Hippocampal perivascular spaces are related to aging and blood pressure but not to cognition

Neurobiology of Aging xxx (2014) 1e8

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Hippocampal perivascular spaces are related to aging and blood pressure but not to cognition Ming Yao a, b, Yi-cheng Zhu a, Aïcha Soumaré c, d, Carole Dufouil c, d, Bernard Mazoyer e, f, g, h, i, Christophe Tzourio c, d, Hugues Chabriat b, j, k, * a

Department of Neurology, Peking Union Medical College Hospital, Beijing, China Univ Paris Diderot, Sorbonne Paris Cité, Paris, France Univ Pierre et Marie Curie Paris 6, Paris, France d INSERM UMR708, Bordeaux, France e Institut Universitaire de France, Paris, France f CNRS-CEA UMR 6232, Caen, France g Groupe Imagerie Neurofonctionelle, Caen, France h University Caen Basse-Normandie, Caen, France i CHU de Caen, Caen, France j INSERM UMR 1161 and DHU NeuroVasc, Paris, France k Department of Neurology, AP-HP, Paris, France b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 October 2013 Received in revised form 11 March 2014 Accepted 14 March 2014

The risk factors of hippocampal dilated perivascular spaces (H-dPVS), their radiological relevance and their impact on cognitive performance remain under investigation. These aspects were evaluated in 1818 stroke- and dementia-free participants enrolled in the 3C-Dijon MRI study, using logistic regression, multiple linear regression, and Cox models. At study entry, the load of H-dPVS was found strongly associated with age and hypertension (degree 2 vs. degree 0: odds ratio: 1.16; 95% confidence interval: 1.02e1.33 and odds ratio: 1.98; 95% confidence interval: 1.39e2.81, respectively) and positively related to the presence of lacunar infarcts, white-matter hyperintensities volume, and hippocampal volume (p
0.024). Load of H-dPVS was not related to baseline cognitive performance (p > 0.05). Cox regression modeling did not show a significant relationship between the load of H-dPVS and incident dementia risk (p > 0.05). The present results support that both aging and blood pressure do play a key role in the development of H-dPVS in the older population. In contrast with the dilated perivascular spaces located in white matter or basal ganglia, the load of H-dPVS does not appear associated with occurrence of dementia. Ó 2014 Elsevier Inc. All rights reserved.

Keywords: Hippocampal dilated perivascular spaces Risk factors MRI Cognition

1. Introduction Dilated perivascular spaces (dPVS) are commonly described from magnetic resonance imaging (MRI) in basal ganglia and in different subcortical white-matter regions (Chen et al., 2011; Cumurciuc et al., 2006; Groeschel et al., 2006; Maclullich et al., 2004; Patankar et al., 2005; Rouhl et al., 2008; Zhu et al., 2010a), while only a few studies have focused on hippocampal dilated perivascular spaces (H-dPVS) (Barboriak et al., 2000; Bartrés-Faz et al., 2001; Bastos-Leite et al., 2006; Chen et al., 2011; Li et al., 2006; Maclullich et al., 2004; Nakada et al., 2005; Sasaki et al., 1993; Yoneoka et al., 2002). The exact relationships between pathologic * Corresponding author at: Service de Neurologie, Hôpital Lariboisière, 2 rue Ambroise Paré, 75010 Paris, France. Tel.: þ33 1 49 95 25 93; fax: þ33 1 49 95 25 96. E-mail address: [email protected] (H. Chabriat). 0197-4580/$ e see front matter Ó 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neurobiolaging.2014.03.021

data and the radiological aspects of H-dPVS as well as their exact clinical correlates are still debated. Various radiological terminologies, such as “cavities” or “sulcal cavities”, have been previously used for these lesions (Barboriak et al., 2000; Bartrés-Faz et al., 2001; Bastos-Leite et al., 2006; Li et al., 2006; Nakada et al., 2005; Sasaki et al., 1993; Yoneoka et al., 2002). However, histologic analyses strongly support that these “cavities” actually correspond to dilated cystic tissue remnants containing small perforating vessels and resulting from incomplete fusion of the hippocampus fissure during development (Sasaki et al., 1993). In the literature, the prevalence of H-dPVS seems to vary largely in healthy older populations (from 36.4% to 77%) (Barboriak et al., 2000; Li et al., 2006; Maclullich et al., 2004; Yoneoka et al., 2002). In most studies, a higher prevalence of H-dPVS is observed with aging (Barboriak et al., 2000; Sasaki et al., 1993; Yoneoka et al., 2002) although this was not confirmed by all authors (Chen et al., 2011; Li et al., 2006).

