Fahd Baig

Alpha‐synuclein RT‐QuIC in the CSF of patients with alpha‐synucleinopathies

We have developed a novel real‐time quaking‐induced conversion RT‐QuIC‐based assay to detect alpha‐synuclein aggregation in brain and cerebrospinal fluid from dementia with Lewy bodies and Parkinsons disease patients. This assay can detect alpha‐synuclein aggregation in Dementia with Lewy bodies and Parkinsons disease cerebrospinal fluid with sensitivities of 92% and 95%, respectively, and with an overall specificity of 100% when compared to Alzheimer and control cerebrospinal fluid. Patients with neuropathologically confirmed tauopathies (progressive supranuclear palsy; corticobasal degeneration) gave negative results. These results suggest that RT‐QuiC analysis of cerebrospinal fluid is potentially useful for the early clinical assessment of patients with alpha‐synucleinopathies.

Prodromal Parkinsonism and Neurodegenerative Risk Stratification in REM Sleep Behavior Disorder

Objectives Rapid eye movement (REM) sleep behavior disorder (RBD) is the most specific marker of prodromal alpha‐synucleinopathies. We sought to delineate the baseline clinical characteristics of RBD and evaluate risk stratification models. Methods Clinical assessments were performed in 171 RBD, 296 control, and 119 untreated Parkinsons (PD) participants. Putative risk measures were assessed as predictors of prodromal neurodegeneration, and Movement Disorders Society (MDS) criteria for prodromal PD were applied. Participants were screened for common leucine‐rich repeat kinase 2 (LRRK2)/glucocerebrosidase gene (GBA) gene mutations. Results Compared to controls, participants with RBD had higher rates of solvent exposure, head injury, smoking, obesity, and antidepressant use. GBA mutations were more common in RBD, but no LRRK2 mutations were found. RBD participants performed significantly worse than controls on Unified Parkinsons Disease Rating Scale (UPDRS)‐III, timed “get‐up‐and‐go”, Flamingo test, Sniffin Sticks, and cognitive tests and had worse measures of constipation, quality of life (QOL), and orthostatic hypotension. For all these measures except UPDRS‐III, RBD and PD participants were equally impaired. Depression, anxiety, and apathy were worse in RBD compared to PD participants. Stratification of people with RBD according to antidepressant use, obesity, and age altered the odds ratio (OR) of hyposmia compared to controls from 3.4 to 45.5. 74% (95% confidence interval [CI] 66%, 80%) of RBD participants met the MDS criteria for probable prodromal Parkinsons compared to 0.3% (95% CI 0.009%, 2%) of controls. Conclusions RBD are impaired across a range of clinical measures consistent with prodromal PD and suggestive of a more severe nonmotor subtype. Clinical risk stratification has the potential to select higher risk patients for neuroprotective interventions.

Parkinson's Disease Subtypes in the Oxford Parkinson Disease Centre (OPDC) Discovery Cohort.

Abstract Background: Within Parkinson’s there is a spectrum of clinical features at presentation which may represent sub-types of the disease. However there is no widely accepted consensus of how best to group patients. Objective: Use a data-driven approach to unravel any heterogeneity in the Parkinson’s phenotype in a well-characterised, population-based incidence cohort. Methods: 769 consecutive patients, with mean disease duration of 1.3 years, were assessed using a broad range of motor, cognitive and non-motor metrics. Multiple imputation was carried out using the chained equations approach to deal with missing data. We used an exploratory and then a confirmatory factor analysis to determine suitable domains to include within our cluster analysis. K-means cluster analysis of the factor scores and all the variables not loading into a factor was used to determine phenotypic subgroups. Results: Our factor analysis found three important factors that were characterised by: psychological well-being features; non-tremor motor features, such as posture and rigidity; and cognitive features. Our subsequent five cluster model identified groups characterised by (1) mild motor and non-motor disease (25.4%), (2) poor posture and cognition (23.3%), (3) severe tremor (20.8%), (4) poor psychological well-being, RBD and sleep (18.9%), and (5) severe motor and non-motor disease with poor psychological well-being (11.7%). Conclusion: Our approach identified several Parkinson’s phenotypic sub-groups driven by largely dopaminergic-resistant features (RBD, impaired cognition and posture, poor psychological well-being) that, in addition to dopaminergic-responsive motor features may be important for studying the aetiology, progression, and medication response of early Parkinson’s.

