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Monday, October 7, 2024

Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures

You, J., et al. (2024).
Neurology, 103(3).

Abstract

Background and Objectives

Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables.

Results

A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785–0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815–0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740–0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD.

Discussion

Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.

The article is cited above, but paywalled.

Here are some thoughts:

A recent study published in Neurology demonstrates the potential for early detection of Parkinson's disease (PD) using machine learning techniques. Researchers developed a predictive model that analyzes blood proteins in conjunction with clinical data, allowing for the identification of individuals at high risk of developing PD up to 15 years before symptoms appear. The study involved over 50,000 participants from the UK Biobank, focusing on 1,463 different blood plasma proteins. By employing machine learning to identify patterns in protein levels alongside clinical information—such as age, history of brain injuries, and blood creatinine levels—the researchers were able to achieve significant accuracy in predicting Parkinson's risk.

The findings revealed 22 specific proteins that are significantly associated with the risk of developing PD, including neurofilament light (NfL), which is linked to brain cell damage, as well as various proteins involved in inflammation and muscle function. This model not only offers a non-invasive and cost-effective screening method but also presents opportunities for early intervention and improved disease management, potentially enabling the development and assessment of neuroprotective treatments.
However, the study does have limitations that warrant consideration. The participant population was predominantly of European descent, which may limit the generalizability of the findings to more diverse groups. Additionally, the reliance on medical records for PD diagnosis raises concerns about potential misdiagnoses. Future research will need to validate the model in diverse populations and utilize more precise measurement techniques for protein levels. Longitudinal studies that incorporate repeated measurements could further enhance the predictive power of the model.

Overall, this groundbreaking research offers new hope for the early detection and intervention of Parkinson's disease, potentially revolutionizing the approach to managing this neurodegenerative disorder.