Compared to oGLD group, SGLT-2i group had lower risk of cardiorenal disease, HF, CKD, stroke, and all-cause mortality with HRs (95% confidence intervals) 0.55 (0.49-0.61), 0.73 (0.61-0.87), 0.45 (0.39-0.52), 0.69 (0.59-0.81), and 0.52 (0.46-0.58), respectively, while no difference in MI. These were consistent in 11 propensity-score matching analysis between SGLT-2i and DPP-4i users (n = 17 232 in each group). In Japanese CVRD-free T2D patients, SGLT-2i initiation was associated with lower risk of cardiorenal diseases, stroke, and all-cause mortality compared to oGLD, suggesting preventive effect of SGLT-2i treatment in the early stage of T2D patients without CVRD manifestation.In Japanese CVRD-free T2D patients, SGLT-2i initiation was associated with lower risk of cardiorenal diseases, stroke, and all-cause mortality compared to oGLD, suggesting preventive effect of SGLT-2i treatment in the early stage of T2D patients without CVRD manifestation. To examine healthcare resource utilization in type 2 diabetes (T2D) patients after initiation of sodium-glucose co-transporter-2 inhibitors (SGLT-2is) versus dipeptidyl peptidase-4 inhibitors (DPP-4is) or other glucose-lowering drugs (oGLDs). A cost-utilization analysis was performed using a nationwide hospital-based administrative claims database (Medical Data Vision) during 2014-2018 in Japan, where universal healthcare coverage is maintained under a single-payer system. Data on T2D patients initiated on either SGLT-2is or oGLDs during the study period (228 514 patients) were extracted and subjected to a 11 propensity score-matching analysis (7626 patient pairs for DPP-4is and 28 484 for oGLDs). Direct healthcare resource utilizations and inpatient and outpatient costs were compared. After matching, baseline characteristics were well balanced, including healthcare costs within 3 and 12 months before the index date (standardized difference <5% for all variables), with a mean age of 61.6-64.1 years. While diabetes medication costs were higher in patients initiated with SGLT-2is than in those initiated with DPP-4is or oGLDs, further breakdown of individual cost components showed that SGLT-2is were associated with a lower hospitalization frequency and a shorter total hospital stay (by 213.0 or 204.6 days/100 patient-years compared with DPP-4is or oGLDs, respectively; P < .001). Accordingly, overall mean cumulative cost per patient at the 2.5-year postindex date was lower in patients with SGLT-2is than in those with DPP-4is or oGLDs by $2545 (1384.6-3759.7) and $2330 (1793.1-2882.9), respectively (P < .001). Our results show the benefits in healthcare resource utilization associated with SGLT-2i use in Japanese T2D patients.Our results show the benefits in healthcare resource utilization associated with SGLT-2i use in Japanese T2D patients.With the widespread use of electronic medical records and administrative claims databases, analytic results from so-called real-world data have become increasingly important in healthcare decision-making. SB431542 Diabetes mellitus is a heterogeneous condition that involves a broad spectrum of patients. Real-world database studies have been recognised as a powerful tool to understand the impact of current practices on clinical courses and outcomes, such as long-term glucose control, development of microvascular or macro-vascular diseases, and mortality. Diabetes is also a major global health issue and poses a significant social and economic burden worldwide. Therefore, it is critical to understand the epidemiology, clinical course, treatment reality, and long-term outcomes of diabetes to determine realistic solutions to a variety of disease-related issues that we are facing. In the present review, we summarise the healthcare system and large-scale databases currently available in Japan, introduce the results from recent database studies involving Japanese patients with diabetes, and discuss future opportunities and challenges for the use of databases in the management of diabetes.Following liver transplant (LT), osteoporosis is a severe complication that causes morbidity. However, the incidence and risk factors of osteoporosis and fractures have not been well described. Single-arm meta-analysis of studies reporting osteopenia, osteoporosis, and fractures post-LT was performed with meta-regression for study period. Dichotomous variables, continuous variables and time-to-event variables were pooled in odds ratio, weighted mean difference and hazard ratio, respectively. For risk factors with limited data, a systematic review of literature was conducted. There was a significant increase in both osteoporosis and fractures compared to non-LT patients. Osteopenia, osteoporosis and incident fractures were newly diagnosed in 34.53% (CI 0.17-0.56, n = 301), 11.68% (CI 0.05-0.24, n = 1251) and 20.40% (CI 0.13-0.30, n = 4322) of LT patients, respectively. Female gender (P = 0.017) increased risks of osteoporosis but not older age and BMI. Older age, lower pre-LT bone mineral density (BMD), presence of bone disease pre-LT were significant risk factors for fractures but not female gender, post-menopausal state, BMI, smoking and alcohol. There is a high incidence of skeletal complications post-LT. Older age, lower pre-LT BMD and presence of bone disease pre-LT are significant risk factors that are associated with incident fractures physicians should be cognisant of in liver transplant recipients.Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as "brain fingerprinting" to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter-subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest-rest pair). Furthermore, high-level cognitive behaviors (e.