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ORIGINAL ARTICLE
Age-dependent association between cigarette smoking on whitematter hyperintensities
Sung Hun Kim • Chang-Ho Yun • Seo-Young Lee •
Kyung-ho Choi • Min Bom Kim • Hee-Kwon Park
Received: 30 August 2010 / Accepted: 28 April 2011 / Published online: 12 May 2011
� Springer-Verlag 2011
Abstract Previous reports have shown that cigarette
smoking is associated with white matter hyperintensities
(WMHs). However, it remains unclear whether this is true
for all ages. We investigated the association between cig-
arette smoking, WMHs, and age. We retrospectively
reviewed charts from 595 patients, who presented as out-
patients from January 2007 to March 2010. Grading of
periventricular WMHs (PVWMHs) and the scores of deep
WMHs (DWMHs) was determined based on criteria
established by the Rotterdam Scan Study. We compared
the degree of WMHs between smokers and non-smokers,
and those younger than the age of 65 years versus those
above. In younger age group, smokers had higher grades of
PVWMHs and more microbleeds than non-smokers. In the
older age group, total burden of DWMHs was much greater
in smokers than nonsmokers. Multivariate regression
analysis showed that cigarette smoking was an independent
risk factor for PVWMHs in the younger age group and for
DWMHs in the older age group. The location of WMHs in
association with smoking seems to differ among age
groups. Age should be considered when interpreting the
effects of smoking on the brain.
Keywords Age � Periventricular � Smoking �White matter hyperintensities
Introduction
Cigarette smoking is a well-known risk factor for stroke
and myocardial infarction [1, 2]. Many studies show that
cigarette smoking is also associated with neurodegenera-
tive diseases such as Alzheimer’s and Parkinson’s disease
[3–5]. In elderly individuals, smoking was associated with
decrease in cerebral perfusion and reduced cerebral gray
matter volume [6, 7]. Some of the effects of smoking on the
cardiovascular or central nervous system are age-depen-
dent [8, 9]. However, the age-dependent effects of smoking
on the brain have not been fully elucidated.
White matter hyperintensities (WMHs) on brain mag-
netic resonance images (MRI) are reportedly related to
stroke, dementia [10], and hypertension [11]. WMHs and
microbleeds in the elderly individuals have been known to
reflect the severity of cerebral small vessel ischemia [12–
15]. Some population-based studies, with middle-aged and
older adults, report that chronic smoking is associated with
increased incidence of WMHs [11, 16–21], but other
investigators have observed an inverse correlation between
WMHs and smoking [16, 22, 23].
In this study, we investigated the possibility of cigarette
smoking affecting the brain in an age-dependent manner by
assessing the degree of WMHs and number of microbleeds
in smokers versus non-smokers.
Patients and methods
Participants and demographic findings
Between January 2007 and March 2010, we retrospectively
reviewed charts from a total of 701 patients who presented
as an outpatient and had a brain MRI. Exclusion criteria
S. H. Kim � S.-Y. Lee � K. Choi � M. B. Kim
Department of Neurology, Kangwon National University
Hospital, Chuncheon, South Korea
C.-H. Yun � H.-K. Park (&)
Department of Neurology, Inha University Hospital,
7-206, 3-Ga, Sinheung-Dong, Jung-gu,
Incheon 400-711, South Korea
e-mail: parkhkwon@gmail.com
123
Neurol Sci (2012) 33:45–51
DOI 10.1007/s10072-011-0617-1
were as follows: (1) incomplete or poor-quality MR ima-
ges; (2) insufficient evaluation of cardiovascular risk fac-
tors; (3) the history or brain images of medical disease
affecting WMHs, such as migraine, intracranial hemor-
rhage or territorial infarction and (4) insufficient history of
cigarette smoking. One hundred and six patients were
excluded due to lack of data or other criteria. Routine
laboratory work-up and brain MRIs (including gradient
echo images) for the remaining 595 subjects were
reviewed. A current smoker was defined as an individual
who smoked currently on a regular basis or during the last
5 years. A non-smoker was defined as an individual who
did not smoke for the last 5 years. We also interviewed the
patients to acquire information such as the age at which
they started smoking. Pack-years were calculated as:
[(number of cigarettes per day/20) 9 (duration of smoking
until present time in years)]. Additional cerebrovascular risk
factors were clarified through routine laboratory findings and
medical history [24]. The following risk factors were
assessed: hypertension, which was defined as a previous
history of blood pressure repeatedly above 140/90 mmHg at
intervals of C1 week or being on antihypertensives; hyper-
lipidemia, which was defined as repeated elevation of total
cholesterol (C221 mg/dl) or being on lipid-lowering medi-
cation and diabetes, which was defined as a fasting serum
glucose over 126 mg/dl or being on anti-diabetic medica-
tion. The amount and type of alcohol consumed over the
past year were also documented.
