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. 2025 May 26;132(11):1635–1644. doi: 10.1007/s00702-025-02950-y

The impact of short-term blood pressure variability on cognitive decline in Parkinson’s disease patients without co-morbidities

Magdalena Kwaśniak-Butowska 1,2,✉, Agnieszka Konkel 2, Agnieszka Skrzypkowska 2,3, Dariusz Wieczorek 4, Piotr Wąż 5, Marta Tomczyk 6, Monika Białecka 7, Edyta Szurowska 8, Ryszard T Smoleński 6, Jarosław Sławek 1,2,✉
PMCID: PMC12630213  PMID: 40418269

Abstract

Cardiovascular dysautonomia, cognitive decline and dementia are common non-motor features of Parkinson’s disease. Short-term blood pressure variability may play a role in the pathogenesis of dementia. Sixty five patients with Parkinson’s disease, without cardiovascular comorbidities, with no concomitant medications affecting cardiovascular system were enrolled in this cross-sectional study. They were divided according to their cognitive status and underwent clinical examination, 24 h ambulatory blood pressure monitoring, orthostatic test, brain magnetic resonance imaging and laboratory tests. Twenty patients were cognitively intact, 23 presented mild cognitive impairment and 20 had dementia. There were no differences in duration of the disease or dopaminergic therapy between the groups. Patients with dementia when compared to those cognitively intact, had higher short-term blood pressure variability, assessed as standard deviation of daytime diastolic blood pressure and average real variability of systolic blood pressure. They also had a higher frequency of supine hypertension and lower nocturnal blood pressure fall (reverse dipping). Average real variability of systolic blood pressure, supine hypertension and reverse dipping correlated with cognitive impairment, especially with visuospatial, language and executive functions. Short-term blood pressure variability, supine hypertension and reverse dipping may contribute to the pathogenesis of dementia in PD.

Keywords: Parkinson’s disease, Dementia, Dysautonomia, Short-term blood pressure variability

Introduction

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders with motor (bradykinesia, rigidity and tremor) and non-motor symptoms (Alster and Madetko-Alster 2024). Among non-motor symptoms cognitive impairment and cardiovascular dysautonomia are frequent and can be present from early stages of the disease (Chaudhuri et al. 2006a, b; Fanciulli et al. 2025). Moreover, cardiovascular dysautonomia, particularly orthostatic hypotension (OH), at prodromal phase may predict phenoconversion and may be associated with more severe disease progression (Goldstein and Sharabi 2023).

Dementia is noticed in up to 17% of PD patients within 5 years from the disease onset, 46% at 10 and even 80% at 20 years and probably has a complex aetiology (Aarsland et al. 2021). Cardiovascular dysautonomia may contribute to this process (Longardner et al. 2022). Orthostatic hypotension is a frequent manifestation of PD with 30% prevalence, increasing with age, disease duration or levodopa equivalent dose (LEDD) (Espay et al. 2016; Malkiewicz and Siuda 2024). It coexists often with supine hypertension (SH) (Espay et al. 2016). Loss of nocturnal blood pressure fall (non-dipping) has been also observed in this group of patients (Tanaka et al. 2018). Furthermore, they tend to have higher blood pressure variability (BPV) compared with healthy subjects (Alves et al. 2023; Kanegusuku et al. 2017).

This study aims to determine whether a short-term BPV (measured within 24 h) and other blood pressure abnormalities may contribute to cognitive impairment in a highly selected group of PD patients without co-morbidities and concomitant medications affecting cardiovascular system.

Subjects and methods

Sixty five patients of Caucasian origin, recruited from Outpatient Movement Disorder Clinic, with clinical diagnosis of idiopathic PD based on Movement Disorders Society Clinical Diagnostic Criteria (Postuma et al. 2015) were enrolled in the cross-sectional study (Table 1). Recruitment lasted 3 years and during that time approximately 500 patients were screened. The exclusion criteria were: secondary or atypical Parkinsonism, advanced therapies for PD (deep brain stimulation or infusion therapies), renal or liver impairment. To assess the real impact of dysautonomia on cognitive impairment in PD we excluded patients with cardiovascular diseases (e.g. hypertension, ischemic heart disease, heart failure, atrial fibrillation) or known as major risk factors (diabetes mellitus) and any medications (except for PD treatment) with known mechanism of action influencing blood pressure (renin-angiotensin system, RAS). Two participants were subsequently excluded due to diabetes mellitus diagnosed after initial evaluation.

Table 1.

