Development and Validation of a Plasma FAM19A5 and MRI-Based Radiomics Model for Prediction of Parkinson's Disease and Parkinson's Disease With Depression

Differentiating idiopathic Parkinson's disease from multiple system atrophy-P using brain MRI-based radiomics: a multicenter study

Background

Differentiating idiopathic Parkinson's disease (IPD) from multiple system atrophy-parkinsonian type (MSA-P) is essential for optimizing patient care and prognosis, given the differences in disease progression and treatment response.

Objectives

This study aimed to develop and evaluate a radiomics-based model using magnetic resonance imaging (MRI)-derived features to distinguish IPD from MSA-P.

Design

A multicenter retrospective study.

Methods

A multicenter retrospective study was conducted with 287 patients (186 IPD and 101 MSA-P) who underwent brain MRI. Radiomic features were extracted from T1-weighted imaging and T2-weighted imaging sequences, and various machine learning classifiers were applied, including logistic regression, support vector machine (SVM), ExtraTrees, extreme gradient boosting, and Light Gradient Boosting Machine. Model performance was assessed using area under the curve (AUC), accuracy, sensitivity, and specificity. A nomogram combining clinical and radiomic features was also evaluated.

Results

The SVM model, selected as the base for the Rad-signature, achieved the best diagnostic performance, with AUCs of 0.885 and 0.900 in the training and testing cohorts, respectively. The Rad-signature significantly outperformed clinical-only models in distinguishing IPD from MSA-P. The nomogram incorporating radiomic and clinical features yielded the highest diagnostic accuracy (AUC = 0.973 and 0.963 for training and testing cohorts, respectively) and balanced sensitivity and specificity. Decision curve analysis confirmed the nomogram's clinical utility.

Conclusion

Radiomics-based MRI analysis offers a powerful tool for distinguishing IPD from MSA-P, enhancing diagnostic accuracy, and aiding personalized treatment planning. Integrating radiomic and clinical data may improve diagnostic workflows in clinical practice.

Identification of Depression Subtypes in Parkinson's Disease Patients via Structural MRI Whole-Brain Radiomics: An Unsupervised Machine Learning Study

OBJECTIVE

Current clinical evaluation may tend to lack precision in detecting depression in Parkinson's disease (DPD). Radiomics features have gradually shown potential as auxiliary diagnostic tools in identifying and distinguishing different subtypes of Parkinson's disease (PD), and a radiomic approach that combines unsupervised machine learning has the potential to identify DPD.

METHODS

Analyze the clinical and imaging data of 272 Parkinson's disease (PD) patients from the PPMI dataset, along with 45 PD patients from the NACC dataset. Extract radiomic features from T1-weighted MRI images and employ principal component analysis (PCA) for dimensionality reduction. Subsequently, apply four unsupervised clustering methods including Gaussian mixture model (GMM), hierarchical clustering, K-means, and partitioning around medoids (PAM) to classify cases in the PPMI dataset into distinct subtypes. Identify high-risk subtypes of DPD on the basis of the time and number of depression progression, and validate these findings using the NACC dataset. The data from the high-risk subtype were divided into a training subtype and a testing subtype in a 7:3 ratio. Multiple logistic regression analysis was conducted on the training subtype data to develop a traditional logistic regression model for the high-risk subtype, which was subsequently compared with a supervised logistic regression model constructed for the entire PPMI cohort. Finally, the performance of both models was evaluated using receiver operating characteristic (ROC) curves. In addition, a decision tree (DT) model was constructed based on independent risk factors of high-risk subtypes and validated using low-risk subtype data. ROC curves were employed to validate this model across training subtype, testing subtype, and low-risk subtype datasets.

RESULTS

The PAM clustering method demonstrates superior performance compared to the other three clustering methods when the number of clusters is 2. High-risk subtypes of DPD can be effectively distinguished in both the PPMI and NACC datasets. A traditional logistic regression model was developed based on rapid-eye-movement behavior disorder, UPDRS I score, UPDRS II score, and ptau in high-risk subgroups. This model exhibits a diagnostic efficacy (AUC = 0.731) that surpasses that of the traditional regression model constructed using the entire PPMI cohort (AUC = 0.674). The prediction model based on high-risk subtypes had AUC values of 0.853 and 0.81 in the training and testing subtypes, sensitivities of 0.765 and 0.786, and specificities of 0.771 and 0.815, respectively. The AUC, sensitivity, and specificity in the nonhigh-risk subtype were 0.859, 0.654, and 0.852, respectively.

CONCLUSION

By identifying MRI structural radiomics and clinical features as potential biomarkers, the radiomic approach and UCA provide new insights into the pathophysiology of DPD to support the clinical diagnosis with high prediction accuracy.

