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0Scientists find way to predict dementia
科学家找到预测痴呆症的方法
Research identifies blood biomarkers to detect disorder 15 years in advance
研究确定血液生物标记物，可提前15年检测疾病
Using a massive databank and artificial intelligence, Shanghai scientists have discovered biomarkers in plasma that can predict dementia 15 years before symptoms begin. The scientists said that their research results may play a major role in early intervention for healthy adults who are at high risk of developing the disorder.
上海科学家利用庞大的数据库和人工智能，在血浆中发现了生物标志物，可以在症状出现前15年预测痴呆症。科学家们表示，其研究结果可能发挥重要作用，帮助对患有痴呆的高风险健康成年人进行早期干预。
It is often difficult to diagnose brain disorders, doctors said. Lumbar punctures are invasive, and examinations of brain images are expensive. In contrast, blood tests are convenient, non-invasive and cost-effective. Fudan University scientists said they are hopeful that blood testing to predict the likelihood of dementia can be applied in clinical settings within six months. The early detection of the disorder opens a door to early intervention, offering the potential to slow down or even halt its progression.
医生们认为，诊断大脑疾病通常很困难。腰椎穿刺是侵入性的，并且脑部图像的检查费用昂贵。相比之下，血液检测方便、无创、更划算。复旦大学科学家希望预测痴呆症可能性的血液检测能够在六个月内应用于临床。痴呆症越早发现，越有利于早期干预，有可能减缓甚至阻止其进展。
According to the World Health Organization, dementia affects over 55 million people worldwide, and that figure is expected to continue to rise. Dementia progresses slowly, from an asymptomatic stage to a fully expressed clinical syndrome, over the course of a decade or more. "By the time patients begin showing cognitive behavioral problems, the disorder may have already progressed to the middle or late stages, and the best intervention time will have been missed," said Feng Jianfeng, a computational biologist at the Fudan University institute.
据世界卫生组织称，全球有超过5500万人患有痴呆症，而且这一数字预计将继续上升。痴呆症进展缓慢，从无症状阶段到完全表达的临床综合征，需要十年或更长时间。复旦大学研究院计算生物学家冯建峰表示：“当患者开始出现认知行为问题时，疾病可能已经发展到中晚期，已经错过了最佳干预时间。”
The researchers employed the help of the massive United Kingdom Biobank cohort, which enrolled more than 52,600 healthy adults and had a median follow-up period of 14 years. Among them, 1,417 people were diagnosed with all-cause dementia, 691 with Alzheimer's disease and 285 with vascular dementia. For each participant, 1,463 proteins in plasma associated with cardio metabolism, inflammation, neurology and oncology were tested, and researchers used survival association analysis and machine learning algorithms to perform modeling analysis.
研究人员借助了英国生物银行庞大队列，该队列招募了超过 52,600名健康成年人，随访期中位数为14年。其中1417人被诊断出患有全因痴呆症，691人患有阿尔茨海默病，285人患有血管性痴呆症。该研究测试了每位参与者血浆中与心脏代谢、炎症、神经学和肿瘤学相关的 1463 种蛋白质，研究人员使用生存关联分析和机器学习算法进行建模分析。
They discovered significant associations of three proteins — GFAP, NEFL, and GDF15 — with the risk of those three types of dementia. They also found that the protein LTBP2 plays a role in the onset of the disorder. These biomarkers, as well as conventional risk factors of age, gender, education level and genetics, were used to facilitate the high accuracy of the predictive model, exceeding 90 percent.
他们发现三种蛋白质（GFAP、NEFL 和 GDF15）与这三种类型的痴呆症风险存在显着关联，还发现 LTBP2 蛋白在疾病发作中发挥作用。这些生物标志物以及年龄、性别、教育水平和遗传学等传统风险因素被用来促进预测模型的高精度，可超过90%。
"Our study provides a great example of how AI can facilitate a research paradigm that fosters interdisciplinary collaboration," Feng said. "Employing machine learning, we extracted and optimized the combinations using a large-scale dataset and established a protein-based dementia prediction model with high accuracy."
冯建峰表示，“我们的研究提供了很好的范例，说明人工智能如何促进跨学科合作的研究范式。”“利用机器学习，我们利用大规模数据集提取并优化了这些组合，并建立了基于蛋白质的高精度痴呆症预测模型。”
The team will now focus on conducting data collection and cross-validation among populations at risk of dementia in China. It will tailor the dementia risk prediction model to fit the characteristics of the Chinese population by gathering relevant data.
该团队现在将重点在中国有痴呆症风险的人群中进行数据收集和交叉验证。它将通过收集相关数据，调整出适合中国人群特征的痴呆风险预测模型。
