Physical activity and risk of Parkinson’s disease and parkinsonism in a prospective population-based study (NEDICES)

Llamas‐Velasco, Sara; Contador, Israel; Méndez-Guerrero, Antonio; Romero, Carmen; Benito‐León, Julián; Villarejo-Galende, Alberto; Bermejo‐Pareja, Félix

doi:10.1016/j.pmedr.2021.101485

Cited by 9 publications

(9 citation statements)

References 30 publications

(42 reference statements)

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“…Our findings are in line with previous studies. The possible mechanisms related to these potential neuroprotective effects of physical activity for preventing NDs are reducing neuro-inflammation, insulin resistance, stress and anxiety [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

Aguayo

Zhang

Vaillant

et al. 2023

BMC Med Res Methodol

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Background In the older general population, neurodegenerative diseases (NDs) are associated with increased disability, decreased physical and cognitive function. Detecting risk factors can help implement prevention measures. Using deep neural networks (DNNs), a machine-learning algorithm could be an alternative to Cox regression in tabular datasets with many predictive features. We aimed to compare the performance of different types of DNNs with regularized Cox proportional hazards models to predict NDs in the older general population. Methods We performed a longitudinal analysis with participants of the English Longitudinal Study of Ageing. We included men and women with no NDs at baseline, aged 60 years and older, assessed every 2 years from 2004 to 2005 (wave2) to 2016–2017 (wave 8). The features were a set of 91 epidemiological and clinical baseline variables. The outcome was new events of Parkinson’s, Alzheimer or dementia. After applying multiple imputations, we trained three DNN algorithms: Feedforward, TabTransformer, and Dense Convolutional (Densenet). In addition, we trained two algorithms based on Cox models: Elastic Net regularization (CoxEn) and selected features (CoxSf). Results 5433 participants were included in wave 2. During follow-up, 12.7% participants developed NDs. Although the five models predicted NDs events, the discriminative ability was superior using TabTransformer (Uno’s C-statistic (coefficient (95% confidence intervals)) 0.757 (0.702, 0.805). TabTransformer showed superior time-dependent balanced accuracy (0.834 (0.779, 0.889)) and specificity (0.855 (0.0.773, 0.909)) than the other models. With the CoxSf (hazard ratio (95% confidence intervals)), age (10.0 (6.9, 14.7)), poor hearing (1.3 (1.1, 1.5)) and weight loss 1.3 (1.1, 1.6)) were associated with a higher DNN risk. In contrast, executive function (0.3 (0.2, 0.6)), memory (0, 0, 0.1)), increased gait speed (0.2, (0.1, 0.4)), vigorous physical activity (0.7, 0.6, 0.9)) and higher BMI (0.4 (0.2, 0.8)) were associated with a lower DNN risk. Conclusion TabTransformer is promising for prediction of NDs with heterogeneous tabular datasets with numerous features. Moreover, it can handle censored data. However, Cox models perform well and are easier to interpret than DNNs. Therefore, they are still a good choice for NDs.

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Section: Discussionmentioning

confidence: 99%

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

Aguayo

Zhang

Vaillant

et al. 2023

BMC Med Res Methodol

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“…The potential mechanisms for this neuroprotective effect of PA may include the increase in serum urate and the release of neurotrophic factors such as brain-derived neurotrophic factor and cerebral dopamine neurotrophic factor, and regulation of dopamine turnover [ 34 – 38 ]. Although a previous meta-analysis of 8 prospective studies revealed an inverse dose-response association between PA and the risk of developing PD among men, the results from some specific individual prospective studies were not statistically significant [ 9 – 16 ]. The discrepant results in previous studies may be attributed to variations in the measured domains of PA across these studies.…”

Section: Discussionmentioning

confidence: 99%

“…Sleep disorders are common non-motor symptoms in patients with PD. However, current published studies about PA and PD did not take into account the effects of different sleep traits on risk of incident PD [ 9 – 16 ]. A previous prospective population-based Rotterdam Study with 5,450 participants found that poor sleep quality (evaluated by Pittsburgh Sleep Quality Index) as well as short sleep duration were linked to a higher PD risk [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…For example, a meta-analysis summarizing 8 studies revealed an inverse dose-response association between PA and PD risk among men but not women [ 8 ]. However, the enrolled studies did not take into account sleep-related factors [ 9 – 16 ]. In fact, sleep issues are tightly correlated to PD [ 17 ], and deterioration of sleep quality has been demonstrated as markers of the prodromal phase of PD [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Physical activity and sleep pattern in relation to incident Parkinson’s disease: a cohort study

