Resources

Although the risk of end-stage renal disease remains high for individuals with type 1 diabetes, lifetime likelihood of ESRD has substantially decreased over the last 50 years.

Study in England links high HbA1c levels to long-term cognitive decline.

Insulin resistance has been proposed as one of the causes of poor glycemic control in youth with type 1 diabetes who have excess weight or obesity.

The suggested hierarchy of usage (AACE Algorithm) for a patient on metformin who has not reached goal according to the new 2018 AACE goal in 3 months is: A. GLP-1RA, SGLT-2i, TZD, Basal Insulin, DPP4i B. SGLT-2i, GLP-1RA, TZD. DPP4i, Basal Insulin C. GLP-IRA, DPP4i, GLP-1RA, Bromocriptine QR D. SGLT-2i, TZD, GLP-1RA, Colesevelem E. SGLT-21, GLP-1RA, Basal Insulin, Colesevelem Follow the link for the answer.

Do you prescribe sleep for your patients? Follow the link to see how you and your colleagues compare.

In part 7, the conclusion of this Exclusive Interview, Dr. Ray Kausik talks with Diabetes in Control Publisher Steve Freed during the ADA 2017 convention in San Diego, CA about the side effects so far of the new inhibitor drug and watching for reactions.

In this week's Homerun Slides, the impact of diabetes medication on the heart and other organs, and the frequency of hypoglycemia.

OSA epidemiology and risk factors: The prevalence of OSA varies considerably between studies, mainly due to differences in the population studied, study designs, and the method and criteria used to diagnose OSA. A prevalence of 4% in men and 2% in women has traditionally been quoted in many populations. The prevalence from three well-conducted studies with similar design from Wisconsin, Pennsylvania, and Spain showed an OSA prevalence of 17–26% in men and 9–28% in women and a prevalence of 9–14% and 2–7% for men and women with moderate to severe OSA. These studies used a two-stage sampling design which allows some degree of estimate of the “self-selection” bias which is usually a significant problem in OSA studies.

In the last four issues, we discussed a new software that can go through over 6 million possible treatments using anywhere from 1 to 5 drugs for an effective treatment for type 2 diabetes. Along with determining the best treatment for the patient, it includes the ability to put in what the patient can afford in their budget. Dr. John Interlandi, one of our readers, has submitted a counterpoint to using this software to decide the best treatment options. In Part 1, Dr. Interlandi shares his concerns about the use of AI in medical decision making. 

Short-term efficacy and safety of SGLT2 inhibitors have been well established, but what happens with long-lasting use of these medications?