A cross-national study of depression in preclinical dementia: A COSMIC collaboration study

Artificial intelligence for dementia prevention

INTRODUCTION

A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.

METHODS

ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.

RESULTS

Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.

DISCUSSION

ML is not yet widely used but has considerable potential to enhance precision in dementia prevention.

HIGHLIGHTS

Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

Depression-related phenotypes at early stages of Aβ and tau accumulation in inducible Alzheimer's disease mouse model: Task-oriented and concept-driven interpretations

Depression is highly prevalent in Alzheimer Disease (AD); however, there is paucity of studies that focus specifically on the assessment of depression-relevant phenotypes in AD mouse models. Conditional doxycycline-dependent transgenic mouse models reproducing amyloidosis (TetOffAPPsi) and/or tau (TetOffTauP301L) pathology starting at middle age (6 months) were used in this study. As AD patients can experience depressive symptoms relatively early in disease, testing was conducted at early, pre-pathology stages of Aβ and/or tau accumulation (starting from 45 days of transgenes expression). Tau-related differences were detected in the Novelty Suppressed Feeding task (NSF), whereas APP-related differences were observed predominantly in measures of the Open Field (OF) and Forced Swim tasks (FST). Effects of combined production of Aβ and tau were detected in immobility during the 1st half of the Tail Suspension task (TST). These data demonstrate that results from different tasks are difficult to reconcile using task/variable-centered interpretations in which a single task/variable is assigned an ad-hoc meaning relevant to depression. An alternative, concept-oriented, approach is based on multiple variables/tests, with an understanding of their possible inter-dependence and utilization of statistical approaches that handle correlated data sets. The existence of strong correlations within and between some of the tasks supported utilization of factor analyses (FA). FA explained a similar amount of variability across the genotypes (∼80%) and identified two factors stable across genotypes and representing motor activity and anxiety measures in OF. In contrast, variables related to FST, TST, and NSFT did not demonstrate a structure of factor loadings that would support the existence of a single integral factor of "depressive state" measured by these tasks. In addition, factor loadings varied between genotypes, indicating that genotype-specific between-task correlations need to be considered for interpretations of findings in any single task. In general, this study demonstrates that utilization of multiple tasks to characterize behavioral phenotypes, an approach that is finally gaining more widespread adoption, requires a step of data integration across different behavioral tests for appropriate interpretations.

The clinical heterogeneity of subjective cognitive decline: a data-driven approach on a population-based sample

BACKGROUND

subjective cognitive decline (SCD) refers to the subjective experience of cognitive decline in the absence of detectable cognitive impairment. SCD has been largely studied as a risk condition for cognitive decline. Empirical observations suggest that persons with SCD are heterogeneous, including individuals with early Alzheimer's disease and others with psychological vulnerabilities and/or physical comorbidity. The semiology of SCD is still in its infancy, and the features predicting cognitive decline are poorly defined. The present study aims to identify subgroups of SCD using a data-driven approach and study their clinical evolution across 8 years.

METHODS

the study population is the InveCe.Ab population-based cohort, including cognitively unimpaired people aged 70-74 years and followed for 8 years. Hierarchical cluster analysis (HCA) was carried out to identify distinct SCD subgroups based on nine clinical and cognitive features. Longitudinal changes by baseline SCD status were estimated using linear mixed models for cognitive decline and Cox proportional-hazard model for all-cause dementia risk.

RESULTS

out of 956 individuals, 513 were female (54%); and the mean age was 72.1 (SD = 1.3), education was 7.2 (3.3), and 370 (39%) reported cognitive complaints (SCD). The HCA resulted in two clusters (SCD1 and SCD2). SCD2 were less educated and had more comorbidities, cardiovascular risk and depressive symptoms than SCD1 and controls. SCD2 presented steeper cognitive decline (Mini-Mental State Examination; β = -0.31) and increased all-cause dementia risk (hazard-ratio = 3.4).

CONCLUSIONS

at the population level, basic clinical information can differentiate individuals with SCD at higher risk of developing dementia, underlining the heterogeneous nature of this population even in a sample selected for a narrow age range, in a specific geographic area.