Demographic and clinical data of the study group

Variable PD-NC (n = 20) PD-MCI (n = 23) PD-D (n = 20) p-values
Sex (female/male) 9/11 9/14 7/13 0.81a
Age (years) 62.6 (± 4.62) 64.6 (± 6.92) 67.7 (± 5.11) 0.02b
Years of education 15.5 (11–23) 14 (10–24) 13 (10–17) 0.02c
Duration of the disease (years) 7 (2–20) 6 (4–20) 9.5 (3–20) 0.25c
LEDD (mg) 1017 (± 418) 1110 (± 492) 1314 (± 511) 0.139b
Levodopa (mg) 693 (± 408) 812 (± 445) 1011 (± 442) 0.075b
Ropinirole dose equivalence (mg) 9.42 (0–20) 7.38 (0–24) 8 (0–20) 0.246c
Hoehn-Yahr scale 2 (1–3) 2 (1–3) 2 (1–3) 0.01c
NMSQ 9.15 (± 3.69) 11.4 (± 3.96) 13.6 (± 3.70) 0.003b
UPDRS (part III) 10 (3–35) 15 (2–40) 19.5 (4–31) 0.06c
Schwab&England scale 90 (70–100) 85 (60–100) 77.5 (40–90) 0.004c
SBP SD daytime 11 (4–57) 11 (6–29) 13.5 (6–43) 0.29c
SBP SD night-time 8.5 (3–15) 9 (3–22) 10 (3–19) 0.25c
SBP SD 24 h 12 (7–41) 10 (6–28) 14 (6–34) 0.27c
SBP CV daytime 0.09 (0.03–0.41) 0.089 (0.05–0.21) 0.11 (0.06–0.35) 0.51c
SBP CV night-time 0.08 (0.03–0.14) 0.07 (0.03–0.2) 0.08 (0.03–0.14) 0.28c
SBP CV 24 h 0.09 (0.05–0.35) 0.08 (0.05–0.18) 0.11 (0.04–0.3) 0.38c
SBP ARV 8.75 (5.78–18.0) 9.46 (7.68–17.6) 10.5 (7.39–17.7) 0.059c
DBP SD daytime 6 (4–54) 7 (3–27) 8 (6–45) 0.028c
DBP SD night-time 7 (3–11) 5 (3–16) 6.5 (3–15) 0.74c
DBP SD 24 h 8 (4–39) 8 (5–22) 8.5 (6–34) 0.47c
DBP CV daytime 0.08 (0.05–0.58) 0.09 (0.04–0.31) 0.1 (0.07–0.55) 0.10c
DBP CV night-time 0.1 (0.04–0.2) 0.08 (0.04–0.25) 0.08 (0.03–0.19) 0.67c
DBP CV 24 h 0.11 (0.05–0.52) 0.11 (0.06–0.28) 0.12 (0.07–0.46) 0.80c
DBP ARV 5.52 (3.61–16) 6.67 (3.95–9.64) 6.58 (4.65–15.5) 0.059c
Homocysteine (µmol/l) 14 (7.9–26.6) 14 (6.8–47) 18.4 (6.8–31.2) 0.09c
%Δ MAP (%) 11.1 (± 9.92) 6.95 (± 9.91) 2.76 (± 10.4) 0.04b
Nocturnal blood pressure profile Dipper—40% Dipper—43.5% Dipper—25% 0.28a
Extreme dipper—10% Extreme dipper—4% Extreme dipper—5%
Reverse dipper—10% Reverse dipper—30% Reverse dipper—45%
Non-dipper—40% Non-dipper—22.5% Non-dipper—25%
Supine hypertension Present—15% Present—26% Present—50% 0.05a
ΔSBP (mmHg) 5.5 (− 12 to 35) 12 (− 6 to 67) 16 (− 17 to 79) 0.20c
ΔDBP (mmHg) − 3 (− 12 to 9) 0 (− 12 to 32) 2 (− 14 to 34) 0.27c
Orthostatic hypotension Present—20% Present—43% Present—35% 0.52a
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Data are presented as a mean (± standard deviation) or a median (minimum–maximum)

LEDD levodopa equivalent dose (mg), UPDRS Unified Parkinson’s Disease Rating Scale, NMSQ non-motor symptoms questionnaire, SBP systolic blood pressure, DBP diastolic blood pressure, SD standard deviation, CV coefficient of variation, ARV average real variability, %Δ MAP nocturnal blood pressure fall in %, ΔSBP orthostatic systolic blood pressure drop, ΔDBP orthostatic diastolic blood pressure drop, PD-NC- patients with normal cognition, PD-MCI patients with mild cognitive impairment, PD-D patients with dementia

aχ2

bANOVA

cKruskal–Wallis test

The severity of the disease was assessed with the use of the Unified Parkinson’s Disease Rating Scale (UPDRS; part III) in “on” state, Hoehn–Yahr staging, and the Schwab-England activities of daily living scale (Poewe 2009). Non motor symptoms were assessed with the non-motor symptoms questionnaire (NMSQ) (Chaudhuri et al. 2006a, b). All assessments were performed by the same physician. All patients received dopaminergic treatment. Dopaminergic therapies (levodopa, dopamine receptors agonists, MAO-B inhibitors, COMT inhibitors and amantadine) were converted to levodopa equivalent dosage (LEDD). All patients, except two, were taking levodopa and the majority were taking dopamine receptors agonists, therefore these two drug groups are listed separately in Table 1. Dopamine agonists were converted to ropinirole dose equivalence (Thobois 2006). Nevertheless, the influence of dopamine agonists and levodopa was analysed separately.

The detailed neuropsychological testing methods are presented in Table 3. Mild cognitive impairment (MCI) was defined as deficits in single cognitive domain and dementia as impairment in more than one cognitive domain with negative impact on daily life, according to Litvan et al. (2011) and by Emre et al. (2007) criteria respectively. Based on the neuropsychological examination, the cohort was divided into three groups: PD with normal cognition (PD-NC), with mild cognitive impairment (PD-MCI) and with dementia (PD-D) (Table 1).

Table 3.

Tests used for cognitive assessment and their correlations with blood pressure variables (performed by the Spearman’s rank correlation coefficient)

