Inhibition of sodium-glucose cotransporter-2 and liver-related complications in individuals with diabetes: a Mendelian randomization and population-based cohort study.

BACKGROUND AND AIMS: No medication has been found to reduce liver-related events. We evaluated the effect of sodium-glucose cotransporter-2 inhibitor (SGLT2i) on liver-related outcomes. APPROACH AND RESULTS: Single nucleotide polymorphisms associated with SGLT2 inhibition were identified, and a genetic risk score (GRS) was computed using the UK Biobank data (n=337,138). Two-sample Mendelian randomization (MR) was conducted using the FinnGen (n=218,792) database and the UK Biobank data. In parallel, a nationwide population-based study using the Korean National Health Insurance Service (NHIS) database was conducted. The development of liver-related complications (ie, hepatic decompensation, HCC, liver transplantation, and death) was compared between individuals with type 2 diabetes mellitus and steatotic liver diseases treated with SGLT2i (n=13,208) and propensity score-matched individuals treated with dipeptidyl peptidase-4 inhibitor (n=70,342). After computing GRS with 6 single nucleotide polymorphisms (rs4488457, rs80577326, rs11865835, rs9930811, rs34497199, and rs35445454), GRS-based MR showed that SGLT2 inhibition (per 1 SD increase of GRS, 0.1% lowering of HbA1c) was negatively associated with cirrhosis development (adjusted odds ratio=0.83, 95% CI=0.70-0.98, p =0.03) and this was consistent in the 2-sample MR (OR=0.73, 95% CI=0.60-0.90, p =0.003). In the Korean NHIS database, the risk of liver-related complications was significantly lower in the SGLT2i group than in the dipeptidyl peptidase-4 inhibitor group (adjusted hazard ratio=0.88, 95% CI=0.79-0.97, p =0.01), and this difference remained significant (adjusted hazard ratio=0.72-0.89, all p <0.05) across various sensitivity analyses. CONCLUSIONS: Both MRs using 2 European cohorts and a Korean nationwide population-based cohort study suggest that SGLT2 inhibition is associated with a lower risk of liver-related events.

Extrahepatic malignancies in metabolic dysfunction-associated fatty liver disease: A nationwide cohort study.

BACKGROUND AND AIMS: Metabolic dysfunction-associated fatty liver disease (MAFLD) encompasses heterogeneous fatty liver diseases associated with metabolic disorders. We aimed to evaluate the association between MAFLD and extrahepatic malignancies based on MAFLD subtypes. METHODS: This nationwide cohort study included 9 298 497 patients who participated in a health-screening programme of the National Health Insurance Service of Korea in 2009. Patients were further classified into four subgroups: non-MAFLD, diabetes mellitus (DM)-MAFLD, overweight/obese-MAFLD and lean-MAFLD. The primary outcome was the development of any primary extrahepatic malignancy, while death, decompensated liver cirrhosis and liver transplantation were considered competing events. The secondary outcomes included all-cause and extrahepatic malignancy-related mortality. RESULTS: In total, 2 500 080 patients were diagnosed with MAFLD. During a median follow-up of 10.3 years, 447 880 patients (6.0%) with extrahepatic malignancies were identified. The DM-MAFLD (adjusted subdistribution hazard ratio [aSHR] = 1.13; 95% confidence interval [CI] = 1.11-1.14; p < .001) and the lean-MAFLD (aSHR = 1.12; 95% CI = 1.10-1.14; p < .001) groups were associated with higher risks of extrahepatic malignancy than the non-MAFLD group. However, the overweight/obese-MAFLD group exhibited a similar risk of extrahepatic malignancy compared to the non-MAFLD group (aSHR = 1.00; 95% CI = .99-1.00; p = .42). These findings were reproduced in several sensitivity analyses. The DM-MAFLD was an independent risk factor for all-cause mortality (adjusted hazard ratio [aHR] = 1.41; 95% CI = 1.40-1.43; p < .001) and extrahepatic malignancy-related mortality (aHR = 1.20; 95% CI = 1.17-1.23; p < .001). CONCLUSION: The diabetic or lean subtype of MAFLD was associated with a higher risk of extrahepatic malignancy than non-MAFLD. As MAFLD comprises a heterogeneous population, appropriate risk stratification and management based on the MAFLD subtypes are required.

