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Coronary heart disease (CHD) risk is influenced by socioeconomic status-related parameters, particularly occupation. We investigated occupational gaps in CHD risk and how the introduction of remote work moderated the observed occupational differences in CHD risk during the coronavirus disease 2019 pandemic in Japan. Data from a web-based, nationwide cohort study, comprising 17,640 workers (aged 20–65 years) with baseline data from December 2020, were analyzed. Participants were grouped by occupation as upper-level nonmanual workers (managers/professionals) and others (reference group). The primary outcome was CHD (angina pectoris/myocardial infarction) onset retrospectively confirmed at the 1-year follow-up survey. Upper-level nonmanual workers exhibited a higher CHD incidence than others (2.3% vs. 1.7%). This association was pronounced in the younger (20–49 years) population, with a significant CHD risk (adjusted risk ratio = 1.88). Upper-level nonmanual workers exhibited nearly 15% higher remote work prevalence, with a significant remote work-related CHD risk (adjusted risk ratio = 1.92). The mediating effects of remote work explained an overall disparity of 32% among the younger population. Occupational gaps in CHD incidence in Japan differ from those in Western countries, where upper-level nonmanual workers have lower cardiovascular risk. In Japan, remote work can mediate CHD risk in the younger population of upper-level nonmanual workers. [ABSTRACT FROM AUTHOR]

We evaluated the association between gestational age at threatened preterm birth (TPTB) diagnosis and preterm birth (PTB) incidence using a nationwide birth cohort. Data of 94,236 women with singleton deliveries from the Japan Environment and Children's Study (enrolled between 2011 and 2014) were analysed. Participants were divided based on parity and gestational age at TPTB diagnosis (22–24, 25–27, 28–30, 31–33, and 34–36 weeks). Multivariable logistic regression models were used to calculate the odds ratios (ORs) for PTB before 37 and 34 weeks in women from all groups, using participants without TPTB as the reference. The adjusted ORs for PTB before 37 weeks were the highest in the latest gestational age group in nulliparous and multiparous women without previous PTB, while those before 34 weeks were the highest in the earliest and latest gestational age group in multiparous women without previous PTB and in the earliest gestational age group in multiparous women with previous PTB. The association between gestational age at TPTB diagnosis and PTB incidence varies based on maternal parity and PTB before 37 or 34 weeks. Further studies with detailed clinical data and a unified TPTB diagnosis protocol are necessary to clarify this association. [ABSTRACT FROM AUTHOR]

Recently, prediction of gestational diabetes mellitus (GDM) using artificial intelligence (AI) from medical records has been reported. We aimed to evaluate GDM-predictive AI-based models using birth cohort data with a wide range of information and to explore factors contributing to GDM development. This investigation was conducted as a part of the Japan Environment and Children's Study. In total, 82,698 pregnant mothers who provided data on lifestyle, anthropometry, and socioeconomic status before pregnancy and the first trimester were included in the study. We employed machine learning methods as AI algorithms, such as random forest (RF), gradient boosting decision tree (GBDT), and support vector machine (SVM), along with logistic regression (LR) as a reference. GBDT displayed the highest accuracy, followed by LR, RF, and SVM. Exploratory analysis of the JECS data revealed that health-related quality of life in early pregnancy and maternal birthweight, which were rarely reported to be associated with GDM, were found along with variables that were reported to be associated with GDM. The results of decision tree-based algorithms, such as GBDT, have shown high accuracy, interpretability, and superiority for predicting GDM using birth cohort data. [ABSTRACT FROM AUTHOR]

Recently, prediction of gestational diabetes mellitus (GDM) using artificial intelligence (AI) from medical records has been reported. We aimed to evaluate GDM-predictive AI-based models using birth cohort data with a wide range of information and to explore factors contributing to GDM development. This investigation was conducted as a part of the Japan Environment and Children's Study. In total, 82,698 pregnant mothers who provided data on lifestyle, anthropometry, and socioeconomic status before pregnancy and the first trimester were included in the study. We employed machine learning methods as AI algorithms, such as random forest (RF), gradient boosting decision tree (GBDT), and support vector machine (SVM), along with logistic regression (LR) as a reference. GBDT displayed the highest accuracy, followed by LR, RF, and SVM. Exploratory analysis of the JECS data revealed that health-related quality of life in early pregnancy and maternal birthweight, which were rarely reported to be associated with GDM, were found along with variables that were reported to be associated with GDM. The results of decision tree-based algorithms, such as GBDT, have shown high accuracy, interpretability, and superiority for predicting GDM using birth cohort data. [ABSTRACT FROM AUTHOR]

