Stratifying by sex, the PR of antidiabetic usage was lower for men
(PR = 0.38, 95% CI = 0.23, 0.63) than women (PR = 0.41, 95% CI = 0.29,
0.59), although the difference was not significant
( p  = 0.830).

Adjusted proportions of diabetes and hypertension medication usage at
index year. Crude prevalence for both People with Multiple Sclerosis
(PwMS) and matched controls is reported in the columns on the left.
For BMI categories, the value represents the proportion of those
falling within the range. Adjusted prevalence ratios between PwMS
and matched controls in the index year were estimated employing
logistic regression models. Models were adjusted for gender, age,
ethnicity (white/non-white), deprivation, smoking status, BMI,
systolic and diastolic blood pressure, electronic frailty index
(eFI) ratio, number of primary care visits in the year before and
year.

PwMS: People with Multiple Sclerosis; PR: prevalence ratio.

Hypertension

Overall, 9.7% of PwMS had a diagnosis of hypertension as compared with 7.3%
of matched controls. Although the difference between the cohorts was
attenuated after controlling for confounders, the prevalence remained 6%
higher (PR = 1.06, 95% CI = 1.05, 1.06). However, among those with a
diagnosis of hypertension ( n  = 4817), PwMS had a 56% lower
prevalence of antihypertensive usage at index year (PR = 0.34, 95%
CI = 0.28, 0.42). The PR was even lower in men (PR = 0.27, 95% CI = 0.18,
0.39) than in women (PR = 0.38, 95% CI = 0.30, 0.49; Figure 1 ), but the difference was
not statistically significant ( p  = 0.097).

Hyperlipidaemia

Treatment with lipid-lowering medications was lower in PwMS, as compared with
matched controls (PR = 0.63, 95% CI = 0.54, 0.74). This was particularly
pronounced for men (women: PR = 0.71, 95% CI = 0.59, 0.87; men: PR = 0.41,
95% CI = 0.37, 0.62).

Differences in risk factor severity

As compared with matched controls, after adjustment, PwMS had a 0.4-mm Hg lower
systolic blood pressure at the index year. The magnitude
was greater for men than women, considering that men had almost a 3-mm Hg lower
blood pressure than matched controls (overall: coeff. = −0.37, 95% CI = −0.60,
−0.13; women: −0.54, 95% CI = −0.96, −0.12; men: −2.81, 95% CI = −3.84, −1.77).
The differences were greater when restricting analyses to only those with a
diagnosis of hypertension at baseline, as PwMS had a 3.3-mm Hg lower systolic
blood pressure then matched controls (coeff. = −3.27, 95% CI = −5.04, −1.50);
differences were confirmed in women but not in men (women: coeff. = −2.56, 95%
CI = −3.84, −1.27; men: −0.27, 95% CI = 0.01, −2.56).

In contrast, PwMS had higher levels of diastolic blood pressure
at baseline, as compared with matched controls (coeff. = 0.29, 95% CI = 0.14,
0.43). However, the differences were not confirmed when restricting analyses to
only those with hypertension at baseline. Sex-stratified analyses for diastolic
blood pressure showed opposing findings for men and women, with finding for the
latter group being consistent with those of the general population. Men had a
0.7 mm Hg lower diastolic blood pressure (coeff. = −0.66, 95% CI = −1.28,
−0.04), as compared with matched controls but men with a diagnosis of
hypertension at baseline had 0.3 mm Hg higher diastolic blood pressure than
controls (coeff. = 0.30, 95% CI = 0.13, 0.48).

PwMS had lower levels of BMI at index year, as compared with matched controls
(coeff. = −0.43, 95% CI = −0.51, −0.35). The differences were attenuated when
progressing towards higher BMI ranges (underweight: coeff. = −0.25, 95%
CI = −0.42, −0.09; normal weight: coeff. = −0.25, 95% CI = −0.42, −0.09;
overweight: coeff. = −0.09, 95% CI = −0.13, −0.05; obese: coeff. = 0.03, 95%
CI = −0.21, 0.27). Differences were confirmed when stratifying analyses by sex
( Figure 2 ).

