scholarly journals Skin intrinsic fluorescence is associated with hemoglobin A1c and hemoglobin glycation index but not mean blood glucose in children with type 1 diabetes. Diabetes Care 2011;34:1816-1820

Diabetes Care ◽  
2013 ◽  
Vol 36 (4) ◽  
pp. 1056-1056
Author(s):  
D. Felipe ◽  
J. Hempe ◽  
S. Liu ◽  
N. Matter ◽  
J. Maynard ◽  
...  
2013 ◽  
Vol 15 (2) ◽  
pp. 117-123 ◽  
Author(s):  
Vanita R. Aroda ◽  
Baqiyyah N. Conway ◽  
Stephen J. Fernandez ◽  
Nathaniel I. Matter ◽  
John D. Maynard ◽  
...  

2020 ◽  
Author(s):  
Helleputte Simon ◽  
De Backer Tine ◽  
Calders Patrick ◽  
Pauwels Bart ◽  
Shadid Samyah ◽  
...  

OBJECTIVE: In type 1 diabetes mellitus (T1DM) management, CGM-derived parameters can provide additional insights, with the concept of time in range (TIR) and other parameters reflecting glycaemic control and variability (GV) being put forward. This study aimed to examine the added and interpretative value of the CGM-derived indices TIR and coefficient of variation (CV%) in T1DM patients stratified according to their level of glycaemic control by means of HbA1c. METHODS: T1DM patients with a minimum disease duration of 10 years and without known macrovascular disease were enrolled. Patients were equipped with a blinded CGM device (Dexcom G4) for seven days. TIR (70–180 mg/dl), time in hypoglycaemia (total: <70 mg/dl; level 2: <54 mg/dl) and hyperglycaemia (total: >180 mg/dl; level 2: >250 mg/dl) were determined, and CV% (=standard deviation(SD)/mean blood glucose(MBG)) was used as parameter for GV. Pearson and Spearman correlations, and regression analysis was used to examine associations. RESULTS: 95 patients (age: 45±10 years; HbAc1: 7.7±0.8%) were included (MBG: 159±31 mg/dl; TIR 55.8±14.9%; CV%: 43.5±7.8%) and labeled as having good (HbA1c ≤7%; n=20), moderate (7–8%; n=44) or poor (>8%; n=31) glycaemic control. HbA1c was significantly associated with MBG (rs=0.48, p<0.001) and time spent in hyperglycaemia (total: rs=0.52; level 2: r=0.46; p<0.001), but not with time in hypoglycaemia and CV%, even after analysis in HbA1c subgroups. Similarly, TIR was negatively associated with HbA1c (r=−0.53; p<0.001), MBG (rs=−0.81; p<0.001) and time in hyperglycaemia (total: rs=−0.90; level 2: rs=−0.84; p<0.001), but not with time in hypoglycaemia. Subgroup analyses, however, showed that TIR did associate with shorter time in level 2 hypoglycaemia in those patients with good (rs=−0.60; p=0.007) and moderate (rs=−0.25; p=0.047) glycaemic control. In contrast, CV% was strongly positively associated with time in hypoglycaemia (total: rs=0.78; level 2: rs=0.76; p<0.001), but not with TIR or time in hyperglycaemia in the entire cohort, although subgroup analyses showed that TIR was negatively associated with CV% in patients with good glycaemic control (r=−0.81, p<0.001) and positively in patients with poor glycaemic control (r=0.47; p<0.01). CONCLUSION: This study demonstrates that CGM-derived metrics TIR and CV% relate with clinically important situations, TIR being strongly dependent on hyperglycaemia and CV% being reflective of hypoglycaemic risk. However, the interpretation and applicability of TIR and CV%, and their relationship, depends on the level of glycaemic control of the individual patient, with CV% generally adding less clinically relevant information in those with poor control. This illustrates the need for further research and evaluation of composite measures of glycaemic control in T1DM. Abbreviations: T1DM = Type 1 diabetes mellitus; CGM = Continuous glucose monitoring; TIR = Time in range; TAR = Time above range; TBR = Time below range; GV = Glycaemic variability; CV% = Coefficient of variation; MBG = Mean blood glucose.


Diabetes Care ◽  
2010 ◽  
Vol 33 (5) ◽  
pp. 1025-1027 ◽  
Author(s):  
J. L. Kamps ◽  
J. M. Hempe ◽  
S. A. Chalew

Author(s):  
Jiamin Guo ◽  
Andrew Paterson ◽  
Delnaz Roshandel

Introduction & Objective: Cumulated advanced glycation end products (AGEs) in the bloodstream and tissues contribute to the pathogenesis of diabetes complications. The skin intrinsic fluorescence (SIF) is a non-invasive measurement of dermal AGEs level using spectrometer, and it can be used as a biomarker in AGEs-related diseases. Previously, specific epigenomic factor has been found to be associated with haemoglobin A1c (HbA1c). HbA1c is a type of glycated haemoglobin – the HbA1c test measures the average glycemic control over the period of 3 months. However, the effect of epigenetic factors on the level of AGEs in the skin remains unknown. We hypothesize that some cytosine-guanine dinucleotides (CpGs) are associated with SIF. An epigenome-wide associations study (EWAS) was performed to identify CpG sites associated with SIF in people with type 1 diabetes. Methods: 499 people with type 1 diabetes that have both methylation and SIF from the Diabetes Control Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study were included. We fit linear regression models for SIF with each CpG site one at a time. The epigenome-wide significance level (p=5e-8) was applied. Then the result is compared with the null hypothesis where CpGs are not associated with SIF to check the inflation. In order to check the assumptions of the multiple linear models at a single CpG, we use diagnostic plots. Results: We did not identify a specific CpG that is significantly associated with neither skin intrinsic fluorescence 1 (SIF1) nor skin intrinsic fluorescence 12 (SIF 12).The CpG site with strongest effect is cg06538183 ([SE] -2.73 [0.61], p = 8.72e-6) on SIF1 and cg12871967 ([SE] 2.52, 0.53, p = 2.71e-6) on SIF12. Conclusion: We did not find any specific CpG that was significantly associated with either SIF 1 or SIF12. In general, the result suggests that DNA methylation does not impact the accumulation of AGEs in skin cells. DNA methylation data has a unique pattern of distribution that drives the non-uniform distribution of the p-values. The group of 275,301 CpGs that have means above the median and standard deviations below the median has the expected uniform p-value distribution.


2015 ◽  
Vol 17 (10) ◽  
pp. 726-734 ◽  
Author(s):  
Karen M. Eny ◽  
Trevor J. Orchard ◽  
Rachel Grace Miller ◽  
John Maynard ◽  
Denis M. Grant ◽  
...  

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1656-P
Author(s):  
ERIN L. TOMASZEWSKI ◽  
TREVOR J. ORCHARD ◽  
MARQUIS S. HAWKINS ◽  
THOMAS SONGER ◽  
JOHN D. MAYNARD ◽  
...  

2018 ◽  
Vol 12 (4) ◽  
pp. 905-906
Author(s):  
Shuyu Zhang ◽  
Ludi Fan ◽  
Qianyi Zhang ◽  
Annette M. Chang ◽  
Edward J. Bastyr ◽  
...  

2010 ◽  
Vol 11 (7) ◽  
pp. 455-461 ◽  
Author(s):  
Arlette A Soros ◽  
Stuart A Chalew ◽  
Robert J McCarter ◽  
Rachel Shepard ◽  
James M Hempe

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