An aid diagnostic platform to detect the transition of mild cognitive impairment (MCI) to Alzheimer's disease (AD) based on 48,116 AD and MCI patients

Abstract Alzheimer's disease (AD) is an incurable, progressive neurodegenerative disorder, necessitating early diagnosis and intervention. Mild cognitive impairment (MCI) often precedes AD, but not all cases progress to AD, emphasizing the need for predictive biomarkers. We analyzed routine blood test data from 43,981 AD patients and 4,537 MCI subjects in Hong Kong hospitals (2000-2019). Among 31 shared biomarkers, five blood biomarkers (Hemoglobin, Hematocrit, Red blood cell related to oxygen carrying capacity, Neutrophils, and White blood cell related to immunity) significantly differentiated MCI from AD. Subjects were divided into four groups (Female 65 ~ 74, Male 65 ~ 74, Female 75 ~ 89, Male 75 ~ 89) to minimize gender and age bias. Models utilizing the five biomarkers along with machine learning yielded the highest accuracy in the Female 65 ~ 74 group (AUC of 0.76 on an independent test set). The other three models were trained with other biomarkers besides these 5 to optimize predictions, capturing models with AUC close to 0.70. We then constructed a platform predicting the risk of MCI converting to AD (MAP, http://lab.malab.cn/~lijing/MAP.html) to help physicians and MCI subjects with early diagnosis and prevention of AD. In conclusion, this study demonstrates the potential for accurate prediction of MCI to AD conversion using routine blood test data and machine learning, offering an economical and practical approach for early AD screening in MCI individuals.