Language：English   Publisher：International Journal of Environmental Research and Public Health  

The physical, psychiatric, and social comorbidities interfere with the everyday activities of community-dwelling individuals with schizophrenia and increase the risk of their readmission. However, these comorbidities have not been investigated comprehensively in Japan. We conducted a self-reported internet survey in February 2022 to identify individuals aged 20–75 years with and without schizophrenia using a prevalence case-control study. The survey compared physical comorbidities such as being overweight, hypertension, and diabetes; psychiatric comorbidities such as depressive symptoms and sleep disturbances; social comorbidities such as employment status, household income, and social support between participants with and without schizophrenia. A total of 223 participants with schizophrenia and 1776 participants without schizophrenia were identified. Participants with schizophrenia were more likely to be overweight and had a higher prevalence of hypertension, diabetes, and dyslipidemia than participants without schizophrenia. Additionally, depressive symptoms, unemployment, and non-regular employment were more prevalent in participants with schizophrenia than those without schizophrenia. These results highlight the necessity of comprehensive support and interventions addressing physical, psychiatric, and social comorbidities in individuals with schizophrenia in the community. In conclusion, effective interventions for managing comorbidities in individuals with schizophrenia are necessary to enable them to continue to live in the community.

DOI： 10.3390/ijerph20054336

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PubMed

Language：English   Publisher：JMIR Formative Research  

Background: In Japan, challenges were reported in accurately estimating the prevalence of schizophrenia among the general population. Retrieving previous studies, we investigated that patients with schizophrenia were more likely to experience poor subjective well-being and various physical, psychiatric, and social comorbidities. These factors might have great potential for precisely classifying schizophrenia cases in order to estimate the prevalence. Machine learning has shown a positive impact on many fields, including epidemiology, due to its high-precision modeling capability. It has been applied in research on mental disorders. However, few studies have applied machine learning technology to the precise classification of schizophrenia cases by variables of demographic and health-related backgrounds, especially using large-scale web-based surveys. Objective: The aim of the study is to construct an artificial neural network (ANN) model that can accurately classify schizophrenia cases from large-scale Japanese web-based survey data and to verify the generalizability of the model. Methods: Data were obtained from a large Japanese internet research pooled panel (Rakuten Insight, Inc) in 2021. A total of 223 individuals, aged 20-75 years, having schizophrenia, and 1776 healthy controls were included. Answers to the questions in a web-based survey were formatted as 1 response variable (self-report diagnosed with schizophrenia) and multiple feature variables (demographic, health-related backgrounds, physical comorbidities, psychiatric comorbidities, and social comorbidities). An ANN was applied to construct a model for classifying schizophrenia cases. Logistic regression (LR) was used as a reference. The performances of the models and algorithms were then compared. Results: The model trained by the ANN performed better than LR in terms of area under the receiver operating characteristic curve (0.86 vs 0.78), accuracy (0.93 vs 0.91), and specificity (0.96 vs 0.94), while the model trained by LR showed better sensitivity (0.63 vs 0.56). Comparing the performances of the ANN and LR, the ANN was better in terms of area under the receiver operating characteristic curve (bootstrapping: 0.847 vs 0.773 and cross-validation: 0.81 vs 0.72), while LR performed better in terms of accuracy (0.894 vs 0.856). Sleep medication use, age, household income, and employment type were the top 4 variables in terms of importance. Conclusions: This study constructed an ANN model to classify schizophrenia cases using web-based survey data. Our model showed a high internal validity. The findings are expected to provide evidence for estimating the prevalence of schizophrenia in the Japanese population and informing future epidemiological studies.

DOI： 10.2196/50193

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PubMed

Language：English   Publisher：Japan Epidemiological Association  

<p><b>Background:</b> Associations of major risk factors for stroke with total and each type of stroke, as well as subtypes of ischemic stroke, and their population attributable fractions had not been examined comprehensively.</p><p><b>Methods:</b> Participants of the Japan Public Health Center-based prospective (JPHC) Study Cohort II without histories of cardiovascular disease and cancer (<i>n</i> = 14,797) were followed from 1993 through 2012. Associations of current smoking, hypertension, diabetes, overweight (body mass index ≥25 kg/m²), non-high-density lipoprotein cholesterol (non-HDLC) categories, low HDLC (<40 mg/dL), urine protein, and history of arrhythmia were examined in a mutually-adjusted Cox regression model that included age and sex. Population attributable fractions (PAFs) were estimated using the hazard ratios and the prevalence of risk factors among cases.</p><p><b>Results:</b> Subjects with hypertension were 1.63 to 1.84 times more likely to develop any type of stroke. Diabetes, low HDLC, current smoking, overweight, urine protein, and arrhythmia were associated with risk of overall and ischemic stroke. Hypertension and urine protein were associated with risk of intracerebral hemorrhage, while current smoking, hypertension, and low non-HDLC were associated with subarachnoid hemorrhage. Hypertension alone accounted for more than a quarter of stroke incidence, followed by current smoking and diabetes. High non-HDLC, current smoking, low HDLC, and overweight contributed mostly to large-artery occlusive stroke. Arrhythmia explained 13.2% of embolic stroke. Combined PAFs of all the modifiable risk factors for total, ischemic, and large-artery occlusive strokes were 36.7%, 44.5%, and 61.5%, respectively.</p><p><b>Conclusion:</b> Although there are differences according to subtypes, hypertension could be regarded as the most crucial target for preventing strokes in Japan.</p>

