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Early life predictors of late life cerebral
small vessel disease in four prospective
cohort studies
Ellen V. Backhouse,
1,2
Susan D. Shenkin,
3
Andrew M. McIntosh,
4
Mark E. Bastin,
1,5,6
Heather C. Whalley,
1,4
Maria Valdez Hernandez,
1,5,6
Susana Mu~
noz Maniega,
1,5,6
Mathew A. Harris,
4
Aleks Stolicyn,
4
Archie Campbell,
4
Douglas Steele,
7
Gordon D. Waiter,
8
Anca-Larisa Sandu,
8
Jennifer M. J. Waymont,
5,8
Alison D. Murray,
8
Simon R. Cox,
9
Susanne R. de Rooij,
10
Tessa J. Roseboom
10
and Joanna M. Wardlaw
1,2,5,6
Development of cerebral small vessel disease, a major cause of stroke and dementia, may be influenced by early
life factors. It is unclear whether these relationships are independent of each other, of adult socio-economic status
or of vascular risk factor exposures.
We examined associations between factors from birth (ponderal index, birth weight), childhood (IQ, education,
socio-economic status), adult small vessel disease, and brain volumes, using data from four prospective cohort
studies: STratifying Resilience And Depression Longitudinally (STRADL) (n= 1080; mean age = 59 years); the Dutch
Famine Birth Cohort (n= 118; mean age = 68 years); the Lothian Birth Cohort 1936 (LBC1936; n= 617; mean age = 73-
years), and the Simpson’s cohort (n= 110; mean age = 78 years). We analysed each small vessel disease feature in-
dividually and summed to give a total small vessel disease score (range 1–4) in each cohort separately, then in
meta-analysis, adjusted for vascular risk factors and adult socio-economic status.
Higher birth weight was associated with fewer lacunes [odds ratio (OR) per 100 g = 0.93, 95% confidence interval
(CI) = 0.88 to 0.99], fewer infarcts (OR = 0.94, 95% CI = 0.89 to 0.99), and fewer perivascular spaces (OR = 0.95, 95%
CI = 0.91 to 0.99). Higher childhood IQ was associated with lower white matter hyperintensity burden (OR per IQ
point = 0.99, 95% CI 0.98 to 0.998), fewer infarcts (OR = 0.98, 95% CI = 0.97 to 0.998), fewer lacunes (OR = 0.98, 95%
CI = 0.97 to 0.999), and lower total small vessel disease burden (OR = 0.98, 95% CI = 0.96 to 0.999). Low education
was associated with more microbleeds (OR = 1.90, 95% CI = 1.33 to 2.72) and lower total brain volume (mean differ-
ence = –178.86 cm
3
, 95% CI = –325.07 to –32.66). Low childhood socio-economic status was associated with fewer
lacunes (OR = 0.62, 95% CI = 0.40 to 0.95).
Early life factors are associated with worse small vessel disease in later life, independent of each other, vascular
risk factors and adult socio-economic status. Risk for small vessel disease may originate in early life and provide a
mechanistic link between early life factors and risk of stroke and dementia. Policies investing in early child devel-
opment may improve lifelong brain health and contribute to the prevention of dementia and stroke in older age.
1 Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
2 MRC UK Dementia Research Institute at the University of Edinburgh, Edinburgh, EH16 4SB, UK
3 Geriatric Medicine, Usher Institute, The University of Edinburgh, Edinburgh, EH16 4SB, UK
4 Division of Psychiatry, Royal EdinburghHospital,UniversityofEdinburgh,Edinburgh,EH105HF,UK
5 Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), Institute of Neuroscience and Psychology,
Glasgow G12 8QB, UK
Received March 04, 2021. Revised June 12, 2021. Accepted July 07, 2021. Advance access publication September 28, 2021
V
CThe Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
https://doi.org/10.1093/brain/awab331 BRAIN 2021: 144; 3769–3778 |3769
6 Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of
Edinburgh, Edinburgh, EH16 4TJ, UK
7 Division of Imaging Sciences and Technology, Medical School, University of Dundee, Dundee, DD1 9SY, UK
8 Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen,
Foresterhill, Aberdeen, AB25 2ZD, UK
9 Lothian Birth Cohorts Group, Department of Psychology, University of Edinburgh, Edinburgh, UK
10 Department of Epidemiology and Data Science, Amsterdam University, Medical Centres, University of Amsterdam,
The Netherlands
Correspondence to: Joanna M. Wardlaw
Centre for Clinical Brain Sciences, University of Edinburgh, The Chancellors Building
49 Little France Crescent, Edinburgh EH16 4SB, UK
E-mail: joanna.wardlaw@ed.ac.uk
Keywords: cerebral small vessel disease; education; childhood; MRI; epidemiology
Abbreviations: LBC1936 = Lothian Birth Cohort 1936; SES = socio-economic status; STRADL = STratifying Resilience
And Depression Longitudinally; SVD = cerebral small vessel disease; WMH = white matter hyperintensities
Introduction
Cerebral small vessel disease (SVD) is common at older ages
1
and
causes 20–25% of strokes and up to 45% of dementias, either as
vascular or mixed with Alzheimer’s disease.
