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Sodium-glucose cotransporter 2 inhibitor luseogliflozin improves glycaemic control, assessed by continuous glucose monitoring, even on a low-carbohydrate diet.

ABSTRACT: This randomized, double-blind, placebo-controlled, crossover study was the first to determine the effects of luseogliflozin in combination with a low-carbohydrate diet (LCD) on 24-h glucose variability, assessed by continuous glucose monitoring (CGM). A total of 18 Japanese patients with type 2 diabetes were randomized into two groups, in which patients first received luseogliflozin 2.5?mg once daily then placebo for 8?days each, or vice versa. Patients took luseogliflozin or placebo with a normal-carbohydrate diet (NCD) on day 7 and with the LCD on day 8. CGM was performed on both days. Luseogliflozin significantly reduced glucose exposure in terms of the area under the curve over the course of 24?h when administered with the NCD (difference vs placebo: -555.6?mg/dl·h [1?mg/dl?=?0.0556?mmol/l]; p?

SUBMITTER: Nishimura R

PROVIDER: S-EPMC5066655 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Sodium-glucose cotransporter 2 inhibitor luseogliflozin improves glycaemic control, assessed by continuous glucose monitoring, even on a low-carbohydrate diet.

Nishimura R R Omiya H H Sugio K K Ubukata M M Sakai S S Samukawa Y Y

Diabetes, obesity & metabolism 20160115 7

This randomized, double-blind, placebo-controlled, crossover study was the first to determine the effects of luseogliflozin in combination with a low-carbohydrate diet (LCD) on 24-h glucose variability, assessed by continuous glucose monitoring (CGM). A total of 18 Japanese patients with type 2 diabetes were randomized into two groups, in which patients first received luseogliflozin 2.5 mg once daily then placebo for 8 days each, or vice versa. Patients took luseogliflozin or placebo with a norm ...[more]

PMID: 26639943

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Project description:INTRODUCTION:The aim of this study was to objectively analyze the correlation between dietary components and blood glucose variation by means of continuous glucose monitoring (CGM). METHODS:Patients with type 1 diabetes mellitus (T1DM) who received CGM to manage their blood glucose levels were enrolled into the study, and the components of their total caloric intake were analyzed. Glycemic variation parameters were calculated, and dietary components, including percentages of carbohydrate, protein and fat in the total dietary intake, were analyzed by a dietitian. The interaction between parameters of glycemic variability and dietary components was analyzed. RESULTS:Sixty-one patients with T1DM (33 females, 28 males) were enrolled. The mean age of the participants was 34.7 years, and the average duration of diabetes was 14 years. Glycated hemoglobin before CGM was 8.54%. Participants with a carbohydrate intake that accounted for < 50% of their total caloric intake had a longer DM duration and a higher protein and fat intake than did those with a carbohydrate intake that accounted for ≥ 50% of total caloric intake, but there was no between-group difference in total caloric intake per day. The group with a carbohydrate intake that accounted for < 50% of their total caloric intake also had lower nocturnal continuous overlapping net glycemic action (CONGA) 1, - 2 and - 4 values. The percentage of protein intake had a slightly negative correlation with mean amplitude of glycemic excursions (MAGE) (r = - 0.286, p < 0.05) and a moderately negative correlation with coefficient of variation (CV) (r = 0.289, p < 0.05). One additional percentage of protein calories of total calories per day decreased the MAGE to 4.25 mg/dL and CV to 0.012 (p < 0.05). The optimal dietary protein percentage for MAGE < 140 mg/dL was 15.13%. The performance of predictive models revealed the beneficial effect of adequate carbohydrate intake on glucose variation when combined with protein consumption. CONCLUSIONS:Adequate carbohydrate consumption-but not more than half the daily total calories-combined with protein calories that amount to approximately 15% of the daily caloric intake is important for glucose stability and beneficial for patients with T1DM.

