Operationalizing the Diagnostic Criteria for Mild Cognitive Impairment: The Salience of Objective Measures in Predicting Incident Dementia

Clinical classification of memory and cognitive impairment with multimodal digital biomarkers

INTRODUCTION

Early detection of Alzheimer's disease and cognitive impairment is critical to improving the healthcare trajectories of aging adults, enabling early intervention and potential prevention of decline.

METHODS

To evaluate multi-modal feature sets for assessing memory and cognitive impairment, feature selection and subsequent logistic regressions were used to identify the most salient features in classifying Rey Auditory Verbal Learning Test-determined memory impairment.

RESULTS

Multimodal models incorporating graphomotor, memory, and speech and voice features provided the stronger classification performance (area under the curve = 0.83; sensitivity = 0.81, specificity = 0.80). Multimodal models were superior to all other single modality and demographics models.

DISCUSSION

The current research contributes to the prevailing multimodal profile of those with cognitive impairment, suggesting that it is associated with slower speech with a particular effect on the duration, frequency, and percentage of pauses compared to normal healthy speech.

High prevalence of subjective cognitive decline in older Chinese adults: a systematic review and meta-analysis

Background

Subjective cognitive decline (SCD) is considered a preclinical stage of Alzheimer's disease. However, reliable prevalence estimates of SCD in the Chinese population are lacking, underscoring the importance of such metrics for policymakers to formulate appropriate healthcare strategies.

Objective

To systematically evaluate SCD prevalence among older Chinese adults.

Methods

PubMed, Web of Science, The Cochrane Library, Embase, CNKI, Wanfang, VIP, CBM, and Airiti Library databases were searched for studies on SCD in older Chinese individuals published before May 2023. Two investigators independently screened the literature, extracted the information, and assessed the bias risk of the included studies. A meta-analysis was then conducted using Stata 16.0 software via a random-effects model to analyze SCD prevalence in older Chinese adults.

Results

A total of 17 studies were included (n = 31,782). The SCD prevalence in older Chinese adults was 46.4% (95% CI, 40.6-52.2%). Further, subgroup analyzes indicated that SCD prevalence was 50.8% in men and 58.9% among women. Additionally, SCD prevalence in individuals aged 60-69, 70-79, and ≥ 80 years was 38.0, 45.2, and 60.3%, respectively. Furthermore, SCD prevalence in older adults with BMI <18.5, 18.5-24.0, and > 24.0 was 59.3, 54.0, and 52.9%, respectively. Geographically, SCD prevalence among older Chinese individuals was 41.3% in North China and 50.0% in South China. In terms of residence, SCD prevalence was 47.1% in urban residents and 50.0% among rural residents. As for retired individuals, SCD prevalence was 44.2% in non-manual workers and 49.2% among manual workers. In the case of education, individuals with an education level of "elementary school and below" had an SCD prevalence rate of 62.8%; "middle school, "52.4%; "high school, "55.0%; and "college and above, "51.3%. Finally, SCD prevalence was lower among married individuals with surviving spouses than in single adults who were divorced, widowed, or unmarried.

Conclusion

Our systematic review and meta-analysis identified significant and widespread SCD prevalence in the older population in China. Therefore, our review findings highlight the urgent requirement for medical institutions and policymakers across all levels to prioritize and rapidly develop and implement comprehensive preventive and therapeutic strategies for SCD.Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023406950, identifier: CRD42023406950.

Apathy and depression in mild cognitive impairment: distinct longitudinal trajectories and clinical outcomes

OBJECTIVES

Apathy is a common symptom in mild cognitive impairment (MCI) and may predict progression to dementia. Little research, however, has investigated the longitudinal trajectory of apathy in patients with MCI or controlled for depression, which can mimic apathy, when examining its clinical correlates. The current study sought to address these issues.

DESIGN

A prospective longitudinal study was conducted over 3 years.

SETTING

Nine memory clinics around Australia.

PARTICIPANTS

One hundred and eighty-five patients with MCI at baseline.

MEASUREMENTS

Measures of cognition, function, neuropsychiatric symptoms, caregiver burden, and medication use were completed annually with additional assessments at 3 and 6 months. Patients were also assessed for dementia by expert clinicians at these time points.

