Dataset Information

PREDICT-PD: identifying risk of Parkinson's disease in the community: methods and baseline results.

ABSTRACT:

Objectives

To present methods and baseline results for an online screening tool to identify increased risk for Parkinson's disease (PD) in the UK population.

Methods

Risk estimates for future PD were derived from the results of a systematic review of risk factors and early features of PD. Participants aged 60-80 years without PD were recruited by self-referral. They completed an online survey (including family history, non-motor symptoms and lifestyle factors), a keyboard-tapping task and the University of Pennsylvania Smell Identification Test. Risk scores were calculated based on survey answers. Preliminary support for the validity of this algorithm was assessed by comparing those estimated to be higher risk for PD with those at lower risk using proxies, including smell loss, REM-sleep behaviour disorder and reduced tapping speed, and by assessing associations in the whole group.

Results

1324 eligible participants completed the survey and 1146 undertook the keyboard-tapping task. Smell tests were sent to 1065 participants. Comparing the 100 highest-risk participants and 100 lowest-risk participants, median University of Pennsylvania Smell Identification Test scores were 30/40 versus 33/40 (p<0.001), mean number of key taps in 30 s were 55 versus 58 (p=0.045), and 24% versus 10% scored above cut-off for REM-sleep behaviour disorder (p=0.008). Regression analyses showed increasing risk scores were associated with worse scores in the three proxies across the whole group (p?0.001).

Conclusions

PREDICT-PD is the first study to systematically combine risk factors for PD in the general population. Validity to predict risk of PD will be tested through longitudinal follow-up of incident PD diagnosis.

SUBMITTER: Noyce AJ

PROVIDER: S-EPMC3888633 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Publications

PREDICT-PD: identifying risk of Parkinson's disease in the community: methods and baseline results.

Noyce Alastair J AJ Bestwick Jonathan P JP Silveira-Moriyama Laura L Hawkes Christopher H CH Knowles Charles H CH Hardy John J Giovannoni Gavin G Nageshwaran Saiji S Osborne Curtis C Lees Andrew J AJ Schrag Anette A

Journal of neurology, neurosurgery, and psychiatry 20130704 1

<h4>Objectives</h4>To present methods and baseline results for an online screening tool to identify increased risk for Parkinson's disease (PD) in the UK population.<h4>Methods</h4>Risk estimates for future PD were derived from the results of a systematic review of risk factors and early features of PD. Participants aged 60-80 years without PD were recruited by self-referral. They completed an online survey (including family history, non-motor symptoms and lifestyle factors), a keyboard-tapping ...[more]

