Sex differences in the association between nonalcoholic fatty liver disease and Parkinson's disease

Risk of Parkinson disease in stroke patients: A nationwide cohort study in South Korea

BACKGROUND AND PURPOSE

Previous studies have examined the risk of stroke in patients with Parkinson disease (PD), but the incidence of PD onset among stroke patients and its risk according to severity of poststroke disabilities have scarcely been investigated. This study aims to determine whether the risk of PD is increased among stroke patients using a retrospective cohort with a large population-based database.

METHODS

We used data collected by the Korean National Health Insurance Service from 2010 to 2018 and examined 307,361 stroke patients and 380,917 sex- and age-matched individuals without stroke to uncover the incidence of PD. Cox proportional hazards regression was used to calculate the hazard ratio (HR) and 95% confidence interval (CI), and the risk of PD was compared according to presence and severity of disability.

RESULTS

During 4.31 years of follow-up, stroke patients had a 1.67 times higher risk of PD compared to individuals without stroke (adjusted HR = 1.67, 95% CI = 1.57-1.78). The risk of PD was greater among stroke patients with disabilities than among those without disabilities, even after adjustment for multiple covariates (adjusted HR = 1.72, 95% CI = 1.55-1.91; and adjusted HR = 1.66, 95% CI = 1.56-1.77, respectively).

CONCLUSIONS

Our study demonstrated an increased risk of PD among stroke patients. Health professionals need to pay careful attention to detecting movement disorders as clues for diagnosing PD.

Rheumatoid Arthritis and Risk of Parkinson Disease in Korea

Importance

Although it has been postulated that chronic inflammation caused by rheumatoid arthritis (RA) contributes to the development of Parkinson disease (PD), the association between these 2 conditions has yet to be determined.

Objective

To evaluate the association between RA and subsequent PD risk.

Design, Setting, and Participants

This retrospective cohort study used the Korean National Health Insurance Service database to collect population-based, nationally representative data on patients with RA enrolled from 2010 to 2017 and followed up until 2019 (median follow-up, 4.3 [IQR, 2.6-6.4] years after a 1-year lag). A total of 119 788 patients who were first diagnosed with RA (83 064 with seropositive RA [SPRA], 36 724 with seronegative RA [SNRA]) were identified during the study period and included those who underwent a national health checkup within 2 years before the RA diagnosis date (64 457 patients). After applying exclusion criteria (eg, age <40 years, other rheumatic diseases, previous PD), 54 680 patients (39 010 with SPRA, 15 670 with SNRA) were included. A 1:5 age- and sex-matched control group of patients without RA was also included for a total control population of 273 400.

Exposures

Rheumatoid arthritis as defined using International Classification of Diseases, Tenth Revision codes M05 for SPRA and M06 (except M06.1 and M06.4) for SNRA; prescription of any disease-modifying antirheumatic drug; and enrollment in the Korean Rare and Intractable Diseases program.

Main Outcomes and Measures

The main outcome was newly diagnosed PD. Data were analyzed from May 10 through August 1, 2022, using Cox proportional hazards regression analyses.

Results

From the 328 080 individuals analyzed (mean [SD] age, 58.6 [10.1] years; 74.9% female and 25.1% male), 1093 developed PD (803 controls and 290 with RA). Participants with RA had a 1.74-fold higher risk of PD vs controls (95% CI, 1.52-1.99). An increased risk of PD was found in patients with SPRA (adjusted hazard ratio [aHR], 1.95; 95% CI, 1.68-2.26) but not in patients with SNRA (aHR, 1.20; 95% CI, 0.91-1.57). Compared with the SNRA group, those with SPRA had a higher risk of PD (aHR, 1.61; 95% CI, 1.20-2.16). There was no significant interaction between covariates on risk of PD.

Conclusions and Relevance

In this study, RA was associated with an increased risk of PD, and seropositivity of RA conferred an augmented risk of PD. The findings suggest that physicians should be aware of the elevated risk of PD in patients with RA and promptly refer patients to a neurologist at onset of early motor symptoms of PD without synovitis.

Machine learning analysis of the UK Biobank reveals IGF-1 and inflammatory biomarkers predict Parkinson's disease risk

INTRODUCTION

Parkinson's disease (PD) is the most common movement disorder, and its prevalence is increasing rapidly worldwide with an ageing population. The UK Biobank is the world's largest and most comprehensive longitudinal study of ageing community volunteers. The cause of the common form of PD is multifactorial, but the degree of causal heterogeneity among patients or the relative importance of one risk factor over another is unclear. This is a major impediment to the discovery of disease-modifying therapies.

