Effect of Positional Changes on Cerebral Perfusion in Parkinson’s Disease Patients With Orthostatic Hypotension
Article information
Abstract
Objective
Orthostatic hypotension (OH) is one of the most common autonomic dysfunctions in Parkinson’s disease (PD) patients. However, many patients with OH are asymptomatic. Conversely, orthostatic dizziness (OD) is not always associated with OH. We investigated the effects of positional changes on cerebral perfusion in patients with PD and OH.
Methods
We enrolled 42 patients, comprising 31 PD patients and 11 healthy controls. All the subjects underwent the following clinical assessments: the OH questionnaire, head-up tilt test (HUTT) with transcranial Doppler (TCD), near-infrared spectroscopy, measurement of the change in oxygenated hemoglobin (ΔHboxy) during the squat-to-stand test (SST), measurement of the time derivative of total hemoglobin (DHbtot), and time taken to reach the peak (peak time [PT]) of DHbtot after restanding.
Results
The mean flow velocity change (ΔMFV) in the TCD during the HUTT failed to differentiate between the PD-OH(+) and PD-OH(-) groups. The change in oxygenated hemoglobin ΔHboxy was greater in the PD-OH(+) group, which persisted for 9 min until the end of the HUTT only in the left hemisphere. During SST, PT was significantly delayed in the left hemisphere in PD-OH(+) patients.
Conclusion
Although TCD demonstrated no significant difference in ΔMFV, the parameters measured by near-infrared spectroscopy, such as ΔHboxy during HUTT and PT during the SST, significantly increased ΔHboxy or delayed PT in the left hemisphere of PD-OH(+). Positional changes have a detrimental effect on cerebral hemodynamics in patients with PD and OH, especially in the left hemisphere.
INTRODUCTION
Orthostatic hypotension (OH) is a common nonmotor symptom of Parkinson’s disease (PD). It is associated with falls, interferes with daily activities, and decreases the 10-year survival rate of patients with PD. OH occurs due to failure of the sympathetic noradrenergic system [1], and the sympathetic noradrenergic outflow increases to augment peripheral vasoconstriction and cardiac output to compensate for the displacement of blood upon standing, which could impair PD patients with OH. α-Synuclein aggregation is found in the central and peripheral autonomic pathways, which could be responsible for the autonomic dysfunction in PD [1,2]. Recent studies have suggested that OH and cerebral hypoperfusion have synergistic effects on PD patients [3,4]. Pathologic aggregation of α-synuclein in vasomotor regulatory areas may be related to neurogenic OH in PD [5].
The head-up tilt test (HUTT) is the gold standard diagnostic tool for OH. Cerebral hypoperfusion in PD and OH patients has been evaluated via brain magnetic resonance imaging or oximetry, which revealed impaired cerebral autoregulation [3,4,6]. In transcranial Doppler (TCD) studies, cerebral blood flow (CBF) has been measured by the mean flow velocity (MFV) of the cerebral vessels, the results of which are variable [7,8]. Near-infrared spectroscopy (NIRS) provides an indirect measure of cerebral perfusion related to regional tissue oxygen saturation by detecting signals of oxygenated hemoglobin (Hboxy) and deoxyhemoglobin (HbR) [6,9-11]. NIRS has not been adopted in previous studies of patients with PD and OH.
In this study, we investigated changes in cerebral perfusion in response to positional changes in patients with PD and OH via NIRS and TCD.
MATERIALS & METHODS
Study design and participants
A total of 43 participants, aged 50–75 years, were enrolled in the outpatient clinic of the Movement Disorder Center at Kyung Hee University Hospital in Korea between February 2020 and January 2021. The diagnosis of PD was confirmed on the basis of the United Kingdom Brain Bank criteria [12]. Patients suspected of having atypical or secondary parkinsonism were excluded. We also excluded one PD patient with nonneurogenic OH. Healthy controls (HCs) were recruited from the caregivers of PD patients via advertisements. A total of 31 patients with PD and 11 HCs were included in the study.
All the participants underwent clinical assessments for orthostatic dizziness (OD), HUTT, TCD, and wearable NIRS devices (NIRSIT and NIRSIT-ON; OBELAB Inc., Seoul, Korea).