Delineating Nonmotor Symptoms in Early Parkinson's Disease and First-Degree Relatives

Nonmotor symptoms (NMS) are an important prodromal feature of Parkinsons disease (PD). However, their frequency, treatment rates, and impact on health‐related quality of life (HRQoL) in the early motor phase is unclear. Rates of NMS in enriched at‐risk populations, such as first‐degree PD relatives, have not been delineated. We assessed NMS in an early cohort of PD, first‐degree PD relatives and control subjects to address these questions. In total, 769 population‐ascertained PD subjects within 3.5 years of diagnosis, 98 first‐degree PD relatives, and 287 control subjects were assessed at baseline across the following NMS domains: (1) neuropsychiatric; (2) gastrointestinal; (3) sleep; (4) sensory; (5) autonomic; and (6) sexual. NMS were much more common in PD, compared to control subjects. More than half of the PD cases had hyposmia, pain, fatigue, sleep disturbance, or urinary dysfunction. NMS were more frequent in those with the postural instability gait difficulty phenotype, compared to the tremor dominant (mean total number of NMS 7.8 vs. 6.2; P < 0.001). PD cases had worse HRQoL scores than controls (odds ratio: 4.1; P < 0.001), with depression, anxiety, and pain being stronger drivers than motor scores. NMS were rarely treated in routine clinical practice. First‐degree PD relatives did not significantly differ in NMS, compared to controls, in this baseline study. NMS are common in early PD and more common in those with postural instability gait difficulty phenotype or on treatment. Despite their major impact on quality of life, NMS are usually under‐recognized and untreated.

Visual short-term memory deficits in REM sleep behaviour disorder mirror those in Parkinson's disease

Individuals with REM sleep behaviour disorder (RBD) are at high risk of Parkinson’s disease. Rolinski, Zokaei et al. show that they also display the same pattern of visual short-term memory deficits as patients with Parkinson’s disease, and suggest that this ‘fingerprint’ of memory impairment could be a prodromal disease marker.

Equating scores of the University of Pennsylvania Smell Identification Test and Sniffin' Sticks test in patients with Parkinson's disease

Background Impaired olfaction is an important feature in Parkinsons disease (PD) and other neurological diseases. A variety of smell identification tests exist such as “Sniffin’ Sticks” and the University of Pennsylvania Smell Identification Test (UPSIT). An important part of research is being able to replicate findings or combining studies in a meta-analysis. This is difficult if olfaction has been measured using different metrics. We present conversion methods between the: UPSIT, Sniffin’ 16, and Brief-SIT (B-SIT); and Sniffin’ 12 and Sniffin’ 16 odour identification tests. Methods We used two incident cohorts of patients with PD who were tested with either the Sniffin’ 16 (n = 1131) or UPSIT (n = 980) and a validation dataset of 128 individuals who took both tests. We used the equipercentile and Item Response Theory (IRT) methods to equate the olfaction scales. Results The equipercentile conversion suggested some bias between UPSIT and Sniffin’ 16 tests across the two groups. The IRT method shows very good characteristics between the true and converted Sniffin’ 16 (delta mean = 0.14, median = 0) based on UPSIT. The equipercentile conversion between the Sniffin’ 12 and 16 item worked well (delta mean = 0.01, median = 0). The UPSIT to B-SIT conversion showed evidence of bias but amongst PD cases worked well (mean delta = −0.08, median = 0). Conclusion We have demonstrated that one can convert UPSIT to B-SIT or Sniffin’ 16, and Sniffin’ 12 to 16 scores in a valid way. This can facilitate direct comparison between tests aiding future collaborative analyses and evidence synthesis.

Statins are underused in recent-onset Parkinson's disease with increased vascular risk: findings from the UK Tracking Parkinson's and Oxford Parkinson's Disease Centre (OPDC) discovery cohorts