RESULTS

Of 164 patients who completed measures of neuropsychiatric symptoms, 59 (36.0%) had apathy and 61 (37.2%) had depression. The proportion affected by apathy and overall apathy scores increased over time, in contrast to measures of depression, which remained relatively stable. Apathy was associated with incident dementia and worse cognition, function, neuropsychiatric symptoms, and caregiver burden independent of both depression and incident dementia. Depression was associated with worse function, albeit to lesser degree than apathy, and neuropsychiatric symptoms.

CONCLUSIONS

Apathy increases in MCI and is associated with worse clinical outcomes. These findings provide further evidence for apathy as a marker of clinical decline in older people and poorer outcomes across neurocognitive disorders.

Utility of Diffusion Modeling of Cogstate Brief Battery Test Performance in Detecting Mild Cognitive Impairment

Cognitive testing data are essential to the diagnosis of mild cognitive impairment (MCI), and computerized cognitive testing, such as the Cogstate Brief Battery, has proven helpful in efficiently identifying harbingers of dementia. This study provides a side-by-side comparison of traditional Cogstate outcomes and diffusion modeling of these outcomes in predicting MCI diagnosis. Participants included 257 older adults (160 = normal cognition; 97 = MCI). Results showed that both traditional Cogstate and diffusion modeling analyses predicted MCI diagnosis with acceptable accuracy. Cogstate measures of recognition learning and working memory accuracy and diffusion modeling variable of decision-making efficiency (drift rate) and nondecisional time were most predictive of MCI. While participants with normal cognition demonstrated a change in response caution (boundary separation) when transitioning tasks, participants with MCI did not evidence this change.

Mild Cognitive Impairment, Reversion Rates, and Associated Factors: Comparison of Two Diagnostic Approaches

BACKGROUND

As mild cognitive impairment (MCI) is typically used to identify prodromal stages of dementia, it is essential to identify MCI criteria with high diagnostic stability and prediction of dementia. Moreover, further investigation into pinpointing key factors for reversion is required to foresee future prognosis of MCI patients accurately.

OBJECTIVE

To explore disparities in diagnostic stability by examining reversion rates produced by two operationalizations of the MCI definition: the widely applied Petersen criteria and a version of the Neuropsychological (NP) criteria and to identify cognitive, lifestyle, and health related factors for reversion.

METHODS

MCI was retrospectively classified in a sample from the Swedish community-based study Good Aging in Skåne with the Petersen criteria (n = 744, median follow-up = 7.0 years) and the NP criteria (n = 375, median follow-up, 6.7 years), respectively. Poisson regression models estimated the effect of various factors on the likelihood of incident reversion.

RESULTS

Reversion rates were 323/744 (43.4%, 95% confidence intervals (CI): 39.8; 47.0) and 181/375 (48.3% 95% CI: 43.2; 53.5) for the Petersen criteria and NP criteria, respectively. Participants with impairment in a single cognitive domain, regular alcohol consumption, living with someone, older age, and lower body mass index had a higher likelihood of reverting to normal.

CONCLUSION

Reversion rates were similar for Petersen and NP criteria indicating that one definition is not superior to the other regarding diagnostic stability. Additionally, the results highlight important aspects such as multiple domain MCI, cohabitation, and the role of alcohol on predicting the trajectory of those diagnosed with MCI.

Observed Improvement in Cognition During a Personalized Lifestyle Intervention in People with Cognitive Decline

BACKGROUND

Alzheimer's disease (AD) is a chronic condition marked by progressive objective cognitive impairment (OCI). No monotherapy has substantially altered disease progression, suggesting the disease is multifactorial and may require a multimodal therapeutic approach.

OBJECTIVE

We sought to determine if cognitive function in a sample with OCI would change in response to a multimodal, individualized care plan based on potential contributors to cognitive decline (e.g., nutritional status, infection, etc.).

METHODS

Participants (n = 34) were recruited from the San Diego, CA area. The multimodal intervention included lifestyle changes (i.e., movement, diet, and stress management), nutraceutical support, and medications. It was delivered pragmatically over four clinical visits, and outcome measures were gathered at four study visits, occurring at baseline, one, three, and six months (primary endpoint). Study participants received weekly phone calls for nutrition support throughout study participation. Outcome measures included the Cambridge Brain Sciences (CBS) battery, and the Montreal Cognitive Assessment (MoCA).