Adjusted differences in risk factors between MS and control subjects at
index year. Adjusted differences in risk factors mean values in the
index years were estimated employing linear regression models. Models
were adjusted for gender, age, ethnicity (white/non-white), deprivation,
smoking status, BMI, systolic and diastolic blood pressure, electronic
frailty index (eFI) ratio, number of primary care visits in the year
before and year. Columns on the left report unadjusted mean values at
index year.

PwMS: People with Multiple Sclerosis; coeff.: coefficient.

Sensitivity analysis

Complete case analysis for differences in study outcomes at index year between
PwMS and matched controls confirmed our main findings ( Supplemental Appendix Table 4 ).

Discussion

We conducted a large population-based study on 12,251 PwMS and 72,572 controls
matched by age, sex and general practice between January 1987 and December 2018 in
England to assess differences in the prevalence and management of vascular risk at
the time of the diagnosis of MS as compared with the general population. We have
found a 30% increased prevalence of diabetes in PwMS, but paradoxically, a 56%
reduced likelihood of being treated with antidiabetic medication. Similarly, the
prevalence of hypertension was 6% greater in PwMS, but the probability of being
treated with antihypertensive medication was 56% lower. Importantly, for PwMS who
had hypertension at time of diagnosis, even if the proportion of antihypertensive
medication usage was lower than in matched controls, the actual systolic blood
pressure values were, on average, lower in PwMS. This result was only partially
confirmed in sex-stratified analyses, as findings had opposite directions for women
and men, with analyses for men with MS showing higher prevalence of hypertension at
diagnosis, lower proportion of antihypertensive medication usage among those with
hypertension and higher diastolic blood pressure values than matched controls. PwMS
also had a 37% lower probability of using lipid-lowering medication at the time of
diagnosis. At time of diagnosis, BMI was 0.4 lower in PwMS as compared with
controls. We found little or no difference between PwMS and matched controls when
restricting analyses to those who were overweight or obese. Overall, results were
confirmed when stratifying by sex.

The 7.2% prevalence of type 2 diabetes at diagnosis was consistent with a prior
Canadian study which examined comorbidity prevalence at diagnosis (5.7%).
6
The prevalence was lower than estimated in a prior meta-analysis (8.6%),
7
which did not focus on prevalence at MS diagnosis specifically.
Interestingly, the prevalence of hypertension at diagnosis (9.7%) was lower than
that reported in the prior Canadian study (15.2%).
24
This finding was also supported by the lower absolute systolic blood pressure
values in PwMS than matched controls in this study. In contrast, no clinically
meaningful differences in diastolic blood pressure values were found, consistent
with recent findings which found no differences in temporal trends in the incidence
of hypertension between PwMS and matched controls.
2
Overall, BMI was lower in PwMS than matched controls and lower than estimates
reported in previous study. 4 , 7 , 24 However, we
observed a significant proportion of PwMS to be underweight (2.4%) which would have
reduced the average BMI.

Generally, the association between comorbid disease and intensity of management of
vascular risk factors varies in magnitude and direction. 25 , 26 We found that PwMS were less
likely than matched controls to be treated if they had type 2 diabetes and
hypertension. While the findings regarding type 2 diabetes are consistent with
recent evidence, 4 , 27 those regarding likelihood of being treated with
antihypertensive medications, contradict recent evidence that found no difference.
4
That study, however, did not focus on differences at diagnosis, and our
findings might have differed if we had focused on prevalent cohorts post-MS
diagnosis since PwMS have higher healthcare resource utilization
following the diagnosis,
9
which could lead to tighter clinical management following diagnosis. A
growing body of evidence shows the benefits of this medication on disease
progression in PwMS. 28 , 29 Nonetheless, we found that PwMS were also less likely to be
treated with statins, consistent with a Canadian study showing that PwMS were less
likely to receive statins following admission for acute myocardial infarction.
30