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In older individuals, the severity of dPVS in basal ganglia or white matter was recently found associated with an increased risk of cognitive decline or incident dementia (Maclullich et al., 2004; Zhu et al., 2010b). Since hippocampus plays a key role in memory performance (Frisoni et al., 2008; Lazarov et al., 2010; Sahay et al., 2011; Squire, 1992), the development of H-dPVS may be also related to cognitive performance in older people. However, their impact on cognition has been previously evaluated only in limited-samples and remains controversial. H-dPVS have been associated respectively with transient global amnesia or cognitive decline (Chen et al., 2011; Nakada et al., 2005). Conversely, other reports suggested that a higher number of hippocampal cerebrospinal fluid (CSF) spaces was not related to global cognitive performance (Bartrés-Faz et al., 2001; Maclullich et al., 2004) and was even possibly related to a lower risk of Alzheimer’s disease in a study of limited sample-size (Li et al., 2006). Otherwise, H-dPVS have not been found related to white-matter hyperintensities (WMH) (Barboriak et al., 2000; Chen et al., 2011), though the severity of dPVS in other cerebral areas has repeatedly shown to be a marker of cerebral small vessel disease (SVD) strongly related to the accumulation of lacunar infarcts, white-matter hyperintensities or microbleeds (Doubal et al., 2010; Patankar et al., 2005; Rouhl et al., 2008). In this study based on data collected in a large-sample of community-dwelling older individuals, we aimed at (1) investigating the risk factors of H-dPVS and their relationships with the other classical MRI markers of SVD; and (2) determining their impact on baseline cognitive performance or incident dementia over 8-years of follow-up. 2. Methods 2.1. Participants Data were obtained from the 3C-Dijon MRI study, a cohort study of community-dwelling persons aged 65 years and older, randomly selected from the electoral rolls in the city of Dijon France. The complete study protocol, approved by the Ethical Committee of the University-Hospital of Bicêtre, has been detailed elsewhere (3C Study Group, 2003). All participants gave a signed informed consent. Among the 4931 participants included in Dijon, those <80 years and enrolled between June 1999 and September 2000 (n ¼ 2763) were initially proposed to undergo a cerebral MRI (83%) with an agreement of 83% (n ¼ 2285) participants, another 87 participants with age 80 years also agreed to perform a cerebral MRI scanning, finally only 1924 scans were performed at baseline because of financial limitations. Exclusion criteria for MRI were: cardiac pacemaker, valvular prosthesis, other internal electrical and/or magnetic devices, history of neurosurgery and/or aneurysm, claustrophobia, the presence of metal fragments (eyes, brain, or spinal cord). Over an 8-year period, 3 follow-up examinations were performed (at years 2, 4, and 7e8). They consisted of a face-to-face interview (socio-demographic characteristics, medical history), cognitive and physical assessments conducted at the participant’s home and at the study center. Of 1924 participants aged 65 years having an MRI examination, 8 participants with tumors, 9 with prevalent dementia, 44 with prevalent stroke, and 45 without interpretable MRI data were excluded. Finally, 1818 stroke- and dementia-free individuals were included for the cross-sectional baseline analysis, 1816 of who had at least one baseline cognitive test. For longitudinal analyses, 71 participants were further excluded because of the lack of follow-up information on their dementia status (27 deaths, 15 refusals to participate, 29 lost to follow-up), leaving a working sample of 1745 participants (mean follow-up time [standard deviation, SD]: 6.12

