A recent study featured in npj Parkinson’s Disease delved into the impact of sex on brain aging in Parkinson’s disease (PD). The researchers conducted an analysis of Magnetic Resonance Imaging (MRI)-derived disparities in brain age and their clinical correlations.

 

Background

 

PD is a neurodegenerative condition characterized by motor symptoms such as tremors and rigidity, along with non-motor symptoms including cognitive decline and emotional disorders. Risk factors for PD encompass genetic predisposition, age, and notably, male sex, as men exhibit a higher prevalence and earlier onset of the disease.

 

Research indicates sex-based distinctions in the clinical presentation of PD, with men being more susceptible to cognitive impairments while women tend to display milder symptoms. Neuroimaging studies have revealed distinct patterns of brain atrophy between genders, suggesting the presence of sex-specific disease mechanisms. Further investigation is imperative to unveil the underlying mechanisms behind these sex differences in PD progression, paving the way for more tailored and effective gender-specific treatments.

 

 

About The Study

 

In this study, a total of 1,054 T1-weighted (T1w) MRI scans were obtained from healthy controls (HCs), sourced from publicly available databases such as the Open Access Series of Imaging Studies (OASIS), IXI, and the Parkinson’s Progression Markers Initiative (PPMI). These scans played a pivotal role in training and validating a brain age estimation model, ensuring that all participants were free from cognitive impairments or neurological diseases.

 

The participants, with a mean age of 49.15 years and a slight female majority, were used to develop a model accurately estimating brain age across diverse groups. The focus then shifted to 373 PD patients, comprising 244 males and 129 females, selected based on their demographic and clinical data, including motor and non-motor symptom scores, mood assessments, and cerebrospinal fluid (CSF) biomarkers.

 

To ensure an accurate comparison of disease severity across genders, a subset of male patients was matched with female patients using propensity score matching. The MRI scans underwent processing using the Computational Anatomy Toolbox version 12 (CAT12) toolbox, incorporating voxel-based morphometry (VBM) techniques for a detailed analysis of the brain.

 

Brain age estimation was performed through support vector regression, factoring in variables such as sex, scanner attributes, and total brain volumes. The model’s accuracy was validated on a separate set of HCs, with the brain-predicted age difference (Brain-PAD) serving as a metric for deviation from the expected healthy brain aging trajectory.

 

Statistical analyses were conducted to explore the relationship between brain PAD and various clinical variables within the PD cohort. Separate models were applied for male and female patients to detect sex-specific associations, considering factors such as age, disease duration, and education.

 

Additionally, multiple regression analysis was employed to identify brain regions where gray matter (GM) and white matter (WM) volumes significantly correlated with brain PAD scores. This approach highlighted differences in brain aging between PD patients and HCs, as well as variations among PD patients based on their sex.

 

 

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Study Result

 

In a thorough examination, researchers identified noteworthy distinctions in clinical and biomarker scores between male and female patients, despite similar demographic factors such as age, education, age at diagnosis, and disease duration. The study emphasized that although overall motor symptom severity did not significantly differ between genders, rigidity was more pronounced in males. Cognitive performance exhibited significant sex differences, as reflected in scores on assessments like the Montreal Cognitive Assessment (MoCA) and the Benton Judgment of Line Orientation Score. However, no significant variations were observed in sleep, anxiety, or depression scores.

 

To account for clinical symptom severity and other variables, the study employed propensity score matching to ensure comparable groups for analysis. This rigorous approach underscored the absence of sex differences in cerebrospinal fluid (CSF) biomarkers and regional brain atrophy, indicating nuanced pathways of disease manifestation and progression between males and females.

 

A crucial aspect of the study involved assessing brain age through a machine learning model, revealing a higher brain PAD in Parkinson’s disease (PD) patients compared to healthy controls, indicative of accelerated aging in PD. Notably, males with PD demonstrated a significantly higher brain PAD than females, suggesting a sex-specific trajectory in brain aging among PD patients. This variation in brain PAD was correlated with motor and cognitive symptom severity, particularly in males, suggesting a potential link between brain aging and clinical manifestations of PD.

 

Furthermore, the study delved into the relationship between brain PAD and various clinical outcomes, uncovering associations with cognitive performance, mood, and biomarkers such as α-Synuclein and Amyloid-β levels. These findings underscored that while brain PAD serves as a robust predictor of disease severity and progression in PD, its implications can differ significantly between sexes.

 

The visual analysis of brain regions contributing to Brain-PAD score estimation revealed noteworthy reductions in gray matter (GM) and white matter (WM) volumes across the brain, particularly in atrophy patterns observed in PD patients. This relationship between brain volume and brain PAD scores varied between males and females, highlighting the importance of considering sex differences in Parkinson’s disease research and treatment strategies.

 

 

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