Decreased Cerebrospinal Fluid Flow Is Associated With Cognitive Deficit in Elderly Patients

Jadwiga Attier-Zmudka1,2, Jean-Marie Sérot1, Jeremy Valluy3, Anne-Sophie Macaret4, Momar Diouf5, Salif Dao6, Youcef Douadi4, Krzysztof Piotr Malinowski7 and Olivier Balédent2,8
1Department of Gerontology, Centre Hospitalier de Saint-Quentin, Saint-Quentin, France
2CHIMERE, EA 7516 Head and Neck Research Group, University of Picardie Jules Verne, Amiens, France
3ReSurg SA, Nyon, Switzerland
4Department of Neurology, Centre Hospitalier de Saint-Quentin, Saint-Quentin, France
5Department of Research, Amiens University Hospital, Amiens, France
6Department of Radiology, Centre Hospitalier de Saint-Quentin, Saint-Quentin, France
7Faculty of Health Sciences, Institute of Public Health, Jagiellonian University Medical College, Kraków, Poland
8BioFlowImage, Image Processing Unit, University Hospital of Amiens, Amiens, France
Background: Disruptions in cerebrospinal fluid (CSF) flow during aging could compromise protein clearance from the brain and contribute to the etiology of Alzheimer’s Disease (AD).
Objective: To determine whether CSF flow is associated with cognitive deficit in elderly patients (>70 years).
Methods: We studied 92 patients admitted to our geriatric unit for non-acute reasons using phase-contrast magnetic resonance imaging (PC-MRI) to calculate their ventricular and spinal CSF flow, and assessed their global cognitive status, memory, executive functions, and praxis. Multivariable regressions with backward selection (criterion p < 0.15) were performed to determine associations between cognitive tests and ventricular and spinal CSF flow, adjusting for depression, anxiety, and cardiovascular risk factors.Results: The cohort comprised 71 women (77%) and 21 (33%) men, aged 84.1 ± 5.2 years (range, 73–96). Net ventricular CSF flow was 52 ± 40 μL/cc (range, 0–210), and net spinal CSF flow was 500 ± 295 μL/cc (range, 0–1420). Ventricular CSF flow was associated with the number of BEC96 figures recognized (β = 0.18, CI, 0.02–0.33; p = 0.025). Spinal CSF flow was associated with the WAIS Digit Span Backward test (β = 0.06, CI, 0.01–0.12; p = 0.034), and categoric verbal fluency (β = 0.53, CI, 0.07–0.98; p = 0.024) and semantic verbal fluency (β = 0.55, CI, 0.07–1.02; p = 0.024).Conclusion: Patients with lower CSF flow had significantly worse memory, visuo-constructive capacities, and verbal fluency. Alterations in CSF flow could contribute to some of the cognitive deficit observed in patients with AD. Diagnosis and treatment of CSF flow alterations in geriatric patients with neurocognitive disorders could contribute to the prevention of their cognitive decline.IntroductionThe cerebrospinal fluid (CSF) is an important part of the central nervous system, as it allows exchange of water, small molecules and proteins between the brain parenchyma and arterial and venous blood (Oreskovic and Klarica, 2010; Brinker et al., 2014), by either passive diffusion or active transport (Oreskovic and Klarica, 2014; Oreskovic et al., 2017b). The CSF therefore plays an important role in regulating brain homeostasis, waste clearance (Puy et al., 2016), as well as intracranial pressure and blood supply (Baledent et al., 2004). During aging, CSF turnover can be disrupted (Rubenstein, 1998; Stoquart-ElSankari et al., 2007) which could contribute to the etiology of age-related neurocognitive disorders (Rubenstein, 1998; Weller et al., 2000; Launer, 2002; Silverberg et al., 2003; Chakravarty, 2004). Several studies revealed that patients with Alzheimer’s disease (AD) have disrupted CSF pressure (Silverberg et al., 2006), turnover (Henry-Feugeas and Intracranial, 2009; Serot et al., 2012), and oscillations (Silverberg et al., 2006; Stoquart-ElSankari et al., 2007). Moreover, biomarkers for AD are found in the CSF, and their abundance was shown to have predictive value for clinical progression (Wolfsgruber et al., 2017).The increase of intracranial pressure during the cardiac cycle causes a flow from the blood and brain interstitial fluid to the CSF, and a net CSF flow toward its extracerebral compartment and venous blood (Oreskovic et al., 2017a). Since this CSF flow is important for protein clearance from the brain (Puy et al., 2016), it is possible that impaired CSF flow could be associated with cognitive decline (Coblentz et al., 1973; Sohn et al., 1973; Rubenstein, 1998). Moreover, CSF flow is linked with brain perfusion (Egnor et al., 2002; Baledent et al., 2004), defects of which are known causes of neurocognitive disorders in the elderly (O’Brien and Thomas, 2015). A number of studies suggested that the choroid plexus and the ventricular walls degenerate with the progression of AD (Serot et al., 2000; Balusu et al., 2016; Daouk et al., 2016), but none could determine whether disrupted CSF flow causes cognitive decline, or whether it is a by-product of AD or normal aging.To the authors’ knowledge, there are no published studies that investigated the relationship between CSF flow alterations and cognitive deficit in the elderly, adjusting for cardiovascular risk factors for the development of neurocognitive disorders. The purpose of this study was therefore to evaluate the association of CSF flow in the brain ventricles and cervical spine with cognitive deficit (assessed using neurocognitive tests in clinical settings) in a cohort of elderly patients (>70 years) admitted to our geriatric unit for non-acute reasons. The hypothesis was that reduced CSF flow would be associated with cognitive deficit. Improved knowledge of such associations could guide the development of medical or surgical treatments to limit or prevent cognitive decline in the elderly (Nakajima et al., 2018).
Materials and Methods
We enrolled 115 consecutive patients admitted to our geriatric unit for non-acute reasons between October 2015 and March 2018. The inclusion criterion was patients aged over 70 years. Twenty-one patients were excluded because of contraindications to phase-contrast magnetic resonance imaging (PC-MRI) for analysis of brain fluid motion, and two patients died before completing neurocognitive assessments. This left a study cohort of 92 patients (Figure 1) who underwent PC-MRI that enabled calculation of ventricular (aqueduct) CSF flow and spinal (C2-C3) CSF flow.

