Use of mild cognitive impairment and prodromal AD/MCI due to AD in clinical care: a European survey

Problems with memory are a very common complaint in the elderly and are not synonymous with dementia. Some degree of cognitive decline, manifested as greater difficulty in learning and retrieving new information for instance, develops with normal aging. Thus many older patients do not perform at the same level they did when they were younger but they do perform well when compared to their peers. For many, cognitive change ends at this stage and they proceed to lead normal, healthy, dementia-free lives.The cohort that has cognitive changes beyond what is expected in normal aging but does not yet meet criteria for dementia concerns clinicians greatly as many of these patients eventually become demented. These patients usually go through a latent stage in which neurodegenerative pathology silently spreads in the brain. Once there is enough pathological burden, cognitive decline beyond what is expected for normal aging can be detected by formal neuropsychological testing. Frequently such patients go through a state called mild cognitive impairment (MCI). In this state patients are still functionally intact and live independently, but show cognitive impairment relative to the age- and education-adjusted norms.The MCI state in itself is a prominent risk factor for developing dementia. Most patients with amnestic MCI develop Alzheimer’s disease (AD) dementia over time. At six years, as many as 80% progress to AD. Thus, MCI is a very important topic of research and an increasingly important topic of clinical care.

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Patterns of brain atrophy in dysexecutive amnestic mild cognitive impairment raise confidence about prodromal AD dementia

Alzheimer s & Dementia ◽

10.1002/alz.046365 ◽

2020 ◽

Vol 16 (S6) ◽

Author(s):

Almudena Junquera Fernández ◽

Estefanía García ◽

Mario A. Parra ◽

Sara Fernández Guinea

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Brain Atrophy ◽

Amnestic Mild Cognitive Impairment ◽

Prodromal Ad

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Mild cognitive impairment in clinical care: A survey of American Academy of Neurology members

Neurology ◽

10.1212/wnl.0b013e3181eb5872 ◽

2010 ◽

Vol 75 (5) ◽

pp. 425-431 ◽

Cited By ~ 57

Author(s):

J. S. Roberts ◽

J. H. Karlawish ◽

W. R. Uhlmann ◽

R. C. Petersen ◽

R. C. Green

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

American Academy ◽

Clinical Care ◽

American Academy Of Neurology

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Robust Discovery of Mild Cognitive Impairment Subtypes and Their Risk of Alzheimer’s Disease Conversion Using Unsupervised Machine Learning and Gaussian Mixture Modeling

Current Alzheimer Research ◽

10.2174/1567205018666210831145825 ◽

2021 ◽

Vol 18 ◽

Author(s):

Fahimeh Nezhadmoghadam ◽

Antonio Martinez-Torteya ◽

Victor Treviño ◽

Emmanuel Martínez ◽

Alejandro Santos ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

High Risk ◽

Gaussian Mixture ◽

Low Risk ◽

Latent Classes ◽

Consensus Clustering ◽

Prodromal Ad

Background: Alzheimer’s disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. The ability to correctly predict the diagnosis of Alzheimer’s disease in its earliest stages can help physicians make more informed clinical decisions on therapy plans. Objective: This study aimed to determine whether the unsupervised discovering of latent classes of subjects with mild cognitive impairment (MCI) may be useful in finding different prodromal AD stages and/or subjects with a low MCI to AD conversion risk. Methods: Total 18 features relevant to the MCI to AD conversion process led to the identification of 681 subjects with early MCI. Subjects were divided into training (70%) and validation (30%) sets. Subjects from the training set were analyzed using consensus clustering, and Gaussian mixture models (GMM) were used to describe the latent classes. The discovered GMM predicted the latent class of the validation set. Finally, descriptive statistics, rates of conversion, and odds ratios (OR) were computed for each discovered class. Results: Through consensus clustering, we discovered three different clusters among MCI subjects. The three clusters were associated with low-risk (OR = 0.12, 95%CI = 0.04 to 0.3|), medium-risk (OR = 1.33, 95%CI = 0.75 to 2.37), and high-risk (OR = 3.02, 95%CI = 1.64 to 5.57) of converting from MCI to AD, with the high-risk and low-risk groups highly contrasting. Hence, prodromal AD subjects were present in only two clusters. Conclusion: We successfully discovered three different latent classes among MCI subjects with varied risks of MCI-to- AD conversion through consensus clustering. Two of the discovered classes may represent two different prodromal presentations of Alzheimer´s disease.

