Background: Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis and lacks clinical biomarkers. Exosomes are extracellular vesicles that facilitate cell–cell communication by distributing macromolecules, such as small RNAs (smRNAs). We assessed the potential of exosome-derived small RNAs (Ex-smRNAs) as PDAC biomarkers. Methods: Peripheral blood was collected from 51 patients with PDAC and 15 control individuals. Exosomes were isolated using an aqueous two-phase system. Ex-smRNAs were analyzed using smRNA sequencing. smRNA-mediated target gene regulation was verified via The Cancer Genome Atlas analysis and in vitro transfection and wound-healing assays using PDAC organoids. Results: The total Ex-smRNA count was substantially reduced in patients with PDAC compared with that in control individuals. The levels of microRNAs (miRNAs) miR-125a-5p, miR-30e-5p, miR-16-2-3p, miR-98-5p, and the let-7 family were significantly suppressed, whereas that of miR-6731-5p was significantly elevated. Let-7c-5p and miR-98-5p were found to interact with the long non-coding RNA OLMALINC to regulate their common target genes, BACH1 and CCND1, thus controlling PDAC proliferation and migration. The expressions of CARS1-AS1 and miR-142-5p were upregulated in treatment-responsive patients.Multivariable Cox regression analyses, adjusting for potential prognostic factors such as sex, Eastern Cooperative Oncology Group performance status, and tumor size and stage, revealed that CARS1-AS1 (adjusted hazard ratio [HR] 0.33; 95% confidence interval [CI], 0.15–0.73; P = 0.0061) and miR-142-5p (adjusted HR 0.79; 95% CI, 0.61–1.01; P = 0.0581) were associated with improved overall survival. Conclusions We identified potential Ex-smRNA biomarkers involved in PDAC progression and prognosis that reflect key molecular alterations in PDAC and may serve as clinically relevant biomarkers for disease monitoring.

The impact of baseline estimated glomerular filtration rate (eGFR) on the risk of adverse outcomes according to metabolic parameter variabilities in chronic kidney disease has rarely been investigated. Methods: We conducted a retrospective nationwide cohort study using the National Health Insurance System data in Korea from 2007 to 2013 to identify individuals with three or more health screenings. The metabolic components variability was defined as intraindividual variability between measurements using the variability independent of the mean. The metabolic variability score was defined as the total number of high-variability metabolic components. Multivariable-adjusted Cox regression analysis was conducted to evaluate the risks of all-cause mortality, myocardial infarction, and ischemic stroke. Results: During a mean follow-up of 6.0 ± 0.7 years, 223,531 deaths, 107,140 myocardial infarctions, and 116,182 ischemic strokes were identified in 9,971,562 patients. Low eGFR categories and higher metabolic variability scores were associated with a higher risk of adverse outcomes. The degree of association between metabolic variability and adverse outcomes was significantly larger in those with low eGFR categories than in those with preserved eGFR (p for interaction < 0.001). Representatively, those with high metabolic variability in the eGFR of <15 mL/min/1.73 m2 group showed a prominently higher risk for all-cause mortality (adjusted hazard ratio [aHR], 5.28; 95% confidence interval [CI], 4.02–6.94) when the degree was compared to the findings in those with preserved (eGFR of ≥60 mL/min/1.73 m2) kidney function (aHR, 2.55; 95% CI, 2.41–2.69). Conclusion: The degree of adverse association between metabolic variability and poor prognosis is accentuated in patients with impaired kidney function.

Early-onset diabetes mellitus has a significant lifetime burden and is associated with higher morbidity and mortality. Since insulin resistance is one of the mechanisms of podocyte injury, we aimed to evaluate the effect of albuminuria on newly developed early-onset type 2 diabetes mellitus (T2DM). Methods: We screened 6,891,399 subjects aged ≥20 and <40 years without a history of prediabetes or diabetes from the Korean National Health Insurance Service database between 2009 and 2012. A multivariate Cox proportional hazard model was used to identify the impact of albuminuria on early-onset T2DM. Results: Among a total of 5,383,779 subjects, 62,148 subjects (1.2%) developed early-onset diabetes over 7.3 ± 1.2 years. Albuminuria was significantly associated with early-onset T2DM (adjusted hazard ratio [aHR], 1.62; 95% confidence interval [CI], 1.55–1.70) after adjustment for age, sex, anthropometric data, physical exercise status, serum glucose, and total cholesterol. The risk of early-onset T2DM increased more in subjects with more components of metabolic syndrome (MetS). Among each component of MetS, hypertriglyceridemia was prominently associated with early-onset T2DM (aHR, 2.02; 95% CI, 1.81–2.25) in subjects with albuminuria. Conclusion: Dipstick albuminuria was significantly associated with early-onset T2DM in young adult populations. Close monitoring of albuminuria is warranted for disease risk modification, especially in subjects with MetS.