Transcriptomic Expression of T2-Inflammation Genes in Peripheral Blood Mononuclear Cells and Longitudinal Clinical Outcomes in Asthma: Insights from the COREA Study.

BACKGROUND: Gene expression can provide distinct information compared to clinical biomarkers in the context of longitudinal clinical outcomes in asthma patients. OBJECTIVE: This study examined the association between the gene expression levels of upstream (IL-25, IL-33, and TSLP) and downstream cytokines (IL-5, IL-4, and IL-13) in the T2 inflammatory pathway with a 12-month follow-up of exacerbation, lung function, and steroid use. METHODS: Transcriptomic sequencing analysis was performed on peripheral blood mononuclear cells from 279 adult asthmatics. Survival analysis and linear mixed-effect models were used to investigate potential differences between the high-level and low-level gene expression groups and the clinical outcomes. Analysis was performed separately for the upstream, downstream, and all 6 cytokines. RESULTS: In general, T2 inflammatory cytokine gene expression showed a weak correlation with blood eosinophil counts (all r < 0.1) and clinical outcomes. Among moderate-to-severe eosinophilic asthma (MSEA) patients, individuals with elevated levels of downstream cytokines were at increased risk of time-to-first exacerbation (p = 0.044) and a greater increase of inhaled corticosteroid use over time (p = 0.002) compared to those with lower gene expression. There was no association between baseline T2 inflammatory cytokine gene expression and the longitudinal changes in lung function over time among MSEA patients. CONCLUSION: These findings suggest that, among MSEA patients, the gene expression levels of downstream cytokines in the T2 inflammatory pathway may serve as indicators for endotyping asthma.

Impact of linkage level on inferences from big data analyses in health and medical research: an empirical study.

BACKGROUND: Linkage errors that occur according to linkage levels can adversely affect the accuracy and reliability of analysis results. This study aimed to identify the differences in results according to personally identifiable information linkage level, sample size, and analysis methods through empirical analysis. METHODS: The difference between the results of linkage in directly identifiable information (DII) and indirectly identifiable information (III) linkage levels was set as III linkage based on name, date of birth, and sex and DII linkage based on resident registration number. The datasets linked at each level were named as databaseIII (DBIII) and databaseDII (DBDII), respectively. Considering the analysis results of the DII-linked dataset as the gold standard, descriptive statistics, group comparison, incidence estimation, treatment effect, and moderation effect analysis results were assessed. RESULTS: The linkage rates for DBDII and DBIII were 71.1% and 99.7%, respectively. Regarding descriptive statistics and group comparison analysis, the difference in effect in most cases was "none" to "very little." With respect to cervical cancer that had a relatively small sample size, analysis of DBIII resulted in an underestimation of the incidence in the control group and an overestimation of the incidence in the treatment group (hazard ratio [HR] = 2.62 [95% confidence interval (CI): 1.63-4.23] in DBIII vs. 1.80 [95% CI: 1.18-2.73] in DBDII). Regarding prostate cancer, there was a conflicting tendency with the treatment effect being over or underestimated according to the surveillance, epidemiology, and end results summary staging (HR = 2.27 [95% CI: 1.91-2.70] in DBIII vs. 1.92 [95% CI: 1.70-2.17] in DBDII for the localized stage; HR = 1.80 [95% CI: 1.37-2.36] in DBIII vs. 2.05 [95% CI: 1.67-2.52] in DBDII for the regional stage). CONCLUSIONS: To prevent distortion of the analyses results in health and medical research, it is important to check that the patient population and sample size by each factor of interest (FOI) are sufficient when different data are linked using DBDII. In cases involving a rare disease or with a small sample size for FOI, there is a high likelihood that a DII linkage is unavoidable.

The Risk of the Aggravation of Diabetic Foot According to Air Quality Factors in the Republic of Korea: A Nationwide Population-Based Study.