RESULTS

At 6 months, mean MoCA scores improved from 19.6±3.1 to 21.7±6.2 (p = 0.013). Significant improvement was observed in mean scores of the CBS memory domain [25.2 (SD 23.3) to 35.8 (SD 26.9); p < 0.01] and CBS overall composite cognition score [24.5 (SD 16.1) to 29.7 (SD 20.5); p = 0.02]. All CBS domains improved.

CONCLUSION

Multiple measures of cognitive function improved after six months of intervention. Our results support the feasibility and impact of a multimodal, individualized treatment approach to OCI, warranting further research.

Subjective, neuropsychological, and neural markers of memory in older adults

OBJECTIVES

To investigate the relationship between the P300 event-related potential, neuropsychological measures of memory, subjective memory complaints (SMCs), and indicators of psychosocial functioning.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study of 79 community-based older adults, aged 60-75 years, participants completed online surveys and in-person neuropsychological and electroencephalogram (EEG) assessments.

MEASUREMENTS

Measures included: the Change subscale of the Metamemory in Adulthood Questionnaire, NIH Toolbox Emotions battery (Perceived Stress and Psychological Well-Being), Geriatric Depression Scale, Geriatric Anxiety Scale, electrocortical measures (EEG), California Verbal Learning Test, 3rd Edition, and diagnostic ratings for mild and major neurocognitive disorders based on full neuropsychological battery, clinical interview, and two-clinician consensus.

RESULTS

P300 amplitude was associated with long-delay verbal memory recall and diagnostic rating. SMCs were not associated with objective memory or diagnostic rating. SMCs were associated with higher perceived stress, anxiety, and depression symptoms and lower psychological well-being.

CONCLUSIONS

Neural indicators such as the P300 may be useful for early detection of cognitive impairment. SMCs were not a reliable indicator of early memory impairment in relation to neuropsychological or neural indicators, but may be a useful indicator of unreported stress and mood symptoms in clinical settings.

Improving community health-care systems' early detection of cognitive decline and dementia

Preliminary estimates suggest that current global health-care systems lack the resource capacity to provide persons with dementia timely access to diagnosis, treatment, and care. There is an increasing need to improve timely identification of individuals who will likely progress to Alzheimer's disease (AD) dementia particularly among under-represented, underserved, and vulnerable populations. The rapidly evolving area of bioinformatics of health system data and the emergence of fluid-based biomarkers for pre-symptomatic AD may provide an innovative strategic option for health system planners. A think-tank style meeting entitled "The Campaign to Prevent Alzheimer's Disease Work Group on Community-Based Detection and Assessment of Cognitive Decline" developed recommendations to guide future sustainability activities, public policy campaigns, and implementation pilots. The group identified and explored different pathways of community-based detection using electronic health records, from different international health-care systems, to detect and surveil individuals with early possible cognitive impairment.

The Role of Cognitive Self-Report Measure Type in Predicting Cognitive Decline Among Older Adults: A Systematic Review

Many types of items are used to measure self-reported cognition, resulting in heterogeneity across studies. Certain cognitive self-report measure types may be more predictive of future decline. Therefore, the purpose of this systematic review was to compare whether specific types of cognitive self-report measures better predict risk for cognitive decline over time when measures are directly compared within the same study. The PRISMA criteria guided the review. Eligibility criteria included: longitudinal studies, outcome of cognitive decline, at least 2 different cognitive self-report measures, and no cognitive impairment at baseline. Nineteen studies were included in the final review. A narrative synthesis of results was completed, resulting in 3 thematic groups of comparisons across self-reported measure types. Self-reported memory decline with worry and peer perceptions of memory were associated with the highest risk for cognitive decline. Future longitudinal investigations of self-reported cognitive problems should focus on using measures that may be most sensitive to predicting cognitive decline risk.

Diagnostic Precision in the Detection of Mild Cognitive Impairment: A Comparison of Two Approaches

OBJECTIVE

This study compared diagnostic rates and clinical predictors of discrepancies between diagnoses conferred via: 1) a comprehensive neuropsychological evaluation and National Institute on Aging-Alzheimer's Association (NIA-AA) criteria versus 2) a cognitive screener and Diagnostic Statistical Manual of Mental Disorders (DSM-5) criteria.

