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Background: Various disease prediction models have been developed, capitalizing on the wide use of electronic health records, but environmental factors that are important in the development of noncommunicable diseases are rarely included in the prediction models. Hypertensive disorders of pregnancy are leading causes of maternal morbidity and mortality and are known to cause several serious complications later in life. Objective: This study aims to develop early hypertensive disorders of pregnancy prediction models using comprehensive environmental factors based on self-report questionnaires in early pregnancy. Study Design: We developed machine learning and artificial intelligence models for the early prediction of hypertensive disorders of pregnancy using early pregnancy data from approximately 23,000 pregnancies in the Tohoku Medical Megabank Birth and Three Generation Cohort Study. We clarified the important features for prediction based on regression coefficients or Gini coefficients of the interpretable artificial intelligence models (i.e., logistic regression, random forest and XGBoost models) among our developed models. Results: The performance of the early hypertensive disorders of pregnancy prediction models reached an area under the receiver operating characteristic curve of 0.93, demonstrating that the early hypertensive disorders of pregnancy prediction models developed in this study retain sufficient performance in hypertensive disorders of pregnancy prediction. Among the early prediction models, the best performing model was based on self-reported questionnaire data in early pregnancy (mean of 20.2 gestational weeks at filling) which consist of comprehensive lifestyles. The interpretation of the models reveals that both eating habits were dominantly important for prediction. Conclusion: We have developed high-performance models for early hypertensive disorders of pregnancy prediction using large-scale cohort data from the Tohoku Medical Megabank project. Our study clearly revealed that the use of comprehensive lifestyles from self-report questionnaires led us to predict hypertensive disorders of pregnancy risk at the early stages of pregnancy, which will aid early intervention to reduce the risk of hypertensive disorders of pregnancy.

Background: Measurement of fractional exhaled nitric oxide (Feno) has been used in the diagnosis and management of asthma. Understanding the distribution of Feno in a larger resident population and its “healthy” subpopulation would contribute to the interpretation of Feno in clinical practice. Objective: This study aimed to investigate the distribution and its associated factors in the adult population and its healthy subpopulations. Methods: We conducted a cross-sectional study of 8,638 men and 17,288 women aged 20 years or older living in Miyagi prefecture, Japan. We investigated the distribution of Feno and its associated factors in all subjects, a subpopulation with no history of upper and lower airway diseases (healthy subpopulation 1), and a subpopulation with no history of upper and lower airway diseases, normal lung function, and no positivity for other biomarkers of type 2 inflammation (healthy subpopulation 2). Results: The distribution of Feno in healthy subpopulations, especially in healthy subpopulation 2 (median [interquartile range], 17 [12-23] with 95th percentile of 36 ppb) was lower than in all subjects (19 [13-26] ppb with 95th percentile of 47 ppb). In healthy subpopulation 1, 10.3% had elevated Feno (≥35 ppb), and elevated Feno was positively associated with factors including obstructive ventilatory defect, blood eosinophilia, house dust mite–specific IgE positivity, and history of hypertension. Male sex was associated with elevated Feno in all subjects and healthy subpopulations. Conclusion: The distribution of Feno in the healthy subpopulation supports the validity of the criteria (≥35 ppb) currently used in Japan for the diagnosis of asthma.

Purpose: To elucidate the differences in ocular biometric parameters by generation and gender and to identify axial length (AL)-associated genetic variants in Japanese individuals, we analyzed Tohoku Medical Megabank Organization (ToMMo) Eye Study data. Design: We designed the ToMMo Eye Study, examined AL variations, and conducted genome-wide association studies (GWASs). Participants: In total, 33 483 participants aged > 18 years who were recruited into the community-based cohort (CommCohort) and the birth and three-generation cohort (BirThree Cohort) of the ToMMo Eye Study were examined. Methods: Each participant was screened with an interview, ophthalmic examinations, and a microarray analysis. The GWASs were performed in 22 379 participants in the CommCohort (discovery stage) and 11 104 participants in the BirThree Cohort (replication stage). We evaluated the associations of single nucleotide polymorphisms (SNPs) with AL using a genome-wide significance threshold (5 × 10-8) in each stage of the study and in the subsequent meta-analysis. Main Outcome Measures: We identified the association of SNPs with AL and distributions of AL in right and left eyes and individuals of different sexes and ages. Results: In the discovery stage, the mean AL of the right eye (23.99 mm) was significantly greater than that of the left eye (23.95 mm). This difference was reproducible across sexes and ages. The GWASs revealed 703 and 215 AL-associated SNPs with genome-wide significance in the discovery and validation stages, respectively, and many of the SNPs in the discovery stage were replicated in the validation stage. Validated SNPs and their associated loci were meta-analyzed for statistical significance (P < 5 × 10-8). This study identified 1478 SNPs spread over 31 loci. Of the 31 loci, 5 are known AL loci, 15 are known refractive-error loci, 4 are known corneal-curvature loci, and 7 loci are newly identified loci that are not known to be associated with AL. Of note, some of them shared functional relationships with previously identified loci. Conclusions: Our large-scale GWASs exploiting ToMMo Eye Study data identified 31 loci linked to variations in AL, 7 of which are newly reported in this article. The results revealed genetic heterogeneity and similarity in SNPs related to ethnic variations in AL.