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Method Article

Factors Associated with Orthostatic Hypotension in Parkinson's Disease Patients: A Systematic Review and Meta-analysis

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DOI:

10.3791/69191

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October 14th, 2025

 ,  ,  ,  ,  ,  ,  , 
1School of Nursing, Chengdu University of Traditional Chinese Medicine, 2School of Nursing, Sun Yat-sen University, 3Department of Dermatology, Hospital of Chengdu University of Traditional Chinese Medicine, 4School of Nursing, Sichuan Nursing Vocational College, 5Department of Endocrinology, Hospital of Chengdu University of Traditional Chinese Medicine

* These authors contributed equally

In This Article

Summary

This study summarizes and quantifies factors associated with orthostatic hypotension in Parkinson's disease patients.

Abstract

Orthostatic hypotension (OH) is a prevalent non-motor symptom in Parkinson's disease (PD), heightening the risk of falls and mortality. This study aimed to identify and quantify the factors associated with OH in PD patients. A comprehensive search of nine databases, PubMed, CINAHL, Web of Science, Scopus, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), and Wanfang, was conducted, covering articles from database inception to November 27, 2024. Two researchers independently screened titles, abstracts, and full texts, evaluating the methodological quality of the studies. Relevant data were analyzed through meta-analysis using R software. Nineteen studies of moderate to high quality were included. The analysis revealed significant associations between PD-related OH and several independent factors: age (OR = 1.06; 95% CI: 1.05-1.08; I2 = 78%; P < 0.01), Hoehn-Yahr stage (H-Y stage) (OR = 1.57; 95% CI: 1.09-2.26; I2 = 51%; P = 0.02), Hemoglobin A1c (HbA1c) (OR = 2.08; 95% CI: 1.28-3.38; I2 = 0%; P < 0.01), REM Sleep Behavior Disorder (RBD) (OR = 5.83; 95% CI: 2.70-12.61; I2 = 24%; P < 0.01), Levodopa-equivalent daily dose (LEDD) (OR = 1.00; 95% CI: 1.00-1.01; I2 = 79%; P = 0.06), PD-autonomic dysfunction (SCOPA-AUT) (OR = 1.15; 95% CI: 0.99-1.34; I2 = 94%; P = 0.06), and hypertension (OR = 3.86; 95% CI: 1.25-11.90; I2 = 71%; P = 0.02). These findings suggest that factors such as age, H-Y stage, HbA1c, LEDD, SCOPA-AUT, hypertension, and RBD are associated with PD-related OH. Further research is essential to refine prevention strategies.

Introduction

Following Alzheimer's disease, Parkinson's disease (PD) is the second most common neurodegenerative disorder in the elderly population1. Orthostatic hypotension (OH) is prevalent in PD2, affecting between 30.1% and 60% of patients3,4. Despite its high prevalence, OH is often underrecognized and underestimated due to its asymptomatic nature, which complicates detection5,6. Common symptoms of OH in PD patients include dizziness, blurred vision, and transient loss of consciousness7, all of which contribute to postural instability and an increased risk of falls. Research by Francois et al. demonstrated that PD patients with OH experienced significantly higher rates of fall-related hospitalizations (7% vs. 3%) and emergency room visits (18% vs. 10%) compared to those without OH8. Additionally, OH is linked to the onset of cardiovascular disease, dementia, and frailty9,10,11,12. The presence of OH severely limits daily activities, diminishes quality of life13, and elevates the risk of disability and mortality14,15.

Given these consequences, early screening and management of PD-related OH are essential. Identifying the factors contributing to its development in PD patients is crucial for prevention. However, most existing studies have primarily addressed how PD-OH serves as a risk factor influencing disease outcomes16. Furthermore, the impact of various factors on PD-OH remains insufficiently explored and contentious. For instance, a study by Yin et al.17 suggested that the Hoehn-Yahr (H-Y) stage is predictive of PD-OH onset, while Ou et al.18 identified cognitive function as a significant risk factor. However, several studies have failed to corroborate these findings, indicating that neither cognitive function19,20,21 nor H-Y stage22,23 consistently correlates with the development of PD-OH. This ongoing debate warrants further investigation.

Previous meta-analyses on PD-OH have predominantly focused on prevalence and medication usage24,25. There is, however, a lack of comprehensive analyses summarizing the factors contributing to PD-OH onset. This study aims to address this gap by employing quantitative statistical methods to explore the factors associated with PD-OH development, challenging conventional assumptions and providing a robust, evidence-based foundation for preventive strategies.

Eligibility criteria for this study included: (a) observational studies, such as case-control, cohort, or cross-sectional designs; (b) populations meeting clinical diagnostic criteria for PD; (c) the outcome measure being the occurrence of OH in PD patients; (d) comparison of at least two groups, one consisting of PD patients without OH and the other of PD patients with OH.

Exclusion criteria were as follows: (a) reviews, conference abstracts, and commentaries; (b) studies lacking full-text availability; (c) studies with incomplete or insufficient data; (d) studies where required data could not be obtained.

