Switching from subcutaneous insulin injection to oral vildagliptin administration in hemodialysis patients with type 2 diabetes: a pilot study

Application of machine learning methods for the development of antidiabetic drugs

Diabetes is a chronic non-communicable disease caused by several different routes, which has attracted increasing attention. In order to speed up the development of new selective drugs, machine learning (ML) technology has been applied in the process of diabetes drug development, which opens up a new blueprint for drug design. This review provides a comprehensive portrayal of the application of ML in antidiabetic drug use.

Deprescription in elderly patients with type 2 diabetes mellitus

AIMS

The Primary aim is to verify physicians' adherence to the 2016 Italian diabetes guidelines therapeutic targets, and their habits on deprescription in elderly persons with Type 2 Diabetes Mellitus (T2DM). Secondary aims are the assessment of the potential impact of the targets' changes in 2018 Italian guidelines, and the outcomes of deprescription in the management of T2DM.

METHODS

Observational retrospective cohort study, enrolling persons with T2DM, aged > 75 years, who attended a visit throughout 2017, and a second visit 6 months later in our outpatient clinic.

RESULTS

Of the 387 patients included, 336 (87, 8%) were on target, according to 2016 guidelines. Deprescription was advisable in 62% of patients on target. Among those, 22% were deprescribed. In patients undergoing deprescription, during the following 6 months, no severe hypoglycemia occurred (versus 5 cases in the prior 6 months). Glycated Hemoglobin (HbA1c) increased (p < 0.05) from 47.0 [41.7-51.0] to 53.0 [45.4-59.5] mmol/mol). Applying to the sample the 2018 Italian Guidelines targets, 57.2% would have been on target, 18.5% above, and 24.3% below (needing deprescription).

CONCLUSION

In our study, a minority of suitable patients received deprescription. Deprescription led to a significant reduction in severe hypoglycemia rate, whereas HbA1c remained on target in the majority of cases.

Deintensification in older patients with type 2 diabetes: A systematic review of approaches, rates and outcomes

AIM

To assess deintensification approaches and rates and evaluate the harm and benefits of deintensification with antidiabetic medication and other therapies among older people (≥ 65 years) with type 2 diabetes with or without cardiometabolic conditions.

METHODS

We identified relevant studies in a literature search of MEDLINE, Embase, Web of Science and Cochrane databases to 30 October 2018. Data were extracted on baseline characteristics, details on deintensification and outcomes, and was synthesized using a narrative approach.

RESULTS

Ten studies (observational cohorts and interventional studies) with data on 26 558 patients with comorbidities were eligible. Deintensification approaches included complete withdrawal, discontinuation, reducing dosage, conversion, or substitution of at least one medication, but the majority of studies were based on complete withdrawal or discontinuation of antihyperglycaemic medication. Rates of deintensification approaches ranged from 13.4%-75%. The majority of studies reported no deterioration in HbA1c levels, hypoglycaemic episodes, falls or hospitalizations on deintensification. On adverse events and mortality, no significant differences were observed among the comparison groups in the majority of studies.

CONCLUSION

Available but limited evidence suggests that the benefits of deintensification outweigh the harm in older people with type 2 diabetes with or without comorbidities. Given the heterogeneity of patients with diabetes, further research is warranted on which deintensification approaches are appropriate and beneficial for each specific patient population.

PATHOPHYSIOLOGY AND MANAGEMENT OF HYPOGLYCEMIAIN END-STAGE RENAL DISEASE PATIENTS: A REVIEW

OBJECTIVE

This review focuses on hypoglycemia in patients with end-stage renal disease (ESRD). It discusses the pathophysiology of glucose metabolism in the kidney, the impact of dialysis on glucose and insulin metabolism, and the challenges of glucose monitoring in ESRD. The clinical relevance of these changes is reviewed in relation to altered blood glucose targets and modification of antidiabetes therapy to prevent hypoglycemia. Based on current data and guidelines, recommendations for the outpatient and inpatient setting are provided for diabetes management in ESRD.

METHODS

PubMed, OVID, and Google Scholar were searched to identify related articles through May 2016 using the following keywords: "glucose metabolism," "kidney," "diabetes," "hypoglycemia," "ESRD," and "insulin" in various combinations for this review.

RESULTS

In ESRD, a combination of impaired insulin clearance, changes in glucose metabolism, and the dialysis process make patients vulnerable to low blood glucose levels. Hypoglycemia accounts for up to 3.6% of all ESRD-related admissions. At admission or during hospitalization, hypoglycemia in ESRD has a poor prognosis, with mortality rates reported at 30%. Several guidelines suggest a modified hemoglobin A1c (A1c) goal of 7 to 8.5% (53 to 69 mmol/mol) and an average blood glucose goal of 150 to 200 mg/dL. Noninsulin antidiabetes agents like dipeptidyl peptidase 4 inhibitors, repaglinide, and glipizide in appropriate doses and reduction of insulin doses up to 50% may help decrease hypoglycemia.

CONCLUSION

Patients with ESRD are at high risk for hypoglycemia. Increased awareness by providers regarding these risks and appropriate diabetes regimen adjustments can help minimize hypoglycemic events.

ABBREVIATIONS

ADA = antidiabetes agent BG = blood glucose CKD = chronic kidney disease DPP-4 = dipeptidyl peptidase 4 eGFR = estimated glomerular filtration rate ESRD = end-stage renal disease GFR = glomerular filtration rate HD = hemodialysis NPH = neutral protamine Hagedorn PD = peritoneal dialysis SA = short acting SU = sulfonylurea.