Levels of Progranulin Protein May Affect Parkinson’s Severity, Progression

possible disease biomarker

Blood levels of progranulin — a protein whose deficiency has been linked to neurodegeneration — may reflect Parkinson’s severity and progression, and serve as a disease biomarker, a recent study suggested.

The research, “Reduced plasma progranulin levels are associated with the severity of Parkinson’s disease,” was published in Neuroscience Letters.

Progranulin is widely distributed throughout the brain. Studies indicate this protein is a potent regulator of neuroinflammation and a promotor of long-term neuronal survival.

Low progranulin levels have been associated with neurodegenerative and lysosomal storage disorders. Blood levels of progranulin are also suggested to be lower than usual in Parkinson’s patients.

But little is known about how blood levels of progranulin and disease severity in Parkinson’s might correlate.

Researchers explored this possibility by measuring progranulin blood concentrations and correlating them with symptom severity.

Their study involved 55 patients (24 men and 31 women, mean age 71.1) and 55 people without the disease serving as controls, (22 men and 33 women, mean age 67.8).

Disease severity was quantified using the Unified Parkinson’s Disease Rating Scale (UPDRS) and the Hoehn and Yahr scale. Patients’ motor symptoms were assessed using the UPDRS motor section (UPDRS-III).

Blood plasma tests revealed that progranulin levels were significantly lower in Parkinson’s patients compared to controls (333.8 vs. 364.2 ng/ml). Blood levels of progranulin were also found to negatively correlate with Parkinson’s severity, motor symptoms, and disease duration.

This means that lower progranulin levels associated with greater disease severity and motor symptoms, and longer disease duration. It also indicates a possible protective role of progranulin against the neurodegeneration process associated with Parkinson’s.

Previous studies have shown that boosting progranulin production protected dopamine-producing neurons from degeneration in mouse models of Parkinson’s, supporting progranulin’s role in better neuronal survival and neuroinflammation control.

“These results indicate that circulating [progranulin] levels might reflect the severity of neuronal loss and might be developed as a potential biomarker of [Parkinson’s disease],” the researchers wrote.

Progranulin deficiency has also been implicated in other neurodegenerative diseases besides Parkinson’s, including frontotemporal dementia, a group of dementias mainly affecting decision-making and behavior or language and speech, depending on the brain area that’s affected.

More research is necessary to investigate the protein’s diagnostic potential in Parkinson’s disease, the researchers advised.

A Phase 1 clinical trial in healthy volunteers (NCT04111666) is expected to test AL101, a therapeutic compound with a potential ability to raise progranulin levels in the brains of people with neurodegenerative diseases. But this study, listed as starting in December 2019, does not yet appear to be enrolling.

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Motion Machine’s Ability to Ease Motor Symptoms in Parkinson’s Entering Trial in Australia

Parkinson's and balance

A trial in Australia will test a motion machine, called the Reviver, to understand whether it can improve balance, mobility and sensory-motor coordination in people with moderate to advanced Parkinson’s disease and atypical parkinsonism.

Exercise has been shown to help ease Parkinson’s symptoms. The Reviver machine, by Isodymanics, is designed to stimulate the vestibular system, the sensory system that provides a sense of balance and information about body position.

The machine works by placing the user at a tilted angle and rotating them in a slow and radial, wave-like motion.

The reaction to being tilted off-balance can induce a brain response that activates muscles across the body, including those that may be dormant as a result of age, infrequent use, damage, or disability. Specifically, it activates nerve pathways that then aid in balance, enhance muscle strength, and help resist the effects of gravity.

Isodynamics reports early evidence suggesting the Reviver’s use can improve mobility and lessen Parkinson’s symptoms, with patients demonstrating a 22% increase in mobility; namely, quicker “up and go” test times over an average of 26 days. This test measures the time it takes for a person to rise from a chair, walk three meters (about 10 feet), turn around and return, then sit down again.

“The anecdotal results with our patients have been very positive,” Geoffrey Redmond, Reviver’s developer, said in a press release. “We’re really glad to see the Reviver being used in a formal trial.”

The trial will assess whether a 12-week program using the Reviver machine improves balance, mobility, and sensory-motor coordination. It plans to enroll 30 patients with moderate to advanced Parkinson’s disease or atypical parkinsonism. People with atypical disease have some evident Parkinson’s symptoms, like muscle stiffness or balance issues, but who do not respond well to standard medications. Their symptoms are caused by other disorders.

