Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures

Dynamics of Cognitive Impairment in MCI Patients over a Three-Year Period: The Informative Role of Blood Biomarkers, Neuroimaging, and Genetic Factors

Given the high growth rates of cognitive decline among the elderly population and the lack of effective etiological treatments, early diagnosis of cognitive impairment progression is an imperative task for modern science and medicine. It is of particular interest to identify predictors of an unfavorable subsequent course of cognitive disorders, specifically, rapid progression. Our study assessed the informative role of various risk factors on the dynamics of cognitive impairment among mild cognitive impairment (MCI) patients. The study included patients with MCI (N = 338) who underwent neuropsychological assessment, magnetic resonance imaging (MRI) examination, blood sampling for general and biochemical analysis, APOE genotyping, and polygenic risk score (PRS) evaluation. The APOE ε4/ε4 genotype was found to be associated with a diminished overall cognitive scores initial assessment and negative cognitive dynamics. No associations were found between cognitive changes and the PRS. The progression of cognitive impairment was associated with the width of the third ventricle and hematological parameters, specifically, hematocrit and erythrocyte levels. The absence of significant associations between the dynamics of cognitive decline and PRS over three years can be attributed to the provided suitable medical care for the prevention of cognitive impairment. Adding other risk factors and their inclusion in panels assessing the risk of progression of cognitive impairment should be considered.

Neuroanatomical and clinical factors predicting future cognitive impairment

Identifying cognitively normal (CN) older adults who will convert to cognitive impairment (CI) due to Alzheimer's disease is crucial for early intervention. Clinical and neuroimaging measures were acquired from 301 CN adults who converted to CI within 15 years of baseline, and 294 who did not. Regional volumes and brain age measures were extracted from T1-weighted magnetic resonance images. Linear discriminant analysis compared non-converters' characteristics against those of short-, mid-, and long-term converters. Conversion was associated with clinical measures such as hearing impairment and self-reported memory decline. Converters' brain volumes were smaller than non-converters' across 48 frontal, temporal, and subcortical structures. Brain age measures of 12 structures were correlated with shorter times to conversion. Conversion prediction accuracy increased from 81.5% to 90.5% as time to conversion decreased. Proximity to CI conversion is foreshadowed by anatomic features of brain aging that enhance the accuracy of predicting conversion.

Sex differences in the relationship between depression and Alzheimer's disease-mechanisms, genetics, and therapeutic opportunities

Depression and Alzheimer's disease (AD) are prevalent neuropsychiatric disorders with intriguing epidemiological overlaps. Their interrelation has recently garnered widespread attention. Empirical evidence indicates that depressive disorders significantly contribute to AD risk, and approximately a quarter of AD patients have comorbid major depressive disorder, which underscores the bidirectional link between AD and depression. A growing body of evidence substantiates pervasive sex differences in both AD and depression: both conditions exhibit a higher incidence among women than among men. However, the available literature on this topic is somewhat fragmented, with no comprehensive review that delineates sex disparities in the depression-AD correlation. In this review, we bridge these gaps by summarizing recent progress in understanding sex-based differences in mechanisms, genetics, and therapeutic prospects for depression and AD. Additionally, we outline key challenges in the field, holding potential for improving treatment precision and efficacy tailored to male and female patients' distinct needs.

Prediction of conversion from mild cognitive impairment to Alzheimer's disease and simultaneous feature selection and grouping using Medicaid claim data

BACKGROUND

Due to the heterogeneity among patients with Mild Cognitive Impairment (MCI), it is critical to predict their risk of converting to Alzheimer's disease (AD) early using routinely collected real-world data such as the electronic health record data or administrative claim data.

METHODS

The study used MarketScan Multi-State Medicaid data to construct a cohort of MCI patients. Logistic regression with tree-guided lasso regularization (TGL) was proposed to select important features and predict the risk of converting to AD. A subsampling-based technique was used to extract robust groups of predictive features. Predictive models including logistic regression, generalized random forest, and artificial neural network were trained using the extracted features.

RESULTS

The proposed TGL workflow selected feature groups that were robust, highly interpretable, and consistent with existing literature. The predictive models using TGL selected features demonstrated higher prediction accuracy than the models using all features or features selected using other methods.

