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

Evaluating the Real-World Representativeness of Participants with Mild Cognitive Impairment in Canadian Research Protocols: a Comparison of the Characteristics of a Memory Clinic Patients and Research Samples

Background

Studies of mild cognitive impairment (MCI) employ rigorous eligibility criteria, resulting in sampling that may not be representative of the broader clinical population.

Objective

To compare the characteristics of MCI patients in a Calgary memory clinic to those of MCI participants in published Canadian studies.

Methods

Clinic participants included 555 MCI patients from the PROspective Registry of Persons with Memory SyMPToms (PROMPT) registry in Calgary. Research participants included 4,981 individuals with MCI pooled from a systematic literature review of 112 original, English-language peer-reviewed Canadian studies. Both samples were compared on baseline sociodemographic variables, medical and psychiatric comorbidities, and cognitive performance for MCI due to Alzheimer’s disease and Parkinson’s disease.

Results

Overall, clinic patients tended to be younger, more often male, and more educated than research participants. Psychiatric disorders, traumatic brain injury, and sensory impairment were commonplace in PROMPT (up to 83% affected) but > 80% studies in the systematic review excluded these conditions. PROMPT patients also performed worse on global cognition measures than did research participants.

Conclusion

Stringent eligibility criteria in Canadian research studies excluded a considerable subset of MCI patients with comorbid medical or psychiatric conditions. This exclusion may contribute to differences in cognitive performance and outcomes compared to real-world clinical samples.

Patterns of Cognitive Decline Prior to Dementia in Persons with Mild Cognitive Impairment

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.

Neuropsychological predictors of conversion to probable Alzheimer disease in elderly with mild cognitive impairment

In the field of neuropsychology, it is essential to determine which neuropsychological tests predict Alzheimer's disease (AD) in people with mild cognitive impairment (MCI) and which cut-off points should be used to identify people at greater risk for converting to dementia. The aim of the present study was to analyse the predictive value of the cognitive tests included in a neuropsychological battery for conversion to AD among MCI participants and to analyse the influence of some sociodemographic variables - sex, age, schooling - and others, such as follow-up time and emotional state. A total of 105 participants were assessed with a neuropsychological battery at baseline and during a 3-year follow-up period. For the present study, the data were analysed at baseline. During the follow-up period, 24 participants (22.85%) converted to dementia (2.79 ± 1.14 years) and 81 (77.14%) remained as MCI. The logistic regression analysis determined that the long delay cued recall and the performance time of the Rey figure test were the best predictive tests of conversion to dementia after an MCI diagnosis. Concerning the sociodemographic factors, sex had the highest predictive power. The results reveal the relevance of the neuropsychological data obtained in the first assessment. Specifically, the data obtained in the episodic verbal memory tests and tests that assess visuospatial and executive components may help to identify people with MCI who may develop AD in an interval not longer than 4 years, with the masculine gender being an added risk factor.

Dementia and mild cognitive impairment in adults with intellectual disability: issues of diagnosis

Individuals with intellectual disability (ID) are now living longer with the majority of individuals reaching middle and even "old age." As a consequence of this extended longevity they are vulnerable to the same age-associated health problems as elderly adults in the general population without ID. This includes dementia, a general term referring to a variety of diseases and conditions causing substantial loss of cognitive ability and functional declines; adults with Down syndrome are at especially high risk. A great deal of recent effort has focused on the very earliest detectable indicators of decline (and even prodromal stages of dementia-causing diseases). A condition called mild cognitive impairment (MCI) has been conceptually defined as a decline in functioning that is more severe than expected with typical brain aging but not severe enough to meet criteria for a diagnosis of dementia. Consensus criteria for both dementia and MCI have been developed for typically developing adults but are of limited applicability for adults with ID, given their pre-existing cognitive impairments. Early diagnosis will continue to be of growing importance, both to support symptomatic treatment and to prevent irreversible neuropathology when interventions are developed to slow or halt the progression of underlying disease. While the intellectual and developmental disabilities field has for some time recognized the need to develop best-practices for the diagnosis of MCI and dementia, there remains a pressing need for empirically based assessment methods and classification criteria.

Neuroimaging-based biomarkers in psychiatry: clinical opportunities of a paradigm shift

Neuroimaging research has substantiated the functional and structural abnormalities underlying psychiatric disorders but has, thus far, failed to have a significant impact on clinical practice. Recently, neuroimaging-based diagnoses and clinical predictions derived from machine learning analysis have shown significant potential for clinical translation. This review introduces the key concepts of this approach, including how the multivariate integration of patterns of brain abnormalities is a crucial component. We survey recent findings that have potential application for diagnosis, in particular early and differential diagnoses in Alzheimer disease and schizophrenia, and the prediction of clinical response to treatment in depression. We discuss the specific clinical opportunities and the challenges for developing biomarkers for psychiatry in the absence of a diagnostic gold standard. We propose that longitudinal outcomes, such as early diagnosis and prediction of treatment response, offer definite opportunities for progress. We propose that efforts should be directed toward clinically challenging predictions in which neuroimaging may have added value, compared with the existing standard assessment. We conclude that diagnostic and prognostic biomarkers will be developed through the joint application of expert psychiatric knowledge in addition to advanced methods of analysis.

