Ethics statement
This study conformed with the World Medical Association Declaration of Helsinki and was approved by the Huashan Hospital Research Ethics Committee (Project-ID: KY2018-224). All patients or their relatives provided written informed consent.
Subjects
We consecutively recruited subjects aged between 50 and 80 years who visited the Sleep Center of Huashan Hospital from September 1, 2018 to August 31, 2019. Brain magnetic resonance imaging (MRI) was routinely recommended for all subjects using a 3-Tesla scanner (Siemens Magneton Verio 3 T). The MRI sequences included T1-weighted, T2-weighted, fluid-attenuated inversion recovery, and susceptibility weighted imaging.
The inclusion criteria for patients diagnosed with arteriosclerotic CSVD included 1) baseline MRI scan mainly showing moderate to severe WMH of presumed vascular origin (Fazekas score of 2-3), with or without the presence of other MRI features of CSVD (lacune of presumed vascular origin, CMBs, EPVSs and brain atrophy)3; 2) one or more characteristic clinical manifestations of CSVD (including cognitive, motor or mood disturbances) or no evident symptoms; and (3) consent to participate in the study. The exclusion criteria were: 1) cortical infarct or large subcortical infarct (>2 cm) on conventional MRI; 2) previous ischemic stroke which was less than 6 months after onset; 3) carotid artery stenosis >50%16; 4) non-arteriosclerotic CSVD, such as inherited CSVD or probable cerebral amyloid angiopathy (CAA); 5) any other cause of white matter disease; 6) major psychiatric disorders; 7) the presence of any ANS disorders or clinically relevant arrhythmia; and 8) a systemic or terminal illness that could not complete examinations.
The inclusion criteria for individuals in the control group were as follows: 1) no characteristic neuroimaging markers of CSVD on baseline MRI, except for mild WMH of presumed vascular origin (Fazekas score of 0-1) and age-matched brain atrophy; 2) no history of definite cerebrovascular disease, Parkinson’s disease, cognitive impairment or psychiatric disorders; and (3) consent to participate in the study. Enrollment exclusions were : 1) a systemic or terminal illness that could not complete examinations; and 2) the presence of any ANS disorders or clinically relevant arrhythmia.
Traditional cerebrovascular risk factors
Cerebrovascular risk factors were ascertained through laboratory examinations and interviews conducted by experienced physicians. Hypertension was defined as systolic blood pressure >140 mmHg or diastolic blood pressure>90 mmHg, or the use of antihypertensive drugs17. Diabetes mellitus (DM) was defined as glycated hemoglobin level>6.5%, fasting glucose level>126 mg/dL, 2-hour glucose level>200 mg/dL, or the current use of insulin or hypoglycemic agents18. Hyperlipidemia was defined as total serum cholesterol level>5.9 mmol/L, total triglyceride level>1.8 mmol/L, or the use of lipid-lowering medications. Previous stroke was defined as the presentation of sudden focal neurological deficits with consistent radiological findings occurred more than 6 months before enrollment. The smoking history (including ex- and current smoker) and body mass index (BMI) were also recorded.
Polysomnography
Compumedics Profusion Polysomnography (PSG) V4.5 (Shanghai, China) was used to monitor sleep parameters for a whole night of sleep, and the permission for its application was obtained. Nocturnal PSG was performed in the Sleep Laboratory Center of Huashan Hospital, and the Pittsburgh Sleep Quality Index (PSQI) was applied to measure the subjective sleep quality during the last month. All subjects were instructed not to use sleep medications, anxiolytics, or antidepressants for at least 2 weeks preceding the examination and not to consume caffeinated beverages, alcohol or strong tea at the afternoon preceding the recording. The monitoring time ranged from approximately 22:00 pm to 6:30 am the next day and was adjusted according to the individual’s habitual bedtime. Sleep stages, including non-rapid eye movement (NREM)sleep (stage N1, stage N2, stage N3) and rapid eye movement (REM) sleep(stage R), were manually scored by an experienced polysomnographic technologist according to criteria from American Academy of Sleep Medicine19 (AASM, version 2.4), who was blinded to the medical history of all participants.
Sleep apnea was defined as a ≥90% reduction in airflow from baseline lasting more than 10 seconds (s). Sleep hypopnea was defined as a ≥30% reduction in airflow from baseline lasting at least 10 s that was associated with either an oxygen desaturation of >3% or an arousal. The apnea-hypopnea index (AHI) was defined as the total number of sleep apnea and hypopnea events per hour during the whole night of sleep. AHI in the NREM sleep and REM sleep were also analyzed. Hypoxia-related parameters were also recorded, including the average oxygen saturation (SaO2), minimum SaO2, average oxygen desaturation, oxygen desaturation index (ODI), time with SaO2<90% (ST90%), and percentage of cumulative time with SaO2<90% (CT90%). The ODI referred to the total number of 3% or greater oxygen desaturation events per hour during sleep. The periodic limb movements index (PLMI) was also calculated as the total number of limb movement events per hour during sleep.
HRV analyses
Electrocardiogram (ECG) data were obtained simultaneously during awake and sleep periods with PSG between the onset and the end of recording. The ECG Add-on for Profusion PSG 4 was applied to analyze the HRV, with a focus on time and frequency domains. Four time-domain measures of HRV were evaluated: the standard deviation of normal-to-normal intervals (SDNN), the root mean square of successive differences in RR intervals (RMSSD) and the percentage of normal R-R intervals that differ by 50 ms (PNN50). The ratio of low to high frequency power (LF/HF) was calculated as a frequency domain measure. HRV parameters during awake and sleep periods defined by PSG were recorded and analyzed separately.
MRI data acquisition and image processing
Based on the MRI results at baseline, all hallmark imaging markers were defined according to the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE) guidelines3. The total CSVD scores were calculated according to previous descriptions20, with a maximum score of four points.
Enrolled patients were invited to further undergo imaging with a three-dimensional brain volume (3D-BRAVO) sequence within one week (acquisition parameters: TR = 8.8 ms, TE =1.0 ms, flip angle = 15°, slice thickness=1 mm iso-voxel, FOV=320 mm×320 mm, matrix=320×320, voxel=1 mm×1 mm×1 mm).Voxel-based morphometry (VBM) was implemented using SPM12 on the MATLAB 2016b workstation. The origin of each participant’s image was adjusted to the anterior commissure. Images were segmented into gray matter, white matter, and cerebrospinal fluid. Normalization and high-dimensional registration were achieved using the DARTEL toolbox. Then, the 3D images were modulated using non-linear methods and spatially smoothed with a full width at half maximum value of 12 mm. Finally, parametric statistical tests were performed for group comparisons of the smoothed gray matter map after including age, sex and AHI as covariates. Differences were considered significant with a family-wise error cluster corrected probability of p<0.0001. Additionally, the volume of gray matter from different areas of the brain was measured quantitatively.
Statistical analyses
The statistical analyses were performed using SPSS 26.0 and STATA software. The categorical variables were presented as counts. The continuous variables were presented as means ± standard deviations (SD), unless their distributions were skewed, in which case the variables were presented as medians (interquartile ranges). The chi-squared test or Fisher’s exact test was used to analyze the categorical variables. The continuous variables were analyzed using a t-test or nonparametric test. The odds ratios (OR) and 95% confidence intervals (CI) for the relationship between HRV parameters and arteriosclerotic CSVD were assessed through multivariate binary logistic regression models. Generalized linear models (GLMs) were applied to analyze the relationships between HRV parameters and cortical thickness in the two groups. Due to the small sample size, variables with a P value<0.05 in the group comparisons were preferentially entered into the models.