Cerebral small vessel disease is associated with gait disturbance among community-dwelling elderly individuals: the Taizhou imaging study

Demographic, imaging, and gait characteristics

The demographic, neuroimaging and gait performance characteristics of the 314 involved participants are summarized in Table 1. The mean age was 59.6 at the time of MRI acquisition and males accounted for 45.9%. Lacunes, severe WMH, CMBs and large PVS were found in 71 (22.6%), 28 (8.9%), 58 (18.5%), and 61 (19.4%) participants, respectively. The mean gait velocity was 0.95 m/s and the average time for TUG test was 10.5 s. All included subjects were divided into two groups based on total CSVD score. High CSVD burden group (with a total CSVD score ≥ 2) accounts for 17.5% of all involved subjects. No differences in age, gender and height were found between the two groups. Compared with low CSVD burden group, the high CSVD burden group had a higher proportion of current smokers, current drinkers and hypertension, although these trends were not statistically significant.

Total CSVD burden and gait

General linear models were used to assess correlations between total CSVD burden and gait performance. Total CSVD burden was grouped into high CSVD burden (defined as total CSVD score ≥ 2) and low CSVD burden. As shown in Table 2 and Table 3, after adjustment for age, sex and height, high CSVD burden was significantly associated with shorter stride length (β=-0.120, P=0.022), lower maximum swing velocity (β=-0.124, P=0.027), prolonged 3-m walking (β=0.127, P=0.025), and poorer TUG performance (β=0.154, P=0.006). After additional adjustment for vascular risk factors and time interval between MRI and gait measurement, high CSVD burden was still related to stride length, 3-m walking and duration of TUG test, and also borderline significant differences were observed in maximum swing velocity (β=-0.109, P=0.052). As gait velocity and Timed-Up-and-Go test mainly represent global gait function, logistic regression was used to identify the association between high CSVD burden and impaired gait (velocity < 0.8 m/s or TUG test > 12 seconds). Participants with high CSVD burden were 2 times more likely to have an impaired gait velocity (Figure 1A) or an impaired TUG test (Figure 1B).

Figure 1. Association between total CSVD burden, amount of lacunes and impaired gait by logistic regression. Model 1: adjusted for age, sex and height. Model 2: additional adjustment for vascular risk factors (hypertension, diabetes, smoking, drinking, hyperlipidemia) and interval between MRI and gait measurement. Impaired gait velocity defined as < 0.8 m/s in gait velocity. Impaired TUG test defined as a TUG test of >12 seconds.

CSVD imaging markers and gait

As total CSVD burden was shown to be related to gait disturbance, we used GLMs to assess the association between different imaging markers of CSVD and gait disturbance estimated by wearable gait tracking devices and clinical rating scales. As shown in Table 2, among quantitative temporal or spatial gait parameters, WMH volume was independently positively associated with stride time (β=0.145, P=0.015) when adjusted for age, sex, height and total intracranial volume, and further adjustments did not change the association. Presence of CMBs was associated with increased stance phase time percentage (β=0.115, P=0.038) after full adjustment. Heel-strike angle and toe-off angle were not associated with any CSVD imaging markers including lacunes, WMH, CMBs and PVS.

Regarding the clinical rating scales (Table 3), no significant associations were found between performance and the CSVD imaging markers of WMH or CMBs. Large PVS was significantly correlated with poorer Tinetti test performance after adjustment for age, sex, and height but did not survive additional adjustment. Presence of lacunes was positively associated with prolonged 3-m walking (β=0.118, P=0.037) and duration of the TUG test (β=0.112, P=0.047) after full adjustment, therefore logistic regression was further applied to evaluate the correlations between the severity of lacunes and impaired gait velocity or TUG test. As shown in Figure 1C and 1D, participants with more lacunes exhibited higher risk of impaired gait velocity (P for trend=0.016 in the fully adjusted model) and impaired TUG test (P for trend=0.006 in the fully adjusted model). For participants with 1 lacune, there was an OR of 2.6 (95%CI: 1.3 to 5.3) for impaired gait velocity and an OR of 2.2 (95%CI: 1.1 to 4.7) for impaired TUG test compared to those without lacunes after full adjustment. Those with 2 or more lacunes had a fully adjusted OR of 4.1 (95%CI: 1.2 to 14.4) for impaired TUG test and a fully adjusted OR of 2.1 (95%CI: 0.6 to 7.2) for impaired gait velocity, although this finding was not statistically significant.

