Tuesday, 17 April 2018

Changing Tack

In a very wide ranging and well written review, two leaders of the Parkinson’s field lay down set the terms of engagement and (to mix my combative metaphors), lay down the gauntlet in the quest to find disease modifying treatments.

After defining three kinds of ‘disease modification’ (neuroprotection: preventing the progression of PD; neurorescue: repairing damaged brain cells; neurorestoration: replacing what has been lost), they provide a brief but comprehensive overview of why every attempt so far has failed, from both a mechanistic perspective as well as a methodological perspective.

The key messages to take from this article are thus:
1.     We really need to stop thinking of Parkinson’s as a single condition. As well as many different ways that people experience their Parkinson’s (the different phenotypes), there are also many different molecular and cellular processes. Each person will likely have a different milieu of process that results in their particular phenotype.
2.     Once we can do this, we can start approaching disease modification in a more personalised way. This means recruiting the people with the right kind of Parkinson’s, at the right time, to the right study. We need to become much more sophisticated in the way we study new treatments, probably recruiting very specific groups of participants, for example people with particular genetic changes related to their Parkinson’s, and testing cocktails of treatments, rather than our current blunderbuss approach.
3.     Given that Parkinson’s is a complex, and slowly progressive condition, we must have better progression markers on which to design our studies.
This is going to require a lot of work, with the Parkinson’s population, scientists, clinicians, and industry. In order to get the highly effective treatments that our oncologists have avaiable to them, we need to change the terms of warfare.

What can we all do in the mean time? Pay attention to these three battles. Not necessarily throw the baby out with the bathwater if a particular study doesn’t prove miraculous (which it almost certainly won’t). And if you have Parkinson’s: exercise more!

At PREDICT-PD towers, we strongly believe that identifying the disease earlier, in the prodromal phase, will bring us closer to many of these objectives. Earlier disease is more likely to be simpler disease; it may be possible to identify early phenotypes that map onto particular pathological pathways; we have an opportunity to identify biomarkers that predict the development of established Parkinson’s, thus giving an ideal method of proving efficacy of new treatments.


Disease modification in Parkinson's Disease: Current approaches, challenges, and future considerations.

Anthony E Lang and Alberto J Espay.

Mov Disord, 2018 vol. 386 p. 896.


The greatest unmet therapeutic need in Parkinson's disease is the development of treatment that slows the relentless progression of the neurodegenerative process. The concept of "disease modification" encompasses intervention types ranging from those designed to slow the underlying degeneration to treatments directed at regenerating or replacing lost neurons. To date all attempts to develop effective disease-modifying therapy have failed. Many reasons have been proposed for these failures including our rudimentary understanding of disease pathogenesis and the assumption that each targeted mechanisms of disease apply to most patients with the same clinical diagnosis. Here we review all aspects of this broad field including general concepts and past challenges followed by a discussion of treatment approaches under the following 4 categories: (1) α-synuclein, (2) pathogenic mechanisms distinct from α-synuclein (most also potentially triggered by α-synuclein toxicity), (3) non-SNCA genetic subtypes of "PD," and (4) possible disease-modifying interventions not directly influencing the underlying PD pathobiology. We emphasize treatments that are currently under active clinical development and highlight a wide range of important outstanding questions and concerns that will need to be considered to advance the field of disease modification in PD. Critically, it is unknown whether the dysfunctional molecular pathways/organelles amenable to modification occur in a sequential fashion across most clinically affected individuals or manifest differentially in independent molecular subtypes of PD. It is possible that there is no "order of disruption" applicable to most patients but, rather, "type of disruption" applicable to subtypes dependent on unknown factors, including genetic variability and other causes for heterogeneity in PD. Knowing when (early vs late), which (eg, synaptic transmission, endosomal sorting and maturation, lysosomal degradation, mitochondrial biogenesis), and in whom (PD subtype) specific disrupted cell pathways are truly pathogenic versus compensatory or even protective, will be important in considering the use of single or combined ("cocktails") putative disease-modifying therapies to selectively target these processes. Beyond the current phase 2 or 3 studies underway evaluating treatments directed at oxidative stress (inosine), cytosolic Ca2+ (isradipine), iron (deferiprone), and extracellular α-synuclein (passive immunization), and upcoming trials of interventions affecting c-Abl, glucagon-like peptide-1, and glucocerebrosidase, it might be argued that further trials in populations not enriched for the targeted pathogenic process are doomed to repeat the failures of the past. © 2018 International Parkinson and Movement Disorder Society.