Digital therapeutics are emerging as treatments for diseases and disabilities. In chronic kidney disease (CKD), gait is a potential biomarker for health status and intervention effectiveness. This study aims to analyze gait characteristics in CKD patients, providing baseline data for digital therapeutics development. Methods: At baseline and after an 8-week intervention, we performed bioimpedance analysis measurements, the Timed Up and Go, Tinetti, and grip strength tests, and gait analysis in 217 healthy individuals and 276 patients with CKD. Demographic and clinical information was collected, including underlying diseases and medications, laboratory tests, and quality of life satisfaction surveys. Gait analysis was performed using skeleton data, which involved acquiring three-dimensional skeleton data of a walker using a single Kinect sensor. The performance of an artificial intelligence-based classification model in distinguishing between healthy individuals and those with CKD was then investigated. Simultaneously, inertia measurement unit analysis was conducted using measurements taken from the wrist and waist. Results: Most subjects received a health intervention via an app, and their gait was assessed for improvements after an 8-week period. Incidents such as falls, fractures, hospitalizations, and deaths will be investigated in years 1 and 3. Conclusion: This study confirmed that the gaits of healthy individuals and CKD patients were different, and the effect of the 8-week app-based health intervention will be analyzed. The study will yield important baseline data for creating digital therapeutics for CKD patients’ diet/exercise in the future.

Further study is warranted to determine the association between estimated glomerular filtration rate (eGFR) or albuminuria and the risk of death from diverse causes. Methods: We screened >10 million general health screening examinees who received health examinations conducted in 2009 using the claims database of Korea. After the exclusion of those previously diagnosed with renal failure and those with missing data, 9,917,838 individuals with available baseline kidney function measurements were included. The primary outcome was mortality and cause-specific death between 2009 and 2019 identified through death certificates based on the diagnostic codes of International Classification of Diseases, 10th revision. Multivariable Cox regression analysis adjusted for various clinicodemographic and social characteristics was used to assess mortality risk. Results: The hazard ratio of death was significantly high in both the eGFR <60 mL/min/1.73 m2 and in the eGFR ≥120 mL/ min/1.73 m2 groups in univariable and multivariable regression analyses when compared to those within the reference range (eGFR of 90–120 mL/min/1.73 m2). The results were similar for death by cardiovascular, cancer, infection, endocrine, respiratory, and digestive causes. We also found that albuminuria was associated with higher risk of death regardless of eGFR range, and those in the higher categories of dipstick albuminuria showed higher risk. Conclusion: We reconfirmed the significant association between eGFR, albuminuria, and mortality. Healthcare providers should keep in mind that albuminuria and decreased eGFR as well as kidney hyperfiltration are independent predictors of mortality.

Until now, rare disease studies have mainly been carried out by detecting simple variants such as single nucleotide substitutions and short insertions and deletions in protein-coding regions of disease-associated gene panels using diagnostic nextgeneration sequencing in association with patient phenotypes. However, several recent studies reported that the detection rate hardly exceeds 50% even when whole-exome sequencing is applied. Therefore, the necessity of introducing wholegenome sequencing is emerging to discover more diverse genomic variants and examine their association with rare diseases.When no diagnosis is provided by whole-genome sequencing, additional omics techniques such as RNA-seq also can be considered to further interrogate causal variants. This paper will introduce a description of these multi-omics techniques and their applications in rare disease studies.

Background: The genetically predicted lipid-lowering effect of HMGCR or PCSK9 variant can be used to assess drug proxy effects on kidney function. Methods: Mendelian randomization (MR) analysis-identified HMGCR and PCSK9 genetic variants were used to predict the low-density lipoprotein (LDL) cholesterol-lowering effects of medications targeting related molecules. Primary summary-level outcome data for log-estimated glomerular filtration rate (eGFR; creatinine) were provided by the CKDGen Consortium (n = 1,004,040 European) from a meta-analysis of CKDGen and UK Biobank data. We also conducted a separate investigation of summary-level data from CKDGen (n = 567,460, log-eGFR [creatinine]) and UK Biobank (n = 436,581, log-eGFR [cystatin C]) samples. Summary-level MRs using an inverse variance weighted method and pleiotropy-robust methods were performed. Results: Summary-level MR analysis indicated that the LDL-lowering effect predicted genetically by HMGCR variants (50-mg/dL decrease) was significantly associated with a decrease in eGFR (–1.67%; 95% confidence interval [CI], –2.20% to –1.13%). Similar significance was found in results from the pleiotropy-robust MR methods when the CKDGen and UK Biobank data were analyzed separately. However, the LDL-lowering effect predicted genetically by PCSK9 variants was significantly associated with an increase in eGFR (+1.17%; 95% CI, 0.10%–2.25%). The results were similarly supported by the weighted median method and in each CKDGen and UK Biobank dataset, but the significance obtained by MR-Egger regression was attenuated. Conclusion Genetically predicted HMG-CoA reductase inhibition was associated with low eGFR, while genetically predicted PCSK9 inhibition was associated with high eGFR. Clinicians should consider that the direct effect of different types of lipid-lowering medication on kidney function can vary.