The trial is being overseen by Terry O’Brien, a neurologist at Monash University and led by Ben Sinclair, a brain imaging expert at Monash and with Alfred Health. Participants will be required to attend twice weekly sessions for 12 weeks at The Alfred in Melbourne.

Enrolled patients will be split into two groups, based on their diagnoses. One group will undertake the Reviver exercise regime on top of their standard of care, and the second (a control group) will continue with standard of care without using the Reviver.

Those interested in participating or receiving more information about the trial can call Isodynamics at 02-9524-2188 (in Australia, country code +61) or email the company.

“We now need to see what kind of results can be generated during a formal, randomised controlled trial,” Sinclair said.

“It’s an exciting project because people affected by Parkinson’s have a limited range of treatment options. This study provides a rare opportunity to explore and uncover a new possible treatment pathway for people affected by Parkinson’s,” he added.

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Palliative Care Leads to Better Quality of Life than Standard Care for PDRD Patients, Study Finds

Palliative care

Palliative care — focusing on physical, psychosocial and spiritual treatment — for people with Parkinson’s disease and related disorders (PDRD) led to a significantly better quality of life (QoL) than standard care alone, a study finds.

Easing of both non-motor and motor symptom severity was linked to palliative care intervention — and those with the greatest needs benefited the most, the researchers said.

The study, “Comparison of Integrated Outpatient Palliative Care With Standard Care in Patients With Parkinson Disease and Related Disorders,” was published in the Journal of the American Medical Association, Neurology

Parkinson’s disease and related disorders (PDRD) are a group of disorders that share core features of Parkinson’s but have additional symptoms. People with PDRD do not respond well to standard Parkinson’s medications and have a poor prognosis. 

Given the additional needs of patients with PDRD, an increasing number of medical centers are providing palliative care for these patients. 

While such care is typically associated with hospice and cancer, “recognition of the potential relevance of [palliative care] in other contexts has expanded substantially over the past decade to include earlier deployment, delivery to noncancer populations, delivery in outpatient settings, and delivery by persons not specializing in palliative medicine,” the researchers said.

Palliative care, known as PC, aims to improve quality of life (QoL) and reduce suffering by addressing medical symptoms, psychosocial issues, and care planning. 

Despite the recent advances in patient care, few studies are available that support the effectiveness of palliative care in the PDRD population.

Thus, a team of investigators from the University of Colorado (UC), the University of California, San Francisco (UCSF), and the University of Alberta (UA) in Canada, designed a study (NCT02533921) to examine the effects of this care approach. The team compared outpatient palliative care with standard care alone to assess any differences in participants’ QoL, the burden on the caregiver, and other patient-related outcomes. 

A total of 210 PDRD patients with moderate-to-high care needs were enrolled in the study, with participants randomly divided into a standard care group and a palliative care group. Of those selected, 104 patients and 88 caregivers were part of the standard care group, while 106 patients and 87 caregivers were assigned to the palliative care intervention group.

Standard care was provided by the patient’s primary care physician and a neurologist. 

Outpatient palliative care included standard care plus visits every three months either in person or by telemedicine — two-way videoconferencing and advanced information communication technologies. The PC team consisted of a specialized neurologist with palliative care workshop training, a nurse, a social worker, a chaplain experienced with Parkinson’s patients, and a physician specializing in this type of care. 

The primary outcomes were defined as differences in patient QoL after six months, measured using the Quality of Life in Alzheimer’s Disease (QoL-AD) scale, and by determining caregiver burden, using the 12-item Zarit Burden Interview (ZBI-12). 

Additional patient outcomes also were assessed, including symptom burden and health-related QoL. Patient and caregiver mood, grief, spiritual well-being, and overall impression of change also were reported. The outcomes for both patients and caregivers were recorded at the beginning of the study, and every three months for 12 months.

The results showed that, after six months, those receiving outpatient palliative care had significantly better QoL compared with those receiving standard care. When QoL assessments of patients and caregivers were combined, the impact was even greater. 

While the ZBI-12 difference in caregiver burden at six months was not significant, reassessment at 12 months showed a statistically significant difference. 

The greatest benefit from palliative care intervention was seen among the patients who were assessed, at the beginning of the study, as having greater needs. After 12 months, palliative care had a greater effect on women compared with men. 