Radiological findings
All patients underwent MRI scans performed on a 1.5 T
superconducting magnet system (GE, Milwaukee, USA).
Whole images were obtained with 27 contiguous, 5 mm
axial slices per scan without an interslice gap, in accor-
dance with the previously described imaging protocol [25].
Two blinded neurologists (H-K. Park and S. H. Kim) rated
the WMHs on a consensus basis and evaluated the number
of microbleeds on gradient echo sequences. WMHs were
defined as hyperintense focal lesions on FLAIR sequences
and iso- or hypointense focal lesions on T1-weighted
sequences and scored according to the method described in
the previous article [26]. The periventricular white matter
hyperintensities (PVWMHs) were assessed and rated in the
three regions—frontal, band, and posterior horns. The sum
of these regional grades was used to calculate the peri-
ventricular (PV) grades, which ranged from 0 to 9. Deep
white matter hyperintensities (DWMHs) were rated by the
number and size on FLAIR images. The DWMH scores
were calculated by multiplying each lesion by a size-
dependent constant, as a quantitative measure of DWMHs
(Fig. 1) [26]. Microbleeds were defined as homogenous
round signal loss (B5 mm) on gradient echo scans [25].
Data analysis
Data were expressed as the number (%) or mean ± SD.
Fisher’s exact tests were used to analyze discontinuous
variables, and unpaired Student’s t tests were used to
analyze continuous variables. To analyze the association
between smoking and WMHs in the younger and older age
groups, the subjects were categorized into two groups by
median value of the age (65 years). To identify whether
smoking was an independent risk factor for DWMHs or
PVWMHs, variables with p values\0.05 were entered into
a multivariate binary regression model. All statistical
analyses were performed using SPSS 14.0 for Windows
(SPSS Inc., Chicago, IL).
Results
Out of a total 595 patients (347 men; mean age
63.5 ± 10.7 years), 336 suffered a lacunar infarct in the
past (Table 1). A total of 144 patients (24.2%) were
smokers. The mean pack-year (PY) of the smokers was
66.2 ± 52.1 PY, and the mean age at onset of smoking was
24.8 ± 9.6 years. Three hundred and eighty-three partici-
pants had a history of hypertension, and 139 had a history
of diabetes mellitus. The mean values of HbA1c and total
cholesterol were 6.08% and 178.3 mg/dl, respectively. The
mean values of total DWMHs scores, PVWMH grades, and
the number of microbleeds were 1.56 ml, 1.50 and 1.11,
respectively. The male:female ratio was higher in smokers
than in non-smokers. Smokers were younger, and more
frequently had a history of diabetes, atrial fibrillation,
alcohol consumption, and lacunar infarction than non-
smokers (Table 1). The PVWMH grades, the total scores of
DWMHs and the number of microbleeds on MRI tended to
be greater in smokers than in non-smokers. Among
smokers, pack-years correlated with the PVWMH grades
(Pearson’s correlation r = 0.206, p = 0.01), even though
there was no association between this and DWMHs scores,
or the number of microbleeds. We were unable to find any
correlation between the first age of smoking and the total
burden of PVWMHs, DWMHs or number of microbleeds
(Pearson correlation: PVWMH grades, p = 0.95; DWMH
scores, p = 0.13; numbers of microbleeds, p = 0.83).
