Can Clinical Data Predict Progression to Dementia in Amnestic Mild Cognitive Impairment?

Fellows, Lesley K.; Bergman, Howard; Wolfson, Christina; Chertkow, Howard

doi:10.1017/s0317167100008891

Cited by 18 publications

(13 citation statements)

References 90 publications

(135 reference statements)

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“…Conversion from all type MCI to any cause dementia was not predicted by smoking status in two epidemiological studies 25;26 , one higher quality 25 , and a clinical study after adjusting for age, gender and education 27 . There was no significant association between risk of AD in people with aMCI and smoking status in a higher quality epidemiological 18 or a clinical study 38 . Ever smoking did not predict conversion from aMCI to any dementia in two further studies 23;24 .…”

Section: Smokingmentioning

confidence: 79%

“…While none of these studies specifically excluded heavy drinkers, few participants drank more than one drink a day in the two studies that reported overall alcohol consumption 18;39 . Two studies, one epidemiological and one clinical, reported whether drinking any alcohol currently, as opposed to abstaining predicted conversion from any type MCI to dementia 26 and from aMCI to AD 38 . Neither study found this relationship was significant.…”

Section: Smokingmentioning

confidence: 99%

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Modifiable Predictors of Dementia in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis

Cooper¹,

Sommerlad²,

Lyketsos³

et al. 2015

AJP

412

290

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Objectives: Public health campaigns encouraging early help-seeking have increased rates of Mild Cognitive Impairment (MCI) diagnosis in Western countries, but we know little about how to treat or prognosticate dementia outcomes in persons with MCI. Method: We searched electronic databases and references for longitudinal studies reporting potentially modifiable risk factors for incident dementia after MCI. Two authors independently evaluated study quality using a checklist. We meta-analysed findings from three or more studies. Results: We identified 76 eligible papers. Diabetes and prediabetes increased risk of conversion from amnestic MCI (aMCI) to Alzheimer's Dementia (AD); risk in treated versus untreated diabetes was lower in one study. Diabetes was also associated with increased risk of conversion from any type or non-amnestic MCI to all cause dementia. Metabolic syndrome and pre-diabetes predicted all cause dementia in people with aMCI and any type MCI respectively. Mediterranean diet decreased risk of conversion from aMCI to AD. Presence of neuropsychiatric symptoms or lower serum folate levels predicted conversion from any type MCI to all cause dementia, but less formal education did not. Depressive symptoms predicted conversion from any type MCI to dementia in epidemiological, but not clinical studies. Conclusions: Diabetes increased the risk of conversion to dementia. Other prognostic factors that are potentially manageable are pre-diabetes and the metabolic syndrome, neuropsychiatric symptoms and low dietary folate. Dietary interventions and interventions to reduce neuropsychiatric symptoms including depression, which increase conversion risk to dementia, may decrease incident dementia.

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Section: Smokingmentioning

confidence: 79%

Section: Smokingmentioning

confidence: 99%

Modifiable Predictors of Dementia in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis

Cooper¹,

Sommerlad²,

Lyketsos³

et al. 2015

AJP

412

290

View full text Add to dashboard Cite

show abstract

“…Examples of classification of MCI Converted subjects based on the outputs of the selected subset of networks. Comprehensive neuropsychological tests alone generally do not allow high accuracy rates, rarely surpassing 70% sensitivity and specificity even when combined with the presence of the APOE epsilon 4 allele (a genetically determined risk factor for dementia) [27][28][29].…”

Section: Discussionmentioning

confidence: 99%

The I.F.A.S.T. Model Allows the Prediction of Conversion to Alzheimer Disease in Patients with Mild Cognitive Impairment with High Degree of Accuracy