In comparison with the standard care group, the PC group had a greater number of patients who experienced a clinically significant benefit in QoL-AD, and a lower number of those who scored worse.

Factors such as age, mood, symptom burden, disease severity, and cognition were not significantly different. However, improvements in non-motor symptoms, motor symptoms severity, and caregiver anxiety were linked to palliative care. 

Standard care alone was not favored for any outcome, the results showed. 

“Outpatient PC is associated with benefits among patients with PDRD compared with standard care alone,” the researchers concluded. “This study supports efforts to integrate [palliative care] into PDRD care.”

The researchers said such efforts are particularly needed for people with more severe symptoms.

“The integration of [palliative care] into PDRD care holds the potential to improve outcomes, particularly for persons who are underserved by current models of care (eg, patients with advanced illness and dementia),” the investigators said.

“Because the PC intervention is time-intensive and resource-intensive, future studies should optimize triage tools and consider alternative models of care delivery, such as telemedicine or care navigators, to provide key aspects of the intervention at lower cost,” they recommended. 

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Way of Detecting Parkinson’s Early via Typing Patterns Being Tested and Refined

typing patterns

A type of computational analysis that works to analyze typing patterns may help in detecting motor signs of Parkinson’s disease at early stages, the researchers who developed the analysis report.

This new method, which appeared to allow researchers to discriminate between people with early Parkinson’s and those without the disease, may also speed data collection and analysis of disease states across neurodegenerative ills.

The study, “Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots,” was published in the IEEE/CAA Journal of Automatica Sinica.

Objective measures of Parkinson’s motor signs are crucial for diagnosing the disease early and correctly, as well as for monitoring progression and assessing treatment response. Early detection of Parkinson’s disease (PD) is particularly relevant, as people at early stages of the disease are more likely to benefit from neuroprotective treatments.

“Because a significant amount of the [midbrain’s] substantia nigra neurons have already been lost or impaired before the onset of motor features, people with PD may first start experiencing symptoms later in the course of the disease,” the researchers wrote.

Current methods to evaluate motor symptoms focus on a person’s movements and balance while walking, which requires a trained specialist and clinic visits. As such, they limit the frequency at which disease state and progression is likely to be assessed.

In addition, these methods involve the collection of data “during relatively long walking periods, causing discomfort to the participants or impracticability of performance in clinical settings,” the researchers wrote.

Increasing efforts are being made to develop easier and more accessible methods of detecting Parkinson’s motor signs, including the use of digital technologies.

A previous study showed that analyzing the time a person takes between pressing and releasing a key while typing (key hold time) could be used to detect motor problems in the early stages of Parkinson’s. The analysis involves a computational self-learning algorithm able to generate a Parkinson’s disease motor index based on key hold times.

This approach — which measures key hold times during the normal use of a computer without any change in hardware — was shown to efficiently distinguish people with and without Parkinson’s using data from either a controlled clinic setting or an uncontrolled at-home setting.

While it has the potential to be an objective and user-convenient tool to detect Parkinson’s, this approach “require the time series of length to be considerably long.”

Researchers at Linköping University (LiU), in Sweden, developed a new way of analyzing typing patterns based on a very short time series of data for machine learning (artificial neural networks that learn from data). It intends to avoid discomfort to participants in performing long physical tasks for data recording, and to effectively differentiate Parkinson’s patients from healthy people.

The team used the first short segments of the key hold time data from 43 healthy individuals and 42 Parkinson’s patients (average time since diagnosis, 3.9 years), part of a publicly available database. Of note, patients were on parkinsonian medication, but stopped their treatments for at least the 18 hours before the typing test.

Researchers first translated the data into a set of two-dimensional, grey-scale images of texture, called fuzzy recurrence plots, which were then used for machine learning with an algorithm named long short-term memory (LSTM)-based deep learning.

The use of fuzzy recurrence plots in machine learning allowed for distinguishing among people with and without Parkinson’s using less data than current methods.

According to a press release, researchers believe their findings are “encouraging,” and plan to further explore the use of fuzzy recurrence plots and improve the algorithm to better determine a patient’s disease state.

They also highlighted that this approach may be applied to other types of data, with a goal of improving machine learning and reducing the amount of data required to achieve good results for differentiating healthy people from those with disease.

The research team plans to evaluate this approach against walking and balance data collected from people with Parkinson’s and other neurodegenerative diseases, such as Huntington’s disease and amyotrophic lateral sclerosis (ALS).