To determine the age-specific effects of cigarette
smoking on cerebral microvessels, we investigated the total
burden of PVWMHs, DWMHs and microbleeds, according
to the smoking status in younger (B64 years, n = 295) and
older (C65 years, n = 300) age groups. We found that
there was an age-dependent association between smoking
and either PVWMHs or DWMHs. In the younger group,
the PVWMH grades was much higher in smokers than in
non-smokers (p = 0.002), but there was no difference in
46 Neurol Sci (2012) 33:45–51
123
DWMH load between the two (p = 0.39, Table 2). In
the older age group, smokers had a larger burden of
DWMHs than non-smokers (p = 0.049), even though there
was no difference in PVWMH grades between smokers
and non-smokers (p = 0.58). Smokers tended to have more
microbleeds than non-smokers in either age group
(Table 2).
To identify whether there were any additional cardio-
vascular risk factors affecting WMHs, we compared the
prevalence of risk factors according to the burden of
PVWMs or DWMHs in both age groups (Table 3). We
found differences in the risk factors, related to PVWMHs
or DWMHs between the younger and older age groups. In
the younger age group, hypertension, hyperlipidemia, and
current smoking were more frequent in patients with
PVWMHs than those without, even though there was no
association of PVWMHs with the frequency of old age
(C57 years) men, diabetes mellitus, or lacunar infarction.
Larger scores of DWMHs (C0.27 ml) were more strongly
associated with advanced age (C57 years), and lacunae
than those with smaller scores. In the older age group, there
was no association between smoking and PVWMHs. The
subjects with higher grade of PVWMHs had the more
frequent history of advanced age (C71 years) and lacunae.
Hypertension and current smoking were also associated
with larger scores of DWMHs (C0.5 ml).
Fig. 1 Typical appearance of PVWMH (a) and DWMH (b) on
FLAIR scans. WMHs were defined as hyperintense focal lesions on
FLAIR sequences and iso- or hypointense focal lesions on T1-
weighted sequences. PVWMHs were assessed and rated in three
regions. The sum of these regional grades was used for the PVWMH
grades and ranged from 0 to 9. DWMH were rated by number and size
and the DWMH scores were added as a quantitative measure of
DWMH by multiplying each lesion by a size-dependent constant
Table 1 Baseline patient characteristics
Characteristics Total (n = 595) Smoker (n = 144) Non-smoker (n = 451) p value
Age*, years ± SD 63.5 ± 10.7 61.1 ± 10.5 64.3 ± 10.7 0.002
Male gender, n (%) 347 (58.3) 133 (92.4) 214 (47.5) \0.001
Medical history
Hypertension, n (%) 383 (64.4) 101 (70.1) 282 (62.5) 0.110
Diabetes mellitus, n (%) 139 (23.4) 45 (31.3) 94 (20.8) 0.013
Hyperlipidemia, n (%) 160 (26.9) 41 (28.5) 119 (26.4) 0.666
Atrial fibrillation, n (%) 95 (16.0) 61 (42.4) 34 (7.5) \0.001
Alcohol consumption, n (%) 130 (21.8) 59 (41.0) 71 (15.7) \0.001
Lacunar infarction, n (%) 336 (56.5) 102 (70.8) 234 (51.9) \0.001
Laboratory findings*
WBC count, /mm3 5,324 ± 1,850 5,575 ± 2,043 5,244 ± 1,780 0.082
HbA1c, % 6.08 ± 0.86 6.21 ± 0.98 6.03 ± 0.81 0.045
T. cholesterol, mg/dl 178.3 ± 35.6 180.6 ± 35.7 177.5 ± 35.6 0.373
Neuroradiologic findings*
Grade of PVWMH, grade ± SD 1.50 ± 1.71 1.85 ± 1.94 1.39 ± 1.61 0.011
Total scores of DWMH, ml ± SD 1.56 ± 2.85 1.94 ± 2.81 1.43 ± 2.85 0.062
Total number of microbleeds, n ± SD 1.11 ± 3.27 1.86 ± 4.64 0.87 ± 2.66 0.015
DWMH deep white matter hyperintensities, PVWMH periventricular white matter hyperintensities
Statistical significant differences were determined by Fisher’s exact test (categorical values) or Student’s t test (continuous values, marked as *)
Neurol Sci (2012) 33:45–51 47
123
We investigated whether smoking was an independent
risk factor for PVWMHs in the younger and DWMHs in
the older age groups. After adjusting for advanced age,
hypertension, hyperlipidemia and lacunar infarction,
current smoking was an independent risk factor for
PVWMHs in the younger age group (OR = 2.13; 95%
CI 1.21–3.72; p = 0.008). In the older age group,
smoking was not associated with PVWMHs indepen-
dently. However, multivariate analysis showed that a
history of smoking was independently associated with a
larger burden of DWMHs (OR = 2.43; 95% CI 1.30–
4.54; p = 0.005) (Table 4).