PwMS have an increased prevalence of comorbidities.
2
Some studies suggest that in primary care, individuals with more unrelated
comorbidities, such as arthritis, are less likely to have their uncontrolled
hypertension addressed. 26 , 31 Some, but not all, studies suggest that comorbidities that are
unrelated or discordant with diabetes are associated with worse diabetes control.
32
This may reflect clinician or patient priorities, competing demands and
challenges with treatment adherence.

Sex differences in vascular risk and risk management are complex. At diagnosis, men
had a higher prevalence of hypertension and diabetes than women in both populations,
consistent with a prior Canadian study.
6
Before the menopause, women in the general population have a lower prevalence
of hypertension than men, and this association reverses post-menopause.
33
Sex-specific differences in vascular risk management have been reported in
some populations. Among individuals with coronary heart disease from Europe, Asian
and the Middle East, women were less likely to reach targets for cholesterol and
glucose than men, but were more likely to reach targets for blood pressure.
34
In a Canadian MS cohort, women were less likely to exhibit good adherence to
statins, angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor
blockers than men.
35

To our knowledge, this was the first study that controlled for important clinical
variables, including blood pressure, BMI and frailty index when assessing vascular
risk management at the time of the diagnosis of MS. We note the frailty index is
strongly associated with MS disease duration, disability and fatigue.
12
Moreover, frailty is associated with higher prevalence of hypertension and
worse hypertension control, 36 , 37 as well as worse cardiovascular outcomes.
38

Several caveats merit discussion. First, when using routinely collected data,
miscoding, misclassification and misdiagnosis may occur. However, the CPRD is a
reliable, widely used data source and is subject to regular quality checks.
13
Second, PwMS diagnosed before availability of Disease Modifying Therapies in
the United Kingdom (1995) may have been more likely to be exposed to corticosteroid
treatment, with subsequent negative impact on their vascular risk profile. However,
only 4.6% of the PwMS in this study population had a diagnosis of MS before 1995;
therefore, the impact on our findings might be limited. Third, although CPRD is a
database representative of the UK population and although we adopted a validated
algorithm to identify PwMS, we cannot be completely certain if the MS population
identified using our case-finding algorithm is fully representative of all people
with MS in England. Fourth, we used lipid-lowering medication as proxy of
dyslipidemia because we lacked sufficient data regarding cholesterol levels which
may have reduced ascertainment of dyslipidemia. Fifth, we could not assess any
non-pharmacologic recommendations, such as changes in diet or physical activity,
that might have been made to manage vascular risk. Finally, this is a
cross-sectional study which limits causal inference regarding our findings.

In summary, at the time of diagnosis, PwMS have an increased prevalence of vascular
risk factors, including hypertension and diabetes, although paradoxically poorer
treatment, with probabilities of initiating treatment being around 40%–60% less than
matched controls. This is concerning, because we know that in PwMS the vascular
burden increases over time
2
and is associated with accelerated MS-related disability,
8
increased healthcare utilization
9
and greater all-cause and vascular mortality. 1 , 3 Further research is needed to
determine the optimal approach to vascular risk management in this population and to
develop appropriate guidelines to guide clinical practice.

Supplemental Material

sj-docx-1-msj-10.1177_13524585231164296 – Supplemental material for
Management of vascular risk in people with multiple sclerosis at the time of
diagnosis in England: A population-based study

Click here for additional data file.

Supplemental material, sj-docx-1-msj-10.1177_13524585231164296 for Management of
vascular risk in people with multiple sclerosis at the time of diagnosis in
England: A population-based study by Raffaele Palladino, Ruth Ann Marrie, Azeem
Majeed and Jeremy Chataway in Multiple Sclerosis Journal