We evaluated the association between gestational age at threatened preterm birth (TPTB) diagnosis and preterm birth (PTB) incidence using a nationwide birth cohort. Data of 94,236 women with singleton deliveries from the Japan Environment and Children's Study (enrolled between 2011 and 2014) were analysed. Participants were divided based on parity and gestational age at TPTB diagnosis (22–24, 25–27, 28–30, 31–33, and 34–36 weeks). Multivariable logistic regression models were used to calculate the odds ratios (ORs) for PTB before 37 and 34 weeks in women from all groups, using participants without TPTB as the reference. The adjusted ORs for PTB before 37 weeks were the highest in the latest gestational age group in nulliparous and multiparous women without previous PTB, while those before 34 weeks were the highest in the earliest and latest gestational age group in multiparous women without previous PTB and in the earliest gestational age group in multiparous women with previous PTB. The association between gestational age at TPTB diagnosis and PTB incidence varies based on maternal parity and PTB before 37 or 34 weeks. Further studies with detailed clinical data and a unified TPTB diagnosis protocol are necessary to clarify this association. [ABSTRACT FROM AUTHOR]

The objective of this study was to identify the prevalence of family history of cancer using cohorts participating in the Japanese National Center Cohort Collaborative for Advancing Population Health (NC-CCAPH). We pooled data from seven eligible cohorts of the Collaborative with available data on family history of cancer. Prevalence of family history of cancer and corresponding 95% confidence intervals are presented for all cancers and selected site-specific cancers for the total population and stratified by sex, age, and birth cohort. Prevalence of family history of cancer increased with age ranging from 10.51% in the 15 to 39 year age category to 47.11% in 70-year-olds. Overall prevalence increased in birth cohorts from ≤ 1929 until 1960 and decreased for the next two decades. Gastric cancer (11.97%) was the most common site recorded for family members, followed by colorectal and lung (5.75%), prostate (4.37%), breast (3.43%) and liver (3.05%) cancer. Women consistently had a higher prevalence of family history of cancer (34.32%) versus men (28.75%). Almost one in three participants had a family history of cancer in this Japanese consortium study highlighting the importance of early and targeted cancer screening services. [ABSTRACT FROM AUTHOR]

Previous studies reported conflicting results regarding an association between serum albumin concentration and the cumulative incidence of remission of proteinuria in adult patients with minimal change disease (MCD). The present study aimed to clarify the clinical impact of serum albumin concentration and the cumulative incidence of remission and relapse of proteinuria in 108 adult patients with MCD at 40 hospitals in Japan, who were enrolled in a 5-year prospective cohort study of primary nephrotic syndrome, the Japan Nephrotic Syndrome Cohort Study (JNSCS). The association between serum albumin concentration before initiation of immunosuppressive treatment (IST) and the cumulative incidence of remission and relapse were assessed using multivariable-adjusted Cox proportional hazards models. Remission defined as urinary protein < 0.3 g/day (or g/gCr) was observed in 104 (96.3%) patients. Of 97 patients with remission within 6 month of IST, 42 (43.3%) developed relapse defined as ≥ 1.0 g/day (or g/gCr) or dipstick urinary protein of ≥ 2+. Serum albumin concentration was significantly associated with remission (multivariable-adjusted hazard ratio [95% confidence interval] per 1.0 g/dL, 0.57 [0.37, 0.87]), along with eGFR (per 30 mL/min/1.73 m2: 1.43 [1.08, 1.90]), whereas they were not associated with relapse. A multivariable-adjusted model showed that patients with high eGFR level (≥ 60 mL/min/1.73 m2) and low albumin concentration (≤ 1.5 g/dL) achieved significantly early remission, whereas those with low eGFR (< 60 mL/min/1.73 m2) and high albumin concentration (> 1.5 g/dL) showed significantly slow remission. In conclusion, lower serum albumin concentration and higher eGFR were associated with earlier remission in MCD, but not with relapse. [ABSTRACT FROM AUTHOR]