Assessment of heterogeneity among participants in the Parkinson's Progression Markers Initiative cohort using α-synuclein seed amplification: a cross-sectional study.

BACKGROUND: Emerging evidence shows that α-synuclein seed amplification assays (SAAs) have the potential to differentiate people with Parkinson's disease from healthy controls. We used the well characterised, multicentre Parkinson's Progression Markers Initiative (PPMI) cohort to further assess the diagnostic performance of the α-synuclein SAA and to examine whether the assay identifies heterogeneity among patients and enables the early identification of at-risk groups. METHODS: This cross-sectional analysis is based on assessments done at enrolment for PPMI participants (including people with sporadic Parkinson's disease from LRRK2 and GBA variants, healthy controls, prodromal individuals with either rapid eye movement sleep behaviour disorder (RBD) or hyposmia, and non-manifesting carriers of LRRK2 and GBA variants) from 33 participating academic neurology outpatient practices worldwide (in Austria, Canada, France, Germany, Greece, Israel, Italy, the Netherlands, Norway, Spain, the UK, and the USA). α-synuclein SAA analysis of CSF was performed using previously described methods. We assessed the sensitivity and specificity of the α-synuclein SAA in participants with Parkinson's disease and healthy controls, including subgroups based on genetic and clinical features. We established the frequency of positive α-synuclein SAA results in prodromal participants (RBD and hyposmia) and non-manifesting carriers of genetic variants associated with Parkinson's disease, and compared α-synuclein SAA to clinical measures and other biomarkers. We used odds ratio estimates with 95% CIs to measure the association between α-synuclein SAA status and categorical measures, and two-sample 95% CIs from the resampling method to assess differences in medians between α-synuclein SAA positive and negative participants for continuous measures. A linear regression model was used to control for potential confounders such as age and sex. FINDINGS: This analysis included 1123 participants who were enrolled between July 7, 2010, and July 4, 2019. Of these, 545 had Parkinson's disease, 163 were healthy controls, 54 were participants with scans without evidence of dopaminergic deficit, 51 were prodromal participants, and 310 were non-manifesting carriers. Sensitivity for Parkinson's disease was 87·7% (95% CI 84·9-90·5), and specificity for healthy controls was 96·3% (93·4-99·2). The sensitivity of the α-synuclein SAA in sporadic Parkinson's disease with the typical olfactory deficit was 98·6% (96·4-99·4). The proportion of positive α-synuclein SAA was lower than this figure in subgroups including LRRK2 Parkinson's disease (67·5% [59·2-75·8]) and participants with sporadic Parkinson's disease without olfactory deficit (78·3% [69·8-86·7]). Participants with LRRK2 variant and normal olfaction had an even lower α-synuclein SAA positivity rate (34·7% [21·4-48·0]). Among prodromal and at-risk groups, 44 (86%) of 51 of participants with RBD or hyposmia had positive α-synuclein SAA (16 of 18 with hyposmia, and 28 of 33 with RBD). 25 (8%) of 310 non-manifesting carriers (14 of 159 [9%] LRRK2 and 11 of 151 [7%] GBA) were positive. INTERPRETATION: This study represents the largest analysis so far of the α-synuclein SAA for the biochemical diagnosis of Parkinson's disease. Our results show that the assay classifies people with Parkinson's disease with high sensitivity and specificity, provides information about molecular heterogeneity, and detects prodromal individuals before diagnosis. These findings suggest a crucial role for the α-synuclein SAA in therapeutic development, both to identify pathologically defined subgroups of people with Parkinson's disease and to establish biomarker-defined at-risk cohorts. FUNDING: PPMI is funded by the Michael J Fox Foundation for Parkinson's Research and funding partners, including: Abbvie, AcureX, Aligning Science Across Parkinson's, Amathus Therapeutics, Avid Radiopharmaceuticals, Bial Biotech, Biohaven, Biogen, BioLegend, Bristol-Myers Squibb, Calico Labs, Celgene, Cerevel, Coave, DaCapo Brainscience, 4D Pharma, Denali, Edmond J Safra Foundation, Eli Lilly, GE Healthcare, Genentech, GlaxoSmithKline, Golub Capital, Insitro, Janssen Neuroscience, Lundbeck, Merck, Meso Scale Discovery, Neurocrine Biosciences, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, VanquaBio, Verily, Voyager Therapeutics, and Yumanity.

The triple aim of clinical research.

The proposed triple aim of health care-enhanced patient experience, improved population health, and reduced per capita costs-can be applied to clinical research. A triple aim for clinical research would (1) improve the individual research participant's experience; (2) promote the health of populations; and (3) reduce per capita costs of clinical research. Such an approach is possible by designing trials around the needs of participants rather than sites, embracing digital measures of health, and advancing decentralized studies. Recent studies, including those evaluating therapies for COVID-19, have demonstrated the value of such an approach. Accelerating the adoption of these methods can help fulfill this new triple aim of clinical research.

Robust Bayesian inference for multivariate longitudinal data by using normal/independent distributions.

Many randomized clinical trials collect multivariate longitudinal measurements in different scales, for example, binary, ordinal, and continuous. Multilevel item response models are used to evaluate the global treatment effects across multiple outcomes while accounting for all sources of correlation. Continuous measurements are often assumed to be normally distributed. But the model inference is not robust when the normality assumption is violated because of heavy tails and outliers. In this article, we develop a Bayesian method for multilevel item response models replacing the normal distributions with symmetric heavy-tailed normal/independent distributions. The inference is conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in BUGS language. Our proposed method is evaluated by simulation studies and is applied to Earlier versus Later Levodopa Therapy in Parkinson's Disease study, a motivating clinical trial assessing the effect of Levodopa therapy on the Parkinson's disease progression rate.

Interrater agreement in the assessment of motor manifestations of Huntington's disease.

With prospects improving for experimental therapeutics aimed at postponing the onset of illness in preclinical carriers of the Huntington's disease (HD) gene, we assessed agreement among experienced clinicians with respect to the motor manifestations of HD, a relevant outcome measure for preventive trials in this population. Seventy-five clinicians experienced in the evaluation of patients with early HD and six non-clinicians were shown a videotape compiled from the film archives of the United States-Venezuela Collaborative HD Research Project. Observers were asked to rate a 2-3-minute segment of the motor examination for each of 17 at-risk subjects. The rating scale ranged from 0 (normal) to 4 (unequivocal extrapyramidal movement disorder characteristic of HD). As measured by a weighted kappa statistic, there was substantial agreement among the 75 clinicians in the judgment of unequivocal motor abnormalities comparing scale ratings of 4 with ratings that were not 4 (weighted kappa = 0.67; standard error (SE) = 0.09). Agreement among the non-clinicians was only fair (weighted kappa = 0.28; SE = 0.10). Even under the artificial conditions of a videotape study, experienced clinicians show substantial agreement about the signs that constitute the motor manifestations of illness in subjects at risk for HD. We expect these findings to translate to a similar level of interobserver agreement in the clinical trial setting involving experienced investigators examining live patients.