Background: Hemoglobin A1c (HbA1c) variability may be a predictor of diabetic complications, but the predictive values of HbA1c trajectories remain unclear. We aimed to classify long-term HbA1c trajectories and to explore their effects on future clinical outcomes in a 10-year cohort with type 2 diabetes mellitus (T2DM). Methods: A total of 2,161 participants with T2DM from the Beijing Community Diabetes Study were included. The 10-year follow-up was divided into two stages for the present data analysis. Stage I (from 2008 to 2014) was used to identify the HbA1c trajectories and to calculate the adjusted SD of HbA1c (HbA1c-adjSD), or the coefficient of variation of HbA1c (HbA1c-CV). Stage II (from 2014 to 2018) was used to collect the records of the new occurrence of diabetes-related clinical outcomes. Latent growth mixture models were used to identify HbA1c trajectories. Cox proportional hazards models were used to explore the relationship between HbA1c trajectories, HbA1c-adjSD, or HbA1c-CV and the future outcomes. Results: Three HbA1c trajectories were identified, including low stable (88.34%), gradual decreasing (5.83%), and pre-stable and post-increase (5.83%). Either the risk of death or the chronic complications were significantly higher in the latter two groups compared to the low stable group after adjustment for average HbA1c and other traditional risk factors, the adjusted hazard ratios (HRs) for renal events, composite endpoint, and all-cause death for the pre-stable and post-increase group were 2.83 [95%CI: 1.25–6.41, p = 0.013], 1.85 (95%CI: 1.10–3.10, p = 0.020), and 3.01 (95%CI: 1.13–8.07, p = 0.028), respectively, and the adjusted HR for renal events for the gradual decreasing group was 2.37 (95%CI: 1.08–5.21, p = 0.032). In addition, both univariate and multivariate Cox HR models indicated that participants in the fourth and third quartiles of HbA1c-CV or HbA1c-adjSD were at higher risk of renal events compared to participants in the first quartile. Conclusions: HbA1c trajectories, HbA1c-CV, and HbA1c-adjSD could all predict diabetes-related clinical outcomes. HbA1c trajectories could reflect long-term blood glucose fluctuation more intuitively, and non-stable HbA1c trajectories may predict increased risk of renal events, all-cause death, and composite endpoint events, independent of average HbA1c.

Obesity increases the risk of developing cardiovascular disease and other metabolic diseases. We intended to compare three different anthropometric indicators of obesity, in predicting the incidence of cardiovascular events in Chinese type 2 diabetes. Beijing Community Diabetes Study was a prospective multi-center study conducted in Beijing community health centers. Type 2 diabetes patients from fourteen community health centers were enrolled at baseline. The primary endpoint was cardiovascular events. The upper quartile of neck circumference (NC) was set as greater NC. A total of 3299 diabetes patients were enrolled. In which, 941 (28.52%) had cardiovascular disease at baseline. Logistic analysis showed that central obesity (waist circumference (WC) above 90 cm in men and 85 cm in women) and greater NC were all related to baseline cardiovascular disease (adjusted OR = 1.49, and 1.55). After 10-year follow-up, 340 (10.31%) had cardiovascular events. Compared with patients without cardiovascular events, those having cardiovascular events had higher BMI, larger WC and NC. Cox regression analysis showed that greater WC and NC were all associated with the occurrence of cardiovascular events (adjusted HR = 1.41, and 1.38). A higher NC and WC might increase the risk of cardiovascular events by about 40% in type 2 diabetes patients in Beijing communities.

Background: White adipose tissue can be classified based on its location as subcutaneous and visceral fat, and the latter accumulation is reported to be more detrimental to metabolism. Endoplasmic reticulum (ER) stress has been demonstrated to regulate lipogenesis. The peptide angiotensin(1–7) [Ang(1–7)], which can be produced from angiotensin II (AngII) by angiotensin-converting enzyme 2 (ACE2), plays its role through Mas receptor, also participates in the regulation of lipid metabolism in adipose tissue, however, whether ER stress is involved in the mechanism remains unclear. Therefore, we aimed to explore the role of Ang(1–7) pathway in regulating visceral adipose tissue expansion and ER stress. Methods: ACE2 knockout (KO), Mas KO and C57BL/6 J mice were fed with high fat diet. Db/db mice were treated with either normal saline, Ang(1–7) or Ang(1–7) combined with Mas receptor inhibitor A779 using mini osmotic pumps. Fat mass was weighted, fat distribution was evaluated by MRI, and lipid profile and adipokines in epididymal adipose tissue were measured by ELISA kits, and histology of epididymal adipose tissue was also analyzed in multiple animal models. Additionally, differentiated 3T3-L1 cells were pre-loaded with palmitic acid to induce ER stress, then treated with drugs as those administrated to db/db mice. ER stress and lipogenesis related proteins in mice adipose and differentiated 3T3L-1 cells were analyzed by Western blot. Results: ACE2 or Mas KO mice exhibited increased visceral adipose tissue, adipocyte size and protein expression of lipogenesis and ER stress related markers in epididymal adipose tissue compared to wild-type mice. Db/db mice treated with Ang(1–7) displayed decreased visceral fat mass, adipocyte size and protein expression of lipogenesis and ER stress markers in epididymal adipose tissue compared to those treated with normal saline, while A779 partly attenuated these effects. Additionally, Ang(1–7) improved ER stress and lipogenesis markers in differentiated 3T3-L1 cells pre-loaded with palmitic acid. Conclusions: Our findings indicated that Ang(1–7) attenuated visceral adipose tissue expansion and lipogenesis by suppression of ER stress via Mas receptor. The present study provides a potential perspective for Ang(1–7) for the therapeutics of obesity and related disorders.