Cognitive domain ΔDBP ΔSBP SHSBP ≥ 140 mmHg SH-DBP ≥ 90 mmHg %ΔMAP SD DBP day ARV SBP ARV DBP CV DBP day
General cognitive functions
 MoCA – 0.14 – 0.24^ – 0.25^ – 0.16 – 0.07 – 0.16 – 0.29* – 0.21 – 0.21
Visuospatial functions
 Benton JoLO – 0.27* – 0.41** – 0.22 – 0.03 – 0.06 – 0.05 – 0.19 – 0.08 – 0.09
 VOSP
 Screening test – 0.01 – 0.05 – 0.02 0.08 – 0.09 – 0.23 – 0.11 – 0.12 – 0.31*
 Incomplete letters – 0.20 – 0.27* – 0.05 – 0.05 – 0.10 – 0.17 – 0.16 – 0.26^ – 0.34*
 Dots counting 0.11 – 0.09 – 0.11 – 0.17 – 0.08 0.00 – 0.25^ 0.10 0.03
 Cube analysis – 0.12 – 0.22 – 0.07 – 0.03 – 0.12 – 0.21 – 0.22 – 0.12 – 0.30*
Verbal memory
 WAIS
 Digit span forward – 0.14 – 0.11 – 0.13 – 0.04 0.08 0.08 – 0.01 – 0.06 – 0.13
 Digit span backwards 0.04 – 0.07 – 0.12 0.15 0.11 0.01 0.03 – 0.08 – 0.25^
 CVLT
 List A Tasks 1–5 – 0.19 – 0.30* – 0.17 – 0.10 – 0.08 – 0.20 – 0.20 – 0.22 – 0.23
 List A Task 1 – 0.20 – 0.24^ – 0.17 – 0.12 – 0.07 – 0.24^ – 0.14 – 0.12 – 0.11
 List A Task 5 – 0.14 – 0.25^ – 0.16 – 0.11 – 0.17 – 0.19 – 0.22 – 0.23 – 0.24^
 List A Free recall after distraction – 0.04 – 0.01 – 0.08 – 0.10 0.03 – 0.07 0.00 – 0.17 – 0.22
 List A Free recall after 20 min – 0.15 – 0.24 – 0.16 – 0.19 – 0.18 – 0.14 – 0.16 – 0.12 – 0.18
 Guided recall after distraction – 0.13 – 0.22 – 0.07 – 0.21 – 0.15 – 0.06 – 0.24^ – 0.18 – 0.10
 Guided recall after 20 min – 0.13 – 0.25 – 0.13^ – 0.10 – 0.05 – 0.22 – 0.30* – 0.25^ – 0.23
 Perseverations – 0.19 – 0.20 – 0.11 – 0.09 – 0.22 – 0.17 – 0.15 – 0.13 – 0.27*
 Confabulations in free recall 0.09 – 0.01 – 0.01 0.18 0.05 0 0.09 – 0.10 – 0.05
 Confabulations in guided recall – 0.08 0.14 – 0.12 – 0.15 0 0.15 0.22 0.18 0.16
 Recognition—total hits 0.08 0.26* 0.16 0 0.02 0.08 0.12 0.11 0.07
 Recognition mistakes 0.03 – 0.12 – 0.09 – 0.16 – 0.20 – 0.14 – 0.23 – 0.24^ – 0.19
Visuospatial memory
 Corsi Block-Tapping test
 Span forward – 0.01 0.02 0.03 0.04 0.06 0.02 0.04 – 0.07 – 0.16
 Span backward 0.01 0.02 – 0.05 0.04 – 0.05 – 0.19 – 0.13 – 0.10 – 0.27^
 CVMT
 Hits 0.07 – 0.09 – 0.15 – 0.10 – 0.03 – 0.12 – 0.32* – 0.20 – 0.17
 False alarms 0.24 0.14 0.17 0.18 0.13 0.01 – 0.03 0.10 – 0.15
 d' – 0.19 – 0.17 – 0.18 – 0.21 – 0.18 – 0.05 – 0.11 – 0.17 0.09
 Total score – 0.16 – 0.27^ – 0.25 – 0.09 – 0.08 – 0.06 – 0.12 – 0.05 0.12
 Delayed recognition 0.02 – 0.13 – 0.10 0 – 0.02 – 0.06 – 0.31* – 0.25 0.06
Language
 BNT-30 – 0.27* – 0.41** – 0.08 0.08 – 0.08 – 0.25 – 0.15^ – 0.08 – 0.30*
Executive functions
 D-KEFS Tower test
 Mean time of first move 0.31* 0.28* – 0.02 0 0.06 0.15 0.12 – 0.04 0.09
 Sum 1–9 trial – 0.22 – 0.26^ – 0.16 – 0.12 – 0.05 – 0.07 – 0.34** – 0.17 – 0.14
Total number of rule violations 0.15 0.15 0.01 – 0.10 – 0.03 0.06 0.19 0.14 0.30*
 COWAT
  Phonemic (“K”) – 0.27* – 0.28* – 0.17 – 0.10 – 0.03 – 0.15 – 0.04 – 0.16 – 0.34**
  Animals – 0.20 – 0.28* – 0.18 – 0.07 – 0.04 – 0.17 – 0.19 – 0.21 – 0.38**
  Sharp items – 0.03 – 0.17 – 0.11 0.05 0.09 – 0.11 – 0.16 – 0.15 – 0.34**
  Dynamic praxis (sum) – 0.12 – 0.35** – 0.33* – 0.28* – 0.14 – 0.08 – 0.20 – 0.21 0.01
Alternating motor pattern
  Total number 0 0.12 – 0.06 – 0.13 – 0.06 0.01 – 0.11 – 0.12 – 0.08
  Number of errors 0.25 0.28^ 0.16 0.22 0.27 0.11 0.04 0.04 – 0.10
  Correct rate – 0.28^ – 0.31* – 0.23 – 0.34* – 0.27^ – 0.11 – 0.11 – 0.12 0.02
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MoCA Montreal cognitive assessment, JoLO judgement of line orientation, VOSP visual objects and space perception battery, WAIS Wechsler Adult Intelligence Scale, CVLT California verbal learning test, CVMT continuous visual memory test, BNT Boston naming test, D-KEFS Delis-Kaplan executive function system, COWAT controlled oral word association test, ΔSBP orthostatic systolic blood pressure drop, ΔDBP orthostatic diastolic blood pressure drop, %Δ MAP nocturnal blood pressure fall in %, SH supine hypertension, SBP systolic blood pressure, DBP diastolic blood pressure, SD standard deviation, CV—coefficient of variation, ARV average real variability

Significance codes: **p-value < 0.01, *p-value < 0.05, ^p-value < 0.1

The examination with 3 T MRI (Philips Achieva, Netherlands) was performed in all subjects to exclude brain structural lesions. The groups did not differ in terms of white matter hyperintensities assessed in Fazekas scale, but specific imaging data were not analysed in this paper.

To measure BP an electronic inflatable brachial sphygmomanometer was used. Blood pressure was measured after at least five minutes of rest in a supine position. Supine hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg (Fanciulli et al. 2018). Orthostatic hypotension was defined as a reduction in SBP ≥ 20 mmHg or ≥ 10 mmHg in DBP after 3 min of standing from a supine position. When the result was negative after 3 min, testing was prolonged to 10 min and BP was additionally measured after 5 and 10 min (Fanciulli et al. 2025).