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Higher Risk of Falls Linked to Longer Disease Duration and PIDG Subtype, Study Suggests

gene mutation cancer risk

In Parkinson’s patients, the risk of falls increases depending on disease duration and having the postural instability/gait disturbance (PIDG) subtype, but is not signficantly correlated with non-motor symptoms, a study suggests.

The study, titled “Falls in persons with Parkinson’s disease: Do non-motor symptoms matter as much as motor symptoms?,” was published in Arquivos de Neuro-Psiquiatria.

Falls can be a major problem for people with Parkinson’s disease, with some individuals being at greater risk of serious falls. However, identifying a person’s fall risk can be challenging because the risk is affected by a multitude of different factors that may or may not be related to Parkinson’s disease itself.

Intuitively, it may seem that the best predictors of falls are likely related to motor symptoms — after all, falling is associated with moving. But, previous studies have suggested that measurements of motor function aren’t good predictors of falls.

The researchers behind this new study set out to investigate whether including non-motor symptoms, in addition to motor symptoms, would help better predict fall risk in people with Parkinson’s.

To test this, the researchers assessed 179 people (average age of 64.6 years, mean disease duration of 10.4 years) with Parkinson’s who were seen at the National Institute of Neurology and Neurosurgery in Mexico City. The participants’ clinical history, including fall history in the past year, was taken.

Participants underwent a series of evaluations, including disease state using the Hoehn and Yahr scale, assessment of motor symptoms using relevant parts of the Movement Disorders Society Unified Parkinson’s Disease Rating Scale, as well as non-motor symptoms using the Non-Motor Symptoms Scale.

Overall, 16.8% had experienced a fall in the last year, with just over half of these having experienced more than one fall — the average was 2.5 falls per month. The researchers noted that this is a “very low” proportion of patients who experienced falls (“fallers”), which “could partially be explained by under-representation of advanced forms of the disease,” a limitation of the study.

The researchers constructed statistical models using several types of data that they had collected, with the aim of identifying factors that were significantly over-represented among the fallers — thus, being predictive of falling.

They found that the severity of motor and non-motor symptoms in the participants was not significantly linked with fall risk.

However, the study did find two factors that were linked with fall risk: the first was disease duration. Patients who had experienced symptoms for longer were more likely to fall — average disease duration was 12.8 years in the fallers group and 7.4 years in the non-fallers group.

The second risk factor was having the PIDG subtype (which accounted for 59.8% of the participants), one of the three groups into which Parkinson’s patients can be divided based on their most prominent motor symptoms. The PIDG subtype is associated primarily with difficulty standing and/or walking and is thought to be associated with rapid progression of disease and cognitive dysfunction.

While about half (53%) of the people in the non-fall group had the PIDG subtype, nearly all (93%) of those in the fall group had the subtype.

“Disease duration and the PIGD subtype were identified as relevant risk factors for falls in [people with Parkinson’s disease]. Non-motor symptoms appear to have a less important role as risk factors for falls,” the researchers wrote, adding that these findings suggest a need for a “more intensive approach in fall prevention” for people with this subtype.


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Devices to Record the Progression of PD


The progression of Parkinson’s disease (PD) is unique to every person, with different early, middle, and late-stage symptoms. However, this view of PD progression may be an artifact of limited data rather than an accurate description. We need new ways of measuring PD symptoms as they change over time. We have the technology to create new devices that people can use over an extended period, across multiple settings and severity of “off periods.”

I see progression as a change in the intensity and duration of “bad” days and off periods. Many longitudinal studies investigate the progression of PD (for example, the rate of progression in exercise), but it is hard to find studies that measure changes in response to treatment. Devices discussed in this column might change that.

Better measurement of PD progression begins with a few assumptions. First, subtle, early motor symptoms will appear before more obvious symptoms, such as tremors or bradykinesia. Second, early motor symptoms will be inconsistent and episodic. Third, we have the technology to build mobile monitoring devices.

I recently read that a patient being evaluated for “internal tremors” showed no signs of tremor during a physical examination of his bare feet. However, once he put his socks and boots on, an astute clinician observed the left bootlace swinging in such a way that, when measured, fit the PD pattern of a tremor. In other words, while the patient didn’t exhibit tremor during a visual examination of his bare feet, his shoelace reflected an underlying tremor!