Clinical evaluation
Clinical evaluations were performed at baseline to assess motor and nonmotor symptoms via the Movement Disorder Society Sponsored Unified Parkinson’s Disease Rating Scale (UPDRS) [13], Parkinson’s Disease Questionnaire-39 (PDQ-39) [14], Mini-Mental State Exam (MMSE) [15], and Montreal Cognitive Assessment (MoCA) [16]. OD was assessed via the Scale for Outcomes in Parkinson’s Disease-Autonomic (SCOPA-AUT) [17], Orthostatic Hypotension Symptom Assessment (OH-SA), and the Orthostatic Hypotension Daily Activity Scale (OH-DAS) [18].
The SCOPA-AUT was developed to quantify autonomic symptoms in patients with PD. The Orthostatic Hypotension Questionnaire [14] consists of two components, the OH-SA and the OH-DAS, for recording comprehensive symptom burden and assessing the severity of OH. OD (SCOPA-AUT) was defined as a value of 1 or higher for the total sum of scores on the three items of the SCOPA-AUT-cardiovascular scale (items 14, 15, and 16) [19]. In the case of the OH-SA, OD (OH-SA) was defined as a score of 1 or higher on item 1 (a question regarding dizziness, lightheadedness, or feeling faint) [20,21]. Since dizziness in other conditions, such as vestibular disorders, can mimic OD, in addition to careful neurological examination, medical history, and neuroimaging data were reviewed to exclude alternative diagnoses.
Diagnostic tests
The HUTT is the gold standard diagnostic tool for OH. All the participants rested in the supine position on a flat table, the blood pressure (BP) cuff device was placed on the mid-upper arm, and the table was elevated with a tilt of 70° after lying flat for 10 minutes. Systolic BP (SBP), diastolic BP (DBP), and heart rate were measured for up to 9 min. The attending physician would stop the test if the patient complained of severe dizziness or signs of syncope.
The TCD was performed at room temperature. The MFV was measured simultaneously in the middle cerebral artery (MCA) during the HUTT. A head-fixed 2 MHz TCD probe was placed at a depth of 45–64 mm in the anterior temporal window.
Two types of NIRS devices were used: a patch type (NIRSIT-ON) and a headgear type (NIRSIT). The former had a total of eight channels (four for each hemisphere), and the latter had a 64-channel system (32 for each hemisphere) for gathering signals, each of which was 30 mm from a source detector, which was suitable for signal detection from the prefrontal cortex (i.e., 20 mm deep from the scalp). Both devices detected the signals at a sampling rate of 8.138 Hz, and they employed light sources with dual wavelengths of 780 nm and 850 nm to measure the concentrations of Hboxy and HbR. The NIRSIT-ON was attached to place the lower edge of each patch at prefrontal (Fp) 1 and Fp2 of the 10–20 system. The NIRSIT was attached to place the center of the lowermost optical probes at frontal pole zero. All the participants underwent a gain calibration process to adjust the source power and detector gain to ensure signal quality before measurement.
We used the NIRSIT-ON during the HUTT and the NIRSIT during the squat-to-stand test (SST). The SST was performed with the participants wearing the NIRSIT headgear. Parameters such as SBP, DBP, and heart rate (HR) were measured when the participants sat on a chair and after standing (at 1 and 2 min). They were subsequently measured when the participants squatted (at 1 and 2 min) and after they stood up (at 1–9 min).
To assess cerebral perfusion using the same temporal set, all studies (HUTT, TCD, and SST) were performed for 9 min.
The NIRS measurement was recorded and transferred wirelessly to a mobile tablet for storage or via USB to a PC for data acquisition with NIRSIT or NIRSIT-ON, as appropriate. Before the analysis, channels with insufficient signal integrity, that is, a signal-to-noise ratio of less than 30 dB, were excluded from further analysis. By utilizing the modified Beer–Lambert law, NIRS devices trace the concentration changes of Hboxy (ΔHboxy) and HbR (ΔHbR) from the measurements compared with the baseline. The results of ΔHboxy and ΔHbR were filtered by using a discrete cosine filter and a lowpass filter with a cutoff frequency of 0.02 Hz to eliminate any instrumental noise and high-frequency noise. ΔHbtot was estimated as the sum of the ΔHboxy and ΔHbR values.
The SST was performed to assess cerebral hemodynamic changes during drastic positional changes [22]. Hbtot recovery during SST is related to cerebral autoregulation mechanisms. However, there is a limitation in identifying the relative hemodynamic changes from Hbtot variations. The time derivative of Hbtot (DHbtot) can be derived from the Hbtot curve with respect to time, which provides useful markers for understanding the characteristics of cerebral perfusion [23]. From the DHbtot curve of the SST, the times taken to reach the minimum and maximum peak values of DHbtot (peak time [PT]) were obtained. A drastic decrease or increase in Hbtot was identified at the minimum and maximum PT, respectively.