Patients underwent 24-h ambulatory blood pressure monitoring (ABPM) using a Watch BP03 device on the non-dominant arm. Recordings were made every 20 min between 6:00 am and 10:00 pm and every 30 min between 10:00 pm and 6:00 am. The classification of nocturnal BP profiles, computed as percent change between daytime and night-time values, was as following: reverse dipper < 0%, non-dipper 0–10%, dipper 10–20%, extreme-dipper > 20%. Measured indices of BPV, based on ABPM, were: standard deviation (SD), coefficient of variation (CV, SD divided by a corresponding mean) both calculated for daytime, night-time and 24 h SBP and DBP as well as average real variability (ARV, calculated as the average of absolute changes between following BP readings) of 24 h SBP and DBP (Schutte et al. 2022).

Blood samples were collected early in the morning, after overnight fasting, at least 8 h after the last dose of dopaminergic medications. Vascular and dementia risk factors (vitamin B12, folic acid, thyrotropic hormone, glucose) together with renal and hepatic functions were assessed.

Informed written consent was obtained from all patients. Study protocol was approved by the Bioethics Committee for Scientific Research of Medical University of Gdansk (NKBBN/415/2018).

Statistical analysis

The Shapiro–Wilk test was used for the assessment of normal distribution for demographic and clinical data. The one-way parametric ANOVA and one-way non-parametric Kruskal–Wallis testing were further performed. For post hoc analyses of parametric and non-parametric data were used Tukey’s test and Dunn’s test, respectively. Spearman's ρ coefficient was used to determine the correlation between the two quantitative variables. Comparisons for categorical variables were performed using χ2 tests. In case of problems with determining the p-value from the asymptotic χ2 distribution of the test statistic, Monte-Carlo simulations with 2000 repetitions were used to determine this value. Cramer's V was used to test the strength of the relation between the two nominal variables.

Ordinal regression models were also constructed. In these models, the dependent variables were categories related to the patients'cognitive functions, and the independent variables were values characterizing the studied individuals. Then multivariate regression analyses, using the forward stepwise selection method, were conducted (compared parameters were: Akaike information criterion—AIC, Bayesian information criterion—BIC, p).

Receiver Operating Characteristic (ROC) curves were determined from the logistic regression models created for patients with normal cognition and dementia, to meet the dichotomous requirements for this method. The sensitivity and specificity values shown in the graph determine the classification correctness of variables describing BP abnormalities and short-term BPV indices as predictors of dementia (Fig. 1).

Fig. 1.

Fig. 1

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ROC curves. Accuracy of short-term BPV indices as predictors for dementia. SBP systolic blood pressure, DBP diastolic blood pressure, ARV average real variability, CV coefficient of variation, SD standard deviation

The calculations used functions and procedures from the R environment (R Core Team 2024). The significance level was set at α = 0.05.

Results

Demographic data

The detailed demographics are presented in Table 1. Among 63 patients, 25 were women (39.7%). Twenty patients had normal cognition, 23 and 20 were diagnosed MCI and dementia respectively. Patients with normal cognition were significantly younger, better educated and had a more benign disease course than PD-D (Table 2). There were no differences in duration of the disease or dopaminergic therapies between the groups.

Table 2.

Post hoc test results (p-values) presenting specific differences between the study groups

Variable PD-NC vs PD-MCI PD-NC vs PD-D PD-MCI vs PD-D
Aged 0.26 0.03 0.24
Years of educatione 0.26 0.01 0.26
NMSQd 0.17 0.003 0.17
Schwab&England scalee 0.31 0.003 0.04
UPDRSe 0.39 0.05 0.40
SBP ARVe 0.42 0.05 0.42
DBP ARVe 0.09 0.09 0.83
DBP SD daytimee 0.28 0.02 0.28
DBP CV daytimee 0.53 0.09 0.53
Homocysteine (µmol/l)e 0.64 0.10 0.19
%Δ MAP (%)d 0.38 0.07 0.28
Supine hypertensionf 0.43 0.05 0.18
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UPDRS Unified Parkinson’s Disease Rating Scale, NMSQ non-motor symptoms questionnaire, SBP systolic blood pressure, DBP diastolic blood pressure, SD standard deviation, CV coefficient of variation, ARV average real variability, %Δ MAP nocturnal blood pressure fall in %, PD-NC patients with normal cognition, PD-MCI patients with mild cognitive impairment, PD-D patients with dementia

dTukey’s test

eDunn’s test

fMcNemar corrected with Bonferroni method

Blood pressure abnormalities

Thirty three percent of patients had OH (12% delayed to 5–10 min), 31% SH and in 14% both coexisted. Although differences in a prevalence of OH in all three groups were not statistically significant, it was correlated with more severe impairment in a verbal memory and visuospatial, language and executive functions (Table 3). Drop in DBP correlated with LEDD (ρ = 0.46, p-value = 0.0001) and separately with levodopa dose (ρ = 0.44, p-value = 0.0003).

Supine hypertension was associated with dementia (Cramer’s V = 0.2562, p-value = 0.0473).

There was a trend towards association between lower nocturnal blood pressure fall (%ΔMAP), reverse dipping pattern and cognitive decline (Cramer’s V = 0.2971, p-value = 0.09; Cramer’s V = 0.2971, p-value = 0.08, respectively).

Short-term blood pressure variability

Standard deviation of daytime DBP was significantly higher in PD-D (Table 1). Also average real variability of SBP was significantly higher in the PD-D when compared to PD-NC (Table 2). Coefficient of variation of daytime DBP and DBP ARV were also higher in PD-D patients, but differences remained at the trend level. Visuospatial, language and executive functions were negatively related to CV of daytime DBP (Table 3). Average real variability of SBP correlated with decreased MoCA scores and deficits in visuospatial memory and executive functions (Table 3). Levodopa equivalent dosage and ropinirole dose equivalence did not show any correlation with assessed parameters.

Laboratory tests

Laboratory tests, including vascular or dementia risk factors (vitamin B12, folic acid, thyrotropic hormone, glucose) did not differ between groups. There was a trend towards higher homocysteine level in PD-D group and it correlated with higher levodopa dosage (Table 2).