A shoelace is not going to be a reliable measuring device, but it proves that slight motor changes that are difficult to detect do exist. I’ve designed two possible motor symptom detection devices: a mobile swing monitor (MSM) and a fine motor skills test (FST). Both devices would record and monitor movement fluctuation over time and across settings in daily life over 10 or so days. Both devices use sensors to track and record movement through three dimensions.

The MSM uses five “movement in 3D space” sensors — one on each wrist, one on each ankle, and one on the belt — with recording hardware for all five. Worn for several days, like a Holter monitor, the MSM would map the sway of the arms, legs, and body over time and across settings. The MSM is very similar in appearance to wearable training weights, which can measure the slightest variations in body movement. Wearable training weights are used by Olympic and World Cup judges to evaluate Shaun White’s amazing snowboard flips and twists.

The FST, illustrated in the graphic below, has a 3D monitor in the “soda can” receptor where the block is inserted. Similar to the game “Operation,” the patient must remove objects from openings in the receptor without setting off the buzzer. The warning light goes off when the sensor plate is touched.

The FST will measure how the person adjusts position and control while using fine motor skills. The FST uses a 3D monitor and four independent, pressure-sensitive plates that record when the patient fails to insert the block and when the block is aligned. The plates can be positioned at different widths using an adjustable difficulty setting, making it harder to insert the block without touching the plates.

Fine skills motor test, designed by Dr. C. (Photo by Dr. C)

Data gathered by these two devices may provide patients and medical professionals with more accurate clinical data about motion, tremors, and fine motor skills over a greater period. They could demonstrate the progression of intensity and duration of bad days and off periods and serve as the beginning of a database on PD progression.

Many people are excited about using technology to provide outcome measures. As we know, technology is not being utilized in offices with patients to help understand the progression of PD. But hopefully, that will change.

If these devices are already being tested in the home of PD patients, sign me up!


Note: Parkinson’s News Today is strictly a news and information website about the disease. It does not provide medical advice, diagnosis, or treatment. This content is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or another qualified health provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read on this website. The opinions expressed in this column are not those of Parkinson’s News Today or its parent company, BioNews Services, and are intended to spark discussion about issues pertaining to Parkinson’s disease.

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Alpha-synuclein Blood Levels May Be Biomarker for Parkinson’s with Motor Symptoms

blood biomarkers of disease

Measuring the levels of alpha-synuclein in red blood cells can reliably distinguish people with Parkinson’s disease and evident motor symptoms from healthy individuals, and could serve as a diagnostic biomarker, a study reports.

These levels in Parkinson’s patients with symptoms of dementia, however, did not measurably differ from healthy people serving as a control group.

The study, “α‐Synuclein in blood cells differentiates Parkinson’s disease from healthy controls,” was published in the journal Annals of Clinical and Translational Neurology.

The hallmark of Parkinson’s disease is the build-up of the protein alpha-synuclein in the brain, which goes on to form clumps of misfolded proteins known as Lewy bodies that damage nerve cells. 

Alpha-synuclein levels in the blood have been evaluated as a biomarker for Parkinson’s, as the ease and accessibility of a blood test would help with treatment during the course of the disease.

Low levels of misfolded alpha-synuclein — originating in neurons — have been found in the blood of Parkinson’s patients and are associated with disease progression.  

However, the primary source of alpha-synuclein in the blood comes from red blood cells, and little is known about the relevance of this source of alpha-synuclein and disease pathology.

To determine if alpha-synuclein levels in blood cells could be a biomarker for Parkinson’s, researchers at The Hebrew University‐Hadassah Medical School in Jerusalem tested the levels of alpha-synuclein in red blood cells isolated from 46 people with Parkinson’s. They compared them to those from 45 healthy controls. 

These blood samples were obtained from The BioFIND Study, an observational clinical study designed to discover and confirm Parkinson’s biomarkers. 

The overall levels of blood cells’ alpha-synuclein and misfolded alpha-synuclein were determined, as were known markers of Parkinson’s: phosphorylated and oxidized forms of alpha-synuclein. 

Alpha-synuclein phosphorylation — a chemical modification in which a phosphate group is added to the protein — and oxidation — which modifies the protein’s side chains —  are known to occur in Parkinson’s disease, and are thought to be critical steps in disease progression. They enhance alpha-synuclein’s toxicity, possibly by increasing the formation of alpha-synuclein aggregates (clumps).

