Life Course Sleep Duration Trajectories and Risk and Age at Onset of Parkinson's Disease
Fang, Y.; Hardy, R.; Yaffe, K.; Little, S. J.; Tanner, C. M.; Leng, Y.
Show abstract
ImportanceInvestigating associations between life course sleep duration and Parkinsons disease (PD) may help clarify the role of sleep in early detection and/or prevention of PD. ObjectiveTo characterize life course sleep duration trajectories and their associations with PD risk and age at onset (AAO). DesignTwo ongoing online cohorts: Parkinsons Progression Markers Initiative (PPMI)-Online (discovery; started in 2021) and Fox Insight (FI; validation; started in 2018). SettingParticipants reported sleep duration across life stages, with PD-related follow-ups every three months. ParticipantsA convenience sample of 5,660 individuals with PD and 10,245 without PD from PPMI-Online, and 1,929 participants with PD from FI. ExposuresSelf-reported sleep duration from ages 18 to 80+ in PPMI-Online and 12 to 66+ in FI. Latent class growth analysis (LCGA) identified sleep trajectories. Main Outcomes and MeasuresPD risk and AAO were assessed using logistic and linear regression, adjusting for demographics, lifestyle, comorbidities. ResultsThe combined sample included 17,834 participants [age at sleep report 67.2{+/-}7.83 years; 9,735 (54.6%) female]. LCGA identified nine sleep trajectories in PPMI-Online, stable in early adulthood but diverging in midlife (stable, increasing, decreasing). Midlife sleep reductions (6-7 to [≤]5-6 hours/day: OR = 1.90, 95% CI 1.61-2.24, P < .001; 7-8 to [≤]6-7 hours/day: OR = 1.64, 95% CI 1.40-1.91, P < .001) and consistent short sleep (<=6 hours/day throughout adulthood: OR = 1.41, 95% CI 1.19-1.67, P < .001) demonstrated increased PD risk. Short sleep in early adulthood or midlife also had earlier AAO. The strongest effects were seen in those with [≤]6 hours/day throughout adulthood (PPMI-Online: {beta} = -2.45 years, 95% CI -3.33 to -1.56, P <.001) and those with a continuous decrease since adolescence (FI: {beta} = -4.23 years, 95% CI -5.52 to -2.93, P < .001). These effects were independent of rapid eye movement sleep behavior disorders. Conclusions and RelevanceSelf-reported short sleep in early adulthood and midlife sleep reductions are associated with increased PD risk and earlier AAO. Self-perceived midlife sleep reduction may be a marker for future PD. Persons with chronic short sleep may be candidates for preventive intervention. Key PointsQuestion: What is the relationship between life course sleep duration trajectories and the risk and age at onset of Parkinsons disease (PD)? Findings: In this cohort study including 7,589 participants with PD and 10,245 without PD, short sleep duration in early adulthood or decreasing sleep duration after age 50 were associated with an increased risk and earlier age at onset of PD. Meaning: Short sleep duration over the life course, even in early adulthood (< 40 years), may contribute to risk of late-life PD; declining sleep duration after age 50 is a potential prodromal sign of PD.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Age-related differences in the association between REM sleep and the polygenic risk for Parkinson's disease 96%
- RBDAct: Home screening of REM sleep behaviour disorder based on wrist actigraphy in Parkinson’s patients 93%
- Development of Parkinson’s disease and its relationship with incidentally-discovered white matter disease and covert brain infarction in a real world cohort 92%
Similar papers in this journal
- Association of sleep abnormalities in older adults with risk of developing Parkinson’s disease 96%
- Wake and non-rapid eye movement sleep dysfunction is associated with colonic neuropathology in Parkinson’s disease 93%
- EEG-based Machine Learning Models for the Prediction of Phenoconversion Time and Subtype in iRBD 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.