2
It is responsible for
up to a fifth of all strokes, doubles the risk of future stroke and
worsens post-stroke recovery.
3
SVD is detected on neuroimaging
or post-mortem
4
as white matter hyperintensities (WMH), lacunes,
microbleeds, perivascular spaces, acute lacunar infarcts and brain
atrophy.
4,5
Several demographic and clinical factors are associated
with increased risk of SVD, including adult socio-economic status
(SES), hypertension and smoking.
6,7
However, a large proportion of
the variance in the presence and severity of SVD is unexplained by
vascular risk factors
7
and factors from earlier in life may also be
important.
8
The Developmental Origins of Adult Heath and Disease
(DOHAD) hypothesis
9
proposes that adverse environmental expo-
sures occurring during gestation can cause permanent changes in
foetal development resulting in increased vulnerability to chronic
diseases in adulthood. Factors affecting foetal growth such as
stress and poor nutrition
10,11
are often hard to measure but an-
thropometric measures such as birth weight and ponderal index
(birth weight/birth length
3
) can be used as proxy measures.
12
Additional confounding or mediating factors in childhood may
also affect later disease risk.
13
A recent meta-analysis
14
found that
lower levels of childhood IQ, poorer childhood SES, and less educa-
tion increased the risk of SVD in later life by approximately 17–
39%. However, it is not clear if these relationships are independent
of each other, or if they persist after adjustment for vascular risk
factors and adult SES. Few studies have examined the effect of
these early life factors in combination and many rely on childhood
measures assessed retrospectively in adulthood so may be subject
to recall bias.
We examined the relationships between birth and childhood
factors and total and individual components of SVD and brain vol-
umes, after adjustment for each other and common adult risk fac-
tors, in four well-phenotyped prospective cohort studies:
STratifying Resilience And Depression Longitudinally (STRADL).
15
the Dutch Famine Birth Cohort,
16
the Lothian Birth Cohort 1936
(LBC1936),
17
and the Simpson’s cohort.
18
All had information on
education and SES, and three cohorts had IQ measured during
childhood. All underwent brain imaging between the ages of 59
and 85 years. We hypothesized that low birth weight, low child-
hood IQ, low education and low childhood SES would be associated
with increased SVD, independent of each other, vascular risk fac-
tors and adult SES.
Materials and methods
Participants
The recruitment procedures and inclusion criteria for STRADL,
15
the Dutch Famine Birth Cohort,
16
the LBC1936,
17
and the
Simpson’s cohort
18
have been described previously in detail (see
Supplementary Fig. 1A–D for recruitment flow charts). All subjects
were community dwelling.
STRADL
STRADL is a population-based study of 1198 adults recruited from
the Generation Scotland: Scottish Family Health Study (GS:SFHS)
and two Scottish longitudinal birth cohorts, the Aberdeen Children
of the 1950s (ACONF) cohort
19
and the Walker cohort.
20
ACONF
consists of surviving participants of the Aberdeen Child
Development Survey (ACDS), a population-based study of school-
children in Aberdeen, conducted in 1962–64. The Walker cohort is
a database of over 48 000 birth records of babies born in hospital in
Dundee, between 1952 and 1966. In 2015 eligible participants were
sent postal questionnaires and between 2015 and 2019 1188
attended in-person assessments. MRI and childhood data were
available for 1080 participants (ACONF 268; Walker 201; GS:SFHS
611) [40% female; mean age = 59.3 years, standard deviation
(SD) = 10.1].
The Dutch Famine Birth Cohort
The Dutch Famine Birth Cohort consists of 2414 individuals born
in the Wilhelmina Gasthuis hospital in Amsterdam between 1
November 1943 and 28 February 1947, a proportion of whom were
exposed to the Dutch famine of 1944–45 in utero. A total of 151 sur-
viving cohort members were recruited for an MRI study in 2012 of
whom 118 had MRI and childhood data (56% female; mean
age = 67.5 years, SD = 0.9).
3770 |BRAIN 2021: 144; 3769–3778 E. V. Backhouse et al.
The Lothian Birth Cohort 1936
The LBC1936 consists of 1091 community-dwelling adults born in
1936 and living in the Lothian area of Scotland. All are surviving
participants of the Scottish Mental Health Survey 1947, which was
a cognitive ability test administered to all age 11 school children in
Scotland in 1947. Between 2007 and 2009, 680 of the original 1091
cohort members underwent MRI, all with childhood data (47% fe-
male; mean age 72.7 years, SD = 0.7).