METHODS

We used an integrated machine learning algorithm (IDEARS) to explore the relative effects of 1,753 measured non-genetic variables in 334,062 eligible UK Biobank participants, including 2,719 who had developed PD since their recruitment into the study.

RESULTS

Male gender was the highest-ranked risk factor, followed by elevated serum insulin-like growth factor 1 (IGF-1), lymphocyte count, and neutrophil/lymphocyte ratio. A group of factors aligned with the symptoms of frailty also ranked highly. IGF-1 and neutrophil/lymphocyte ratio were also elevated in both sexes before PD diagnosis and at the point of diagnosis.

DISCUSSION

The use of machine learning with the UK Biobank provides the best opportunity to explore the multidimensional nature of PD. Our results suggest that novel risk biomarkers, including elevated IGF-1 and NLR, may play a role in, or are indicative of PD pathomechanisms. In particular, our results are consistent with PD being a central manifestation of a systemic inflammatory disease. These biomarkers may be used clinically to predict future PD risk, improve early diagnosis and provide new therapeutic avenues.

Sex-stratified associations between fatty liver disease and Parkinson's disease: The Rotterdam study

Fatty liver disease was not associated with Parkinsonism or Parkinson's disease in an elderly European population, the Rotterdam Study, (n = 8.848), neither in men nor women. Results were consistent either using non-alcoholic fatty liver disease (NAFLD) or metabolic-dysfunction associated fatty liver disease (MAFLD) as exposure defined by either fatty liver index (FLI) or ultrasound.

Risk of Parkinson's disease in multiple sclerosis and neuromyelitis optica spectrum disorder: a nationwide cohort study in South Korea

BACKGROUND

Neurodegeneration is associated with pathogenesis of both multiple sclerosis (MS) and neuromyelitis optica (NMOSD). Parkinson's disease (PD) is a representative neurodegenerative disease, however, whether MS or NMOSD is associated with risk of PD is not known.

METHODS

MS and NMOSD cohorts were collected from the Korean National Health Insurance Service between 1 January 2010 and 31 December 2017, using International Classification of Diseases 10th revision diagnosis codes and information in the Rare Intractable Disease management programme. The PD incidence rate that occurred after a 1-year lag period was calculated and compared with that of a control cohort matched for age, sex, hypertension, diabetes and dyslipidaemia in a 1:5 ratio.

RESULTS

The incidence rates of PD in patients with MS and NMOSD were 3.38 and 1.27 per 1000 person-years, respectively, and were higher than that of their matched control groups. The adjusted HR of PD was 7.73 (95% CI, 3.87 to 15.47) in patients with MS and 2.61 (95% CI, 1.13 to 6.02) in patients with NMOSD compared with matched controls. In both patients with MS and NMOSD, there were no significant differences in relative risk when stratified by sex, age, diabetes, hypertension and dyslipidaemia.

CONCLUSION

The PD risk was higher in patients with MS and NMOSD compared with healthy controls and was particularly high in patients with MS. Further investigations should be performed to determine the pathophysiology and occurrence of PD in patients with MS and NMOSD.

Machine Learning Analysis Reveals Biomarkers for the Detection of Neurological Diseases

It is critical to identify biomarkers for neurological diseases (NLDs) to accelerate drug discovery for effective treatment of patients of diseases that currently lack such treatments. In this work, we retrieved genotyping and clinical data from 1,223 UK Biobank participants to identify genetic and clinical biomarkers for NLDs, including Alzheimer's disease (AD), Parkinson's disease (PD), motor neuron disease (MND), and myasthenia gravis (MG). Using a machine learning modeling approach with Monte Carlo randomization, we identified a panel of informative diagnostic biomarkers for predicting AD, PD, MND, and MG, including classical liver disease markers such as alanine aminotransferase, alkaline phosphatase, and bilirubin. A multinomial model trained on accessible clinical markers could correctly predict an NLD diagnosis with an accuracy of 88.3%. We also explored genetic biomarkers. In a genome-wide association study of AD, PD, MND, and MG patients, we identified single nucleotide polymorphisms (SNPs) implicated in several craniofacial disorders such as apnoea and branchiootic syndrome. We found evidence for shared genetic risk loci among NLDs, including SNPs in cancer-related genes and SNPs known to be associated with non-brain cancers such as Wilms tumor, leukemia, and colon cancer. This indicates overlapping genetic characterizations among NLDs which challenges current clinical definitions of the neurological disorders. Taken together, this work demonstrates the value of data-driven approaches to identify novel biomarkers in the absence of any known or promising biomarkers.