Metformin is the first-line pharmacotherapy for type 2 diabetes mellitus; however, many patients respond poorly to this drug in clinical practice. The potential involvement of microbiota-mediated intestinal immunity and related signals in metformin responsiveness has not been previously investigated. In this study, we successfully constructed a humanized mouse model by fecal transplantation of the gut microbiota from clinical metformin-treated - responders and non-responders, and reproduced the difference in clinical phenotypes of responsiveness to metformin. The abundance of Bacteroides thetaiotaomicron, considered a representative differential bacterium of metformin responsiveness, and the level of secretory immunoglobulin A (SIgA) in intestinal immunity increased significantly in responder recipient mice following metformin treatment. In contrast, no significant alterations in B. thetaiotaomicron and SIgA were observed in non-responder recipient mice. The study of IgA^-/- mice confirmed that downregulated expression or deficiency of SIgA resulted in non-response to metformin, meaning that metformin was unable to improve dysfunctional glucose metabolism and reduce intestinal and adipose tissue inflammation, ultimately leading to systemic insulin resistance. Furthermore, supplementation with succinate, a microbial product of B. thetaiotaomicron, potentially reversed the non-response to metformin by inducing the production of SIgA. In conclusion, we demonstrated that upregulated SIgA, which could be regulated by succinate, was functionally involved in metformin response through its influence on immune cell-mediated inflammation and insulin resistance. Conversely, an inability to regulate SIgA may result in a lack of response to metformin.

The effects of AER and eGFR on outcomes of CVD in patients with T2DM in an urban community over 8 years of multifactorial treatment: the Beijing Communities Diabetes Study 18 Xue-Lian Zhang,1 Ming-Xia Yuan,1 Gang Wan,2 Guang-Ran Yang,1 Dong-Mei Li,3 Han-Jing Fu,1 Liang-Xiang Zhu,1 Rong-Rong Xie,1 Jian-Dong Zhang,4 Yu-Jie Lv,5 Yu-Ling Li,6 Xue-Ping Du,7 Zi-Ming Wang,8 Xue-Li Cui,9 De-Yuan Liu,10 Ying Gao,11 Shu-Yan Cheng,12 Qian Wang,13 Yu Ji,14 Guang-Wei Li,15,16 Shen-Yuan Yuan1 1Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Medical Records and Statistics Department, Beijing Ditan Hospital, Capital Medical University, Beijing, People’s Republic of China; 3Clinical and Translational Science Institute, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA; 4Department of General Practice, Jinsong Community Health Service Center, Beijing, People’s Republic of China; 5Department of General Practice, Cuigezhuang Community Health Service Center, Beijing, People’s Republic of China; 6Department of General Practice, Xinjiekou Community Health Service Center, Beijing, People’s Republic of China; 7Department of General Practice, Yuetan Community Health Service Center of Fuxing Hospital, Capital Medical University, Beijing, People’s Republic of China; 8Department of General Practice, Jiangtai Community Health Service Center, Beijing, People’s Republic of China; 9Department of General Practice, Sanlitun Community Health Service Center, Beijing, People’s Republic of China; 10Department of General Practice, Zuojiazhuang Community Health Service Center, Beijing, People’s Republic of China; 11Department of General Practice, The First People’s Hospital of Chongwen District, Beijing, People’s Republic of China; 12Department of General Practice, Balizhuang Community Health Service Center, Beijing, People’s Republic of China; 13Department of General Practice, Majiapu Community Health Service Center, Beijing, People’s Republic of China; 14Department of Endocrinology, Beijing Aerospace General Hospital, Beijing, People’s Republic of China; 15Department of Endocrinology, China-Japan Friendship Hospital, Beijing, People’s Republic of China; 16Center of Endocrinology and Cardiovascular Disease, Department of Endocrinology, National Center of Cardiology and Fuwai Hospital, Beijing, People’s Republic of China Objective: It is well known that diabetic kidney disease is a risk factor for cardiovascular diseases (CVD) in patients with type 2 diabetes mellitus (T2DM). In this study, the effects of urine albumin excretion rate (AER) and estimated glomerular filtration rate (eGFR) on CVD outcomes were analyzed in a population of T2DM. Methods: The study was carried out using recorded information of a cohort study. A total of 1,914 patients with T2DM with no prevalent CVD were enrolled in an 8 years prospective study and received multifactorial intervention. The risk of CVD outcomes was assessed according to chronic kidney disease staging, which was categorized using AER (mg/d) and eGFR (mL/min/1.73 m2). The effects of AER and eGFR on risk of CVD onset were also analyzed. Results: During the follow-up period (median 6.8 years), 71 CVD events occurred. At baseline, those with AER ≥300 mg/d and coexisting eGFR 60–89 mL/min/ 1.73 m2 or