The Simpson’s cohort
The Simpson’s cohort consists of 130 individuals born 1921–26 in
three Edinburgh hospitals. In 2000, 28 people were recruited as
part of the Lothian Birth Cohort 1921, 19 were traced through hos-
pital records from 1921 and 80 people were recruited through local
advertisements. MRI and childhood data were available for 110
people (67% female, mean age = 78.4 years , SD = 1.5).
Participants in all cohorts provided written informed consent
and research was approved by Local or Multicentre Research
Ethics Committees. (STRADL: 14/SS/0039; LBC1936: MREC/01/0/56
and LREC/2003/2/29; Simpson’s cohort LREC 1702/1998/4/183–
Amendment.)
Early life factors
The early life data available varied between cohorts (Fig. 1). Where
possible, data were harmonized to allow direct comparison be-
tween the studies. We examined birth weight in grams (all
cohorts) and ponderal index (birth weight/birth length
3
) (Dutch
Famine Birth Cohort, LBC1936 and Simpson’s cohort). In childhood,
we examined: childhood IQ (STRADL, LBC1936 and Simpson’s co-
hort) measured using raw test scores adjusted for age at testing
and placed on an IQ scale; education (all cohorts) dichotomized at
compulsory education (STRADL), lower secondary (Dutch Famine
Birth Cohort) and 11 years (LBC1936 and Simpson’s cohort); and
childhood SES (all cohorts) classified according to parental occupa-
tion (manual and non-manual). Further details are provided in
Supplementary Table 1.
MRI acquisition and analysis
Brain imaging acquisition for STRADL,
21
the Dutch Famine Birth
Cohort,
22
LBC1936
23
and the Simpson’s cohort
24
have been
described previously. Participants were scanned on a Philips
Achieva 3.0 T TX (STRADL, Aberdeen), Siemens 3 T Prisma-FIT
(STRADL, Dundee), a 3 T Philips Ingenia (Best, The Netherlands)
with a 16-channel DStream Head-Spin coil (Dutch Famine Birth
Cohort), or the same 1.5 T GE Signa scanner operating in research
mode in its original LX format (Simpson’s cohort) or following an
upgrade to HDx format (LBC1936) (Supplementary Table 2).
Cerebral small vessel disease visual ratings
Trained researchers using the same rating methods, and blind to
all other data, performed all image analyses. An experienced certi-
fied and registered neuroradiologist (J.M.W.) cross-checked 20% of
Figure 1 The life course perspective of the risk of SVD and stroke. Adapted from Figure 1 in Backhouse et al.
8
AF = atrial fibrillation; BP = blood pres-
sure; Chol = cholesterol.
Early life risk factors for SVD BRAIN 2021: 144; 3769–3778 |3771
scans. The presence of WMH, lacunes, micro-bleeds and perivas-
cular spaces were rated according to STRIVE criteria and estab-
lished protocols, published previously using validated visual
scales,
23,25,26
converted to dichotomous point scores and summed
to create the total SVD score (0–4; higher score represents higher
SVD burden).
6,27–29
We noted any imaging evidence of infarcts in
the cortical or subcortical regions using a validated stroke lesion
rating scale.
30
Superficial and deep atrophy scores were coded sep-
arately using a valid template,
31
summed to give a total score and
dichotomized into ‘none or mild’ and ‘moderate or severe’.
White matter hyperintensity volumes and whole
brain volume
We conducted structural image analysis, blind to all non-imaging
data, including measurements of volumes of the intracranial com-
partment, whole brain and total WMH volume in STRADL, LBC1936
and the Simpson’s cohort and WMH volume only in the Dutch
Famine Birth Cohort. For tissue segmentation we used the process-
ing protocol with the lesion growth algorithm (LGA), provided by
the Lesion Segmentation Toolbox for SPM (STRADL) and a semi-
automatic segmentation tool MCMxxxVI previously validated
32
(LBC1936 and Simpson’s cohort). We visually inspected all seg-
mented images and manually edited any incorrectly classified tis-
sues. Analyses were performed using Freesurfer 5.3 and Analyze
TM
software.
Statistical analysis
We assessed descriptive characteristics using means, SD, medians
and interquartile range (IQR), counts and percentages as appropri-
ate. We used v
2
for categorical data and Mann-Whitney U-test for
continuous data to compare differences between participants who
underwent MRI and those who did not and to examine gender dif-
ferences in SVD burden.
Few in the cohorts had the highest SVD scores, which likely
reflects the generally good health of the cohorts. We therefore
dichotomized the SVD score into 0–1 (‘no or mild disease’) and 2–4
(‘moderate-severe disease’).
We performed logistic regression for differences in early life
factors for higher versus lower SVD scores and for presence of
each individual SVD component and linear regression analysis to
assess early life factors and brain volumes. Brain volumes were
adjusted for intracranial volume. For all main analyses we ana-
lysed the cohorts individually and meta-analysed them using a
random effects model in Review Manager 5.3. Because of the small
sample size for some analyses we did not adjust for all available
vascular risk factors. Based on previous research,
6,7
we included
age, sex, hypertension, smoking behaviour and adult SES at the
time of the MRI (manual versus non-manual occupation) as covari-
ates in all models. We adjusted analyses including birth weight
and ponderal index for gestational age taken from birth records.
