Abstract
Alzheimer’s disease (AD) is the most common subtype of dementia, followed by Vascular Dementia (VaD), and Dementia with Lewy Bodies (DLB). Recently, microRNAs (miRNAs) have received a lot of attention as the novel biomarkers for dementia. Here, using serum miRNA expression of 1,601 Japanese individuals, we investigated potential miRNA biomarkers and constructed risk prediction models, based on a supervised principal component analysis (PCA) logistic regression method, according to the subtype of dementia. The final risk prediction model achieved a high accuracy of 0.873 on a validation cohort in AD, when using 78 miRNAs: Accuracy = 0.836 with 86 miRNAs in VaD; Accuracy = 0.825 with 110 miRNAs in DLB. To our knowledge, this is the first report applying miRNA-based risk prediction models to a dementia prospective cohort. Our study demonstrates our models to be effective in prospective disease risk prediction, and with further improvement may contribute to practical clinical use in dementia.
Highlights
With an increasingly aging global human population, the number of people with dementia is rapidly increasing, and is estimated to reach 75 million by 2030 and 135 million by 2050, worldwide[1]
The adjusted models were evaluated on the validation cohort, which was completely independent from the discovery cohort
The role of serum miRNAs has recently been reviewed with emphasis on their impact on the etiopathogenesis of sporadic AD32 and cancers[21,22,23,33,34]
Summary
With an increasingly aging global human population, the number of people with dementia is rapidly increasing, and is estimated to reach 75 million by 2030 and 135 million by 2050, worldwide[1]. We performed a comprehensive miRNA expression analysis using 1601 serum samples, composed of dementia patients and individuals with cognitive normal function (referred to as normal controls (NC)), in order to investigate new biomarkers for earlier diagnosis and therapeutic intervention and to construct risk prediction models using the biomarkers. We determined the optimal miRNA and PC score set though crossvalidation This final risk prediction model, constructed based on the entire discovery cohort, was evaluated with an independent validation cohort by the area under the receiver operating characteristic curve (AUC). Our findings indicate that the prediction models using serum miRNA expression data may be useful as biomarkers for dementia and contribute to the development of future therapeutic measurement for this common but serious disorder
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