Background Cardiovascular disease (CVD) influences phenotypic variation in Parkinsons disease (PD), and is usually an indication for statin therapy. It is less clear whether cardiovascular risk factors influence PD phenotype, and if statins are prescribed appropriately. Objectives To quantify vascular risk and statin use in recent-onset PD, and examine the relationship between vascular risk, PD severity and phenotype. Methods Cardiovascular risk was quantified using the QRISK2 calculator (high ≥20%, medium ≥10 and <20%, low risk <10%). Motor severity and phenotype were assessed using the Movement Disorder Society Unified PD Rating Scale (UPDRS) and cognition by the Montreal cognitive assessment. Results In 2909 individuals with recent-onset PD, the mean age was 67.5 years (SD 9.3), 63.5% were men and the mean disease duration was 1.3 years (SD 0.9). 33.8% of cases had high vascular risk, 28.7% medium risk, and 22.3% low risk, while 15.2% of cases had established CVD. Increasing vascular risk and CVD were associated with older age (p<0.001), worse motor score (p<0.001), more cognitive impairment (p<0.001) and worse motor phenotype (p=0.021). Statins were prescribed in 37.2% with high vascular risk, 15.1% with medium vascular risk and 6.5% with low vascular risk, which compared with statin usage in 75.3% of those with CVD. Conclusions Over 60% of recent-onset PD patients have high or medium cardiovascular risk (meriting statin usage), which is associated with a worse motor and cognitive phenotype. Statins are underused in these patients, compared with those with vascular disease, which is a missed opportunity for preventive treatment. Trial registration number GN11NE062, NCT02881099.

The borderland of neuromyelitis optica

Neuromyelitis optica (NMO), also known as Devic’s disease, is an emerging clinical and pathological entity originally thought to be a variant of multiple sclerosis. Characterised by episodes of demyelination confined to the optic nerve and spinal cord, the discovery in such patients of antibodies to the aquaporin-4 channel has been largely responsible for defining the phenotype to date. Recently it has become clear that there is a borderland where there are patients with optic neuritis-only and myelitis-only forms of the disease, and these may be seronegative in the early phase. We describe two cases of optic neuritis-only NMO, and explore the current understanding of the diagnosis and spectrum of NMO disorders.

Personality and addictive behaviours in early Parkinson's disease and REM sleep behaviour disorder.

Introduction Changes in personality have been described in Parkinsons disease (PD), with suggestion that those with established disease tend to be risk averse with a disinclination for addictive behaviour. However, little is known about the earliest and prodromal stages. Personality and its relationship with addictive behaviours can help answer important questions about the mechanisms underlying PD and addiction. Methods 941 population-ascertained PD subjects within 3.5 years of diagnosis, 128 patients with rapid eye movement sleep behaviour disorder (RBD) and 292 control subjects were fully characterised for motor symptoms, non-motor symptoms and across the following 5 personality domains: 1) neuroticism 2) extraversion 3) conscientiousness 4) agreeableness 5) openness using the Big Five Inventory. Results Patients with early PD were more neurotic (p < 0.001), less extraverted (p < 0.001) and less open than controls (p < 0.001). RBD subjects showed the same pattern of being more neurotic (p < 0.001), less extraverted (p = 0.03) and less open (p < 0.001). PD patients had smoked less (p = 0.02) and drunk less alcohol (p = 0.03) than controls, but caffeine beverage consumption was similar. Being more extraverted (p < 0.001), more open (p < 0.001), and less neurotic (p < 0.001) predicted higher alcohol use, while being more extravert (p = 0.007) and less agreeable (p < 0.001) was associated with smoking more. Conclusions A similar pattern of personality changes is seen in PD and RBD compared to a control population. Personality characteristics were associated with addictive behaviours, suggestive of a common link, but the lower rates of addictive behaviours before and after the onset of motor symptoms in PD persisted after accounting for personality.

What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm

The K-means algorithm is one of the most popular clustering algorithms in current use as it is relatively fast yet simple to understand and deploy in practice. Nevertheless, its use entails certain restrictive assumptions about the data, the negative consequences of which are not always immediately apparent, as we demonstrate. While more flexible algorithms have been developed, their widespread use has been hindered by their computational and technical complexity. Motivated by these considerations, we present a flexible alternative to K-means that relaxes most of the assumptions, whilst remaining almost as fast and simple. This novel algorithm which we call MAP-DP (maximum a-posteriori Dirichlet process mixtures), is statistically rigorous as it is based on nonparametric Bayesian Dirichlet process mixture modeling. This approach allows us to overcome most of the limitations imposed by K-means. The number of clusters K is estimated from the data instead of being fixed a-priori as in K-means. In addition, while K-means is restricted to continuous data, the MAP-DP framework can be applied to many kinds of data, for example, binary, count or ordinal data. Also, it can efficiently separate outliers from the data. This additional flexibility does not incur a significant computational overhead compared to K-means with MAP-DP convergence typically achieved in the order of seconds for many practical problems. Finally, in contrast to K-means, since the algorithm is based on an underlying statistical model, the MAP-DP framework can deal with missing data and enables model testing such as cross validation in a principled way. We demonstrate the simplicity and effectiveness of this algorithm on the health informatics problem of clinical sub-typing in a cluster of diseases known as parkinsonism.