Development and Evaluation of a Data Quality Management System for the Korea Biobank Network.

As the reliance on clinical epidemiological information from human specimens grows, so does the need for effective clinical information management systems, particularly for biobanks. Our study focuses on enhancing the Korea Biobank Network's (KBN) system with data quality verification features. By comparing the quality of data collected before and after these enhancements, we observed a notable improvement in data accuracy, with the error rate decreasing from 0.1198% to 0.0492%. This advancement underscores the importance of robust data quality management in supporting high-quality clinical research and sets a precedent for the development of clinical information management systems.

Development of a Big Data Platform for the Collection and Utilization of Korea Biobank Network Clinical Information.

Biobanks serve as vital repositories for human biospecimens and clinical data, promoting biomedical and clinical research. The integration of electronic health records particularly enhances research opportunities in the era of genomics and personalized medicine, improving understanding of tumor development and disease progression. Based on the Korea Biobank Network Common Data Model, it is possible to expand data collection across various diseases. We have developed an innovative big data platform designed to efficiently collect large-scale clinical information within the KBN. By implementing the system structure, data quality management processes, and basic statistical preprocessing functionalities, we have collected data from 136,473 individuals from 2021 to 2023, demonstrating the platform's continuous and efficient data collection capabilities. Integration with hospital systems and robust quality management ensure the acquisition of high-quality data.

Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea.

A machine learning model was developed for cardiovascular diseases prediction based on 21,118 patient checkups data from a tertiary medical institution in Seoul, Korea, collected between 2009 and 2021. XGBoost algorithm showed the highest predictive performance, with an average AUROC of 0.877. In survival analysis, XGBSE achieved an AUROC exceeding 0.9 for 2-9 year predictions, with a C-index of 0.878 across all diseases, outperforming Cox regression (C-index of 0.887). A high-performance prediction model for cardiovascular diseases using the XGBSE algorithm was successfully developed and is poised for real-world clinical application following external simplification and validation.

Comparison of AI with and without hand-crafted features to classify Alzheimer's disease in different languages.

BACKGROUND: Detecting and analyzing Alzheimer's disease (AD) in its early stages is a crucial and significant challenge. Speech data from AD patients can aid in diagnosing AD since the speech features have common patterns independent of race and spoken language. However, previous models for diagnosing AD from speech data have often focused on the characteristics of a single language, with no guarantee of scalability to other languages. In this study, we used the same method to extract acoustic features from two language datasets to diagnose AD. METHODS: Using the Korean and English speech datasets, we used ten models capable of real-time AD and healthy control classification, regardless of language type. Four machine learning models were based on hand-crafted features, while the remaining six deep learning models utilized non-explainable features. RESULTS: The highest accuracy achieved by the machine learning models was 0.73 and 0.69 for the Korean and English speech datasets, respectively. The deep learning models' maximum achievable accuracy reached 0.75 and 0.78, with their minimum classification time of 0.01s and 0.02s. These findings reveal the models' robustness regardless of Korean and English and real-time diagnosis of AD through a 30-s voice sample. CONCLUSION: Non-explainable deep learning models that directly acquire voice representations surpassed machine learning models utilizing hand-crafted features in AD diagnosis. In addition, these AI models could confirm the possibility of extending to a language-agnostic AD diagnosis.

A male mouse model for metabolic dysfunction-associated steatotic liver disease and hepatocellular carcinoma.

The lack of an appropriate preclinical model of metabolic dysfunction-associated steatotic liver disease (MASLD) that recapitulates the whole disease spectrum impedes exploration of disease pathophysiology and the development of effective treatment strategies. Here, we develop a mouse model (Streptozotocin with high-fat diet, STZ + HFD) that gradually develops fatty liver, metabolic dysfunction-associated steatohepatitis (MASH), hepatic fibrosis, and hepatocellular carcinoma (HCC) in the context of metabolic dysfunction. The hepatic transcriptomic features of STZ + HFD mice closely reflect those of patients with obesity accompanying type 2 diabetes mellitus, MASH, and MASLD-related HCC. Dietary changes and tirzepatide administration alleviate MASH, hepatic fibrosis, and hepatic tumorigenesis in STZ + HFD mice. In conclusion, a murine model recapitulating the main histopathologic, transcriptomic, and metabolic alterations observed in MASLD patients is successfully established.

A Review of Current Digital Mental Health Care Applications for Anxiety Symptoms and Future Prospects.

