Association between screen time, including that for smartphones, and overweight/obesity among children in Japan: NICE EVIDENCE Study 4.

The association between screen time (ST), including that for smartphones, and overweight/obesity in children was examined separately for boys and girls, considering the influence of lifestyle factors. A cross-sectional study was conducted in 2,242 Japanese children (1,278 girls) aged 10-14 years. Overweight/obesity was defined by the International Obesity Task Force. Logistic regression analysis showed that only for girls, total ST (≥4 h), smartphone ST (≥3 h), and non-smartphone ST (≥2 h) were all independently and significantly associated with overweight/obesity compared to <2 h total ST, non-use of smartphones, and <1 h non-smartphone ST. Thus, smartphone ST ≥3 h and non-smartphone ST ≥2 h were additively associated with overweight/obesity in girls only. Girls having smartphone ST ≥3 h and non-smartphone ST ≥2 h were 6.79 times (95% CI: 3.11-14.81) more likely to have overweight/obesity than girls with less usage of both. In girls, when total ST was ≥4 < 5 h or smartphone ST was ≥2 h, the significant association with overweight/obesity disappeared when physical activity was ≥60 min/day and sleep time was ≥8.5 h. In addition, none of these associations was significant in boys. In Japanese girls, smartphone ST, non-smartphone ST, and total ST were all significantly associated with overweight/obesity. To avoid overweight/obesity, it is suggested to keep smartphone ST, non-smartphone ST, and total ST to <3 h, <2 h, and <4 h, respectively, and to engage in sufficient physical activity and sleep time.

Machine learning approach to predict body weight in adults.

Background: Obesity is an established risk factor for non-communicable diseases such as type 2 diabetes mellitus, hypertension and cardiovascular disease. Thus, weight control is a key factor in the prevention of non-communicable diseases. A simple and quick method to predict weight change over a few years could be helpful for weight management in clinical settings. Methods: We examined the ability of a machine learning model that we constructed to predict changes in future body weight over 3 years using big data. Input in the machine learning model were three-year data on 50,000 Japanese persons (32,977 men) aged 19-91 years who underwent annual health examinations. The predictive formulas that used heterogeneous mixture learning technology (HMLT) to predict body weight in the subsequent 3 years were validated for 5,000 persons. The root mean square error (RMSE) was used to evaluate accuracy compared with multiple regression. Results: The machine learning model utilizing HMLT automatically generated five predictive formulas. The influence of lifestyle on body weight was found to be large in people with a high body mass index (BMI) at baseline (BMI ≥29.93 kg/m2) and in young people (<24 years) with a low BMI (BMI <23.44 kg/m2). The RMSE was 1.914 in the validation set which reflects ability comparable to that of the multiple regression model of 1.890 (p = 0.323). Conclusion: The HMLT-based machine learning model could successfully predict weight change over 3 years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning model could contribute to individualized weight management.

Fast walking and physical activity are independent contributors to improved physical function.

BACKGROUND AND OBJECTIVES: To clarify whether the presence or absence of fast walking and habitual physical activity are independently associated with the incidence of functional disability. METHODS: This historical cohort study was comprised of 9,652 (4,412 men, mean age 65 years) individuals aged 39-98 years without functional disability at baseline. Functional disability was determined based on the Japanese long-term care insurance system, which specified requirements for assistance in the activities of daily living. The impact of fast walking and habitual physical activity on the incidence of functional disability was analysed by Cox proportional hazards models. RESULTS: The follow-up period was a median of 3.7 years during which 165 patients were newly certified as having functional disability. In the multivariate analysis, baseline age in 5-year increments (hazard ratio 2.42 [95% confidence interval 2.18-2.69]), no habitual physical activity (1.56 [1.07-2.27]), and not fast walking (1.89 [1.32-2.69]) significantly increased the risk of functional disability after adjustment for covariates. The stratified analysis showed that compared with physical activity (+), the impact of physical activity (-) on the incidence of functional disability was observed in those aged ≥75 years regardless of fast walking (+). Fast walking (-) significantly increased the risk of disability compared with fast walking (+) in those aged <75 years regardless of a physical activity habit. CONCLUSION: In Japanese, slow walking speed and lack of a physical activity habit were shown to be independent risk factors for incident functional disability, with their impact differing according to age.

Impact of metabolic syndrome and metabolic dysfunction-associated fatty liver disease on cardiovascular risk by the presence or absence of type 2 diabetes and according to sex.

