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The apolipoprotein ε4 allele (APOE4) is the strongest genetic risk factor for Alzheimer's disease (AD). APOE4 carriers develop systemic metabolic dysfunction decades before showing AD symptoms. Accumulating evidence shows that the metabolic dysfunction accelerates AD development, including exacerbated amyloid-beta (Aβ) retention, neuroinflammation and cognitive decline. Therefore, preserving metabolic function early on may be critical to reducing the risk for AD. Here, we show that inulin increases beneficial microbiota and decreases harmful microbiota in the feces of young, asymptomatic APOE4 transgenic (E4FAD) mice and enhances metabolism in the cecum, periphery and brain, as demonstrated by increases in the levels of SCFAs, tryptophan-derived metabolites, bile acids, glycolytic metabolites and scyllo-inositol. We show that inulin also reduces inflammatory gene expression in the hippocampus. This knowledge can be utilized to design early precision nutrition intervention strategies that use a prebiotic diet to enhance systemic metabolism and may be useful for reducing AD risk in asymptomatic APOE4 carriers.

Neurovascular integrity, including cerebral blood flow (CBF) and blood-brain barrier (BBB) function, plays a major role in determining cognitive capability. Recent studies suggest that neurovascular integrity could be regulated by the gut microbiome. The purpose of the study was to identify if ketogenic diet (KD) intervention would alter gut microbiome and enhance neurovascular functions, and thus reduce risk for neurodegeneration in young healthy mice (12–14 weeks old). Here we show that with 16 weeks of KD, mice had significant increases in CBF and P-glycoprotein transports on BBB to facilitate clearance of amyloid-beta, a hallmark of Alzheimer’s disease (AD). These neurovascular enhancements were associated with reduced mechanistic target of rapamycin (mTOR) and increased endothelial nitric oxide synthase (eNOS) protein expressions. KD also increased the relative abundance of putatively beneficial gut microbiota ( Akkermansia muciniphila and Lactobacillus ), and reduced that of putatively pro-inflammatory taxa ( Desulfovibrio and Turicibacter ). We also observed that KD reduced blood glucose levels and body weight, and increased blood ketone levels, which might be associated with gut microbiome alteration. Our findings suggest that KD intervention started in the early stage may enhance brain vascular function, increase beneficial gut microbiota, improve metabolic profile, and reduce risk for AD.

Cerebrovascular dysfunction and cognitive decline are highly prevalent in aging, but the mechanisms underlying these impairments are unclear. Cerebral blood flow decreases with aging and is one of the earliest events in the pathogenesis of Alzheimer's disease (AD). We have previously shown that the mechanistic/mammalian target of rapamycin (mTOR) drives disease progression in mouse models of AD and in models of cognitive impairment associated with atherosclerosis, closely recapitulating vascular cognitive impairment. In the present studies, we sought to determine whether mTOR plays a role in cerebrovascular dysfunction and cognitive decline during normative aging in rats. Using behavioral tools and MRI‐based functional imaging, together with biochemical and immunohistochemical approaches, we demonstrate that chronic mTOR attenuation with rapamycin ameliorates deficits in learning and memory, prevents neurovascular uncoupling, and restores cerebral perfusion in aged rats. Additionally, morphometric and biochemical analyses of hippocampus and cortex revealed that mTOR drives age‐related declines in synaptic and vascular density during aging. These data indicate that in addition to mediating AD‐like cognitive and cerebrovascular deficits in models of AD and atherosclerosis, mTOR drives cerebrovascular, neuronal, and cognitive deficits associated with normative aging. Thus, inhibitors of mTOR may have potential to treat age‐related cerebrovascular dysfunction and cognitive decline. Since treatment of age‐related cerebrovascular dysfunction in older adults is expected to prevent further deterioration of cerebral perfusion, recently identified as a biomarker for the very early (preclinical) stages of AD, mTOR attenuation may potentially block the initiation and progression of AD. Aging is associated with increased cerebrovascular dysfunction, synaptic loss, and cognitive decline. Attenuation of mTOR negates these age‐associated changes in rats, suggesting that mTOR activity underlies the etiology of age‐associated microvascular and neuronal dysfunction and may thus drive cognitive impairment in normative aging.