FIGURE 1

Figure 1. Flowchart of patient inclusion.

Data Acquisition
The PC-MRI was performed using a 1.5-T machine. Conventional morphologic image sequences were first acquired in the sagittal and axial planes. The CSF flow acquisition planes were then selected perpendicular to the presumed direction of flow through the Sylvius aqueduct [representing the ventricular flow (Jacobson et al., 1996)] and the spinal C2-C3 sub-arachnoid spaces (representing the spinal flow). Flow images were acquired using a velocity-encoded phase-contrast pulse sequence with peripheral gating, as previously described (Baledent et al., 2001, 2004). Velocity sensitization was set at 10 cm/s for the ventricular flow and 5 cm/s for the spinal flow.
Data Analysis
Phase-contrast magnetic resonance imaging data were transferred to a Sparc 10 workstation (SUN Microsystems) and analyzed using an in-house image processing software with Interactive Data Language (Baledent et al., 2001). This software automatically measures the CSF flow curve over the cardiac cycle for a given region of interest. Cranial–caudal flows were positive (CSF flush), whereas caudal–cranial flows were negative (CSF fill). The difference between CSF fill and flush flows is the net CSF flow, which reflects the volume of CSF produced (Nilsson et al., 1994). For technical reasons, intracranial sub-arachnoid CSF flow was not investigated.
Neurocognitive Assessment
All patients underwent a battery of neurocognitive tests that assessed global cognitive status, memory, executive functions, praxis, as well as depression and anxiety (Table 2). Global cognitive efficiency was assessed by the Mini-Mental State Examination (MMSE) (Folstein et al., 1975), standard of Kalafat et al. (2003), following the GRECO standardization and calibration, and the Mattis Dementia Rating scale (MDRS) (Hersch, 1979; Gardner et al., 1981). The memory domain was assessed using the Wechsler Adult Intelligence Scale (WAIS-III) – Digit Span task (Iverson and Tulsky, 2003; Hill et al., 2010) and the Grober-Buschke (GB) test (French version of the Free and Cued Selective Reminding Test) (Buschke, 1984; Grober et al., 1988; Grober and Kawas, 1997; Van der Linden and Juillerat, 2004). Instrumental cognition was assessed with Signoret’s Battery of Cognitive Efficacy (BEC 96) (visuo-constructive subscale) (Jacus et al., 2001). Attention and executive domains were assessed using the Stroop Color and Words Test (Comalli et al., 1962), as well as two categoric and semantic verbal fluency tests, as impaired semantic fluency is a predictor of progression to AD (Vaughan et al., 2018) and categoric fluency may be impaired in amnesic MCI (Balthazar et al., 2007). In addition, the patients were evaluated with the Montgomery-Asberg Depression rating scale (MADRS) (Montgomery and Asberg, 1979), on 60 points, and the Goldberg anxiety scale (Goldberg et al., 1987; Huber et al., 1999) on 9 points, to rule out effects of depression or anxiety on cognitive test results.
Mild cognitive impairment was diagnosed based on the criteria of Petersen et al. (Petersen et al., 2001), while AD and AD-like diseases were diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) and the recommendations of the National Institute on Aging – Alzheimer’s Association workgroups (McKhann et al., 2011).
Vascular risk factors were diagnosed based on a comprehensive geriatric assessment performed at our geriatric unit. Blood samples were obtained after a minimum of 10 h of fasting. The diagnosis of diabetes was attributed with blood glucose levels above 7 mmol/L, anemia with hemoglobin levels below 12 (women) or 13 (men) g/dL and inflammation with CRP levels above 10 mg/L. Likewise, the reference range was 4.1–6.5 mmol/L for total cholesterol, 0.6–1.8 mmol/L for triglycerides and 35–50 g/L for Albumin. Malnutrition was defined by Albumin levels

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