We performed further multiple regression analyses adjusting for
the other early life factors and where sample size allowed, using
an event per variable of 10, vascular risk factors and SES in adult-
hood. A Bonferroni correction for multiple testing was not appro-
priate, as the variables are not independent. Therefore to mitigate
the problem of multiple testing, we defined our hypotheses a priori
based on our previous meta-analysis.
33
All analyses were performed using SPSS version 24 (IBM Corp.,
Armonk, NY) using pairwise deletion to deal with missing data.
Data availability
The data that support the findings of this study are available upon
reasonable request.
Results
Demographic and key characteristics of all participants are dis-
played in Table 1.
Differences in demographic and key characteristics between
those who underwent MRI and those who did not are provided in
the Supplementary material and Supplementary Tables 3–6, along
with comparisons between the participants in this study and pre-
vious waves of each. Where data were available in comparable for-
mat, we have also provided key characteristics of the wider
Scottish and Dutch population in Supplementary Tables 3–6.
Gender differences were observed in some markers of SVD.
Moderate to severe SVD and WMH burden were more common in
females compared to males in the LBC1936 [SVD: 22.4% versus
15.9%, v
2
(1) = 4.7, P= 0.03; WMH: 26.5% versus 18.8%; v
2
(1) = 5.8,
P= 0.02] and Dutch Famine Birth Cohort [SVD: 31.3% versus 14.0%,
v
2
(1) = 4.6, P= 0.03]. Atrophy was more common in males com-
pared to females in STRADL [11.2% versus 3.1%, v
2
(1) = 28.0,
P50.001], the LBC1936 [60.7% versus 40.7%, v
2
(1) = 27.1, P50.001]
and Simpson’s cohort [36.4% versus 18.2%, v
2
(1) = 4.2, P= 0.04]. No
other gender differences were observed in SVD burden.
Results from our main analyses are given below. Analysis of
ponderal index are detailed in the Supplementary material and
Supplementary Fig. 2.
Birth weight
Across all four cohorts, each increase in birth weight of 100 g was
associated with fewer lacunes (OR = 0.93, 95% CI = 0.88 to 0.99),
fewer infarcts (OR = 0.94, 95% CI = 0.89 to 0.99) and decreased mod-
erate-severe perivascular spaces (OR = 0.95, 95% CI = 0.91 to 0.99;
Fig. 2A) independent of age, sex, hypertension, smoking behaviour
and adult SES. Results for the remaining lesions were in the
expected direction (increasing birth weight and lower risk of SVD
features) but did not reach significance.
Associations were attenuated but remained significant after
additional adjustment for education and childhood SES (lacunes
OR = 0.94, 95% CI = 0.89 to 0.99; infarcts OR = 0.94, 95% CI = 0.89 to
1.00; perivascular spaces OR = 0.95, 95% CI = 0.91 to 0.99;
Supplementary Table 7).
Increasing birth weight was not associated with WMH volume
or brain volume in the Dutch Famine Birth Cohort, LBC1936 or
Simpson’s cohort (Supplementary Table 8).
Childhood IQ
Across STRADL, LBC1936, Simpson’s, each point increase in IQ
assessed in childhood was associated with decreased risk of mod-
erate or severe WMH (OR per point increase 0.99, 95% CI = 0.98 to
1.00), lacunes (OR = 0.98, 95% CI = 0.97 to 0.99), infarcts (OR = 0.98,
95% CI = 0.97 to 1.00), and total SVD burden (OR = 0.98, 95%
CI = 0.96 to 1.00; Fig. 2B) independent of age, sex, hypertension,
smoking behaviour and adult SES.
Additional adjustment for education and childhood SES attenu-
ated all associations between childhood IQ and individual SVD fea-
tures (Supplementary Table 9), but the associations with total SVD
burden (OR = 0.98, 95% CI = 0.97 to 0.997) and infarcts (OR = 0.98,
95% CI = 0.97 to 1.00; Supplementary Table 9) remained.
3772 |BRAIN 2021: 144; 3769–3778 E. V. Backhouse et al.