The COVID-19 pandemic has underscored the urgent need for rapid and accurate diagnosis facilitated by artificial intelligence (AI), particularly in computer-aided diagnosis using medical imaging. However, this context presents two notable challenges: high diagnostic accuracy demand and limited availability of medical data for training AI models. To address these issues, we proposed the implementation of a Masked AutoEncoder (MAE), an innovative self-supervised learning approach, for classifying 2D Chest X-ray images. Our approach involved performing imaging reconstruction using a Vision Transformer (ViT) model as the feature encoder, paired with a custom-defined decoder. Additionally, we fine-tuned the pretrained ViT encoder using a labeled medical dataset, serving as the backbone. To evaluate our approach, we conducted a comparative analysis of three distinct training methods: training from scratch, transfer learning, and MAE-based training, all employing COVID-19 chest X-ray images. The results demonstrate that MAE-based training produces superior performance, achieving an accuracy of 0.985 and an AUC of 0.9957. We explored the mask ratio influence on MAE and found ratio = 0.4 shows the best performance. Furthermore, we illustrate that MAE exhibits remarkable efficiency when applied to labeled data, delivering comparable performance to utilizing only 30% of the original training dataset. Overall, our findings highlight the significant performance enhancement achieved by using MAE, particularly when working with limited datasets. This approach holds profound implications for future disease diagnosis, especially in scenarios where imaging information is scarce.

Many people with diabetes mellitus experience minimal or no complications. Our objective was to determine the proportion of Alaska Native people who experienced four major complications or mortality and to identify factors that may be associated with these outcomes. We used records in a diabetes registry and clinical and demographic variables in our analyses. We used logistic regression and Cox Proportional Hazards models to evaluate associations of these parameters with death and complications that occurred prior to 2013. The study included 591 Alaska Native people with non-type 1 diabetes mellitus, diagnosed between 1986 and 1992. Over 60% of people in this study remained free of four major diabetes-related complications for the remainder of life or throughout the approximately 20-year study period. Lower BMI, higher age at diagnosis of diabetes, and use of at least one diabetes medication were associated with death and a composite of four complications. A majority of Alaska Native people with DM had none of four major complications over a 20-year period. Lower BMI and use of diabetes medications were associated with higher hazard for some deleterious outcomes. This suggests that goals in care of elders should be carefully individualised. In addition, we discuss several programme factors that we believe contributed to favourable outcomes.

Advancing age is the top risk factor for the development of neurodegenerative disorders, including Alzheimer's disease (AD). However, the contribution of aging processes to AD etiology remains unclear. Emerging evidence shows that reduced brain metabolic and vascular functions occur decades before the onset of cognitive impairments, and these reductions are highly associated with low-grade, chronic inflammation developed in the brain over time. Interestingly, recent findings suggest that the gut microbiota may also play a critical role in modulating immune responses in the brain via the brain-gut axis. In this study, our goal was to identify associations between deleterious changes in brain metabolism, cerebral blood flow (CBF), gut microbiome and cognition in aging, and potential implications for AD development. We conducted our study with a group of young mice (5-6 months of age) and compared those to old mice (18-20 months of age) by utilizing metabolic profiling, neuroimaging, gut microbiome analysis, behavioral assessments and biochemical assays. We found that compared to young mice, old mice had significantly increased levels of numerous amino acids and fatty acids that are highly associated with inflammation and AD biomarkers. In the gut microbiome analyses, we found that old mice had increased / ratio and alpha diversity. We also found impaired blood-brain barrier (BBB) function and reduced CBF as well as compromised learning and memory and increased anxiety, clinical symptoms often seen in AD patients, in old mice. Our study suggests that the aging process involves deleterious changes in brain metabolic, vascular and cognitive functions, and gut microbiome structure and diversity, all which may lead to inflammation and thus increase the risk for AD. Future studies conducting comprehensive and integrative characterization of brain aging, including crosstalk with peripheral systems and factors, will be necessary to define the mechanisms underlying the shift from normal aging to pathological processes in the etiology of AD.

Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze Alzheimer’s disease (AD) brain images for a better understanding of the disease progress or predicting the conversion from cognitively impaired (CU) or mild cognitive impairment status. It is well-known that training 3D-CNN is computationally expensive and with the potential of overfitting due to the small sample size available in the medical imaging field. Here we proposed a novel 3D-2D approach by converting a 3D brain image to a 2D fused image using a Learnable Weighted Pooling (LWP) method to improve efficient training and maintain comparable model performance. By the 3D-to-2D conversion, the proposed model can easily forward the fused 2D image through a pre-trained 2D model while achieving better performance over different 3D and 2D baselines. In the implementation, we chose to use ResNet34 for feature extraction as it outperformed other 2D CNN backbones. We further showed that the weights of the slices are location-dependent and the model performance relies on the 3D-to-2D fusion view, with the best outcomes from the coronal view. With the new approach, we were able to reduce 75% of the training time and increase the accuracy to 0.88, compared with conventional 3D CNNs, for classifying amyloid-beta PET imaging from the AD patients from the CU participants using the publicly available Alzheimer’s Disease Neuroimaging Initiative dataset. The novel 3D-2D model may have profound implications for timely AD diagnosis in clinical settings in the future.