DESIGN

Cross-sectional examination of baseline data from the Prevention of Alzheimer's dementia (AD) using Cognitive remediation and transcranial direct current stimulation in Mild Cognitive Impairment (MCI) and Depression (PACt-MD; ClinicalTrials.gov Identifier: NCT02386670) trial.

SETTING

Five geriatric psychiatry and memory clinics located at academic hospitals affiliated with the Department of Psychiatry, University of Toronto.

PARTICIPANTS

Older adults (N = 431) with a history of major depressive disorder (MDD) in remission, MCI, or both.

MEASUREMENTS

Main outcome was a comparison of NIA-AA diagnostic rates of MCI or dementia versus DSM-5 rates of mild or major neurocognitive disorder. Secondary analyses examined demographic, race, gender, premorbid intellectual ability, psychosocial, health-related, and genetic predictors of discrepancy between DSM-5 and NIA-AA diagnoses.

RESULTS

There were 103 (23.8%) discrepant cases, with most (91; 88.3%) of these discrepant cases reflecting more impairment with the detailed neuropsychological testing and NIA-AA criteria. Discrepancies were more likely in individuals with a history of MDD or who had at least one ApoE4 allele.

CONCLUSION

The NIA-AA criteria, in conjunction with comprehensive neuropsychological testing, identified a greater prevalence of cognitive impairment than DSM-5 criteria, in conjunction with the Montreal Cognitive Assessment. Detailed neuropsychological evaluations are recommended for older adults who have a history of MDD or a genetic vulnerability to dementia.

Subjective Cognitive Decline: Level of Risk for Future Dementia and Mild Cognitive Impairment, a Meta-Analysis of Longitudinal Studies

Subjective Cognitive Decline (SCD) in older adults has been identified as a risk factor for dementia, although the literature is inconsistent, and it is unclear which factors moderate progression from SCD to dementia. Through separate meta-analyses, we aimed to determine if SCD increased the risk of developing dementia or mild cognitive impairment (MCI). Furthermore, we examined several possible moderators. Longitudinal studies of participants with SCD at baseline, with data regarding incident dementia or MCI, were extracted from MEDLINE and PsycINFO. Articles were excluded if SCD occurred solely in the context of dementia, MCI, or as part of a specific disease. Pooled estimates were calculated using a random-effects model, with moderator analyses examining whether risk varied according to SCD definition, demographics, genetics, recruitment source, and follow-up duration. Risk of study bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. 46 studies with more than 74,000 unique participants were included. SCD was associated with increased risk of developing dementia (HR = 1.90, 95% CI 1.52-2.36; OR = 2.48, 95% CI 1.97-3.14) and MCI (HR = 1.73, 95% CI 1.18-2.52; OR = 1.83, 95% CI 1.56-2.16). None of the potential moderating factors examined influenced the HR or OR of developing dementia. In contrast, including worry in the definition of SCD, younger age, and recruitment source impacted the OR of developing MCI, with clinic samples demonstrating highest risk. SCD thus represents an at-risk phase, ideal for early intervention, with further research required to identify effective interventions for risk reduction, and cognitive-behavioural interventions for cognitive management. PROSPERO, protocol number: CRD42016037993.

Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia

BACKGROUND

Longitudinal neuroimaging provides spatiotemporal brain data (STBD) measurement that can be utilised to understand dynamic changes in brain structure and/or function underpinning cognitive activities. Making sense of such highly interactive information is challenging, given that the features manifest intricate temporal, causal relations between the spatially distributed neural sources in the brain.

METHODS

The current paper argues for the advancement of deep learning algorithms in brain-inspired spiking neural networks (SNN), capable of modelling structural data across time (longitudinal measurement) and space (anatomical components). The paper proposes a methodology and a computational architecture based on SNN for building personalised predictive models from longitudinal brain data to accurately detect, understand, and predict the dynamics of an individual's functional brain state. The methodology includes finding clusters of similar data to each individual, data interpolation, deep learning in a 3-dimensional brain-template structured SNN model, classification and prediction of individual outcome, visualisation of structural brain changes related to the predicted outcomes, interpretation of results, and individual and group predictive marker discovery.

