Associations of MRI-derived kidney volume, kidney function, body composition and physical performance in ≈38 000 UK Biobank participants: a population-based observational study.

Background: Kidney volume is used as a predictive and therapeutic marker for several clinical conditions. However, there is a lack of large-scale studies examining the relationship between kidney volume and various clinicodemographic factors, including kidney function, body composition and physical performance. Methods: In this observational study, MRI-derived kidney volume measurements from 38 526 UK Biobank participants were analysed. Major kidney volume-related measures included body surface area (BSA)-adjusted total kidney volume (TKV) and the difference in bilateral kidneys. Multivariable-adjusted linear regression and cubic spline analyses were used to explore the association between kidney volume-related measures and clinicodemographic factors. Cox or logistic regression was used to identify the risks of death, non-kidney cancer, myocardial infarction, ischaemic stroke and chronic kidney disease (CKD). Results: The median of BSA-adjusted TKV and the difference in kidney volume were 141.9 ml/m2 [interquartile range (IQR) 128.1-156.9] and 1.08-fold (IQR 1.04-1.15), respectively. Higher BSA-adjusted TKV was significantly associated with higher estimated glomerular filtration rate {eGFR; ß = 0.43 [95% confidence interval (CI) 0.42-0.44]; P < .001}, greater muscle volume [ß = 0.50 (95% CI 0.48-0.51); P < .001] and greater mean handgrip strength [ß = 0.15 (95% CI 0.13-0.16); P < .001] but lower visceral adipose tissue volume [VAT; ß = -0.09 (95% CI -0.11 to -0.07); P < .001] in adjusted models. A greater difference in bilateral kidney volumes was associated with lower eGFR, muscle volume and physical performance but with higher proteinuria and VAT. Higher BSA-adjusted TKV was significantly associated with a reduced risk of CKD [odds ratio (OR) 0.7 (95% CI 0.63-0.77); P < .001], while a greater difference in kidney volume was significantly associated with an increased risk of CKD [OR 1.13 (95% CI 1.07-1.20); P < .001]. Conclusion: Higher BSA-adjusted TKV and lower differences in bilateral kidney volumes are associated with higher kidney function, muscle volume and physical performance and a reduced risk of CKD.

Continual learning framework for a multicenter study with an application to electrocardiogram.

Deep learning has been increasingly utilized in the medical field and achieved many goals. Since the size of data dominates the performance of deep learning, several medical institutions are conducting joint research to obtain as much data as possible. However, sharing data is usually prohibited owing to the risk of privacy invasion. Federated learning is a reasonable idea to train distributed multicenter data without direct access; however, a central server to merge and distribute models is needed, which is expensive and hardly approved due to various legal regulations. This paper proposes a continual learning framework for a multicenter study, which does not require a central server and can prevent catastrophic forgetting of previously trained knowledge. The proposed framework contains the continual learning method selection process, assuming that a single method is not omnipotent for all involved datasets in a real-world setting and that there could be a proper method to be selected for specific data. We utilized the fake data based on a generative adversarial network to evaluate methods prospectively, not ex post facto. We used four independent electrocardiogram datasets for a multicenter study and trained the arrhythmia detection model. Our proposed framework was evaluated against supervised and federated learning methods, as well as finetuning approaches that do not include any regulation to preserve previous knowledge. Even without a central server and access to the past data, our framework achieved stable performance (AUROC 0.897) across all involved datasets, achieving comparable performance to federated learning (AUROC 0.901).

The complete mitochondrial genome of Hyperhalosydna striata (Kinberg, 1856) (Annelida: Polynoidae) collected from Jejudo Island, Korea.

The mitogenome sequence of Hyperhalosydna striata was determined for the first time in the present study. The genome is 15,226 bp long and contains 13 protein-coding genes (PCGs), two ribosomal RNA genes (rRNAs), and 22 transfer RNA genes (tRNA). The overall base composition was 28.0% A, 21.9% C, 13.0% G, and 37.1% T. A phylogenetic tree was constructed to infer the phylogenetic position of H. striata among polynoid species whose mitochondrial genome sequences are available in GenBank. Hyperhalosydna striata was closely related to the species of subfamily Lepidonotinae.

