UPDATE: Shortly after publishing this blog, I had a thought—does any literature exist on prioritisations? It turns out there is, which inspired a new blog: Identifying Research Gaps in Parkinson’s Disease – Prioritised and Compared.

“All gaps are weighted equally so it would also be interesting to see if it could provide some sort of ranking.” Building on yesterday’s discussion on research priorities, I leveraged AI/LLMs to do the heavy lifting. I asked two separate LLMs (ChatGPT and DeepSeek) to prioritize the original list based on things such as potential impact on disease burden, equity, and feasibility of implementation. I then used the LLMs to reconcile their suggestions, leading to the following final prioritization:

Top Priority (High Impact, Feasible, and Addresses Urgent Needs & Equity)

These research areas have the highest potential to significantly alter disease progression, improve early diagnosis, and enhance patient quality of life, while being feasible for near-term implementation.

1. Lack of Reliable Biomarkers for Early Detection

  • Why? Early diagnosis is critical for slowing disease progression and improving quality of life. Biomarkers (e.g., α-synuclein, mitochondrial DNA, blood-based markers) could transform Parkinson’s care by enabling earlier intervention.
  • Feasibility: Advances in genetic, imaging, and blood-based biomarkers are progressing rapidly.
  • Equity: Biomarkers would increase access to earlier diagnosis, especially if they can be non-invasive and cost-effective.

2. Non-Invasive Biomarkers for Disease Detection & Progression Monitoring

  • Why? Saliva, blood, and microbiota-based markers offer less invasive and more accessible alternatives to expensive imaging or invasive procedures.
  • Feasibility: These biomarkers are already under active investigation and could be implemented relatively quickly with further validation.
  • Equity: Improves accessibility in low-resource settings, benefiting underserved populations.

3. Integration of AI for Early & Accurate Diagnosis

  • Why? AI-based tools enhance diagnostic accuracy, potentially reducing misdiagnoses and allowing earlier intervention.
  • Feasibility: AI-based algorithms for imaging, digital cognitive assessments, and motor function tracking are already in development and testing.
  • Equity: If designed with diverse datasets, AI can expand diagnostic access to regions with limited specialists.

4. Understanding Non-Motor Symptoms as Early Indicators

  • Why? Non-motor symptoms (e.g., cognitive decline, gut microbiota changes, depression, psychiatric symptoms) often appear years before motor symptoms, making them valuable for early diagnosis.
  • Feasibility: Ongoing research suggests non-motor symptoms could be integrated into diagnostic criteria with further validation.
  • Equity: Improves early detection and personalized treatment in diverse populations.

5. Role of GLP-1 Receptor Agonists as Disease-Modifying Treatments

  • Why? GLP-1 agonists (e.g., Lixisenatide, Exenatide) have shown promise in slowing disease progression, addressing a major unmet need.
  • Feasibility: These drugs are already approved for diabetes and other conditions, making repurposing feasible.
  • Equity: If effective, they could be integrated into treatment regimens globally.

6. Best Exercise Type & Intensity for Motor Symptoms

  • Why? Exercise is a low-cost, widely accessible intervention with proven benefits for motor symptoms and no significant side effects.
  • Feasibility: Exercise is already widely studied, but optimal regimens for symptom management still need refinement.
  • Equity: Can be implemented globally at low cost, benefiting all socioeconomic groups.

7. Cognitive Training Interventions for Parkinson’s-Related Dementia & Mild Cognitive Impairment

  • Why? Cognitive decline significantly impacts quality of life and caregiver burden, yet current treatments are limited.
  • Feasibility: Digital and behavioral cognitive training interventions are emerging and could be scaled up.
  • Equity: Can be widely accessible via mobile apps, online programs, and community health initiatives.

Final Adjustments & Notes on Reconciliation

  • Both original lists shared the same core priorities, but some terminology and emphasis differed.
  • Equity considerations were harmonized—especially in how early detection, AI, and exercise could reach underserved populations.
  • Feasibility analysis was refined to ensure short-to-medium-term implementation is realistic.
  • The ranking maintains a balance between high-impact, scalable interventions and urgent research gaps.

Final comments: Looks and sounds plausible but we have no way of verifying the ‘accuracy’ of the above. As with yesterday, and interesting experiment none-the-less!