Buscema¹,

Grossi²,

Capriotti³

et al. 2010

CAR

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This paper presents the results obtained with the innovative use of special types of artificial neural networks (ANNs) assembled in a novel methodology named IFAST (implicit function as squashing time) capable of compressing the temporal sequence of electroencephalographic (EEG) data into spatial invariants. The aim of this study is to test the potential of this parallel and nonlinear EEG analysis technique in providing an automatic classification of mild cognitive impairment (MCI) subjects who will convert to Alzheimer's disease (AD) with a high degree of accuracy. Eyes-closed resting EEG data (10-20 electrode montage) were recorded in 143 amnesic MCI subjects. Based on 1-year follow up, the subjects were retrospectively classified to MCI converted to AD and MCI stable. The EEG tracks were successively filtered according to four different frequency ranges, in order to evaluate the hypotheses that a specific range, corresponding to specific brain wave type, could provide a better classification (0.12 Hz, 12.2 - 29.8 Hz; 30.2 - 40 Hz, and finally Notch Filter 48 - 50 Hz). The spatial content of the EEG voltage was extracted by IFAST step-wise procedure using ANNs. The data input for the classification operated by ANNs were not the EEG data, but the connections weights of a nonlinear auto-associative ANN trained to reproduce the recorded EEG tracks. These weights represented a good model of the peculiar spatial features of the EEG patterns at scalp surface. The classification based on these parameters was binary and performed by a supervised ANN. The best results distinguishing between MCI stable and MCI/AD reached to 85.98%.(012 Hz band). And confirmed the working hypothesis that a correct automatic classification can be obtained extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG. These results suggest that this low-cost procedure can reliably distinguish eyes-closed resting EEG data in individual MCI subjects who will have different prognosis at 1-year follow up, and is promising for a large-scale periodic screening of large populations at amnesic MCI subjects at risk of AD.

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“…The majority of the studies assessing the cognitive profile in MCI are predictive studies aiming to identify the factors/neuropsychological tests that are predictive of future progression to dementia [10][11][12] (for a review, see [13]). In turn, natural history studies aim to characterize the changes that occur in different cognitive domains as individuals progress during the early phase of MCI, and to provide the opportunity to study the trajectory of the symptoms, per cognitive domain.…”

Section: Natural Historymentioning

confidence: 99%

O2‐07‐03: Patterns of Cognitive Decline Prior to Dementia in Persons With Mild Cognitive Impairment

Cloutier

Chertkow

Lepage

et al. 2014

Alzheimer's & Dementia

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Abstract. Only a limited number of studies have investigated the decline of discrete cognitive domains as individuals progress from mild cognitive impairment (MCI) to dementia. Thus, the goal of this longitudinal study was to evaluate the cognitive changes underway during the years preceding a diagnosis of probable Alzheimer's disease (AD), and to compare these changes to those found in MCI participants who do not progress to dementia. Participants were compared as a function of whether they later converted to AD (n = 47) or not (n = 74). Cognitive change was assessed prior to the conversion year, using that year as a starting point. A combination of polynomial regression analyses and mixed ANOVAs assessed 1) the trajectory of cognitive decline for each domain and 2) the differences between non-progressors and those who had converted to AD. The different cognitive domains demonstrated very different patterns of decline in the group of MCI progressors. A quadratic function, i.e., many years of stable performance followed by a rapid decline just prior to diagnosis, was observed for delayed recall, working memory, and spatial memory. In contrast, a gradual linear decline was observed for immediate recall, executive function, and visuo-spatial abilities. Finally, language in progressors was impaired on all time periods relative to non-progressors, but there was no further change between the first assessments and conversion to AD. Individuals with MCI who progress to AD show abnormal cognition at least two years prior to their dementia diagnosis. The pattern of symptom change observed appears to depend upon the cognitive domain and thus, clinical studies should not assume similar rate of decline across domains. In contrast and, apart from verbal memory, the non-progressors present a performance similar to that of healthy older adults.

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Can Clinical Data Predict Progression to Dementia in Amnestic Mild Cognitive Impairment?

Cited by 18 publications

References 90 publications

Modifiable Predictors of Dementia in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis

Modifiable Predictors of Dementia in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis

The I.F.A.S.T. Model Allows the Prediction of Conversion to Alzheimer Disease in Patients with Mild Cognitive Impairment with High Degree of Accuracy

O2‐07‐03: Patterns of Cognitive Decline Prior to Dementia in Persons With Mild Cognitive Impairment

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