Deep learning-based long-term risk evaluation of incident type 2 diabetes using electrocardiogram in a non-diabetic population: a retrospective, multicentre study.

Background: Diabetes is a major public health concern. We aimed to evaluate the long-term risk of incident type 2 diabetes in a non-diabetic population using a deep learning model (DLM) detecting prevalent type 2 diabetes using electrocardiogram (ECG). Methods: In this retrospective study, participants who underwent health checkups at two tertiary hospitals in Seoul, South Korea, between Jan 1, 2001 and Dec 31, 2022 were included. Type 2 diabetes was defined as glucose ≥126 mg/dL or glycated haemoglobin (HbA1c) ≥ 6.5%. For survival analysis on incident type 2 diabetes, we introduced an additional variable, diabetic ECG, which is determined by the DLM trained on ECG and corresponding prevalent diabetes. It was assumed that non-diabetic individuals with diabetic ECG had a higher risk of incident type 2 diabetes than those with non-diabetic ECG. The one-dimensional ResNet-based model was adopted for the DLM, and the Guided Grad-CAM was used to localise important regions of ECG. We divided the non-diabetic group into the diabetic ECG group (false positive) and the non-diabetic ECG (true negative) group according to the DLM decision, and performed a Cox proportional hazard model, considering the occurrence of type 2 diabetes more than six months after the visit. Findings: 190,581 individuals were included in the study with a median follow-up period of 11.84 years. The areas under the receiver operating characteristic curve for prevalent type 2 diabetes detection were 0.816 (0.807-0.825) and 0.762 (0.754-0.770) for the internal and external validations, respectively. The model primarily focused on the QRS duration and, occasionally, P or T waves. The diabetic ECG group exhibited an increased risk of incident type 2 diabetes compared with the non-diabetic ECG group, with hazard ratios of 2.15 (1.82-2.53) and 1.92 (1.74-2.11) for internal and external validation, respectively. Interpretation: In the non-diabetic group, those whose ECG was classified as diabetes by the DLM were at a higher risk of incident type 2 diabetes than those whose ECG was not. Additional clinical research on the relationship between the phenotype of ECG and diabetes to support the results and further investigation with tracked data and various ECG recording systems are suggested for future works. Funding: National Research Foundation of Korea.

The Risk of COVID-19 and Its Outcomes in Korean Patients With Gout: A Multicenter, Retrospective, Observational Study.

This retrospective cohort study aimed to compare coronavirus disease 2019 (COVID-19)-related clinical outcomes between patients with and without gout. Electronic health record-based data from two centers (Seoul National University Hospital [SNUH] and Boramae Medical Center [BMC]), from January 2021 to April 2022, were mapped to a common data model. Patients with and without gout were matched using a large-scale propensity-score algorithm based on population-level estimation methods. At the SNUH, the risk for COVID-19 diagnosis was not significantly different between patients with and without gout (hazard ratio [HR], 1.07; 95% confidence interval [CI], 0.59-1.84). Within 30 days after COVID-19 diagnosis, no significant difference was observed in terms of hospitalization (HR, 0.57; 95% CI, 0.03-3.90), severe outcomes (HR, 2.90; 95% CI, 0.54-13.71), or mortality (HR, 1.35; 95% CI, 0.06-16.24). Similar results were obtained from the BMC database, suggesting that gout does not increase the risk for COVID-19 diagnosis or severe outcomes.

Paclitaxel and Carboplatin in Combination with Low-intensity Pulsed Ultrasound for Glioblastoma.