DOI： 10.2188/jea.je20220364

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CiNii Research

Language：English   Publisher：Hypertension Research  

Studies have reported that short-term blood pressure (BP) variability (BPV) is associated with type 2 diabetes mellitus (T2DM) incidence, but the association with long-term BPV remains unclear. The present study investigated the associations of long-term BPV as well as the time trend of BP changes over time with the incidence of T2DM. This study followed a cohort of 3017 Japanese individuals (2446 male, 571 female) aged 36–65 years from 2007 through March 31, 2019. The root-mean-square error (RMSE) and the slope of systolic BP (SBP) change regressed on year were calculated individually using SBP values obtained from 2003 to baseline (2007). A multivariable Cox proportional hazard model was applied to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) for tertiles of SBP RMSE and continuous SBP slopes adjusted for age, sex, smoking status, regular exercise, sodium intake, family history of diabetes, sleep disorder, body mass index (BMI), SBP, and fasting blood glucose (FBG) at baseline, and BMI slope from 2003 to 2007. The highest RMSE tertile compared to the lowest was associated with a significantly higher incidence of T2DM after adjusting for covariates (HR: 1.79, 95% CI: 1.15, 2.78). The slope was also significantly associated with T2DM incidence until baseline SBP and FBG were adjusted (HR: 1.03, 95% CI: 0.99, 1.07). In conclusion, long-term SBP variability was significantly associated with an increased incidence of T2DM independent of baseline age, sex, BMI, SBP, FBG, lifestyle factors and BMI slope from 2003 until baseline.

DOI： 10.1038/s41440-022-00993-2

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PubMed

Language：English   Publisher：Japan Atherosclerosis Society  

<p> <b>Aim:</b> We aimed to examine the association of obesity-related cancer and cardiovascular disease (CVD) with body mass index (BMI) and the estimated population attributable fraction in lean Asians.</p><p><b>Methods:</b> We studied 102,535 participants aged 40–79 years without histories of cancer or CVD at baseline between 1988 and 2009. The cause-specific hazard ratios (csHRs) of BMI categories (＜18.5, 18.5–20.9, 21.0–22.9 [reference], 23.0–24.9, 25.0–27.4, and ≥ 27.5 kg/m²) were estimated for each endpoint. The events considered were mortalities from obesity-related cancer (esophageal, colorectal, liver, pancreatic, kidney, female breast, and endometrial cancer) and those from CVD (coronary heart disease and stroke). Population attributable fractions (PAFs) were calculated for these endpoints.</p><p><b>Results:</b> During a 19.2-year median follow-up, 2906 died from obesity-related cancer and 4532 died from CVD. The multivariable-adjusted csHRs (95% confidence interval) of higher BMI categories (25–27.4 and ≥ 27.5 kg/m²) for obesity-related cancer mortality were 0.93 (0.78, 1.10) and 1.18 (0.92, 1.50) in men and 1.25 (1.04, 1.50) and 1.48 (1.19, 1.84) in women, respectively. The corresponding csHRs for CVD mortality were 1.27 (1.10, 1.46) and 1.59 (1.30, 1.95) in men and 1.10 (0.95, 1.28) and 1.44 (1.21, 1.72) in women, respectively. The PAF of a BMI ≥ 25 kg/m² for obesity-related cancer was −0.2% in men and 6.7% in women and that for CVD was 5.0% in men and 4.5% in women.</p><p><b>Conclusion:</b> A BMI ≥ 25 kg/m² is associated with an increased risk of obesity-related cancer in women and CVD in both sexes.</p>

DOI： 10.5551/jat.63143

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CiNii Research

Language：English   Publisher：Nutrients  

The aim of the present study was to derive dietary patterns to explain variation in a set of nutrient intakes or in the measurements of waist circumference (WC) and fasting blood glucose (FBG) using reduced rank regression (RRR) and to prospectively investigate these patterns in relation to the risk of developing metabolic syndrome (MetS) and its components during the follow-up. The study participants were comprised of 2944 government employees aged 30–59 years without MetS. RRR was applied with 38 food groups as predictors and with two sets of response variables. The first set included intake of putatively beneficial nutrients, and the first factor retained was named the Healthy Dietary Pattern (HDP). The second one included baseline WC and FBG, and the first factor was named the Unhealthy Dietary Pattern (UHDP). Multivariable Cox proportional hazard model was used to estimate hazard ratio and 95% confidence intervals with adjustments for age, sex, total energy consumption and other potential confounders. During the 5-year median follow-up, we ascertained 374 cases of MetS. The HDP score was inversely associated with the incidence of MetS (p-trend = 0.009) and hypertension (p-trend = 0.002) and marginally significantly associated with elevated triglyceride and decreased high-density lipoprotein cholesterol (p-trend = 0.08). The UHDP score was linearly positively associated with the incidence of MetS and all its components (all p-trend < 0.05). Both the HDP and UHDP predicted the development of MetS and its components.

DOI： 10.3390/nu14153019

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PubMed