Discussion
In this study, we found an overall association between
cigarette smoking and PVWMHs. Age-based stratification
showed that smoking was associated with PVWMHs, but
not with DVWMHs, in the younger age group. In the older
Table 2 Clinical characteristics and radiological findings according to age
Younger group (B64 years), (n = 295) Older group (C65 years), (n = 300)
Smoker
(n = 87)
Non-smoker
(n = 208)
p value Smoker
(n = 57)
Non-smoker
(n = 243)
p value
Grades of PVWMH, grade ± SD 1.92 ± 2.06 1.14 ± 1.60 0.002 1.74 ± 1.76 1.60 ± 1.59 0.582
Total scores of DWMH, cc ± SD 1.78 ± 2.95 1.47 ± 2.82 0.390 2.18 ± 2.61 1.40 ± 2.88 0.049
Number of microbleeds, n ± SD 1.80 ± 4.69 0.66 ± 2.53 0.034 1.95 ± 4.61 1.04 ± 2.75 0.054
DWMH deep white matter hyperintensities, PVWMH periventricular white matter hyperintensities
Statistical significances of differences were analyzed by Student’s t tests
Table 3 Clinical characteristics and WMHS according to age groups
Characteristics Total
(n = 295)
Lower grades
of PVWMH (=0)
(n = 139)
Higher grades
of PVWMH (C1)
(n = 156)
p value Smaller scores
of DWMH
(\0.27 cc)
(n = 148)
Larger scores
of DWMH
(C0.27 cc)
(n = 147)
p value
Younger group (B64 years)
Age*, years ± SD 55.3 ± 8.3 54.9 ± 8.4 55.7 ± 8.2 0.39 54.2 ± 8.6 56.4 ± 7.9 0.02
Male gender, n (%) 165 (55.9) 70 (50.4) 95 (60.9) 0.08 79 (53.4) 86 (58.5) 0.41
Hypertension, n (%) 160 (54.2) 63 (45.3) 97 (62.2) 0.005 69 (46.6) 91 (61.9) 0.10
Diabetes mellitus, n (%) 69 (23.4) 35 (25.2) 34 (21.8) 0.50 35 (23.6) 34 (23.1) 1.00
Hyperlipidemia, n (%) 70 (23.7) 25 (18.0) 45 (28.8) 0.04 38 (25.7) 32 (21.8) 0.49
Lacunar infarction, n (%) 165 (55.9) 70 (50.4) 95 (60.9) 0.08 72 (48.6) 93 (63.3) 0.01
Current smoking, n (%) 87 (29.5) 28 (20.1) 59 (37.8) 0.001 36 (24.3) 51 (34.7) 0.06
Characteristics Total
(n = 300)
Lower grades
of PVWMH (B1)
(n = 163)
Higher grades
of PVWMH (C2)
(n = 137)
p value Smaller scores
of DWMH
(\0.5 cc)
(n = 147)
Larger scores
of DWMH
(C0.5 cc)
(n = 153)
p value
Older group (C65 years)
Age*, years ± SD 71.6 ± 5.2 70.8 ± 4.8 72.6 ± 5.5 0.003 71.1 ± 4.8 72.2 ± 5.4 0.07
Male gender, n (%) 182 (60.7) 99 (60.7) 83 (60.6) 1.00 86 (58.5) 96 (62.7) 0.48
Hypertension, n (%) 223 (74.3) 115 (70.6) 108 (78.8) 0.11 98 (66.7) 125 (81.7) 0.004
Diabetes mellitus, n (%) 70 (23.3) 32 (19.6) 38 (27.7) 0.10 31 (21.1) 39 (25.5) 0.41
Hyperlipidemia, n (%) 90 (30.0) 44 (27.0) 46 (33.6) 0.26 43 (29.3) 47 (30.7) 0.80
Lacunar infarction, n (%) 171 (57.0) 82 (50.3) 89 (65.0) 0.01 80 (54.4) 91 (59.5) 0.42
Current smoking, n (%) 57 (19.0) 26 (16.0) 31 (22.6) 0.18 18 (12.2) 39 (25.5) 0.005
DWMH deep white matter hyperintensities, PVWMH periventricular white matter hyperintensities
Statistical significant differences were determined by Fisher’s exact test (categorical values) or Student’s t test (continuous values, marked as *)
48 Neurol Sci (2012) 33:45–51
123
group, smokers had a greater burden of DWMHs than non-
smokers.