Statistical analysis
The demographic and clinical characteristics of the participants are presented as means and standard deviations. The participants were divided into three groups on the basis of the OH criteria (diagnosed as a drop in SBP ≥20 mmHg or DBP ≥10 mmHg in 3 min during the HUTT): HCs, PD patients without OH (PD-OH(-)), and PD patients with OH (PD-OH(+)). Further grouping was performed on the basis of the onset of clinical symptoms (right-onset vs. left-onset), the presence of OD (OD (SCOPA-AUT)+ vs. OD (SCOPA-AUT)- and OD (OH-SA)+ vs. OD (OH-SA)-), and the reduction in MFV (decreased MFV vs. normal MFV). For repetitive comparisons at different time points, two-way repeated-measures analysis of variance was adopted. Other comparisons among the groups were performed via the Kruskal‒Wallis test or Fisher’s exact test. The Mann–Whitney test and Bonferroni correction were used for post hoc paired tests. Correlation analyses were conducted via the phi correlation coefficient to compare the OD with the OH, OD, and TCD cutoff values. Statistical analyses were performed using SPSS software (version 25.0; IBM Corp., Armonk, NY, USA) and Prism software (GraphPad Prism version 7 for Windows; GraphPad Software, San Diego, CA, USA), with statistical significance set at a two-sided p < 0.05. DHbtot data analysis was performed using MATLAB (R2013b; MathWorks Inc, Natick, MA, USA).
Standard protocol approvals, registrations, and patient consent
The study was approved by the Local Ethics Committee (Institutional Review Boards of Kyung Hee University Hospital, #KHUH 2019-09-011), and all the subjects provided written informed consent before inclusion in the study according to the Code of Ethics of the World Medical Association (Declaration of Helsinki).
RESULTS
A total of 31 patients with PD (nineteen with onset symptoms on the right side) and eleven HCs completed the study. None of the tests was terminated prematurely because of severe dizziness or signs of syncope. The demographic and clinical characteristics of the participants are summarized in Table 1. The average age was 69.0 ± 5.2 years, and there were 23 females and 19 males. The disease duration was 3.9 ± 3.1 years, and the levodopa equivalent daily dose was 452.1 ± 297.4 mg. No significant differences in clinical parameters, such as age, sex, years of education, hypertension status, diabetes mellitus status, MMSE score, or MoCA score, were detected among the participants. The comparison of PD-OH(-) and PD-OH(+) patients via the UPDRS, PDQ-39, MMSE, and MoCA revealed no significant differences.
The frequency of OD (SCOPA-AUT)/OD (OH-SA) differed among the three groups, but it did not differ between the PD-OH(-) and PD-OH(+) groups in the post hoc analysis (Supplementary Table 1 in the online-only Data Supplement). There was no correlation between the OD (SCOPA-AUT)/OD (OH-SA) and OH.
In the HUTT, both the change in SBP (ΔSBP) and DBP (ΔDBP) of PD-OH(+) patients were significantly greater than those of HCs and PD-OH(-) patients, whereas the difference in HR (ΔHR) was not significant (Supplementary Figure 1 in the online-only Data Supplement). For all the PD-OH(+) groups, the ratio of HR change to SBP change (ΔHR/ΔSBP) was less than 0.5 after 3 min of standing. When OD (SCOPA-AUT)/OD (OH-SA)+ and OD (SCOPA-AUT)/OD (OH-SA) were compared in patients with PD, there were no significant differences in ΔSBP and ΔDBP; however, the ΔHR was significantly greater in the OD(+) group. (Supplementary Figure 2 in the online-only Data Supplement). There were low to moderate correlations between OD(OH-SA) and ΔHR (Rho = 0.373, 0.502, and 0.469, respectively; at 3, 5, and 9 minutes after head-up) and between OD(SCOPA-AUT) and ΔHR (Rho = 0.429 and 0.365, respectively; at 5 and 9 minutes after head-up).