This study aims to examine the association between the occurrence of diabetic foot and air quality (SO2, CO, NO2, O3). Open data were collected to conduct a big data study. Patient information was gathered from the National Health Insurance Service, and the National Institute of Environmental Science's air quality data were used. A total study population of 347,543 cases were reviewed (case = 13,353, control = 334,190). The lag period from air quality changes to the actual amputation operation was calculated for each factor. The frequency of diabetic foot amputation in each region was identified and analyzed using a distributed lag non-linear model. Gangwon-do showed the highest relative risks (RRs) for SO2 and CO, while Chungcheongnam-do exhibited the highest RR for NO2. Jeju had the highest RR for O3. Regions like Incheon, Busan, and the capital region also showed significant risk increases. These findings emphasize the importance of tailored air quality management to address diabetic foot complications effectively.

Propensity score matching for comparative studies: a tutorial with R and Rex.

Recently, there has been considerable progress in developing new technologies and equipment for the medical field, including minimally invasive surgeries. Evaluating the effectiveness of these treatments requires study designs like randomized controlled trials. However, due to the nature of certain treatments, randomization is not always feasible, leading to the use of observational studies. The effect size estimated from observational studies is subject to selection bias caused by confounders. One method to reduce this bias is propensity scoring. This study aimed to introduce a propensity score matching process between two groups using a practical example with R. Additionally, Rex, an Excel add-in graphical user interface statistical program, is provided for researchers unfamiliar with R programming. Further techniques, such as matching with three or more groups, propensity score weighting and stratification, and imputation of missing values, are summarized to offer approaches for more complex studies not covered in this tutorial.

Sex differences in the applicability of Western cardiovascular disease risk prediction equations in the Asian population.

AIMS: Cardiovascular diseases (CVDs) are the most common cause of death, but they can be effectively managed through appropriate prevention and treatment. An important aspect in preventing CVDs is assessing each individual's comprehensive risk profile, for which various risk engines have been developed. The important keys to CVD risk engines are high reliability and accuracy, which show differences in predictability depending on disease status or race. Framingham risk score (FRS) and the atherosclerotic cardiovascular disease risk equations (ASCVD) were applied to the Korean population to assess their suitability. METHODS: A retrospective cohort study was conducted using National Health Insurance Corporation sample cohort from 2003 to 2015. The enrolled participants over 30 years of age and without CVD followed-up for 10 years. We compared the prediction performance of FRS and ASCVD and calculated the relative importance of each covariate. RESULTS: The AUCs of FRS (men: 0.750; women: 0.748) were higher than those of ASCVD (men: 0.718; women: 0.727) for both sexes (Delong test P <0.01). Goodness of fits (GOF) were poor for all models (Chi-square P < 0.001), especially, underestimation of the risk was pronounced in women. When the men's coefficients were applied to women's data, AUC (0.748; Delong test P<0.01) and the GOF (chi-square P = 0.746) were notably improved in FRS. Hypertension was found to be the most influential variable for CVD, and this is one of the reasons why FRS, having the highest relative weight to blood pressure, showed better performance. CONCLUSION: When applying existing tools to Korean women, there was a noticeable underestimation. To accurately predict the risk of CVD, it was more appropriate to use FRS with men's coefficient in women. Moreover, hypertension was found to be a main risk factor for CVD.

Effects of polygenic risk score and sodium and potassium intake on hypertension in Asians: A nationwide prospective cohort study.