Parkinson’s patients were divided into two groups: 32 people with motor symptoms and 14 with symptoms of dementia as determined by the Montreal Cognitive Assessment. Blood cell alpha-synuclein levels of these two groups were then compared to healthy controls. 

While the average levels of blood cell alpha-synuclein from both Parkinson’s groups combined were slightly lower than those of controls, alpha-synuclein levels in patients with motor symptoms were significantly higher than both controls and patients with dementia symptoms.

The levels of misfolded alpha-synuclein, in addition to its phosphorylated form, followed the same pattern — both were significantly higher in motor symptom patients and were found to correlate with disease severity. 

The test for oxidized alpha-synuclein found no differences between groups.

To validate these three tests as potential Parkinson’s biomarkers, the team collected a second set of blood samples from the Hadassah hospital, comprising 35 Parkinson’s patients with motor symptoms and 28 healthy controls. The levels of total, misfolded, and phosphorylated alpha-synuclein were measured.

This analysis confirmed that these three markers were able to reliably distinguish between Parkinson’s patients with motor symptoms and those without the disease. 

“We conclude that blood cells expressed [alpha-synuclein] can differentiate [Parkinson’s with motor symptoms] and [healthy controls] with a high degree of accuracy. It provides a reliable classification rate, correlates with the severity of disease and is reproducible,” the researchers wrote.

“A longitudinal study that will determine whether alterations in blood cell-expressed [alpha-synuclein] forms are associated with disease progression is required,” they added.

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Cerevance’s Phase 2 Trial Recruiting Patients to Test Oral CVN424 Therapy for Motor Symptoms

CVN424 Phase 2 trial

Cerevance has initiated a Phase 2 clinical trial to evaluate the safety and efficacy of its oral investigational therapy CVN424 for treating Parkinson’s disease motor symptoms.

CVN424 is small molecule that can penetrate the brain and modulate the activity of specific nerve cells in the striatum region that control body movement. In contrast to available therapies, CVN424 specifically targets a non-dopaminergic protein involved in signaling pathways that can activate brain cells.

This new strategy is anticipated to induce positive therapeutic effects similar to current standard-care treatments for Parkinson’s while avoiding side effects such as dyskinesia.

“We are pleased to further advance the clinical development of CVN424 in Parkinson’s, as there remain significant shortcomings with current therapeutics,” Brad Margus, Cerevance’s CEO, said in a press release.

Preclinical studies have shown that CVN424 can improve locomotor activity in animal models of Parkinson’s disease.

Results from a previous Phase 1 study (NCT03657030) showed that CVN424 was safe and well-tolerated compared with placebo. The trial enrolled 64 healthy volunteers who received either single doses or seven daily doses of CVN424, ranging from 1 mg to 225 mg, or placebo. No serious or severe adverse events or clinically significant changes were reported or associated with the therapy.

Oral administration of CVN424 was rapidly absorbed by the body and its stability profile supported a once-daily dosing regimen as the optimal treatment approach for future studies.

These positive data supported the launch of the multicenter, placebo-controlled Phase 2 trial that will now evaluate CVN424’s efficacy and safety in Parkinson’s patients with motor fluctuations who are being treated with levodopa.

The trial is expected to enroll approximately 70 participants, ages 30 to 80, who will be randomly assigned to receive one of two doses of CVN424 or a placebo.

The researchers will evaluate CVN424’s potential for reducing “off” time — periods of the day when Parkinson’s symptoms return despite ongoing medication — as well as other functional outcome measures.

“CVN424 activates key motor pathways, but not the neurons implicated in dyskinesias, a common side effect of dopaminergic Parkinson’s disease treatments,” said David H. Margolin, MD, PhD, senior vice president of clinical and translational medicine at Cerevance. “This selectivity should allow CVN424 to augment the positive effects of the current standard of care, levodopa, without exacerbating its side effects.”

More information about the trial, including participating clinical sites and contacts, is available here.

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SleepFit App Can Help Improve Daily Monitoring of Parkinson’s Motor Symptoms, Study Says

SleepFit monitor motor, Parkinson's

A new application for Android tablets, called SleepFit, may help to monitor the daily progression and response to treatment of motor symptoms associated with Parkinson’s disease, according to a study.