PURPOSE: We recently reported on clinical trials for patients with recurrent glioblastoma where low-intensity pulsed ultrasound and microbubbles (LIPU/MB) improved paclitaxel or carboplatin delivery into the brain. Here, we report variable local tumor control with paclitaxel at the maximal/target dose in our phase I trial (NCT04528680). To address this, we investigated the combination of paclitaxel with carboplatin in preclinical glioma models. EXPERIMENTAL DESIGN: We performed MRI-based analysis to evaluate disease control in patients from our trial. We studied the cytotoxicity of paclitaxel and carboplatin against 11 human glioma lines as monotherapy and in combination at concentrations derived from human intraoperative studies. Synergy was assessed with the Loewe model and the survival benefit evaluated in two xenografts. We examined the effects on cell cycle progression, DNA damage, and apoptosis. RESULTS: Patients treated with paclitaxel and LIPU/MB exhibited variable local tumor control, which correlated with overall survival. We observed limited cross-resistance to paclitaxel and carboplatin in glioma lines, with almost a third of them being exclusively susceptible to one drug. This combination led to susceptibility of 81% of lines and synergy in 55% of them. The combination proved more efficacious in two intracranial xenografts when administered with LIPU/MB, leading to complementary effects on cell cycle arrest. CONCLUSIONS: Combining paclitaxel and carboplatin in gliomas may be more efficacious than monotherapy, as in other cancers, due to synergy and independent susceptibility to each drug. These results form the basis for an ongoing phase II trial (NCT04528680) where we investigate this combination with LIPU/MB.

Primary sclerosing cholangitis causally affects kidney function decline: A Mendelian randomization study.

BACKGROUND AND AIM: The causal linkage between primary sclerosing cholangitis (PSC) and kidney function is unexplored despite their potential for long-term detrimental effects on kidney function. METHODS: Two-sample summary-level Mendelian randomization (MR) study was conducted to identify the association between PSC and kidney function. The genetic variants were extracted from the PSC-specific multi-trait analyzed genome-wide association study (GWAS) of European ancestry. Summary-level data for kidney function traits, including estimated glomerular filtration rate (eGFR), annual eGFR decline, and chronic kidney disease (CKD), were obtained from the CKDGen consortium. Multiplicative random-effects inverse-variance weighted (MR-IVW), and a series of pleiotropy-robust analyses were performed to investigate the causal effects and ascertain their robustness. RESULTS: Significant causal associations between genetically predicted PSC and kidney function traits were identified. Genetically predicted PSC was associated with decreased log-transformed eGFR (MR-IVW; beta = -0.41%; standard error [SE] = 0.02%; P < 0.001), increased rate of annual eGFR decline (MR-IVW; beta = 2.43%; SE = 0.18%; P < 0.001), and higher risk of CKD (MR-IVW; odds ratio = 1.07; 95% confidence interval = 1.06-1.08; P < 0.001). The main findings were supported by pleiotropy-robust analysis, including MR-Egger with bootstrapped error and weighted median. CONCLUSIONS: Our study demonstrates that genetically predicted PSC is causally associated with kidney function impairment. Further studies are warranted to identify the underlying mechanisms.

ERK1/2 Phosphorylation Predicts Survival in Recurrent Glioblastoma Following Intracerebral and Adjuvant PD-1/CTLA-4 Immunotherapy: A REMARK-guided Analysis.

PURPOSE: Evidence suggests that MAPK pathway activation, as measured by ERK1/2 phosphorylation (p-ERK), predicts overall survival (OS) in patients with recurrent glioblastoma receiving anti-PD-1 therapy. We aimed to validate these findings in independent cohorts. EXPERIMENTAL DESIGN: In a 24-patient clinical trial on recurrent glioblastoma and high-grade gliomas, we examined the link between p-ERK levels and OS. Patients received intravenous nivolumab, followed by maximal safe resection and an intracerebral injection of either ipilimumab alone or combined with nivolumab. Biweekly adjuvant nivolumab was then administered up to five times (NCT03233152). Using REporting recommendations for tumor MARKER prognostic studies (REMARK) criteria, we conducted independent analyses for p-ERK quantification and statistical evaluations. Additional comparative analysis included prior cohorts, totaling 65 patients. Cox proportional hazards models and meta-analysis were employed to assess p-ERK as a predictive biomarker after immunotherapy. RESULTS: Lower median p-ERK+ cell density was observed compared with prior studies, likely due to variable tissue processing across cohorts. Nonetheless, high p-ERK was associated with prolonged OS, particularly in isocitrate dehydrogenase wild-type glioblastomas (P = 0.036). Median OS for high and low p-ERK patients were 55.6 and 30 weeks, respectively. Multivariable analysis reinforced p-ERK's significance in survival prediction (P = 0.011). Upon p-ERK normalization across cohorts (n = 65), meta-analysis supported the survival benefit of elevated tumor p-ERK levels (P = 0.0424). CONCLUSIONS: This study strengthens the role of p-ERK as a predictive biomarker for OS in patients with glioblastoma on immune checkpoint blockade. Future research should focus on further validation in prospective trials and the standardization of preanalytical variables influencing p-ERK quantification.