Automatic substantia nigra segmentation with Swin-Unet in susceptibility- and T2-weighted imaging: application to Parkinson disease diagnosis

Background

Accurately distinguishing between Parkinson disease (PD) and healthy controls (HCs) through reliable imaging method is crucial for appropriate therapeutic intervention. However, PD diagnosis is hindered by the subjective nature of the evaluation. We aimed to develop an automatic deep-learning method that can segment the substantia nigra areas on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) and further differentiate patients with PD from HCs using a machine learning algorithm.

Methods

Magnetic resonance imaging (MRI) data from 83 patients with PD and 83 age- and sex-matched HCs were obtained on the same 3.0-T MRI scanner. A deep learning method with Swin-Unet was developed to segment volumes of interest (VOIs) on SWI and then map the VOIs on SWI to the corresponding T2WI; features were then extracted from the VOIs on SWI and T2WI. Three machine learning models were developed and compared to differentiate those with PD from HCs.

Results

Swin-Unet achieved a better Dice coefficient than did U-Net in SWI segmentation (0.832 vs. 0.712). Machine learning models outperformed visual analysis (P>0.05), and logistic regression (LR) achieved the best performance [area under the curve (AUC) ≥0.819] and the most stable (relative standard deviations in AUC ≤0.05). The test results showed that the AUC of the LR model based on SWI segmentation was 0.894 while that of the LR model based on T2WI segmentation was 0.876. There was no significant difference in VOIs based on manual labeling or automatic segmentation across T2WI, SWI, or a combination of the two (P>0.05). The AUCs of the LR model based on automatic segmentation were close to those of the model based on manual labeling (P>0.05).

Conclusions

Our approach could provide a powerful and useful method for automatically and rapidly diagnosing PD in the clinic with only T2WI.

FAM19A5 in vascular aging and osteoporosis: Mechanisms and the "calcification paradox"

Aging induces a progressive decline in the vasculature's structure and function. Vascular aging is a determinant factor for vascular ailments in the elderly. FAM19A5, a recently identified adipokine, has demonstrated involvement in multiple vascular aging-related pathologies, including atherosclerosis, cardio-cerebral vascular diseases and cognitive deficits. This review summarizes the current understanding of FAM19A5' role and explores its putative regulatory mechanisms in various aging-related disorders, including cardiovascular diseases (CVDs), metabolic diseases, neurodegenerative diseases and malignancies. Importantly, we provide novel insights into the underlying therapeutic value of FAM19A5 in osteoporosis. Finally, we outline future perspectives on the diagnostic and therapeutic potential of FAM19A5 in vascular aging-related diseases.

Can Adipokine FAM19A5 Be a Biomarker of Metabolic Disorders?

Aim

One of the most critical functions of adipose tissue is the production of adipokines, ie, numerous active substances that regulate metabolism. One is the newly discovered FAM19A5, whose older name is TAFA-5.

Purpose

The study aimed to review the literature on the FAM19A5 protein.

Methods

The review was conducted in December 2023 using the PubMed (Medline) search engine. Sixty-four papers were included in the review.

Results

This protein exhibits the characteristics of an adipokine with positive features for maintaining homeostasis. The results showed that FAM19A5 was highly expressed in adipose tissue, with mild to moderate expression in the brain and ovary. FAM19A5 may also inhibit vascular smooth muscle cell proliferation and migration through the perivascular adipose tissue paracrine pathway. Serum levels of FAM19A5 were decreased in obese children compared with healthy controls. There are negative correlations between FAM19A5, body mass index, and fasting insulin. Serum FAM19A5 level is correlated with type 2 diabetes, waist circumference, waist-to-hip ratio, glutamic pyruvic transferase, fasting plasma glucose, HbA1c, and mean shoulder pulse wave velocity. FAM19A5 expression was reduced in mice with obesity. However, the data available needs to be clarified or contradictory.

Conclusion

Considering today's knowledge about FAM19A5, we cannot consider this protein as a biomarker of the metabolic syndrome. According to current knowledge, FAM19A5 cannot be considered a marker of metabolic disorders because the results of studies conducted in this area are unclear.

The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis

Background

In recent years, radiomics has been increasingly utilized for the differential diagnosis of Parkinson's disease (PD). However, the application of radiomics in PD diagnosis still lacks sufficient evidence-based support. To address this gap, we carried out a systematic review and meta-analysis to evaluate the diagnostic value of radiomics-based machine learning (ML) for PD.

Methods

We systematically searched Embase, Cochrane, PubMed, and Web of Science databases as of November 14, 2022. The radiomics quality assessment scale (RQS) was used to evaluate the quality of the included studies. The outcome measures were the c-index, which reflects the overall accuracy of the model, as well as sensitivity and specificity. During this meta-analysis, we discussed the differential diagnostic value of radiomics-based ML for Parkinson's disease and various atypical parkinsonism syndromes (APS).

