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Abstract Hematoma expansion occasionally occurs in patients with intracerebral hemorrhage (ICH), associating with poor outcome. Multimodal neural networks incorporating convolutional neural network (CNN) analysis of images and neural network analysis of tabular data are known to show promising results in prediction and classification tasks. We aimed to develop a reliable multimodal neural network model that comprehensively analyzes CT images and clinical variables to predict hematoma expansion. We retrospectively enrolled ICH patients at four hospitals between 2017 and 2021, assigning patients from three hospitals to the training and validation dataset and patients from one hospital to the test dataset. Admission CT images and clinical variables were collected. CT findings were evaluated by experts. Three types of models were developed and trained: (1) a CNN model analyzing CT images, (2) a multimodal CNN model analyzing CT images and clinical variables, and (3) a non-CNN model analyzing CT findings and clinical variables with machine learning. The models were evaluated on the test dataset, focusing first on sensitivity and second on area under the receiver operating curve (AUC). Two hundred seventy-three patients (median age, 71 years [59–79]; 159 men) in the training and validation dataset and 106 patients (median age, 70 years [62–82]; 63 men) in the test dataset were included. Sensitivity and AUC of a CNN model were 1.000 (95% confidence interval [CI] 0.768–1.000) and 0.755 (95% CI 0.704–0.807); those of a multimodal CNN model were 1.000 (95% CI 0.768–1.000) and 0.799 (95% CI 0.749–0.849); and those of a non-CNN model were 0.857 (95% CI 0.572–0.982) and 0.733 (95% CI 0.625–0.840). We developed a multimodal neural network model incorporating CNN analysis of CT images and neural network analysis of clinical variables to predict hematoma expansion in ICH. The model was externally validated and showed the best performance of all the models.

Abstract Little is known about the association of prolonged cough, a common and troublesome symptom, with metabolic pathways. We aimed to clarify this association using data from the Nagahama cohort, a prospective study of participants from the general population. Self-report questionnaires on prolonged cough were collected at baseline and 5-year follow-up assessments. Blood tests at follow-up were used for gas chromatography-mass spectrometry-based metabolomics. The association between metabolites and prolonged cough was examined using the partial least squares discriminant analysis and multiple regression analysis. Among the 7432 participants, 632 had newly developed prolonged cough at follow-up, which was defined as “new-onset prolonged cough”. Low plasma citric acid was significantly associated with new-onset prolonged cough, even after the adjustment of confounding factors including the presence of asthma, upper airway cough syndrome (UACS), and gastroesophageal reflux disease (GERD). A similar association was observed for isocitric acid, 3-hydroxybutyric acid, and 3-hydroxyisobutyric acid. The analysis of these four metabolites revealed that citric acid had the strongest association with new-onset prolonged cough. This significant association remained even when the analysis was confined to participants with UACS or GERD at baseline or follow-up, and these associations were also observed in participants (n = 976) who had prolonged cough at follow-up regardless of baseline status. In conclusion, low blood citric acid may be associated with prolonged cough.

Abstract Sleep disordered breathing (SDB), mainly obstructive sleep apnea (OSA), constitutes a major health problem due to the large number of patients. Intermittent hypoxia caused by SDB induces alterations in metabolic function. Nevertheless, metabolites characteristic for SDB are largely unknown. In this study, we performed gas chromatography-mass spectrometry-based targeted metabolome analysis using data from The Nagahama Study (n = 6373). SDB-related metabolites were defined based on their variable importance score in orthogonal partial least squares discriminant analysis and fold changes in normalized peak-intensity levels between moderate-severe SDB patients and participants without SDB. We identified 20 metabolites as SDB-related, and interestingly, these metabolites were frequently included in pathways related to fructose. Multivariate analysis revealed that moderate-severe SDB was a significant factor for increased plasma fructose levels (β = 0.210, P = 0.006, generalized linear model) even after the adjustment of confounding factors. We further investigated changes in plasma fructose levels after continuous positive airway pressure (CPAP) treatment using samples from patients with OSA (n = 60) diagnosed by polysomnography at Kyoto University Hospital, and found that patients with marked hypoxemia exhibited prominent hyperfructosemia and their plasma fructose levels lowered after CPAP treatment. These data suggest that hyperfructosemia is the abnormality characteristic to SDB, which can be reduced by CPAP treatment.

Abstract Blood eosinophil count is a useful measure in asthma or COPD management. Recent epidemiological studies revealed that body mass index (BMI) is positively associated with eosinophil counts. However, few studies focused on the role of adiposity and fatty acid-related metabolites on eosinophil counts, including the effect of genetic polymorphism. In this community-based study involving 8265 participants (30–74 year old) from Nagahama city, we investigated the relationship between eosinophil counts and serum levels of fatty acid-related metabolites. The role of MDC1, a gene that is related to eosinophil counts in our previous study and encodes a protein that is thought to be involved in the repair of deoxyribonucleic acid damage, was also examined taking into account its interaction with adiposity. Serum levels of linoleic acid (LA) and β-hydroxybutyric acid (BHB) were negatively associated with eosinophil counts after adjustment with various confounders; however, there were positive interactions between serum LA and BMI and between serum BHB and BMI/body fat percentages in terms of eosinophil counts. In never-smokers, there was positive interaction for eosinophil counts between the CC genotype of MDC1 rs4713354 and BMI/body fat percentages. In conclusion, both serum LA and BHB have negative impacts on eosinophil counts, while adiposity shows robust positive effects on eosinophil counts, partly via genetic background in never-smokers.