Table 1 Demographic and health characteristics, early life characteristics and imaging characteristics of STRADL, the Dutch Famine Birth cohort, the LBC1936 and the Simpson’s
cohort
STRADL Dutch Famine LBC1936 Simpson’s
Total nn(%) Total nn(%) Total nn(%) Total nn(%)
Demographic and health characteristics
Age (y) at MRI, mean (SD), range 1080 59.3 (10.1), 26–84 118 67.5 (0.9), 65–69 685 72.7 (0.7), 71–74 110 78.4 (1.5), 75–81
Sex, male 1080 437 (40.5) 118 52 (44.1) 685 361 (52.7) 110 33 (30)
Manual adult SES 1070 345 (31.9) 118 44 (37.3) 674 141 (20.9) 110 64 (58.2)
History of stroke 1080 33 (3.1) 117 3 (2.6) 685 47 (6.9) 110 16 (14.6)
Hypertension 1080 299 (27.7) 117 62 (53.0) 685 336 (49.1) 110 49 (44.6)
Diabetes 1069 84 (7.9) 118 24 (20.3) 685 72 (10.5) 110 7 (6.4)
Hypercholesterolemia 930 221 (23.7) 117 56 (47.9) 685 287 (41.9) – –
Smoking history 962 118 685 110
Ever smoker 435 (40.3) 72 (61.0) 362 (52.9) 60 (54.6)
Never smoked 527 (48.8) 46 (39.0) 323 (47.2) 50 (45.5)
Early life characteristics
Years of birth 1933–1993 1944–1947 1936 1921–1926
Median year 1955
Birth factors
Ponderal index (kg/m
3
) mean (SD) – – 115 26.2 (2.3) 79 27.3 (5.3) 107 25.8 (4.2)
Birth weight (g), mean (SD) 154 3309.3 (529.4) 118 3417.5 (503.4) 140 3351.5 (482.1) 110 3333.6 (457.2)
Low birth weight (55lbs), n(%) 253 6 (2.4) – – – – – –
Birth length (cm), mean (SD) – – 118 51.9 (8.0) 79 50.0 (3.3) 107 50.7 (2.8)
Childhood factors
Childhood IQ 246 102.0 (8.9) – – 648 100.8 (15.3) 30 101.7 (14.5)
Low versus high level of education
a
1078 259 (24.0) 118 74 (62.7) 685 491 (71.7) 110 89 (80.9)
Manual father’s occupation 1070 719 (67.2) 96 64 (66.7) 627 465 (74.2) 110 76 (69.1)
Imaging characteristics
Visual ratings
Total SVD score 1058 114 680 96
0 461 (43.6) 52 (45.6) 302 (44.4) 12 (12.5)
1 414 (39.1) 35 (30.7) 249 (36.6) 53 (55.2)
2 145 (13.7) 16 (14.0) 98 (14.4) 20 (20.8)
3 31 (2.9) 9 (6.0) 27 (4.0) 8 (8.3)
4 7 (0.7) 2 (1.3) 4 (0.6) 3 (3.1)
Mod/sev total SVD score 1058 188 (17.3) 114 27 (23.7) 680 129 (19.0) 97 31 (32.0)
Mod/sev WMH 1075 114 (10.6) 118 30 (25.4) 685 154 (22.5) 110 27 (24.6)
Mod/sev EPVS 1063 511 (48.1) 114 28 (24.6) 680 276 (40.6) 110 83 (75.5)
1 + Lacune 1076 86 (7.2) 118 26 (22.0) 680 33 (4.9) 110 27 (24.5)
1 + CMB 1074 119 (11.1) 117 16 (13.7) 680 79 (11.6) 97 11 (11.3)
Imaging evidence of 1 + infarcts 1076 60 (5.0) 118 22 (18.6) 685 99 (14.5) 110 10 (7.7)
Mod/sev atrophy 1076 70 (5.8) 118 23 (19.5) 685 189 (27.6) 110 64 (58.2)
Brain volumes
Whole brain volume (mm
3
), mean (SD) 882 1 064 225.8
(107 249.4)
– – 657 990322.7 (89 401.9) 110 1 137 480.3 (98 056.8)
ICV (mm
3
), mean (SD) 893 1 376 151.5
(226471.3)
– – 659 1 438 223.1 (133 870.1) 95 1 454 751.50
(123 117.80)
WMH volume (mm
3
), median (IQR) 471 1510.0 (2942.5) – – 656 7896.0 (11 531.0) 107 25 755.4 (27 166.0)
A dash is used where data are not available; mod/sev WMH = periventricular WMH with a score of 3 and/or deep WMH with a score of 2–3 on the Fazekas scale
34
; mod/sev EPVS = moderate or severe enlarged perivascular spaces; a score
of 2–3 on a semi-quantitative scale in the basal ganglia
35
; CMB = cerebral microbleed.
a
Low education defined as compulsory education and below (STRADL), lower secondary school and below (Dutch Famine Birth Cohort) and 11years and below (LBC1936 and Simpson’s cohort).
Early life risk factors for SVD BRAIN 2021: 144; 3769–3778 |3773
Figure 2 Forest plots showing associations between features of SVD and (A) birth weight, (B) childhood IQ, (C) low education, (D) low childhood SES.
All analyses are adjusted for age, sex, hypertension, smoking behaviour and adult SES.
3774 |BRAIN 2021: 144; 3769–3778 E. V. Backhouse et al.