Regression analyses

Univariate regression analyses in models for dementia identified age, Hoehn-Yahr’s more advanced stage, NMSQ score, SH, reverse dipping pattern and inversely: nocturnal blood pressure fall, years of education and scores in Schwab-England scale as significant variables. Higher SBP ARV was associated with cognitive impairment at the trend level (Table 4). Multivariate regression analyses models identifying significant predictors for the development of dementia were comparable and did not have sufficient parameters to choose the best one.

Table 4.

Ordinal regression analysis for risk of dementia

Explanatory variables (predictors) Ordinal regression
B OR CI p-value
Lower Upper
Age 0.1 1.11 1.03 1.21 0.01
Years of education − 0.23 0.79 0.67 0.93 0.005
Hoehn-Yahr scale 1.1 2.99 1.348 7.092 0.01
Schwab-England scale − 0.08 0.92 0.876 0.969 0.002
NMSQ 0.21 1.24 1.095 1.419  < 0.001
SBP ARV 0.19 1.21 1.002 1.494 0.059
Reverse dipping 1.39 4.02 1.199 14.470 0.03
SH 1.29 3.63 1.297 10.783 0.02
% ΔMAP − 0.06 0.94 0.894 0.985 0.01
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B-unstandarized regression coefficient, SH supine hypertension, % ΔMAP nocturnal blood pressure fall in %, SBP ARV average real variability of systolic blood pressure, NMSQ non-motor symptoms questionnaire

The highest accuracy as predictors of dementia was achieved for SBP ARV, DBP ARV and DBP SD and DBP CV, both calculated for daytime period. (Fig. 1).

Discussion

This is to our knowledge the first study assessing the impact of short-term BPV on cognition in a highly selected group of PD patients without comorbidities and co-medications. We found that PD-D patients presented higher BPV, reverse dipping and SH when compared to PD-NC. Moreover, results of our study suggest, that those variabilities may be associated with cognitive decline in PD.

Blood pressure variability

Short-term BPV characterizes BP fluctuations through 24 h (ABPM) (Schutte et al. 2022). It can be assessed with several indices and the classic ones include: SD (correlated with average BP) and CV (independent on the average BP), both influenced by diurnal BP variation (Schutte et al. 2022). Average real variability is not affected by alterations in circadian rhythm, but depends on the number of BP measurements (Mena et al. 2005; Schutte et al. 2022). In the literature there are no clear recommendations which of the indices should be used and which has the strongest prognostic value. Results of a recent, large meta-analysis indicate an association of long-term systolic and diastolic BPV (visit to visit assessments during months or years) with cognitive decline in a general population, stronger than for mean BP (Heus et al. 2021). However, there are only few studies assessing such relationship between short-term BPV and cognition (Sun 2023). Arterial stiffness related to ageing, high BP values and reduced hippocampal volume may be the underlying mechanisms linking systolic BPV with cognitive impairment, whereas higher diastolic BPV is correlated with greater white matter hyperintensities (WMH) volume, endothelial dysfunction and increased sympathetic activity (Gutteridge et al. 2022; Schillaci et al. 2012). Furthermore increased BPV may cause hemodynamic instability, what may lead to cerebral hypoperfusion (Sun 2023). An Alves et al. (2023) showed a tendency to asymptomatic organ damage (carotid intima-media thickness, extracranial plaques and increased pulse pressure) in PD patients with higher systolic BPV, what is in concordance with described above studies based on general population (Heus et al. 2021; Schillaci et al. 2012). Higher levels of BPV were also observed among advanced PD patients (Shen et al. 2022).

The results of studies assessing short-term BPV in PD patients are inconsistent and difficult to compare as different indices were used for the assessments (Alves et al. 2023; Chen et al. 2024; Kanegusuku et al. 2017; Kim et al. 2012; Sebastian et al. 2022; Shen et al. 2022; Tsukamoto et al. 2013; Yoo et al. 2020). In the majority of studies using classical ones, only daytime BPV was significantly increased, when compared to healthy controls (Kanegusuku et al. 2017; Alves et al. 2023; Shen et al. 2022). Standard deviation and CV from night-time and 24 h period may not show the BPV adequately in this group of patients due to reduced nocturnal blood pressure fall.

The association between short-term BPV and cognition in PD was previously analysed in one study with drug naive patients (Kim et al. 2012). Participants with hypertension or diabetes mellitus were also enrolled and BPV of DBP was not studied. In our study we excluded such patients to make the results more clear and related to PD or PD medications itself. Although our group was treated for PD, LEDD was not statistically different between the groups. In Kim et al. (2012) study SD and CV of SBP were significantly higher in PDD. Moreover, frontal executive functions were negatively related to the SD of SBP. It is partly consistent with our results, where higher levels of BPV also characterized the PD-D group (together with decrease in executive functions) for SBP ARV, SD of daytime DBP and on a trend level for daytime DBP CV and DBP ARV. Furthermore, our findings suggest, that increased SBP ARV may predict dementia and it was precisely associated with the worse global cognitive function and poor performance in visuospatial memory and executive function (Table 3, Fig. 1).

Orthostatic hypotension

Orthostatic hypotension along with SH are supposed to be a risk factors for cognitive decline (Espay et al. 2016). The prevalence of both increases with the disease duration (Kwaśniak-Butowska et al. 2021). The association of OH with dementia was not confirmed in our study, however orthostatic changes in SBP and DBP affected certain cognitive domains (verbal memory, visuospatial, language and executive functions) (Table 3). This may be due to limited number of recruited patients, early disease stage or the assessment method, as OH occurs more frequently after tilting than on standing (Jamnadas-Khoda et al. 2009). It seems to be consisted with the findings by Vallelonga et al. (2019, 2024), who observed that a number of ABPM hypotensive episodes (defined as systolic drop ≥ 15 mmHg compared with the average 24-h SBP during the waking period) have a stronger association with dementia than the simple bedside assessment. Executive performances in PD patients with OH are also temporarily worsen upon postural change, owing to impair cerebral autoregulation during orthostatic stress, with a negative influence on patients quality of life (Centi et al. 2017). Prevalence of SH in our study was similar to that observed by other authors and it was associated with dementia (Kim et al. 2012; Umehara et al. 2016). Longitudinal presence of SH was recently found to be associated with verbal memory decline, possibly resulting from hippocampal atrophy (Miller-Patterson et al. 2024).