RESULTS

To demonstrate the functionality of the proposed methodology, the paper presents experimental results on a longitudinal magnetic resonance imaging (MRI) dataset derived from 175 older adults of the internationally recognised community-based cohort Sydney Memory and Ageing Study (MAS) spanning 6 years of follow-up.

SIGNIFICANCE

The models were able to accurately classify and predict 2 years ahead of cognitive decline, such as mild cognitive impairment (MCI) and dementia with 95% and 91% accuracy, respectively. The proposed methodology also offers a 3-dimensional visualisation of the MRI models reflecting the dynamic patterns of regional changes in white matter hyperintensity (WMH) and brain volume over 6 years.

CONCLUSION

The method is efficient for personalised predictive modelling on a wide range of neuroimaging longitudinal data, including also demographic, genetic, and clinical data. As a case study, it resulted in finding predictive markers for MCI and dementia as dynamic brain patterns using MRI data.

The Destiny of Multiple Domain Amnesic Mild Cognitive Impairment: Effect of Alternative Neuropsychological Definitions and Their Adjunctive Role in Respect of Memory Impairment

OBJECTIVE

Mild cognitive impairment is the main risk factor of dementia. Previous evidence has claimed that subjects with memory disturbances associated with impairment of other cognitive domains (multiple domain amnesic MCI) are at the highest risk of developing dementia. To date, a shared definition of amnesic MCI multiple domain (aMCI-MD) is still lacking.

METHOD

163 subjects with aMCI were enrolled and followed-up for 2 years. They underwent a baseline comprehensive neuropsychological battery. The cut-off point for each test was set at 1, 1.5, and 2 SD below the mean obtained in normative studies; aMCI-MD was defined as the occurrence of abnormal scores on at least one, two, or three tests not assessing memory. The Episodic Memory Score (EMS), that measures the severity of memory impairment, was determined. Logistic regressionand Cox's proportional hazard risk models were carried out. The adjunctive effect of the definitions of aMCI-MD on the severity of memory impairment was assessed.

RESULTS

Fifty-four subjects progressed to dementia. Only restrictive definitions of aMCI-MD (at least three tests below 1.5 SD; at least two tests below 2 SD) predicted conversion to dementia in both logistic regression and survival analysis. None of the conditions showed a significant adjunctive effect on the EMS.

CONCLUSIONS

The predictive effect of impairment in tests assessing cognitive domains other than memory depends on its psychometric definition. The use of a restrictive definition would be of some usefulness, but the adjunctive effect of such a definition on an integrated analysis of memory impairment may be questionable.

Comparison of the predictive accuracy of multiple definitions of cognitive impairment for incident dementia: a 20-year follow-up of the Whitehall II cohort study

BACKGROUND

Studies generally use cognitive assessment done at one timepoint to define cognitive impairment in order to examine conversion to dementia. Our objective was to examine the predictive accuracy and conversion rate of seven alternate definitions of cognitive impairment for dementia.

METHODS

In this prospective study, we included participants from the Whitehall II cohort study who were assessed for cognitive impairment in 2007-09 and were followed up for clinically diagnosed dementia. Algorithms based on poor cognitive performance (defined using age-specific and sex-specific thresholds, and subsequently thresholds by education or occupation levels) and objective cognitive decline (using data from cognitive assessments in 1997-99, 2002-04, and 2007-09) were used to generate seven alternate definitions of cognitive impairment. We compared predictive accuracy using Royston's R 2, the Akaike information criterion (AIC), sensitivity, specificity, and Harrell's C-statistic.

FINDINGS

5687 participants, with a mean age of 65·7 years (SD 5·9) in 2007-09, were included and followed up for a median of 10·5 years (IQR 10·1-10·9). Over follow-up, 270 (4·7%) participants were clinically diagnosed with dementia. Cognitive impairment defined using both cognitive performance and decline had higher hazard ratios (from 5·08 [95% CI 3·82-6·76] to 5·48 [4·13-7·26]) for dementia than did definitions based on cognitive performance alone (from 3·25 [2·52-4·17] to 3·39 [2·64-4·36]) and cognitive decline alone (3·01 [2·37-3·82]). However, all definitions had poor predictive performance (C-statistic ranged from 0·591 [0·565-0·616] to 0·631 [0·601-0·660]), primarily due to low sensitivity (21·6-48·4%). A predictive model containing age, sex, and education without measures of cognitive impairment had better predictive performance (C-statistic 0·783 [0·758-0·809], sensitivity 74·2%, specificity 72·2%) than all seven definitions of cognitive impairment (all p<0·0001).

