Gender variations in brain aging among Parkinson's Disease patients

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In a recent study published in npj Parkinson’s Disease, researchers explored how sex influences brain aging in Parkinson's disease (PD) by analyzing Magnetic Resonance Imaging (MRI)-derived brain age differences and their clinical correlations.

Study: Differences in brain aging between sexes in Parkinson’s disease. Image Credit: SpeedKingz/Shutterstock.comStudy: Differences in brain aging between sexes in Parkinson’s disease. Image Credit: SpeedKingz/Shutterstock.com

Background 

PD is a neurodegenerative disorder marked by motor symptoms like tremors and rigidity, as well as non-motor symptoms, including cognitive decline and emotional disorders.

Risk factors for PD include genetic predisposition, age, and, notably, male sex, with men having a higher prevalence and earlier onset.

Studies reveal sex differences in PD's clinical presentation, with men more prone to cognitive impairments and women exhibiting milder symptoms.

Neuroimaging research has shown differing patterns of brain atrophy between sexes, suggesting sex-specific disease mechanisms. Further research is needed to elucidate the underlying mechanisms of sex differences in PD progression and tailor more effective, gender-specific treatments.

About the study 

In the present study, researchers utilized 1,054 T1-weighted (T1w) MRI scans from healthy controls (HCs) sourced from publicly available databases such as Open Access Series of Imaging Studies (OASIS), IXI, and the Parkinson’s Progression Markers Initiative (PPMI).

These scans were crucial for training and validating a brain age estimation model, ensuring all participants were free from cognitive impairments or neurological diseases.

With a mean age of 49.15 years and a slight female majority, the model was developed to gauge brain age across different groups accurately.

The study then focused on 373 PD patients, comprising 244 males and 129 females. These patients were selected based on their demographic and clinical data, including motor and non-motor symptom scores, mood assessments, and cerebrospinal fluid (CSF) biomarkers.

To align the severity of disease across genders for an accurate comparison, a subset of male patients was matched with female patients using propensity score matching.

MRI scans underwent processing using the Computational Anatomy Toolbox version 12 (CAT12) toolbox, integrating voxel-based morphometry (VBM) techniques for detailed brain analysis.

The brain age estimation was executed through support vector regression, considering factors like sex, scanner attributes, and total brain volumes.

This model's accuracy was validated on a separate set of HCs, with the brain-predicted age difference (Brain-PAD) serving as a measure of deviation from the expected healthy brain aging trajectory.

Statistical analyses examined the relationship between Brain-PAD and various clinical variables within the PD cohort.

The analyses included models for motor symptom severity, non-motor symptom severity, and mood symptom severity, considering factors like age, disease duration, and education.

These models were applied separately for male and female patients to detect sex-specific associations.

Furthermore, the study utilized multiple regression analysis to identify brain regions where gray matter (GM) and white matter (WM) volumes were significantly correlated with Brain-PAD scores.

This approach highlighted differences in brain aging between PD patients and HCs and among PD patients based on their sex. 

Study results 

In the detailed examination, researchers found notable differences in clinical and biomarker scores between male and female patients despite similar demographics like age, education, age at diagnosis, and disease duration.

The study highlighted that while overall motor symptom severity did not significantly vary between sexes, rigidity was more pronounced in males.

Cognitive performance also varied, with significant sex differences in scores on tests such as the Montreal Cognitive Assessment (MoCA) and Benton Judgment of Line Orientation Score. However, no significant differences were found in sleep, anxiety, or depression scores.

To adjust for clinical symptom severity and other variables, propensity score matching was employed, ensuring comparable groups for analysis.

This rigorous approach underscored the absence of sex differences in CSF biomarkers and regional brain atrophy, indicating nuanced pathways of disease manifestation and progression between males and females.

A core component of the study was the assessment of brain age through a machine learning model, which showed a higher Brain-PAD in PD patients compared to healthy controls, signifying accelerated aging in PD.

Notably, males with PD had a significantly higher Brain-PAD than females, suggesting a sex-specific trajectory in brain aging among PD patients.

This variation in Brain-PAD was also correlated with motor and cognitive symptom severity, particularly in males, indicating a potential link between brain aging and clinical manifestations of PD.

Furthermore, the study explored the relationship between Brain-PAD and various clinical outcomes, revealing associations with cognitive performance, mood, and biomarkers such as α-Synuclein and Amyloid-β levels.

The findings suggest that while Brain-PAD is a powerful predictor of disease severity and progression in PD, its implications can differ markedly between sexes.

The visual analysis of brain regions contributing to Brain-PAD score estimation highlighted significant reductions in GM and WM volumes across the brain, particularly atrophy patterns in PD patients.

This relationship between brain volume and Brain-PAD scores differed between males and females, further emphasizing the importance of considering sex differences in PD research and treatment strategies.

Journal reference:
Vijay Kumar Malesu

Written by

Vijay Kumar Malesu

Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.    

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