Education
Across all cohorts, low education was associated with increased
risk of micro-bleeds (versus high education level, OR = 1.90, 95%
CI = 1.33 to 2.72; Fig. 2C) independent of age, sex, hypertension,
smoking behaviour and adult SES. This was attenuated by add-
itional adjustment for childhood IQ and SES (OR = 1.24, 95%
CI = 0.71 to 2.18; Supplementary Table 9). The Simpson’s cohort
were not included in this multiple regression analysis due to the
small number of participants with childhood IQ scores.
Low education was associated with lower brain volume
(mean difference = –178.86 cm
3
, 95% CI = –325.07 to –32.66;
Supplementary Fig. 5A) but this was attenuated after adjustment
for vascular risk factors and adult SES (b= 0.01, 95% CI = –0.04 to
0.06; Supplementary Table 10).
Childhood SES
Across all cohorts manual childhood SES (i.e. more deprived) was
associated with decreased risk of lacunes (OR = 0.62, 95% CI = 0.40
to 0.95; Fig. 2D).
Discussion
Early life factors are thought to influence health later in life but
there are few studies with such a wealth of data from birth, child-
hood and later life to tease out which early life factors are import-
ant and if they are independent of each other and of exposures in
later life. By combining data from almost 2000 participants from
four prospective birth cohorts we confirm that low birth weight,
low childhood IQ and less education increase SVD burden five to
eight decades later. SVD is important since it increases dementia
and stroke risk, two of the largest sources of loss of independence,
health and societal costs in older age across the world. Dementia
and stroke prevention are government priorities. Life-course mod-
els are increasingly recognized in dementia prevention
36
but have
largely been ignored in stroke and SVD, which too often focus on
mid to later life only, thereby missing major opportunities to pre-
vent these devastating diseases much earlier, as well as gaining
other health benefits.
Our findings confirm previous findings that some early life fac-
tors may increase risk of SVD burden in later life, but importantly
also demonstrate that the associations are independent of vascu-
lar risk factors and adult SES and persist after adjustment for the
other early life factors. Lower birth weight increased the risk of
lacunes, infarcts and perivascular spaces across four cohorts, inde-
pendent of education and childhood SES. In STRADL, the LBC1936
and Simpson’s cohort, higher childhood IQ was associated with
fewer infarcts and lacunes, lower WMH and total SVD burden.
Associations between childhood IQ, infarcts and total SVD burden
were independent of education and childhood SES. Across all
cohorts, low education level was associated with more micro-
bleeds. These new data show that lower birth weight, childhood IQ
and low education are independently associated with increased
SVD lesions many decades later.
Low childhood SES was not found to be associated with SVD
and associations between childhood SES and lacunes were in the
opposite direction to what we expected. This was true for univari-
ate analyses (Supplementary Table 8) and multivariate analyses.
This may be because childhood SES reflects SES in adulthood,
whereas the other early life factors such as cognitive ability and
education capture different aspects of early life adversity.
Alternatively, parental occupation, which we used as a measure of
SES to allow direct comparison between cohorts, may not have
been a sufficiently sensitive measure of actual SES in childhood.
Jobs traditionally classed as ‘manual’ such as farmer or skipper
trawler can have a high income and the wartime occupations of
the parents of some cohort members would have been limited. In
the LBC1936 we have previously shown a trend towards an associ-
ation between SVD at age 72 and age 11 deprivation index.
37,38
Deprivation index encompasses several socio-economic markers
so may be a better measure of SES and thus of associations with
SVD in later life.
Increasing age and traditional vascular risk factors, particularly
hypertension, are important risk factors for SVD
1,39
but together
explain little variance in WMH (2%)
7,40
suggesting that other fac-
tors, as identified here, may contribute to SVD pathology. The ef-
fect sizes are small when considered per point difference in IQ
score or per 100 g difference in birth weight, and the early life vari-
ables examined here only explained 1% of the variance in SVD
risk. However, the fact that these effects are evident for such small
differences in scores or weights, and at up to seven decades later,
underscores that factors influencing early stages in life, including
during foetal development and childhood, can impact on brain
health in older age and are rightly public health priorities.
Furthermore it is likely that our effects are an underestimate of
population effects given that our cohorts are healthier with higher
IQ than average members of the population. For example, the
mean age 11 IQ score of the LBC1936 was relatively high with a
narrow range compared with the mean age 11 IQ for Scotland in
1947.
41
Our associations between birth weight and SVD are independ-
ent of gestational age and therefore reflect the impact of variations
in growth rather than prematurity. The relationship between size
at birth and brain structure is biologically plausible: lack of
nutrients at particular stages of gestation can impair foetal growth
resulting in small size at birth, indicated by low birth weight or dis-
proportionate growth such as low birth weight to length ratio (pon-
deral index). Long-lasting physiological changes in the structure of
foetal organs and tissues can increase risk of later disease in adult-
hood.
42,43
Relations between size at birth and disease in later life
including coronary heart disease
44,45
are well established, but
fewer studies have examined brain health, particularly with this
sample size or age range. The current study is one of the few
examining the effect of size at birth on brain volumes in later life
and the first to examine multiple markers of SVD.
