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<p>Blood transcriptomic signatures for Parkinson’s disease (PD) diagnosis have failed to integrate into clinical practice despite decades of translational efforts. We evaluated the classification performance of 13 published coding RNA-based signatures using both a large public dataset and data we collected prospectively in a controlled clinical study of levodopa-naïve patients and healthy controls. Our results show that gene overlap between signatures is low but significant (2.7%, <i>p</i> &lt; 0.001) and enriched for lipid metabolism genes. Most signatures (10/13) remained significant when tested on the Parkinson’s Progression Markers Initiative (PPMI) dataset, though with lower classification performance than previously reported (median AUC: 59.7%). Performance improved for <i>GBA1</i>-associated PD. Rigorous standardization of clinical and environmental parameters in our prospective study (30 participants) failed to improve transcriptome-based classification. We conclude that while the search for a universal blood-based PD transcriptome may elucidate disease pathophysiology, its clinical utility is inherently limited.</p>

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Inherent variability limits clinical utility of reproducible Parkinson’s transcriptomics signatures

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月19日

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  • Roy Dayan,
  • Serafima Dubnov,
  • Hagit Turm,
  • Michelle Grunin,
  • Shahar Shohat,
  • Salim T. Khoury,
  • Ami Citri,
  • Tamar Harel,
  • David Arkadir

摘要

Blood transcriptomic signatures for Parkinson’s disease (PD) diagnosis have failed to integrate into clinical practice despite decades of translational efforts. We evaluated the classification performance of 13 published coding RNA-based signatures using both a large public dataset and data we collected prospectively in a controlled clinical study of levodopa-naïve patients and healthy controls. Our results show that gene overlap between signatures is low but significant (2.7%, p < 0.001) and enriched for lipid metabolism genes. Most signatures (10/13) remained significant when tested on the Parkinson’s Progression Markers Initiative (PPMI) dataset, though with lower classification performance than previously reported (median AUC: 59.7%). Performance improved for GBA1-associated PD. Rigorous standardization of clinical and environmental parameters in our prospective study (30 participants) failed to improve transcriptome-based classification. We conclude that while the search for a universal blood-based PD transcriptome may elucidate disease pathophysiology, its clinical utility is inherently limited.