Nocturnal blood pressure

Abnormal nocturnal BP profile in PD patients is common, independently from coexisting hypertension (Milazzo et al. 2018). Reverse pattern, according to Milazzo et al. (2018), can be a marker of cardiovascular dysautonomia and may lead to left ventricular hypertrophy, similar to essential hypertensive patients (Di Stefano et al. 2020) and development of WMH (Tanaka et al. 2018). In our study it was also identified as a predictor of dementia, what was confirmed in Tanaka et al. (2018) and Chen et al. (2024) studies as well.

Role of laboratory findings

We failed to demonstrate an association between Hcy, cognitive dysfunction and WMH described in previous study (Sławek et al. 2013). However Hcy levels correlated with higher levodopa doses and potentially may be harmful due to its vascular and neurotoxic properties (Sławek et al. 2013).

Role of medications

Levodopa dose in our study group correlated with orthostatic drop of DBP. However, like other authors, we did not observe such association for orthostatic drop of SBP (Earl et al. 2024; Jost et al. 2020). Levodopa may affect BP due to baroreflex impairment (Earl et al. 2024). Stimulation of central D1 receptors (Durrieu et al. 1990) and peripheral D2 receptors on postgangliotic sympathetic nerves (Mannelli et al. 1999) can lead to lower norepinephrine plasma levels, as it was found in previous studies (Bouhaddi et al. 2004). It might be hypothesized that due to autonomic denervation in PD, dopaminergic therapy enhance a deficit in blood pressure regulation, what needs to be investigated in further studies.

Strengths and limitations

The major strength was the study design. Neuropsychological testing was detailed and based on both general and highly specific outcome measures. Each cognitive domain was assessed with two tests and the number of patients in each group according to cognitive status was equal. This was in concordance with the current dementia and PD related MCI definitions (Emre et al. 2007; Litvan et al. 2011). Groups also did not differ in terms of dopaminergic therapies. One of the most important strengths of the study was the exclusion of patients with cardiovascular diseases or taking medications affecting RAS, thus potential confounders leading to dysautonomia or having influence on BPV were excluded. However, this limited the total number of patients enrolled. A weakness of this study is a relatively small sample size, but it was related to the mentioned above restrictions in recruiting patients without co-morbidities and commonly used in this population medications. Nonetheless this had a negative impact on the statistical power of our analyses and strength of our conclusions. Furthermore, ABPM was performed only once and OH was assessed on standing, not on tilting, but it resembles the clinical practice (Fanciulli et al. 2025).

Conclusions

Increased SBP ARV, SH and reverse dipping correlated with dementia in PD. The ABPM should be considered in the monitoring of PD patients as it is an easy to perform and inexpensive. The procedure should be standardized, as some of the BPV indices depend on the number of the measurements. Recognition and treatment of circadian BP abnormalities in PD patients potentially may protect them from further cognitive decline what requires further longitudinal studies.

Acknowledgements

Authors would like to thank the patients for their participation in the study.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Magdalena Kwaśniak-Butowska, Agnieszka Konkel, Agnieszka Skrzypkowska, Dariusz Wieczorek and Piotr Wąż. The first draft of the manuscript was written by Magdalena Kwaśniak-Butowska and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by statutory funds of Medical University of Gdansk.

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Magdalena Kwaśniak-Butowska, Email: magdalena.butowska@gumed.edu.pl.

Jarosław Sławek, Email: jaroslaw.slawek@gumed.edu.pl.

