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OBJECTIVE To evaluate the performance of a real-time continuous glucose monitor (CGM) in individuals with diabetes on peritoneal dialysis (PD). RESEARCH DESIGN AND METHODS Thirty participants with type 2 diabetes on continuous ambulatory PD wore a Guardian Sensor 3 on the upper arm paired with Guardian Connect for 14 days. We compared CGM readings against Yellow Springs Instrument (YSI) venous glucose during an 8-h in-clinic session with glucose challenge. RESULTS The mean absolute relative difference (MARD) was 10.4% (95% CI 9.6, 11.7) from 941 CGM-YSI matched pairs; 81.3% of readings were within %15/15 of YSI values in the full glycemic range. Consensus error grid analysis showed 99.9% of sensor values in zones A and B. There were no correlations between pH, uremia, hydration status, and MARD. CONCLUSIONS We showed satisfactory performance of a real-time CGM sensor in PD patients with diabetes, supporting future use to facilitate treatment decisions.

IntroductionObesity and diabetes are public health concerns worldwide, but few studies have examined the habitual intake of minerals on body composition in people with prediabetes.MethodsIn this prospective cross-sectional study, 155 Chinese subjects with IGT [median age: 59 (53–62) years, 58% female] had an assessment of body composition including body fat percentage, oral glucose tolerance tests (OGTT), Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and 3-day food records from nutritional programme analysis.ResultsDietary intake of minerals was negatively correlated with body fat. People with obesity had the lowest daily consumption of iron median (IQR) 10.3 (6.9–13.3) mg, magnesium 224 (181–282) mg, and potassium 1973 (1563–2,357) mg when compared to overweight [10.5 (8.0–14.5) mg, 273 (221–335) mg, and 2,204 (1720–2,650) mg] and normal weight individuals [13.2 (10.0–18.6) mg, 313 (243–368) mg, and 2,295 (1833–3,037) mg] (p = 0.008, ConclusionDietary magnesium and potassium intake may be associated with lower body fat in people with impaired glucose tolerance. Inadequate dietary mineral intake may play contribute to obesity and metabolic disorders independent of macronutrients and fibre consumption.

Introduction To compare glycemic variability (GV) and time in range (TIR) in Chinese patients with type 2 diabetes (T2D) initiated on once-daily bedtime insulin glargine 300U/ml (Gla-300) versus neutral protamine Hagedorn (NPH) insulin using continuous glucose monitoring (CGM). Methods This was a 24-week, open-label exploratory study with 1:1 randomization comparing patient-adjusted titration of Gla-300 (n = 23) versus NPH (n = 23) at bedtime in insulin-naïve T2D patients on maximum oral glucose-lowering drugs. The starting dose was 0.2 U/kg/day and with self-titration of one unit per week to achieve a target fasting glucose of 4.4–6 mmol/l, without hypoglycemia. Participants had masked CGM at baseline, weeks 11 and 24. The primary outcome was between-treatment differences in CGM glucose standard deviation (SD) at week 24. Results HbA1c at week 24 were similar, with 21% of Gla-300 versus 4% of NPH-treated patients achieving HbA1c

The non-absorbable disaccharide lactulose is mostly used in the treatment of various gastrointestinal disorders such as chronic constipation and hepatic encephalopathy. The mechanism of action of lactulose remains unclear, but it elicits more than osmotic laxative effects. As a prebiotic, lactulose may act as a bifidogenic factor with positive effects in preventing and controlling diabetes. In this review, we summarized the current evidence for the effect of lactulose on gut metabolism and type 2 diabetes (T2D) prevention. Similar to acarbose, lactulose can also increase the abundance of the short-chain fatty acid (SCFA)-producing bacteria Lactobacillus and Bifidobacterium as well as suppress the potentially pathogenic bacteria Escherichia coli. These bacterial activities have anti-inflammatory effects, nourishing the gut epithelial cells and providing a protective barrier from microorganism infection. Activation of peptide tyrosine tyrosine (PYY) and glucagon-like peptide 1 (GLP1) can influence secondary bile acids and reduce lipopolysaccharide (LPS) endotoxins. A low dose of lactulose with food delayed gastric emptying and increased the whole gut transit times, attenuating the hyperglycemic response without adverse gastrointestinal events. These findings suggest that lactulose may have a role as a pharmacotherapeutic agent in the management and prevention of type 2 diabetes via actions on the gut microbiota.

In developed countries, diabetes is the leading cause of chronic kidney disease (CKD) and accounts for 50% of incidence of end stage kidney disease. Despite declining prevalence of micro- and macrovascular complications, there are rising trends in renal replacement therapy in diabetes. Optimal glycemic control may reduce risk of progression of CKD and related death. However, assessing glycemic control in patients with advanced CKD and on dialysis (G4-5) can be challenging. Laboratory biomarkers, such as glycated haemoglobin (HbA1c), may be biased by abnormalities in blood haemoglobin, use of iron therapy and erythropoiesis-stimulating agents and chronic inflammation due to uraemia. Similarly, glycated albumin and fructosamine may be biased by abnormal protein turnover. Patients with advanced CKD exhibited heterogeneity in glycemic control ranging from severe insulin resistance to ‘burnt-out’ beta-cell function. They also had high risk of hypoglycaemia due to reduced renal gluconeogenesis, frequent use of insulin and dysregulation of counterregulatory hormones. Continuous glucose monitoring (CGM) systems measure glucose in interstitial fluid every few minutes and provide an alternative and more reliable method of glycemic assessment, including asymptomatic hypoglycaemia and hyperglycaemic excursions. Recent international guidelines recommended use of CGM-derived Glucose Management Index (GMI) in patients with advanced CKD although data are scarce in this population. Using CGM, patients with CKD were found to experience marked glycemic fluctuations with hypoglycemia due to loss of glucose and insulin during haemodialysis (HD) followed by hyperglycemia in the post-HD period. On the other hand, during peritoneal dialysis, patients may experience glycemic excursions with influx of glucose from dialysate solutions. These undesirable glucose exposure and variability may accelerate decline of residual renal function. Although CGM may improve the quality of glycemic monitoring and control in populations with CKD, further studies are needed to confirm the accuracy, optimal mode and frequency of CGM as well as their cost-effectiveness and user-acceptability in patients with advanced CKD and dialysis.

