Prognosis of Early-Onset vs. Late-Onset Mild Cognitive Impairment: Comparison of Conversion Rates and Its Predictors

Tábuas‐Pereira, Miguel; Baldeiras, Inês; Duro, Diana; Santiago, Beatriz; Ribeiro, Maria Helena; Leitão, Maria João; Oliveira, Catarina R.; Santana, Isabel

doi:10.3390/geriatrics1020011

Cited by 55 publications

(40 citation statements)

References 68 publications

(150 reference statements)

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“…Some patients in their MCI stages are converted to AD within a limit of the time window after baseline, while some are not. It has been reported that MCI patients progress to AD at a rate of 10% to 15% per year and 80% of these MCI patients will have converted to AD after approximately six years of follow-up 5,6 . It is an ongoing topic among AD-related researches to identify biomarkers that classify patients with MCI who later progress to AD (MCI converter) from those with MCI who do not progress to AD (MCI non-converter).…”

Section: Introductionmentioning

confidence: 99%

Predicting Alzheimer’s disease progression using multi-modal deep learning approach

Initiative

2019

Sci Rep

266

139

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Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.

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Section: Introductionmentioning

confidence: 99%

Predicting Alzheimer’s disease progression using multi-modal deep learning approach

Initiative

2019

Sci Rep

266

139

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“…Patients who already suffer from mild cognitive impairment (MCI) are at higher risk of developing AD [ 2 , 3 ]. Studies have shown that the conversion rate from MCI to AD is between 10 and 15% per year with 80% of these MCI patients progressing to AD after approximately 6 years of follow-up [ 4 , 5 ]. Identifying those who are at greatest risk of progression to AD is a central problem.…”

Section: Introductionmentioning

confidence: 99%

Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease

et al. 2021

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Background In Alzheimer’s disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A β pathology. Methods A cohort of n=2293 participants, of whom n=749 were A β positive, was selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A β pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A β pathology, models fit only to A β-positive subjects were compared to models fit to an extended cohort including subjects without established A β pathology, adjusting for covariate differences between the cohorts. Results A β pathology status was determined based on the A β42/A β40 ratio. The best predictive model of change in cognitive test scores for A β-positive subjects at the 2-year follow-up achieved an R2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F1 score of 0.791. A β-positive subjects declined faster on average than those without A β pathology, but the specific level of CSF A β was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R2 score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R2 score of 0.228. Conclusion Our analysis shows that CSF levels of A β are not strong predictors of the rate of cognitive decline in A β-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.

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“…Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia, and its presence is associated with a higher risk of progression to clinically probable Alzheimer's disease (AD) [1][2][3]. The annual conversion rate from MCI to AD has been reported as 10 to 15% [4]. After 6 years of follow-up, approximately 80% of MCI patients will have converted to AD (MCI converters [MCI-C]) [4,5], although some MCI patients remain stable or convert back to normal (MCI non-converters [MCI-NC]) [6].…”

Section: Introductionmentioning

confidence: 99%

“…The annual conversion rate from MCI to AD has been reported as 10 to 15% [4]. After 6 years of follow-up, approximately 80% of MCI patients will have converted to AD (MCI converters [MCI-C]) [4,5], although some MCI patients remain stable or convert back to normal (MCI non-converters [MCI-NC]) [6]. To date, there are no curative treatments for patients who already have AD, and available treatments are only able to postpone the progression of the disease [7].…”

Section: Introductionmentioning

confidence: 99%

Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer’s disease created by integrative analysis of multi-omics data

et al. 2020

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Background Mild cognitive impairment (MCI) is a precursor to Alzheimer’s disease (AD), but not all MCI patients develop AD. Biomarkers for early detection of individuals at high risk for MCI-to-AD conversion are urgently required. Methods We used blood-based microRNA expression profiles and genomic data of 197 Japanese MCI patients to construct a prognosis prediction model based on a Cox proportional hazard model. We examined the biological significance of our findings with single nucleotide polymorphism-microRNA pairs (miR-eQTLs) by focusing on the target genes of the miRNAs. We investigated functional modules from the target genes with the occurrence of hub genes though a large-scale protein-protein interaction network analysis. We further examined the expression of the genes in 610 blood samples (271 ADs, 248 MCIs, and 91 cognitively normal elderly subjects [CNs]). Results The final prediction model, composed of 24 miR-eQTLs and three clinical factors (age, sex, and APOE4 alleles), successfully classified MCI patients into low and high risk of MCI-to-AD conversion (log-rank test P = 3.44 × 10−4 and achieved a concordance index of 0.702 on an independent test set. Four important hub genes associated with AD pathogenesis (SHC1, FOXO1, GSK3B, and PTEN) were identified in a network-based meta-analysis of miR-eQTL target genes. RNA-seq data from 610 blood samples showed statistically significant differences in PTEN expression between MCI and AD and in SHC1 expression between CN and AD (PTEN, P = 0.023; SHC1, P = 0.049). Conclusions Our proposed model was demonstrated to be effective in MCI-to-AD conversion prediction. A network-based meta-analysis of miR-eQTL target genes identified important hub genes associated with AD pathogenesis. Accurate prediction of MCI-to-AD conversion would enable earlier intervention for MCI patients at high risk, potentially reducing conversion to AD.

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Prognosis of Early-Onset vs. Late-Onset Mild Cognitive Impairment: Comparison of Conversion Rates and Its Predictors

Cited by 55 publications

References 68 publications

Predicting Alzheimer’s disease progression using multi-modal deep learning approach

Predicting Alzheimer’s disease progression using multi-modal deep learning approach

Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease

Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer’s disease created by integrative analysis of multi-omics data

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