Abstract
BackgroundWith demographic shifts toward older populations, the number of people with dementia is steadily increasing. Alzheimer’s disease (AD) is the most common cause of dementia, and no curative treatment is available. The current best strategy is to delay disease progression and to practice early intervention to reduce the number of patients that ultimately develop AD. Therefore, promising novel biomarkers for early diagnosis are urgently required.MethodsTo identify blood-based biomarkers for early diagnosis of AD, we performed RNA sequencing (RNA-seq) analysis of 610 blood samples, representing 271 patients with AD, 91 cognitively normal (CN) adults, and 248 subjects with mild cognitive impairment (MCI). We first estimated cell-type proportions among AD, MCI, and CN samples from the bulk RNA-seq data using CIBERSORT and then examined the differentially expressed genes (DEGs) between AD and CN samples. To gain further insight into the biological functions of the DEGs, we performed gene set enrichment analysis (GSEA) and network-based meta-analysis.ResultsIn the cell-type distribution analysis, we found a significant association between the proportion of neutrophils and AD prognosis at a false discovery rate (FDR) < 0.05. Furthermore, a similar trend emerged in the results of routine blood tests from a large number of samples (n = 3,099: AD, 1,605; MCI, 994; CN, 500). In addition, GSEA and network-based meta-analysis based on DEGs between AD and CN samples revealed functional modules and important hub genes associated with the pathogenesis of AD. The risk prediction model constructed by using the proportion of neutrophils and the most important hub genes (EEF2 and RPL7) achieved a high AUC of 0.878 in a validation cohort; when further applied to a prospective cohort, the model achieved a high accuracy of 0.727.ConclusionsOur model was demonstrated to be effective in prospective AD risk prediction. These findings indicate the discovery of potential biomarkers for early diagnosis of AD, and their further improvement may lead to future practical clinical use.
Highlights
With demographic shifts toward older populations, the number of people with dementia is steadily increasing
After low-quality read sequences were discarded and reads with adaptor sequences were trimmed, 43.8, 47.3, and 43.2 million reads of cleaned data remained for the Alzheimer’s disease (AD), mild cognitive impairment (MCI), and cognitively normal (CN) samples, respectively, of which 82.7%, 82.1%, and 82.1% uniquely mapped to the human reference genome (GRCh37) (Supplementary Table S3)
The proportion of neutrophils was significantly increased in AD prognosis at an false discovery rate (FDR) < 0.05
Summary
With demographic shifts toward older populations, the number of people with dementia is steadily increasing. Alzheimer’s disease (AD) is the most common cause of dementia, and no curative treatment is available. The current best strategy is to delay disease progression and to practice early intervention to reduce the number of patients that develop AD. Since there is no treatment or prevention for AD, the current best strategy is to delay disease progression and to practice early intervention to reduce the number of patients that develop AD [2]. The current AD diagnosis is generally based on assessing patients’ cognitive function. These examinations are not performed routinely, because they are time-consuming and the results largely depend on the physician’s experience [9, 10]. Cerebrospinal fluid (CSF) biomarkers, including amyloid-beta 1–42 (Aβ1-42), total tau (T-tau), and phosphorylated tau 181 (P-tau181) [11, 12], and positron emission tomography (PET) imaging scans [13,14,15] are effective for AD diagnosis, but because of the highly invasive nature of CSF collection and high cost of PET, using these biomarkers as part of a general physical examination to facilitate early diagnosis and therapeutic intervention remains challenging
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