Alzheimer’s Disease: Physical Activities as an Effective Intervention Tool - A Mini-Review

Overlooked cases of mild cognitive impairment: Implications to early Alzheimer's disease

Mild cognitive impairment (MCI) marks the initial phase of memory decline or other cognitive functions like language or spatial perception, while individuals typically retain the capacity to carry out everyday tasks independently. Our comprehensive article investigates the intricate landscape of cognitive disorders, focusing on MCI and Alzheimer's disease (AD) and Alzheimer's disease-related dementias (ADRD). The study aims to understand the signs of MCI, early Alzheimer's disease, and healthy brain aging while assessing factors influencing disease progression, pathology development and susceptibility. A systematic literature review of over 100 articles was conducted, emphasizing MCI, AD and ADRD within the elderly populations. The synthesis of results reveals significant findings regarding ethnicity, gender, lifestyle, comorbidities, and diagnostic tools. Ethnicity was found to influence MCI prevalence, with disparities observed across diverse populations. Gender differences were evident in cognitive performance and decline, highlighting the need for personalized management strategies. Lifestyle factors and comorbidities were identified as crucial influencers of cognitive health. Regarding diagnostic tools, the Montreal Cognitive Assessment (MoCA) emerged as superior to the Mini-Mental State Examination (MMSE) in early MCI detection. Overall, our article provides insights into the multifaceted nature of cognitive disorders, emphasizing the importance of tailored interventions and comprehensive assessment strategies for effective cognitive health management.

Detecting Smartwatch-based Behavior Change in Response to a Multi-domain Brain Health Intervention

In this study, we introduce and validate a computational method to detect lifestyle change that occurs in response to a multi-domain healthy brain aging intervention. To detect behavior change, digital behavior markers (DM) are extracted from smartwatch sensor data and a Permutation-based Change Detection (PCD) algorithm quantifies the change in marker-based behavior from a pre-intervention, one-week baseline. To validate the method, we verify that changes are successfully detected from synthetic data with known pattern differences. Next, we employ this method to detect overall behavior change for n=28 BHI subjects and n=17 age-matched control subjects. For these individuals, we observe a monotonic increase in behavior change from the baseline week with a slope of 0.7460 for the intervention group and a slope of 0.0230 for the control group. Finally, we utilize a random forest algorithm to perform leave-one-subject-out prediction of intervention versus control subjects based on digital marker delta values. The random forest predicts whether the subject is in the intervention or control group with an accuracy of 0.87. This work has implications for capturing objective, continuous data to inform our understanding of intervention adoption and impact.

Joint Effect of ABCA7 rs4147929 and Body Mass Index on Progression from Mild Cognitive Impairment to Alzheimer's Disease: The Shanghai Aging Study

BACKGROUND

Approximately 40 independent Single Nucleotide Polymorphisms (SNPs) have been associated with Alzheimer's Disease (AD) or cognitive decline in genome-wide association studies.

OBJECTIVE

We aimed to evaluate the joint effect of genetic polymorphisms and environmental factors on the progression from Mild Cognitive Impairment (MCI) to AD (MCI-AD progression) in a Chinese community cohort.

METHODS

Demographic, DNA and incident AD diagnosis data were derived from the follow-up of 316 participants with MCI at baseline of the Shanghai Aging Study. The associations of 40 SNPs and environmental predictors with MCI-AD progression were assessed using the Kaplan-Meier method with the log-rank test and Cox regression model.

RESULTS

Rs4147929 at ATP-binding cassette family A member 7 (ABCA7) (AG/AA vs. GG, hazard ratio [HR] = 2.43, 95% confidence interval [CI] 1.24-4.76) and body mass index (BMI) (overweight vs. non-overweight, HR = 0.41, 95% CI 0.22-0.78) were independent predictors of MCI-AD progression. In the combined analyses, MCI participants with the copresence of non-overweight BMI and the ABCA7 rs4147929 (AG/AA) risk genotype had an approximately 6-fold higher risk of MCI-AD progression than those with an overweight BMI and a non-risk genotype (HR = 6.77, 95% CI 2.60-17.63). However, a nonsignificant result was found when participants carried only one of these two risk factors (nonoverweight BMI and AG/AA of ABCA7 rs4147929).

CONCLUSION

ABCA7 rs4147929 and BMI jointly affect MCI-AD progression. MCI participants with the rs4147929 risk genotype may benefit from maintaining an overweight BMI level with regard to their risk for incident AD.