Characterisation and diagnosis of idiopathic Parkinson’s disease (iPD) is a current challenge that hampers both clinical assessment and clinical trial development with the potential inclusion of non-PD cases. Here, we used a targeted mass spectrometry approach to quantify 38 metabolites extracted from the serum of 231 individuals. This cohort is currently one of the largest metabolomic studies including iPD patients, drug-naïve iPD, healthy controls and patients with Alzheimer’s disease as a disease-specific control group. We identified six metabolites (3-hydroxykynurenine, aspartate, beta-alanine, homoserine, ornithine (Orn) and tyrosine) that are significantly altered between iPD patients and control participants. A multivariate model to predict iPD from controls had an area under the curve (AUC) of 0.905, with an accuracy of 86.2%. This panel of metabolites may serve as a potential prognostic or diagnostic assay for clinical trial prescreening, or for aiding in diagnosing pathological disease in the clinic.

特发性帕金森病 (iPD) 的特征和诊断是当前的一项挑战，它阻碍了临床评估和临床试验的开展，并可能纳入非帕金森病病例。在这里，我们使用靶向质谱方法对从 231 名个体的血清中提取的 38 种代谢物进行了定量。该队列是目前最大的代谢组学研究之一，包括 iPD 患者、未接受药物治疗的 iPD、健康对照以及作为疾病特异性对照组的阿尔茨海默病患者。我们确定了六种代谢物（3-羟基犬尿氨酸、天冬氨酸、β-丙氨酸、高丝氨酸、鸟氨酸 (Orn) 和酪氨酸），它们在 iPD 患者和对照参与者之间存在显着变化。从对照组预测 iPD 的多变量模型的曲线下面积 (AUC) 为 0.905，准确度为 86.2%。这组代谢物可以作为临床试验预筛选的潜在预后或诊断测定，或帮助诊断临床病理疾病。