Sex differences in risk factors that predict progression from mild cognitive impairment to Alzheimer's dementia

HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression

Objective

We aimed to develop and validate a novel multimodal framework Hierarchical Multi-task Auxiliary Learning (HiMAL) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD).

Materials and Methods

HiMAL utilized multimodal longitudinal visit data including imaging features, cognitive assessment scores, and clinical variables from MCI patients in the Alzheimer's Disease Neuroimaging Initiative dataset, to predict at each visit if an MCI patient will progress to AD within the next 6 months. Performance of HiMAL was compared with state-of-the-art single-task and multitask baselines using area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. An ablation study was performed to assess the impact of each input modality on model performance. Additionally, longitudinal explanations regarding risk of disease progression were provided to interpret the predicted cognitive decline.

Results

Out of 634 MCI patients (mean [IQR] age: 72.8 [67-78], 60% male), 209 (32%) progressed to AD. HiMAL showed better prediction performance compared to all state-of-the-art longitudinal single-modality singe-task baselines (AUROC = 0.923 [0.915-0.937]; AUPRC = 0.623 [0.605-0.644]; all P < .05). Ablation analysis highlighted that imaging and cognition scores with maximum contribution towards prediction of disease progression.

Discussion

Clinically informative model explanations anticipate cognitive decline 6 months in advance, aiding clinicians in future disease progression assessment. HiMAL relies on routinely collected electronic health records (EHR) variables for proximal (6 months) prediction of AD onset, indicating its translational potential for point-of-care monitoring and managing of high-risk patients.

Alzheimer's Disease Has Its Origins in Early Life via a Perturbed Microbiome

Alzheimer's disease (AD) is a neurodegenerative disorder with limited therapeutic options. Accordingly, new approaches for prevention and treatment are needed. One focus is the human microbiome, the consortium of microorganisms that live in and on us, which contributes to human immune, metabolic, and cognitive development and that may have mechanistic roles in neurodegeneration. AD and Alzheimer's disease-related dementias (ADRD) are recognized as spectrum disorders with complex pathobiology. AD/ADRD onset begins before overt clinical signs, but initiation triggers remain undefined. We posit that disruption of the normal gut microbiome in early life leads to a pathological cascade within septohippocampal and cortical brain circuits. We propose investigation to understand how early-life microbiota changes may lead to hallmark AD pathology in established AD/ADRD models. Specifically, we hypothesize that antibiotic exposure in early life leads to exacerbated AD-like disease endophenotypes that may be amenable to specific microbiological interventions. We propose suitable models for testing these hypotheses.

Cerebrovascular dysregulation and postoperative cognitive alterations after carotid endarterectomy

There are controversial data about the effect of carotid endarterectomy regarding postoperative cognitive function. Our aim was to analyze the effect of cerebral tissue saturation monitored by near-infrared spectroscopy (NIRS) on cognitive function. Perioperative data of 103 asymptomatic patients undergoing elective carotid surgery under general anesthesia were analyzed. Preoperatively and 3 months after the operation, MMSE (Mini Mental State Examination) and MoCA (Montreal Cognitive Assessment) tests were conducted. For cerebral monitoring, NIRS was used, and the lowest rSO2 value and the degree of desaturation were calculated. Cognitive changes were defined as one standard deviation change from the preoperative test scores, defined as postoperative neurocognitive decline (PNCD) and cognitive improvement (POCI). PNCD was found in 37 patients (35.92%), and POCI was found in 18 patients (17.47%). Female gender, patients with diabetes, and the degree of desaturation were independently associated with PNCD. The degree of desaturation during the cross-clamp period negatively correlated with the change in the MoCA scores (R =  - 0.707, p = 0.001). The 15.5% desaturation ratio had 86.5% sensitivity and 78.8% specificity for discrimination. For POCI, a desaturation of less than 12.65% had 72.2% sensitivity and 67.1% specificity. POCI was associated with lower preoperative MOCA scores and a lower degree of desaturation. We found a significant relation between the change of postoperative cognitive function proven by the MoCA test and cerebral tissue saturation during the clamping period in patients undergoing carotid endarterectomy.

Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease

The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.

Investigating Sex Differences in Risk and Protective Factors in the Progression of Mild Cognitive Impairment to Dementia: A Systematic Review

BACKGROUND

Developing effective strategies for reducing dementia risk requires a detailed understanding of the risk and protective factors associated with the progression of mild cognitive impairment (MCI) to dementia.

OBJECTIVE

We aimed to systematically review the evidence for sex differences in these factors.

METHODS

Five online databases (PubMed/CINAHL/EMBASE/PsycINFO/Cochrane) were searched from inception until 17 October 2022 for cohort studies that focused on sex differences in risk and protective factors in the progression of MCI to dementia.

RESULTS

A total of 2,972 studies were identified, of which 12 studies from five countries were included in the systematic review. There was substantial variability in study designs, study populations and outcome measures. Sex differences were present in the associations of sociodemographic, health, psychological factors, genetic and other biomarkers with the progression of MCI to dementia. APOE ɛ4 status and depression appeared to increase the risk of progression for females, whereas history of stroke, MRI markers and cerebrospinal fluid biomarkers appeared to increase the risk of progression for males. APOE ɛ2 status and marital status (unmarried) were observed to reduce risk of progression in males and females, respectively.

CONCLUSIONS

The ability of studies to accurately detail risk factors for dementia are likely limited when solely controlling for the effects of sex. Although the heterogeneity and underpowered nature of the studies made it difficult to synthesize the findings for each risk factor, this study highlights the apparent need for further research examining risk factors for dementia in males and females with MCI separately.

Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer's Disease

Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer's disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55-88 years old (n = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows and averaged to predict progression to AD within 24-48 months. The MLA outperformed the mini-mental state examination (MMSE) and three comparison models at all prediction windows on most metrics. Exceptions include sensitivity at 18 months (MLA and MMSE each achieved 0.600); and sensitivity at 30 and 42 months (MMSE marginally better). For all prediction windows, the MLA achieved AUROC ≥ 0.857 and NPV ≥ 0.800. With averaged data for the 24-48-month lookahead timeframe, the MLA outperformed MMSE on all metrics. This study demonstrates that machine learning may provide a more accurate risk assessment than the standard of care. This may facilitate care coordination, decrease healthcare expenditures, and maintain quality of life for patients at risk of progressing from MCI to AD.

Harnessing Transfer Learning for Dementia Prediction: Leveraging Sex-Different Mild Cognitive Impairment Prognosis

This paper presents a machine learning-based prediction for dementia, leveraging transfer learning to reuse the knowledge learned from prediction of mild cognitive impairment, a precursor of dementia. We also examine the impacts of temporal aspects of longitudinal data and sex differences. The methodology encompasses key components such as setting the duration window, comparing different modeling strategies, conducting comprehensive evaluations, and examining the sex-specific impacts of simulated scenarios. The findings reveal that cognitive deficits in females, once detected at the mild cognitive impairment stage, tend to deteriorate over time, while males exhibit more diverse decline across various characteristics without highlighting specific ones. However, the underlying reasons for these sex differences remain unknown and warrant further investigation.