Previous studies revealed that cigarettes contain various
compounds, such as nicotine, and the effects of smoking on
the central nervous system are various and complex [27].
Cigarette smoking has been shown to be associated with
cardiovascular diseases, stroke and neurodegenerative
diseases, although the relationship between smoking and
neurodegeneration was controversial [8, 9]. It is interesting
to note that the effects of smoking in humans were affected
by age. For example, smoking causes marked tachycardia
and suppression of central sympathetic outflow in young
individuals, whereas in middle-aged subjects sympathetic
vasoconstrictor activity is not suppressed even though
tachycardia occurs to a lesser degree [8]. Cigarette smoking
is protective in younger individuals with Parkinson’s dis-
ease but not in the elderly individuals [9].
Smoking is also a well-known risk factor for WMHs
[11, 17–23]. Cytotoxic cell edema secondary to nicotine-
induced osmotic imbalance and vasogenic edema has been
suggested as a possible mechanism of increased WMHs in
smokers [28]. In elderly individuals, chronic exposure to
smoking is related to low cerebral blood flow [6], which
may be a potential mechanism underlying the association
between smoking and WMH burden. Nicotine has been
shown to evoke age-dependent changes in gene expression
in white matter microstructures of rat brains [29, 30].
However, some previous authors showed an inverse asso-
ciation between smoking and the presence of WMHs [22,
23]. Previous clinical studies did not consider the location
and quantity of WMHs or age-based stratification for
smoking effect. Here, we compared the effects of cigarette
smoking on PVWMHs and DWMHS in younger and older
age groups and found that effects of smoking on WMHs
differed among the young and the elderly individuals. We
thought that the age-dependent effects of smoking on brain
and the different age distribution of study population might
cause the discrepancy in the results among the previous
clinical studies [18, 20, 22, 23].
DWMHs are known to be mainly associated with cere-
brovascular disease [11]. We discovered that there was a
clear association between DWMHs and smoking individuals
over the age of 65 years, which implies that smoking might
increase the risk of stroke in the elderly individuals. Previous
studies have shown that PVWMHs are more frequently
observed in patients with neurodegenerative disorders, such
as Parkinson’s disease, and associated with memory and gait
disturbance [31], even though the pathology and mecha-
nisms underlying PVWMHs and DWMHs still remain
unclear [32, 33]. Recent studies reported that small vessel
changes could be due to abnormal venous drainage, hence
lead to WMHs [34, 35]. The periventricular and subcortical
areas differ in venous drainage systems [35], which might
explain the differences in mechanisms of the two. Our results
showed that smoking was also associated with an increase in
PVWMHs in the younger age group. This fact indicated that
the smoking may accelerate aging of the brain in our younger
age population.