Project description:IntroductionWe investigated the impact of reduced renal function on 24-h glucose variability in Japanese patients with type 2 diabetes mellitus (T2DM) treated with luseogliflozin.MethodsIn this double-blind, placebo-controlled, crossover study, 37 Japanese patients with T2DM [glycated hemoglobin (HbA1c) 7.0-10.0%] and estimated glomerular filtration rate (eGFR) ≥45 mL/min/1.73 m(2) were randomized into two groups in which patients first received luseogliflozin then placebo, or vice versa, for 7 days each. Twenty-four-hour glucose variability was measured on day 7 in each period and was compared among patients divided into three groups according to their baseline eGFR (mL/min/1.73 m(2)): normal (≥90; n = 13; normal group), normal-to-mildly reduced renal function (≥75 to <90; n = 12; normal-mild group), and mild-to-moderately reduced renal function (<75; n = 9; mild-moderate group).ResultsThe mean [95% confidence interval (CI)] placebo-subtracted 24-h cumulative urinary glucose excretion (g) was 82.1 (72.7, 91.5), 82.5 (73.4, 91.5), and 62.2 (51.2, 73.3); the placebo-subtracted 24-h mean glucose concentration (mg/dL) was -24.39 (-32.53, -16.26), -28.28 (-39.35, -17.22), and -11.53 (-23.93, 0.86); and the placebo-subtracted peak postprandial glucose (mg/dL) was -26.9 (-46.9, -6.9), -38.1 (-59.6, -16.6), and 1.5 (-25.5, 28.4) in the normal, normal-mild, and mild-moderate groups, respectively. The mean lowest glucose concentrations (placebo vs. luseogliflozin, mg/dL) decreased to similar levels in the normal (115.4 vs. 93.4), normal-mild (121.0 vs. 97.9), and mild-moderate (104.0 vs. 91.1) groups.ConclusionThis post hoc subanalysis revealed that although mild-to-moderately reduced renal function attenuated the glucose-lowering effects of luseogliflozin on peak postprandial glucose, it did not attenuate the effects of luseogliflozin on fasting glucose. These findings may explain the smaller increase in urinary glucose excretion in these patients relative to patients with normal renal function or normal-to-moderately reduced renal function. Further studies may be needed to examine these findings in large populations of patients with T2DM and reduced renal function.Trial registrationJapicCTI-142548.FundingTaisho Pharmaceutical Co., Ltd.

Project description:BackgroundContinuous glucose monitors (CGMs) provide high-frequency information regarding daily glucose variation and are recognised as effective for improving glycaemic control in individuals living with diabetes. Despite increased use in individuals with non-diabetic blood glucose concentrations (euglycemia), their utility as a health tool in this population remains unclear.ObjectivesTo characterise variation in time in range (TIR) and glycaemic variability in large populations without diabetes or impaired glucose tolerance; describe associations between CGM-derived glycaemic metrics and metabolic and cardiometabolic health traits; identify key diet and lifestyle factors associated with TIR and glycaemic variability.DesignGlycaemic variability (coefficient of variation) and time spent in both the ADA secondary target range (TIRADA; 3.9-7.8 mmol/L) and a more stringent range (TIR3.9-5.6; 3.9-5.6 mmol/L) were calculated during free-living in PREDICT 1, PREDICT 2, and PREDICT 3 euglycaemic community-based volunteer cohorts. Associations between CGM derived glycaemic metrics, markers of cardiometabolic health, diet (food frequency questionnaire and logged diet records), diet-habits, and lifestyle were explored.ResultsData from N=4135 participants (Mean SD; Age: 47 12 y; Sex: 83% Female, BMI: 27 6 kg/m2). Median glycaemic variability was 14.8% (IQR 12.6-17.6%), median TIRADA was 95.8% (IQR 89.6-98.6%) and TIR3.9-5.6 was 75.0% (IQR 64.6-82.8%). Greater TIR3.9-5.6 was associated with lower HbA1c, ASCVD 10y risk and HOMA-IR (all p < 0.05). Lower glycaemic variability was associated with lower % energy derived from carbohydrate (rs: 0.17, p < 0.01), ultra-processed foods (NOVA 4, % EI; rs: 0.12, p = 0.01) and a longer overnight fasting duration (rs: -0.10, p = 0.01).ConclusionsA stringent TIR target provides sensitivity to detect changes in HOMA-IR, ASCVD 10 y risk and HbA1c that were not detected using ADA secondary targets. Associations among TIR, glycaemic variability, dietary intake (e.g. carbohydrate and protein) and habits (e.g. nocturnal fasting duration) highlight potential strategic targets to improve glycaemic metrics derived from continuous glucose monitors.

Dataset Information

Sodium-glucose cotransporter 2 inhibitor luseogliflozin improves glycaemic control, assessed by continuous glucose monitoring, even on a low-carbohydrate diet.

Publications

Sodium-glucose cotransporter 2 inhibitor luseogliflozin improves glycaemic control, assessed by continuous glucose monitoring, even on a low-carbohydrate diet.

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