Bacterial cancer therapy using the attenuated fowl-adapted Salmonella enterica serovar Gallinarum.

We report here a novel anti-cancer therapy based on an avian-host-specific serotype Salmonella enterica serovar Gallinarum (S. Gallinarum) deficient in ppGpp synthesis. To monitor the tumor targeting, a bioluminescent ΔppGpp S. Gallinarum was constructed and injected intravenously into mice bearing syngeneic and human xenograft tumors. Strong bioluminescent signals were detected specifically in all grafted tumors at 2 days post-injection (dpi). The bacterial counts in normal and tumor tissue at 1 dpi revealed that ΔppGpp S. Gallinarum reached >108 CFU/g in tumor tissue and 106-107 CFU/g in endothelial organs; counts were much lower in other organs. At 16 dpi, ΔppGpp S. Gallinarum counts in tumor tissue decreased to ∼106 CFU/g, while those in the other organs became undetectable. A strong anti-cancer effect was observed after the injection of ΔppGpp S. Gallinarum into BALB/c mice grafted with CT26 colon cancer cells. This could be attributed to reduced virulence, which allowed the administration of at least a 10-fold greater dose (108 CFU) of ΔppGpp S. Gallinarum than other attenuated strains of S. enterica serovar Typhimurium (≤107 CFU). An advantage of the avian-specific S. Gallinarum as a cancer therapeutic should be a reduced capacity to cause infections or harm in humans.

Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and Transformer architecture.

OBJECTIVES: Automatic detection of atrial fibrillation and flutter (AF/AFL) is a significant concern in preventing stroke and mitigating hemodynamic instability. Herein, we developed a Transformer-based deep learning model for AF/AFL segmentation in single-lead electrocardiograms (ECGs) by self-supervised learning with masked signal modeling (MSM). MATERIALS AND METHODS: We retrieved data from 11 open-source databases on PhysioNet; 7 of these databases included labeled ECGs, while the other 4 were without labels. Each database contained ECG recordings with durations of ≥30 s. A total of 24 intradialytic ECGs with paroxysmal AF/AFL during 4 h of hemodialysis sessions at Seoul National University Hospital were used for external validation. The model was pretrained by predicting masked areas of ECG signals and fine-tuned by predicting AF/AFL areas. Cross-database validation was used for evaluation, and the intersection over union (IOU) was used as a main performance metric in external database validation. RESULTS: In the 7 labeled databases, the areas marked as AF/AFL constituted 41.1% of the total ECG signals, ranging from 0.19% to 51.31%. In the evaluation per ECG segment, the model achieved IOU values of 0.9254 and 0.9477 for AF/AFL segmentation and other segmentation tasks, respectively. When applied to intradialytic ECGs with paroxysmal AF/AFL, the IOUs for the segmentation of AF/AFL and non-AF/AFL were 0.9896 and 0.9650, respectively. Model performance by different training procedure indicated that pretraining with MSM and the application of an appropriate masking ratio both contributed to the model performance. It also showed higher IOUs of AF/AFL labels than in previous studies when training and test databases were matched. CONCLUSION: The present model with self-supervised learning by MSM performs robustly in segmenting AF/AFL.

Subtyping of COVID-19 samples based on cell-cell interaction in single cell transcriptomes.

In single-cell transcriptome analysis, numerous biomarkers related to COVID-19 severity, including cell subtypes, genes, and pathways, have been identified. Nevertheless, most studies have focused on severity groups based on clinical features, neglecting immunological heterogeneity within the same severity level. In this study, we employed sample-level clustering using cell-cell interaction scores to investigate patient heterogeneity and uncover novel subtypes. The clustering results were validated using external datasets, demonstrating superior reproducibility and purity compared to gene expression- or gene set enrichment-based clustering. Furthermore, the cell-cell interaction score-based clusters exhibited a strong correlation with the WHO ordinal severity score based on clinical characteristics. By characterizing the identified subtypes through known COVID-19 severity-associated biomarkers, we discovered a "Severe-like moderate" subtype. This subtype displayed clinical features akin to moderate cases; however, molecular features, such as gene expression and cell-cell interactions, resembled those of severe cases. Notably, all patients who progressed from moderate to severe belonged to this subtype, underscoring the significance of cell-cell interactions in COVID-19 patient heterogeneity and severity.

