We previously developed a novel machine-learning-based brain age model that was sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility using independent clinical data. We recruited 650 participants from South Korean memory clinics to undergo magnetic resonance imaging and clinical assessments. We employed a pretrained brain age model that used data from an independent set of largely Caucasian individuals (n = 757) who had no or relatively low levels of amyloid as confirmed by positron emission tomography (PET). We investigated the association between brain age residual and cognitive decline. We found that our pretrained brain age model was able to reliably estimate brain age (mean absolute error = 5.68 years, r(650) = 0.47, age range = 49–89 year) in the sample with 71 participants with subjective cognitive decline (SCD), 375 with mild cognitive impairment (MCI), and 204 with dementia. Greater brain age was associated with greater amyloid and worse cognitive function [Odds Ratio, (95% Confidence Interval {CI}): 1.28 (1.06–1.55), p = 0.030 for amyloid PET positivity; 2.52 (1.76–3.61), p < 0.001 for dementia]. Baseline brain age residual was predictive of future cognitive worsening even after adjusting for apolipoprotein E e4 and amyloid status [Hazard Ratio, (95% CI): 1.94 (1.33–2.81), p = 0.001 for total 336 follow-up sample; 2.31 (1.44–3.71), p = 0.001 for 284 subsample with baseline Clinical Dementia Rating ≤ 0.5; 2.40 (1.43–4.03), p = 0.001 for 240 subsample with baseline SCD or MCI]. In independent data set, these results replicate our previous findings using this model, which was able to delineate significant differences in brain age according to the diagnostic stages of dementia as well as amyloid deposition status. Brain age models may offer benefits in discriminating and tracking cognitive impairment in older adults.

Objective We aimed to present the study design and baseline cross-sectional participant characteristics of biobank innovations for chronic cerebrovascular disease with Alzheimer’s disease study (BICWALZS) participants.Methods A total of 1,013 participants were enrolled in BICWALZS from October 2016 to December 2020. All participants underwent clinical assessments, basic blood tests, and standardized neuropsychological tests (n=1,013). We performed brain magnetic resonance imaging (MRI, n=817), brain amyloid positron emission tomography (PET, n=713), single nucleotide polymorphism microarray chip (K-Chip, n=949), locomotor activity assessment (actigraphy, n=200), and patient-derived dermal fibroblast sampling (n=175) on a subset of participants.Results The mean age was 72.8 years, and 658 (65.0%) were females. Based on clinical assessments, total of 168, 534, 211, 80, and 20 had subjective cognitive decline, mild cognitive impairment (MCI), Alzheimer’s dementia, vascular dementia, and other types of dementia or not otherwise specified, respectively. Based on neuroimaging biomarkers and cognition, 199, 159, 78, and 204 were cognitively normal (CN), Alzheimer’s disease (AD)-related cognitive impairment, vascular cognitive impairment, and not otherwise specified due to mixed pathology (NOS). Each group exhibited many differences in various clinical, neuropsychological, and neuroimaging results at baseline. Baseline characteristics of BICWALZS participants in the MCI, AD, and vascular dementia groups were generally acceptable and consistent with 26 worldwide dementia cohorts and another independent AD cohort in Korea.Conclusion The BICWALZS is a prospective and longitudinal study assessing various clinical and biomarker characteristics in older adults with cognitive complaints. Details of the recruitment process, methodology, and baseline assessment results are described in this paper.

Recently, research on human disease network has succeeded and has become an aid in figuring out the relationship between various diseases. In most disease networks, however, the relationship between diseases has been simply represented as an association. This representation results in the difficulty of identifying prior diseases and their influence on posterior diseases. In this paper, we propose a causal disease network that implements disease causality through text mining on biomedical literature. To identify the causality between diseases, the proposed method includes two schemes: the first is the lexicon-based causality term strength, which provides the causal strength on a variety of causality terms based on lexicon analysis. The second is the frequency-based causality strength, which determines the direction and strength of causality based on document and clause frequencies in the literature. We applied the proposed method to 6,617,833 PubMed literature, and chose 195 diseases to construct a causal disease network. From all possible pairs of disease nodes in the network, 1011 causal pairs of 149 diseases were extracted. The resulting network was compared with that of a previous study. In terms of both coverage and quality, the proposed method showed outperforming results; it determined 2.7 times more causalities and showed higher correlation with associated diseases than the existing method. This research has novelty in which the proposed method circumvents the limitations of time and cost in applying all possible causalities in biological experiments and it is a more advanced text mining technique by defining the concepts of causality term strength.

Although drug discoveries can provide meaningful insights and significant enhancements in pharmaceutical field, the longevity and cost that it takes can be extensive where the success rate is low. In order to circumvent the problem, there has been increased interest in ‘Drug Repositioning’ where one searches for already approved drugs that have high potential of efficacy when applied to other diseases. To increase the success rate for drug repositioning, one considers stepwise screening and experiments based on biological reactions. Given the amount of drugs and diseases, however, the one-by-one procedure may be time consuming and expensive. In this study, we propose a machine learning based approach for efficiently selecting candidate diseases and drugs. We assume that if two diseases are similar, then a drug for one disease can be effective against the other disease too. For the procedure, we first construct two disease networks; one with disease-protein association and the other with disease-drug information. If two networks are dissimilar, in a sense that the edge distribution of a disease node differ, it indicates high potential for repositioning new candidate drugs for that disease. The Kullback-Leibler divergence is employed to measure difference of connections in two constructed disease networks. Lastly, we perform repositioning of drugs to the top 20% ranked diseases. The results showed that F-measure of the proposed method was 0.75, outperforming 0.5 of greedy searching for the entire diseases. For the utility of the proposed method, it was applied to dementia and verified 75% accuracy for repositioned drugs assuming that there are not any known drugs to be used for dementia. This research has novelty in that it discovers drugs with high potential of repositioning based on disease networks with the quantitative measure. Through the study, it is expected to produce profound insights for possibility of undiscovered drug repositioning.