Ph.D. Program in Health Data Science, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal.
Center for Minimally Invasive Therapies, Institute of Medical Science Research and Development, Tokyo Medical University, Tokyo 160-8402, Japan.
Int J Mol Sci. 2022 Apr 29;23(9):4962. doi: 10.3390/ijms23094962.
Alzheimer's disease (AD) has become a problem, owing to its high prevalence in an aging society with no treatment available after onset. However, early diagnosis is essential for preventive intervention to delay disease onset due to its slow progression. The current AD diagnostic methods are typically invasive and expensive, limiting their potential for widespread use. Thus, the development of biomarkers in available biofluids, such as blood, urine, and saliva, which enables low or non-invasive, reasonable, and objective evaluation of AD status, is an urgent task. Here, we reviewed studies that examined biomarker candidates for the early detection of AD. Some of the candidates showed potential biomarkers, but further validation studies are needed. We also reviewed studies for non-invasive biomarkers of AD. Given the complexity of the AD continuum, multiple biomarkers with machine-learning-classification methods have been recently used to enhance diagnostic accuracy and characterize individual AD phenotypes. Artificial intelligence and new body fluid-based biomarkers, in combination with other risk factors, will provide a novel solution that may revolutionize the early diagnosis of AD.
阿尔茨海默病(AD)已成为一个问题,因为在老龄化社会中,该病的发病率很高,且在发病后尚无治疗方法。然而,由于该病进展缓慢,早期诊断对于预防干预以延迟发病至关重要。目前的 AD 诊断方法通常具有侵入性且昂贵,限制了其广泛应用的潜力。因此,在可利用的生物体液(如血液、尿液和唾液)中开发生物标志物,从而能够以低侵入性或非侵入性、合理且客观的方式评估 AD 状态,是一项紧迫的任务。在这里,我们回顾了研究 AD 早期检测中生物标志物候选物的文章。一些候选物显示出了有潜力的生物标志物,但仍需要进一步的验证研究。我们还回顾了用于 AD 的非侵入性生物标志物的研究。鉴于 AD 连续体的复杂性,最近已经使用机器学习分类方法的多种生物标志物来提高诊断准确性并表征个体 AD 表型。人工智能和新的基于体液的生物标志物,结合其他风险因素,将提供一种可能彻底改变 AD 早期诊断的新解决方案。