Genetic factors, lifestyle, and diet have been shown to play important roles in the development of hypertension. Increased salt intake is an important risk factor for hypertension. However, research on the involvement of genetic factors in the relationship between salt intake and hypertension in Asians is lacking. We aimed to investigate the risk of hypertension in relation to sodium and potassium intake and the effects of genetic factors on their interactions. We used Korean Genome and Epidemiology Study data and calculated the polygenic risk score (PRS) for the effect of systolic and diastolic blood pressure (SBP and DBP). We also conducted multivariable logistic modeling to evaluate associations among incident hypertension, PRSSBP, PRSDBP, and sodium and potassium intake. In total, 41,351 subjects were included in the test set. The top 10% PRSSBP group was the youngest of the three groups (bottom 10%, middle, top 10%), had the highest proportion of women, and had the highest body mass index, baseline BP, red meat intake, and alcohol consumption. The multivariable logistic regression model revealed the risk of hypertension was significantly associated with higher PRSSBP, higher sodium intake, and lower potassium intake. There was significant interaction between sodium intake and PRSSBP for incident hypertension especially in sodium intake ≥2.0 g/day and PRSSBP top 10% group (OR 1.27 (1.07-1.51), P = 0.007). Among patients at a high risk of incident hypertension due to sodium intake, lifestyle modifications and sodium restriction were especially important to prevent hypertension.

Outcomes of non-vitamin K oral anticoagulants for secondary prevention in ischemic stroke with atrial fibrillation.

Previous studies have rarely investigated the role of non-vitamin K oral anticoagulants (NOAC) and warfarin in the secondary prevention of ischemic stroke patients with nonvalvular atrial fibrillation (NVAF). In this study, we compared the effectiveness and safety of NOAC and warfarin for secondary prevention in Korean ischemic stroke patients with NVAF. Based on the Korean National Health Insurance Service Database, this study included 21,064 oral anticoagulants-naïve acute ischemic stroke patients with NVAF between July 2015 and June 2019. The main study outcomes included ischemic stroke, systemic embolism, major bleeding, and death. During the observational periods, NOAC users had a significantly decreased risk of ischemic stroke + systemic embolism (adjusted hazard ratio [aHR] 0.86; 95% confidence interval [CI] 0.78-0.95), ischemic stroke (aHR 0.89; 95% CI 0.81-0.99), major bleeding (aHR 0.78; 95% CI 0.68-0.89), and all-cause death (aHR 0.87; 95% CI 0.81-0.93). Standard-dose NOAC users had a lower risk of ischemic stroke, systemic embolism, and major bleeding events than warfarin users. In contrast, low-dose NOAC users did not differ in risk from warfarin users for all outcomes. In conclusion, NOACs were associated with a lower risk of secondary thromboembolic events and bleeding complications in Korean ischemic stroke patients with NVAF than warfarin.

Association between dyslipidemia and the risk of incident chronic kidney disease affected by genetic susceptibility: Polygenic risk score analysis.

BACKGROUND: The effect of dyslipidemia on kidney disease outcomes has been inconclusive, and it requires further clarification. Therefore, we aimed to investigate the effects of genetic factors on the association between dyslipidemia and the risk of chronic kidney disease (CKD) using polygenic risk score (PRS). METHODS: We analyzed data from 373,523 participants from the UK Biobank aged 40-69 years with no history of CKD. Baseline data included plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride, as well as genome-wide genotype data for PRS. Our primary outcome, incident CKD, was defined as a composite of estimated glomerular filtration rate < 60 ml/min/1.73 m2 and CKD diagnosis according to International Classification of Disease-10 codes. The effects of the association between lipid levels and PRS on incident CKD were assessed using the Cox proportional hazards model. To investigate the effect of this association, we introduced multiplicative interaction terms into a multivariate analysis model and performed subgroup analysis stratified by PRS tertiles. RESULTS: In total, 4,424 participants developed CKD. In the multivariable analysis, PRS was significantly predictive of the risk of incident CKD as both a continuous variable and a categorized variable. In addition, lower total cholesterol, LDL-C, HDL-C, and higher triglyceride levels were significantly associated with the risk of incident CKD. There were interactions between triglycerides and intermediate and high PRS, and the interactions were inversely associated with the risk of incident CKD. CONCLUSIONS: This study showed that PRS presented significant predictive power for incident CKD and individuals in the low-PRS group had a higher risk of triglyceride-related incident CKD.

Gut microbiome signature of metabolically healthy obese individuals according to anthropometric, metabolic and inflammatory parameters.

