scholarly journals Predictors of Change in Skin Intrinsic Fluorescence in Type 1 Diabetes: The Epidemiology of Diabetes Complications study

2021 ◽  
pp. 193229682110143
Author(s):  
Erin L. Tomaszewski ◽  
Trevor J. Orchard ◽  
Marquis S. Hawkins ◽  
Rebecca B.N. Conway ◽  
Jeanine M. Buchanich ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetes Complications ◽  
Glycosylated Hemoglobin ◽  
Albumin Excretion Rate ◽  
Linear Regression Models ◽  
Score Change ◽  
Indirect Measures ◽  
Glycation End Products ◽  
Skin Intrinsic Fluorescence

Background Skin intrinsic fluorescent (SIF) scores are indirect measures of advanced glycation end-products (AGEs). SIF scores are cross-sectionally associated with type 1 diabetes (T1D) complications such as increased albumin excretion rate (AER), coronary artery calcification (CAC) and neuropathy. We assessed predictors of SIF score change in those with T1D. Methods Data from the 30-year longitudinal Epidemiology of Diabetes Complications (EDC) study of childhood-onset T1D were used to assess AGEs measured with a SIF score produced by the SCOUT DS® device. SIF scores were assessed twice in 83 participants: between 2007-08 and again between 2010-14. Regression analyses were used to assess independent predictors of SIF score change Results At baseline, mean age was 47.9 ± 6.9 years, diabetes duration was 36.7 ± 6.4 years, and median glycosylated hemoglobin (HbA1c) was 7.1 (interquartile range: 6.5, 8.5). During a mean follow-up of 5.2 ± 0.9 years, mean change in SIF score was 2.9 ± 2.8 arbitrary units. In multivariable linear regression models, log HbA1c ( P < 0.001), log estimated glomerular filtration rate (eGFR) ( P < 0.001), overt nephropathy (defined as AER ≥ 200 µg/min, P = 0.06), and multiple daily insulin shots/pump use (MDI) exposure years ( P = 0.02) were independent predictors of SIF score change. Conclusions Increases in SIF score over 5 years were related to increased glycemic levels and decreased kidney function (eGFR). MDI and glomerular damage were related to a decreased SIF score. This is one of the first studies with repeated SIF assessments in T1D and provides unique, albeit preliminary, insight about these associations.

scholarly journals Epigenetics of skin intrinsic florescence

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Jiamin Guo ◽  
Andrew Paterson ◽  
Delnaz Roshandel
Keyword(s):  
Dna Methylation ◽  
Type 1 Diabetes ◽  
Intrinsic Fluorescence ◽  
P Value ◽  
Haemoglobin A1c ◽  
Linear Regression Models ◽  
Significance Level ◽  
Cpg Site ◽  
Skin Intrinsic Fluorescence

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.

scholarly journals The Association of Skin Intrinsic Fluorescence With Type 1 Diabetes Complications in the DCCT/EDIC Study

Diabetes Care ◽  
2013 ◽  
Vol 36 (10) ◽  
pp. 3146-3153 ◽  
Author(s):  
T. J. Orchard ◽  
T. J. Lyons ◽  
P. A. Cleary ◽  
B. H. Braffett ◽  
J. Maynard ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetes Complications ◽  
Intrinsic Fluorescence ◽  
Skin Intrinsic Fluorescence

1514-P: Suboptimal Glycemic Control and High Prevalence of Diabetes Complications in Adults with Type 1 Diabetes (T1D) in the SAGE Study

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1514-P ◽  
Author(s):  
ERIC RENARD ◽  
ZSOLT BOSNYAK ◽  
FELIPE LAUAND ◽  
PAOLO POZZILLI ◽  
HIROSHI IKEGAMI ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Diabetes Complications ◽  
High Prevalence

scholarly journals Insulin doses requirements in patients with type 1 diabetes using glargine U300 or degludec in routine clinical practice

2021 ◽  
pp. jim-2020-001633
Author(s):  
Florentino Carral San Laureano ◽  
Mariana Tomé Fernández-Ladreda ◽  
Ana Isabel Jiménez Millán ◽  
Concepción García Calzado ◽  
María del Carmen Ayala Ortega
Keyword(s):  
Type 1 Diabetes ◽  
Clinical Practice ◽  
Retrospective Study ◽  
Real World ◽  
Glycosylated Hemoglobin ◽  
Routine Clinical Practice ◽  
Insulin Analogs ◽  
Total P ◽  
Real Clinical Practice

There are not many real-world studies evaluating daily insulin doses requirements (DIDR) in patients with type 1 diabetes (T1D) using second-generation basal insulin analogs, and such comparison is necessary. The aim of this study was to compare DIDR in individuals with T1D using glargine 300 UI/mL (IGlar-300) or degludec (IDeg) in real clinical practice. An observational, retrospective study was designed in 412 patients with T1D (males: 52%; median age 37.0±13.4 years, diabetes duration: 18.7±12.3 years) using IDeg and IGla-300 ≥6 months to compare DIDR between groups. Patients using IGla-300 (n=187) were more frequently males (59% vs 45.8%; p=0.004) and had lower glycosylated hemoglobin (HbA1c) (7.6±1.2 vs 8.1%±1.5%; p<0.001) than patients using IDeg (n=225). Total (0.77±0.36 unit/kg/day), basal (0.43±0.20 unit/kg/day) and prandial (0.33±0.23 unit/kg/day) DIDR were similar in IGla-300 and IDeg groups. Patients with HbA1c ≤7% (n=113) used significantly lower basal (p=0.045) and total (p=0.024) DIDR, but not prandial insulin (p=0.241), than patients with HbA1c between 7.1% and 8% and >8%. Patients using IGla-300 and IDeg used similar basal, prandial and total DIDR regardless of metabolic control subgroup. No difference in basal, prandial and total DIDR was observed between patients with T1D using IGla-300 or IDeg during at least 6 months in routine clinical practice.

scholarly journals Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an in silico Trial

2021 ◽  
pp. 193229682110123
Author(s):  
Chiara Roversi ◽  
Martina Vettoretti ◽  
Simone Del Favero ◽  
Andrea Facchinetti ◽  
Pratik Choudhary ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
In Silico ◽  
Open Loop ◽  
Random Errors ◽  
Linear Regression Models ◽  
Reference Case ◽  
Counting Error ◽  
The Impact

Background: In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control. Methods: The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes. Results: Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD ( R2>0.95), with slopes of [Formula: see text], [Formula: see text] for ∆TIR, [Formula: see text], [Formula: see text] for ∆TAR, and [Formula: see text], [Formula: see text] for ∆TBR. Conclusions: The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

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