The association between cholesterol and WMHs has
also been controversial. Some reports have indicated that
subjects with WMH had lower levels of cholesterol than
those without WMHs, even though others showed that
increased cholesterol levels were associated with WMHs
[36–38]. Our study revealed that hyperlipidemia tended to
Table 4 Multivariate analysis
DWMH deep white matter
hyperintensities, PVWMHperiventricular white matter
hyperintensities
Statistical significant
differences were determined by
multiple binary regression tests
Characteristics PVWMH grades (C1) DWMH (C0.27 cc)
OR (95% CI) p value OR (95% CI) p value
Younger group (B64 years)
Age (C57 years) 1.14 (0.70–1.85) 0.61 2.18 (1.33–3.55) 0.002
Hypertension 1.83 (1.13–2.97) 0.01 1.57 (0.97–2.55) 0.07
Hyperlipidemia 1.69 (0.95–3.01) 0.08 0.65 (0.37–1.15) 0.14
Lacunar infarction 1.14 (0.69–1.89) 0.60 1.64 (0.99–2.73) 0.05
Current smoking 2.13 (1.21–3.72) 0.008 1.50 (0.86–2.61) 0.15
Characteristics PVWMH grades (C2) DWMH (C0.50 cc)
OR (95% CI) p value OR (95% CI) p value
Older group (C65 years)
Age (C71 years) 1.74 (1.08–2.79) 0.02 1.52 (0.95–2.43) 0.08
Hypertension 1.34 (0.77–2.31) 0.30 2.07 (1.19–3.58) 0.01
Hyperlipidemia 1.30 (0.78–2.18) 0.31 1.04 (0.62–1.74) 0.88
Lacunar infarction 1.79 (1.11–2.89) 0.02 1.12 (0.69–1.80) 0.66
Current smoking 1.61 (0.89–2.93) 0.12 2.43 (1.30–4.54) 0.005
Neurol Sci (2012) 33:45–51 49
123
be associated with PVWMHs in the younger age group and
had an age-dependent effect on white matter, which might
have made it difficult to delineate any association between
cholesterol and WMHs in the previous studies [36–38].
This result suggests that an age-dependent effect of
hyperlipidemia on human white matter is also plausible. It
does not seem to happen unfrequently that the white matter
changes evoked by some factors might differ according to
age and the location of white matter.
Previous studies have shown that microbleeds located in
the deep white matter or infratentorial areas were associ-
ated with smoking and WMHs [39]. We found that
smoking was associated with microbleeds only in the
younger group. The association between microbleeds and
smoking was stronger in younger than in older individuals,
and the age-dependent pattern of microbleeds was quite
similar to that of PVWMHs, but not of DWMHs. This
suggested that smoking might have a similar effect on
PVWMHs and microbleeds, but different on DWMHs.
Individuals may exhibit an age-dependent susceptibility to
smoking, which could lead to periventricular white matter
lesions or microbleeds in young smokers and deep white
matter changes in older individuals.
There was also a correlation between PVWMHs and
pack-years of smoking, but not between DWMHs and
pack-years of smoking. Age at the onset of smoking was
not associated with DWMHs or PVWMHs. This implied
that smoking, regardless of the amount, plays an important
role in the development of DWMHs, even though both the
amount and history of smoking contributed to the devel-
opment of PVWMHs.
This study has some limitations. First, this was the ret-
rospective study and we could not exclude the possibility that
selection bias due to the overall premature death of smokers
might cause an age-dependent difference in WMH distri-
bution in smokers, which was believed to result in the inverse
association between smoking and Alzheimer’s disease [5].
The large number of recruited stroke patients also might lead
to a selection bias. To avoid this bias, the history of stroke
was adjusted in the multivariate analysis. Second, the fre-
quency and severity of PVWMH and DWMH increase pro-
gressively with the age [40], and therefore the decision to
categorize the patients into two groups based on median age
(65 years) might be controversial. Age of 65 years is sig-
nificant in cerebrovascular disease, because nearly three
quarters of all strokes occur over the age of 65 years and the
stroke of the patients younger than 65 years was considered
as young age stroke [41]. However, to confirm such age-
related effects of smoking on the cerebral white matter and
the true discrimination threshold age, a large prospective
study including patients recruited from the general popula-
tion should be performed.
Conclusion
To our knowledge, our study was the first to investigate the
age-dependent effects of smoking on WMHs. We maintain
that the cigarette smoking affects the brain in an age-
dependent manner. Such findings can assist the interpre-
tation of WMHs and cardiovascular risk factors in a
clinical setting.
Acknowledgments This work was supported by INHA UNIVER-
SITY Research Grant (INHA-42157-01).
Conflict of interest The authors report no conflicts of interest.