In this study, we investigated the characteristics of gut microbiome in the metabolically healthy obese (MHO) patients, and how they correlate with metabolic and inflammatory profiles. A total of 120 obese people without metabolic comorbidities were recruited, and their clinical phenotypes, metabolic and inflammatory parameters were analysed. The faecal microbial markers originating from bacterial cell and extracellular vesicle (EV) were profiled using 16S rDNA sequencing. The total study population could be classified into two distinct enterotypes (enterotype I: Prevotellaceae-predominant, enterotype II: Akkermansia/Bacteroides-predominant), based on their stool EV-derived microbiome profile. When comparing the metabolic and inflammatory profiles, subjects in enterotype I had higher levels of serum IL-1ß [false discovery rate (FDR) q = 0.050] and had a lower level of microbial diversity than enterotype II (Wilcoxon rank-sum test p < 0.01). Subjects in enterotype I had relatively higher abundance of Bacteroidetes, Prevotellaceae and Prevotella-derived EVs, and lower abundance of Actinobacteria, Firmicutes, Proteobacteria, Akkermansia and Bacteroides-derived EVs (FDR q < 0.05). In conclusion, HMO patients can be categorised into two distinct enterotypes by the faecal EV-derived microbiome profile. The enterotyping may be associated with different metabolic and inflammatory profiles. Further studies are warranted to elucidate the long-term prognostic impact of EV-derived microbiome in the obese population.

Blood transcriptome differentiates clinical clusters for asthma.

Background: In previous studies, several asthma phenotypes were identified using clinical and demographic parameters. Transcriptional phenotypes were mainly identified using sputum and bronchial cells. Objective: We aimed to investigate asthma phenotypes via clustering analysis using clinical variables and compare the transcription levels among clusters using gene expression profiling of the blood. Methods: Clustering analysis was performed using 6 parameters: age of asthma onset, body mass index, pack-years of smoking, forced expiratory volume in 1 s (FEV1), FEV1/forced vital capacity, and blood eosinophil counts. Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood samples and RNA was extracted from selected PBMCs. Transcriptional profiles were generated (Illumina NovaSeq 6000) and analyzed using the reference genome and gene annotation files (hg19.refGene.gft). Pathway enrichment analysis was conducted using GO, KEGG, and REACTOME databases. Results: In total, 355 patients with asthma were included in the analysis, of whom 72 (20.3%) had severe asthma. Clustering of the 6 parameters revealed 4 distinct subtypes. Cluster 1 (n = 63) had lower predicted FEV1 % and higher pack-years of smoking and neutrophils in sputum. Cluster 2 (n = 43) had a higher proportion and number of eosinophils in sputum and blood, and severe airflow limitation. Cluster 3 (n = 110) consisted of younger subjects with atopic features. Cluster 4 (n = 139) included features of late-onset mild asthma. Differentially expressed genes between clusters 1 and 2 were related to inflammatory responses and cell activation. Th17 cell differentiation and interferon gamma-mediated signaling pathways were related to neutrophilic inflammation in asthma. Conclusion: Four clinical clusters were differentiated based on clinical parameters and blood eosinophils in adult patients with asthma form the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA) cohort. Gene expression profiling and molecular pathways are novel means of classifying asthma phenotypes.

Associations between plasma metabolites and heavy metal exposure in residents of environmentally polluted areas.

Heavy metals are commonly released into the environment through industrial processes such as mining and refining. The rapid industrialization that occurred in South Korea during the 1960s and 1970s contributed significantly to the economy of the country; however, the associated mining and refining led to considerable environmental pollution, and although mining is now in decline in South Korea, the detrimental effects on residents inhabiting the surrounding areas remain. The bioaccumulation of toxic heavy metals leads to metabolic alterations in human homeostasis, with disruptions in this balance leading to various health issues. This study used metabolomics to explore metabolomic alterations in the plasma samples of residents living in mining and refining areas. The results showed significant increases in metabolites involved in glycolysis and the surrounding metabolic pathways, such as glucose-6-phosphate, phosphoenolpyruvate, lactate, and inosine monophosphate, in those inhabiting polluted areas. An investigation of the associations between metabolites and blood clinical parameters through meet-in-the-middle analysis indicated that female residents were more affected by heavy metal exposure, resulting in more metabolomic alterations. For women, inhabiting the abandoned mine area, metabolites in the glycolysis and pentose phosphate pathways, such as ribose-5-phosphate and 3-phosphoglycerate, have shown a negative correlation with albumin and calcium. Finally, Mendelian randomization(MR) was used to determine the causal effects of these heavy metal exposure-related metabolites on heavy metal exposure-related clinical parameters. Metabolite biomarkers could provide insights into altered metabolic pathways related to exposure to toxic heavy metals and improve our understanding of the molecular mechanisms underlying the health effects of toxic heavy metal exposure.