Clinical Approach to Diagnosis of Pre-Dementia Alzheimer's Disease (CAD-PAD)

Objective

Our aim was to evaluate clinical and neuropsychological features to identify pre-dementia Alzheimer's disease (PAD) among mild cognitive impairment (MCI) patients.

Methods

A longitudinal prospective study of 89 consecutive patients affected with MCI was conducted. Clinical evaluation consisted of a clinical interview protocol, neuropsychological evaluation, standard laboratory tests, and CT scan. A 6-month clinical check-up was made to determine whether patients remained in MCI, improved or progressed to AD or another dementia.

Results

At 3-year follow-up, 47% patients developed AD dementia. Seventeen variables were significant and were evaluated by logistic regression analysis to identify the remaining optimal diagnostic criteria: age, gender, repeating comments, difficulties in understanding explanations, time of symptom evolution, history of depression, and word fluency (with animals) were identified with a sensitivity of 100% and a specificity of 93%. A computer application was developed with all these variables which we have named Clinical Approach to Diagnosis of PAD (CAD-PAD).

Conclusions

These results suggest that CAD-PAD can help in the clinical diagnosis of PAD.

Identifying and characterizing trajectories of cognitive change in older persons with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) represents a state of high risk for dementia but is heterogeneous in its course. To date, the trajectories reflecting distinct developmental courses of cognition among patients with MCI have not been well defined.

AIM

To identify the developmental trajectory of groups with distinct cognitive change patterns among a cohort of MCI patients.

METHODS

187 MCI patients from 2 geriatric outpatient clinics were evaluated serially with the Mini-Mental State Examination (MMSE) for up to 3.5 years. Group-based trajectory analysis was applied to identify distinct trajectories. Estimates of decline for each group were compared with the mean rate of decline obtained from mixed modeling of the entire sample.

RESULTS

5 trajectories were identified and labeled based on their baseline MMSE score and course: (1) 29/stable (6.5%); (2) 27/stable (53.9%); (3) 25/slow decline (23.8%); (4) 24/slow decline (11.6%); (5) 25/rapid decline (4.2%). Annual rate of change in the MMSE score for these 5 groups was 0.09, -0.43, -1.23, -1.84, and -4.6 points, respectively. None corresponded to the mean rate of -0.82 points estimated for the group as a whole. A majority of MCI patients (60.4%) follow stable cognitive trajectories over time. Within the 3 groups with declining trajectories, cognitive decline occurs slowly in a vast majority of MCI patients (98.5%).

CONCLUSIONS

Results provide direct evidence for the heterogeneous course of cognitive decline that has been suggested by the variable prognosis for patients with MCI.

A method to combine cognitive and neurophysiological assessments of the elderly

BACKGROUND

The development of better treatments for brain diseases of the elderly will necessitate more sensitive and efficient means of repeatedly assessing an individual's neurocognitive status.

AIM

To illustrate the development of an assessment combining episodic memory and working memory tasks with simultaneous electroencephalography and evoked potential (EP) brain function measures.

METHODS

Data from matched groups of elderly subjects with mildly impaired episodic verbal memory on neuropsychological tests and those with no objective signs of impairment were used for scale development. An exploratory multivariate divergence analysis selected task performance and neurophysiological variables that best recognized impairment. Discriminant validity was then initially assessed on separate impaired and unimpaired groups.

RESULTS

Decreased response accuracy and parietal late positive component EP amplitude in the episodic memory task best characterized impaired subjects. Sensitivity in recognizing impairment in the validation analysis was 89% with 79% specificity (area under the curve = 0.94). Retest reliability was 0.89 for the unimpaired and 0.74 for the impaired validation groups.

CONCLUSION

These promising initial results suggest that with further refinement and testing, an assessment combining cognitive task performance with simultaneous neurofunctional measures could eventually provide an important benefit for clinicians and researchers.

EEG markers discriminate among different subgroup of patients with mild cognitive impairment

Aim of the study is to discriminate among participants with mild cognitive impairment through electroencephalography brain rhythms. A total of 79 participants with MCI were classified into 4 subgroups based on the beginning of memory complaints up to the time of first visit. All participants underwent electroencephalography recording, magnetic resonance imaging, apolipoprotein E characterization, and volumetric morphometry estimation of hippocampal region. Electroencephalography markers show 2 distinct patterns: (1) increase of theta/ delta power ratio and highest value of alpha2 band power in the group with shorter duration of disease, the greater right-left hippocampal volume difference and worst memory performance; (2) the highest value of alpha3 band power and the highest alpha3/alpha2 power ratio in the group with the lesser total hippocampal volume but preserved memory performance. Apolipoprotein E4 is linked to a major risk of early beginning of disease. Electroencephalography markers allow a mean correct percentage of correct classification up to 89%.