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.

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.].

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.

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.

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.

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.

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.

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.

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.

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.

Behavioral and transcriptional effects of repeated electroconvulsive seizures in the neonatal MK-801-treated rat model of schizophrenia.

RATIONALE: Electroconvulsive therapy (ECT) is an effective treatment modality for schizophrenia. However, its antipsychotic-like mechanism remains unclear. OBJECTIVES: To gain insight into the antipsychotic-like actions of ECT, this study investigated how repeated treatments of electroconvulsive seizure (ECS), an animal model for ECT, affect the behavioral and transcriptomic profile of a neurodevelopmental animal model of schizophrenia. METHODS: Two injections of MK-801 or saline were administered to rats on postnatal day 7 (PN7), and either repeated ECS treatments (E10X) or sham shock was conducted daily from PN50 to PN59. Ultimately, the rats were divided into vehicle/sham (V/S), MK-801/sham (M/S), vehicle/ECS (V/E), and MK-801/ECS (M/E) groups. On PN59, prepulse inhibition and locomotor activity were tested. Prefrontal cortex transcriptomes were analyzed with mRNA sequencing and network and pathway analyses, and quantitative real-time polymerase chain reaction (qPCR) analyses were subsequently conducted. RESULTS: Prepulse inhibition deficit was induced by MK-801 and normalized by E10X. In M/S vs. M/E model, Egr1, Mmp9, and S100a6 were identified as center genes, and interleukin-17 (IL-17), nuclear factor kappa B (NF-κB), and tumor necrosis factor (TNF) signaling pathways were identified as the three most relevant pathways. In the V/E vs. V/S model, mitophagy, NF-κB, and receptor for advanced glycation end products (RAGE) pathways were identified. qPCR analyses demonstrated that Igfbp6, Btf3, Cox6a2, and H2az1 were downregulated in M/S and upregulated in M/E. CONCLUSIONS: E10X reverses the behavioral changes induced by MK-801 and produces transcriptional changes in inflammatory, insulin, and mitophagy pathways, which provide mechanistic insight into the antipsychotic-like mechanism of ECT.

Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients.

BACKGROUND: More than 200 asthma-associated genetic variants have been identified in genome-wide association studies (GWASs). Expression quantitative trait loci (eQTL) data resources can help identify causal genes of the GWAS signals, but it can be difficult to find an eQTL that reflects the disease state because most eQTL data are obtained from normal healthy subjects. METHODS: We performed a blood eQTL analysis using transcriptomic and genotypic data from 433 Korean asthma patients. To identify asthma-related genes, we carried out colocalization, Summary-based Mendelian Randomization (SMR) analysis, and Transcriptome-Wide Association Study (TWAS) using the results of asthma GWASs and eQTL data. In addition, we compared the results of disease eQTL data and asthma-related genes with two normal blood eQTL data from Genotype-Tissue Expression (GTEx) project and a Japanese study. RESULTS: We identified 340,274 cis-eQTL and 2,875 eGenes from asthmatic eQTL analysis. We compared the disease eQTL results with GTEx and a Japanese study and found that 64.1% of the 2,875 eGenes overlapped with the GTEx eGenes and 39.0% with the Japanese eGenes. Following the integrated analysis of the asthmatic eQTL data with asthma GWASs, using colocalization and SMR methods, we identified 15 asthma-related genes specific to the Korean asthmatic eQTL data. CONCLUSIONS: We provided Korean asthmatic cis-eQTL data and identified asthma-related genes by integrating them with GWAS data. In addition, we suggested these asthma-related genes as therapeutic targets for asthma. We envisage that our findings will contribute to understanding the etiological mechanisms of asthma and provide novel therapeutic targets.

Glutathione dynamics is a potential predictive and therapeutic trait for neoadjuvant chemotherapy response in bladder cancer.