Study population

For the present study, we used the baseline data from Phase I of the Taizhou Imaging Study (TIS), an ongoing population-based cohort study nested within the Taizhou Longitudinal Study (TZL) [26]. TIS was designed to investigate the risk factors and consequences of structural and functional brain changes in community-dwelling elderly people in a rural Han Chinese population. In 2013, two villages with the previous highest response rate were chosen from TZL and involved in the TIS. Inclusion criteria were: 1) aged between 55 and 65 years; 2) being Han Chinese whom have settled at Taizhou for over the past 10 years and lived locally for more than 9 months per year; 3) able to walk and communicate normally and independently, and able to provide information by self-reporting and participate in physical examinations. Exclusion criteria were: 1) history of stroke or cancer; 2) history of clinically diagnosed neurological or psychiatric disorders; 3) signs of abnormalities in neurological examination; 4) prominent visual or hearing impairment. Accordingly, a total of 624 participants were involved. Among them, 562 individuals responded and agreed to participate in our study. The response rate of participation was 90.1%. Informed consent has been obtained. The Ethics Committee of the School of Life Sciences, Fudan University, Shanghai, China approved the study (institutional review board approval number: 469).

At the baseline, all of 562 participants accepted questionnaire interviews, physical and laboratory examinations and brain MRI scans, 317 of them accomplished quantitative gait analysis. From the 317 participants, we excluded 2 with incomplete data and 1 with current mobility aid use due to fracture, leaving 314 for the current analysis.

Collection of clinical characteristics

As reported previously [27, 28], current smokers were defined as individuals that reported regular smoking for at least 6 months before enrollment; current alcohol drinkers were individuals consuming more than three drinks per week for at least 6 months before enrollment. Blood pressure was measured in the right upper limb in a sitting position after resting for 10 minutes in a quiet room. Hypertension was defined by systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥90 mmHg, a previous diagnosis of hypertension, or use of antihypertensive drugs. Venous blood samples were collected by certified nurses between 7 AM and 8 AM after overnight fasting. Diabetes was determined by a fasting level of plasma glucose ≥ 7.0 mmol/L, a previous diagnosis of diabetes, or treatment with antidiabetic drugs. Hyperlipidemia was defined by plasma total cholesterol (TC) ≥ 5.2 mmol/L, triglycerides (TG) ≥ 1.7 mmol/L, a previous diagnosis of hyperlipidemia, or current use of lipid lowering drugs. All examinations were conducted at the Taizhou People’s Hospital, Taizhou City, Jiangsu Province, China.

MRI scanning and processing

MRI examination for all participants were performed on the same 3.0-Tesla scanner (Magnetom Veiro Tim scanner; Simens, Erlangen, Germany) with a pre-determined multi-sequence protocol. The MRI sequences included T1−weighted, T2-weighted, fluid attenuated inversion recovery (FLAIR), T2^*−weighted gradient recalled echo (GRE), proton density-weighted imaging (PDWI), perfusion-weighted imaging (PWI), diffusion tensor imaging (DTI), and time of flight (TOF) 3D magnetic resonance angiography (MRA). The specific parameters of each sequences have been reported previously [28, 29].

Two experienced neurologists (M.C. and Q.Y.) were involved in reading and assessment of MRI images. Each of them read images independently and the final report was based on their consensus. Conflicts during assessment were handled to a senior neuroradiologist (W.T.) for a final judgment. The inter-rater agreements were tested by Cohen’s kappa statistics and showed good reliability (kappa=0.81). Lacunes, WMH and CMBs were diagnosed based on the Standards for Reporting Vascular Changes on Neuroimaging (STRIVE) criteria proposed in 2013 [4]. Presence of severe WMH were defined as those with either periventricular white matter hyperintensities (PVWMHs) of deep white matter hyperintensities (DWMHs) rated at least two on the Fazekas scale [16]. As recent studies revealed that large PVS, compared with small PVS with a maximum diameter ≤ 2 mm, may have a stronger relevance with CSVD-related functional deficit, we defined PVS as round or tubular lesions with CSF-like signal and a short axis ≥ 3 mm in the subcortical area in the current study. Imaging markers of CSVD, including lacunes, CMBs, severe WMH and PVS, were summed in an ordinal “total CSVD score” (with a range of 0–4) by counting presence of each of these four MRI features [14].