OBJECTIVE: Since the impact of the coronavirus disease-2019 pandemic, the need for efficiency in medical services has become more urgent than ever. The digital treatment market is rapidly growing worldwide and digital therapeutics (DTx), a major part of the digital medical services, is also emerging as a new paradigm for treatment, with its industry growing rapidly as well. Increasing research is done on the effectiveness of mobile DTx in improving mental health conditions such as insomnia, panic, and depression. METHODS: This review paper investigates 1) the functions and characteristics of mobile digital mental health care applications for the treatment of anxiety symptoms, 2) extracts common attributes of the applications, and 3) compares them with existing traditional treatment mechanisms. RESULTS: Among the 20,000 mental health management applications that have been developed so far, 8 applications that are relatively widely used were selected and reviewed. Check-in, self-help tips, quick relief, journal, courses for practice are common features of the digital mental health care applications for anxiety and are also widely used feature in the cognitive behavioral therapy. CONCLUSION: Based on this review, we have proposed the essential elements and directions for the development of a Korean digital mental health care applications for anxiety disorders.

Long-Term Prognostic Value of Infarct Transmurality Determined by Contrast-Enhanced Cardiac Magnetic Resonance after ST-Segment Elevation Myocardial Infarction.

The long-term prognostic significance of maximal infarct transmurality evaluated by contrast-enhanced cardiac magnetic resonance (CE-CMR) in ST-segment elevation myocardial infarction (STEMI) patients has yet to be determined. This study aimed to see if maximal infarct transmurality has any additional long-term prognostic value over other CE-CMR predictors in STEMI patients, such as microvascular obstruction (MVO) and intramyocardial hemorrhage (IMH). The study included 112 consecutive patients who underwent CE-CMR after STEMI to assess established parameters of myocardial injury as well as the maximal infarct transmurality. The primary clinical endpoint was the occurrence of major adverse cardiac events (MACE), which included all-cause death, non-fatal reinfarction, and new heart failure hospitalization. The MACE occurred in 10 patients over a median follow-up of 7.9 years (IQR, 5.8 to 9.2 years) (2 deaths, 3 nonfatal MI, and 5 heart failure hospitalization). Patients with MACE had significantly higher rates of transmural extent of infarction, infarct size >5.4 percent, MVO, and IMH compared to patients without MACE. In stepwise multivariable Cox regression analysis, the transmural extent of infarction defined as 75 percent or more of infarct transmurality was an independent predictor of the MACE after correction for MVO and IMH (hazard ratio 8.7, 95% confidence intervals [CIs] 1.1-71; p=0.043). In revascularized STEMI patients, post-infarction CE-CMR-based maximal infarct transmurality is an independent long-term prognosticator. Adding maximal infarct transmurality to CE-CMR parameters like MVO and IMH could thus identify patients at high risk of long-term adverse outcomes in STEMI.

Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications.

Background: Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa. Methods: In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy. Results: A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05). Conclusions: DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

The E592K variant of SF3B1 creates unique RNA missplicing and associates with high-risk MDS without ring sideroblasts.

ABSTRACT: Among the most common genetic alterations in myelodysplastic syndromes (MDS) are mutations in the spliceosome gene SF3B1. Such mutations induce specific RNA missplicing events, directly promote ring sideroblast (RS) formation, and generally associate with a more favorable prognosis. However, not all SF3B1 mutations are the same, and little is known about how distinct hotspots influence disease. Here, we report that the E592K variant of SF3B1 associates with high-risk disease features in MDS, including a lack of RS, increased myeloblasts, a distinct comutation pattern, and a lack of favorable survival seen with other SF3B1 mutations. Moreover, compared with other hot spot SF3B1 mutations, E592K induces a unique RNA missplicing pattern, retains an interaction with the splicing factor SUGP1, and preserves normal RNA splicing of the sideroblastic anemia genes TMEM14C and ABCB7. These data have implications for our understanding of the functional diversity of spliceosome mutations, as well as the pathobiology, classification, prognosis, and management of SF3B1-mutant MDS.

Testing the structural disconnection hypothesis: Myelin content correlates with memory in healthy aging.