INTERPRETATION

These findings suggest that cognitive impairment in early old age might not be useful for dementia prediction, even when it is defined using longitudinal data on cognitive decline and thresholds of poor cognitive performance additionally defined by education or occupation.

FUNDING

National Institutes of Health, UK Medical Research Council.

Functional objective parameters which may discriminate patients with mild cognitive impairment from cognitively healthy individuals: a systematic review and meta-analysis using an instrumented kinematic assessment

BACKGROUND

a systematic review in 2015 showed kinematic gait and balance parameters which can discriminate patients with mild cognitive impairment (MCI) from cognitively healthy individuals.

OBJECTIVE

this systematic review and meta-analysis aims to summarise and synthesise the evidence published after the previous review about the functional objective parameters obtained by an instrumented kinematic assessment which could discriminate patients with MCI from cognitively healthy individuals, as well as to assess the level of evidence per outcome.

METHODS

major electronic databases were searched from inception to August 2019 for cross-sectional studies published after 2015 examining kinematic gait and balance parameters, which may discriminate patients with MCI from cognitively healthy individuals. Meta-analysis was carried out for each parameter reported in two or more studies.

RESULTS

Ten cross-sectional studies with a total of 1,405 patients with MCI and 2,277 cognitively healthy individuals were included. Eight of the included studies reported a low risk of bias. Patients with MCI showed a slower gait speed than cognitively healthy individuals. Thus, single-task gait speed (d = -0.44, 95%CI [-0.60 to -0.28]; P < 0.001), gait speed at fast pace (d = -0.48, 95%CI [-0.72 to -0.24]; P < 0.001) and arithmetic dual-task gait speed (d = -1.20, 95%CI [-2.12 to -0.28]; P = 0.01) were the functional objective parameters which best discriminated both groups.

CONCLUSION

the present review shows kinematic gait parameters which may discriminate patients with MCI from cognitively healthy individuals. Most of the included studies reported a low risk of bias, but the grading of recommendations assessment, development and evaluation criteria showed a low level of evidence per outcome.

Aggregation of Abnormal Memory Scores and Risk of Incident Alzheimer's Disease Dementia: A Measure of Objective Memory Impairment in Amnestic Mild Cognitive Impairment

OBJECTIVES

The criteria for objective memory impairment in mild cognitive impairment (MCI) are vaguely defined. Aggregating the number of abnormal memory scores (NAMS) is one way to operationalise memory impairment, which we hypothesised would predict progression to Alzheimer's disease (AD) dementia.

METHODS

As part of the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing, 896 older adults who did not have dementia were administered a psychometric battery including three neuropsychological tests of memory, yielding 10 indices of memory. We calculated the number of memory scores corresponding to z ≤ -1.5 (i.e., NAMS) for each participant. Incident diagnosis of AD dementia was established by consensus of an expert panel after 3 years.

RESULTS

Of the 722 (80.6%) participants who were followed up, 54 (7.5%) developed AD dementia. There was a strong correlation between NAMS and probability of developing AD dementia (r = .91, p = .0003). Each abnormal memory score conferred an additional 9.8% risk of progressing to AD dementia. The area under the receiver operating characteristic curve for NAMS was 0.87 [95% confidence interval (CI) .81-.93, p < .01]. The odds ratio for NAMS was 1.67 (95% CI 1.40-2.01, p < .01) after correcting for age, sex, education, estimated intelligence quotient, subjective memory complaint, Mini-Mental State Exam (MMSE) score and apolipoprotein E ϵ4 status.

CONCLUSIONS

Aggregation of abnormal memory scores may be a useful way of operationalising objective memory impairment, predicting incident AD dementia and providing prognostic stratification for individuals with MCI.