The application was evaluated in the study “A New Prospective, Home-Based Monitoring of Motor Symptoms in Parkinson’s Disease,” which was published in the Journal of Parkinson’s Disease.

Development and approval of new therapies for Parkinson’s disease has made it possible for many patients to retain a relatively active lifestyle. However, therapies do not have the same effect on all patients, so doses and treatment regimens need to be tailored to each patient’s needs.

In routine clinical practice, adjusting antiparkinsonian therapy relies on a combination of objective evaluation of symptoms and subjective analysis of the patient’s perspectives and experiences.

Clinicians have several clinical assessment tools to help evaluate the progression of Parkinson’s symptoms, including the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), which is commonly used to assess motor skills and symptoms.

Reviewing a patient’s perspectives and experiences can, in contrast, be a challenge rather than a helpful strategy. Some patients show subclinical cognitive dysfunction, even at early stages of the disease, which can impact the way they perceive their symptoms and surrounding environment.

“The importance of accurately assessing motor symptoms is pivotal in the clinical follow-up of patients with [Parkinson’s disease],” Pietro Luca Ratti, MD, PhD, researcher at the Neurocenter of Southern Switzerland, in Switzerland, and lead author of the study, said in a press release.

“In fact, physicians’ therapeutic decisions rely on the subjective information provided by a patient just as much as on the physical examination,” he added. “This is particularly important considering that antiparkinsonian medications need to be prescribed at their minimal effective doses to optimize mobility, while minimizing undesirable side effects.”

To inform clinical decision-making, researchers have developed SleepFit, an easy-to-use application (app) for Android tablets that can help patients record of their motor symptoms at given times of the day.

Using questions and tests to collect data on the patient’s motor and mobility skills, the app can be a source of subjective data that’s unbiased by patient recall. It integrates the items measuring motor symptoms from the Scales for Outcome in Parkinson Assessment Diary Card (m-SCOPA-DC) and a new Visual Analogue Scale assessing global mobility (m-VAS).

Researchers evaluated the potential of this new home-based tool and compared it to data collected from standard interviews among 42 patients with mild to moderate Parkinson’s who participated in the Sleep and Move study (NCT02723396).

All participants were asked to use the app four times a day for 14 days to record their symptoms: in the morning, 30 minutes after waking up and one hour after intake of dopaminergic medication; in the afternoon before taking dopaminergic medication; and in the evening before bedtime.

On the last day, patients completed the MDS-UPDRS parts II and IV questionnaires during an office consultation in order to assess their subjective recall of motor symptoms.

In general, data collected through the two methods showed similar results for overall scores, with m-VAS scores differing by 10% and SCOPA-DC by 18.3%. For single motor symptoms, such as involuntary movements, hand dexterity, walking, and ability to change position, results were also similar with differences of less than 20% between the two methods.

However, some individuals with more advanced disease, higher fatigue, or worse sleep quality had more difficulty recalling their symptoms.

Evaluation of patients’ self-reported information did not reveal a tendency towards positive or negative thinking about their symptoms. Still, 16.7% of the participants did tend to over- or underestimate their symptoms in their recollection.

“Knowing of a given patient’s tendency towards positive or negative thinking could thus critically inform clinical decision-making with regard to dopaminergic medication adjustment,” the researchers wrote.

These preliminary results demonstrate that the SleepFit app could be useful in routine clinical practice to reduce retrospective self-reporting bias, particularly for patients who have more advanced disease or cognitive impairments.

“We believe that a prospective approach would enable better clinical evaluation of patients’ subjective symptoms and, thus, better clinical management of the patients themselves,” said Ratti, who is also a researcher at the Pierre Zobda-Quitman University Hospital in Fort-de-France, Martinique.

“Although SleepFit is still under development, we believe it will eventually become a powerful tool to support patient evaluation in real-life conditions, encompassing motor and non-motor symptoms of [Parkinson’s disease],” he said.

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Algorithm for Wearable Sensors Ably Measures Tremor Severity as Patients Go About Daily Life, Study Says

algorithm for tremor sensors

Researchers have developed algorithms that work with wearable sensors to continuously monitor tremor, and estimate total tremor, in Parkinson’s patients as they go about their daily routines.

Analyses of sensor results using one algorithm, in particular, were similar to an established test assessing tremor without being dependent on the time the test is given.

The study, “Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements,” was published in Sensors.