Patients with moderate-to-advanced diabetic kidney disease (DKD) are at high risk of hypoglycemia. There are few studies evaluating personal continuous glucose monitoring (CGM) use in this group. We investigated whether flash glucose monitoring (FGM) was effective in reducing hypoglycemia compared with standard self-monitoring of blood glucose (SMBG) in individuals with chronic kidney disease (CKD) stages 3b-5 (non-dialysis). This was a single-centre, parallel-group randomised clinical trial of 94 adults with diabetes and CKD [G3b (n=36), G4 (n=44) and G5 (n=14)] and a history of hypoglycemia. Patients were randomly assigned 1:1 to FGM or SMBG for 16 weeks. Both groups received structured education on hypoglycemia and monthly reviews by healthcare professionals. Blinded CGM was performed at baseline and end of study in both groups. Patients were aged (mean±SD) 65.4±9.0 years with estimated glomerular filtration rate (eGFR) 26.1±9.6 ml/min/1.73m2, HbA1c 7.4±0.8% and median duration of diabetes for 20 years. 93 patients had type 2 diabetes and 99% were on insulin. After 16 weeks, percentage time Disclosure J. Ling: None. J. K. Ng: None. R. C. Ma: Other Relationship; Self; AstraZeneca, Medtronic, Research Support; Self; AstraZeneca, Bayer Healthcare Pharmaceuticals Inc., Novo Nordisk, Pfizer Inc., Sanofi-Aventis, Tricida, Inc. A. P. Kong: Advisory Panel; Self; Lilly Diabetes, Speaker’s Bureau; Self; Abbott, AstraZeneca, Bayer Healthcare Pharmaceuticals Inc., Lilly Diabetes, Sanofi, Stock/Shareholder; Self; Aptorum. A. Luk: None. C. Szeto: None. J. C. Chan: Consultant; Self; Bayer AG, Boehringer Ingelheim International GmbH, MSD Corporation, Sanofi, Other Relationship; Self; Asia Diabetes Foundation, GemVCare, Research Support; Self; Applied Therapeutics, AstraZeneca, Hua Medicine, Lilly Diabetes, Merck KGaA. E. Chow: Research Support; Self; Medtronic, Speaker’s Bureau; Self; Novartis Pharmaceuticals Corporation, Sanofi-Aventis. Funding Hong Kong College of Physicians; University Grants Committee Research Matching Grant Scheme

Background: Increased glycemic variability (GV) is thought to represent abnormalities of glucose homeostasis in the progression from prediabetes to diabetes. We evaluated the association between GV, beta-cell function and glucagon-like peptide 1 (GLP-1) response among individuals with prediabetes. Methods: 61 individuals with prediabetes (age Mean±SD 56±9 years, 31 male) each wore a blinded continuous glucose monitor (Medtronic iPro2) for 72 hours. Standard deviation (SD) and coefficient of variation (CV) were calculated from CGM glucose. Participants underwent a 2-hour mixed-meal tolerance test with measurement of insulin, C-peptide, GLP-1 for modelling of beta-cell function. Results: Early and late GLP-1/glucose ratio were positively correlated with CGM sensor glucose SD (r = 0.29, p=0.02 and r= 0.28 p = 0.048 respectively). Beta-cell glucose sensitivity (βCGS) was negatively correlated with SD (r = -0.28, p = 0.03). In multivariate analyses, only βCGS and GLP-1 response were significant predictors of SD, while early phase insulin and insulin secretion rate predicted average sensor glucose but not indices of GV. Conclusions: Higher GLP-1 response and reduced βCGS are associated with greater glucose fluctuations in the prediabetic range. Disclosure E. Chow: Research Support; Self; Powder Pharmaceuticals Inc., Sanofi-Aventis. A. Luk: None. J. Ling: None. A.P. Kong: Advisory Panel; Self; Lilly Diabetes. Research Support; Self; AstraZeneca, Lilly Diabetes. Speaker's Bureau; Self; Abbott. Other Relationship; Self; AstraZeneca, Novartis Pharmaceuticals Corporation, Sanofi. R.C. Ma: Advisory Panel; Self; Boehringer Ingelheim International GmbH. Research Support; Self; AstraZeneca, Bayer AG, Pfizer Inc. Stock/Shareholder; Self; GemVCare. Other Relationship; Self; AstraZeneca. E. Ferrannini: None. J.C. Chan: Board Member; Self; Asia Diabetes Foundation. Consultant; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Medtronic, Merck Sharp & Dohme Corp., Sanofi-Aventis. Research Support; Self; Amgen Inc., AstraZeneca, Lee Powder, Lilly Diabetes, Pfizer Inc., Sanofi-Aventis. Speaker's Bureau; Self; Ascensia Diabetes Care. Stock/Shareholder; Self; GemVCare.