Chen,

Sun,

et al. 2024

Int J Behav Nutr Phys Act

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Background How physical activity (PA) and different sleep traits and overall sleep pattern interact in the development of Parkinson’s disease (PD) remain unknown. Objective To prospectively investigate the joint associations of PA and sleep pattern with risk of PD. Methods Included were 339,666 PD-free participants from the UK Biobank. Baseline PA levels were grouped into low (< 600 MET-mins/week), medium (600 to < 3000 MET-mins/week) and high (≥ 3000 MET-mins/week) according to the instructions of the UK Biobank. Healthy sleep traits (chronotype, sleep duration, insomnia, snoring, and daytime sleepiness) were scored from 0 to 5 and were categorized into “ideal sleep pattern” (≥ 3 sleep scores) and “poor sleep pattern” (0–2 sleep scores). Hazard ratios (HRs) and 95% confidence intervals (CIs) of PD were estimated by Cox proportional hazards models. Results During a median of 11.8 years of follow-up, 1,966 PD events were identified. The PD risk was lower in participants with high PA (HR = 0.73; 95% CI: 0.64, 0.84), compared to those with low PA; and participants with ideal sleep pattern also had a lower risk of PD (HR = 0.78; 95% CI: 0.69, 0.87), compared to those with poor sleep pattern. When jointly investigating the combined effect, participants with both high PA and ideal sleep pattern had the lowest risk of incident PD (HR = 0.55; 95% CI: 0.44, 0.69), compared to those with low PA and poor sleep pattern; notably, participants with high PA but poor sleep pattern also gained benefit on PD risk reduction (HR = 0.74; 95% CI: 0.55, 0.99). Conclusions Both high PA and ideal sleep pattern were independently associated with lower risk of developing PD, and those with both high PA level and ideal sleep pattern had the lowest risk. Our results suggest that improving PA levels and sleep quality may be promising intervention targets for the prevention of PD.

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“…2 A few cohort studies examined the association between PA and PD incidence, with inconsistent results. [3][4][5][6][7][8][9] A meta-analysis of 8 studies estimated that participants with the highest PA level, defined in different ways across studies, had 21% lower PD incidence. 10 This association was statistically significant in men and weaker and not significant in women; however, only 4 studies examined this association in women.…”

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confidence: 99%

Association of Physical Activity and Parkinson Disease in Women

et al. 2023

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Background and Objectives:Previous cohort studies reported that a single measure of physical activity (PA) assessed at baseline was associated with lower Parkinson’s disease (PD) incidence, but a meta-analysis suggested that this association was restricted to men. Due to the long prodromal phase of the disease, reverse causation could not be excluded as a potential explanation. Our objective was to study the association between time-varying PA and PD using lagged analyses to address the potential for reverse causation, and to compare PA trajectories in patients prior to diagnosis and matched controls.Methods:We used data from E3N (1990-2018), a cohort study of women affiliated with a national health insurance plan for persons working in education. PA was self-reported in six questionnaires over the follow-up. As questions changed across questionnaires, we created a time-varying latent PA (LPA) variable using latent-process mixed models. PD was ascertained using a multistep validation process based on medical records, or a validated algorithm based on drug claims. We set-up a nested case-control study to examine differences in LPA trajectories using multivariable linear mixed models with a retrospective time scale. Cox proportional hazards models with age as the timescale and adjusted for confounders were used to estimate the association between time-varying LPA and PD incidence. Our main analysis used a 10y-lag to account for reverse causation; sensitivity analyses used 5y, 15y, and 20y-lags.Results:Analyses of trajectories (1,196 cases, 23,879 controls) showed that LPA was significantly lower in cases than in controls throughout the follow-up, including 29y before diagnosis; the difference between cases and controls started to increase ∼10y before diagnosis (P-interaction=0.003). In our main survival analysis, of 95,354 women free of PD in 2000, 1,074 women developed PD over a mean follow-up of 17.2y. PD incidence decreased with increasing LPA (P-trend=0.001), with 25% lower incidence in those in the highest quartile compared to the lowest (adjusted hazard ratio=0.75, 95% confidence interval=0.63-0.89). Using longer lags yielded similar conclusions.Discussion:Higher PA level is associated with lower PD incidence in women, not explained by reverse causation. These results are important for planning interventions for PD prevention.

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Physical activity and risk of Parkinson’s disease and parkinsonism in a prospective population-based study (NEDICES)

Cited by 9 publications

References 30 publications

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

Physical activity and sleep pattern in relation to incident Parkinson’s disease: a cohort study

Association of Physical Activity and Parkinson Disease in Women

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