PMID: 23828833

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Project description:BackgroundA number of early features can precede the diagnosis of Parkinson's disease (PD).ObjectiveTo test an online, evidence-based algorithm to identify risk indicators of PD in the UK population.MethodsParticipants aged 60 to 80 years without PD completed an online survey and keyboard-tapping task annually over 3 years, and underwent smell tests and genotyping for glucocerebrosidase (GBA) and leucine-rich repeat kinase 2 (LRRK2) mutations. Risk scores were calculated based on the results of a systematic review of risk factors and early features of PD, and individuals were grouped into higher (above 15th centile), medium, and lower risk groups (below 85th centile). Previously defined indicators of increased risk of PD ("intermediate markers"), including smell loss, rapid eye movement-sleep behavior disorder, and finger-tapping speed, and incident PD were used as outcomes. The correlation of risk scores with intermediate markers and movement of individuals between risk groups was assessed each year and prospectively. Exploratory Cox regression analyses with incident PD as the dependent variable were performed.ResultsA total of 1323 participants were recruited at baseline and >79% completed assessments each year. Annual risk scores were correlated with intermediate markers of PD each year and baseline scores were correlated with intermediate markers during follow-up (all P values < 0.001). Incident PD diagnoses during follow-up were significantly associated with baseline risk score (hazard ratio = 4.39, P = .045). GBA variants or G2019S LRRK2 mutations were found in 47 participants, and the predictive power for incident PD was improved by the addition of genetic variants to risk scores.ConclusionsThe online PREDICT-PD algorithm is a unique and simple method to identify indicators of PD risk. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Project description:Mild cognitive impairment in Parkinson's disease is associated with progression to dementia (Parkinson's disease dementia) in a majority of patients. Determining structural imaging biomarkers associated with prodromal Parkinson's disease dementia may allow for the earlier identification of those at risk, and allow for targeted disease modifying therapies. One hundred and five non-demented subjects with newly diagnosed idiopathic Parkinson's disease and 37 healthy matched controls had serial 3 T structural magnetic resonance imaging scans with clinical and neuropsychological assessments at baseline, which were repeated after 18 months. The Movement Disorder Society Task Force criteria were used to classify the Parkinson's disease subjects into Parkinson's disease with mild cognitive impairment (n = 39) and Parkinson's disease with no cognitive impairment (n = 66). Freesurfer image processing software was used to measure cortical thickness and subcortical volumes at baseline and follow-up. We compared regional percentage change of cortical thinning and subcortical atrophy over 18 months. At baseline, cases with Parkinson's disease with mild cognitive impairment demonstrated widespread cortical thinning relative to controls and atrophy of the nucleus accumbens compared to both controls and subjects with Parkinson's disease with no cognitive impairment. Regional cortical thickness at baseline was correlated with global cognition in the combined Parkinson's disease cohort. Over 18 months, patients with Parkinson's disease with mild cognitive impairment demonstrated more severe cortical thinning in frontal and temporo-parietal cortices, including hippocampal atrophy, relative to those with Parkinson's disease and no cognitive impairment and healthy controls, whereas subjects with Parkinson's disease and no cognitive impairment showed more severe frontal cortical thinning compared to healthy controls. At baseline, Parkinson's disease with no cognitive impairment converters showed bilateral temporal cortex thinning relative to the Parkinson's disease with no cognitive impairment stable subjects. Although loss of both cortical and subcortical volume occurs in non-demented Parkinson's disease, our longitudinal analyses revealed that Parkinson's disease with mild cognitive impairment shows more extensive atrophy and greater percentage of cortical thinning compared to Parkinson's disease with no cognitive impairment. In particular, an extension of cortical thinning in the temporo-parietal regions in addition to frontal atrophy could be a biomarker in therapeutic studies of mild cognitive impairment in Parkinson's disease for progression towards dementia.

Project description:Background: The characterization of adolescents at high risk for developing depression has traditionally relied on the presence or absence of single risk factors. More recently, the use of composite risk scores combining information from multiple variables has gained attention in prognostic research in the field of mental health. We previously developed a sociodemographic composite score to estimate the individual level probability of depression occurrence in adolescence, the Identifying Depression Early in Adolescence Risk Score (IDEA-RS). Objectives: In this report, we present the rationale, methods, and baseline characteristics of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo), a study designed for in-depth examination of multiple neurobiological, psychological, and environmental measures associated with the risk of developing and with the presence of depression in adolescence, with a focus on immune/inflammatory and neuroimaging markers. Methods: Using the IDEA-RS as a tool for risk stratification, we recruited a new sample of adolescents enriched for low (LR) and high (HR) depression risk, as well as a group of adolescents with a currently untreated major depressive episode (MDD). Methods for phenotypic, peripheral biological samples, and neuroimaging assessments are described, as well as baseline clinical characteristics of the IDEA-RiSCo sample. Results: A total of 7,720 adolescents aged 14-16 years were screened in public state schools in Porto Alegre, Brazil. We were able to identify individuals at low and high risk for developing depression in adolescence: in each group, 50 participants (25 boys, 25 girls) were included and successfully completed the detailed phenotypic assessment with ascertainment of risk/MDD status, blood and saliva collections, and magnetic resonance imaging (MRI) scans. Across a variety of measures of psychopathology and exposure to negative events, there was a clear pattern in which either the MDD group or both the HR and the MDD groups exhibited worse indicators in comparison to the LR group. Conclusion: The use of an empirically-derived composite score to stratify risk for developing depression represents a promising strategy to establish a risk-enriched cohort that will contribute to the understanding of the neurobiological correlates of risk and onset of depression in adolescence.

Dataset Information

PREDICT-PD: identifying risk of Parkinson's disease in the community: methods and baseline results.

Objectives

Methods

Results

Conclusions

Publications

PREDICT-PD: identifying risk of Parkinson's disease in the community: methods and baseline results.

Similar Datasets

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