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Protocol

This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines26. It was registered in PROSPERO (CRD42023397565).

1. Software installation

  1. R software installation
    1. Visit the official R software website (see Table of Materials). Click Download R for Windows, then select base and click Download. Choose the appropriate platform installation version to download the installation package.
    2. Run the executable file by double-clicking it, then follow the installation prompts: Click Next, select the desired installation directory, and proceed with Next. Complete the setup by selecting Finish.
  2. EndNote 21 software installation
    1. Access the official EndNote website (see Table of Materials) to obtain the EndNote 21 installation package in ZIP format.
    2. Extract the ZIP file to a local directory. Launch the installer by double-clicking it, and follow the setup prompts: Click Next, review the 'Welcome to EndNote 21.0' screen, enter the product key, fill in user details, accept the license terms, choose Typical installation, and finalize by selecting Finish.

2. Data sources and search strategy

  1. Develop a detailed search strategy incorporating both MeSH (Medical Subject Headings) subject terms and free-text terms based on the research topics "Parkinson's disease," "Orthostatic hypotension," and "factors."
    NOTE: The search period ranged from the establishment of the databases to November 27, 2024, with no restrictions on publication status, country, or language. The complete search strategy used in this study is documented in Supplementary File 1.
  2. Conduct comprehensive searches across nine databases: PubMed, Web of Science, Scopus, Cochrane Library, Embase, CINAHL, China National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), and Wanfang Database (see Table of Materials). Additionally, relevant research results from Google Scholar were considered in this study. For example, in PubMed: Navigate to PubMed's online database platform. Input the search strategy following PubMed's query syntax guidelines. The search query in this investigation was as follows (see Supplementary Figure 1A)
    1. Parkinson's disease
      1. MeSH Terms: Parkinson Disease
      2. Title/ ABSTRACT: "Parkinson Disease" OR "Idiopathic Parkinson's Disease" OR "Lewy Body Parkinson's Disease" OR "Parkinson's Disease, Idiopathic" OR "Parkinson's Disease, Lewy Body" OR "Parkinson Disease, Idiopathic" OR "Parkinson's Disease" OR "Idiopathic Parkinson Disease" OR "Lewy Body Parkinson Disease" OR "Primary Parkinsonism" OR "Parkinsonism, Primary" OR "Paralysis Agitans".
      3. 2.2.1.1 OR 2.2.1.2.
    2. Hypotension, Orthostatic
      1. MeSH Terms: Hypotension, Orthostatic
      2. Title/ ABSTRACT: "Hypotension, Orthostatic" OR "Hypotension, Postural" OR "Postural Hypotension"OR "Orthostatic Hypotension".
      3. 2.2.2.1 OR 2.2.2.2.
    3. Risk factor
      1. Title/ ABSTRACT: "risk factor*" OR "relevant factor*" OR "predictor" OR "associated factor*" OR "correlate" OR "influence factor*"
      2. 2.2.1.3 AND 2.2.2.3 AND 2.2.3.1
  3. Export all retrieved records from PubMed and save as a file (see Supplementary Figure 1B).
  4. Conduct the literature screening process.
    NOTE: Literature exported from PubMed was used to demonstrate how to manage and screen references in EndNote.
    1. Import the search results by selecting: File > Import > Options, then choosing PubMed (NLM) under Import Options and clicking Import (see Supplementary Figure 2A).
    2. Establish the inclusion and exclusion criteria groups by right-clicking the My Groups folder in the left panel of EndNote and selecting Create Group. Add references to the corresponding groups either by dragging and dropping them or by right-clicking the target references, selecting Add References To, and choosing the appropriate group (Supplementary Figure 2B).
    3. Remove duplicate references by navigating to Library > Find Duplicates > Cancel, which generates a temporary Duplicate References Group with all duplicates pre-selected. Drag the highlighted references to the Trash folder for removal. Perform a final manual review to ensure no duplicates were overlooked (Supplementary Figure 2C).
      NOTE: The studies were independently screened by two researchers using EndNote Version 21. In the first screening phase, articles unrelated to OH in PD patients were excluded based on titles and abstracts. In the second phase, full texts were reviewed by both researchers according to the inclusion and exclusion criteria. Any disagreements were resolved through discussion with a third researcher.
  5. Generate a PRISMA diagram in Microsoft Word to visualize the count of included and excluded studies along with their respective exclusion criteria (see Supplementary File 2).

3. Data extraction

  1. Use the developed Excel template to systematically record key data elements from each selected study. Extract the following data: first author, year of publication, country, study type, duration of the study, sample size, age (mean ± standard deviation/age range), number of women, number of individuals with OH, and associated factors.
    NOTE: Data were independently extracted and reconciled by two researchers, with any discrepancies and uncertainties reviewed by a third researcher.