Mild cognitive impairment in Aboriginal Australians

INTRODUCTION

Aboriginal Australians have among the highest rates of dementia worldwide, yet no study has investigated the subtypes, risk factors, or longer term outcomes of mild cognitive impairment (MCI) in this population.

METHODS

A total of 336 community-dwelling Aboriginal Australians aged ≥60 years participated in a longitudinal study, completing a structured interview at baseline. MCI (amnestic subtype, aMCI; non-amnestic subtype, naMCI) and dementia were diagnosed via cognitive screening, medical assessment, and clinical consensus. Associations between life-course factors and baseline MCI subtypes were examined using logistic regression. Conversion to dementia was assessed at 6-year follow-up.

RESULTS

Prevalent aMCI (n = 24) was associated with older age (odds ratio [OR] = 1.68, 95% confidence interval [CI]: 1.12 to 2.53), head injury (OR = 3.19, 95% CI: 1.35 to 7.56), symptoms of depression (OR = 1.52, 95% CI: 1.04 to 2.24), and lower blood pressure (OR = 0.53, 95% CI: 0.33 to 0.86). Prevalent naMCI (n = 29) was associated with low education (OR = 4.46, 95% CI: 1.53 to 13.05), unskilled work history (OR = 5.62, 95% CI: 2.07 to 13.90), higher body mass index (OR = 1.99, 95% CI: 1.30 to 3.04), and moderate to severe hearing loss (OR = 2.82, 95% CI: 1.06 to 7.55). A small proportion of MCI cases reverted to intact at follow-up (15%), but most remained stable (44%), developed dementia and/or died (41%).

DISCUSSION

Sociodemographic and clinical factors both contributed to baseline MCI and were distinct for MCI subtypes, with similar patterns of conversion to dementia for amnestic and non-amnestic MCI.

Functional parameters indicative of mild cognitive impairment: a systematic review using instrumented kinematic assessment

BACKGROUND

Patients with mild cognitive impairment (MCI) experience alterations of functional parameters, such as an impaired balance or gait. The current systematic review set out to investigate whether functional objective performance may predict a future risk of MCI; to compare functional objective parameters in patients with MCI and a control group; and to assess changes in these parameters after different physical activity interventions.

METHODS

Electronic databases, including PubMed, AMED, CINAHL, EMBASE, PEDro and Web of Science as well as grey literature databases, were searched from inception to February 2020. Cohort studies and Randomized Controlled Trials (RCTs) were included. The risk of bias of the included studies was assessed independently by reviewers using quality assessment checklists. The level of evidence per outcome was assessed using the GRADE criteria.

RESULTS

Seventeen studies met inclusion criteria including patients with MCI. Results from RCTs suggested that gait speed, gait variability and balance may be improved by different physical activity interventions. Cohort studies showed that slower gait speed, above all, under Dual Task (DT) conditions, was the main impaired parameter in patients with MCI in comparison with a Control Gorup. Furthermore, cohort studies suggested that gait variability could predict an incident MCI. Although most of included cohort studies reported low risk of bias, RCTs showed an unclear risk of bias.

CONCLUSIONS

Studies suggest that gait variability may predict an incident MCI. Moreover, different gait parameters, above all under DT conditions, could be impaired in patients with MCI. These parameters could be improved by some physical activity interventions. Although cohort studies reported low risk of bias, RCTs showed an unclear risk of bias and GRADE criteria showed a low level of evidence per outcome, so further studies are required to refute our findings.

PROSPERO

CRD42019119180.

Tai Chi is Effective in Delaying Cognitive Decline in Older Adults with Mild Cognitive Impairment: Evidence from a Systematic Review and Meta-Analysis

To determine whether Tai Chi (TC) is effective in slowing cognitive decline in older populations with mild cognitive impairment (MCI), we performed a systematic review and meta-analysis of randomized controlled trials (RCTs) on Tai Chi and MCI. We searched eight electronic databases (PubMed, PsycINFO, Wanfang, Web of Science, MEDLINE, CNKI, EBSCO, and the Cochrane Central Register of Controlled Trials) for appropriate RCTs published up to August 2019. For those studies included, the data were extracted, methodological quality was evaluated, and then meta-analysis was performed using Review Manager software (version 5.3). A total of 11 of the studies were available for systematic review, which together included 1061 participants, met the inclusion criteria, and ten of these were included in the meta-analysis. For most RCTs, the methodological quality was moderate. The meta-analysis revealed that Tai Chi could significantly improve global cognitive function; memory and learning; mental speed and attention; ideas, abstraction, figural creations, and mental flexibility; and visuospatial perception. The present review adds to the evidence showing that Tai Chi is potentially beneficial in improving cognitive functions among elderly people with MCI. However, strictly designed and well-reported RCTs are required.

