Summary of recent progress in the field of diabetes research (11.29)
November 29, 2017 Source: WuXi PharmaTech
Window._bd_share_config={ "common":{ "bdSnsKey":{ },"bdText":"","bdMini":"2","bdMiniList":false,"bdPic":"","bdStyle":" 0","bdSize":"16"},"share":{ }};with(document)0[(getElementsByTagName('head')[0]||body).appendChild(createElement('script')) .src='http://bdimg.share.baidu.com/static/api/js/share.js?v=89860593.js?cdnversion='+~(-new Date()/36e5)];1. New discovery feedback mechanism is expected to improve insulin levels in diabetic patients
Type 2 diabetes affects more than 30 million Americans, and chronic tissue inflammation is the underlying cause of insulin resistance and type 2 diabetes. Inflammation in the islets promotes obesity in patients and leads to failure of beta-cell secretion of insulin, but its regulatory mechanisms are still not fully understood. Recently, researchers at the University of Basel and the University Hospital Basel in Switzerland have discovered a feedback mechanism that has the potential to help overweight patients maintain insulin secretion. The paper was published in Immunity.
In this study, the researchers focused on ILC2 immune cells recently found in the pancreas. ILC2 cells are type 2 innate lymphoid cells. The researchers found that in the case of diabetes, proteins called interleukin-33 (IL-33) are activated in ILC2 cells. IL-33 is produced by islet mesenchymal cells and is enhanced by the diabetic environment (glucose, IL-1β and palmitic acid). Activated IL-33 promotes retinoic acid production in macrophages and dendritic cells via ICL2 cells in islets, and local retinoic acid can signal beta cells to increase insulin secretion, so may be used To improve the poor secretion of insulin in overweight patients.
The investigators also found that this IL-33 - ILC2 axis was activated after acute beta cell stress, but was dysfunctional during chronic obesity. Therefore, injection of IL-33 can improve islet function in obese mice. The results of this research are expected to bring new hope for treatment to diabetic patients.
2. Three molecules are expected to become predictors of insulin resistance
Diabetes can occur when the body is unable to properly metabolize glucose. Under normal circumstances, insulin regulates glucose metabolism, but this regulatory mechanism has become increasingly inefficient in some people, called insulin resistance. Recently, a team led by researchers from the University of Sydney in Australia identified three molecules that accurately indicate insulin resistance, a measure of pre-diabetic metabolic syndrome. The study was conducted in mice and has the potential to help doctors detect early pre-diabetes and is expected to provide patients with more personalized and effective treatments in the future. The paper was published in the Journal of Biological Chemistry.
Researchers combine machine learning with metabolomics techniques that detect various molecules in cells to identify specific molecules in mouse cells. They fed mice with a high-fat diet or a regular diet and then measured metabolomics of skeletal muscle. They found that there were large differences between mouse strains, recipes, and even different individuals. The computer found 113 molecules associated with the metabolic phenotype, and then screened them by machine learning, leaving the last three molecules with the highest insulin sensitivity: C22:1-CoA, C2-carnitine, and C16-ceramide . The researchers also found that when considering these three molecules simultaneously, they indicate insulin sensitivity is more accurate than considering each molecule individually. Moreover, C22:1-CoA was 2.3 times higher in insulin-resistant mice than in normal mice, and it was significantly associated with insulin resistance.
“We identified three molecular markers that can diagnose insulin resistance or pre-diabetes. This study suggests that there may be multiple factors that contribute to pre-diabetes, which is why many traditional methods fail to identify such highly predictive indicators. Even these three molecules are more predictive than the independent predictive power of the merger," said Jacqueline Stöckli, one of the main authors of the paper. "The next step in the research is to use these techniques to reveal at a large population level. The full range of pathways and factors for pre-diabetes, including genetic, environmental, and possible epigenetic effects."
3. Canagliflozin reduces patient admission risk by 43%
Recently, Janssen's analysis of real-world data from patients with type 2 diabetes and confirmed cardiovascular disease (CVD) showed that after an average of 1.6 years of follow-up, INVOKANA® (canagliflozin) or another sodium was used. Adult patients treated with glucose cotransporter-2 inhibitor (SGLT2i) were hospitalized for mortality (ACM) and heart failure (HHF) compared with similar patients who received non-SGLT2i medication for intention to treat (ITT) The risk is reduced by 43%. In addition, patients treated with SGLT2i had a 33% lower risk of cardiovascular adverse effects (MACE), including ACM, nonfatal myocardial infarction (MI), and nonfatal stroke. This new retrospective study was based on EASEL clinical trial data and published in the journal Circulation.
Diabetes is a growing global pandemic, and as of 2015, 400 million adults worldwide were affected by diabetes. Among them, there are about 100 million adults with diabetes in China. People with type 2 diabetes are resistant to insulin or are unable to secrete enough insulin to maintain normal blood sugar levels.
The clinical study, called EASEL, consisted of two cohorts: 12,629 patients treated with SGLT2i and 12,629 patients treated with non-SGLT2i diabetes medication, both on a standard basis. The study used a model that covered a variety of characteristics to match the propensity scores of the two groups, including demographics, duration of diabetes, baseline comorbidities and medications, diagnosis and procedures, and various health care. Use of resources. In the SGLT2i cohort, the proportion of patients using canagliflozin, empagliflozin, and dapagliflozin was 58.1%, 26.4%, and 15.5%, respectively. According to the ITT method, the median follow-up time for the two cohorts was 1.6 years.
The primary clinical endpoint of this real-world analysis is the combination of ACM and HHF. Other CVD-related adverse events were also assessed as secondary endpoints. The ITT analysis showed that the SGLT2i cohort showed significant reductions in risk at multiple clinical endpoints compared to the non-SGLT2i cohort, including:
- The combined risk of ACM and HHF (primary endpoint) was reduced by 43%: 1.73 vs. 3.01 (per 100 people); hazard ratio (HR) 0.57, 95% confidence interval (CI): 0.50 - 0.65; P < 0.0001.
- ACM decreased by 43% (secondary endpoint): 1.29 vs. 2.26 (per 100 people); HR 0.57, CI: 0.49 -0.66; P < 0.0001.
- HHF decreased by 43% (secondary endpoint): 0.51 vs. 0.90 (per 100 people); HR 0.57, CI: 0.45 - 0.73; P < 0.0001.
- MACE decreased by 33% (secondary endpoint): 2.31 vs. 3.45 (per 100 people); HR 0.67, CI: 0.60-0.75; P < 0.0001. The incidence of individual endpoint events in nonfatal myocardial infarction and nonfatal stroke did not differ significantly between the two groups.
- The MACE and HHF (secondary endpoint) composites were reduced by 34%: 2.72 vs. 4.11 (per 100 people); HR 0.66, CI: 0.60 - 0.74; P < 0.0001.
Dr. Paul Burton, vice president of medical affairs at Janssen, said: "This is the first to demonstrate that adult patients with type 2 diabetes can reduce the risk of major adverse cardiovascular events (MACE) with SGLT2i in the context of established cardiovascular disease (CVD). Real-world research. These findings will further strengthen the positive impact of INVOKANA® and SGLT2is on reducing cardiovascular risk."
Reference materials:
[1] Diabetes: Immune system can regulate insulin
[2] Pre-diabetes discovery marks step towards precisionmedicine
[3] Janssen's New Real-World Analysis Shows INVOKANA (canagliflozin) and Other SGLT2 Inhibitors Reduced the Risk of Death and Cardiovascular Events Compared to Other Diabetes Medicines
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