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Background: Better identification of high-risk individuals of type 2 diabetes mellitus (T2DM) for focused delivery of preventive measures requires risk prediction models using novel predictors. We evaluated the predictive value of long-term variability of systolic blood pressure (SBPV), which was recently reported to be associated with T2DM incidence, if added to a model with conventional T2DM predictors in a Japanese cohort study. Methods: A cohort of 3017 Japanese individuals (2446 male, 571 female) ages 36-65 years were followed from 2007 to 2019. Root-mean-square error (RMSE) and slope of systolic blood pressure (SBP) change regressed on year were calculated per individual using SBP values obtained consecutively from 2003 to 2007 to represent SBPV. An initial Cox model included age, sex, smoking status, regular exercise, family history of diabetes, body mass index (BMI), baseline SBP, blood levels of triglycerides (TG), high-density lipoprotein cholesterol (HDLC) and fasting blood glucose (FBG), and backward elimination was used for variable selection. The c-statistics, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the performance of prediction models without (Model 1) and with SBPV (Model 2). Results: During 9.8-year (median) follow-up, 135 developed T2DM. We confirmed that RMSE was significantly associated with T2DM incidence independent of other variables used in a conventional model. Backwards elimination procedure selected BMI, TG, HDLC, FBG, SBP RMSE, and SBP slope for the final model (Table 1). Although the c-indices were not statistically different between Model 1 (0.77) and Model 2 (0.78) as well as the NRI (7.1%), the IDI was statistically significant (0.8%, p Conclusions: The present study revealed that long-term variability of SBP slightly improved the predictive value of T2DM if added to a conventional prediction model.

Introduction: Low density lipoprotein cholesterol (LDL-C) is an established causal factor for coronary heart disease (CHD) and ischemic stroke. A positive linear association between LDL-C and risk of CHD or ischemic stroke was reported from western populations but among Japanese the evidence still insufficient especially on the risk of ischemic stroke. This study used a longitudinal data from the Aichi Worker’s Cohort Study to explore the associations of LDL-C levels with the incidence of CHD, and stroke subtypes. Methods: Pooled data of 6325 adults (5001 men and 1324 women) who responded to the second (2002) and third (2007) wave surveys of the study were used for the current analysis. Propensity scores for LDL-C categories were generated using multinomial logistic regression that included age, sex, smoking, alcohol drinking, physical activity, body mass index, high-density lipoprotein cholesterol, triglycerides, history of diabetes and hypertension, antihypertensive medication, taking dyslipidemia medication and survey year. Hazard ratios (HRs) and the 95% confidence intervals (95% CIs) were estimated from inverse probability weighted (IPW) cox proportional hazards model for LDL-C categories associations with risks of CHD, stroke and its subtypes, and CVD. We also used restricted cubic spline to examine the possible nonlinear relationship. Results: During a median of 14 years of follow-up, 73 strokes (40 ischemic stroke, 30 hemorrhagic stroke and 3 unknown) and 60 CHD were observed. In comparison with LDL-C < 120 mg/ dl, LDL-C ≥160 mg/dl was significantly associated with the increased risk of CVD (HR 1.79, 95% CI: 1.12-2.86) and CHD (HR 3.82, 95% CI: 1.80-8.06), but not with stroke (HR 1.05, 95% CI: 0.54-2.06), hemorrhagic stroke (HR 0.47, 95% CI: 0.13-1.67) or ischemic stroke (HR 1.56, 95% CI: 0.66-3.68). The results of restricted cubic spline analysis showed that the risks of CVD and CHD gradually increased from LDL-C of 120 mg/dl. On the other hand, the risk of ischemic stroke was flat until around LDL-C of 160 mg/dl and then increased afterward. The risk of hemorrhagic stroke was flat from LDL-C of 120 mg/dl or above, but showed an increased risk trend towards lower levels of LDL-C. Conclusions: Based on this recent, long-term prospective study among middle-aged Japanese workers and by applying the IPW method to adjust for several confounding variables, we found that LDL-C was linearly and positively associated with CHD incidence while higher LDL-C levels tended to be at an increased risk of ischemic stroke, though non-significant.

Objective: Preventive services including screening for diabetes and its potential risk factorsare available to more Americans under Obamacare Preventive Care. Stratifying individuals by the predicted risk of developing type 2 diabetes mellitus (T2DM) would be useful for improving public health with efficient interventions. Although a number of T2DM prediction models have been reported, there is little evidence in East Asians, especially that from long-term follow-up studies. They are reported to have lower ability of innate insulin secretion and develop diabetes at much lower body mass index (BMI) than Caucasians and African Americans. Thus, this study aims to develop a point-based prediction model for 10-year risk of developing T2DM incidence in middle-aged Japanese men. Method: We followed 3,540 males in a worksite in Japan who were aged 35-64 years and free of diabetes in 2002 until March 31, 2015. Relationships of baseline age (continuous), BMI (2 ), current smoking status (yes, no [Ref]), alcohol consumption(0 [Ref], Result: During the median follow-up of 12.2 years, 342 males developed T2DM. The point-based model employing BMI, current smoking status, family history of diabetes, and blood levels of triglycerides and fasting blood glucose showed reasonable discrimination (c-statistics: 0.73) and goodness of fit (Hosmer-Lemeshow p=0.22). Conclusion: Our point-based prediction model showed applicability in terms of identifying middle-aged Japanese men at high risk of developing T2DM. The present findings warrants further investigations to determine whether using the point-based prediction models is effective to reduce T2DM incidence.