Watanabe Yumi, Hirao Yoshitoshi, Kasuga Kensaku, Kitamura Kaori, Nakamura Kazutoshi, Yamamoto Tadashi
Division of Preventive Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
Biofluid and Biomarker Center, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan.
Front Neurol. 2023 Mar 16;14:1134976. doi: 10.3389/fneur.2023.1134976. eCollection 2023.
Non-invasive and simple methods enabling easy identification of individuals at high risk of cognitive decline are needed as preventive measures against dementia. This pilot study aimed to explore protein biomarkers that can predict cognitive decline using urine, which can be collected non-invasively. Study subjects were selected from participants in a cohort study of middle-aged and older community-dwelling adults who underwent cognitive testing using the Mini-Mental State Examination and provided spot urine samples at two time points with an interval of approximately 5 years. Seven participants whose cognitive function declined 4 or more points from baseline (Group D) and 7 sex- and age-matched participants whose cognitive function remained within the normal range during the same period (Group M) were selected. Urinary proteomics using mass spectrometry was performed and discriminant models were created using orthogonal partial least squares-discriminant analysis (OPLS-DA). OPLS-DA yielded two models that significantly discriminated between the two groups at baseline and follow-up. Both models had ORM1, ORM2, and SERPINA3 in common. A further OPLS-DA model using baseline ORM1, ORM2, and SERPINA3 data showed similar predictive performance for data at follow-up as it did for baseline data (sensitivity: 0.85, specificity: 0.85), with the receiver operating characteristic curve analysis yielding an area under the curve of 0.878. This prospective study demonstrated the potential for using urine to identify biomarkers of cognitive decline.
作为预防痴呆症的措施，需要有非侵入性且简单的方法，以便轻松识别认知能力下降高危个体。这项试点研究旨在探索能够利用可无创收集的尿液来预测认知能力下降的蛋白质生物标志物。研究对象从一项针对社区居住的中老年人队列研究的参与者中选取，这些参与者接受了简易精神状态检查的认知测试，并在两个时间点（间隔约5年）提供了即时尿液样本。选取了7名认知功能较基线下降4分或更多的参与者（D组）以及7名在同一时期认知功能保持在正常范围内的性别和年龄匹配的参与者（M组）。采用质谱法进行尿液蛋白质组学分析，并使用正交偏最小二乘判别分析（OPLS-DA）创建判别模型。OPLS-DA生成了两个在基线和随访时能显著区分两组的模型。两个模型都有ORM1、ORM2和SERPINA3这几个共同因素。使用基线ORM1、ORM2和SERPINA3数据的进一步OPLS-DA模型对随访数据的预测性能与对基线数据的预测性能相似（敏感性：0.85，特异性：0.85），受试者工作特征曲线分析得出曲线下面积为0.878。这项前瞻性研究证明了利用尿液识别认知能力下降生物标志物的潜力。