Results

Twenty-eight articles with a total of 6,057 participants were included. The mean RQS score for all included articles was 10.64, with a relative score of 29.56%. The pooled c-index, sensitivity, and specificity of radiomics for predicting PD were 0.862 (95% CI: 0.833-0.891), 0.91 (95% CI: 0.86-0.94), and 0.93 (95% CI: 0.87-0.96) in the training set, and 0.871 (95% CI: 0.853-0.890), 0.86 (95% CI: 0.81-0.89), and 0.87 (95% CI: 0.83-0.91) in the validation set, respectively. Additionally, the pooled c-index, sensitivity, and specificity of radiomics for differentiating PD from APS were 0.866 (95% CI: 0.843-0.889), 0.86 (95% CI: 0.84-0.88), and 0.80 (95% CI: 0.75-0.84) in the training set, and 0.879 (95% CI: 0.854-0.903), 0.87 (95% CI: 0.85-0.89), and 0.82 (95% CI: 0.77-0.86) in the validation set, respectively.

Conclusion

Radiomics-based ML can serve as a potential tool for PD diagnosis. Moreover, it has an excellent performance in distinguishing Parkinson's disease from APS. The support vector machine (SVM) model exhibits excellent robustness when the number of samples is relatively abundant. However, due to the diverse implementation process of radiomics, it is expected that more large-scale, multi-class image data can be included to develop radiomics intelligent tools with broader applicability, promoting the application and development of radiomics in the diagnosis and prediction of Parkinson's disease and related fields.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=383197, identifier ID: CRD42022383197.

Amygdala's T1-weighted image radiomics outperforms volume for differentiation of anxiety disorder and its subtype

Introduction

Anxiety disorder is the most common psychiatric disorder among adolescents, with generalized anxiety disorder (GAD) being a common subtype of anxiety disorder. Current studies have revealed abnormal amygdala function in patients with anxiety compared with healthy people. However, the diagnosis of anxiety disorder and its subtypes still lack specific features of amygdala from T1-weighted structural magnetic resonance (MR) imaging. The purpose of our study was to investigate the feasibility of using radiomics approach to distinguish anxiety disorder and its subtype from healthy controls on T1-weighted images of the amygdala, and provide a basis for the clinical diagnosis of anxiety disorder.

Methods

T1-weighted MR images of 200 patients with anxiety disorder (including 103 GAD patients) as well as 138 healthy controls were obtained in the Healthy Brain Network (HBN) dataset. We extracted 107 radiomics features for the left and right amygdala, respectively, and then performed feature selection using the 10-fold LASSO regression algorithm. For the selected features, we performed group-wise comparisons, and use different machine learning algorithms, including linear kernel support vector machine (SVM), to achieve the classification between the patients and healthy controls.

Results

For the classification task of anxiety patients vs. healthy controls, 2 and 4 radiomics features were selected from left and right amygdala, respectively, and the area under receiver operating characteristic curve (AUC) of linear kernel SVM in cross-validation experiments was 0.6739±0.0708 for the left amygdala features and 0.6403±0.0519 for the right amygdala features; for classification task for GAD patients vs. healthy controls, 7 and 3 features were selected from left and right amygdala, respectively, and the cross-validation AUCs were 0.6755±0.0615 for the left amygdala features and 0.6966±0.0854 for the right amygdala features. In both classification tasks, the selected amygdala radiomics features had higher discriminatory significance and effect sizes compared with the amygdala volume.

Discussion

Our study suggest that radiomics features of bilateral amygdala potentially could serve as a basis for the clinical diagnosis of anxiety disorder.

Increased level of FAM19A5 is associated with cerebral small vessel disease and leads to a better outcome

Objective

FAM19A5 plays an essential role in the development and acute or chronic inflammation of the central nervous system. The present study aimed to explore the association between FAM19A5 and cerebral small vessel disease (cSVD).

Methods

A total of 344 recent small subcortical infarct (RSSI) patients and 265 healthy controls were included in this study. The difference in the FAM19A5 level between the two groups was compared and the correlation between FAM19A5 and cerebral infarction volume was analyzed. Also, the association between FAM19A5 and the total magnetic resonance imaging (MRI) burden with its imaging characteristics was explored. Moreover, the correspondence of FAM19A5 with the outcome was assessed via Δ National Institutes of Health Stroke Scale score (NIHSS) and the percentage of NIHSS improvement.

Results

FAM19A5 was highly expressed in the RSSI group (P = 0.023), showing a positive correlation with cerebral infarction volume (P < 0.01). It was positively correlated with total MRI cSVD burden (P < 0.001) and reflected the severity of white matter hyperintensity (WMH) (P < 0.01) and enlarged perivascular space (EPVS) (P < 0.01), but did not show any association with cerebral microbleed (CMB) and lacune. Moreover, FAM19A5 suggested a larger Δ NIHSS (P = 0.021) and NIHSS improvement percentage (P = 0.007).

Conclusion

Serum FAM19A5 was increased in RSSI and positively correlated with the infarct volume. It also reflects the total MRI burden of cSVD, of which the imaging characteristics are positively correlated with WMH and EPVS. In addition, higher FAM19A5 levels reflect better outcomes in RSSI patients.