Radical cystectomy with preoperative cisplatin-based neoadjuvant chemotherapy (NAC) is the standard care for muscle-invasive bladder cancers (MIBCs). However, the complete response rate to this modality remains relatively low, and current clinicopathologic and molecular classifications are inadequate to predict NAC response in patients with MIBC. Here, we demonstrate that dysregulation of the glutathione (GSH) pathway is fundamental for MIBC NAC resistance. Comprehensive analysis of the multicohort transcriptomes reveals that GSH metabolism and immune-response genes are enriched in NAC-resistant and NAC-sensitive MIBCs, respectively. A machine-learning-based tumor/stroma classifier is applied for high-throughput digitalized immunohistochemistry analysis, finding that GSH dynamics proteins, including glutaminase-1, are associated with NAC resistance. GSH dynamics is activated in cisplatin-resistant MIBC cells, and combination treatment with a GSH dynamics modulator and cisplatin significantly suppresses tumor growth in an orthotopic xenograft animal model. Collectively, these findings demonstrate the predictive and therapeutic values of GSH dynamics in determining the NAC response in MIBCs.

Possible link between statin and iron deficiency anemia: A South Korean nationwide population-based cohort study.

An extensive evaluation of disease occurrence after statin use based on a "hypothesis-free" approach remains scarce. To examine the effect of statin use on the potential risk of developing diseases, a propensity score-matched cohort study was executed using data from the National Sample Cohort in South Korea. A total of 7847 statin users and 39,235 nonstatin users were included in the final analysis. The period of statin use was defined as our main time-dependent exposure and was divided into three periods: current, recent, and past. The main outcomes were defined as new-onset diseases with ≥100 events based on the International Statistical Classification of Diseases, 10th Revision. We calculated the adjusted hazard ratios and 95% confidence intervals (CIs) using Cox regression. We found that statin use significantly increased the risk of developing iron deficiency anemia up to 5.04 times (95% CI, 2.11 to 12.03). Therefore, the iron levels of patients using statins should be monitored carefully.

Comparative analysis of batch correction methods for FDG PET/CT using metabolic radiogenomic data of lung cancer patients.

In radiomics research, the issue of different instruments being used is significant. In this study, we compared three correction methods to reduce the batch effects in radiogenomic data from fluorodeoxyglucose (FDG) PET/CT images of lung cancer patients. Texture features of the FDG PET/CT images and genomic data were retrospectively obtained. The features were corrected with different methods: phantom correction, ComBat method, and Limma method. Batch effects were estimated using three analytic tools: principal component analysis (PCA), the k-nearest neighbor batch effect test (kBET), and the silhouette score. Finally, the associations of features and gene mutations were compared between each correction method. Although the kBET rejection rate and silhouette score were lower in the phantom-corrected data than in the uncorrected data, a PCA plot showed a similar variance. ComBat and Limma methods provided correction with low batch effects, and there was no significant difference in the results of the two methods. In ComBat- and Limma-corrected data, more texture features exhibited a significant association with the TP53 mutation than in those in the phantom-corrected data. This study suggests that correction with ComBat or Limma methods can be more effective or equally as effective as the phantom method in reducing batch effects.

Predicting mild cognitive impairments from cognitively normal brains using a novel brain age estimation model based on structural magnetic resonance imaging.

Brain age prediction is a practical method used to quantify brain aging and detect neurodegenerative diseases such as Alzheimer's disease (AD). However, very few studies have considered brain age prediction as a biomarker for the conversion of cognitively normal (CN) to mild cognitive impairment (MCI). In this study, we developed a novel brain age prediction model using brain volume and cortical thickness features. We calculated an acceleration of brain age (ABA) derived from the suggested model to estimate different diagnostic groups (CN, MCI, and AD) and to classify CN to MCI and MCI to AD conversion groups. We observed a strong association between ABA and the 3 diagnostic groups. Additionally, the classification models for CN to MCI conversion and MCI to AD conversion exhibited acceptable and robust performances, with area under the curve values of 0.66 and 0.76, respectively. We believe that our proposed model provides a reliable estimate of brain age for elderly individuals and can identify those at risk of progressing from CN to MCI. This model has great potential to reveal a diagnosis associated with a change in cognitive decline.

Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies.