The prognostic value of a combined immune score in tumor and immune cells assessed by immunohistochemistry in triple-negative breast cancer.

BACKGROUND: This study aimed to develop a novel combined immune score (CIS)-based model assessing prognosis in triple-negative breast cancer (TNBC). METHODS: The expression of eight immune markers (PD-1, PD-L1, PD-L2, IDO, TIM3, OX40, OX40L, and H7-H2) was assessed with immunohistochemistry on the tumor cells (TCs) and immune cells (ICs) of 227 TNBC cases, respectively, and subsequently associated with selected clinicopathological parameters and survival. Data retrieved from The Cancer Genome Atlas (TCGA) were further examined to validate our findings. RESULTS: All immune markers were often expressed in TCs and ICs, except for PD-1 which was not expressed in TCs. In ICs, the expression of all immune markers was positively correlated between one another, except between PD-L1 and OX40, also TIM3 and OX40. In ICs, PD-1, PD-L1, and OX40L positive expression was associated with a longer progression-free survival (PFS; p = 0.040, p = 0.020, and p = 0.020, respectively). In TCs, OX40 positive expression was associated with a shorter PFS (p = 0.025). Subsequently, the TNBC patients were classified into high and low combined immune score groups (CIS-H and CIS-L), based on the expression levels of a selection of biomarkers in TCs (TCIS-H or TCIS-L) and ICs (ICIS-H or ICIS-L). The TCIS-H group was significantly associated with a longer PFS (p < 0.001). Furthermore, the ICIS-H group was additionally associated with a longer PFS (p < 0.001) and overall survival (OS; p = 0.001), at significant levels. In the multivariate analysis, both TCIS-H and ICIS-H groups were identified as independent predictors of favorable PFS (p = 0.012 and p = 0.001, respectively). ICIS-H was also shown to be an independent predictor of favorable OS (p = 0.003). The analysis of the mRNA expression data from TCGA also validated our findings regarding TNBC. CONCLUSION: Our novel TCIS and ICIS exhibited a significant prognostic value in TNBC. Additional research would be needed to strengthen our findings and identify the most efficient prognostic and predictive biomarkers for TNBC patients.

Analysis of random mutations in Salmonella Gallinarum dihydropteroate synthase conferring sulfonamide resistance.

In bacteria and primitive eukaryotes, sulfonamide antibiotics block the folate pathway by inhibiting dihydropteroate synthase (FolP) that combines para-aminobenzoic acid (pABA) and dihydropterin pyrophosphate (DHPP) to form dihydropteroic acid (DHP), a precursor for tetrahydrofolate synthesis. However, the emergence of resistant strains has severely compromised the use of pABA mimetics as sulfonamide drugs. Salmonella enterica serovar Gallinarum (S. Gallinarum) is a significant source of antibiotic-resistant infections in poultry. Here, a sulfonamide-resistant FolP mutant library of S. Gallinarum was generated through random mutagenesis. Among resistant strains, substitution of amino acid Arginine 171 with Proline (R171P) in the FolP protein conferred the highest resistance against sulfonamide. Substitution of Phe28 with Leu or Ile (F28L/I) led to modest sulfonamide resistance. Structural modeling indicates that R171P and Phenylalanine 28 with leucine or isoleucine (F28L/I) substitution mutations are located far from the substrate-binding site and cause insignificant conformational changes in the FolP protein. Rather, in silico studies suggest that the mutations altered the stability of the protein, potentially resulting in sulfonamide resistance. Identification of specific mutations in FolP that confer resistance to sulfonamide would contribute to our understanding of the molecular mechanisms of antibiotic resistance.

Predictive worker safety assessment through on-site correspondence using multi-layer fuzzy logic in outdoor construction environments.