The change in MFV (ΔMFV) was not significantly different in the TCD test during the HUTT (Figure 1). Despite the physical and temporal limitations of the simultaneous measurement of TCD and NIRSIT-ON, TCD was performed in either the right or left hemisphere, and no difference was found in the ΔMFV between the right and left hemispheres. To understand OD in terms of cerebral hypoperfusion in TCD, ΔMFV was compared between PD patients with OD and no OH (OD (SCOPA-AUT)/OD (OH-SA)+ and OH-) and those with OD and OH (OD (SCOPA-AUT)/OD (OH-SA)+ and OH+), and the results were not significantly different. Alternatively, when the participants were grouped according to the previous criteria for MFV reduction (20% or 30%), there was no significant difference in the frequencies of (SCOPA-AUT)/OD (OH-SA) and OH between patients with increased and normal ΔMFV.
In the NIRS using NIRSIT-ON during the HUTT, the ΔHboxy in the left hemisphere in the PD-OH(+) group was significantly different from that in the other groups (p = 0.025), whereas the recovery of Hboxy was not different (p = 0.083). Additional comparisons of the three groups at each time point also revealed that ΔHboxy was significantly greater in the left hemisphere of the PD-OH(+) group than in the other groups, starting 4 min after a 70° tilt. (Figure 2; Supplementary Table 2 in the online-only Data Supplement). There were no significant ΔHbR or ΔHbtot during the HUTT (Figure 2).
During the SST, the ΔSBP and ΔDBP were greater in the PD-OH(+) group than in the PD-OH(-) group at the initial standing up and standing up without a significant change in the ΔHR, whereas the ΔSBP, ΔDBP, and ΔHR demonstrated no significant difference in squatting. The DHbtot curve revealed an increased maximum PT on the left side of the PD-OH(+) group, which was not observed in the PD-OH(-) group (Figure 3).
In the comparison of PD patients with right- and left-onset clinical symptoms, no significant differences were observed in the clinical scales for OD, ΔSBP, ΔDBP (HUTT), ΔMFV (TCD), ΔHboxy (HUTT), or DHbtot (SST).
Four subjects experienced delayed hypotension at 9 min during the HUTT (3 PD patients and 1 HC; delayed OH). One patient (PD) had OD. There was no difference between the analyses of those with and without delayed OH. In addition, 8 patients with supine hypertension (SBP ≥140 mmHg and/or DBP ≥90 mmHg) were included; 10 patients were PD-OH(+) and 2 were PD-OH(-). Further analyses comparing those with and without supine hypertension revealed no significant differences (Supplementary Table 3 in the online-only Data Supplement).
DISCUSSION
In patients with PD, cerebral hypoperfusion can impact various nonmotor symptoms, including OD and cognition [3,4,24]. OD serves as one of the clinical indicators used in the diagnosis of OH. However, in our study, the frequency of OD did not significantly differ between PD-OH(+) and PD-OH(-) patients. Surprisingly, the presence of OD did not correlate with the presence of OH, which aligns with previous reports highlighting the limited sensitivity of OD in identifying OH [11,19,25]. For example, a prior study revealed that only 21% of patients who presented with OD were ultimately diagnosed with OH [11]. Conversely, among patients with OH, only 31% had OD as part of their clinical profile [25]. Interestingly, although OD questionnaires are less sensitive in detecting OH, individuals with positive OD questionnaire responses presented greater ΔHR and significant correlations between these responses and the ΔHR. This discrepancy between OD assessments and OH could be linked to an underestimation of the ΔHR, especially considering that rapid heart rate changes are also associated with conditions such as postural orthostatic tachycardia syndrome [26].
TCD is frequently used to study cerebral autoregulation and cerebral perfusion [27]. However, in patients with PD, TCD during the HUTT has rarely been studied [7,8,28]. A greater ΔMFV was indicative of cerebral hypoperfusion in patients with OD+ and OH- [28]. However, ΔMFV was not significantly different between OH+ and OH- or between OD+ and OH- in this study. Conversely, no significant difference was observed in the frequency of OD+ or OH+ between patients with a higher ΔMFV (MFV reduction by 20% or 30%, respectively) and those with a normal ΔMFV. Previous studies have also reported variable results regarding the usefulness of ΔMFV as a reliable CBF monitoring marker [7,8,28].
In NIRS, the signals are dependent on the volume fraction of the microvasculature (diameter ≤200 μm) and larger blood vessels (diameter ≤1 mm) in the case of dynamic perturbation [9,10,29,30]. During the HUTT, NIRS revealed that ΔHboxy was significantly greater in the PD-OH(+) group than in the PDOH(-) group only in the left hemisphere. During SST, hemodynamic recovery assessed by DHbtot was also impaired in the left hemisphere in the PD-OH(+) group. These findings suggest that CBF is more severely crippled by positional changes in the left hemisphere.