We found no associations between birth weight or ponderal
index and WMH burden or brain volumes. This is consistent with
data from the (AGES)-Reykjavik study,
46
which reported no associ-
ation between ponderal index and WMH burden at age 75 after ad-
justment for vascular risk factors. Birth weight and size are
indirect measures of the foetal environment and may not reflect
all adverse prenatal circumstances that can affect later life health.
The Dutch Famine Birth Cohort previously showed that foetal mal-
nutrition can lead to accelerated cognitive ageing and advanced
structural brain ageing, measured using the BrainAGE method (a
composite measure based mainly on tissue loss) independent of
birth weight.
47
From a life course perspective, a disadvantaged foetal environ-
ment may interact with factors during childhood to increase risk
of later disease. Development of neural pathways in the brain
extends well into childhood and may therefore mean the brain
remains vulnerable to insults for a longer period of time.
48
Our two
recent meta-analyses
14,49
found small but statistically significant
associations between increasing childhood IQ and lower WMH
burden (r= –0.07) and a 17% lower risk of stroke. Low education
(defined by attainment or years) was associated with a 35% relative
increased risk of stroke and a 17% increased risk of SVD. Manual
paternal occupation (SES measure) was associated with a 28%
increased risk of stroke and increased WMH (only one study
Early life risk factors for SVD BRAIN 2021: 144; 3769–3778 |3775
identified). However, the previous literature did not allow us to de-
termine the independent effect of these three inter-related early
life factors from each other, or from risk factor exposures in adult-
hood, which we are now able to do.
In many high-income countries age-specific incidence rates of
dementia are declining.
36,50
Improved health in old age, including
cerebrovascular disease and SVD,
51
has been reported across gen-
erations and epidemiological studies have found that age-adjusted
incidence rates of dementia are lower in more recent cohorts com-
pared with cohorts from previous decades.
36,50,51
This can in part
be attributed to population public health strategies, advances in
treatment and management of patients with cerebrovascular dis-
ease and dementia, and improved management of key modifiable
risk factors such as smoking and hypertension. Additionally, in-
vestment in early life, particularly improvements in living condi-
tions and education, explain some of the decline in incidence of
dementia.
52–54
More recent generations of older adults have
received more years of statutory education than older cohorts,
which may increase cognitive reserve and therefore reduce risk of
dementia or cerebrovascular disease. This is particularly relevant
to our cohorts, whose years of birth span the 20th century. Low
education increased with increasing age of our cohorts, as did SVD
burden. In STRADL (median year of birth 1955) 24% had low educa-
tion and 17.3% had moderate to severe SVD burden. In the
Simpson’s cohort (born 1921–26) 81% had low education and 32%
had moderate to severe SVD burden. Increases in life expectancy
means that the global population is ageing, therefore identifying
factors that contribute to reductions in the prevalence and inci-
dence of dementia and cerebrovascular disease is a major priority.
Our findings support the suggestion that reducing inequalities,
including improvements in education, will contribute to improve-
ments in health in older age and a reduction in the risk of demen-
tia and cerebrovascular disease.
Why might the early life factors increase the risk of SVD in later
life? There are numerous potential explanations. Children with
higher IQ or from higher socio-economic backgrounds are likely to
receive better diets, medical care, more educational opportunities
and hence better job opportunities or less hazardous working con-
ditions. In adulthood, they may be more likely to engage in better
lifestyle behaviours and self-management of vascular risk factors.
Alternatively, positive early life factors may be associated with, or
lead to, an increase in the resilience and integrity of the brain
resulting in less SVD. These remain important empirical questions
to be addressed in future work.
Strengths and limitations
Strengths include data collected prospectively in early life through
to middle or later life, including brain imaging, from different stud-
ies in two western European countries. Detailed birth records
allowed correction for gestational age and did not rely on retro-
spective estimations of birth weight. We used ponderal index and
birth weight as measures of infant growth. Ponderal index may be
a better indicator of gestational problems than birth-weight per-
centiles as it provides information on the neonate’s body propor-
tionality and can detect situations in which weight growth
exceeds or fails to match growth in the infant’s length.
55
We
adjusted for key adult vascular risk factors and other early life fac-
tors in our analyses with a relatively large sample size for some
analyses. We also did a detailed characterization of SVD using
multiple individual assessments as well as a summary score.
Limitations include availability of birth data only for some par-
ticipants in STRADL and the LBC1936. Participants in the Dutch
Famine Birth Cohort may be unusual due to their famine exposure,
and we have demonstrated excess mortality up to the age of
63 years in females exposed to famine in early gestation.
56
This
may have resulted in selective participation of people who were in
sufficient health to participate in the present study at age 68 years.
Participants with birth data were born in hospitals, which was un-
common at the time of their births. In the Netherlands females
largely delivered at home supported by a midwife. Whilst little is
known about the actual referring pattern during this period most
referrals to hospital were because of social or medical reasons and
most referred females were from lower or middle social classes.
