Vitamin D and Folate as Predictors of MMSE in Alzheimer's Disease: A Machine Learning Analysis

Vitamin D: An Essential Nutrient in the Dual Relationship between Autoimmune Thyroid Diseases and Celiac Disease-A Comprehensive Review

Autoimmune thyroid diseases (AITD) are among the most frequent autoimmune disorders, with a multifactorial etiology in which both genetic and environmental determinants are probably involved. Celiac disease (CeD) also represents a public concern, given its increasing prevalence due to the recent improvement of screening programs, leading to the detection of silent subtypes. The two conditions may be closely associated due to common risk factors, including genetic setting, changes in the composition and diversity of the gut microbiota, and deficiency of nutrients like vitamin D. This comprehensive review discussed the current evidence on the pivotal role of vitamin D in modulating both gut microbiota dysbiosis and immune system dysfunction, shedding light on the possible relevance of an adequate intake of this nutrient in the primary prevention of AITD and CeD. While future technology-based strategies for proper vitamin D supplementation could be attractive in the context of personalized medicine, several issues remain to be defined, including standardized assays for vitamin D determination, timely recommendations on vitamin D intake for immune system functioning, and longitudinal studies and randomized controlled trials to definitely establish a causal relationship between serum vitamin D levels and the onset of AITD and CeD.

Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network

This study aimed to address the issue of larger prediction errors existing in intelligent predictive tasks related to Alzheimer's disease (AD). A cohort of 487 enrolled participants was categorized into three groups: normal control (138 individuals), mild cognitive impairment (238 patients), and AD (111 patients) in this study. An improved multifeature squeeze-and-excitation-dilated residual network (MFSE-DRN) was proposed for two important AD predictions: clinical scores and conversion probability. The model was characterized as three modules: squeeze-and-excitation-dilated residual block (SE-DRB), multifusion pooling (MF-Pool), and multimodal feature fusion. To assess its performance, the proposed model was compared with two other novel models: ranking convolutional neural network (RCNN) and 3D vision geometrical group network (3D-VGGNet). Our method showed the best performance in the two AD predicted tasks. For the clinical scores prediction, the root-mean-square errors (RMSEs) and mean absolute errors (MAEs) of mini-mental state examination (MMSE) and AD assessment scale-cognitive 11-item (ADAS-11) were 1.97, 1.46 and 4.20, 3.19 within 6 months; 2.48, 1.69 and 4.81, 3.44 within 12 months; 2.67, 1.86 and 5.81, 3.83 within 24 months; 3.02, 2.03 and 5.09, 3.43 within 36 months, respectively. At the AD conversion probability prediction, the prediction accuracies within 12, 24, and 36 months reached to 88.0, 85.5, and 88.4%, respectively. The AD predication would play a great role in clinical applications.

Differential predictability of cognitive profiles from brain structure in older males and females

Structural brain imaging parameters may successfully predict cognitive performance in neurodegenerative diseases but mostly fail to predict cognitive abilities in healthy older adults. One important aspect contributing to this might be sex differences. Behaviorally, older males and females have been found to differ in terms of cognitive profiles, which cannot be captured by examining them as one homogenous group. In the current study, we examined whether the prediction of cognitive performance from brain structure, i.e. region-wise grey matter volume (GMV), would benefit from the investigation of sex-specific cognitive profiles in a large sample of older adults (1000BRAINS; N = 634; age range 55-85 years). Prediction performance was assessed using a machine learning (ML) approach. Targets represented a) a whole-sample cognitive component solution extracted from males and females, and b) sex-specific cognitive components. Results revealed a generally low predictability of cognitive profiles from region-wise GMV. In males, low predictability was observed across both, the whole sample as well as sex-specific cognitive components. In females, however, predictability differences across sex-specific cognitive components were observed, i.e. visual working memory (WM) and executive functions showed higher predictability than fluency and verbal WM. Hence, results accentuated that addressing sex-specific cognitive profiles allowed a more fine-grained investigation of predictability differences, which may not be observable in the prediction of the whole-sample solution. The current findings not only emphasize the need to further investigate the predictive power of each cognitive component, but they also emphasize the importance of sex-specific analyses in older adults.