Cerebral vasodilatation is required to compensate for the impaired CBF provoked by the HUTT or SST, in which the cholinergic drive via parasympathetic activity may be more critical [31-33]. The left hemisphere is asymmetrically involved in PD patients, especially in the early stage, regardless of motor asymmetry, and parasympathetic control is dominated by the left hemisphere [34,35]. Although we did not study hemispheric volume asymmetry, since the disease duration of our patients was less than 10 years, the dominance of left hemispheric dysfunction in this study may be in line with the results of previous studies [33,35]. However, without direct evidence of parasympathetic dysfunction in this study, the direct role of parasympathetic dysfunction in the asymmetric impairment of CBF during standing remains to be further studied.
Another possible explanation for left hemispheric dysfunction is the relationship between hemispheric asymmetry and asymmetry in the clinical presentation and symptom severity of PD. The onset of motor symptoms is fairly common in the dominant hand [36]. In the case of nonmotor symptoms, autonomic symptoms, including cardiovascular symptoms, are more severe in patients with right-onset PD [37]. The sympathetic skin response is asymmetrically worse in hemibodies with more severe symptoms [38]. These studies suggest that the mode of clinical presentation could indicate more severe dysfunction of the cerebral blood response to positional changes in the left hemisphere of patients with PD-OH(+). However, in this study, clinical and neurophysiological data were not significantly different between patients with left-onset and right-onset clinical symptoms or between those with more severe symptoms on the left and right sides, which does not support an association between symptom onset or severity and asymmetric hemodynamic dysfunction of the left hemisphere.
In a previous study, hemispheric recovery during SST was delayed in the right hemisphere in patients with orthostatic intolerance, but the participants were younger (21.2 years old) and healthier (without PD and OH) than our patients were [23]. The derangement of cerebral perfusion in the left hemisphere under hypotensive challenge may characterize PD-OH(+).
One of the limitations of this study was its small sample size, which was similar to that of previous studies [7,8,28]. Second, to match the protocols of the HUTT, TCD, and SST, all studies were performed for 9 minutes, which could be inadequate for assessing delayed OH. There were a few subjects with delayed OH (4 subjects, 9.3%), but the results of the analyses were not affected by the data from the subjects with delayed OH. Third, because we planned to perform HUTT, TCD, and NIRS simultaneously, we were unable to perform TCD separately due to physical and temporal constraints. However, there was no difference in the TCD data between the right and left hemispheres. Another limitation is the lack of supplemental autonomic tests, such as the Valsalva test, to evaluate the sympathetic and parasympathetic systems. The role of the parasympathetic nervous system in patients with PD and OH should be further explored via more sophisticated statistical analysis via a mediation model, which could not be performed in this study without independent data related to parasympathetic function.
In conclusion, this is the first study to investigate the relationship between cerebral perfusion and positional changes via NIRS in patients with PD. Noninvasive real-time blood flow monitoring via NIRS technology is expected to be helpful in observing cerebral perfusion in PD patients. NIRS revealed delayed recovery of cerebral perfusion in the left hemisphere during the HUTT or SST in PD patients with OH, suggesting a dominant role of the left hemisphere in hypotensive challenge via positional changes.
Supplementary Materials
The online-only Data Supplement is available with this article at https://doi.org/10.14802/jmd.24104.
Notes
Conflicts of Interest
The authors have no financial conflicts of interest.
Funding Statement
None
Author Contributions
Conceptualization: Tae-Beom Ahn, Chaewon Shin, Jae-Myoung Kim. Data curation: Jae-Myoung Kim. Formal analysis: Jae-Myoung Kim, Jae Young Joo. Investigation: Tae-Beom Ahn, Dallah Yoo. Methodology: Jae-Myoung Kim, Dallah Yoo. Project administration: Tae-Beom Ahn. Resources: Chaewon Shin, Chaewon Shin, Dallah Yoo. Software: Jae-Myoung Kim. Supervision: Tae-Beom Ahn. Validation: Tae-Beom Ahn. Visualization: Jae-Myoung Kim, Dallah Yoo. Writing—original draft: Jae Young Joo. Writing—review & editing: Tae-Beom Ahn.
Acknowledgements
None