591-P: Glucose Dynamics during OGTT Identify Metabolic Subphenotypes in Individuals with Glucose Dysregulation

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 591-P
Author(s):  
AHMED A. METWALLY ◽  
DALIA PERELMAN ◽  
HEYJUN PARK ◽  
ALESSANDRA CELLI ◽  
TRACEY MCLAUGHLIN ◽  
...  
2007 ◽  
Vol 46 (02) ◽  
pp. 222-226 ◽  
Author(s):  
H. Ogata ◽  
K. Tokuyama ◽  
S. Nagasaka ◽  
A. Ando ◽  
I. Kusaka ◽  
...  

Summary Objectives : Our objective is to investigate diabetes- related alteration of glucose control in diurnal fluctuations in normal daily life by detrended fluctuation analysis (DFA). Methods : The fluctuations of glucose of 12 non-diabetic subjects and 15 diabetic patients were measured using a continuous glucose monitoring system (CGMS) over a period of one day. The glucose data was calculated by the DFA method, which is capable of revealing the presence of long-range correlations in time series with inherent non-stationarity. Results : Compared with the non-diabetic subjects, the mean glucose level and the standard deviation are significantly higher in the diabetic group.The DFA exponent α is calculated, and glucose time series are searched for the presence of negatively (0.5 < α <1.5) or positively (1.5 < α) correlated fluctuations. A crossover phenomenon, i.e. a change in the level of correlations, is observed in the non-diabetic subjects at about two hours; the net effects of glucose flux/reflux causing temporal changes in glucose concentration are negatively correlated in a “long-range" (> two hours) regime. However, for diabetic patients, the DFA exponent α = 1.65 ± 0.30, and in the same regime positively correlated fluctuations are observed, suggesting that the net effects of the flux and reflux persist for many hours. Conclusions : Such long-range positive correlation in glucose homeostasis may reflect pathogenic mechanisms of diabetes, i.e., the lack of the tight control in blood glucose regulation. Using modern time series analysis methods such as DFA, continuous evaluation of glucose dynamics could promote better diagnoses and prognoses of diabetes and a better understanding of the fundamental mechanism of glucose dysregulation in diabetes.


Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 790-P
Author(s):  
SIGNE SCHMIDT ◽  
AJENTHEN RANJAN ◽  
MERETE B. CHRISTENSEN ◽  
KIRSTEN NØRGAARD
Keyword(s):  

Author(s):  
Spencer Frank ◽  
Abdulrahman Jbaily ◽  
Ling Hinshaw ◽  
Rita Basu ◽  
Ananda Basu ◽  
...  

Author(s):  
Kimimasa Saito ◽  
Yosuke Okada ◽  
Keiichi Torimoto ◽  
Yoko Takamatsu ◽  
Yoshiya Tanaka

Abstract Purpose Glycemic variability (GV) and hypoglycemia during nighttime are presumed to be associated with fatal bradycardia. The aim of this prospective study was to evaluate blood glucose dynamics during sleep in patients with obstructive sleep apnea syndrome (OSA) and normal glucose tolerance. Methods Patients with OSA and no diabetes who underwent type 1 overnight polysomnography from December 2018 to May 2020 participated in this study. GV was evaluated in all participants for 14 days using a flash glucose monitoring device. Correlations were examined between GV indexes and indexes related to sleep breathing disorders, the effects of treatment with continuous positive airway pressure (CPAP) on these GV indexes, and the characteristics of glucose dynamics in different OSA subtypes classified by sleep stage. Results Among 42 patients with OSA and no diabetes, the standard deviation of GV during sleep correlated significantly with sleep time spent with oxygen saturation <90% (r=0.591, p=0.008). High blood glucose index during sleep correlated significantly with stage N1% (r=0.491, p=0.032) and negatively with stage N2% (r=−0.479, p=0.038). High blood glucose index correlated significantly with sleep time spent with oxygen saturation <90% (r=0.640, p=0.003). The rapid eye movement–related OSA group had a higher incidence of hypoglycemia. One-week with CPAP treatment significantly improved GV during sleep, standard deviation of GV (from 12.1 to 9.0 mg/dL, p<0.001), and high blood glucose index (from 0.7 to 0.4, p=0.006). Conclusions To evaluate GV during sleep in patients with OSA may be useful for clinical risk management. CPAP treatment for 1 week may have an improving GV and high blood glucose index. Clinical trial registration UMIN000038489 2019/11/04, UMIN 000025433 2016/12/27


