Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction

Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (β-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice

Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings

Background: Predicting dementia early has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools for stratifying patients early, resulting in patients being undiagnosed or wrongly diagnosed. Despite rapid expansion in machine learning models for dementia prediction, limited model interpretability and generalizability impede translation to the clinic.Methods: We build a robust and interpretable predictive prognostic model (PPM) and validate its clinical utility using real-world, routinely-collected, non-invasive, and low-cost (cognitive tests, structural MRI) patient data. To enhance scalability and generalizability to the clinic, we: 1) train the PPM with clinically-relevant predictors (cognitive tests, grey matter atrophy) that are common across research and clinical cohorts, 2) test PPM predictions with independent multicenter real-world data from memory clinics across countries (UK, Singapore).Findings: PPM robustly predicts (accuracy: 81.66%, AUC: 0.84, sensitivity: 82.38%, specificity: 80.94%) whether patients at early disease stages (MCI) will remain stable or progress to Alzheimer's Disease (AD). PPM generalizes from research to real-world patient data across memory clinics and its predictions are validated against longitudinal clinical outcomes. PPM allows us to derive an individualized AI-guided multimodal marker (i.e. predictive prognostic index) that predicts progression to AD more precisely than standard clinical markers (grey matter atrophy, cognitive scores; PPM-derived marker: hazard ratio = 3.42, p = 0.01) or clinical diagnosis (PPM-derived marker: hazard ratio = 2.84, p &lt; 0.01), reducing misdiagnosis.Interpretation: Our results provide evidence for a robust and explainable clinical AI-guided marker for early dementia prediction that is validated against longitudinal, multicenter patient data across countries, and has strong potential for adoption in clinical practice.Funding: Wellcome Trust, Royal Society, Alzheimer’s Research UK, Alzheimer’s Drug Discovery Foundation Diagnostics Accelerator, Alan Turing Institute.</div

A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset

Early detection of Alzheimer's Disease (AD) is vital to reduce the burden of dementia and for developing effective treatments. Neuroimaging can detect early brain changes, such as hippocampal atrophy in Mild Cognitive Impairment (MCI), a prodromal state of AD. However, selecting the most informative imaging features by machine-learning requires many cases. While large publically-available datasets of people with dementia or prodromal disease exist for Magnetic Resonance Imaging (MRI), comparable datasets are missing for Magnetoencephalography (MEG). MEG offers advantages in its millisecond resolution, revealing physiological changes in brain oscillations or connectivity before structural changes are evident with MRI. We introduce a MEG dataset with 324 individuals: patients with MCI and healthy controls. Their brain activity was recorded while resting with eyes closed, using a 306-channel MEG scanner at one of two sites (Madrid or Cambridge), enabling tests of generalization across sites. A T1-weighted MRI is provided to assist source localisation. The MEG and MRI data are formatted according to international BIDS standards and analysed freely on the DPUK platform (https://portal.dementiasplatform.uk/Apply). Here, we describe this dataset in detail, report some example (benchmark) analyses, and consider its limitations and future directions

MicroRNAs and Periodontal Disease: Helpful Therapeutic Targets?

Periodontal disease is the most common oral disease. This disease can be considered as an inflammatory disease. The immune response to bacteria accumulated in the gum line plays a key role in the pathogenesis of periodontal disease. In addition to immune cells, periodontal ligament cells and gingival epithelial cells are also involved in the pathogenesis of this disease. miRNAs which are small RNA molecules with around 22 nucleotides have a considerable relationship with the immune system affecting a wide range of immunological events. These small molecules are also in relation with periodontium tissues especially periodontal ligament cells. Extensive studies have been performed in recent years on the role of miRNAs in the pathogenesis of periodontal disease. In this review paper, we have reviewed the results of these studies and discussed the role of miRNAs in the immunopathogenesis of periodontal disease comprehensively. miRNAs play an important role in the pathogenesis of periodontal disease and maybe helpful therapeutic targets for the treatment of periodontal disease

Physical growth in under five years old children in villages around Gorgan

This study has been carried out on the 491 local children whom have been selected on random sampling in 20 villages around Gorgan. The size of the height and weight of NCHS standard has been used for comparison. The results of this study shows that the height and weight of all the children in any age group is below NCHS standard. It take more time to reach to acceptable height than reaching to a standard growth and weight. Children in any age experience delay in height monotonously but it has been recorded than in initial years of life, children experience shorter delay in growth and weight but by increasing the age, the delay in the former indices increased as well. In the whole, the rate of the malnutrition will be worsen after the breast feeding period is stopped

The comparison of waist circumference, waist-to-hip ratio, and waist-to-height ratio among rural women adults in the north of Iran, between the years 2004 and 2013

BACKGROUND: Central obesity is a common health disorder, and the main objective of this study was to compare its changings among rural women in the north of Iran, between the years 2004 and 2013. METHODS: Two cross-sectional studies were established on the 2839 and 2478 subjects in 2004 (first stage) and 2013 (second stage), respectively. Among 118 villages, 20 were selected using random sampling; they were the same in two studies. Central obesity was defined as waist circumference (WC) > 88 cm, waist-to-hip ratio (WHR) > 0.8, and waist-to-height ratio (WHtR) > 0.5. RESULTS: The prevalence of central obesity in 2013 based on WC, WHR, and WHtR were 37.4, 73.5, and 67.8, respectively. Compared with 2004, the prevalence of central obesity based on WHR increased as 5.4 (68.1 vs. 73.5) (P = 0.001), whereas morbid obesity (WHtR > 0.6) based on WHtR decreased as 3.7 in 2013 (28.8 vs. 25.1) (P = 0.004). Central obesity based on WHR significantly decreased in less or equal 24-year-old group (76.6 vs. 70.1) (P = 0.003), while it increased in 25-34-(65.1 vs. 74.0) and in equal or more than 35-year-old group (54.1 vs. 78.9) (P = 0.001 for all). Moreover, morbid obesity decreased in all age, economic, and education groups (except uneducated one) (P < 0.050 for all). CONCLUSION: Despite the decrease in central obesity based on WC and WHR indices in 2004-2013 duration, we found the evidence of a decline in sever obesity based on WHtR in that period. These trends have an alarm for health policy makers, not only in this area but also in same communities. Comprehensive studies are recommended to determine the best obesity indicator related to health in future. © 2018, Isfahan University of Medical Sciences(IUMS). All rights reserved