CONCLUSIONS

The identified feature groups provide insights into the progression from MCI to AD and can potentially improve risk prediction in clinical practice and trial recruitment.

Pangenomics: A new era in the field of neurodegenerative diseases

A pangenome is composed of all the genetic variability of a group of individuals, and its application to the study of neurodegenerative diseases may provide valuable insights into the underlying aspects of genetic heterogenetiy for these complex ailments, including gene expression, epigenetics, and translation mechanisms. Furthermore, a reference pangenome allows for the identification of previously undetected structural commonalities and differences among individuals, which may help in the diagnosis of a disease, support the prediction of what will happen over time (prognosis) and aid in developing novel treatments in the perspective of personalized medicine. Therefore, in the present review, the application of the pangenome concept to the study of neurodegenerative diseases will be discussed and analyzed for its potential to enable an improvement in diagnosis and prognosis for these illnesses, leading to the development of tailored treatments for individual patients from the knowledge of the genomic composition of a whole population.

Plasma Insulin Predicts Early Amyloid-β Pathology Changes in Alzheimer's Disease

Background

Evidence suggests that type 2 diabetes (T2D) is an independent risk factor for Alzheimer's disease (AD), sharing similar pathophysiological traits like impaired insulin signaling.

Objective

To test the association between plasma insulin and cerebrospinal fluid (CSF) AD pathology.

Methods

A total of 304 participants were included in the Alzheimer's Disease Neuroimaging Initiative, assessing plasma insulin and CSF AD pathology. We explored the cross-sectional and longitudinal associations between plasma insulin and AD pathology and compared their associations across different AD clinical and pathological stages.

Results

In the non-demented group, amyloid-β (Aβ)+ participants (e.g., as reflected by CSF Aβ42) exhibited significantly lower plasma insulin levels compared to non-demented Aβ-participants (p < 0.001). This reduction in plasma insulin was more evident in the A+T+ group (as shown by CSF Aβ42 and pTau181 levels) when compared to the A-T- group within the non-dementia group (p = 0.002). Additionally, higher plasma insulin levels were consistently associated with more normal CSF Aβ42 levels (p < 0.001) across all participants. This association was particularly significant in the Aβ-group (p = 0.002) and among non-demented individuals (p < 0.001). Notably, baseline plasma insulin was significantly correlated with longitudinal changes in CSF Aβ42 (p = 0.006), whereas baseline CSF Aβ42 did not show a similar correlation with changes in plasma insulin over time.

Conclusions

These findings suggest an association between plasma insulin and early Aβ pathology in the early stages of AD, indicating that plasma insulin may be a potential predictor of changes in early Aβ pathology.

Ultrasensitive Bio-H2S Gas Sensor Based on Cu2O-MWCNT Heterostructures

Developing a respiratory analysis disease diagnosis platform for the H2S biomarker has great significance for the real-time detection of various diseases. However, achieving highly sensitive and rapid detection of H2S gas at the parts per billion level at low temperatures is one of the most critical challenges for developing portable exhaled gas sensors. Herein, Cu2O-multiwalled carbon nanotube (MWCNT) heterostructures with excellent gas sensitivity to H2S at room temperature and a lower temperature were successfully synthesized by a facile two-dimensional (2D) electrodeposition in situ assembly method. The combination of Cu2O and MWCNTs via the principle of optimal conductance growth not only reduced the initial resistance of the material but also provided an ideal interfacial barrier structure. Compared to the response of the pure Cu2O sensor, that of the Cu2O-MWCNT sensor to 1 ppm of H2S increased nearly 800 times at room temperature, and the response time decreased by more than 500 s. In addition to the excellent sensitivity with detection limits as low as 1 ppb, the Cu2O-MWCNT sensor was extremely selective with low-temperature adaptability. The sensor had a response value of 80.6 to 0.1 ppm of H2S at -10 °C, which is difficult to achieve with sensors based on oxygen adsorption/desorption mechanisms. The sensor was used for the detection of real oral exhaled breath, confirming its feasibility as a real-time disease monitoring sensor. The Cu2O-MWCNT heterostructures maximized the advantages of the individual components and laid the experimental foundation for future applications of highly sensitive portable breath analysis platforms for monitoring H2S.