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Parkinson's disease determinants, prediction and gene-environment interactions in the UK Biobank.

ABSTRACT: OBJECTIVE:To systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson's disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate whether existing risk prediction algorithms are improved by the inclusion of genetic risk scores. METHODS:We identified individuals with an incident diagnosis of PD (n=1276) and controls (n=500?406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. We constructed polygenic risk scores (PRSs) using external weights and selected the best PRS from a subset of the cohort (30%). The PRS was used in a separate testing set (70%) to examine gene-environment interactions and compare predictive models for PD. RESULTS:Strong evidence of association (false discovery rate <0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, daytime somnolence, epilepsy and earlier menarche. Individuals with the highest 10% of PRSs had increased risk of PD (OR 3.37, 95%?CI 2.41 to 4.70) compared with the lowest risk decile. A higher PRS was associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm led to a modest improvement in model performance. We found evidence of an interaction between the PRS and diabetes. INTERPRETATION:Here, we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity of a PRS and to demonstrate a novel gene-environment interaction, whereby the effect of diabetes on PD risk appears to depend on background genetic risk for PD.

SUBMITTER: Jacobs BM

PROVIDER: S-EPMC7509524 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Parkinson's disease determinants, prediction and gene-environment interactions in the UK Biobank.

Jacobs Benjamin Meir BM Belete Daniel D Bestwick Jonathan J Blauwendraat Cornelis C Bandres-Ciga Sara S Heilbron Karl K Dobson Ruth R Nalls Mike A MA Singleton Andrew A Hardy John J Giovannoni Gavin G Lees Andrew John AJ Schrag Anette-Eleonore AE Noyce Alastair J AJ

Journal of neurology, neurosurgery, and psychiatry 20201001 10

<h4>Objective</h4>To systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson's disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate whether existing risk prediction algorithms are improved by the inclusion of genetic risk scores.<h4>Methods</h4>We identified individuals with an incident diagnosis of PD (n=1276) and controls (n=500 406) in UK Biobank. We determined the association of risk factors ...[more]

PMID: 32934108

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Project description:BACKGROUND:Impaired lung health represents a significant burden on global health, including chronic obstructive pulmonary disease (COPD) and lung cancer. Given its global health impact, it is important to understand the determinants of impaired lung function and its relation to lung cancer risk independent of smoking. However, to date, no study has evaluated determinants of impaired lung function in a cohort exclusively of never-smokers, who also represent a growing proportion of all lung cancers. METHODS:A total of 222,274 never-smokers with reproducible spirograms were identified in the UK Biobank population-based cohort and included in the analysis. Baseline volumetric measures of lung function, including forced expiratory volume in 1-s (FEV1) and forced vital capacity (FVC), were used to define lung function impairment. Determinants of impaired lung function were evaluated using Poisson regression with robust variance estimation. The added value of lung function in lung cancer prediction was evaluated using Fine and Gray regression accounting for the competing risk of all-cause mortality. FINDINGS:Lung function impairment was associated with low birthweight, ambient air pollution (PM2·5 ?g/mm3), and overweight, after adjustment for other important risk factors. We observed modest improvement in discrimination by adding lung function to our lung cancer prediction model for never-smokers. The highest optimism-corrected AUC at 3 (0·700, 95% CI: 0·654-0·734) and 5?years (0·694, 95% CI: 0·658-0·736) included FEV1 (% of GLI predicted FEV1), while the highest AUC at 7?years was based on the inclusion of FEV1/FVC (0·722, 95% CI: 0·687-0·762). INTERPRETATION:We identified several modifiable risk factors associated with increased risk of lung function impairment among lifetime never-smokers in UKB. We achieved moderate discrimination for lung cancer risk-prediction for never-smokers, and found modest improvement with the inclusion of lung function. FUND: This study was supported by a Canada Research Chair to RJH.

Dataset Information

Parkinson's disease determinants, prediction and gene-environment interactions in the UK Biobank.

Publications

Parkinson's disease determinants, prediction and gene-environment interactions in the UK Biobank.

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