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Increased prediction value of biomarker combinations for the conversion of mild cognitive impairment to Alzheimer's dementia.

ABSTRACT: BACKGROUND:Progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia can be predicted by clinical features and a combination of biomarkers may increase the predictive power. In the present study, we investigated whether the combination of olfactory function and plasma neuronal-derived exosome (NDE) A?1-42 can best predict progression to AD dementia. METHODS:87 MCI patients were enrolled and received the cognitive assessment at 2-year and 3-year follow-up to reevaluate cognition. In the meanwhile, 80 healthy controls and 88?AD dementia patients were enrolled at baseline as well to evaluate the diagnose value in cross-section. Olfactory function was evaluated with the sniffin sticks (SS-16) and A?1-42 levels in NDEs were determined by ELISA. Logistic regression was performed to evaluate the risk factors for cognitive decline in MCI at 2-year and 3-year revisits. RESULTS:In the cross cohort, lower SS-16 scores and higher A?1-42 levels in NDEs were found in MCI and AD dementia compared to healthy controls. For the longitudinal set, 8 MCI individuals developed AD dementia within 2?years, and 16 MCI individuals developed AD dementia within 3?years. The two parameter-combination of SS-16 scores and A?1-42 level in NDEs showed better prediction in the conversion of MCI to AD dementia at 2-year and 3-year revisit. Moreover, after a 3-year follow-up, SS-16 scores also significantly predicted the conversion to AD dementia, where lower scores were associated with a 10-fold increased risk of developing AD dementia (p?=?0.006). Similarly, higher A?1-42 levels in NDEs in patients with MCI increased the risk of developing AD dementia by 8.5-fold (p?=?0.002). CONCLUSION:A combination of two biomarkers of NDEs (A?1-42) and SS-16 predicted the conversion of MCI to AD dementia more accurately in combination. These findings have critical implications for understanding the pathophysiology of AD dementia and for developing preventative treatments for cognitive decline.

SUBMITTER: Zhao A

PROVIDER: S-EPMC7397685 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Increased prediction value of biomarker combinations for the conversion of mild cognitive impairment to Alzheimer's dementia.

Zhao Aonan A Li Yuanyuan Y Yan Yi Y Qiu Yinghui Y Li Binyin B Xu Wei W Wang Ying Y Liu Jun J Deng Yulei Y

Translational neurodegeneration 20200803 1

<h4>Background</h4>Progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia can be predicted by clinical features and a combination of biomarkers may increase the predictive power. In the present study, we investigated whether the combination of olfactory function and plasma neuronal-derived exosome (NDE) Aβ<sub>1-42</sub> can best predict progression to AD dementia.<h4>Methods</h4>87 MCI patients were enrolled and received the cognitive assessment at 2-year and 3-year ...[more]

PMID: 32741361

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Project description:Multiple biomarkers can capture different facets of Alzheimer's disease. However, statistical models of biomarkers to predict outcomes in Alzheimer's rarely model nonlinear interactions between these measures. Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Measures included: demographics, APOE4 genotype, CSF (amyloid-β42, total tau, phosphorylated tau), [18F ]florbetapir, hippocampal volume and brain-age. We examined: (a) the independent value of each biomarker; and (b) whether modelling nonlinear interactions between biomarkers improved predictions. Each measured added complementary information when predicting conversion to Alzheimer's. A linear model classifying stable from progressive MCI explained over half the variance (R2 = 0.51, p < .001); the strongest independently contributing biomarker was hippocampal volume (R2 = 0.13). When comparing sensitivity of different models to progressive MCI (independent biomarker models, additive models, nonlinear interaction models), we observed a significant improvement (p < .001) for various two-way interaction models. The best performing model included an interaction between amyloid-β-PET and P-tau, while accounting for hippocampal volume (sensitivity = 0.77, AUC = 0.826). Closely related biomarkers contributed uniquely to predict conversion to Alzheimer's. Nonlinear biomarker interactions were also implicated, and results showed that although for some patients adding additional biomarkers may add little value (i.e., when hippocampal volume is high), for others (i.e., with low hippocampal volume) further invasive and expensive examination may be warranted. Our framework enables visualisation of these interactions, in individual patient biomarker 'space', providing information for personalised or stratified healthcare or clinical trial design.

Dataset Information

Increased prediction value of biomarker combinations for the conversion of mild cognitive impairment to Alzheimer's dementia.

Publications

Increased prediction value of biomarker combinations for the conversion of mild cognitive impairment to Alzheimer's dementia.

Similar Datasets

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