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Aim: This study aims to investigate the association of glycemia risk index (GRI), a novel composite metric derived from continuous glucose monitoring (CGM), with arterial stiffness in patients with type 2 diabetes. Materials and Methods: A total of 342 adults with type 2 diabetes were enrolled between April and June 2023 from 11 communities in Shanghai, China. Medical examinations, including measurements of anthropometric parameters, blood pressure, and venous blood samples were conducted. Brachial‐ankle pulse wave velocity (baPWV) was examined to evaluate arterial stiffness. All the participants underwent a 14 day CGM recording and GRI was calculated from the CGM data. Results: The mean age was 70.3 ± 6.8 years, and 162 (47.4%) were male. Participants with a higher baPWV had significantly higher levels of GRI and hyperglycemia component (both P for trend < 0.05). Linear regression revealed the significant positive linear associations of the GRI with baPWV in unadjusted or adjusted models (All P < 0.05). In the multivariable logistic analysis, each increase in the GRI quartile was associated with a 1.30‐fold (95% CI 1.01–1.68, P for trend < 0.05) higher prevalence of increased arterial stiffness after adjustment for age, sex, BMI, diabetes duration, current smoking status, blood pressure, and lipid profile. Subgroup analyses showed that the association between the GRI quartiles and increased arterial stiffness was stronger among participants with a diabetes duration ≥15 years (P for interaction = 0.014). Conclusion: Glycemia risk index assessed by continuous glucose monitoring is associated with increased arterial stiffness in type 2 diabetes. [ABSTRACT FROM AUTHOR]

[Display omitted] Energy efficiency in residential buildings can be improved considerably by optimizing the allocation of thermal resistance (R) and capacitance (C) of external walls. However, it is difficult to identify which RC allocation yields the minimum heat loss or heat gain for a given climate region, due to a lack of understanding of how regional climate affects optimal RC allocation. In this study, we determine the effect of the climate characteristics on the development of optimal RC allocation by combining the RC network model and the particle swarm optimization algorithm, taking five representative climates in different Chinese cities as examples. The energy efficiency potential and the ratio of heating to cooling load are introduced to comparatively analyse the energy savings after optimization for the five cities. The results indicate that the energy efficiency potential of residential buildings in Harbin is the largest, ranging from 6.2% to almost 20.6% for different total building capacitance. In Harbin, Beijing and Kunming, the energy efficiency potential is larger than those in Guangzhou and Shanghai, which is attributed to the larger heating to cooling load ratio of the former than that of the latter. An approximately linear relationship between the energy efficiency potential and the ratio of heating to cooling load is also built to fast predict the maximum energy savings for any given climate regions. This study could serve as useful references for the optimal design of thermal resistance and capacitance in different climate regions to save energy of residential buildings. [ABSTRACT FROM AUTHOR]