[1.67] years with a range from 0.75 to 8.56 years). Ninety-four of these participants (5.4%) had only 1 follow-up visit, 19.4% had 2 (95.3% of which were followed during 4 consecutive years), and 75.2% had the 3 follow-up visits. Compared with those included in the analyses, participants excluded were more likely to be apolipoprotein E4 carriers, to present depressive symptoms and to have history of cardiovascular disease; they also had significantly more cardiovascular disease and risk factors, lower hippocampus volume, and worse baseline cognitive performance (data not shown). 2.2. Cognitive measurements, dementia diagnosis, depression indicator, and risk factors assessment A battery of cognitive tests was used to assess different areas of cognitive functions. The Mini Mental State Examination (MMSE) is used as a global measure of cognition. The Isaacs Set Test (IST) provided a measure of verbal fluency and semantic access. The Benton Visual Retention Test assessed visual memory, and the Trail Making Tests (TMT) A and B psychomotor speed and executive functions. For TMTA and TMTB, we calculated the time needed to make a correct connection by dividing the time to complete each part by the number of correct connections (expressed in seconds per correct connection). All tests were administered at baseline and follow-up. A higher score on TMT reflected lower performance, whereas a lower score on IST, Benton Visual Retention Test, or MMSE indicated lower performance. Dementia cases were identified through a 3-step procedure as previously detailed (Barberger-Gateau et al., 2007; The 3C Study Group, 2003). First, screening for dementia was based on a thorough neuropsychological examination. Second, a neurologist further examined those screened positive on the basis of their performance on MMSE and IST, collected data on severity of cognitive disorders and activities of daily living, and established a provisional diagnosis. Third, an independent committee of neurologists reviewed all potential cases of dementia and reached a consensus on diagnosis based on DSM-IV criteria. The date of onset of dementia was set as the date of the follow-up interview when dementia was diagnosed. The depressive symptomatology at baseline was evaluated with the Center for Epidemiological Studies Depression scale (Radloff, 1977). High depressive symptomatology was defined by Center for Epidemiological Studies Depression score 17 in men or 23 in women based on validated data previously obtained in a French population (Fuhrer and Rouillon, 1989). A face-to-face administered questionnaire assessed history of vascular disease and vascular risk factors including diabetes mellitus (defined by fasting blood glucose 7 mmol/L or by the use of antidiabetic drugs), hypercholesterolemia (defined as total cholesterol 6.2 mmol/L or taking lipid-lowering drugs), hypertension (defined by systolic blood pressure 140 mmHg or diastolic blood pressure 90 mmHg or by the use of antihypertensive drugs), and history of ischemic heart disease. Smoking or drinking status was categorized as current and noncurrent. Polymorphism of the APOE gene was assessed using a procedure described elsewhere and the presence of allele 4 of APOE versus the absence was considered for the analyses (Dufouil et al., 2005). Number of risk factors for cerebrovascular diseases corresponded to a score based on the absence and/or the presence of hypertension, diabetes, hypercholesterolemia, history of ischemic heart disease, and current smoking. 2.3. MRI data MRI scans were performed using a 1.5-tesla Magnetom (Siemens, Erlangen). A 3-dimensional high-resolution T1-weighted brain

M. Yao et al. / Neurobiology of Aging xxx (2014) 1e8

images were acquired using a 3-dimensional inversion recovery fast spoiled-gradient echo sequence (repetitive time ¼ 97 ms; echo time ¼ 4 ms; inversion time ¼ 600 ms; coronal acquisition). The axially reoriented 3-dimensional volume matrix size was 256  192  256 with a 1.0  0.98  0.98 mm3 voxel size. T2- and proton density-weighted brain volumes were acquired using a 2dimensional dual spin echo sequence with 2 echo times (repetitive time ¼ 4400 ms; echo time 1 ¼16 ms; echo time 2 ¼ 98 ms). T2- and proton density-weighted acquisitions consisted of 35 axial slices with 3.5 mm thickness (0.5 mm between slices spacing) having a 256  256 matrix size and a 0.98  0.98 mm2 in-plane resolution. 2.4. Rating of dPVS in hippocampus H-dPVS were defined as CSF-like signal-intensity changes (hypointense on T1 and hyperintense on T2) that were round, curvilinear, or crescent, <3 mm in their maximum diameter, with smooth delineated contours, and typically located in the lateral portion of the hippocampus (Fig. 1) (Li et al., 2006). A special care was taken to avoid the inclusion of pulsation artifacts. The evaluation of H-dPVS was performed using 3-dimensional T1-weighted images in association with T2-weighted and Fluid Attenuated Inversion Recovery images. dPVS were individually and separately scored on the right and left side of hippocampus by the same experienced reader (YZ) who was blind to all clinical data. The sum of bilateral sides was used for further rating. The load of H-dVRS at individual level was categorized in a 3-degree scale according to the sum of H-dPVS in the left and the right hippocampus: degree 0 (no H-dPVS), degree 1 (1-2 H-dPVS), and degree 2 (>2 H-dPVS). This cutoff corresponds to the median number in individuals with at least one H-dPVS. 2.5. Other MRI parameters The T1 and T2 weighted images for each subject were first aligned to each other (Woods et al., 1992) and then analyzed with SPM99 (http://www.fil.ion.ucl.ac.uk/spm/). We used the optimized voxelbased morphometry protocol (Ashburner and Friston, 2000; Good et al., 2001) with 2 modifications to account for the structural

