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<b>Objective</b><b></b> <p>We aimed to explore the associations between type 2 diabetes onset age and cardiovascular disease (CVD) and all-cause mortality in Chinese population.</p> <p><b>Research design and methods</b></p> <p>This study included 101,080 participants free of prevalent diabetes and CVD at baseline from the Kailuan study. All participants were followed biennially until December 31, 2017. A total of 11,384 participants were diagnosed as type 2 diabetes during follow-up. For each case, one control was randomly selected matched for age (±1 years) and sex. The final analysis comprised 10,777 case-control pairs. Weighted Cox regression models were used to evaluate the average hazard ratios (AHRs) and 95% confidence intervals (CIs) of incident CVD and all-cause mortality among patients with new-onset type 2 diabetes <i>versus </i>controls across age groups.</p> <p><b>Results</b><b></b></p> <p>During a median follow-up of 5.57 years, 1794 incident events (907 CVD events, of which were 725 strokes, and 887 deaths) occurred. After adjustment for potential confounders, participants with type 2 diabetes diagnosed at age < 45 years had the highest risks of CVD and all-cause mortality relative to the matched controls, with AHRs of 3.21 (95% CI 1.18–8.72) for CVD, 2.99 (95% CI 1.01–9.17) for stroke, and 4.79 (95% CI 1.95–11.76) for all-cause mortality. The risks gradually attenuated with each decade increase in type 2 diabetes onset age. </p> <p><b>Conclusions</b><b></b></p> <p>The relative risks of CVD and all-cause mortality differed across type 2 diabetes onset age groups, and the associations were more evident in younger-onset type 2 diabetes. </p>

Extreme rainfall is one of the primary meteorological hazards in Singapore, as well as elsewhere in the deep tropics, and it can lead to significant local flooding. Since 2013, the Meteorological Service Singapore (MSS) and the United Kingdom Met Office (UKMO) have been collaborating to develop a convective-scale Numerical Weather Prediction (NWP) system, called SINGV. Its primary aim is to provide improved weather forecasts for Singapore and the surrounding region, with a focus on improved short-range prediction of localized heavy rainfall. This paper provides an overview of the SINGV development, the latest NWP capabilities at MSS and some key results of evaluation. The paper describes science advances relevant to the development of any km-scale NWP suitable for the deep tropics and provides some insights into the impact of local data assimilation and utility of ensemble predictions.

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