Impact of hyperuricemia on CKD risk beyond genetic predisposition in a population-based cohort study.

The bidirectional effect of hyperuricemia on chronic kidney disease (CKD) underscores the importance of hyperuricemia as a risk factor for CKD. We evaluated the effect of hyperuricemia on the presence and development of CKD after considering genetic background by calculating polygenic risk scores (PRSs). We employed genome-wide association study summary statistics-excluding the United Kingdom Biobank (UKB) datasets among published CKD Gen Consortium papers-to calculate the PRSs for CKD in white background subjects. To validate PRS performance, we divided the UKB into two datasets to validate and test the data. We used logistic regression analysis to evaluate the association between hyperuricemia and CKD, and performed Kaplan-Meier survival analysis exclusively for subjects with available follow-up data. In total, 438,253 clinical data and 4,307,940 single nucleotide polymorphisms from 459,155 samples were included. We observed a significant positive association between PRS and CKD and the presence and development of CKD. Hyperuricemia significantly increased CKD risk (adjusted odds ratio 1.55, 95% confidence interval 1.48-1.61). The impact of hyperuricemia on CKD was maintained irrespective of PRS range. In addition, negative interaction between hyperuricemia and PRS for CKD was found. Survival analysis indicates that the presence of hyperuricemia significantly increased the risk of CKD development. The PRS for CKD thoroughly reflects the risk of CKD development. Hyperuricemia is a significant indicator of CKD risk, even after incorporating the genetic risk score for CKD. Irrespective of genetic risk, patients with a prospective risk of developing CKD require uric acid monitoring and management.

Genome-wide association study implicates the role of TBXAS1 in the pathogenesis of depressive symptoms among the Korean population.

Although depression is an emerging disorder affecting many people worldwide, most genetic studies have been performed in European descent populations. Herein, a genome-wide association study (GWAS) was conducted in Korean population to elucidate the genomic loci associated with depressive symptoms. Two independent cohorts were used as discovery datasets, which consisted of 6474 (1484 cases and 4990 controls) and 1654 (557 cases and 1097 controls) Korean participants, respectively. The participants were divided into case and control groups based on the Beck Depression Inventory (BDI). Meta-analysis using the two cohorts revealed that rs6945590 was significantly associated with the risk of depressive symptoms [P = 2.83 × 10-8; odds ratio (OR) = 1.23; 95% confidence interval (CI): 1.15-1.33]. This association was validated in other independent cohorts which were another Korean cohort (258 cases and 1757 controls) and the East Asian study of the Psychiatric Genomics Consortium (PGC) (12,455 cases and 85,548 controls). The predicted expression levels of thromboxane A synthase 1 gene (TBXAS1), which encodes the enzyme thromboxane A synthase 1 and participates in the arachidonic acid (AA) cascade, was significantly decreased in the whole blood tissues of the participants with depressive symptoms. Furthermore, Mendelian randomization (MR) analysis showed a causal association between TBXAS1 expression and the risk of depressive symptoms. In conclusion, as the number of risk alleles (A) of rs6945590 increased, TBXAS1 expression decreased, which subsequently caused an increase in the risk of depressive symptoms.

Epigenetic link between Agent Orange exposure and type 2 diabetes in Korean veterans.