Prediction of cognitive performance differences in older age from multimodal neuroimaging data

Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.

The Role of Micronutrients in Neurological Disorders

Trace elements and vitamins, collectively known as micronutrients, are essential for basic metabolic reactions in the human body. Their deficiency or, on the contrary, an increased amount can lead to serious disorders. Research in recent years has shown that long-term abnormal levels of micronutrients may be involved in the etiopathogenesis of some neurological diseases. Acute and chronic alterations in micronutrient levels may cause other serious complications in neurological diseases. Our aim was to summarize the knowledge about micronutrients in relation to selected neurological diseases and comment on their importance and the possibilities of therapeutic intervention in clinical practice.

Perspectives on Neuronutrition in Prevention and Treatment of Neurological Disorders

The term neuronutrition has been proposed as part of nutritional neuroscience, studying the effects of various dietary components on behavior and cognition. Other researchers underline that neuronutrition includes the use of various nutrients and diets to prevent and treat neurological disorders. The aim of this narrative review was to explore the current understanding of the term neuronutrition as the key concept for brain health, its potential molecular targets, and perspectives of its nutritional approach to the prevention and treatment of Alzheimer's and Parkinson's diseases, multiple sclerosis, anxiety, depressive disorders, migraine, and chronic pain. Neuronutrition can be defined as a part of neuroscience that studies the influence of various aspects of nutrition (nutrients, diet, eating behavior, food environment, etc.) on the development of nervous disorders and includes nutrition, clinical dietetics, and neurology. There is evidence that the neuronutritional approach can influence neuroepigenetic modifications, immunological regulation, metabolic control, and behavioral patterns. The main molecular targets in neuronutrition include neuroinflammation, oxidative/nitrosative stress and mitochondrial dysfunction, gut-brain axis disturbance, and neurotransmitter imbalance. To effectively apply neuronutrition for maintaining brain health, a personalized approach is needed, which includes the adaptation of the scientific findings to the genetic, biochemical, psycho-physiological, and environmental features of each individual.

Biomarkers of Micronutrients and Phytonutrients and Their Application in Epidemiological Studies

Nutritional biomarkers can be used as important indicators of nutritional status and play crucial roles in the prevention as well as prognosis optimization of various metabolism-related diseases. Measuring dietary with the deployment of biomarker assessments provides quantitative nutritional information that can better predict the health outcomes. With the increased availability of nutritional biomarkers and the development of assessment tools, the specificity and sensitivity of nutritional biomarkers have been greatly improved. This enables efficient disease surveillance in nutrition research. A wide range of biomarkers have been used in different types of studies, including clinical trials, observational studies, and qualitative studies, to reflect the relationship between diet and health. Through a comprehensive literature search, we reviewed the well-established nutritional biomarkers of vitamins, minerals, and phytonutrients, and their association with epidemiological studies, to better understand the role of nutrition in health and disease.

A novel deep learning approach for diagnosing Alzheimer's disease based on eye-tracking data

Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to build an eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks.

Role of micronutrients in Alzheimer's disease: Review of available evidence

Alzheimer's disease (AD) is one of the most common age-related neurodegenerative disorders that have been studied for more than 100 years. Although an increased level of amyloid precursor protein is considered a key contributor to the development of AD, the exact pathogenic mechanism remains known. Multiple factors are related to AD, such as genetic factors, aging, lifestyle, and nutrients. Both epidemiological and clinical evidence has shown that the levels of micronutrients, such as copper, zinc, and iron, are closely related to the development of AD. In this review, we summarize the roles of eight micronutrients, including copper, zinc, iron, selenium, silicon, manganese, arsenic, and vitamin D in AD based on recently published studies.

A machine learning analysis to predict the response to intravenous and subcutaneous immunoglobulin in inflammatory myopathies. A proposal for a future multi-omics approach in autoimmune diseases

OBJECTIVE

To evaluate the response to treatment with intravenous (IVIg) and subcutaneous (20%SCIg) immunoglobulin in our series of patients with Inflammatory idiopathic myopathies (IIM) by the means of artificial intelligence.