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characteristics of aged brains. First, gray matter, white matter, and CSF templates specific to our database (3C priors) were used for tissue segmentation. Second, segmentation of the CSF class was refined using T2 images (Lemaitre et al., 2005). The CSF partition images were derived from a multi-spectral combining T1 and T2 volumes, while the final gray matter and white matter partition images were derived from the segmentation of the T1 volumes only. Finally, we applied a so-called “modulation” to each cerebral partition image, adjusting their voxel intensities for the strength of the deformation they were submitted to during the spatial normalization process. Modulation preserves the subject’s original tissue quantity after its transfer to the reference space. For each individual, the gray matter, white matter, and CSF volumes were computed as the integral of the voxel intensities in the corresponding modulated tissue image. Total intracranial volume was calculated as the sum of the gray matter, white matter, and CSF volumes. Brain parenchymal fraction was defined as the ratio of brain tissue volume to total intracranial volume. WMH volume was measured with a validated automated imaging processing method of tissue segmentation and quantification of lesion size and analyzed as a continuous variable (Maillard et al., 2008). Infarcts were determined on T1, T2, and PDweighted images by the same rater (Y.Z). Infarcts were defined as focal lesions 3 mm in diameter, with the same signal characteristics as CSF on all sequences (Zhu et al., 2010a). 2.6. Statistical analyses A description of the baseline potential risk factors as well as their crude distribution according to H-dPVS degrees was obtained. Polytomous logistic regression models controlling for age and gender were computed to evaluate their association with categorized HdPVS load (dependent variable). Each response category (the odds of having H-dPVS respectively of degree 1 or 2) was contrasted against the reference category (degree 0). The interaction terms (age  hypertension and age  blood pressure) were separately added into the original regression model. To examine the relationships between the severity of H-dPVS and the other MRI markers of SVD or the hippocampus volume, similar polytomous logistic regression models were performed, except that intracranial cavity volume was forced

Fig. 1. Examples of dilated perivascular spaces in the hippocampus from 3 different participants (each column corresponds to 1 participant). White arrows show hippocampal dilated perivascular spaces on axial (top row), sagittal (left side of hippocampus, middle row), and coronal T1-weighted images (bottom row).

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into the model when appropriate for WMH volume and hippocampus volume. Hypertension or use of antihypertensive drugs was additionally introduced into the models. The following interaction terms (age  hypertension, age  WMH volume, age  lacunar infarcts, age  hippocampus volume, hypertension  WMH volume, hypertension  lacunar infarcts, and hypertension  hippocampus volume) were also separately introduced into the model, indicating nonsignificant interactions (p > 0.05). In preliminary analyses, we observed that age, APOE genotype, and depressive symptoms were the strongest predictors of dementia among the potential studied risk factors, while education level, age, depressive symptoms, gender, and number of cardiovascular diseases or risk factors were associated with cognition (at least 1 cognitive test) (data not shown). Multiple linear regression models (dependent variables ¼ each baseline cognitive score) and Cox proportional hazard models were performed to evaluate the relationships between the severity of H-dPVS and different baseline cognitive performance or incident dementia. Because cognitive tests scores were not normally distributed, they were logtransformed. Analyses for association between the severity of HdPVS and cognition or dementia were performed in 3 steps. First, age, gender, and education level were controlled (model 1). A preliminary search for potential interaction between H-dPVS and age, gender, or education level in relation to the different outcomes (cognitive function and dementia) was performed, but none was significant. Second, models were further adjusted for covariates that were associated with dementia, cognition (at least 1 cognitive test) or for covariates known to be risk factors for dementia or cognitive function, that is, APOE genotype, depressive symptoms, and number of cardiovascular diseases or risk factors (model 2). Third, to assess whether other brain lesions or cerebral atrophy could modify the association between H-dPVS and cognitive performance or incident dementia, additional adjustments for WMH volume, the presence and/or the absence of brain infarcts, and brain parenchymal fraction were obtained (model 3). Statistical analyses were performed using SPSS version 20.0 for Windows (SPSS Inc). All p-values were 2-tailed and criteria for significance were p < 0.05. 3. Results 3.1. Baseline characteristics The demographic, clinical, and main MRI features at baseline are summarized in Table 1. The mean age of participants was 72.46 years (SD ¼ 4.14), 38.8% (n ¼ 706) participants were men. Regarding H-dPVS, 44.5% of individuals had at least 1 H-dPVS with a maximum number of 10. 3.2. Risk factors of H-dPVS As shown in Table 2, the load of H-dPVS increased with age; each SD increase in age was associated with a higher odds of H-dPVS (for degree 2 vs. degree 0: odds ratio [OR] 1.16; 95% confidence interval [CI]: 1.02e1.33, p ¼ 0.022). Comparison of degree 1 and degree 0 resulted in a similar but nonsignificant trend (p ¼ 0.08). After adjustment for age and gender, the load of H-dPVS was found to increase both with systolic and with diastolic blood pressure (p
0.037). Baseline hypertension was also associated with H-dPVS with a doubling of the adjusted odds when degree 2 was compared with degree 0 (p < 0.001). No interaction was detected between age and blood pressure on the load of H-dPVS. HdPVS of degree 2 was also significantly related to the use of antihypertensive drugs (p < 0.001). In contrast, the load of H-dPVS was not found related to gender, smoking or drinking status,