Construction sites remain highly perilous work environments globally, exposing employees to numerous hazards that can result in severe injuries or fatalities. To resolve this several solutions based on quantitative approaches have been developed. However the wide adoption of preexisting solutions is hindered by lack of accuracy. To this aim the development of an efficient fuzzy inference system has become a de-facto necessity. In this paper, we propose an edge inference framework based on multi-layered fuzzy logic for safety of construction workers. The proposed system employs an edge computing-based framework where IoT devices collect, store, and manage data to offer safety services. Multi-layer fuzzy logic is applied to infer the worker safety index based on rules that consist of construction environment factors. The multi-layer fuzzy logic is fed with weather, building and worker data collected from IoT nodes as inputs. The safety risk assessment process involves analyzing various factors. Weather information, such as temperature, humidity, and rainfall data, is considered to assess the risk to safety. The condition of the building is evaluated by analyzing load, strain, and inclination data. Additionally, the safety risk to workers is analyzed by taking into account their heart rate and location information. The initial layer's outputs are utilized as inputs for the subsequent layer, where an integrated safety index is inferred. Ultimately, the safety index is generated as the final outcome. The system's results are conveyed through warnings and an error measurement on a safety scale ranging from 1 to 10. Furthermore, web service is developed to allow the construction management to check the worker safety condition of the construction site in real-time, while also monitoring the operational status of the IoT devices, allowing for the early detection of sensor malfunction and the subsequent guarantee of worker safety. Extensive evaluations conducted to test the performance of the developed framework verify its efficiency to provide improved risk assessment, real-time monitoring, and proactive safety actions, encouraging a safer and more productive work environment.

Real-time dual prediction of intradialytic hypotension and hypertension using an explainable deep learning model.

Both intradialytic hypotension (IDH) and hypertension (IDHTN) are associated with poor outcomes in hemodialysis patients, but a model predicting dual outcomes in real-time has never been developed. Herein, we developed an explainable deep learning model with a sequence-to-sequence-based attention network to predict both of these events simultaneously. We retrieved 302,774 hemodialysis sessions from the electronic health records of 11,110 patients, and these sessions were split into training (70%), validation (10%), and test (20%) datasets through patient randomization. The outcomes were defined when nadir systolic blood pressure (BP) < 90 mmHg (termed IDH-1), a decrease in systolic BP ≥ 20 mmHg and/or a decrease in mean arterial pressure ≥ 10 mmHg (termed IDH-2), or an increase in systolic BP ≥ 10 mmHg (i.e., IDHTN) occurred within 1 h. We developed a temporal fusion transformer (TFT)-based model and compared its performance in the test dataset, including receiver operating characteristic curve (AUROC) and area under the precision-recall curves (AUPRC), with those of other machine learning models, such as recurrent neural network, light gradient boosting machine, random forest, and logistic regression. Among all models, the TFT-based model achieved the highest AUROCs of 0.953 (0.952-0.954), 0.892 (0.891-0.893), and 0.889 (0.888-0.890) in predicting IDH-1, IDH-2, and IDHTN, respectively. The AUPRCs in the TFT-based model for these outcomes were higher than the other models. The factors that contributed the most to the prediction were age and previous session, which were time-invariant variables, as well as systolic BP and elapsed time, which were time-varying variables. The present TFT-based model predicts both IDH and IDHTN in real time and offers explainable variable importance.

Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach.

OBJECTIVES: The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. METHODS: A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable timeseries model. RESULTS: The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. CONCLUSIONS: Implementing a multicenter-based timeseries classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.

Causal Effect of Chondroitin, Glucosamine, Vitamin, and Mineral Intake on Kidney Function: A Mendelian Randomization Study.