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Robust Discovery of Mild Cognitive impairment subtypes and their Risk of Alzheimer’s Disease conversion using unsupervised machine learning and Gaussian Mixture Modeling

10.1101/2020.10.14.20212696 ◽

2020 ◽

Author(s):

Fahimeh Nezhadmoghadam ◽

Antonio Martinez-Torteya ◽

Victor Treviño ◽

Emmanuel Martínez ◽

Alejandro Santos ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

High Risk ◽

Gaussian Mixture ◽

Low Risk ◽

Latent Classes ◽

Consensus Clustering ◽

Prodromal Ad

ABSTRACTBackgroundAlzheimer’s disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. The ability to correctly predict the diagnosis of Alzheimer’s disease in its earliest stages can help physicians make more informed clinical decisions on therapy plans.ObjectiveTo determine whether the unsupervised discovering of latent classes of subjects with mild cognitive impairment (MCI) may be useful in finding different prodromal AD stages and/or subjects that have a low MCI to AD conversion risk.Methods18 features relevant with the MCI to AD conversion process described 681 subjects with early MCI. Subjects were split into training (70%) and validation (30%) sets. Subjects from the training set were analyzed using consensus clustering and Gaussian mixture models (GMM) were used to describe the shape of the discovered latent classes. The discovered GMM predicted the latent class of the validation set. Finally, descriptive statistics, rates of conversion, and odds ratios (OR) were computed for each discovered class.ResultsThrough consensus clustering we discovered three different clusters among MCI subjects. The three clusters were associated with low-risk (OR = 0.12, 95%CI = 0.04 to 0.3|), medium-risk (OR = 1.33, 95%CI = 0.75 to 2.37), and high-risk (OR = 3.02, 95%CI = 1.64 to 5.57) of converting from MCI to AD, with the high-risk and low-risk groups highly contrasting. Hence, prodromal AD subjects were present on only two clusters.ConclusionWe successfully discovered three different latent classes among MCI subjects with varied risk of MCI-to-AD conversion through consensus clustering. Two of the discovered classes may represent two different prodromal presentations of the Alzheimer’s disease.

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Frontal Assessment Battery as a Useful Tool to Differentiate Mild Cognitive Impairment due to Subcortical Ischemic Vascular Disease from Alzheimer Disease

Dementia and Geriatric Cognitive Disorders ◽

10.1159/000452762 ◽

2016 ◽

Vol 42 (5-6) ◽

pp. 331-341 ◽

Cited By ~ 8

Author(s):