References

  1. Aarsland D, Batzu L, Halliday GM, Geurtsen GJ, Ballard C, Ray Chaudhuri K, Weintraub D (2021) Parkinson disease-associated cognitive impairment. Nat Rev Dis Primers 7(1):1–21. 10.1038/s41572-021-00280-3 [DOI] [PubMed] [Google Scholar]
  2. Alster P, Madetko-Alster N (2024) Significance of dysautonomia in Parkinson’s disease and atypical Parkinsonisms. Neurol Neurochir Pol 58(2):147–149. 10.5603/pjnns.98678 [DOI] [PubMed] [Google Scholar]
  3. Alves M, Caldeira D, Ferreira JJ (2023) Blood pressure variability in Parkinson’s disease patients – case control study. Clin Parkinsonism Relat Disord 8(February):100191. 10.1016/j.prdoa.2023.100191 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bouhaddi M, Vuillier F, Fortrat JO, Cappelle S, Henriet MT, Rumbach L, Regnard J (2004) Impaired cardiovascular autonomic control in newly and long-term-treated patients with Parkinson’s disease: involvement of L-dopa therapy. Auton Neurosci 116(1–2):30–38. 10.1016/j.autneu.2004.06.009 [DOI] [PubMed] [Google Scholar]
  5. Centi J, Freeman R, Gibbons CH, Neargarder S, Canova AO, Cronin-Golomb A (2017) Effects of orthostatic hypotension on cognition in Parkinson disease. Neurology 88(1):17–24. 10.1212/WNL.0000000000003452 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chaudhuri KR, Healy DG, Schapira AH, National Institute for Clinical Excellence (2006a) Non-motor symptoms of Parkinson’s disease: diagnosis and management. Lancet Neurol 5(3):235–245. 10.1016/S1474-4422(06)70373-8 [DOI] [PubMed] [Google Scholar]
  7. Chaudhuri KR, Martinez-Martin P, Schapira AHV, Stocchi F, Sethi K, Odin P et al (2006b) International multicenter pilot study of the first comprehensive self-completed nonmotor symptoms questionnaire for Parkinson’s disease: the NMSQuest study. Mov Disord 21(7):916–923. 10.1002/mds.20844 [DOI] [PubMed] [Google Scholar]
  8. Chen L, Jiang L, Wang C, Qiao T, Ma C, Chen Y, Liu C, Wang X, Xu Y (2024) Parkinson’s disease patients with absence of normal dipping status were more vulnerable to cognitive impairment from the early stages. Parkinsonism Relat Disord 121(January):106013. 10.1016/j.parkreldis.2024.106013 [DOI] [PubMed] [Google Scholar]
  9. de Heus RAA, Tzourio C, Lee EJL, Opozda M, Vincent AD, Anstey KJ, Hofman A, Kario K, Lattanzi S et al (2021) Association between blood pressure variability systematic review and meta-analysis. Hypertension. 10.1161/HYPERTENSIONAHA.121.17797 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Di Stefano C, Sobrero G, Milazzo V, Vallelonga F, Romagnolo A, Zibetti M, Milan A, Veglio F, Maule S (2020) Cardiac organ damage in patients with Parkinson’s disease and reverse dipping. J Hypertens 38(2):289–294. 10.1097/HJH.0000000000002249 [DOI] [PubMed] [Google Scholar]
  11. Durrieu G, Senard JM, Rascol O, Tran MA, Lataste X, Rascol A et al (1990) Blood pressure and plasma catecholamines in nevertreated parkinsonian patients: efect of a selective D1 agonist (CY 208–243). Neurology 40(4):707–709 [DOI] [PubMed] [Google Scholar]
  12. Earl T, Jridi A, Thulin PC et al (2024) Effect of levodopa on postural blood pressure changes in Parkinson disease: a randomized crossover study. Clin Auton Res 34(1):117–124. 10.1007/s10286-024-01024-5 [DOI] [PubMed] [Google Scholar]
  13. Emre M, Aarsland D, Brown R, Burn DJ, Duyckaerts C, Mizuno Y, Broe GA et al (2007) Clinical diagnostic criteria for dementia associated with Parkinson’s disease. Mov Disord 22(12):1689–1707. 10.1002/mds.21507 [DOI] [PubMed] [Google Scholar]
  14. Espay AJ, LeWitt PA, Hauser RA, Merola A, Masellis M, Lang AE (2016) Neurogenic orthostatic hypotension and supine hypertension in Parkinson’s disease and related synucleinopathies: prioritisation of treatment targets. Lancet Neurol 15(9):954–966. 10.1016/S1474-4422(16)30079-5 [DOI] [PubMed] [Google Scholar]
  15. Fanciulli A, Jordan J, Biaggioni I et al (2018) 2018) Consensus statement on the definition of neurogenic supine hypertension in cardiovascular autonomic failure by the American Autonomic Society (AAS) and the European Federation of Autonomic Societies (EFAS): Endorsed by the European Academy of Neurology (EAN) and the European Society of Hypertension (ESH). Clin Auton Res 28(4):355–362. 10.1007/s10286-018-0529-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Fanciulli A, Sixel-Döring F, Buhmann C, Krismer F, Hermann W, Winkler C, Woitalla D, Jost WH, German Parkinson’s Guideline Group, Trenkwalder C, Höglinger G (2025) Diagnosis and treatment of autonomic failure, pain and sleep disturbances in Parkinson’s disease: guideline “Parkinson’s disease” of the German Society of Neurology. J Neurol 272(1):90. 10.1007/s00415-024-12730-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Goldstein DS, Sharabi Y (2023) Baroreflex-sympathoneural dysfunction characterizes at-risk individuals with preclinical central Lewy body diseases. Clin Auton Res 33(1):41–49. 10.1007/s10286-022-00912-y [DOI] [PubMed] [Google Scholar]
  18. Gutteridge DS, Tully PJ, Ghezzi ES, Jamadar S, Smith AE et al (2022) Blood pressure variability and structural brain changes: a systematic review. J Hypertens 40(6):1060–1070. 10.1097/HJH.0000000000003133 [DOI] [PubMed] [Google Scholar]
  19. Jamnadas-Khoda J, Koshy S, Mathias CJ, Muthane UB, Ragothaman M, Dodaballapur SK (2009) Are current recommendations to diagnose orthostatic hypotension in Parkinson’s disease satisfactory? Mov Disord 24(12):1747–1751. 10.1002/mds.22537 [DOI] [PubMed] [Google Scholar]
  20. Jost WH, Altmann C, Fiesel T, Becht B, Ringwald S, Hoppe T (2020) Influence of levodopa on orthostatic hypotension in Parkinson’s disease. Neurol Neurochir Pol 54(2):200–203. 10.5603/pjnns.a2020.0019 [DOI] [PubMed] [Google Scholar]
  21. Kanegusuku H, Silva-Batista C, Peçanha T, Silva-Junior N, Queiroz A, Costa L et al (2017) Patients with Parkinson disease present high ambulatory blood pressure variability. Clin Physiol Funct Imaging 37(5):530–535. 10.1111/cpf.12338 [DOI] [PubMed] [Google Scholar]