References
1. Kannel WB, Higgins M (1990) Smoking and hypertension as
predictors of cardiovascular risk in population studies. J Hyper-
tens Suppl 8:S3–S8
2. Hallstrom AP, Cobb LA, Ray R (1986) Smoking as a risk factor
for recurrence of sudden cardiac arrest. N Engl J Med
314:271–275
3. Tyas SL, White LR, Petrovitch H et al (2003) Mid-life smoking
and late-life dementia: the Honolulu-Asia Aging Study. Neuro-
biol Aging 24:589–596
4. Reitz C, den Heijer T, van Duijn C, Hofman A, Breteler MM (2007)
Relation between smoking and risk of dementia and Alzheimer
disease: the Rotterdam Study. Neurology 69:998–1005
5. Hernan MA, Alonso A, Logroscino G (2008) Cigarette smoking
and dementia: potential selection bias in the elderly. Epidemiol-
ogy 19:448–450
6. Siennicki-Lantz A, Reinprecht F, Wollmer P, Elmstahl S (2008)
Smoking-related changes in cerebral perfusion in a population of
elderly men. Neuroepidemiology 30:84–92
7. Brody AL, Mandelkern MA, Jarvik ME et al (2004) Differences
between smokers and nonsmokers in regional gray matter vol-
umes and densities. Biol Psychiatry 55:77–84
8. Hering D, Somers VK, Kara T et al (2006) Sympathetic neural
responses to smoking are age dependent. J Hypertens 24:691–695
9. Tzourio C, Rocca WA, Breteler MM et al (1997) Smoking and
Parkinson’s disease. An age-dependent risk effect? The EURO-
PARKINSON Study Group. Neurology 49:1267–1272
10. Garde E, Mortensen EL, Krabbe K, Rostrup E, Larsson HB
(2000) Relation between age-related decline in intelligence and
cerebral white-matter hyperintensities in healthy octogenarians: a
longitudinal study. Lancet 356:628–634
11. Longstreth WT Jr, Arnold AM, Beauchamp NJ Jr et al (2005)
Incidence, manifestations, and predictors of worsening white
matter on serial cranial magnetic resonance imaging in the
elderly: the Cardiovascular Health Study. Stroke 36:56–61
12. Khan U, Porteous L, Hassan A, Markus HS (2007) Risk factor
profile of cerebral small vessel disease and its subtypes. J Neurol
Neurosurg Psychiatry 78:702–706
13. Neuropathology Group of the Medical Research Council Cogni-
tive Function and Ageing Study (MRC CFAS) (2001) Patho-
logical correlates of late-onset dementia in a multicentre,
community-based population in England and Wales. Lancet
357:169–175
14. Pantoni L, Garcia JH (1997) Pathogenesis of leukoaraiosis: a
review. Stroke 28:652–659
50 Neurol Sci (2012) 33:45–51
123
15. Wardlaw JM, Lewis SC, Keir SL, Dennis MS, Shenkin S (2006)
Cerebral microbleeds are associated with lacunar stroke defined
clinically and radiologically, independently of white matter
lesions. Stroke 37:2633–2636
16. Murray AD, Staff RT, Shenkin SD, Deary IJ, Starr JM, Whalley
LJ (2005) Brain white matter hyperintensities: relative impor-
tance of vascular risk factors in nondemented elderly people.
Radiology 237:251–257
17. Fukuda H, Kitani M (1996) Cigarette smoking is correlated with
the periventricular hyperintensity grade of brain magnetic reso-
nance imaging. Stroke 27:645–649
18. Longstreth WT Jr, Manolio TA, Arnold A et al (1996) Clinical
correlates of white matter findings on cranial magnetic resonance
imaging of 3301 elderly people. The Cardiovascular Health
Study. Stroke 27:1274–1282
19. Ding J, Nieto FJ, Beauchamp NJ et al (2003) A prospective analysis
of risk factors for white matter disease in the brain stem: the Car-
diovascular Health Study. Neuroepidemiology 22:275–282
20. Jeerakathil T, Wolf PA, Beiser A et al (2004) Stroke risk profile
predicts white matter hyperintensity volume: the Framingham
Study. Stroke 35:1857–1861
21. Liao D, Cooper L, Cai J et al (1997) The prevalence and severity
of white matter lesions, their relationship with age, ethnicity,
gender, and cardiovascular disease risk factors: the ARIC Study.
Neuroepidemiology 16:149–162
22. Bots ML, van Swieten JC, Breteler MM et al (1993) Cerebral
white matter lesions and atherosclerosis in the Rotterdam Study.