Yen-Hsuan Hsu ◽

Ching-Feng Huang ◽

Chung-Ping Lo ◽

Tzu-Lan Wang ◽

Chi-Cheng Yang ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

White Matter ◽

Alzheimer Disease ◽

Vascular Disease ◽

Verbal Learning ◽

Executive Dysfunction ◽

Frontal Assessment Battery ◽

Assessment Battery ◽

Mci Due To Ad

Background: Prominent executive dysfunction can differentiate vascular dementia from Alzheimer disease (AD). However, it is unclear whether the Frontal Assessment Battery (FAB) screening tool can differentiate subcortical ischemic vascular disease (SIVD) from AD at the pre-dementia stage. In addition, the neural correlates of FAB performance have yet to be clarified. Methods: Patients with mild cognitive impairment (MCI) due to SIVD (MCI-V), MCI due to AD (MCI-A), and demographically matched controls completed the Mini-Mental State Examination, Taiwanese FAB (TFAB), Category Fluency, and Chinese Version of the Verbal Learning Test, and underwent magnetic resonance imaging. White matter hyperintensities were rated according to the Scheltens scale. Results: TFAB total scale and its Orthographical Fluency subtest were the only measures that could differentiate MCI-V from MCI-A. Discriminative analysis showed that Orthographical Fluency scores successfully identified 73.2% of the cases with MCI-V, with 85.0% sensitivity. Orthographical Fluency scores were specifically associated with lesion load within frontal periventricular, frontal deep white matter, and basal ganglia regions. Conclusion: The TFAB, and especially its 1-min Orthographical Fluency subtest, is a useful screening procedure to differentiate MCI due to SIVD from MCI due to AD. The discriminative ability is probably due to frontosubcortical white matter pathologies disproportionately involved in the two disease entities.

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Use of Cerebrospinal Fluid Biomarkers of Alzheimer’s Disease Risk in Mild Cognitive Impairment and Subjective Cognitive Decline in Routine Clinical Care in Germany

Journal of Alzheimer s Disease ◽

10.3233/jad-200794 ◽

2020 ◽

Vol 78 (3) ◽

pp. 1137-1148

Author(s):

Claudia Bartels ◽

Anna Kögel ◽

Mark Schweda ◽

Jens Wiltfang ◽

Michael Pentzek ◽

...

Keyword(s):

Cerebrospinal Fluid ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Cognitive Decline ◽

Clinical Care ◽

Subjective Cognitive Decline ◽

Csf Biomarkers ◽

Routine Clinical Care ◽

Biomarker Analysis ◽

Csf Biomarker

Background: The National Institute of Aging and Alzheimer’s Association’s diagnostic recommendations for preclinical Alzheimer’s disease (AD) and mild cognitive impairment (MCI) define AD by pathological processes which can be detected by biomarkers. These criteria were established as part of a research framework intended for research purposes but progressively enter the clinical practice. Objective: We investigated the availability, frequency of use, interpretation, and therapeutic implications of biomarkers for the etiologic diagnosis and prognosis in MCI and subjective cognitive decline (SCD) in routine clinical care. Methods: We conducted a cross-sectional questionnaire survey among 215 expert dementia centers (hospitals and memory clinics) in Germany. Results: From the 98 centers (45.6% of contacted centers) included, two-thirds reported use of the cerebrospinal fluid (CSF) biomarkers Aβ42, tau, and phospho-tau in the diagnostic workup of MCI and one third in SCD. CSF biomarker analysis was more often employed by neurological (MCI 84%; SCD 42%) compared to psychiatric institutions (MCI 61%; SCD 33%; p≤0.001). Although dementia experts disagreed on the risk of progression associated with different CSF biomarker constellations, CSF biomarker results guided therapeutic decisions: ∼40% of responders reported to initiate cholinesterase inhibitor therapy in MCI and 18% in SCD (p = 0.006), given that all CSF biomarkers were in the pathological range. Conclusion: Considering the vast heterogeneity among dementia expert centers in use of CSF biomarker analysis, interpretation of results, and therapeutic consequences, a standardization of biomarker-based diagnosis practice in pre-dementia stages is needed.

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Biomarker counseling, disclosure of diagnosis, and follow‐up in patients with mild cognitive impairment: A European survey of EADC centers

Alzheimer s & Dementia ◽

10.1002/alz.039026 ◽

2020 ◽

Vol 16 (S7) ◽

Author(s):

Kristian Steen Frederiksen ◽

Rune Nielsen ◽

David Robinson ◽

Lucrezia Hausner ◽

Bernard J Hanseeuw ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

European Survey ◽

Disclosure Of Diagnosis

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Precuneus and Cingulate Cortex Atrophy and Hypometabolism in Patients with Alzheimer’s Disease and Mild Cognitive Impairment: MRI and18F-FDG PET Quantitative Analysis Using FreeSurfer