Friday, 6 April 2018

Costing the earth

We know that Parkinson’s exacts a heavy price. The individual has a raft of motor and non-motor symptoms that significantly reduce their quality of life. Their loved ones share that burden, and research has shown that their quality of life of suffers greatly, too.

To use they hackneyed phrase, “in these times of austerity”, it is vital to legitamise and acount for every penny spent. In order to pursuade people (more explicitly, funders) of the need to provide a service, talking about loss of quality of life is not enough. It all comes down to the ‘bottom line’.

We heartily welcome the comprehensive and up to date analysis of the cost of Parkinson’s in the UK. The authors (including Professor Anette Schrag, one of the leading investigators of the PREDICT-PD study) report the results of a review of UK national GP and health records from 1993 – 2013. They compared the health service use of everyone 30 years or older who were given a new diagnosis of Parkinson’s during this 20-year period, with people of the same age, gender and location (attend the same GP surgery).

Their results are both astounding and unsuprising. GP and hospital care costs £5000 per year on average for people with Parkinson’s, compared to only £2000 per year for the controls. Furthermore, as the condition became more advanced, so did the costs associated with it. These costs were due to GP visits, outpatient hospital visits, inpatient care, A&E attendances and medications.

What this study brings is an up to date assessment of the cost of Parkinson’s. When considering the number of people with the condition is likely to increase, this represents an invaluable method of identifying how crucial it is to provide better, more efficient services to the Parkinson’s community. It also underscores just how important it is to be able to provide a disease modifying treatment, that will reduce the rate of progression to the more advance, more expensive stages of the disease.

What this study was unable to account for is the non-NHS based costs. These have recently been estimated at around £16,500 per person per year: through lost earnings, time off work, and other out-of-pocket expenses (see figure – from Parkinson’s UK). Including all of these brings the average cost of PD for British society to about £25,000 per person per year!

Financial cost of living with Parkinson's including out of pocket expenses



Short- and long-term cost and utilization of health care resources in Parkinson's disease in the UK.

Mov Disord. 2018 Mar 30;12(12):956.

BACKGROUND:There is currently no robust long-term data on costs of treating patients with Parkinson's disease. The objective of this study was to report levels of health care utilization and associated costs in the 10 years after diagnosis among PD patients in the United Kingdom.

METHODS:We undertook a retrospective population-based cohort study using linked data from the UK Clinical Practice Research Datalink and Hospital Episode Statistics databases. Total health care costs of PD patients were compared with those of a control group of patients without PD selected using 1:1 propensity score matching based on age, sex, and comorbidity.

RESULTS:Between 1994 and 2013, 7271 PD patients who met study inclusion criteria were identified in linked Clinical Practice Research Datalink-Hospital Episode Statistics; 7060 were matched with controls. The mean annual health care cost difference (at 2013 costs) between PD patients and controls was £2471 (US$3716) per patient in the first year postdiagnosis (P < 0.001), increasing to £4004 (US$6021) per patient (P < 0.001) 10 years following diagnosis because of higher levels of use across all categories of health care utilization. Costs in patients with markers of advanced PD (ie, presence of levodopa-equivalent daily dose > 1100 mg, dyskinesias, falls, dementia, psychosis, hospital admission primarily due to PD, or nursing home placement) were on average higher by £1069 (US$1608) per patient than those with PD without these markers.

CONCLUSIONS:This study provides comprehensive estimates of health care costs in PD patients based on routinely collected data. Health care costs attributable to PD increase in the year following diagnosis and are higher for patients with indicators of advanced disease. © 2018 International Parkinson and Movement Disorder Society.

Wednesday, 4 April 2018

Application of the Movement Disorder Society prodromal criteria in healthy G2019S-LRRK2 carriers

This is an interesting application of the MDS Task Force criteria for prodromal PD. Here the Ashkenazi Jewish consortium applied the MDS criteria to non-manifesting carriers and non-carriers of the G2019S-LRRK2 mutation. 

Despite relatively small numbers of participants being diagnosed with PD during the follow-up period, they report impressive specificity of the criteria for idenitfying those in the pre-diagnostic (prodromal) phase of PD. 8 of the 10 patients (80%) that were diagnosed during follow-up were in the highest probability group at baseline. Unfortunately I could not find the raw data in the manuscript to determine how they arrived at 91.8 % specificity but working backwards from the reported specificity and positive predictive value I think there must have 9 out of 110 (8.2%) who were in the high probability group  at baseline, but did not subsequently get diagnosed with PD during follow-up. It will be interesting to see what happens to those participants with more time.

