High concentration of plasma methoxytyramine: dopamine-producing tumour or Parkinson’s disease therapy?

Background Levodopa (L-DOPA) provided to patients with Parkinson’s disease causes an increase in dopamine and methoxytyramine blood concentration which may lead to erroneous diagnosis of dopamine-producing tumours based on a plasma fractionated metanephrines and methoxytyramine assay. Considering that oral L-DOPA is mainly transformed in the gut wall into dopamine and methoxytyramine, we hypothesize that patients treated with L-DOPA produce predominantly sulphated methoxytyramine, whereas dopamine-producing tumours, devoid of sulfotransferase, will secrete free methoxytyramine. These metabolic differences may allow for discrimination between the two groups of patients through methoxytyramine plasma concentration. Methods We retrospectively investigated a cohort of 16 patients with a dopamine-secreting pheochromocytoma or paraganglioma and 22 patients treated for Parkinson’s disease to see whether the metabolic ratio of free and sulphated methoxytyramine differs. Results Receiver operating characteristic curve analysis indicates an absolute separation between the two groups when using a cut-off of free/total methoxytyramine (sum of free and sulphated methoxytyramine) ratio of 0.0059, corresponding to a free methoxytyramine fraction of 0.59% ( P < 0.0001, AUC 1.0 indicating 100% sensitivity and specificity). Conclusion Dopamine secreted by tumours and exogenous dopamine (from Parkinson’s disease treatment) follow different metabolic pathways. We observed that free/total methoxytyramine ratio may be a useful tool in distinguishing between patients with a dopamine-secreting tumour from patients treated with L-DOPA when clinical information is incomplete or lacking.

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PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33011118 ◽

2019 ◽

Vol 33 ◽

pp. 1118-1125 ◽

Cited By ~ 4

Author(s):

Patrick Schwab ◽

Walter Karlen

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Clinical Assessment ◽

Operating Characteristic ◽

Characteristic Curve ◽

Predictive Performance ◽

Learning Approach ◽

Machine Learning Approach ◽

Term Data

Parkinson’s disease is a neurodegenerative disease that can affect a person’s movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson’s disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson’s disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson’s disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson’s disease.

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Serum miR-214 Serves as a Biomarker for Prodromal Parkinson’s Disease

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.700959 ◽

2021 ◽

Vol 13 ◽

Author(s):

Lanting Li ◽

Jingru Ren ◽

Chenxi Pan ◽

Yuqian Li ◽

Jianxia Xu ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Area Under Curve ◽

Operating Characteristic ◽

De Novo ◽

Characteristic Curve ◽

The Other ◽

Circulating Micrornas ◽

Potential Biomarker ◽

Operating Characteristic Curve

Circulating microRNAs (miRNAs) have been proposed to be accessible biomarkers for Parkinson’s disease (PD). However, there is a lack of known miRNAs that can serve as biomarkers for prodromal PD (pPD). We previously identified that miR-31 and miR-214 were dysregulated in PD. The aim of this study was to explore the roles of miR-31 and miR-214 in pPD. We recruited 25 pPD patients, 20 patients with de novo PD (dnPD), 24 advanced PD (aPD) patients and 21 controls. Next, we investigated the expression of miR-31 and miR-214. Compared to controls, miR-214 was found to be significantly upregulated in pPD patients while miR-31 was significantly upregulated in aPD patients. In addition, the expression of miR-214 was lower in aPD patients compared to both dnPD or pPD patients, while the expression of miR-31 was higher in aPD patients compared to dnPD patients. In order to predict pPD via miRNA expression, the receiver operating characteristic curve was constructed and the area under curve (AUC) was calculated. For pPD prediction by miR-214, the AUC was 0.756. The optimal cut-off value of miR-214 was 0.1962, and the sensitivity and specificity were 72.0% and 76.2%, respectively. On the other hand, the AUC for aPD detection by miR-31 was 0.744. The optimal cut-off value for miR-31 was 0.0148, with a sensitivity of 87.5% and a specificity of 71.4%. In conclusion, miR-214 can distinguish pPD patients from controls and may be used as a potential biomarker for pPD diagnosis.

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A Comparative Study of the Diagnostic Potential of Plasma and Erythrocytic α-Synuclein in Parkinson’s Disease

Neurodegenerative Diseases ◽

10.1159/000506480 ◽

2019 ◽

Vol 19 (5-6) ◽

pp. 204-210

Author(s):

Luxuan Wang ◽

Guowei Wang ◽

Yangyang Duan ◽

Feng Wang ◽

Shaoqing Lin ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Predictive Power ◽

Roc Analysis ◽

Operating Characteristic ◽

Characteristic Curve ◽

Expression Level ◽

Enzyme Linked Immunosorbent Assay ◽

Control Subjects ◽

Diagnostic Potential

Background: Parkinson’s disease (PD) is a neurodegenerative disease characterized by intracellular α-synuclein (α-Syn) deposition. Alternation of the α-Syn expression level in plasma or erythrocytes may be used as a potential PD biomarker. However, no studies have compared their prognostic value directly with the same cohort. Methods: The levels of α-Syn in plasma and erythrocytes, obtained from 45 PD patients and 45 control subjects, were measured with enzyme-linked immunosorbent assay. Then, correlation and receiver operating characteristic curve (ROC) analysis were performed to characterize the predictive power of erythrocytic and plasma α-Syn. Results: Our results showed that α-Syn expression levels in both plasma and erythrocytes were significantly higher in PD patients than in control subjects (823.14 ± 257.79 vs. 297.10 ± 192.82 pg/mL, p < 0.0001 in plasma; 3,104.14 ± 143.03 vs. 2,944.82 ± 200.41 pg/mL, p < 0.001 in erythrocytes, respectively). The results of the ROC analysis suggested that plasma α-Syn exhibited better predictive power than erythrocytic α-Syn with a sensitivity of 80.0%, specificity of 97.7%, and a positive predictive value of 77.8%. The expression level of plasma α-Syn correlated well with the age of patients, H-Y stage, MoCA scale, and UPDRS motor scale. On the contrary, there was no correlation between erythrocytic α-Syn level and clinical parameters in this study. Conclusion: Our results suggest that plasma α-Syn could be a specific and sensitive potential diagnostic biomarker for PD.

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