INTRODUCTION: The "structural disconnection" hypothesis of cognitive aging suggests that deterioration of white matter (WM), especially myelin, results in cognitive decline, yet in vivo evidence is inconclusive. METHODS: We examined age differences in WM microstructure using Myelin Water Imaging and Diffusion Tensor Imaging in 141 healthy participants (age 20-79). We used the Virginia Cognitive Aging Project and the NIH Toolbox® to generate composites for memory, processing speed, and executive function. RESULTS: Voxel-wise analyses showed that lower myelin water fraction (MWF), predominantly in prefrontal WM, genu of the corpus callosum, and posterior limb of the internal capsule was associated with reduced memory performance after controlling for age, sex, and education. In structural equation modeling, MWF in the prefrontal white matter and genu of the corpus callosum significantly mediated the effect of age on memory, whereas fractional anisotropy (FA) did not. DISCUSSION: Our findings support the disconnection hypothesis, showing that myelin decline contributes to age-related memory loss and opens avenues for interventions targeting myelin health.

Preface: Special issue: 14th International Conference on Brain Energy Metabolism: Energy substrates and microbiome govern brain bioenergetics and cognitive function with aging.

This Preface introduces the Special Issue entitled, "Energy Substrates and Microbiome Govern Brain Bioenergetics and Cognitive Function with Aging", which is comprised of manuscripts contributed by invited speakers and program/organizing committee members who participated in the 14th International Conference on Brain Energy Metabolism (ICBEM) held on October 24-27, 2022 in Santa Fe, New Mexico, USA. The conference covered the latest developments in research related to neuronal energetics, emerging roles for glycogen in higher brain functions, the impact of dietary intervention on aging, memory, and Alzheimer's disease, roles of the microbiome in gut-brain signaling, astrocyte-neuron interactions related to cognition and memory, novel roles for mitochondria and their metabolites, and metabolic neuroimaging in aging and neurodegeneration. The special issue contains 25 manuscripts on these topics plus three tributes to outstanding scientists who have made important contributions to brain energy metabolism and participated in numerous ICBEM conferences. In addition, two of the manuscripts describe important directions and the rationale for future research in many thematic areas covered by the conference.

A Study to Evaluate the Effectiveness and Safety of Prephase Steroid Treatment before Remission Induction Chemotherapy in Patients with Pediatric Acute Lymphoblastic Leukemia Using Common Data Model-Based Real-World Data: A Retrospective Observational Study.

Background: Rapid reduction of leukemic cells in the bone marrow during remission induction chemotherapy (RIC) can lead to significant complications such as tumor lysis syndrome (TLS). We investigated whether prephase steroid treatment before RIC could decrease TLS incidence and improve overall survival in pediatric patients with acute lymphoblastic leukemia (ALL). Methods: Data were extracted from the Common Data Model databases in two tertiary-care hospitals in Seoul, South Korea. Patients were classified into the treated or untreated group if they had received RIC with prephase steroid treatment ≥7 days before RIC in 2012-2021 or not, respectively. Stabilized Inverse Probability of Treatment Weighting (sIPTW) was applied to ensure compatibility between the treated and untreated groups. The incidence of TLS within 14 days of starting RIC, overall survival (OS), and the incidence of adverse events of special interest were the primary endpoints. Multiple sensitivity analyses were performed. Results: Baseline characteristics were effectively balanced between the treated (n=308.4) and untreated (n=246.6) groups after sIPTW. Prephase steroid treatment was associated with a significant 88% reduction in the risk of TLS (OR 0.12, 95% CI: 0.03-0.41). OS was numerically greater in the treated group than in the untreated group although the difference was not statistically significant (HR 0.64, 95% CI 0.25-1.64). The treated group experienced significantly elevated risks for hyperbilirubinemia and hyperglycemia. The reduction in TLS risk by prephase steroid treatment was maintained in all of the sensitivity analyses. Conclusion: Prephase steroid treatment for ≥7 days before RIC in pediatric patients with ALL reduces the risk of TLS, while careful monitoring for toxicities is necessary. If adequately analyzed, real-world data can provide crucial effectiveness and safety information for proper management of pediatric patients with ALL, for whom prospective randomized studies may be difficult to perform for ethical and practical reasons.

Angiotensin Receptor Blockers and the Risk of Suspected Drug-Induced Liver Injury: A Retrospective Cohort Study Using Electronic Health Record-Based Common Data Model in South Korea.

