Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care

Assessing Plasma APLP1 for the Progression of Parkinson's Disease: Insights from HSPD and PPMI Cohorts

BACKGROUND

Amyloid precursor-like protein 1 (APLP1) is involved in pathological α-synuclein transmission, but its role in Parkinson's disease (PD) progression has not been explored.

OBJECTIVE

This study investigates APLP1 as a potential predictor for motor and cognitive deterioration in PD.

METHODS

Plasma APLP1 levels were measured in PD patients from the Huashan Hospital for Parkinson's Disease (HSPD) and Parkinson's Disease Progression Markers Initiative (PPMI) cohorts. A total of 916 participants were recruited in the HSPD cohort, and 171 participants were in the PPMI cohort. Longitudinal analysis examined the association between baseline APLP1 levels and PD progression.

RESULTS

A significant increase in APLP1 levels was observed in PD patients compared to healthy controls. Longitudinal analysis showed that patients with elevated baseline APLP1 levels experienced faster motor deterioration in HSPD cohort (HR = 3.627, P < 0.0001).

CONCLUSIONS

The data indicate that APLP1 is associated with the progression of PD, potentially offering a measurable indicator of disease progression. © 2025 International Parkinson and Movement Disorder Society.

ZKSCAN3 affects the autophagy‑lysosome pathway through TFEB in Parkinson's disease

The present study aimed to explore the effects of zinc finger with KRAB and SCAN domains 3 (ZKSCAN3)/transcription factor EB (TFEB) on autophagy-lysosome pathway in Parkinson's disease (PD). SH-SY5Y cells were treated with 6-hydroxydopamine to establish a PD cell model. A ZKSCAN3 overexpression vector and short interfering (si)RNAs were also constructed. The TFEB overexpression vector was transfected into the cells with ZKSCAN3 siRNA and the TFEB siRNA was transfected into the cells with the ZKSCAN3 overexpression vector. Reverse transcription-quantitative and western blotting were performed to detect the expression levels of Beclin-1, LC3II/I, α-synuclein and lysosomal-associated membrane protein 1 (Lamp-1). Lysosomes were labelled with LysoTracker Red and morphological changes in the lysosomes were detected by using laser confocal scanning microscopy. Transmission electron microscopy was used to observe changes in autophagosomes and lysosomes. Compared with those in the normal group, the model group presented decreases in the LC3B, ZKSCAN3, TFEB, Beclin-1 and Lamp-1 mRNA levels and increases in the LC3A, LC3II/I and α-synuclein protein levels. ZKSCAN3 overexpression resulted in a decrease in Beclin-1, LC3I mRNA, LC3 II/I protein and α-synuclein levels, as well as an increase in LC3II mRNA levels. ZKSCAN3 interference resulted in an increase in LC3A mRNA, LC3 II/I protein, Beclin-1, α-synuclein mRNA and Lamp-1 and a decrease in LC3B mRNA and α-synuclein. TFEB reversed the effects of ZKSCAN3. The results of lysosome detection revealed that, compared with that of the normal group, the fluorescence intensity of the model group was lower. The fluorescence intensity of the ZKSCAN3 interference group and TFEB interference group was greater than that of the interference empty group. Compared with those in the overexpression empty group, the fluorescence intensity and number of lysosomes in the ZKSCAN3 overexpression group and the TFEB overexpression group were lower. In conclusion, ZKSCAN3 affected the occurrence and development of PD through the TFEB-mediated autophagy-lysosome pathway.

Precision neurology

Over the past several decades, high-resolution brain imaging, blood and cerebrospinal fluid analyses, and other advanced technologies have changed diagnosis from an exercise depending primarily on the history and physical examination to a computer- and online resource-aided process that relies on larger and larger quantities of data. In addition, randomized controlled trials (RCT) at a population level have led to many new drugs and devices to treat neurological disease, including disease-modifying therapies. We are now at a crossroads. Combinatorially profound increases in data about individuals has led to an alternative to population-based RCTs. Genotyping and comprehensive "deep" phenotyping can sort individuals into smaller groups, enabling precise medical decisions at a personal level. In neurology, precision medicine that includes prediction, prevention and personalization requires that genomic and phenomic information further incorporate imaging and behavioral data. In this article, we review the genomic, phenomic, and computational aspects of precision medicine for neurology. After defining biological markers, we discuss some applications of these "-omic" and neuroimaging measures, and then outline the role of computation and ultimately brain simulation. We conclude the article with a discussion of the relation between precision medicine and value-based care.

Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors

BACKGROUND AND OBJECTIVES

Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored.

METHODS

We analyzed the gait pattern related to high CSVD burden from 720 participants (aged 55-65 years, 42.5 % male) free of neurological disease in the Taizhou Imaging Study. All participants underwent detailed quantitative gait assessments (obtained from an insole-like wearable gait tracking device) and brain magnetic resonance imaging examinations. Thirty-three gait parameters were summarized into five gait domains. Sparse sliced inverse regression was developed to extract the gait pattern related to high CSVD burden.

RESULTS

The specific gait pattern derived from several gait domains (i.e., angles, phases, variability, and spatio-temporal) was significantly associated with the CSVD burden (OR=1.250, 95 % CI: 1.011-1.546). The gait pattern indicates that people with a high CSVD burden were prone to have smaller gait angles, more stance time, more double support time, larger gait variability, and slower gait velocity. Furthermore, people with this gait pattern had a 25 % higher risk of a high CSVD burden.

CONCLUSIONS

We established a more stable and disease-specific quantitative gait pattern related to high CSVD burden, which is prone to facilitate the identification of individuals with high CSVD burden among the community residents or the general population.

Improved interpretation of 18F-florzolotau PET in progressive supranuclear palsy using a normalization-free deep-learning classifier

While 18F-florzolotau tau PET is an emerging biomarker for progressive supranuclear palsy (PSP), its interpretation has been hindered by a lack of consensus on visual reading and potential biases in conventional semi-quantitative analysis. As clinical manifestations and regions of elevated 18F-florzolotau binding are highly overlapping in PSP and the Parkinsonian type of multiple system atrophy (MSA-P), developing a reliable discriminative classifier for 18F-florzolotau PET is urgently needed. Herein, we developed a normalization-free deep-learning (NFDL) model for 18F-florzolotau PET, which achieved significantly higher accuracy for both PSP and MSA-P compared to semi-quantitative classifiers. Regions driving the NFDL classifier's decision were consistent with disease-specific topographies. NFDL-guided radiomic features correlated with clinical severity of PSP. This suggests that the NFDL model has the potential for early and accurate differentiation of atypical parkinsonism and that it can be applied in various scenarios due to not requiring subjective interpretation, MR-dependent, and reference-based preprocessing.

Phenomic Studies on Diseases: Potential and Challenges

The rapid development of such research field as multi-omics and artificial intelligence (AI) has made it possible to acquire and analyze the multi-dimensional big data of human phenomes. Increasing evidence has indicated that phenomics can provide a revolutionary strategy and approach for discovering new risk factors, diagnostic biomarkers and precision therapies of diseases, which holds profound advantages over conventional approaches for realizing precision medicine: first, the big data of patients' phenomes can provide remarkably richer information than that of the genomes; second, phenomic studies on diseases may expose the correlations among cross-scale and multi-dimensional phenomic parameters as well as the mechanisms underlying the correlations; and third, phenomics-based studies are big data-driven studies, which can significantly enhance the possibility and efficiency for generating novel discoveries. However, phenomic studies on human diseases are still in early developmental stage, which are facing multiple major challenges and tasks: first, there is significant deficiency in analytical and modeling approaches for analyzing the multi-dimensional data of human phenomes; second, it is crucial to establish universal standards for acquirement and management of phenomic data of patients; third, new methods and devices for acquirement of phenomic data of patients under clinical settings should be developed; fourth, it is of significance to establish the regulatory and ethical guidelines for phenomic studies on diseases; and fifth, it is important to develop effective international cooperation. It is expected that phenomic studies on diseases would profoundly and comprehensively enhance our capacity in prevention, diagnosis and treatment of diseases.