The causal effects of chondroitin, glucosamine, and vitamin/mineral supplement intake on kidney function remain unknown, despite being commonly used. We conducted a two-sample summary-level Mendelian randomization (MR) analysis to test for causal associations between regular dietary supplement intake and kidney function. Genetic instruments for chondroitin, glucosamine, and vitamin/mineral supplement intake were obtained from a genome-wide association study of European ancestry. Summary statistics for the log-transformed estimated glomerular filtration rate (log-eGFR) were provided by the CKDGen consortium. The multiplicative random-effects inverse-variance weighted method showed that genetically predicted chondroitin and glucosamine intake was causally associated with a lower eGFR (chondroitin, eGFR change beta = -0.113%, standard error (SE) = 0.03%, p-value = 2 × 10-4; glucosamine, eGFR change beta = -0.240%, SE = 0.035%, p-value = 6 × 10-12). However, a genetically predicted vitamin/mineral supplement intake was associated with a higher eGFR (eGFR change beta = 1.426%, SE = 0.136%, p-value = 1 × 10-25). Validation analyses and pleiotropy-robust MR results for chondroitin and vitamin/mineral supplement intake supported the main results. Our MR study suggests a potential causal effect of chondroitin and glucosamine intake on kidney function. Therefore, clinicians should carefully monitor their long-term effects.

Epilepsy phenotype and gene ontology analysis of the 129 genes in a large neurodevelopmental disorders cohort.

Objective: Although pediatric epilepsy is an independent disease entity, it is often observed in pediatric neurodevelopmental disorders (NDDs) as a major or minor clinical feature, which might provide diagnostic clues. This study aimed to identify the clinical and genetic characteristics of patients with epilepsy in an NDD cohort and demonstrate the importance of genetic testing. Methods: We retrospectively analyzed the detailed clinical differences of pediatric NDD patients with epilepsy according to their genetic etiology. Among 1,213 patients with NDDs, 477 were genetically diagnosed by exome sequencing, and 168 had epilepsy and causative variants in 129 genes. Causative genes were classified into two groups: (i) the "epilepsy-genes" group resulting in epilepsy as the main phenotype listed in OMIM, Epi25, and ClinGen (67 patients) and (ii) the "NDD-genes" group not included in the "epilepsy-genes" group (101 patients). Results: Patients in the "epilepsy-genes" group started having seizures, often characterized by epilepsy syndrome, at a younger age. However, overall clinical features, including treatment responses and all neurologic manifestations, showed no significant differences between the two groups. Gene ontology analysis revealed the close interactions of epilepsy genes associated with ion channels and neurotransmitters. Conclusion: We demonstrated a similar clinical presentation of different gene groups regarding biological/molecular processes in a large NDDs cohort with epilepsy. Phenotype-driven genetic analysis should cover a broad scope, and further studies are required to elucidate integrated pathomechanisms.

Causal effects from tobacco smoking initiation on obesity-related traits: a Mendelian randomization study.

BACKGROUND: There is a widespread notion that tobacco smoking controls weight based on the appetite suppressive effect of nicotine. However, the causal relationship between smoking initiation and obesity-related traits in the general population are unclear. METHODS: This Mendelian randomization analysis utilized 378 genetic variants associated with tobacco smoking initiation (usually in adolescence or young adulthood) identified in a genome-wide association study (meta-analysis) of 1.2 million individuals. Outcome data for body mass index, waist circumference, hip circumference, and waist-to-hip ratio were extracted from the 337,138 white British-ancestry UK Biobank participants aged 40-69 years. Replication analyses were performed for genome-wide association study meta-analysis for body mass index, including the GERA/GIANT data including 364,487 samples from mostly European individuals. In addition, summary-level Mendelian randomization by inverse variance weighted method and pleiotropy-robust Mendelian randomization methods, including median-based and MR-Egger regression, was performed. RESULTS: Summary-level Mendelian randomization analysis indicated that genetically predicted smoking initiation is causally linked to higher body mass index [+0.28 (0.18-0.38) kg/m2], waist circumference [+0.88 (0.66-1.10) cm], hip circumference [+0.40 (0.23-0.57) cm], and waist-to-hip ratio [+0.006 (0.005-0.007)]. These results were consistent with those of the pleiotropy-robust Mendelian randomization analysis. Additionally, in replication analysis, genetically predicted smoking initiation was significantly associated with a higher body mass index [+0.03 (0.01, 0.05] kg/m2). CONCLUSION: Tobacco initiation may lead to worse obesity-related traits in the general 40- to 69-year-old individuals. Therefore, tobacco-use initiation as a long-term weight-control measure should be discouraged.