Table 1 Main characteristics of the cohort (N ¼ 1818) Characteristics Age (y), mean  SD (range) Male gender Hypertension Hypercholesterolemia Diabetes Ischemic heart disease Current smoker Current drinker Apolipoprotein E4 allele carrier Cognitive scores Mini-Mental State Examination score, mean  SD BENTON visual, mean  SD Isaacs set test, mean  SD Trail making tests part A, mean  SD Trail making tests part B, mean  SD MRI markers Absolute volume of WMH in cm3, mean  SD Presence of lacunar infarcts Absolute volume of gray matter in cm3, mean  SD Absolute volume of hippocampus in cm3, mean  SD BPF, mean  SD Number of H-dPVS 0 1e2 >2

72.46  4.14 (65e85) 38.8% (706) 76.8% (1396) 56.8% (1025) 8.3% (150) 8.1% (147) 5.9% (108) 80.0% (1450) 22.0% (396) 27.67  1.87 11.73  1.91 34.07  6.73 2.22  0.83 6.69  8.44 5.54  5.04 6.8% (121) 508.18  51.03 6.61  0.85 71.82  3.2 55.5% (1009) 28.5% (519) 16.0% (290)

Key: BPF, brain parenchymal fraction; H-dPVS, dilated perivascular spaces in hippocampus; MRI, magnetic resonance imaging; SD, standard deviation; WMH, whitematter hyperintensities.

hypercholesterolemia, diabetes, ischemic heart disease, or APOE4 genotype. 3.3. Relationships between the severity of H-dPVS and the other MRI markers of SVD As shown in Table 3, participants with H-dPVS of degree 2 had the highest prevalence of lacunar infarcts and the highest volume of WMH, deep WMH, and perivascular WMH, whereas the contrary was found for those with degree 0. After adjustment for age, gender, and total intracranial volume, degree 2 of H-dPVS (vs. degree 0) was found strongly related to the total WMH volume (p < 0.001,OR: 1.34; 95% CI: 1.19e1.51). Degree 2 of H-dPVS was also found significantly related both to the volume of deep WMH and to that of perivascular WMH (p < 0.001), and associated with the presence of lacunar infarcts (p ¼ 0.007). Conversely, no significant association was detected for degree 1 neither with the WMH volume (p ¼ 0.11) nor with the presence of lacunar infarcts (p ¼ 0.12) although a similar trend was detected. Finally, the hippocampal volume was found to increase with the load of H-dPVS (p ¼ 0.005) after adjustment on the previously mentioned covariates. Each SD increase in the hippocampus volume was found associated with a higher risk of presenting more H-dPVS as compared with degree 0 (odds for degree 1 of H-dPVS: OR, 1.26; 95% CI, 1.08e1.47; for degree 2 of H-dPVS: OR, 1.23; 95% CI, 1.02e1.49). All these results remained significant even after adjustment for hypertension or use of anti-hypertensive drugs (data not shown). 3.4. Association between H-dPVS and baseline cognitive performance or incident dementia No significant relationship between the load of H-dPVS and baseline cognitive performance was observed in different multiple linear regression models (Table 4, all p > 0.05).

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Table 2 Potential risk factors by the load of dilated perivascular spaces in the hippocampus Distribution by load

Age, mean  SD Male Hypertension Systolic blood pressure, mean (SD) Diastolic blood pressure, mean  SD Use of antihypertensive drug Hypercholesterolemia Diabetes mellitus Ischemic heart disease Current smoking Current drinking apoE4 allele carrier

Degree 1 versus 0

Degree 2 versus 0

Degree 0 (n ¼ 1009)

Degree 1 (n ¼ 519)

Degree 2 (n ¼ 290)

OR (95% CI)

p

OR (95% CI)

p

72.24  4.10 40.9 73.5 147 (23) 84 (12) 39.3 57.5 7.9 8.1 6.4 81.1 23.7

72.64  4.22 36.4 78.8 149 (21) 86 (11) 43.5 54.4 8.7 7.5 5.0 77.9 19.6

72.88  4.09 35.9 84.5 153 (22) 86 (12) 53.1 58.3 9.0 9.0 5.9 80.0 20.1

1.1 0.83 1.35 1.13 1.18 1.16 0.85 1.13 0.92 0.81 0.86 0.80

0.08 0.09 0.021 0.037 0.004 0.19 0.13 0.53 0.7 0.38 0.27 0.10

1.16 0.81 1.98 1.34 1.28 1.68 1 1.17 1.11 0.97 0.98 0.83

0.022 0.13 <0.001 <0.001 <0.001 <0.001 0.98 0.50 0.67 0.92 0.92 0.26

(0.99e1.22) (0.67e1.03) (1.05e1.75) (1.01e1.26) (1.05e1.31) (0.93e1.44) (0.68e1.05) (0.77e1.66) (0.62e1.38) (0.51e1.30) (0.66e1.12) (0.62e1.04)