Two of our cohort’s early childhood or early adulthood were spent
during World War II, which may have influenced the development
of cognitive ability or educational opportunities. Although this
seems unlikely as IQ scores of those who took the Moray House
Test No. 12 in 1947 (born 1936) were higher than the cohort who
took them in 1932 (born 1921). The four cohorts recruited commu-
nity-dwelling volunteers who may be healthier, with less socio-
economic adversity than non-volunteers. Within our cohorts those
who completed the MRI were younger and healthier than those
who declined. Participants in all but one cohort were largely fe-
male and when compared with the Scottish and Dutch population
had lower risk factor profiles, were more educated and from higher
adult socio economic class. Even in our oldest cohort aged 80 years,
less than 30% of participants had moderate or severe SVD. The
large sample size of some of our cohorts mean that there are par-
ticipants with a range of socio economic backgrounds and medical
conditions, but our samples may not be truly representative of the
populations from which they are drawn. Our samples came from
three regions of Scotland and one region of the Netherlands, which
may introduce effects due to local variations in socio-economic
strata but may also increase the generalizability of our findings
and may also be considered a strength of our study. Years of edu-
cation were not available for all cohorts and the education system
in the Netherlands differs from that in Scotland, which meant the
division into ‘low’ and ‘high’ education level was relatively crude.
Whilst we adjusted our models for key vascular risk factors, it was
not possible to separate the confounding effects of other prenatal
environmental or genetic influences that may affect foetal brain
development. In this study we did not adjust for multiple compari-
sons as a Bonferroni-style correction would have been inappropri-
ate when our variables are not independent. We dealt with
multiple comparisons as recommended by Perneger
57
by transpar-
ently reporting all results, including those with borderline signifi-
cance. We also specified our hypotheses a priori based on previous
research. However, given the number of statistical comparisons in
our analysis it is still possible that some of our associations may
be due to Type I error.
SVD frequently coexists with neurodegenerative disease. We
did not examine associations between early life factors and bio-
markers such as amyloid-b, tau or synuclein but given the overlap
between neurodegenerative and cerebrovascular pathologies,
including shared risk factors,
58
it is possible that the associations
observed here may interact with degenerative neuropathologies.
Conclusions
Our findings suggest an important effect of early life factors, par-
ticularly childhood IQ, on brain vascular disease in later life, inde-
pendent of common vascular risk factors, adult SES and other
early life factors. Positive early life factors may influence health
behaviours and access to socio-economic resources beneficial to
health, or may increase brain integrity and resilience reducing sus-
ceptibility to cerebrovascular disease. Brain vascular disease
increases the risk of cognitive impairment, dementia and stroke
1
and worsens chances of recovery after stroke.
3
The current find-
ings may provide a possible mechanistic link between early life
3776 |BRAIN 2021: 144; 3769–3778 E. V. Backhouse et al.
factors and risk of stroke and dementia. Health disparities are well
known and these findings suggest that such disparities may have
effects that persist across more than seven decades of life, high-
lighting the importance of identifying modifiable early life factors
as targets for future social policy interventions with long-lasting
impacts.
Funding
Generation Scotland received core support from the Chief Scientist
Office of the Scottish Government Health Directorates [CZD/16/6]
and the Scottish Funding Council [HR03006] and is currently sup-
ported by the Wellcome Trust [216767/Z/19/Z]. The MRI data collec-
tion was funded by the Wellcome Trust [Wellcome Trust Strategic
Award ‘STratifying Resilience and Depression Longitudinally’
(STRADL) Reference 104036/Z/14/Z)]. The LBC1936 is supported by
Age UK [MR/M01311/1] (http://www.disconnectedmind.ed.ac.uk)
and the Medical Research Council [G1001245/96099]. LBC1936 MRI
brain imaging was supported by Medical Research Council (MRC)
grants [G0701120], [G1001245], [MR/M013111/1] and [MR/R024065/1]
and Row Fogo Charitable Trust (Grant No. BROD.FID3668413).
Simpson’s cohort was supported by the UK MRC and Chest Heart
Stroke Scotland. J.M.J.W. received funding from TauRx
Pharmaceuticals Ltd. E.B. received funding from the Sackler
Foundation. J.M.W. received funding from the UK Dementia
Research Institute (DRI Ltd, funded by the UK Medical Research
Council, Alzheimer’s Society and Alzheimer’s Research UK) and
SVDs@Target, the Fondation Leducq Transatlantic Network of
Excellence for the Study of Perivascular Spaces in Small Vessel
Disease, [16CVD05]. This research was funded in whole or in part,
by the Wellcome Trust [104036/Z/14/Z]. For the purpose of Open
Access, the author has applied a CC BY public copyright licence to
any author accepted manuscript version arising from this
submission.
Competing interests
The authors report no competing interests.
Supplementary material
Supplementary material is available on Brain online.
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