BioMed Research International ◽

10.1155/2015/583931 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 39

Author(s):

Matthieu Bailly ◽

Christophe Destrieux ◽

Caroline Hommet ◽

Karl Mondon ◽

Jean-Philippe Cottier ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Cingulate Cortex ◽

Posterior Cingulate Cortex ◽

Regions Of Interest ◽

Posterior Cingulate ◽

Amnestic Mci ◽

Prodromal Ad

Objective.The objective of this study was to compare glucose metabolism and atrophy, in the precuneus and cingulate cortex, in patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI), using FreeSurfer.Methods.47 individuals (17 patients with AD, 17 patients with amnestic MCI, and 13 healthy controls (HC)) were included. MRI and PET images using18F-FDG (mean injected dose of 185 MBq) were acquired and analyzed using FreeSurfer to define regions of interest in the hippocampus, amygdala, precuneus, and anterior and posterior cingulate cortex. Regional volumes were generated. PET images were registered to the T1-weighted MRI images and regional uptake normalized by cerebellum uptake (SUVr) was measured.Results.Mean posterior cingulate volume was reduced in MCI and AD. SUVr were different between the three groups: mean precuneus SUVr was 1.02 for AD, 1.09 for MCI, and 1.26 for controls (p<0.05); mean posterior cingulate SUVr was 0.96, 1.06, and 1.22 for AD, MCI, and controls, respectively (p<0.05).Conclusion.We found graduated hypometabolism in the posterior cingulate cortex and the precuneus in prodromal AD (MCI) and AD, whereas atrophy was not significant. This suggests that the use of18F-FDG in these two regions could be a neurodegenerative biomarker.

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Cerebrospinal Fluid Biomarkers for the Diagnosis of Prodromal Alzheimer’s Disease in Amnestic Mild Cognitive Impairment

Dementia and Geriatric Cognitive Disorders Extra ◽

10.1159/000496920 ◽

2019 ◽

Vol 9 (1) ◽

pp. 100-113 ◽

Cited By ~ 5

Author(s):

Jung Eun Park ◽

Kyu Yeong Choi ◽

Byeong C. Kim ◽

Seong-Min Choi ◽

Min-Kyung Song ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Cerebrospinal Fluid ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Tau Protein ◽

Amnestic Mild Cognitive Impairment ◽

Csf Biomarkers ◽

Cutoff Values ◽

Prodromal Ad ◽

T Tau

Background/Aims: Disease-modifying therapy for Alzheimer’s disease (AD) has led to a need for biomarkers to identify prodromal AD and very early stage of AD dementia. We aimed to identify the cutoff values of cerebrospinal fluid (CSF) biomarkers for detecting prodromal AD. Methods: We assessed 56 patients with amnestic mild cognitive impairment (aMCI) who underwent lumbar puncture. Additionally, 87 healthy elderly individuals and 34 patients with AD dementia served as controls. Positron emission tomography was performed using florbetaben as a probe. We analyzed the concentration of Aβ1–42, total tau protein (t-Tau), and tau protein phosphorylated at threonine 181 (p-Tau181) in CSF with INNOTEST enzyme-linked immunosorbent assay. Results: For the detection of prodromal AD in patients with aMCI, the cutoff values of CSF Aβ1–42, t-Tau, and p-Tau181 were 749.5 pg/mL, 225.6 pg/mL, and 43.5 pg/mL, respectively. To discriminate prodromal AD in patients with aMCI, the t-Tau/Aβ1–42 and p-Tau181/Aβ1–42 ratios defined cutoff values at 0.298 and 0.059, respectively. Conclusions: CSF biomarkers are very useful tools for the differential diagnosis of prodromal AD in aMCI patients. The concentration of CSF biomarkers is well correlated with the stages of the AD spectrum.

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