(1.02e1.33) (0.62e1.07) (1.39e2.81) (1.17e1.53) (1.12e1.46) (1.29e2.19) (0.76e1.31) (0.74e1.87) (0.69e1.77) (0.56e1.69) (0.70e1.38) (0.60e1.15)

All data are presented as percentage unless otherwise indicated. p-Values correspond to the relationship between each variable and the severity of dilated perivascular spaces in hippocampus. In each polytomous logistic regression model, the severity of dilated perivascular spaces in the hippocampus was considered as the dependent variable, age or/and gender as confounding factors. For continuous variables, the OR estimates the association related to an increase of 1 SD. Key: CI, confidence interval, OR, odds ratio; SD, standard deviation.

Interestingly, a similar association with age and hypertension was recently detected for dPVS located in basal ganglia or in white matter in different populations (Cumurciuc et al., 2006; Maclullich et al., 2004; Rouhl et al., 2008) as well as in the 3C-Dijon MRI cohort (Zhu et al., 2010a). These results emphasize that aging and hypertension may promote the development of dPVS throughout the whole brain, not only in the white matter and basal ganglia but also in the hippocampus. Histologic analyses suggest that H-dPVS may actually correspond to dilated cystic tissue remnants containing small perforating vessels and resulting from incomplete fusion of the hippocampus fissure during development (Sasaki et al., 1993). The dilation and increased visibility of H-dPVS on MRI may result from fibrosis, stiffening, or changes in the permeability of the vascular wall but also from spiral elongation of perforating vessels or from some failure in drainage of interstitial fluid as previously reported with increased blood pressure or during aging (Awad et al., 1986; Kwee and Kwee, 2007). In the present study, the load of H-dPVS was also found strongly related to the extent of WMH and to the presence of lacunar infarcts, which are strongly associated with aging and hypertension. These data further support that H-dPVS should be actually considered as a significant marker of cerebral SVD. In this study, we failed to find any significant association between the load of H-dPVS and baseline cognitive performance or incident dementia over 8-years of follow-up. Recently, the load of

During the 10,674 person-years of follow-up (mean duration of follow-up: 6.12 years), 94 participants developed dementia (crude incidence rate: 8.8/1000 person-years). Cox regression models adjusted for age, gender, and education level did not show a significant relationship between load of H-dPVS and incident dementia (Table 5, both p > 0.05). Further adjustment on other confounders did not alter these results. 4. Discussion In this cohort of 1818 individuals, we observed that both age and hypertension are strong risk factors of H-dPVS. A similar age effect was previously detected in previous samples of less than 130 individuals (Barboriak et al., 2000; Sasaki et al., 1993; Yoneoka et al., 2002) but was not observed in a population of 158 individuals among whom only 50 participants presented with normal cognitive performance (Chen et al., 2011). The significant relationship between the load of H-dPVS and hypertension in the present study has never been reported. Herein, the association with hypertension was found significant even when participants having only 1 visible H-dPVS were compared with those without H-dPVS (data not shown). No association between H-dPVS and blood pressure was previously detected in 2 studies including a limited number of participants in samples with a 2-fold lower prevalence of hypertension (Barboriak et al., 2000; Bartrés-Faz et al., 2001).

Table 3 Distribution of MRI features across the load of dilated perivascular spaces in the hippocampus Distribution by load

Absolute total WMH volume (cm3)a Absolute deep WMH volume (cm3)a Absolute periventricular WMH volume (cm3)a Presence of lacunar infarctsb Absolute hippocampus volume (cm3)a

Degree 1 versus 0

Degree 2 versus 0

Degree 0 (n ¼ 1009)

Degree 1 (n ¼ 590)

Degree 2 (n ¼ 290)

p

OR (95% CI)

p

OR (95% CI)

p

5.13 1.41 3.73 5.6 6.60

5.56 1.50 4.06 7.5 6.65

6.87 1.81 5.06 10.2 6.61

<0.001 <0.001 <0.001 0.024 0.005

1.10 1.11 1.09 1.41 1.26

0.11 0.10 0.17 0.12 0.003

1.34 1.35 1.29 1.96 1.23

<0.001 <0.001 <0.001 0.007 0.033

(4.48) (1.23) (3.74) (0.86)

(5.00) (1.17) (4.24) (0.85)

(6.47) (1.49) (5.47) (0.79)

(0.98e1.25) (0.98e1.25) (0.96e1.23) (0.91e2.18) (1.08e1.47)

(1.19e1.51) (1.20e1.53) (1.15e1.46) (1.20e3.19) (1.02e1.49)

All data are presented as mean (SD) unless otherwise indicated. p Values correspond to the relationship between each variable and the severity of dilated perivascular spaces in hippocampus. In each polytomous logistic regression model, the severity of dilated perivascular spaces in hippocampus was considered as the dependent variable. For continuous variables, the OR estimates the association related to an increase of 1 SD. Key: CI, confidence interval; MRI, magnetic resonance imaging; OR, odds ratio; SD, standard deviation; WMH, white-matter hyperintensities. a Model a: adjustment on age, gender and intracranial cavity volume. b Model b: adjustment on age and gender.

