Guang-Ran Yang

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.

Objective There is a lack of consensus on whether a high BMI increases the risk of diabetic retinopathy (DR). We aimed to investigate the association between BMI, overweight, obesity, and DR using the data of diabetes respondents in the 2015 US Behavioral Risk Factor Surveillance System survey. Methods Diabetes respondents aged over 18-year-old with complete information as well as undergone fundus examination in the past 2 years or had been diagnosed with DR were included. Weighted logistic regression analyses were used to identify the association of BMI with DR. Results Among the 21,647 diabetes respondents, 4588 respondents had DR with a weighted prevalence of 22.5%. The mean BMI of all diabetes respondents was 31.50 ± 6.95 kg/m² with 18,498 (86.5%) overweight and 11,353 (54.6%) obese. The mean BMI of the DR group (31.83 ± 7.41 kg/m²) was significantly higher than that of the non-DR group (31.41 ± 6.81 kg/m², P < 0.05). The proportion of obese respondents in the DR group was higher than the non-DR group (54.3%, P < 0.001). The weighted prevalence of DR was 0.8, 13.8, 29.7, and 55.7% for the emaciation group, the normal weight group, the overweight group, and the obesity group, respectively (P < 0.001). Weighted logistic regression analysis showed that both BMI (adjusted OR = 1.004, 95% CI 1.003–1.004) and obesity (adjusted OR = 1.051, 95% CI 1.048–1.055) were associated with DR after adjusting for the confounding variables. However, overweight was not significantly associated with DR. Conclusion The prevalence of DR in the normal weight, overweight, and obesity groups increased gradually. Obesity, rather than overweight, was significantly associated with increased DR prevalence. Abstract Objective There is a lack of consensus on whether a high BMI increases the risk of diabetic retinopathy (DR). We aimed to investigate the association between BMI, overweight, obesity, and DR using the data of diabetes respondents in the 2015 US Behavioral Risk Factor Surveillance System survey. Methods Diabetes respondents aged over 18-year-old with complete information as well as undergone fundus examination in the past 2 years or had been diagnosed with DR were included. Weighted logistic regression analyses were used to identify the association of BMI with DR. Results Among the 21,647 diabetes respondents, 4588 respondents had DR with a weighted prevalence of 22.5%. The mean BMI of all diabetes respondents was 31.50 ± 6.95 kg/m² with 18,498 (86.5%) overweight and 11,353 (54.6%) obese. The mean BMI of the DR group (31.83 ± 7.41 kg/m²) was significantly higher than that of the non-DR group (31.41 ± 6.81 kg/m², P < 0.05). The proportion of obese respondents in the DR group was higher than the non-DR group (54.3%, P < 0.001). The weighted prevalence of DR was 0.8, 13.8, 29.7, and 55.7% for the emaciation group, the normal weight group, the overweight group, and the obesity group, respectively (P < 0.001). Weighted logistic regression analysis showed that both BMI (adjusted OR = 1.004, 95% CI 1.003–1.004) and obesity (adjusted OR = 1.051, 95% CI 1.048–1.055) were associated with DR after adjusting for the confounding variables. However, overweight was not significantly associated with DR. Conclusion The prevalence of DR in the normal weight, overweight, and obesity groups increased gradually. Obesity, rather than overweight, was significantly associated with increased DR prevalence.

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.

Objective: Diabetic retinopathy (DR) was reported to have higher prevalence of coronary heart disease (CHD) and stroke compared with people without DR. However, whether DR had similar effects on CHD and stroke in US adults with diabetes was not well known. This study was to compare the association between DR, CHD and stroke in US adults with diabetes using the 2015 Behavioral Risk Factor Surveillance System (BRFSS). Methods: Respondents with diabetes aged over 18 years and undergone a fundus examination over the past 2 years were included in this study. DR, CHD and stroke were self-reported. A weighted hierarchical logistic regression was used to examine the association of DR with CHD and stroke. Results: A total of 21,049 respondents with diabetes aged over 18 years having undergone a fundus examination over the past 2 years were included in the analysis. There were 4690 people reported having CHD, accounting for the weighted prevalence 22.1%. The weighted prevalence of stroke was 9.6%. There was 28.3% of CHD people who had DR which was higher than that in people without CHD. Differences in weighted frequency distributions of gender, age category (over 45 years or not), current smoking, hypercholesterolemia, hypertension and insulin treatment were significantly different between the CHD and without CHD groups (p< 0.001). Similar results were found in people with stroke. Logistic analysis showed that DR was related to CHD and stroke (crude OR =1.876, 2.263, respectively, p all < 0.001). After adjusting other variables, these associations persisted (Adjusted OR=1.632 (95% CI=1.625– 1.639), 1.846 (95% CI=1.836– 1.856), respectively, p all< 0.001). Conclusion: The BRFSS 2015 data indicated that DR was not only associated with CHD but also associated with stroke in US adults with diabetes, independently of other risk factor. DR might be more strongly associated with stroke than with CHD.

Objectives Diabetes mellitus (DM) and metabolic syndrome (MS) are both associated with heart attack. Evidence regarding which condition—MS or DM—is better associated with heart attack, however, is limited. The purpose of this study is to examine DM and MS, and their comparative associations with heart attack, using the 2015 Behavioral Risk Factor Surveillance System (BRFSS). Design Cross-sectional study. Methods A total of 332 008 subjects aged over 18 years were included in the analysis. All subjects were classified into four groups based on their DM and MS status: neither DM nor MS, DM without MS, MS without DM, and both DM and MS. A weighted hierarchical logistic regression was used to examine the difference between the four groups in their association with the risk of a heart attack. Results Differences in weighted frequency distributions of gender, age category (over 45 years or not), smoking status, education, race, physical activity and daily vegetable and fruit consumption were significantly different across the four groups (pConclusion The BRFSS 2015 data indicated that MS without DM and DM without MS had comparable effects on heart attack, and the odds of risk are doubled than US adults with neither DM nor MS.

Aims: Neck circumference (NC) was found to be related to the risk factors for coronary heart disease (CHD). However, the effects of NC on CHD are still controversial. To evaluate the relationship between NC and CHD, a meta-analysis of observational studies was performed. Method: Eligible studies on the association between NC and CHD were searched in Medline, Embase, Ovid, and Web of Science databases published in English from January 1980 to December 2016. Moreover, studies published in Chinese in Wanfang and China Hospital Knowledge databases were also searched. Random effects models in the metafor package in statistical analysis software R 3.3.3 were used for the meta-analysis. Heterogeneity was analyzed with Q statistics. Results: Eight studies were selected for the meta-analysis. A larger NC was associated with a higher prevalence of CHD (OR = 1.18, 95% CI 1.04–1.34, p = 0.0108). The eight studies were further divided into three subgroups according to the criteria for diagnosing CHD. In the subgroup of coronary angiography, NC was also found to be associated with the prevalence of CHD with low heterogeneity (OR = 1.17, 95% CI 1.07–1.28, p = 0.0007, I2 = 17.02%). However, in the subgroup of computed tomography or past history, no association between NC and CHD was found. In addition, subgroup analyses were also conducted according to the regions of the study. No association between NC and CHD was identified in either Chinese studies or Brazil studies (OR = 1.20, 95% CI 0.96–1.49; OR = 1.31, 95% CI 0.82–2.09, respectively). Conclusion: Larger NC is associated with increased risk of CHD, especially when coronary angiography was taken to diagnose CHD.