Resting tremor, or the rhythmic shaking of muscles while relaxed, is among the motor symptoms of Parkinson’s disease (PD), and some patients also have active tremor, or shaking while engaged in voluntary muscle movement. Others motor symptoms are slowness of movement (bradykinesia), rigidity, and problems with posture, balance, and gait.

Currently, Parkinson’s motor symptoms are assessed using the Unified Parkinson’s Disease Rating Scale (UPDRS) Part III, scores of whose tests (like the finger-to-nose test) are evaluated by doctors. This test requires office visits where the tasks are performed, providing essentially only a snapshot of a person’s tremor experience in day-to-day life.

“A single, clinical examination in a doctor’s office often fails to capture a patient’s complete continuum of tremors in his or her routine daily life,” Behnaz Ghoraani, PhD, an assistant professor at Florida Atlantic University’s (FAU) Institute for Sensing and Embedded Network Systems (I-SENSE) and FAU’s Brain Institute (I-BRAIN), and lead author of the study, said in a press release.

“Wearable sensors, combined with machine-learning algorithms, can be used at home or elsewhere to estimate a patient’s severity rating of tremors based on the way that it manifests itself in movement patterns,” Ghoraani added.

Investigators developed two distinct machine-learning algorithms that, when combined with wearable sensors, could estimate total Parkinsonian tremor as patients performed a variety of free body movements.

In a collaboration between FAU, the Icahn School of Medicine at Mount Sinai and the University of Rochester Medical Center, researchers developed two algorithms: gradient tree boosting and long short-term memory (LSTM)-based deep learning. These tools can estimate tremor severity both in a resting and action state.  

A total of 24 Parkinson’s patients (10 women and 14 men; mean age, 58.9) had data on their movements recorded in two studies.

“In both protocols, the subjects stopped their medication the night before the experiment, and the experiment started in the morning. Yet, if the subjects were unable to withdraw their medication overnight, they came to the laboratory near the time of a scheduled dose of their PD medication,” the researchers noted.

For the experiment, doctors placed one motion sensor (consisting of a gyroscope and an accelerometer) on patients’ wrist and another on the ankle of the most disease-affected body side. Movement was recorded as they went about daily life activities.

Fifteen individuals were instructed to perform four rounds of specific activities, like walking, resting, eating, drinking, dressing, combing hair, putting groceries on a table, and cutting food. Motion data were recorded only while performing these activities.

Patients then took their routine “morning dose” of Parkinson’s medications. Later, they repeated the same activities at the start of every hour for up to four hours. For comparison purposes, standard UPDRS-based assessment were also given preformed every hour of the testing period before each round of daily life activities.

The other nine patients had their motion recorded continuously for the entire experiment. These people were instructed to cycle through six stations in a home-like setting, performing tasks like personal hygiene, dressing, eating, desk work, entertainment, and laundry. They took their medications after finishing a first round of activities, and when the treatment kicked in, they repeated the previous exercises. This set of activities lasted up to two hours, and the motor part of UPDRS (Part III) was assessed before and after the experiment.

“The data from the 15 subjects who performed rounds of specific [activities of daily living] was used to train the [artificial intelligence] models, and the data from the remaining nine subjects who performed continuous [daily life activities] were held out for testing the models,” the researchers wrote.

Results revealed the gradient tree boosting method estimated total tremor as well as resting tremor with high accuracy, and in most cases, with the same results found by doctors scoring UPDRS Part III.

Importantly, gradient tree boosting-based sensors were able to detect decreases in tremors after patients took their medication, even in cases where results did not match total tremor sub-scores from the UPDRS assessments. The LSTM-based algorithm was less effective in doing the same.

“These results indicate that our approach holds great promise in providing a full spectrum of the patients’ tremor from continuous monitoring of the subjects’ movement in their natural environment,” the researchers wrote.

“It is especially interesting that the method we developed successfully detected hand and leg tremors using only one sensor on the wrist and ankle, respectively,” said Murtadha Hssayeni, a study co-author and a PhD student at FAU’s Department of Computer and Electrical Engineering and Computer Science.

The new gradient tree boosting algorithm combined with wearable sensor technology resulted  “in the highest correlation … reported in the literature when using unconstrained body movements’ data,” the researchers wrote.

“This finding is important because our method is able to provide a better temporal resolution to estimate tremors to provide a measure of the full spectrum of tremor changes over time,” Ghoraani added.

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