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Table 4 Associations of the load of hippocampal dilated perivascular spaces with cognitive performance at baseline MMSEa

Load of H-dPVS

Model 1 Degree Degree Degree Model 2 Degree Degree Degree Model 3 Degree Degree Degree

BVRTa

ISTa

TMTAb

TMTBb

p

b

p

b

p

b

p

b

p

b

0 1 2

REF 0.92 0.83

0.003 0.006

REF 0.41 0.81

0.021 0.007

REF 0.62 0.29

0.012 0.029

REF 0.41 0.19

0.021 0.036

REF 0.85 0.43

0.005 0.021

0 1 2

REF 0.79 0.94

0.007 0.002

REF 0.34 0.70

0.024 0.011

REF 0.64 0.20

0.012 0.034

REF 0.39 0.16

0.022 0.039

REF 0.93 0.47

0.002 0.020

0 1 2

REF 0.80 0.89

0.007 0.004

REF 0.57 0.51

0.015 0.019

REF 0.65 0.39

0.012 0.024

REF 0.33 0.24

0.025 0.034

REF 0.96 0.36

0.001 0.026

Multiple linear stepwise models were built to assess the association between degree of dilated perivascular spaces in hippocampus and cognitive performance. In each model, neurologic tests were considered as the dependent variable. Model 1: adjustment on age, gender, and educational level. Model 2: model 1 þ further adjustment on apoE4 genotype, risk factors for cerebrovascular disease, and the presence of depressive symptoms. Model 3: model 2 þ further adjustment on brain parenchymal fraction, normalized volume of white matter hyperintensities, and the presence of lacunar infarcts. Key: BVRT, Benton Visual Retention Test; H-dPVS, dilated perivascular spaces in hippocampus; IST, Isaacs set test; MMSE, Mini Mental State Examination; REF, reference; TMTA and TMTB, Trail Making Tests part A and B, respectively a Lower scores indicate worse performance. b Higher scores indicate worse performance.

dPVS in basal ganglia or white matter was found to increase with the risk of cognitive decline or incident dementia in older populations (Maclullich et al., 2004; Zhu et al., 2010b). This was observed independently of the association of dPVS with WMH and lacunar infarcts, which are themselves related to cognitive decline (Viswanathan et al., 2009; Zhu et al., 2010b). The underlying mechanisms remain unknown. A possible explanation is that dPVS may be partly due to the blockage of drainage of interstitial fluid in the perivascular spaces by amyloid-b deposit as found in Alzheimer’s disease and cerebral amyloid angiopathy (Roher et al., 2003; Weller et al., 2008). Hippocampal functional and structural changes have long been demonstrated to play a critical role in learning, memory, and cognitive impairments (Lazarov et al., 2010; Sahay et al., 2011; Squire, 1992). The lack of association between HdPVS and cognition, as previously observed in a few studies of small sample-sizes (Bartrés-Faz et al., 2001; Maclullich et al., 2004), suggests that the global load of dPVS rather than the load of dPVS specific to hippocampus is associated with occurrence of dementia. In line with these negative findings and contrary to our initial expectation, the hippocampus volume was found to increase with the load of H-dPVS in the present study while the load of dPVS in basal ganglia or in white matter was not previously found related to brain volume in the same cohort (Zhu et al., 2010a). The results obtained in the present study also contrast with the lack of association between H-dPVS and the global or regional cerebral volume reported in small sample-size studies of patients with Alzheimer’s disease or older healthy individuals (Barboriak et al., 2000; BastosLeite et al., 2006; Li et al., 2006). They should be interpreted with caution. If the development of H-dPVS actually results from