Gut dysbiosis has been identified as a crucial factor of Alzheimer's disease (AD) development for apolipoprotein E4 ( APOE4 ) carriers. Inulin has shown the potential to mitigate dysbiosis. However, it remains unclear whether the dietary response varies depending on sex. In the study, we fed 4-month-old APOE 4 mice with inulin for 16 weeks and performed shotgun metagenomic sequencing to determine changes in microbiome diversity, taxonomy, and functional gene pathways. We also formed the same experiments with APOE 3 mice to identify whether there are APOE -genotype dependent responses to inulin. We found that APOE4 female mice fed with inulin had restored alpha diversity, significantly reduced Escherichia coli and inflammation-associated pathway responses. However, compared with APOE4 male mice, they had less metabolic responses, including the levels of short-chain fatty acids-producing bacteria and the associated kinases, especially those related to acetate and Erysipelotrichaceae . These diet- and sex- effects were less pronounced in the APOE3 mice, indicating that different APOE variants also play a significant role. The findings provide insights into the higher susceptibility of APOE4 females to AD, potentially due to inefficient energy production, and imply the importance of considering precision nutrition for mitigating dysbiosis and AD risk in the future.

Metabolic uncoupling has been well-characterized during the first minutes-to-days after a traumatic brain injury (TBI), yet mitochondrial bioenergetics during the weeks-to-months after a brain injury is poorly defined, particularly after a mild TBI. We hypothesized that a closed head injury (CHI) would be associated with deficits in mitochondrial bioenergetics at one month after the injury. A significant decrease in state-III (adenosine triphosphate production) and state-V (complex-I) driven mitochondrial respiration was found at one month post-injury in adult C57Bl/6J mice. Isolation of synaptic mitochondria demonstrated that the deficit in state-III and state-V was primarily neuronal. Injured mice had a temporally consistent deficit in memory recall at one month post-injury. Using proton magnetic resonance spectroscopy (1H MRS) at 7-Tesla, we found significant decreases in phosphocreatine, N-Acetylaspartic acid, and total choline. We also found regional variations in cerebral blood flow, including both hypo- and hyperperfusion, as measured by a pseudocontinuous arterial spin labeling MR sequence. Our results highlight a chronic deficit in mitochondrial bioenergetics associated with a CHI that may lead toward a novel approach for neurorestoration after a mild TBI. MRS provides a potential biomarker for assessing the efficacy of candidate treatments targeted at improving mitochondrial bioenergetics.

Background: Alzheimer’s disease (AD) is a leading cause of dementia, and it is significantly influenced by the apolipoprotein E4 (APOE4) gene and gender. This study aimed to use machine learning (ML) algorithms to predict brain age and assess AD risk by considering the effects of the APOE4 genotype and gender. Methods: We collected brain volumetric MRI data and medical records from 1100 cognitively unimpaired individuals and 602 patients with AD. We applied three ML regression models—XGBoost, random forest (RF), and linear regression (LR)—to predict brain age. Additionally, we introduced two novel metrics, brain age difference (BAD) and integrated difference (ID), to evaluate the models’ performances and analyze the influences of the APOE4 genotype and gender on brain aging. Results: Patients with AD displayed significantly older brain ages compared to their chronological ages, with BADs ranging from 6.5 to 10 years. The RF model outperformed both XGBoost and LR in terms of accuracy, delivering higher ID values and more precise predictions. Comparing the APOE4 carriers with noncarriers, the models showed enhanced ID values and consistent brain age predictions, improving the overall performance. Gender-specific analyses indicated slight enhancements, with the models performing equally well for both genders. Conclusions: This study demonstrates that robust ML models for brain age prediction can play a crucial role in the early detection of AD risk through MRI brain structural imaging. The significant impact of the APOE4 genotype on brain aging and AD risk is also emphasized. These findings highlight the potential of ML models in assessing AD risk and suggest that utilizing AI for AD identification could enable earlier preventative interventions.