INTRODUCTION: Angiotensin receptor blockers are widely used antihypertensive drugs in South Korea. In 2021, the Korea Ministry of Food and Drug Safety acknowledged the need for national compensation for a drug-induced liver injury (DILI) after azilsartan use. However, little is known regarding the association between angiotensin receptor blockers and DILI. OBJECTIVE: We conducted a retrospective cohort study in incident users of angiotensin receptor blockers from a common data model database (1 January, 2017-31 December, 2021) to compare the risk of DILI among specific angiotensin receptor blockers against valsartan. METHODS: Patients were assigned to treatment groups at cohort entry based on prescribed angiotensin receptor blockers. Drug-induced liver injury was operationally defined using the International DILI Expert Working Group criteria. Cox regression analyses were conducted to derive hazard ratios and the inverse probability of treatment weighting method was applied. All analyses were performed using R. RESULTS: In total, 229,881 angiotensin receptor blocker users from 20 university hospitals were included. Crude DILI incidence ranged from 15.6 to 82.8 per 1000 person-years in treatment groups, most were cholestatic and of mild severity. Overall, the risk of DILI was significantly lower in olmesartan users than in valsartan users (hazard ratio: 0.73 [95% confidence interval 0.55-0.96]). In monotherapy patients, the risk was significantly higher in azilsartan users than in valsartan users (hazard ratio: 6.55 [95% confidence interval 5.28-8.12]). CONCLUSIONS: We found a significantly higher risk of suspected DILI in patients receiving azilsartan monotherapy compared with valsartan monotherapy. Our findings emphasize the utility of real-world evidence in advancing our understanding of adverse drug reactions in clinical practice.

Protocol for a single-arm, pilot trial of creatine monohydrate supplementation in patients with Alzheimer's disease.

BACKGROUND: Impaired brain bioenergetics is a pathological hallmark of Alzheimer's disease (AD) and is a compelling target for AD treatment. Patients with AD exhibit dysfunction in the brain creatine (Cr) system, which is integral in maintaining bioenergetic flux. Recent studies in AD mouse models suggest Cr supplementation improves brain mitochondrial function and may be protective of AD peptide pathology and cognition. AIMS: The Creatine to Augment Bioenergetics in Alzheimer's disease (CABA) study is designed to primarily assess the feasibility of supplementation with 20 g/day of creatine monohydrate (CrM) in patients with cognitive impairment due to AD. Secondary aims are designed to generate preliminary data investigating changes in brain Cr levels, cognition, peripheral and brain mitochondrial function, and muscle strength and size. METHODS: CABA is an 8-week, single-arm pilot study that will recruit 20 patients with cognitive impairment due to AD. Participants attend five in-person study visits: two visits at baseline to conduct screening and baseline assessments, a 4-week visit, and two 8-week visits. Outcomes assessment includes recruitment, retention, and compliance, cognitive testing, magnetic resonance spectroscopy of brain metabolites, platelet and lymphocyte mitochondrial function, and muscle strength and morphology at baseline and 8 weeks. DISCUSSION: CABA is the first study to investigate CrM as a potential treatment in patients with AD. The pilot data generated by this study are pertinent to inform the design of future large-scale efficacy trials. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05383833 , registered on 20 May 2022.

Seizure-induced LIN28A disrupts pattern separation via aberrant hippocampal neurogenesis.

Prolonged seizures can disrupt stem cell behavior in the adult hippocampus, an important brain structure for spatial memory. Here, using a mouse model of pilocarpine-induced status epilepticus (SE), we characterized spatiotemporal expression of Lin28a mRNA and proteins after SE. Unlike Lin28a transcripts, induction of LIN28A protein after SE was detected mainly in the subgranular zone, where immunoreactivity was found in progenitors, neuroblasts, and immature and mature granule neurons. To investigate roles of LIN28A in epilepsy, we generated Nestin-Cre:Lin28aloxP/loxP (conditional KO [cKO]) and Nestin-Cre:Lin28a+/+ (WT) mice to block LIN28A upregulation in all neuronal lineages after acute seizure. Adult-generated neuron- and hippocampus-associated cognitive impairments were absent in epileptic LIN28A-cKO mice, as evaluated by pattern separation and contextual fear conditioning tests, respectively, while sham-manipulated WT and cKO animals showed comparable memory function. Moreover, numbers of hilar PROX1-expressing ectopic granule cells (EGCs), together with PROX1+/NEUN+ mature EGCs, were significantly reduced in epileptic cKO mice. Transcriptomics analysis and IHC validation at 3 days after pilocarpine administration provided potential LIN28A downstream targets such as serotonin receptor 4. Collectively, our findings indicate that LIN28A is a potentially novel target for regulation of newborn neuron-associated memory dysfunction in epilepsy by modulating seizure-induced aberrant neurogenesis.