Conflicting findings have been reported regarding the association between Agent Orange (AO) exposure and type 2 diabetes. This study aimed to examine whether AO exposure is associated with the development of type 2 diabetes and to verify the causal relationship between AO exposure and type 2 diabetes by combining DNA methylation with DNA genotype analyses. An epigenome-wide association study and DNA genotype analyses of the blood of AO-exposed and AO-unexposed individuals with type 2 diabetes and that of healthy controls were performed. Methylation quantitative trait locus and Mendelian randomisation analyses were performed to evaluate the causal effect of AO-exposure-identified CpGs on type 2 diabetes. AO-exposed individuals with type 2 diabetes were associated with six hypermethylated CpG sites (cg20075319, cg21757266, cg05203217, cg20102280, cg26081717, and cg21878650) and one hypo-methylated CpG site (cg07553761). Methylation quantitative trait locus analysis showed the methylation levels of some CpG sites (cg20075319, cg20102280, and cg26081717) to be significantly different. Mendelian randomisation analysis showed that CpG sites that were differentially methylated in AO-exposed individuals were causally associated with type 2 diabetes; the reverse causal effect was not significant. These findings reflect the need for further epigenetic studies on the causal relationship between AO exposure and type 2 diabetes.

HLA-DRB1 is associated with cefaclor-induced immediate hypersensitivity.

Background: Drug-induced hypersensitivity such as anaphylaxis is an important cause of drug-related morbidity and mortality. Cefaclor is a leading cause of drug induced type I hypersensitivity in Korea, but little is yet known about genetic biomarkers to predict this hypersensitivity reaction. We aimed to evaluate the possible involvement of genes in cefaclor induced type I hypersensitivity. Methods: Whole exome sequencing (WES) and HLA genotyping were performed in 43 patients with cefaclor induced type I hypersensitivity. In addition, homology modeling was performed to identify the binding forms of cefaclor to HLA site. Results: Anaphylaxis was the most common phenotype of cefaclor hypersensitivity (90.69%). WES results show that rs62242177 and rs62242178 located in LIMD1 region were genome-wide significant at the 5 × 10-8 significance level. Cefaclor induced type I hypersensitivity was significantly associated with HLA-DRB1∗04:03 (OR 4.61 [95% CI 1.51-14.09], P < 0.002) and HLA-DRB1∗14:54 (OR 3.86 [95% CI 1.09-13.67], P < 0.002). Conclusion: LIMD1, HLA-DRB1∗04:03 and HLA-DRB1∗14:54 may affect susceptibility to cefaclor induced type I hypersensitivity. Further confirmative studies with a larger patient population should be performed to ascertain the role of HLA-DRB1 and LIMD1 in the development of cefaclor induced hypersensitivity.

Correction: Limitations of rapid diagnostic tests in malaria surveys in areas with varied transmission intensity in Uganda 2017-2019: Implications for selection and use of HRP2 RDTs.

[This corrects the article DOI: 10.1371/journal.pone.0244457.].

Development and external validation of a logistic and a penalized logistic model using machine-learning techniques to predict suicide attempts: A multicenter prospective cohort study in Korea.

Despite previous efforts to build statistical models for predicting the risk of suicidal behavior using machine-learning analysis, a high-accuracy model can lead to overfitting. Furthermore, internal validation cannot completely address this problem. In this study, we created models for predicting the occurrence of suicide attempts among Koreans at high risk of suicide, and we verified these models in an independent cohort. We performed logistic and penalized regression for suicide attempts within 6 months among suicidal ideators and attempters in The Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS). We then validated the models in a test cohort. Our findings indicated that several factors significantly predicted suicide attempts in the models, including young age, suicidal ideation, previous suicidal attempts, anxiety, alcohol abuse, stress, and impulsivity. The area under the curve and positive predictive values were 0.941 and 0.484 after variable selection and 0.751 and 0.084 in the test cohort. The corresponding values for the penalized regression model were 0.943 and 0.524 in the original training cohort and 0.794 and 0.115 in the test cohort. The prediction model constructed through a prospective cohort study of the suicide high-risk group showed satisfactory accuracy even in the test cohort. The accuracy with penalized regression was greater than that with the "classical" logistic model.

Introduction to the Forensic Research via Omics Markers in Environmental Health Vulnerable Areas (FROM) study.

This research group (FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas­including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter­along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.