Search

Background: Not many large‐sample investigations are available that compare the potency of the relationship of remnant cholesterol (RC) and other lipid parameters with diabetes and prediabetes. The goals of our study are to discover the relationship between RC and prediabetes, diabetes, and insulin resistance (IR) and to investigate RC, high‐density lipoprotein cholesterol (HDL‐C), non‐HDL‐C, triglycerides (TG), low‐density lipoprotein cholesterol (LDL‐C), total cholesterol (TC), TC/HDL‐C, LDL‐C/HDL‐C, and TG/HDL‐C, which are the lipid parameters that are most positively related to diabetes, prediabetes, and IR. Methods: This research enrolled 36 684 subjects from China's eight provinces. We employed multiple logistic regression analysis for testing the relationship between lipid parameters and diabetes, prediabetes, and IR. Results: After adjusting for potential confounders, and comparing the results with other lipid parameters, the positive relationship between RC and diabetes (odds ratio [OR] 1.417, 95% confidence interval [CI]: 1.345–1.492), prediabetes (OR 1.555, 95% CI: 1.438–1.628), and IR (OR 1.488, 95% CI: 1.404–1.577) was highest. RC was still related to diabetes, prediabetes, and IR even when TG <2.3 mmol/L (diabetes: OR 1.256, 95% CI: 1.135–1.390; prediabetes: OR 1.503, 95% CI: 1.342–1.684; and IR: OR 1.278, 95% CI: 1.140–1.433), LDL‐C <2.6 mmol/L (diabetes: OR 1.306, 95% CI: 1.203–1.418; prediabetes: OR 1.597, 95% CI: 1.418–1.798; and IR: OR 1.552, 95% CI: 1.416–1.701), or HDL‐C ≥1 mmol/L (diabetes: OR 1.456, 95% CI: 1.366–1.550; prediabetes: OR 1.553, 95% CI: 1.421–1.697; and IR: OR 1.490, 95% CI: 1.389–1.598). Conclusion: RC is more positively related to diabetes, prediabetes, and IR than conventional lipids and lipid ratios in the general population, the relationships between RC and diabetes, prediabetes, and IR are stable, even if HDL‐C, LDL‐C, or TG are at appropriate levels. [ABSTRACT FROM AUTHOR]

Background: Novel diabetes phenotypes were proposed by the Europeans through cluster analysis, but Chinese community diabetes populations might exhibit different characteristics. This study aims to explore the clinical characteristics of novel diabetes subgroups under data‐driven analysis in Chinese community diabetes populations. Methods: We used K‐means cluster analysis in 6369 newly diagnosed diabetic patients from eight centers of the REACTION (Risk Evaluation of cAncers in Chinese diabeTic Individuals) study. The cluster analysis was performed based on age, body mass index, glycosylated hemoglobin, homeostatic modeled insulin resistance index, and homeostatic modeled pancreatic β‐cell functionality index. The clinical features were evaluated with the analysis of variance (ANOVA) and chi‐square test. Logistic regression analysis was done to compare chronic kidney disease and cardiovascular disease risks between subgroups. Results: Overall, 2063 (32.39%), 658 (10.33%), 1769 (27.78%), and 1879 (29.50%) populations were assigned to severe obesity‐related and insulin‐resistant diabetes (SOIRD), severe insulin‐deficient diabetes (SIDD), mild age‐associated diabetes mellitus (MARD), and mild insulin‐deficient diabetes (MIDD) subgroups, respectively. Individuals in the MIDD subgroup had a low risk burden equivalent to prediabetes, but with reduced insulin secretion. Individuals in the SOIRD subgroup were obese, had insulin resistance, and a high prevalence of fatty liver, tumors, family history of diabetes, and tumors. Individuals in the SIDD subgroup had severe insulin deficiency, the poorest glycemic control, and the highest prevalence of dyslipidemia and diabetic nephropathy. Individuals in MARD subgroup were the oldest, had moderate metabolic dysregulation and the highest risk of cardiovascular disease. Conclusion: The data‐driven approach to differentiating the status of new‐onset diabetes in the Chinese community was feasible. Patients in different clusters presented different characteristics and risks of complications. [ABSTRACT FROM AUTHOR]

Abstract It is already known that there are many factors responsible for the successful formation of a graft union. However, the role of light has been little studied. In an anatomical study, Scanning Electronic Microscope (SEM) was used to explore the effects of different light-emitting diodes (LEDs) on graft union formation in grafted tomato. In addition, the expression genes related to Auxin hormone signaling pathway (SAUR67, AUX1, ARF30, and LAX3) was investigated. The obtained results showed that the concrescence process occurred faster under R7:B3 light conditions, as compared to blue (B) and white fluorescent (WFL) lights. Red light application caused a delay in the vascular tissue differentiation, which may lead to callus development on both sides, causing junctional failure and resulting in ineffective graft junctional arrangement. The expression of genes related to Auxin hormone significantly increased by R7:B3 application. We suggest that LED spectra affects the graft development of tomato plants and can improve the performance of grafted tomato seedlings.