The positive predictive value (PPV) tells us about the probability of getting a diagnosis of PD over time if you are in the highest risk group at baseline. Based on the follow-up so far, 47% of people that screened positive at baseline have been diagnosed with PD. Whilst that's approximately 50:50 - if you imagine a scenario where a preventive treatment were available and the toxicity or side effects of that treatment were low then perhaps 50% PPV for a screening test is not widely inappropriate. Of course these data only apply to carriers of the G2019S LRRK2 mutation which in itself puts people at significantly higher risk of PD. The MDS Task Force criteria have been applied to other at risk cohorts, but we are in the final stages of applying them to the PREDICT-PD population-based cohort... watch this space.

- Alastair Noyce

Mov Disord. 2018 Mar 30. doi: 10.1002/mds.27342. [Epub ahead of print]
Mirelman A, Saunders-Pullman R, Alcalay RN, Shustak S, Thaler A, Gurevich T, Raymond D, Mejia-Santana H, Orbe Reilly M, Ozelius L, Clark L, Gana-Weisz M, Bar-Shira A, Orr-Utreger A, Bressman SB, Marder K, Giladi N; AJ LRRK2 Consortium.


BACKGROUND: In 2015, the International Parkinson and Movement Disorder Society Task Force recommended research criteria for the estimation of prodromal PD.

OBJECTIVES: We aimed to evaluate, for the first time, the criteria in first-degree relatives of Ashkenazi Jewish G2019S-LRRK2 PD patients, who are considered a population at risk for developing PD, and assess the sensitivity and specificity of the criteria in identifying phenoconverters.

METHODS: Participants were evaluated longitudinally over a period of 5 years (average follow-up: 49.2 ± 12.3 months). Likelihood ratios and probability estimations were calculated based on the International Parkinson and Movement Disorder Society Research Criteria for Prodromal Parkinson's Disease markers and examined for each assessment point.

RESULTS: One hundred twenty healthy carriers (49.53 ± 13.4 years; 54% female) and 111 healthy noncarriers (48.43 ± 15.79 years; 49% female) participated in this study. Probability scores were significantly higher in healthy carriers than healthy noncarriers (P < 0.0001). Of the 20 participants (8.6%) who met criteria for probable prodromal PD at baseline, 17 were healthy carriers. Participants who reached the threshold were older (P < 0.0001), had higher UPDRS-III (P < 0.001), lower cognitive function (P = 0.001), and more nonmotor symptoms (P < 0.0001), compared to those who did not. Ten participants were diagnosed with incident PD within 5 years from baseline resulting in a specificity of 91.82% (95% confidence interval: 86.69-96.94), sensitivity of 80% (95% confidence interval: 55.21-100), positive predictive value of 47.06% (95% confidence interval: 23.33-70.79), and negative predictive value of 98.06% (95% confidence interval: 95.39-100). All 10 phenoconvertors were G2019S-LRRK2 carriers.

CONCLUSIONS: The results showed the utility of using the criteria and high sensitivity and specificity in identifying prodromal PD in this high-risk unique cohort. These results may be valuable for future disease modification clinical trials. © 2018 International Parkinson and Movement Disorder Society.

 © 2018 International Parkinson and Movement Disorder Society.

Sunday, 1 April 2018

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score

Smart phone technology and app-based approaches for measuring PD symptoms and signs have been around for some time and there are seemingly innumerable companies and groups that all suggest that their approach is the best...

The results of this recent study published in JAMA Neurology do however look really impressive. I have always felt that given the heterogeneity of PD (motor and non-motor), a good measuring device will take account of multiple domains. This app measures voice, gait, balance, reaction time and finger tapping (which you might argue are exclusively motor)... but the results suggest that it does measure these really well. 

The hardest thing is to develop a tool that captures fluctuation well and objectively. Some purpose-built devices do (the PKG for example), but it is impressive to see these kind of results through utilisation of the standard hardware that comes with a smart phone. Furthermore the objective response to dopaminergic therapy is substantial and apparently clinically meaningful. 

Will we see this app used in clinical trials in the coming years... I expect we might!

- Alastair Noyce

JAMA Neurol. 2018 Mar 26. doi: 10.1001/jamaneurol.2018.0809. [Epub ahead of print]
Zhan A, Mohan S, Tarolli C, Schneider RB, Adams JL, Sharma S, Elson MJ, Spear KL, Glidden AM, Little MA, Terzis A, Dorsey ER, Saria S.