  22. Kim JS, Oh YS, Lee KS, Kim YI, Yang DW, Goldstein DS (2012) Association of cognitive dysfunction with neurocirculatory abnormalities in early Parkinson disease. Neurology 79(13):1323–1331. 10.1212/WNL.0b013e31826c1acd [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kwaśniak-Butowska M, Dulski J, Pierzchlińska A, Białecka M, Wieczorek D, Sławek J (2021) Cardiovascular dysautonomia and cognition in Parkinson’s disease— a possible relationship. Neurol Neurochir Pol 55(6):525–535. 10.5603/PJNNS.a2021.0040 [DOI] [PubMed] [Google Scholar]
  24. Litvan I, Aarsland D, Adler CH, Goldman JG, Kulisevsky J, Mollenhauer B et al (2011) MDS task force on mild cognitive impairment in Parkinson’s disease: critical review of PD-MCI. Mov Disord 26(10):1814–1824. 10.1002/mds.23823 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Longardner K, Merola A, Litvan I, De Stefano AM, Maule S, Vallelonga F, Lopiano L, Romagnolo A (2022) Differential impact of individual autonomic domains on clinical outcomes in Parkinson’s disease. J Neurol 269(10):5510–5520. 10.1007/s00415-022-11221-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Malkiewicz JJ, Siuda J (2024) Comparison of autonomic dysfunction in patients with Parkinson’s disease, progressive supranuclear palsy, and multiple system atrophy. Neurol Neurochir Pol 58(2):193–202. 10.5603/pjnns.96939 [DOI] [PubMed] [Google Scholar]
  27. Mannelli M, Ianni L, Lazzeri C, Castellani W, Pupilli C, La Villa G et al (1999) In vivo evidence that endogenous dopamine modulates sympathetic activity in man. Hypertension 34(3):398–402 [DOI] [PubMed] [Google Scholar]
  28. Mena L, Pintos S, Queipo NV, Aizpúrua JA, Maestre G, Sulbarán T (2005) A reliable index for the prognostic significance of blood pressure variability. J Hypertens 23(3):505–511. 10.1097/01.hjh.0000160205.81652.5a [DOI] [PubMed] [Google Scholar]
  29. Milazzo V, Di Stefano C, Vallelonga F, Sobrero G, Zibetti M, Romagnolo A, Merola A et al (2018) Reverse blood pressure dipping as marker of dysautonomia in Parkinson disease. Parkinsonism Relat Disord 56(March):82–87. 10.1016/j.parkreldis.2018.06.032 [DOI] [PubMed] [Google Scholar]
  30. Miller-Patterson C, Hsu JY, Barrett MJ, Cloud LJ, Berman BD, Chelimsky TC (2024) Supine hypertension is longitudinally associated with verbal memory decline in Parkinson disease. Clin Auton Res 34(2):293–296. 10.1007/s10286-024-01026-3 [DOI] [PubMed] [Google Scholar]
  31. Poewe W (2009) Clinical measures of progression in Parkinson’s disease. Mov Disord. 10.1002/mds.22600 [DOI] [PubMed] [Google Scholar]
  32. Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, Obeso J, Marek K et al (2015) MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord 30(12):1591–1601. 10.1002/mds.26424 [DOI] [PubMed] [Google Scholar]
  33. R Core Team (2024) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Accessed on 26 Jan 2025
  34. Schillaci G, Bilo G, Pucci G, Laurent S, Macquin-Mavier I, Boutouyrie P, Battista F, Settimi L et al. (2012) Relationship between short-term blood pressure variability and large-artery stiffness in human hypertension findings from 2 large databases. 10.1161/HYPERTENSIONAHA.112.197491 [DOI] [PubMed]
  35. Schutte AE, Kollias A, Stergiou GS (2022) Blood pressure and its variability: classic and novel measurement techniques. Nat Rev Cardiol 19(10):643–654. 10.1038/s41569-022-00690-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Sebastian I, Kate MP, Khatter H, Singh B, Pandian JD (2022) Spectrum of cardiovascular autonomic dysfunction and 24-hour blood pressure variability in idiopathic Parkinson’s disease. Ann Indian Acad Neurol 25(5):902–908. 10.4103/aian.aian_289_22 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Shen L, Yang X, Lu W, Chen W, Ye X, Wu D (2022) 24-hour ambulatory blood pressure alterations in patients with Parkinson’s disease. Brain Behav 12(1):1–10. 10.1002/brb3.2428 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Sławek J, Roszmann A, Robowski P, Dubaniewicz M, Sitek EJ, Honczarenko K et al (2013) The impact of MRI white matter hyperintensities on dementia in Parkinson’s disease in relation to the homocysteine level and other vascular risk factors. Neurodegener Dis 12(1):1–12. 10.1159/000338610 [DOI] [PubMed] [Google Scholar]
  39. Sun F (2023) The impact of blood pressure variability on cognition: current limitations and new advances. J Hypertens. 10.1097/HJH.0000000000003422 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Tanaka R, Shimo Y, Yamashiro K, Ogawa T, Nishioka K, Oyama G, Umemura A, Hattori N (2018) Association between abnormal nocturnal blood pressure profile and dementia in Parkinson’s disease. Parkinsonism Relat Disord 46:24–29. 10.1016/j.parkreldis.2017.10.014 [DOI] [PubMed] [Google Scholar]
  41. Thobois S (2006) Proposed dose equivalence for rapid switch between dopamine receptor agonists in Parkinson’s disease: A review of the literature. Clin Ther 28(1):1–12. 10.1016/j.clinthera.2005.12.003 [DOI] [PubMed] [Google Scholar]
  42. Tsukamoto T, Kitano Y, Kuno S (2013) Blood pressure fluctuation and hypertension in patients with Parkinson’s disease. Brain and Behavior 3(6):710–714. 10.1002/brb3.179 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Umehara T, Matsuno H, Toyoda C, Oka H (2016) Clinical characteristics of supine hypertension in de novo Parkinson disease. Clin Auton Res 26(1):15–21. 10.1007/s10286-015-0324-8 [DOI] [PubMed] [Google Scholar]
  44. Vallelonga F, Romagnolo A, Merola A, Sobrero G, Di Stefano C et al (2019) Detection of orthostatic hypotension with ambulatory blood pressure monitoring in parkinson’s disease. Hypertens Res 42(10):1552–1560. 10.1038/s41440-019-0267-x [DOI] [PubMed] [Google Scholar]
  45. Vallelonga F, Valente M, Tangari MM, Covolo A, Milazzo V, Di Stefano C (2024) Hypotensive episodes at 24-h ambulatory blood pressure monitoring predict adverse outcomes in Parkinson’s disease. Clin Auton Res 34(2):281–291. 10.1007/s10286-024-01030-7 [DOI] [PubMed] [Google Scholar]
  46. Yoo SW, Yun E, Bang M, Yoon U, Yoo JY, Lee KS, Shin NY, Kim JS (2020) Blood pressure lability is associated with subcortical atrophy in early Parkinson’s disease. J Hypertens 38(10):2043–2049. 10.1097/HJH.0000000000002505 [DOI] [PubMed] [Google Scholar]

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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