Later life depression as risk factor for developing dementia: epidemiological evidence, predictive models, preventive strategies and future trends

Current investigations in pre-symptomatic dementia have suggested that depressive mood, a treatable condition, may play an important role in the development of the disorder. However, whether depression in adulthood constitute a risk factor, or a prodrome of dementia remains unclear. A major implication in such dispute is the analytic framework used to identify putative risk factors. Indeed, if evaluated in the years immediately prior to dementia diagnosis the association between depression and dementia may reflect depressive symptoms as a prodrome of yet-undiagnosed dementia. Unfortunately, long term prospective cohort investigations, reaching back into the preclinical phase of dementia are sparse. Here, we have surveyed high-quality evidence (systematic reviews and meta-analyses) on the association between depressive symptoms and increased odds of dementia. Meta-analytic findings are also presented and discussed regarding depression as a prodromal stage of dementia, or a consequence of underlying neurodegenerative processes. Additionally, the potential confounding effect of several variables on the risk association between depression and dementia, an aspect hardly investigated, is discussed. While early onset late-life depression - defined as starting before 60 years of age - increases the odds of developing dementia in predisposed subjects, late-onset depression appears to be a prodrome and a clear accelerating factor for cognitive deterioration. Since it is increasingly important to consider the potential of preemptive approaches to decrease the impact of dementia, evidence on potentially effective preventive strategies targeting depression as a risk factor, and next steps in further research are presented as concluding remarks.

lncRNA MIR155HG Alleviates Depression-Like Behaviors in Mice by Regulating the miR-155/BDNF Axis

Depression is one of most common psychiatric disorders, and the detailed molecular mechanism remains to be fully elucidated. Brain-derived neurotrophic factor (BDNF) is a critical neurotrophic factor that is decreased and closely involved in the development of depression. Noncoding RNAs are central regulators of cellular activities that modulate target genes. However, the roles of long noncoding RNA (lncRNA) MIR155HG and miRNA-155 (miR-155) in the pathophysiology of depression are unclear. In the present study, we aimed to explore the effects of lncRNA MIR155HG and miR-155 on the development of depression and uncover the underlying molecular mechanism. Real-time quantitative polymerase chain reaction was used to examine the expression of MIR155HG and miR-155. Western blotting was applied to measure the expression of BDNF. A luciferase reporter assay was utilized to determine the regulatory relationship between MIR155HG and miR-155. Our current work found that lncRNA MIR155HG and BDNF levels decreased while miR-155 levels increased in the hippocampal region of CUMS (chronic unpredictable mild stress) mice, a well-accepted mouse model of depression. Moreover, MIR155HG rescued while miR-155 exacerbated the depression-like behaviors of CUMS mice. Through bioinformatics analysis and luciferase reporter assays, we found that MIR155HG directly bound to and negatively modulated the expression of miR-155. Moreover, increased miR-155 was found to repress the expression of BDNF, a critical neurotrophic factor that has been reported to alleviate the depression-like behaviors of CUMS mice. Our present study revealed that lncRNA MIR155HG protected CUMS mice by regulating the miR-155/BDNF axis. Our study aimed to understand the pathophysiology of depression and provided potential therapeutic targets to diagnose and treat depression.

A Novel Score for Predicting Alzheimer's Disease Risk from Late Life Psychopathological and Health Risk Factors

With the increasing size of the aging population, dementia risk reduction has become a main public health concern. Dementia risk models or indices may help to identify individuals in the community at high risk to develop dementia. We have aimed to develop a novel dementia risk index focused on the late-life (65 years or more) population, that addresses risk factors for Alzheimer's disease (AD) easily identifiable at primary care settings. These risk factors include some shown to be associated with the risk of AD but not featured in existing indices, such as hearing loss and anxiety. Our index is also the first to account for the competing risk of death. The Zaragoza Dementia and Depression Project (ZARADEMP) Alzheimer Dementia Risk Score predicts an individual´s risk of developing AD within 5 years. The probability of late onset AD significantly increases in those with risk scores between 21 and 28 and, furthermore, is almost 4-fold higher for those with risk scores of 29 or higher. Our index may provide a practical instrument to identify subjects at high risk of AD and to design preventive strategies targeting the contributing risk factors.