Background: Early identification of patients with coronavirus disease 2019 (COVID-19) who are at high risk of mortality is of vital importance for appropriate clinical decision making and delivering optimal treatment. We aimed to develop and validate a clinical risk score for predicting mortality at the time of admission of patients hospitalized with COVID-19. Methods: Collaborating with the Korea Centers for Disease Control and Prevention (KCDC), we established a prospective consecutive cohort of 5,628 patients with confirmed COVID-19 infection who were admitted to 120 hospitals in Korea between January 20, 2020, and April 30, 2020. The cohort was randomly divided using a 7:3 ratio into a development (n = 3,940) and validation (n = 1,688) set. Clinical information and complete blood count (CBC) detected at admission were investigated using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-Mortality Score).The discriminative power of the risk model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic curves. Results: The incidence of mortality was 4.3% in both the development and validation set.A COVID-Mortality Score consisting of age, sex, body mass index, combined comorbidity, clinical symptoms, and CBC was developed. AUCs of the scoring system were 0.96 (95% confidence interval [CI], 0.85–0.91) and 0.97 (95% CI, 0.84–0.93) in the development and validation set, respectively. If the model was optimized for > 90% sensitivity, accuracies were 81.0% and 80.2% with sensitivities of 91.7% and 86.1% in the development and validation set, respectively. The optimized scoring system has been applied to the public online risk calculator (https://www.diseaseriskscore.com). Conclusion This clinically developed and validated COVID-Mortality Score, using clinical data available at the time of admission, will aid clinicians in predicting in-hospital mortality.

Purpose: Acute kidney injury (AKI) in cancer patients is associated with increased morbidity and mortality. The incidence of AKI in lung cancer seems to be relatively higher compared with other solid organ malignancies, although its impact on patient outcomes remains unclear. Materials and Methods: The patients newly diagnosed with lung cancer from 2004 to 2013 were enrolled in this retrospective cohort study. The patients were categorized according to the presence and severity of AKI. We compared all-cause mortality and long-term renal outcome according to AKI stage. Results: A total of 3,202 patients were included in the final analysis. AKI occurred in 1,783 (55.7%) patients during the follow-up period, with the majority having mild AKI stage 1 (75.8%). During the follow-up of 2.6±2.2 years, total 1,251 patients (53.7%) were died and 5-year survival rate was 46.9%. We found that both AKI development and severity were independent risk factors for all-cause mortality in lung cancer patients, even after adjustment for lung cancer-specific variables including the stage or pathological type. In addition, patients suffered from more severe AKI tend to encounter de novo chronic kidney disease development, worsening kidney function, and end-stage kidney disease progression. Conclusion In this study, more than half of the lung cancer patients experienced AKI during their diagnosis and treatment period. Moreover, AKI occurrence and more advanced AKI were associated with a higher mortality risk and adverse kidney outcomes.

Background: Early identification of patients with coronavirus disease 2019 (COVID-19) who are at high risk of mortality is of vital importance for appropriate clinical decision making and delivering optimal treatment. We aimed to develop and validate a clinical risk score for predicting mortality at the time of admission of patients hospitalized with COVID-19. Methods: Collaborating with the Korea Centers for Disease Control and Prevention (KCDC), we established a prospective consecutive cohort of 5,628 patients with confirmed COVID-19 infection who were admitted to 120 hospitals in Korea between January 20, 2020, and April 30, 2020. The cohort was randomly divided using a 7:3 ratio into a development (n = 3,940) and validation (n = 1,688) set. Clinical information and complete blood count (CBC) detected at admission were investigated using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-Mortality Score).The discriminative power of the risk model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic curves. Results: The incidence of mortality was 4.3% in both the development and validation set.A COVID-Mortality Score consisting of age, sex, body mass index, combined comorbidity, clinical symptoms, and CBC was developed. AUCs of the scoring system were 0.96 (95% confidence interval [CI], 0.85–0.91) and 0.97 (95% CI, 0.84–0.93) in the development and validation set, respectively. If the model was optimized for > 90% sensitivity, accuracies were 81.0% and 80.2% with sensitivities of 91.7% and 86.1% in the development and validation set, respectively. The optimized scoring system has been applied to the public online risk calculator (https://www.diseaseriskscore.com). Conclusion This clinically developed and validated COVID-Mortality Score, using clinical data available at the time of admission, will aid clinicians in predicting in-hospital mortality.