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders.

Prevalence and Incidence of Mild Cognitive Impairment across Subtypes, Age, and Sex

OBJECTIVE

The purpose of this study was to report on the prevalence and incidence of mild cognitive impairment (MCI) across age, sex, and subtypes according to various criteria in a population-based sample.

METHODS

The sample was drawn from the Swedish Good Aging in Skåne (GÅS) population study, and data from 3,752 participants aged 60 years and more were used to calculate the MCI prevalence. The incidence was calculated using 2,093 participants with 6-year follow-up data. MCI was defined according to the expanded Mayo Clinic criteria: cognitive complaint, objective cognitive impairment (two different criteria depending on the severity of impairment), preserved functional abilities, and no dementia.

RESULTS

The prevalence estimates ranged from 5.13 to 29.9% depending on age and severity of impairment. The incidence rates of overall MCI were 22.6 (95% confidence interval [CI]: 19.6-25.9) and 8.67 (95% CI: 7.0-10.7) per 1,000 person-years for less severe and severe cognitive impairment, respectively. The highest prevalence and incidence estimates were found for "non-amnestic MCI single domain." The older age groups had a higher prevalence, and no sex or age differences in MCI incidence were detected.

CONCLUSION

Our findings concur with previous research advocating that MCI is a heterogeneous concept, since the prevalence and incidence estimates differed substantially according to age, MCI subtype, and severity of cognitive impairment.

Mild Cognitive Impairment and Caregiver Burden: A 3-Year-Longitudinal Study

OBJECTIVES

Mild cognitive impairment (MCI) is common, affecting 10%-35% of people over 65, and poses unique challenges for patients and their caregivers. Comparatively little research has examined caregiver burden in this population, with longitudinal research, in particular, lacking. We examined caregiver burden in a sample of people with MCI over 3 years.

DESIGN

Three-year observational study.

SETTING

Nine memory clinics in Australia.

PARTICIPANTS

One-hundred-and-eighty-five people with MCI and their caregivers.

MEASUREMENTS

Measures of caregiver burden, cognition, function, neuropsychiatric symptoms, driving status, and medication use were completed with patients and their caregivers at regular intervals over a 3-year period.

RESULTS

Between 21.1% and 29.5% of caregivers reported a clinically significant level of burden over the study. Patients' higher levels of neuropsychiatric symptoms, lower functional ability, and lack of driving ability, and caregivers' employment were associated with greater caregiver burden over time. Caregiver burden did not increase over time when controlling for patient and caregiver characteristics.

CONCLUSIONS

High levels of caregiver burden are present in a significant proportion of caregivers of people with MCI. Clinical characteristics of patients - including severity of neuropsychiatric symptoms and functional impairment - and the employment status of caregivers predict burden. Such characteristics may help identify caregivers at greater risk of burden to target for intervention.

Problems in Classifying Mild Cognitive Impairment (MCI): One or Multiple Syndromes?

As the conceptual, methodological, and technological advances applied to dementias have evolved the construct of mild cognitive impairment (MCI), one problem encountered has been its classification into subtypes. Here, we aim to revise the concept of MCI and its subtypes, addressing the problems of classification not only from the psychometric point of view or by using alternative methods, such as latent class analysis, but also considering the absence of normative data. In addition to the well-known influence of certain factors on cognitive function, such as educational level and cultural traits, recent studies highlight the relevance of other factors that may significantly affect the genesis and evolution of MCI: subjective memory complaints, loneliness, social isolation, etc. The present work will contemplate the most relevant attempts to clarify the issue of MCI categorization and classification, combining our own data with that from recent studies which suggest the role of relevant psychosocial factors in MCI.