Correlates of axonal content in healthy adult span: Age, sex, myelin, and metabolic health.

As the emerging treatments that target grey matter pathology in Alzheimer's Disease have limited effectiveness, there is a critical need to identify new neural targets for treatments. White matter's (WM) metabolic vulnerability makes it a promising candidate for new interventions. This study examined the age and sex differences in estimates of axonal content, as well the associations of with highly prevalent modifiable health risk factors such as metabolic syndrome and adiposity. We estimated intra-axonal volume fraction (ICVF) using the Neurite Orientation Dispersion and Density Imaging (NODDI) in a sample of 89 cognitively and neurologically healthy adults (20-79 years). We showed that ICVF correlated positively with age and estimates of myelin content. The ICVF was also lower in women than men, across all ages, which difference was accounted for by intracranial volume. Finally, we found no association of metabolic risk or adiposity scores with the current estimates of ICVF. In addition, the previously observed adiposity-myelin associations (Burzynska et al., 2023) were independent of ICVF. Although our findings confirm the vulnerability of axons to aging, they suggest that metabolic dysfunction may selectively affect myelin content, at least in cognitively and neurologically healthy adults with low metabolic risk, and when using the specific MRI techniques. Future studies need to revisit our findings using larger samples and different MRI approaches, and identify modifiable factors that accelerate axonal deterioration as well as mechanisms linking peripheral metabolism with the health of myelin.

Developing a Cost-Effectiveness Model of Digital Therapeutics for Smoking Cessation.

The purpose of this research was to construct a Markov model of digital therapeutics to predict the lifetime costs and consequences that would be incurred by a hypothetical group of adult smokers in Korea who only made a single attempt to stop smoking. To determine the efficacy of DTx, we created an annual cycle Markov model. The result shows that the NRT strategy is determined as the dominant strategy. Digital therapeutics acts as a complement to pharmacotherapy and is a low-cost option.

Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model.

The amount of research on the gathering and handling of healthcare data keeps growing. To support multi-center research, numerous institutions have sought to create a common data model (CDM). However, data quality issues continue to be a major obstacle in the development of CDM. To address these limitations, a data quality assessment system was created based on the representative data model OMOP CDM v5.3.1. Additionally, 2,433 advanced evaluation rules were created and incorporated into the system by mapping the rules of existing OMOP CDM quality assessment systems. The data quality of six hospitals was verified using the developed system and an overall error rate of 0.197% was confirmed. Finally, we proposed a plan for high-quality data generation and the evaluation of multi-center CDM quality.

Pacing-induced cardiomyopathy in patients with preserved ejection fraction undergoing permanent cardiac pacemaker placement.

BACKGROUND: Chronic right-ventricular (RV) pacing can worsen heart failure in patients with a low ejection fraction (EF), but little is known about pacing-induced cardiomyopathy (PICM) in patients with preserved EF. We aimed to investigate risk factors of PICM in these patients during long-term follow-up. METHODS: The prospective registry at Chosun University Hospital, South Korea, included de novo patients with preserved EF undergoing transvenous permanent pacemaker (PPM) implantation for atrioventricular blockage from 2017 to 2021. Patients with EF ≥ 50% and expected ventricular pacing ≥ 40% were included. Composite outcomes were cardiac death (pump failure), hospitalization because of heart failure, PICM, and biventricular pacing (BVP) upgrade. RESULTS: A total of 168 patients (69 men, 76.3 ± 10.4 years) were included. During three years of follow-up, one patient died, 14 were hospitalized, 16 suffered PICM, and two underwent BVP upgrade. PICM were associated with reduced global longitudinal strain (GLS), prolonged paced QRS duration (pQRSd) and diastolic variables (E/e', LAVI). Cox regression analysis identified pQRSd (hazard ratio [HR], 1.111; 95% confidence interval [CI], 1.011-1.222; P = 0.03) and reduced GLS (HR, 1.569; 95% CI, 1.163-2.118; P = 0.003) as independent predictors of PICM. GLS showed high predictive accuracy for PICM, with an area under the curve of 0.84 (95% CI 0.779-0.894; P < 0.001) [GLS -12.0, 62.5% sensitivity, and 86.1% specificity]. CONCLUSION: RV pacing increased the risk of PICM in patients with preserved EF. Reduced GLS and prolonged pQRSd could help identify individuals at high risk of PICM even with preserved EF.