4. Quality assessment

  1. Appraise the methodological quality of case-control and cohort studies using the Newcastle-Ottawa Scale (NOS). Note that the scale includes 8 items across 3 sections with a total of 9 scores. A score of 0-3 indicates low quality, 4-6 indicates medium quality, and 7-9 indicates high quality27 (see Supplementary File 3).
    NOTE: Two researchers independently assessed the quality of the literature, with disagreements resolved through discussion or by a third researcher.
  2. Summarize the literature quality assessment results in a table, categorizing them into cohort and case-control studies.
    NOTE: The results of the literature quality assessment were summarized by categorizing them into cohort studies and case-control studies in a table.

5. Meta-analysis

NOTE: The meta-analysis was conducted using R software. Variables with homogeneous definitions reported in at least two studies were aggregated and presented as odds ratios (ORs) with 95% confidence intervals (CIs). The impact of these variables was assessed by pooling the data. A P-value ≤ 0.05 was considered statistically significant. The heterogeneity of the included studies was assessed using Cochran's Q test (with a significance level set at P = 0.10) and the I2 statistic. A random-effects model was applied when the I2 statistic was ≥ 50% or P≤ 0.10. Otherwise, a fixed-effects model was used. Sensitivity analysis was conducted by modifying the effect model to assess the robustness of the combined results. Meta-regression was employed to identify potential sources of heterogeneity. Additionally, Egger's test was used to detect potential publication bias. In the presence of publication bias, the trim-and-fill method was applied to assess its impact on the sensitivity and robustness of the pooled results.

  1. Enter the first author of the study, year of publication, OR value, and the upper and lower limits of the 95% confidence interval (UCI, LCI) into an Excel file, and save the data in .csv format (see Supplementary Figure 3A).
  2. Open the R software, download the Meta package, and load it. Read the pre-saved CSV format data file, take the natural logarithm of the OR value and its 95% confidence interval, conduct a meta-analysis, and generate forest plots. The execution code for the analysis of "Age" is as follows (see Supplementary Figure 3B,C)
    install.packages("meta")
    library("meta")
    m1<-read.csv(file="C:/Users/DELL/Desktop/age.csv")
    m1
    #forest plot
    meta1<-metagen(log(m1$or), (log(m1$uci)-log(m1$lci))/3.92, sm="or", data=m1, comb.fixed=F,comb.random=T, studlab=paste(Study))
    forest(meta1)
    forest(meta1, xlim=c(0.05, 10))
    forest(meta1, col.square="red", col.diamond="red", col.diamond.lines="black")
    NOTE: The analysis code for other factors is identical; simply replace "age" with other factors to run it.

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Results

This study strictly followed the procedures and methods outlined in the protocol to conduct a meta-analysis. The findings include: (1) the screening of 19 relevant original studies; (2) the completion of quality assessments for these 19 articles; and (3) the extraction and quantitative analysis of 14 influencing factors to explore their correlation with the occurrence of OH in PD patients. The combined results were considered statistically significant at a P value of ≤ 0.05.

Study char...

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Discussion

This study presents a meta-analys is aimed at identifying and quantifying the factors associated with OH in PD patients. The analysis revealed that among 14 factors, age, H-Y stage, HbA1c, LEDD, SCOPA-AUT, hypertension, and RBD were significantly associated with PD-OH. This study further demonstrates a strong association between elevated HbA1c levels and orthostatic hypotension (OH), corroborating previous reports39,40. The underlying mechan...

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Disclosures

The authors declare that they have no conflict of interest.

Acknowledgements

This work was supported by the Sichuan Science and Technology Program (No. 2024YFFK0159), the Hospital Special Project of Chengdu Health Commission (WXLH202403127).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
R sofeware Lucent TechnologiesOfficial VersionR is a  software environment for statistical computing and graphics.R provides a wide variety of statistical and graphical techniques, and is highly extensible.Official website:https://www.r-project.org
EndnoteClarivateOfficial VersionEndNote is a widely used reference management tool to help you collect,organize, and share your references.Official website: https://endnote.com/downloads.
Microsoft Excel  MicrosoftOfficial VersionMicrosoft Excel, a spreadsheet software with an intuitive interface, excellent computing capabilities and charting tools, is one of the most popular data processing software for personal computers.
Microsoft WordMicrosoftOfficial VersionMicrosoft Word offers a number of easy-to-use document creation tools, as well as a rich set of features for creating complex documents, as well as text formatting or image manipulation.
Database websites
Pubmedhttps://pubmed.ncbi.nlm.nih.gov/
Web of Sciencehttp://www.web of science.com/
 Scopushttps://www.scopus.com/
Cochrane Libraryhttps://www.cochranelibrary.com/
Embasehttps://www.embase.com/
CINAHLhttps://www.ebsco.com/zh-cn/products/research-databases/cinahl-database
CNKIhttps://www.cnki.net/
CBMhttp://www.sinomed.ac.cn/
Wanfanghttps://c.wanfangdata.com.cn/

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