The Synergic Effect of AT(N) Profiles and Depression on the Risk of Conversion to Dementia in Patients with Mild Cognitive Impairment

Exploring small non-coding RNAs as blood-based biomarkers to predict Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) diagnosis relies on clinical symptoms complemented with biological biomarkers, the Amyloid Tau Neurodegeneration (ATN) framework. Small non-coding RNA (sncRNA) in the blood have emerged as potential predictors of AD. We identified sncRNA signatures specific to ATN and AD, and evaluated both their contribution to improving AD conversion prediction beyond ATN alone.

METHODS

This nested case-control study was conducted within the ACE cohort and included MCI patients matched by sex. Patients free of type 2 diabetes underwent cerebrospinal fluid (CSF) and plasma collection and were followed-up for a median of 2.45-years. Plasma sncRNAs were profiled using small RNA-sequencing. Conditional logistic and Cox regression analyses with elastic net penalties were performed to identify sncRNA signatures for A+(T|N)+ and AD. Weighted scores were computed using cross-validation, and the association of these scores with AD risk was assessed using multivariable Cox regression models. Gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) enrichment analysis of the identified signatures were performed.

RESULTS

The study sample consisted of 192 patients, including 96 A+(T|N)+ and 96 A-T-N- patients. We constructed a classification model based on a 6-miRNAs signature for ATN. The model could classify MCI patients into A-T-N- and A+(T|N)+ groups with an area under the curve of 0.7335 (95% CI, 0.7327 to 0.7342). However, the addition of the model to conventional risk factors did not improve the prediction of AD beyond the conventional model plus ATN status (C-statistic: 0.805 [95% CI, 0.758 to 0.852] compared to 0.829 [95% CI, 0.786, 0.872]). The AD-related 15-sncRNAs signature exhibited better predictive performance than the conventional model plus ATN status (C-statistic: 0.849 [95% CI, 0.808 to 0.890]). When ATN was included in this model, the prediction further improved to 0.875 (95% CI, 0.840 to 0.910). The miRNA-target interaction network and functional analysis, including GO and KEGG pathway enrichment analysis, suggested that the miRNAs in both signatures are involved in neuronal pathways associated with AD.

CONCLUSIONS

The AD-related sncRNA signature holds promise in predicting AD conversion, providing insights into early AD development and potential targets for prevention.

Association of amyloid-beta with depression or depressive symptoms in older adults without dementia: a systematic review and meta-analysis

Several lines of evidence have indicated that depression might be a prodromal symptom of Alzheimer's disease (AD). This systematic review and meta-analysis investigated the cross-sectional association between amyloid-beta, one of the key pathologies defining AD, and depression or depressive symptoms in older adults without dementia. A systematic search in PubMed yielded 689 peer-reviewed articles. After full-text screening, nine CSF studies, 11 PET studies, and five plasma studies were included. No association between amyloid-beta and depression or depressive symptoms were found using cerebrospinal fluid (CSF) (0.15; 95% CI: -0.08; 0.37), positron emission topography (PET) (Cohen's d: 0.09; 95% CI: -0.05; 0.24), or plasma (-0.01; 95% CI: -0.23; 0.22). However, subgroup analyses revealed an association in plasma studies of individuals with cognitive impairment. A trend of an association was found in the studies using CSF and PET. This systematic review and meta-analysis suggested that depressive symptoms may be part of the prodromal stage of dementia.

Depression Is Associated with the Aberration of Resting State Default Mode Network Functional Connectivity in Patients with Amyloid-Positive Mild Cognitive Impairment

Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia, and a significant number of individuals with MCI progress to develop dementia. Depression is prevalent in MCI patients and has been found to influence the disease progression of MCI. The default mode network (DMN), a brain network associated with Alzheimer's disease (AD), and its functional connectivity might be a neurological mechanism linking depression and AD. However, the relationship between depression, DMN functional connectivity, and cerebral beta-amyloid (Aβ) pathology remains unclear. This study aimed to investigate DMN functional connectivity differences in Aβ-positive MCI patients with depression compared to those without depression. A total of 126 Aβ-positive MCI patients were included, with 66 having depression and 60 without depression. The results revealed increased functional connectivity in the anterior DMN in the depression group compared to the non-depression group. The functional connectivity of the anterior DMN positively correlated with depression severity but not with Aβ deposition. Our findings suggest that depression influences DMN functional connectivity in Aβ-positive MCI patients, and the depression-associated DMN functional connectivity aberrance might be an important neural mechanism linking depression, Aβ pathology, and disease progression in the trajectory of AD.