Dataset Information

Glycemic dispersion: a new index for screening high glycemic variability.

ABSTRACT:

Objective

For patients with diabetes, high-frequency and -amplitude glycemic variability may be more harmful than continuous hyperglycemia; however, there is still a lack of screening indicators that can quickly and easily assess the level of glycemic variability. The aim of this study was to investigate whether the glycemic dispersion index is effective for screening high glycemic variability.

Methods

A total of 170 diabetes patients hospitalized in the Sixth Affiliated Hospital of Kunming Medical University were included in this study. After admission, the fasting plasma glucose, 2-hour postprandial plasma glucose, and glycosylated hemoglobin A1c were measured. The peripheral capillary blood glucose was measured seven times in 24 h, before and after each of three meals and before bedtime. The standard deviation of the seven peripheral blood glucose values was calculated, and a standard deviation of > 2.0 was used as the threshold of high glycemic variability. The glycemic dispersion index was calculated and its diagnostic efficacy for high glycemic variability was determined by the Mann-Whitney U test, receiver operating characteristic (ROC) curve and, Pearson correlation analysis.

Results

The glycemic dispersion index of patients with high glycemic variability was significantly higher than that of those with low glycemic variability (p < 0.01). The best cutoff value of the glycemic dispersion index for screening high glycemic variability was 4.21. The area under the curve (AUC) was 0.901 (95% CI: 0.856-0.945) and had a sensitivity of 0.781 and specificity of 0.905. It was correlated with the standard deviation of blood glucose values (r = 0.813, p < 0.01).

Conclusions

The glycemic dispersion index had good sensitivity and specificity for screening high glycemic variability. It was significantly associated with the standard deviation of blood glucose concentration and is simple and easy to calculate. It was an effective screening indicator of high glycemic variability.

SUBMITTER: Shi R

PROVIDER: S-EPMC10169464 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

Glycemic dispersion: a new index for screening high glycemic variability.

Shi Rui R Feng Lei L Liu Yan-Mei YM Xu Wen-Bo WB Luo Bei-Bei BB Tang Ling-Tong LT Bi Qian-Ye QY Cao Hui-Ying HY

Diabetology & metabolic syndrome 20230509 1

<h4>Objective</h4>For patients with diabetes, high-frequency and -amplitude glycemic variability may be more harmful than continuous hyperglycemia; however, there is still a lack of screening indicators that can quickly and easily assess the level of glycemic variability. The aim of this study was to investigate whether the glycemic dispersion index is effective for screening high glycemic variability.<h4>Methods</h4>A total of 170 diabetes patients hospitalized in the Sixth Affiliated Hospital ...[more]

PMID: 37158980

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Project description:We previously reported that a low versus high glycemic index (GI) diet on a high fat (30% kcal fat) background (LGI and HGI, respectively) significantly retarded adverse health effects in C57BL/6J male mice. The LGI diet enhanced whole body insulin sensitivity and repressed high fat diet-induced body and adipose tissue weight gain, resulting in reduced serum leptin and resistin levels (Faseb J 2009; 23: 1092-1101). How white adipose tissue (WAT) is effected is examined in the present study. We characterized the molecular mechanisms underlying the GI-mediated effects in WAT using whole genome transcriptomics technology. We show that a LGI vs. HGI diet mainly exerts its beneficial effects on substrate metabolism, especially insulin signaling of fatty acid metabolism. In addition, cell adhesion and cytoskeleton remodeling showed reduced expression in line with lower WAT mass, but it might also be due to altered insulin sensitivity. An important transcription factor showing enhanced expression is PPARgamma. Furthermore, serum levels of triglycerides, total cholesterol, HDL- and LDL-cholesterol were significantly reduced by a LGI vs. HGI diet, and muscle insulin sensitivity was significantly increased as analyzed by PKB/Akt phosphorylation. Cumulatively, even though these mice were fed a high fat diet, the low versus high GI induced significantly favorable changes in metabolism in WAT. These effects suggest a partial overlap with pharmacological approaches by thiazolidinediones (TZDs) to treat insulin resistance and statins and plantsterols/stanols for hypercholesterolemia. It is therefore tempting to speculate that such a dietary approach might beneficially support pharmacological treatment of insulin resistance or hypercholesterolemia in humans. We analyzed 19 epididymal whie adipose tissue (epiWAT) samples from a 13 week High fat diet, Low glycemic index dietary group (LGI, n=9) versus a High fat diet, High glycemic index dietary group (HGI, n=10) after 13 weeks of feeding wildtype C57BL/6J male adult mice. Of the 19 arrays, we excluded 2 arrays for downstream analysis based on quality control (total final set contains 8 LGI and 9 HGI samples).

Project description:BackgroundThe utility of glycemic index (GI) values for chronic disease risk management remains controversial. Although absolute GI value determinations for individual foods have been shown to vary significantly in individuals with diabetes, there is a dearth of data on the reliability of GI value determinations and potential sources of variability among healthy adults.ObjectiveWe examined the intra- and inter-individual variability in glycemic response to a single food challenge and methodologic and biological factors that potentially mediate this response.DesignThe GI value for white bread was determined by using standardized methodology in 63 volunteers free from chronic disease and recruited to differ by sex, age (18-85 y), and body mass index [BMI (in kg/m2): 20-35]. Volunteers randomly underwent 3 sets of food challenges involving glucose (reference) and white bread (test food), both providing 50 g available carbohydrates. Serum glucose and insulin were monitored for 5 h postingestion, and GI values were calculated by using different area under the curve (AUC) methods. Biochemical variables were measured by using standard assays and body composition by dual-energy X-ray absorptiometry.ResultsThe mean ± SD GI value for white bread was 62 ± 15 when calculated by using the recommended method. Mean intra- and interindividual CVs were 20% and 25%, respectively. Increasing sample size, replication of reference and test foods, and length of blood sampling, as well as AUC calculation method, did not improve the CVs. Among the biological factors assessed, insulin index and glycated hemoglobin values explained 15% and 16% of the variability in mean GI value for white bread, respectively.ConclusionsThese data indicate that there is substantial variability in individual responses to GI value determinations, demonstrating that it is unlikely to be a good approach to guiding food choices. Additionally, even in healthy individuals, glycemic status significantly contributes to the variability in GI value estimates. This trial was registered at clinicaltrials.gov as NCT01023646.

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Dataset Information

Glycemic dispersion: a new index for screening high glycemic variability.

Objective

Methods

Results

Conclusions

Publications

Glycemic dispersion: a new index for screening high glycemic variability.

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