The volume of WMH was determined using SPM8 software [30]. Total intracranial volume (TIV) was estimated using FreeSurfer v6.0.0 software (http://surfer.nmr.mgh.harvard.edu/fswiki/). The results of SPM and FreeSurfer were reviewed for accuracy by the consensus of two experienced neurologists (Y.W. and H.W.) There were 19 (6.1%) missing data in total intracranial volume (TIV) and 3 (1.0%) missing data in WMH volume due to motion artifacts and extreme outliers to maintain imaging quality control.

Measurement of gait

Quantitative gait analysis was performed with an insole-like wearable gait tracking device (Senno gait, Sennotech Co. Ltd., China) [31]. A result of comparison testing, by the China National Institute of Standardization (CNIS) showed that this wearable gait analysis system accurately captured gait parameters as compared to a widely used optoelectronic motion capture system (Vicon, Oxford Metrics, UK). Participants walked freely at a comfortable, self-chosen speed in an obstacle free and flat environment for 2×10 meters. After the first 10-meter walk, participants were instructed to turn 180 degrees. Gait parameters were extracted from each stride and data were streamed wirelessly via Bluetooth^®. Data were automatically ordered and the top and bottom 15% were excluded thus turning strides would not be analyzed. Temporal parameters included stride time (s, the duration of one gait cycle), cadence (the number of steps per minute), stance phase time percentage (%, the portion of the cycle during which part of the foot touches the ground) and maximum swing velocity (m/s, the maximum forward speed of the foot during swing). Spatial parameters included stride length (m, the distance between the heel points of two consecutive footprints of the same foot), heel-strike angle (°, the angle between the foot and the ground at heel strike on a vertical plane) and toe-off angle (°, the angle between the foot and the ground just at take-off on a vertical plane). Clinical rating scales assessment consisted of the Tinetti test [32] with 17 items (9 for balance and 8 for gait) with a maximum score of 28 as well as the Timed-Up-and-Go (TUG) test [33]. For measurement of gait velocity, 3-m walking test was performed. Participants walked at usual pace on a 3-m course and time for walking was measured by a stopwatch.

Statistical analysis

Continuous variables were expressed as mean and standard deviation, and categorical variables were expressed as frequencies and proportions. Shapiro-Wilk test was used to evaluate the normality of continuous variables, and logarithmic transformation was applied for skewed variables. The two-sample t-test or chi-square test was applied to evaluate group differences according to requirement.

In consideration that participants with 2 or more CSVD imaging markers constituted only 17.5% of the study population, high CSVD burden was defined as a total CSVD score ≥ 2 in the current study. To evaluate the relationship between low/high CSVD burden and gait performances, general linear models (GLMs) were applied with the group as a determinant and quantitative gait parameters (stride time, cadence, stance phase time percentage, maximum swing velocity, stride length, heel-strike angle, toe-off angle) or clinical rating scale performances (3-m walking, Tinetti test and Timed-Up-and-Go test) as outcome variables. Covariates included age, sex, height, vascular risk factors (hypertension, diabetes, smoking, drinking, hyperlipidemia) and interval between MRI and gait measurement. The relationships between CSVD imaging markers and gait performances were investigated by using similar GLMs with each CSVD marker (WMH volume, presence of lacunes, presence of PVS and presence of CMBs) as a determinant. For analyses between WMH volume and gait, total intracranial volume was additionally adjusted. The risk of impaired gait by high CSVD burden or the severity of lacunes was assessed with logistic regression analysis with adjustment for age, sex, height, vascular risk factors, and interval between MRI and gait measurement. As a previous study reported that there were significant differences between multiple lacunes and single lacune in vascular factors and prognosis [34], the severity of lacunes was grouped as none, one and two or more lacunes in our study. According to previous studies, impaired TUG test was defined as a duration of TUG test >12 seconds [35]. Impaired gait velocity in this study was defined as a gait velocity under the first quartile (< 0.8 m/s).

Statistical analysis was performed using SPSS version 21.0. A probability value of < 0.05 was considered significant.