Lancet 341:1232–1237
23. van Swieten JC, Kappelle LJ, Algra A, van Latum JC, Koudstaal
PJ, van Gijn J (1992) Hypodensity of the cerebral white matter in
patients with transient ischemic attack or minor stroke: influence
on the rate of subsequent stroke. Dutch TIA Trial Study Group.
Ann Neurol 32:177–183
24. Kim WJ, Kim JH, Ko Y et al (2010) Can early ischemic lesion
recurrence on diffusion-weighted MRI affect functional outcome
after acute ischemic stroke? J Clin Neurol 6:19–26
25. Lee SH, Kim BJ, Roh JK (2006) Silent microbleeds are associ-
ated with volume of primary intracerebral hemorrhage. Neurol-
ogy 66:430–432
26. Kruit MC, van Buchem MA, Hofman PA et al (2004) Migraine as
a risk factor for subclinical brain lesions. JAMA 291:427–434
27. Paul RH, Grieve SM, Niaura R et al (2008) Chronic cigarette
smoking and the microstructural integrity of white matter in
healthy adults: a diffusion tensor imaging study. Nicotine Tob
Res 10:137–147
28. Gazdzinski S, Durazzo TC, Studholme C, Song E, Banys P,
Meyerhoff DJ (2005) Quantitative brain MRI in alcohol
dependence: preliminary evidence for effects of concurrent
chronic cigarette smoking on regional brain volumes. Alcohol
Clin Exp Res 29:1484–1495
29. Jacobsen LK, Picciotto MR, Heath CJ et al (2007) Prenatal and
adolescent exposure to tobacco smoke modulates the develop-
ment of white matter microstructure. J Neurosci 27:13491–13498
30. Polesskaya OO, Fryxell KJ, Merchant AD et al (2007) Nicotine
causes age-dependent changes in gene expression in the adoles-
cent female rat brain. Neurotoxicol Teratol 29:126–140
31. Silbert LC, Nelson C, Howieson DB, Moore MM, Kaye JA
(2008) Impact of white matter hyperintensity volume progression
on rate of cognitive and motor decline. Neurology 71:108–113
32. Piccini P, Pavese N, Canapicchi R et al (1995) White matter
hyperintensities in Parkinson’s disease. Clinical correlations.
Arch Neurol 52:191–194
33. Fazekas F (1989) Magnetic resonance signal abnormalities in
asymptomatic individuals: their incidence and functional corre-
lates. Eur Neurol 29:164–168
34. Black S, Gao F, Bilbao J (2009) Understanding white matter
disease: imaging-pathological correlations in vascular cognitive
impairment. Stroke 40:S48–S52
35. Chung CP, Hu HH (2010) Pathogenesis of leukoaraiosis: role of
jugular venous reflux. Med Hypotheses 75:85–90
36. Breteler MM, van Swieten JC, Bots ML et al (1994) Cerebral
white matter lesions, vascular risk factors, and cognitive function
in a population-based study: the Rotterdam Study. Neurology
44:1246–1252
37. Leppala JM, Virtamo J, Fogelholm R, Albanes D, Heinonen OP
(1999) Different risk factors for different stroke subtypes: asso-
ciation of blood pressure, cholesterol, and antioxidants. Stroke
30:2535–2540
38. Schmidt R, Hayn M, Fazekas F, Kapeller P, Esterbauer H (1996)
Magnetic resonance imaging white matter hyperintensities in
clinically normal elderly individuals. Correlations with plasma
concentrations of naturally occurring antioxidants. Stroke
27:2043–2047
39. Vernooij MW, van der Lugt A, Ikram MA et al (2008) Prevalence
and risk factors of cerebral microbleeds: the Rotterdam Scan
Study. Neurology 70:1208–1214
40. Enzinger C, Fazekas F, Ropele S, Schmidt R (2007) Progression
of cerebral white matter lesions–clinical and radiological con-
siderations. J Neurol Sci 257:5–10
41. Feigin VL, Lawes CM, Bennett DA, Anderson CS (2003) Stroke
epidemiology: a review of population-based studies of incidence,
prevalence, and case-fatality in the late 20th century. Lancet
Neurol 2:43–53
Neurol Sci (2012) 33:45–51 51
123
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