Dataset Information

Features of glycemic variations in drug naive type 2 diabetic patients with different HbA_1c values.

ABSTRACT: To define the features of glycemic variations in drug naïve type 2 diabetic (T2D) patients with different HbA_1c values using continuous glucose monitoring (CGM), a total of 195 drug naïve T2D patients were admitted. The subjects were divided into the following groups: lower HbA_1c values (?8%), moderate HbA_1c values (>8% and ?10%), and higher HbA_1c values (>10%). The patients underwent oral glucose tolerance tests and were then subjected to 3-day CGM. The primary endpoint was the differences in the 24-hr mean amplitude of glycemic excursions (MAGE) in patients with different HbA_1c values. Patients with higher HbA_1c values had larger MAGEs than those in the moderate and lower groups (7.44?±?3.00 vs. 6.30?±?2.38, P?1c values. Moreover, the patients with higher HbA_1c values exhibited higher peak glucose concentrations and prolongation in the time to peak glucose. Patients with higher HbA_1c values had larger MAGE compared with those with lower and moderate HbA_1c values. Our data indicated patients with higher HbA_1c values should receive special therapy aimed at reducing the larger glycemic variations.

SUBMITTER: Li FF

PROVIDER: S-EPMC5431480 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Features of glycemic variations in drug naïve type 2 diabetic patients with different HbA1c values.

Li Feng-Fei FF Liu Bing-Li BL Yan Reng-Na RN Zhu Hong-Hong HH Zhou Pei-Hua PH Li Hui-Qin HQ Su Xiao-Fei XF Wu Jin-Dan JD Zhang Dan-Feng DF Ye Lei L Ma Jian-Hua JH

Scientific reports 20170508 1

To define the features of glycemic variations in drug naïve type 2 diabetic (T2D) patients with different HbA1c values using continuous glucose monitoring (CGM), a total of 195 drug naïve T2D patients were admitted. The subjects were divided into the following groups: lower HbA1c values (≤8%), moderate HbA1c values (>8% and ≤10%), and higher HbA1c values (>10%). The patients underwent oral glucose tolerance tests and were then subjected to 3-day CGM. T ...[more]

PMID: 28484269

Similar Datasets

Project description:BackgroundLong-term glycemic control reduces retinopathy risk, but transient worsening can occur with glucose control intensification. Glucagon-like peptide 1 receptor agonists (GLP-1RA) lower glucose, but the long-term impact on retinopathy is unknown. GLP-1RA cardiovascular outcome trials (CVOTs) provide long-term follow-up, allowing examination of retinopathy outcomes.PurposeTo examine the associations between retinopathy, HbA1c, systolic blood pressure (SBP), and weight in GLP-1RA CVOTs.Data sourcesSystematic review identified six placebo-controlled GLP-1RA CVOTs reporting prespecified retinopathy outcomes.Study selectionPublished trial reports were used as the primary data sources.Data extractionHbA1c, SBP, and weight data throughout follow-up by treatment group were extracted.Data synthesisRandom-effects model meta-analysis showed no association between GLP-1RA treatment and retinopathy (odds ratio [OR] 1.10; 95% CI 0.93, 1.30), with high heterogeneity between studies (I 2 = 52.2%; Q statistic P = 0.063). Univariate meta-regression showed an association between retinopathy and average HbA1c reduction during the overall follow-up (slope = 0.77, P = 0.007), but no relationship for SBP or weight. Sensitivity analyses for HbA1c showed a relationship at 3 months (P = 0.006) and 1 year (P = 0.002). A 0.1% (1.09 mmol/mol) increase in HbA1c reduction was associated with 6%, 14%, or 8% increased Ln(OR) for retinopathy at the 3-month, 1-year, and overall follow-up, respectively.LimitationsCVOTs were not powered to assess retinopathy outcomes and differed in retinopathy-related criteria and methodology. The median follow-up of 3.4 years is short compared with the onset of retinopathy.ConclusionsHbA1c reduction was significantly associated with increased retinopathy risk in meta-regression for GLP-1RA CVOTs. The magnitude of HbA1c reduction was correlated with retinopathy risk in people with diabetes and additional cardiovascular risk factors, but the long-term impact of improved glycemic control on retinopathy was unmeasured in these studies. Retinopathy status should be assessed when intensifying glucose-lowering therapy.

Project description:Aims/hypothesisThe aim of the study was to evaluate longitudinal associations between HbA1c levels, diabetes status and subsequent cognitive decline over a 10 year follow-up period.MethodsData from wave 2 (2004-2005) to wave 7 (2014-2015) of the English Longitudinal Study of Ageing (ELSA) were analysed. Cognitive function was assessed at baseline (wave 2) and reassessed every 2 years at waves 3-7. Linear mixed models were used to evaluate longitudinal associations.ResultsThe study comprised 5189 participants (55.1% women, mean age 65.6 ± 9.4 years) with baseline HbA1c levels ranging from 15.9 to 126.3 mmol/mol (3.6-13.7%). The mean follow-up duration was 8.1 ± 2.8 years and the mean number of cognitive assessments was 4.9 ± 1.5. A 1 mmol/mol increment in HbA1c was significantly associated with an increased rate of decline in global cognitive z scores (-0.0009 SD/year, 95% CI -0.0014, -0.0003), memory z scores (-0.0005 SD/year, 95% CI -0.0009, -0.0001) and executive function z scores (-0.0008 SD/year, 95% CI -0.0013, -0.0004) after adjustment for baseline age, sex, total cholesterol, HDL-cholesterol, triacylglycerol, high-sensitivity C-reactive protein, BMI, education, marital status, depressive symptoms, current smoking, alcohol consumption, hypertension, CHD, stroke, chronic lung disease and cancer. Compared with participants with normoglycaemia, the multivariable-adjusted rate of global cognitive decline associated with prediabetes and diabetes was increased by -0.012 SD/year (95% CI -0.022, -0.002) and -0.031 SD/year (95% CI -0.046, -0.015), respectively (p for trend <0.001). Similarly, memory, executive function and orientation z scores showed an increased rate of cognitive decline with diabetes.Conclusions/interpretationSignificant longitudinal associations between HbA1c levels, diabetes status and long-term cognitive decline were observed in this study. Future studies are required to determine the effects of maintaining optimal glucose control on the rate of cognitive decline in people with diabetes.