dysfunction of the blood-brain barrier and/or from alterations of the drainage of interstitial fluid (Kwee et al., 2007), we can hypothesize that similar mechanisms may participate in some hippocampal tissue edema. In Cerebral Autosomal-Dominant Arteriopathy with Subcortial Infarcts and Leukoencephalopathy, an hereditary ischemic SVD, white-matter edema was found strongly related to the accumulation of dPVS in temporal lobes (Yamamoto et al., 2009) and possibly involved in the increase of brain volume with accumulation of WMH (Yao et al., 2012). This hypothesis is further supported by our findings that compared with those without H-dPVS, participants with at least 1 visible H-dPVS presented with a lower risk of having severe hippocampal atrophy rate at 4-years of follow-up (data not shown). The volume of H-dPVS which was included in the total volume of hippocampus appears unlikely responsible for these findings because most dPVS have a nonsignificant volume effect due to their low number and size. In this cohort, only 5.8% of individuals presented with H-dPVS of diameter larger than 3 mm (Zhu et al., 2011). Our interpretation should remain cautious since we cannot exclude that this association between hippocampal volume and load of H-dPVS is not related to detection biases. Particularly, the detectability of dPVS on MRI is mainly related to local contrasts observed in the hippocampus that may change with the shrinking of this small anatomic structure. Finally, this population-based study results in an estimated prevalence of H-dPVS of 54.5% in individuals over 65 years-old. In the literature, the prevalence of H-dPVS in much smaller groups of healthy older individuals was found highly variable ranging from 36.4% to 77%.Varying sample sizes but also differences in voxel size,

Table 5 Association between hippocampal dilated perivascular spaces and the risk of incident dementia H-dPVS

% (n)

Event/1000 person-year

Model 1

Degree 0 Degree 1 Degree 2

4.9 (48) 6.9 (34) 4.3 (12)

8.0 11.2 7.3

REF 0.19 0.87

p

Model 2 HR (95% CI)

p

1.34 (0.86e2.08) 0.95 (0.50e1.80)

REF 0.41 0.75

Model 3 HR (95% CI)

p

HR (95% CI)

1.21 (0.77e1.90) 0.90 (0.47e1.72)

REF 0.61 0.76

1.13 (0.70e1.84) 0.90 (0.46e1.78)

Model 1: adjustment for age, gender and educational level. Model 2: model 1þfurther adjustment on apoE4 genotype, risk number of cerebral vascular disease, and the presence of depressive symptoms. Model 3: model 2 þ additional adjustment on brain parenchymal fraction, normalized volume of white matter hyperintensities, and the presence of lacunar infarcts. Key: CI, confidence interval; H-dPVS, dilated perivascular spaces in hippocampus; HR, hazard ratio; REF, reference.

M. Yao et al. / Neurobiology of Aging xxx (2014) 1e8

slice thickness, MRI sequences, and orientation that largely influence the detectability of dPVS most likely explain these discrepancies. The methodological strengths of this study include the populationbased design, the large sample size, and the use of 3D T1 acquisitions to obtain reliable identifications of dPVS. Our study also has potential limitations. First, very small ischemic cavities in the cerebral tissue might have been misclassified as dPVS and vice versa despite the use of 3D imaging analysis. Second, the use of a semi-quantitative evaluation for rating the load of dPVS can limit our ability to detect small effects or differences. Third, it remains possible that the d-PVS do impact episodic memory function, and that no relationship was found with the cognitive indices in this study because they did not directly assess these functions. The Benton Visual Retention Test assessed visual memory but not the declarative memory, which was the most representative of hippocampal function (Squire, 1992). However, dHPVS was not found either related to MMSE which includes some evaluation of declarative memory. Fourth, the relatively small number of participants (n ¼ 94) with incident dementia may attenuate the ability to detect a small risk associated with H-dPVS. Meanwhile, 71 participants did not have follow-up information and were not considered in the analysis. However, even if we assume that all of them developed dementia during the follow-up, no global association or linear trend would have been detected between the load of H-dPVS and dementia (data not shown). Fifth, the lack of pathologic confirmation of dPVS is an inevitable limitation of a large imaging study obtained in healthy participants. In conclusion, the present findings strongly suggest that in the older individuals, the number of dPVS in hippocampus increases with age, hypertension, WMH volume, and the presence of lacunar infarctions of presumed ischemic origin. These findings are in accordance with previous results obtained for dPVS in white matter and basal ganglia. Altogether, these data suggest that aging and blood pressure are actually related to the development of dPVS throughout the whole brain in the older individuals and that HdPVS can be considered as an additional radiological hallmark of age-related SVD. Our results also strongly support that the load of H-dPVS is not related to the occurrence of dementia. Disclosure statement All authors confirm that they have no actual or potential conflicts of interest to disclose. The data contained in the manuscript being submitted have not been previously published, have not been submitted elsewhere, and will not be submitted elsewhere while under consideration at Neurobiology of Aging. The study was approved by the Ethical Committee of the University-Hospital of Bicêtre. Written and informed consent was obtained from all patients participating in the study. All coauthors have seen and agree with the contents of the manuscript. Acknowledgements The Three-City (3C) Study is conducted under a partnership agreement among the Institut National de la Santé et de la Recherche Médicale (INSERM), the Victor SegaleneBordeaux II University, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, MGEN, Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of ResearcheINSERM Programme “Cohortes et collections de donne’es biologiques.” Ming Yao and Yicheng Zhu are funded by the French Chinese Foundation for Science

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