MOTIVATION: Allowance for increasingly large samples is a key to identify the association of genetic variants with Alzheimer's disease (AD) in genome-wide association studies (GWAS). Accordingly, we aimed to develop a method that incorporates patients with mild cognitive impairment and unknown cognitive status in GWAS using a machine learning-based AD prediction model. RESULTS: Simulation analyses showed that weighting imputed phenotypes method increased the statistical power compared to ordinary logistic regression using only AD cases and controls. Applied to real-world data, the penalized logistic method had the highest AUC (0.96) for AD prediction and weighting imputed phenotypes method performed well in terms of power. We identified an association (P<5.0×10-8) of AD with several variants in the APOE region and rs143625563 in LMX1A. Our method, which allows the inclusion of individuals with mild cognitive impairment, improves the statistical power of GWAS for AD. We discovered a novel association with LMX1A. AVAILABILITY AND IMPLEMENTATION: Simulation codes can be accessed at https://github.com/Junkkkk/wGEE_GWAS.

Mendelian randomization shows depression increases the risk of type 2 diabetes.

Introduction: Type 2 diabetes (T2D) is associated with severe mental illnesses (SMIs), such as schizophrenia, bipolar disorder, and depression. However, causal relationships between SMIs and T2D remain unclear owing to potential bias in observational studies. We aimed to characterize the causal effect of SMI liability on T2D using two-sample Mendelian randomization (MR). Methods: The causality between liability to SMI and T2D was investigated using the inverse-variance weighted (IVW), MREgger, MR-Egger with a simulation extrapolation, weighted median, and the MR pleiotropy residual sum and outlier method. Similarly, we performed additional MR which can detect the reverse causation effect by switching exposure and outcome for T2D liability for SMI. To further consider pleiotropic effects between SMIs, multivariable MR analysis was performed after accounting for the other traits. Results: In the univariable IVW method, depression showed a causal effect on T2D (odds ratio [OR]: 1.128, 95% confidence interval [CI]: 1.024-1.245, p = 0.014). Multinomial MR more strongly supported these results (IVW OR: 1.197, 95% CI: 1.069, 1.340, p = 0.002; MR-Egger OR: 1.198, 95% CI: 1.062, 1.349, p = 0.003). Bidirectional MR showed absence of reversecausality between depression and T2D. However, causal relationship of bipolar and schizophrenia on T2D was not detected. Discussion: Careful attention is needed for patients with depression regarding T2D prevention and treatment.

Automating Rey Complex Figure Test scoring using a deep learning-based approach: a potential large-scale screening tool for cognitive decline.

BACKGROUND: The Rey Complex Figure Test (RCFT) has been widely used to evaluate the neurocognitive functions in various clinical groups with a broad range of ages. However, despite its usefulness, the scoring method is as complex as the figure. Such a complicated scoring system can lead to the risk of reducing the extent of agreement among raters. Although several attempts have been made to use RCFT in clinical settings in a digitalized format, little attention has been given to develop direct automatic scoring that is comparable to experienced psychologists. Therefore, we aimed to develop an artificial intelligence (AI) scoring system for RCFT using a deep learning (DL) algorithm and confirmed its validity. METHODS: A total of 6680 subjects were enrolled in the Gwangju Alzheimer's and Related Dementia cohort registry, Korea, from January 2015 to June 2021. We obtained 20,040 scanned images using three images per subject (copy, immediate recall, and delayed recall) and scores rated by 32 experienced psychologists. We trained the automated scoring system using the DenseNet architecture. To increase the model performance, we improved the quality of training data by re-examining some images with poor results (mean absolute error (MAE) [Formula: see text] 5 [points]) and re-trained our model. Finally, we conducted an external validation with 150 images scored by five experienced psychologists. RESULTS: For fivefold cross-validation, our first model obtained MAE = 1.24 [points] and R-squared ([Formula: see text]) = 0.977. However, after evaluating and updating the model, the performance of the final model was improved (MAE = 0.95 [points], [Formula: see text] = 0.986). Predicted scores among cognitively normal, mild cognitive impairment, and dementia were significantly different. For the 150 independent test sets, the MAE and [Formula: see text] between AI and average scores by five human experts were 0.64 [points] and 0.994, respectively. CONCLUSION: We concluded that there was no fundamental difference between the rating scores of experienced psychologists and those of our AI scoring system. We expect that our AI psychologist will be able to contribute to screen the early stages of Alzheimer's disease pathology in medical checkup centers or large-scale community-based research institutes in a faster and cost-effective way.