BACKGROUND: To determine the impact of metabolic syndrome (MetS) and/or metabolic dysfunction-associated fatty liver disease (MAFLD), which are pathophysiologically similar and include insulin resistance, on the development of new-onset cardiovascular disease with and without type 2 diabetes and according to sex. METHODS: This study included 570,426 individuals without a history of cardiovascular disease who were enrolled in a nationwide claims database from 2008 to 2016 and were classified by the presence or absence of MetS and/or MAFLD stratified by the presence or absence of type 2 diabetes and sex. The fatty liver index was used to determine the presence or absence of fatty liver that required a diagnosis of MAFLD. Risks of developing coronary artery disease (CAD) and cerebrovascular disease (CVD) in each category were analyzed using a multivariate Cox proportional hazard model. RESULTS: During a median follow-up of 5.2 years, 2252 CAD and 3128 CVD events occurred. Without type 2 diabetes the hazard ratio (HR) (95% CI) for CAD/CVD compared with neither MAFLD nor MetS was 1.32 (1.17-1.50)/1.41(1.28-1.57) for MAFLD only (without MetS), 1.78 (1.22-2.58)/1.66 (1.34-2.06) for MetS only (without MAFLD), and 2.10 (1.84-2.39)/1.73 (1.54-1.95) for MAFLD + MetS. For those with type 2 diabetes, the HR for CAD for MAFLD only (compared with neither MAFLD nor MetS) was 1.29 (1.06-1.58), for MetS only 1.34 (0.84-2.13), and for MAFLD + MetS 1.22 (1.02-1.47). For CVD, there was a significant increase in HR only in MAFLD + MetS [1.44 (1.18-1.76)]. The results of the analysis stratified by sex showed that MAFLD had a greater impact in men, and MetS had a greater impact in women regarding the development of CAD. CONCLUSIONS: Distinguishing between MetS and/or MAFLD in the presence or absence of type 2 diabetes and according to sex may aid in accurately identifying patients at high risk of cardiovascular disease.

Accuracy of Japanese claims data in identifying diabetes-related complications.

PURPOSE: To evaluate the accuracy of various claims-based definitions of diabetes-related complications (coronary artery disease [CAD], heart failure, cerebrovascular disease and dialysis). METHODS: We evaluated data on 1379 inpatients who received care at the Niigata University Medical & Dental Hospital in September 2018. Manual electronic medical chart reviews were conducted for all patients with regard to diabetes-related complications and were used as the gold standard. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of each claims-based definition associated with diabetes-related complications based on Diagnosis Procedure Combination (DPC), International Classification of Diseases, Tenth Revision (ICD-10) codes, procedure codes and medication codes were calculated. RESULTS: DPC-based definitions had higher sensitivity, specificity, and PPV than ICD-10 code definitions for CAD and cerebrovascular disease, with sensitivity of 0.963-1.000 and 0.905-0.952, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. Sensitivity, specificity, and PPV were high using procedure codes for CAD and dialysis, with sensitivity of 0.963 and 1.000, specificity of 1.000 and 1.000, and PPV of 1.000 and 1.000, respectively. DPC and/or ICD-10 codes + medication were better for heart failure than the ICD-10 code definition, with sensitivity of 0.933, specificity of 1.000, and PPV of 1.000. The PPVs were lower than 60% for all diabetes-related complications using ICD-10 codes only. CONCLUSION: The DPC-based definitions for CAD and cerebrovascular disease, procedure codes for CAD and dialysis, and DPC or ICD-10 codes with medication codes for heart failure could accurately identify these diabetes-related complications from claims databases.

Relationship Between Number of Multiple Risk Factors and Coronary Artery Disease Risk With and Without Diabetes Mellitus.

PURPOSE: To determine the degree of control of multiple risk factors under real-world conditions for coronary artery disease (CAD) according to the presence or absence of diabetes mellitus (DM) and to determine whether reaching multifactorial targets for blood pressure (BP), low-density lipoprotein-cholesterol (LDL-C), HbA1c, and current smoking is associated with lower risks for CAD. METHODS: We investigated the effects on subsequent CAD of the number of controlled risk factors among BP, LDL-C, HbA1c, and current smoking in a prospective cohort study using a nationwide claims database of 220,894 individuals in Japan. Cox regression examined risks over a 4.8-year follow-up. RESULTS: The largest percentage of participants had two risk factors at target in patients with DM (39.6%) and subjects without DM (36.4%). Compared with those who had two targets achieved, the risks of CAD among those who had any one and no target achieved were two and four times greater, respectively, regardless of the presence of DM. The effect of composite control was sufficient to bring CAD risk in patients with DM below that for subjects without DM with any two targets achieved, whereas the risk of CAD in the DM group with all four risk factors uncontrolled was 9.4 times more than in the non-DM group who had achieved two targets. CONCLUSIONS: These findings show that composite control of modifiable risk factors has a large effect in patients with and without DM. The effect was sufficient to bring CAD risk in patients with DM below that in the non-DM group who had two targets achieved.