BACKGROUND

IIM are rare diseases mainly involving the skeletal muscle with particular clinical, laboratory and radiological characteristics. Artificial intelligence (AI) represents computer processes which allows to perform complex calculations and data analyses, with the least human intervention. Recently, the use an AI in medicine significantly expanded, especially through machine learning (ML) which analyses huge amounts of information and accordingly makes decisions, and deep learning (DL) which uses artificial neural networks to analyse data and automatically learn.

METHODS

In this study, we employed AI in the evaluation of the response to treatment with IVIg and 20%SCIg in our series of patients with IIM. The diagnoses were determined on the established EULAR/ACR criteria. The treatment response was evaluated employing the following: serum creatine kinase levels, muscle strength (MMT8 score), disease activity (MITAX score) and disability (HAQ-DI score). We evaluated all the above parameters, applying, with R, different supervised ML algorithms, including Least Absolute Shrinkage and Selection Operator, Ridge, Elastic Net, Classification and Regression Trees and Random Forest to estimate the most important predictors for a good response to IVIg and 20%SCIg treatment.

RESULTS AND CONCLUSION

By the means of AI we have been able to identify the scores that best predict a good response to IVIg and 20%SCIg treatment. The muscle strength as evaluated by MMT8 score at the follow-up is predicted by the presence of dysphagia and of skin disorders, and the myositis activity index (MITAX) at the beginning of the treatment. The relationship between muscle strength and MITAX indicates a better action of IVIg therapy in patients with more active systemic disease. Considering our results, Elastic Net and similar approaches were seen to be the most viable, efficient, and effective ML methods for predicting the clinical outcome (MMT8 and MITAX at most) in myositis.

Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ_1–42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

Association Between Serum Vitamins and the Risk of Alzheimer's Disease in Chinese Population

BACKGROUND

Alzheimer's disease (AD) is a chronic and fatal neurodegenerative disease; accumulating evidence suggests that vitamin deficiency is associated with the risk of AD. However, studies attempting to elucidate the relationship between vitamins and AD varied widely.

OBJECTIVE

This study aimed to investigate the relationship between serum vitamin levels and AD in a cohort of the Chinese population.

METHODS

A total of 368 AD patients and 574 healthy controls were recruited in this study; serum vitamin A, B1, B6, B9, B12, C, D, and E were measured in all participants.

RESULTS

Compared with the controls, vitamin B2, B9, B12, D, and E were significantly reduced in AD patients. Lower levels of vitamin B2, B9, B12, D, and E were associated with the risk of AD. After adjusting for age and gender, low levels of vitamin B2, B9, and B12 were still related to the risk of AD. In addition, a negative correlation was determined between vitamin E concentration and Activity of Daily Living Scale score while no significant association was found between serum vitamins and age at onset, disease duration, Mini-Mental State Examination, and Neuropsychiatric Inventory Questionnaire score.

CONCLUSION

We conclude that lower vitamin B2, B9, B12, D, and E might be associated with the risk of AD, especially vitamin B2, B9, and B12. And lower vitamin E might be related to severe ability impairment of daily activities.

A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation

Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations; however, this would lead to a risk of overtesting, with considerable costs for the health system and an unnecessary burden for the patients. To this extent, Machine Learning (ML) algorithms could represent a useful add-on to the current clinical practice for diagnostic purposes and could help retrieve the most useful exams to be carried out for diagnostic purposes. Method: Here, we retrospectively collected high resolution computed tomography, pulmonary function tests, esophageal pH impedance tests, esophageal manometry and reflux disease questionnaires of 38 patients with SSc, applying, with R, different supervised ML algorithms, including lasso, ridge, elastic net, classification and regression trees (CART) and random forest to estimate the most important predictors for pulmonary involvement from such data. Results: In terms of performance, the random forest algorithm outperformed the other classifiers, with an estimated root-mean-square error (RMSE) of 0.810. However, this algorithm was seen to be computationally intensive, leaving room for the usefulness of other classifiers when a shorter response time is needed. Conclusions: Despite the notably small sample size, that could have prevented obtaining fully reliable data, the powerful tools available for ML can be useful for predicting early lung involvement in SSc patients. The use of predictors coming from spirometry and pH impedentiometry together might perform optimally for predicting early lung involvement in SSc.