Gastric Motility in Parkinson’s Disease is Altered Depending on the Digestive Phase and Does Not Correlate with Patient-Reported Motor Fluctuations

Background: Altered gastric motility is a frequent non-motor symptom of Parkinson’s disease (PD). It has been hypothesized that disturbed gastric motility contributes to motor fluctuations in PD due to an erratic gastro-duodenal transport and an unpredictable absorption of drugs. Objective: We investigated whether patient-reported fluctuations are associated with parameters of gastric motility visualized by real-time magnetic resonance imaging (MRI) of the stomach. Methods: We analyzed real-time MRI-scans of the stomach after an overnight fasting period in 16 PD patients and 20 controls. MRI was performed 1) in the fasting state, 2) directly after a test meal, and 3) 4 hours postprandially. Gastric motility indices were calculated and compared between groups. Results: MRI showed an attenuated gastric motility in PD patients compared to controls. The difference was most obvious in the early postprandial phase. Gastric motility was not associated with patient-reported motor fluctuations. Using an iron-containing capsule we were able to retrace retention of drugs in the stomach. Conclusion: The results of this study stress the importance of considering the phase of digestion when investigating gastric motility in PD. Despite theoretical considerations, we did not find robust evidence for an association between MRI parameters of gastric motility and patient-reported motor fluctuations. For future studies that aim to investigate gastric motility in PD by MRI, we suggest multiple short-time MRIs to better track the whole gastro-duodenal phase in PD. Such an approach would also allow to retrace the retention of drugs in the stomach as shown by our approach using an iron-containing capsule.

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Tu1479 Real-Time Evaluation of Altered Gastric Motility by Ultrasonography in Patients With Parkinson's Disease

Gastroenterology ◽

10.1016/s0016-5085(12)63273-9 ◽

2012 ◽

Vol 142 (5) ◽

pp. S-844

Author(s):

Annalisa Tortora ◽

Maria Assunta Zocco ◽

Matteo Garcovich ◽

Francesca Romana Ponziani ◽

Alfonso Fasano ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Real Time ◽

Gastric Motility ◽

Time Evaluation

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Real-time visualization of altered gastric motility by magnetic resonance imaging in patients with Parkinson's disease

Movement Disorders ◽

10.1002/mds.22841 ◽

2010 ◽

Vol 25 (5) ◽

pp. 623-628 ◽

Cited By ~ 20

Author(s):

Marcus M. Unger ◽

Katja Hattemer ◽

Jens C. Möller ◽

Katrin Schmittinger ◽

Katharina Mankel ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Magnetic Resonance ◽

Real Time ◽

Gastric Motility ◽

Resonance Imaging ◽

Real Time Visualization

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The role of brain perivascular space burden in early-stage Parkinson’s disease

npj Parkinson s Disease ◽

10.1038/s41531-021-00155-0 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Ting Shen ◽

Yumei Yue ◽

Shuai Zhao ◽

Juanjuan Xie ◽

Yanxing Chen ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

White Matter ◽

Early Stage ◽

Mean Diffusivity ◽

Perivascular Space ◽

Motor Symptom ◽

Imaging Features ◽

Subcortical Nuclei ◽

The Difference

AbstractPerivascular space (PVS) is associated with neurodegenerative diseases, while its effect on Parkinson’s disease (PD) remains unclear. We aimed to investigate the clinical and neuroimaging significance of PVS in basal ganglia (BG) and midbrain in early-stage PD. We recruited 40 early-stage PD patients and 41 healthy controls (HCs). Both PVS number and volume were calculated to evaluate PVS burden on 7 T magnetic resonance imaging images. We compared PVS burden between PD and HC, and conducted partial correlation analysis between PVS burden and clinical and imaging features. PD patients had a significantly more serious PVS burden in BG and midbrain, and the PVS number in BG was significantly correlated to the PD disease severity and L-dopa equivalent dosage. The fractional anisotropy and mean diffusivity values of certain subcortical nuclei and white matter fibers within or nearby the BG and midbrain were significantly correlated with the ipsilateral PVS burden indexes. Regarding to the midbrain, the difference between bilateral PVS burden was, respectively, correlated to the difference between fiber counts of white fiber tract passing through bilateral substantia nigra in PD. Our study suggests that PVS burden indexes in BG are candidate biomarkers to evaluate PD motor symptom severity and aid in predicting medication dosage. And our findings also highlight the potential correlations between PVS burden and both grey and white matter microstructures.

