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The role of interleukin-33 in patients with mild cognitive impairment and Alzheimer's disease.

ABSTRACT: BACKGROUND:The neuroprotective role of interleukin (IL)-33 is supported by numerous preclinical studies, but it remains uninvestigated in clinical studies of Alzheimer's disease (AD). We aimed to examine the association between human blood levels of IL-33 and cognitive preservation in amnestic mild cognitive impairment (aMCI) and AD. METHODS:A total of 100 participants (26 controls, 35 aMCI patients, and 39 AD patients) completed two Mini-Mental State Examinations (MMSEs) over a 1-year interval. In all 100 participants at the second MMSE, we examined the plasma levels of IL-33, IL-?, IL-1 receptor agonist (IL-1RA), beta amyloid (A?), and tau and apolipoprotein E (ApoE) genotyping; we also performed Hopkins Verbal Learning Test, Trail Making Test, forward and backward digit span, and Clinical Dementia Rating. RESULTS:IL-33 expression showed a positive trend among controls (1/26?=?3.8%), aMCI (9/35?=?25.7%), and AD (17/39?=?43.6%) (trend analysis: P?

SUBMITTER: Liang CS

PROVIDER: S-EPMC7367330 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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The role of interleukin-33 in patients with mild cognitive impairment and Alzheimer's disease.

Liang Chih-Sung CS Su Kuan-Pin KP Tsai Chia-Lin CL Lee Jiunn-Tay JT Chu Che-Sheng CS Yeh Ta-Chuan TC Su Ming-Wei MW Lin Guan-Yu GY Lin Yu-Kai YK Chu Hsuan-Te HT Tsai Chia-Kuang CK Yang Fu-Chi FC

Alzheimer's research & therapy 20200716 1

<h4>Background</h4>The neuroprotective role of interleukin (IL)-33 is supported by numerous preclinical studies, but it remains uninvestigated in clinical studies of Alzheimer's disease (AD). We aimed to examine the association between human blood levels of IL-33 and cognitive preservation in amnestic mild cognitive impairment (aMCI) and AD.<h4>Methods</h4>A total of 100 participants (26 controls, 35 aMCI patients, and 39 AD patients) completed two Mini-Mental State Examinations (MMSEs) over a 1 ...[more]

PMID: 32678011

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Project description:Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers.

Dataset Information

The role of interleukin-33 in patients with mild cognitive impairment and Alzheimer's disease.

Publications

The role of interleukin-33 in patients with mild cognitive impairment and Alzheimer's disease.

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