Risk factors and outcomes of internet gaming disorder identified in Korean prospective adolescent cohort study

Background and aims: Internet gaming disorder (IGD) is known to cause various psychological and physical complications. Through data collected from an adolescent prospective longitudinal cohort, we examined how IGD is related to lifestyle and physical symptoms, as well as the temporal relationship between them. Methods: This study was conducted as part of iCURE (Internet user Cohort for Unbiased Recognition of gaming disorder in Early Adolescence) in Korea between 2015 and 2019. Sleep and physical activity time, dry eye symptoms, musculoskeletal pain, and near-miss accidents were measured at baseline and followed-up after one year. IGD risk was evaluated using the Internet Game Use – Elicited Symptom Screen (IGUESS). The association between IGD risk and measured variables was analyzed, both at baseline and at follow-up after one year. Results: At baseline, the IGD risk group had significantly less physical activity time and sleep time and had more dry eye symptoms, musculoskeletal pain, and near-miss accidents than the IGD non-risk group. Additionally, in the IGD risk group at baseline, dry eye symptoms, musculoskeletal pain, and near-miss accidents occurred significantly more after one year of follow-up. Discussion and conclusion: The results of this study show that IGD is a significant risk factor that increases the probability of physical disease and trauma in adolescents. Therefore, interventions aimed at reducing IGD risk and protecting the physical and mental health of adolescents are imperative

Sex differences in the progression of glucose metabolism dysfunction in Alzheimer’s disease

Abstract Alzheimer’s disease (AD) is a common neurodegenerative disease characterized by amyloid plaques and impaired brain metabolism. Because women have a higher prevalence of AD than men, sex differences are of great interest. Using cross-sectional and longitudinal data, we showed sex-dependent metabolic dysregulations in the brains of AD patients. Cohort 1 (South Korean, n = 181) underwent Pittsburgh compound B-PET, fluorodeoxyglucose-PET, magnetic resonance imaging, and blood biomarker (plasma tau and beta-amyloid 42 and 40) measurements at baseline and two-year follow-ups. Transcriptome analysis of data from Cohorts 2 and 3 (European, n = 78; Singaporean, n = 18) revealed sex differences in AD-related alterations in brain metabolism. In women (but not in men), all imaging indicators displayed consistent correlation curves with AD progression. At the two-year follow-up, clear brain metabolic impairment was revealed only in women, and the plasma beta-amyloid 42/40 ratio was a possible biomarker for brain metabolism in women. Furthermore, our transcriptome analysis revealed sex differences in transcriptomes and metabolism in the brains of AD patients as well as a molecular network of 25 female-specific glucose metabolic genes (FGGs). We discovered four key-attractor FGG genes (ALDOA, ENO2, PRKACB, and PPP2R5D) that were associated with amyloid/tau-related genes (APP, MAPT, BACE1, and BACE2). Furthermore, these genes successfully distinguished amyloid positivity in women. Understanding sex differences in the pathogenesis of AD and considering these differences will improve development of effective diagnostics and therapeutic treatments for AD

Machine learning application for classification of Alzheimer's disease stages using 18F-flortaucipir positron emission tomography

Abstract Background The progression of Alzheimer’s dementia (AD) can be classified into three stages: cognitive unimpairment (CU), mild cognitive impairment (MCI), and AD. The purpose of this study was to implement a machine learning (ML) framework for AD stage classification using the standard uptake value ratio (SUVR) extracted from 18F-flortaucipir positron emission tomography (PET) images. We demonstrate the utility of tau SUVR for AD stage classification. We used clinical variables (age, sex, education, mini-mental state examination scores) and SUVR extracted from PET images scanned at baseline. Four types of ML frameworks, such as logistic regression, support vector machine (SVM), extreme gradient boosting, and multilayer perceptron (MLP), were used and explained by Shapley Additive Explanations (SHAP) to classify the AD stage. Results Of a total of 199 participants, 74, 69, and 56 patients were in the CU, MCI, and AD groups, respectively; their mean age was 71.5 years, and 106 (53.3%) were men. In the classification between CU and AD, the effect of clinical and tau SUVR was high in all classification tasks and all models had a mean area under the receiver operating characteristic curve (AUC) > 0.96. In the classification between MCI and AD, the independent effect of tau SUVR in SVM had an AUC of 0.88 (p < 0.05), which was the highest compared to other models. In the classification between MCI and CU, the AUC of each classification model was higher with tau SUVR variables than with clinical variables independently, which yielded an AUC of 0.75(p < 0.05) in MLP, which was the highest. As an explanation by SHAP for the classification between MCI and CU, and AD and CU, the amygdala and entorhinal cortex greatly affected the classification results. In the classification between MCI and AD, the para-hippocampal and temporal cortex affected model performance. Especially entorhinal cortex and amygdala showed a higher effect on model performance than all clinical variables in the classification between MCI and CU. Conclusions The independent effect of tau deposition indicates that it is an effective biomarker in classifying CU and MCI into clinical stages using MLP. It is also very effective in classifying AD stages using SVM with clinical information that can be easily obtained at clinical screening

Performance of the QPLEX™ Alz plus assay, a novel multiplex kit for screening cerebral amyloid deposition

Background Alzheimers disease (AD) is an irreversible neurodegenerative disease characterized by the hallmark finding of cerebral amyloid deposition. Many researchers have tried to predict the existence of cerebral amyloid deposition by using easily accessible blood plasma samples, but the effectiveness of such strategies remains controversial. Methods We developed a new multiplex kit, the QPLEX™ Alz plus assay kit, which uses proteomics-based blood biomarkers to prescreen for cerebral amyloid deposition. A total of 300 participants who underwent Pittsburgh compound B (PiB)-positron emission tomography (PET) which allows imaging of cerebral amyloid deposition were included in this study. We compared the levels of QPLEX™ biomarkers between patients who were classified as PiB-negative or PiB-positive, regardless of their cognitive function. Logistic regression analysis followed by receiver operating characteristic (ROC) curve analysis was performed. The kit accuracy was tested using a randomized sample selection method. Results The results obtained using our assay kit reached 89.1% area under curve (AUC) with 80.0% sensitivity and 83.0% specificity. Further validation of the QPLEX™ Alz plus assay kit using a randomized sample selection method showed an average accuracy of 81.5%. Conclusions Our QPLEX™ Alz plus assay kit provides preliminary evidence that it can be used as blood marker to predict cerebral amyloid deposition but independent validation is needed.This work was supported by grants from the National Research Foundation (NRF) of Korea (NRF2019R1I1A1A01063525 to S-H.Han), from the Korea Health Technology R&D Project through Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI18C0630 and HI19C1132 to I. Mook-Jung), from the NRF (2018R1A5A2025964 to I. Mook-Jung, 2014M3C7A1046042 to D.Y.Lee), and from KHIDI (HI18C0630 and HI19C0149 to D.Y.Lee)

Predicting progression to dementia with &quot;comprehensive visual rating scale&quot; and machine learning algorithms

Background and ObjectiveIdentifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. MethodsWe included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer&apos;s Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. ResultsOf the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701-0.711) were used than when clinical data and cortical thickness (accuracy 0.650-0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. ConclusionsTree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.N