Diabetes ◽  
1977 ◽  
Vol 26 (4) ◽  
pp. 262-270 ◽  
Author(s):  
L. Sacca ◽  
B. Trimarco ◽  
G. Perez ◽  
F. Rengo
Keyword(s):  

2021 ◽  
Vol 195 ◽  
pp. 110870
Author(s):  
Jennifer K. Mann ◽  
Liza Lutzker ◽  
Stephanie M. Holm ◽  
Helene G. Margolis ◽  
Andreas M. Neophytou ◽  
...  

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Christopher D Gardner ◽  
Michelle Hauser ◽  
Liana Del Gobbo ◽  
John Trepanowski ◽  
Joseph Rigdon ◽  
...  

Background: Dietary modification remains an essential component of successful weight loss strategies. No one dietary strategy has been determined to be superior to others for the general population. Studies that contrast reducing dietary fat vs. carbohydrate report consistently high within-group variability in dietary adherence and weight loss. Previous research by our group and others suggest that insulin-glucose dynamics or genotype patterns may modify diet effects. Objective: To determine if within-group weight loss variability on a Healthy Low-Fat (HLF) vs. a Healthy Low Carbohydrate (HLC) diet can be attributed to underlying factors such as insulin-glucose dynamics (i.e., insulin resistance and secretion) or genotype pattern. We hypothesized the above factors would be effect modifiers of HLF and HLC diets on 12-month weight loss. Methods: Generally healthy, non-diabetic adults, 18-50 years, BMI 28-40 kg/m 2 , were randomized to HLF or HLC with no specific prescribed energy restriction for 12 months (n=609). Health educators delivered the intervention in 22 1-hr group classes. Data were collected at 0, 3, 6, & 12 months. Dietary intake was assessed by three 24-hour recalls/time point. Clinical data includes: 75-g glucose oral glucose tolerance tests (insulin concentration at 30 minutes [Ins-30], a measure of insulin secretion), genotyping (3-SNP multilocus genotype: Low-Fat Genotype vs. Low-Carb Genotype, UK Biobank Axiom® array), body composition (DXA), resting energy expenditure (indirect calorimetry), epigenetics, proteomics, subcutaneous adipose tissue, microbiota, and standard CVD risk indicators. Results: At 12 months participants collectively lost 6,559 lbs. Retention was 79%, with equal dropout between arms. Range of weight change in both diet arms was ~80 lbs (-60 to +20 lbs). Macronutrient distribution at 12 months was 48% vs. 30% carbohydrate, 29% vs. 45% fat, and 21% vs. 23% protein for HLF and HLC, respectively. Both groups reported achieving and maintaining an average ~500 kcal deficit relative to baseline. Weight loss was similar for HLF vs. HLC: -12.1 ± 1.1 lbs vs. -13.8 ± 1.0 lbs, mean ± SEM. Neither Ins-30 (p for interaction = 0.84) nor genotype pattern (p for interaction = 0.20) modified the effect of diet on 12-month weight loss. Conclusions: Despite substantial weight loss, high within-group variability, and strong dietary differentiation between groups, neither baseline Ins-30 nor genotype pattern modified the effect of diet on 12-month weight loss. Focus on a healthy diet in both diet arms is novel in the context of many previous Low-Fat vs. Low-Carb studies and may have diminished expected effect modification. The extensive data set collected will be used to explore this and other potential explanatory factors.


2019 ◽  
Vol 16 (1) ◽  
pp. 37-42 ◽  
Author(s):  
Whitney A. Welch ◽  
Scott J. Strath ◽  
Michael Brondino ◽  
Renee Walker ◽  
Ann M. Swartz

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