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Pelvic Floor Health in Women with Parkinson’s Disease

Journal of Parkinson s Disease ◽

10.3233/jpd-202491 ◽

2021 ◽

Vol 11 (2) ◽

pp. 857-864 ◽

Cited By ~ 1

Author(s):

Ankita Gupta ◽

Kathrin LaFaver ◽

Kevin R. Duque ◽

Anushree Lingaiah ◽

Kate V. Meriwether ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Pelvic Floor ◽

Rating Scale ◽

Short Form ◽

Pelvic Floor Dysfunction ◽

Measurement Information ◽

Cognitive Score ◽

Patient Reported ◽

The Difference

Background: Urinary dysfunction and constipation, manifestations of pelvic floor dysfunction are common sources of disability and impaired quality of life in women with Parkinson’s disease (PD). Objective: We sought to evaluate the pelvic floor health amongst women with PD and their reporting of bladder and bowel symptoms. Methods: We surveyed women with PD and age-matched controls about pelvic floor health using validated questionnaires. All participants completed the Pelvic Floor Disability Index (PFDI-20), the Pelvic Floor Impact Questionnaire (PFIQ-7) and the Patient-Reported Outcomes Measurement Information System (PROMIS) short form version 2.0 Cognitive Function 8a. Additionally, PD patients underwent the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) scale and the Montreal Cognition Assessment (MoCA). Results: Women with PD (n = 59; age, 70.4±8.6 years, PROMIS cognitive score, 52.0±7.8) self-reported urinary symptoms to a greater extent than controls (n = 59; age, 70.2±8.7 years, PROMIS cognitive score, 51.0±10) (68% vs 43%, p < 0.01). The difference was mirrored by higher (worse) scores on both PFDI-20 (35.4 vs 15.6; p = 0.01) and PFIQ-7 (4.8 vs 0; p < 0.01) for PD women compared to controls. Only 63% of all participants with self-reported pelvic floor symptoms had previously reported these symptoms to a health care provider. There was no difference in utilization of specialty care between the two groups (30% vs 46%, p = 0.2). Conclusion: Pelvic floor dysfunction, more common amongst women with PD, is underreported and undertreated. Our study identifies a key gap in care of women with PD.

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Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges

Sensors ◽

10.3390/s21124188 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4188

Author(s):

Mercedes Barrachina-Fernández ◽

Ana María Maitín ◽

Carmen Sánchez-Ávila ◽

Juan Pablo Romero

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Classification Problem ◽

Influential Factors ◽

Motor Fluctuations ◽

Machine Learning Techniques ◽

Motor Symptom ◽

Significant Heterogeneity ◽

Self Assessment ◽

Meta Analyses

Monitoring of motor symptom fluctuations in Parkinson’s disease (PD) patients is currently performed through the subjective self-assessment of patients. Clinicians require reliable information about a fluctuation’s occurrence to enable a precise treatment rescheduling and dosing adjustment. In this review, we analyzed the utilization of sensors for identifying motor fluctuations in PD patients and the application of machine learning techniques to detect fluctuations. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten studies were included between January 2010 and March 2021, and their main characteristics and results were assessed and documented. Five studies utilized daily activities to collect the data, four used concrete scenarios executing specific activities to gather the data, and only one utilized a combination of both situations. The accuracy for classification was 83.56–96.77%. In the studies evaluated, it was not possible to find a standard cleaning protocol for the signal captured, and there is significant heterogeneity in the models utilized and in the different features introduced in the models (using spatiotemporal characteristics, frequential characteristics, or both). The two most influential factors in the good performance of the classification problem are the type of features utilized and the type of model.

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Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson’s disease

Scientific Data ◽

10.1038/s41597-021-00830-0 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Jean-Francois Daneault ◽

Gloria Vergara-Diaz ◽

Federico Parisi ◽

Chen Admati ◽

Christina Alfonso ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Symptom Severity ◽

Neurodegenerative Disorder ◽

Open Data ◽

Motor Fluctuations ◽

Sensor Data ◽

Motor Symptom ◽

Data Repository ◽

Accelerometer Data

AbstractParkinson’s disease (PD) is a neurodegenerative disorder associated with motor and non-motor symptoms. Current treatments primarily focus on managing motor symptom severity such as tremor, bradykinesia, and rigidity. However, as the disease progresses, treatment side-effects can emerge such as on/off periods and dyskinesia. The objective of the Levodopa Response Study was to identify whether wearable sensor data can be used to objectively quantify symptom severity in individuals with PD exhibiting motor fluctuations. Thirty-one subjects with PD were recruited from 2 sites to participate in a 4-day study. Data was collected using 2 wrist-worn accelerometers and a waist-worn smartphone. During Days 1 and 4, a portion of the data was collected in the laboratory while subjects performed a battery of motor tasks as clinicians rated symptom severity. The remaining of the recordings were performed in the home and community settings. To our knowledge, this is the first dataset collected using wearable accelerometers with specific focus on individuals with PD experiencing motor fluctuations that is made available via an open data repository.

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