Project description:BACKGROUND/OBJECTIVE:There is substantial interest in dietary approaches to reducing postprandial glucose (PPG) responses, but the quantitative contribution of PPG to longer-term glycemic control (reflected in glycated hemoglobin, HbA1c) in the general population is not known. This study quantified the associations of preprandial glucose exposure, PPG exposure, and glycemic variability with HbA1c and estimated the explained variance in HbA1c in individuals with and without type 2 diabetes (T2D). SUBJECTS/METHODS:Participants in the A1c-Derived Average Glucose (ADAG) study without T2D (n = 77) or with non-insulin-treated T2D and HbA1c<6.5% (T2DHbA1c?<?6.5%, n = 63) or HbA1c???6.5% (T2DHbA1c???6.5%, n = 34) were included in this analysis. Indices of preprandial glucose, PPG, and glycemic variability were calculated from continuous glucose monitoring during four periods over 12 weeks prior to HbA1c measurement. In linear regression models, we estimated the associations of the glycemic exposures with HbA1c and calculated the proportion of variance in HbA1c explained by glycemic and non-glycemic factors (age, sex, body mass index, and ethnicity). RESULTS:The factors in the analysis explained 35% of the variance in HbA1c in non-diabetic individuals, 49% in T2DHbA1c?<?6.5%, and 78% in T2DHbA1c???6.5%. In non-diabetic individuals PPG exposure was associated with HbA1c in confounder-adjusted analyses (P?<?0.05). In the T2DHbA1c?<?6.5% group, all glycemic measures were associated with HbA1c (P?<?0.05); preprandial glucose and PPG accounted for 14 and 18%, respectively, of the explained variation. In T2DHbA1c???6.5%, these glycemic exposures accounted for more than 50% of the variation in HbA1c and with equal relative contributions. CONCLUSIONS:Among the glycemic exposures, PPG exposure was most strongly predictive of HbA1c in non-diabetic individuals, suggesting that interventions targeting lowering of the PPG response may be beneficial for long-term glycemic maintenance. In T2D, preprandial glucose and PPG exposure contributed equally to HbA1c.

Project description:BackgroundCombining HbA1c with glycated albumin (GA) may improve detection of dysglycaemia. As BMI correlates positively with HbA1c and negatively with GA, HbA1c may be more effective in obese and GA in nonobese individuals.MethodsTo relate these findings to Africans, we assessed in 1274 South Africans living in CapeTown (male 26%; age 48±16y; BMI 28.7 kg/m2 (range 15.6-73.8); obesity 39.9% and no prior diabetes history) the: (1) correlation of BMI with HbA1c and GA, (2) ability of HbA1c and GA separately and jointly, to detect OGTT-diagnosed dysglycaemia (diabetes plus prediabetes). Data collection took place between 2014 and 2016 in the City of Cape Town. Dysglycaemia was diagnosed by glucose criteria for the OGTT. Youden index was used to optimize diagnostic thresholds for HbA1c and GA.FindingsNormal glucose tolerance, prediabetes and diabetes occurred in 76%, 17% and 7%, respectively. BMI positively correlated with HbA1c [r = 0·34 [95%CI: 0·29,0·39)] and negatively with GA [-0·08 (0·13,0·03)]. For HbA1c the optimal threshold by Youden-index for dysglycaemia diagnosis was: 6·0% (95%CI: 5·8,6·2) and for GA: 13·44% (12·72,14·71). In the nonobese, obese and total cohort, HbA1c-alone detected: 51% (42-60), 72% (65,78), 63% (57,68), respectively; GA-alone detected 55% (52% (46,63), 52% (44, 59) and 53% (47,53), respectively; whereas: HbA1c+GA detected: 69% (60,76), 82% (75,87) and 76% (71, 81). Therefore, for the total cohort detection of dysglycaemia HbA1c-alone vs HbA1c+GA detected 63% (57,68) vs 76% (71,81).InterpretationThe opposite correlations of HbA1c and GA with BMI have now been demonstrated in an African-based population. Improving detection of dysglycaemia by combining HbA1c and GA has important implications for diabetes risk screening.FundingAES is supported by the intramural programs of the National Institute of Diabetes and Digestive and Kidney Diseases and the National Institute of Minority Health and Health Disparities of the National Institutes of Health (NIH, Bethesda, Maryland, USA). DBS is supported by the intramural program of the Clinical Center of NIH. The South African Medical Research Council (SAMRC) funded the VMH study with funds from the National Treasury under its Economic Competitiveness and Support Package (MRC-RFA-UFSP-01-2013/VMH Study).

Dataset Information

Features of glycemic variations in drug naive type 2 diabetic patients with different HbA_1c values.

Publications

Features of glycemic variations in drug naïve type 2 diabetic patients with different HbA<sub>1c</sub> values.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Features of glycemic variations in drug naive type 2 diabetic patients with different HbA1c values.

Publications

Features of glycemic variations in drug naïve type 2 diabetic patients with different HbA<sub>1c</sub> values.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Features of glycemic variations in drug naive type 2 diabetic patients with different HbA_1c values.