IMPORTANCE: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.

OBJECTIVES: To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy.

DESIGN, SETTING, AND PARTICIPANTS: This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months.

MAIN OUTCOMES AND MEASURES: Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication.

RESULTS: The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy.

CONCLUSIONS AND RELEVANCE: Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.

Friday, 30 March 2018

Keep it simple..

Predictive medicine is becoming increasingly complex, with predictive models which incorporate both clinical information and a huge range of biomarkers. Biomarkers commonly used in neurodegenerative disease include molecules in bodily fluids such as saliva, blood and spinal fluid and genetic data from these sources, scans for both structural aspects of the brain and molecular studies which can quantify the amount of protein build up in the brain. These biomarkers vary in how invasive and risky they are to collect and while they may help us understand the disease process, many people are uncomfortable with going through an invasive procedure such as lumbar puncture. 

The basis of the current PREDICT-PD model is to use participant survey to collect clinical information to predict risk. The study below, from a team in Quebec, also uses a clinical approach. They looked at a group of people with Parkinsons' disease and used an 8 item scoring scale including age, evidence of cognitive impairment at baseline, motor features such as freezing or falls, drop in blood pressure on standing and features of rapid eye movement sleep behaviour disorder. 

Interestingly, the predictive value of this model seems to be as good as studies which include biomarker values such as amyloid protein levels in the spinal fluid. Biomarker studies in this area will continue to be important, especially as they hold the hope of elucidating the molecular processes which can be interrupted to disrupt the progression of disease. But this is a good reminder of the power that can be harnessed from information which is available simply by asking questions of and examining our patients. 


Office-Based Screening for Dementia in Parkinson Disease: The Montreal Parkinson Risk of Dementia Scale in 4 Longitudinal Cohorts.
Parkinson disease dementia dramatically increases mortality rates, patient expenditures, hospitalization risk, and caregiver burden. Currently, predicting Parkinson disease dementia risk is difficult, particularly in an office-based setting, without extensive biomarker testing.
To appraise the predictive validity of the Montreal Parkinson Risk of Dementia Scale, an office-based screening tool consisting of 8 items that are simply assessed.
This multicenter study (Montreal, Canada; Tottori, Japan; and Parkinson Progression Markers Initiative sites) used 4 diverse Parkinson disease cohorts with a prospective 4.4-year follow-up. A total of 717 patients with Parkinson disease were recruited between May 2005 and June 2016. Of these, 607 were dementia-free at baseline and followed-up for 1 year or more and so were included. The association of individual baseline scale variables with eventual dementia risk was calculated. Participants were then randomly split into cohorts to investigate weighting and determine the scale's optimal cutoff point. Receiver operating characteristic curves were calculated and correlations with selected biomarkers were investigated.
Dementia, as defined by Movement Disorder Society level I criteria.
Of the 607 patients (mean [SD] age, 63.4 [10.1]; 376 men [62%]), 70 (11.5%) converted to dementia. All 8 items of the Montreal Parkinson Risk of Dementia Scale independently predicted dementia development at the 5% significance level. The annual conversion rate to dementia in the high-risk group (score, >5) was 14.9% compared with 5.8% in the intermediate group (score, 4-5) and 0.6% in the low-risk group (score, 0-3). The weighting procedure conferred no significant advantage. Overall predictive validity by the area under the receiver operating characteristic curve was 0.877 (95% CI, 0.829-0.924) across all cohorts. A cutoff of 4 or greater yielded a sensitivity of 77.1% (95% CI, 65.6-86.3) and a specificity of 87.2% (95% CI, 84.1-89.9), with a positive predictive value (as of 4.4 years) of 43.90% (95% CI, 37.76-50.24) and a negative predictive value of 96.70% (95% CI, 95.01-97.85). Positive and negative likelihood ratios were 5.94 (95% CI, 4.08-8.65) and 0.26 (95% CI, 0.17-0.40), respectively. Scale results correlated with markers of Alzheimer pathology and neuropsychological test results.
Despite its simplicity, the Montreal Parkinson Risk of Dementia Scale demonstrated predictive validity equal or greater to previously described algorithms using biomarker assessments. Future studies using head-to-head comparisons or refinement of weighting would be of interest.

Changing Tack

In a very wide ranging and well written review, two leaders of the Parkinson’s field lay down set the terms of engagement and (to mix my co...