| Literature DB >> 31620092 |
Zhen-Ye Zhang1, Ling-Feng Miao1, Ling-Ling Qian1, Ning Wang1, Miao-Miao Qi1, Yu-Min Zhang1, Shi-Peng Dang1, Ying Wu1, Ru-Xing Wang1.
Abstract
Accumulating evidence indicates the occurrence and development of diabetic complications relates to not only constant high plasma glucose, but also glucose fluctuations which affect various kinds of molecular mechanisms in various target cells and tissues. In this review, we detail reactive oxygen species and their potentially damaging effects upon glucose fluctuations and resultant downstream regulation of protein signaling pathways, including protein kinase C, protein kinase B, nuclear factor-κB, and the mitogen-activated protein kinase signaling pathway. A deeper understanding of glucose-fluctuation-related molecular mechanisms in the development of diabetic complications may enable more potential target therapies in future.Entities:
Keywords: diabetes; glucose fluctuations; mitogen-activated protein kinase; protein kinase B; protein kinase C; reactive oxygen species
Year: 2019 PMID: 31620092 PMCID: PMC6759481 DOI: 10.3389/fendo.2019.00640
Source DB: PubMed Journal: Front Endocrinol (Lausanne) ISSN: 1664-2392 Impact factor: 5.555
Commonly used indicators for blood glucose fluctuations.
| Mean amplitude of glycemic excursions (MAGE) | The mean of blood glucose values exceeding one SD from the 24-h mean blood glucose. | The most commonly used term as an indicator of blood glucose fluctuations. |
| Standard deviation of the mean(SD) | Standard deviation of patient's mean blood glucose level in a certain period of time. | It represents the change and dispersion rate of the average blood glucose concentrations. |
| Coefficient of variation(CV) | SD/mean × 100%. | It normalizes glycemic variability at different mean blood glucose values. |
| Glycemic lability index(LI) | It processes three glucose values to calculate a lability value and then moves to the next three glucose values, and so on. | It can serve as an indicator of patients' prognosis. |
| Mean of daily difference (MODD) | It is calculated as the average of the difference between values on different days but at the same time. | It can be used to assess the continuous changes of blood glucose between different days. |
| Continuous overlapping net glycemic action (CONGA) | It is calculated by determining the difference between values at different set intervals, and the difference is then applied to the CONGA formula. | It is a parameter that reflects the variability of blood glucose over a certain time interval. |
| High blood glucose index (HBGI) and low blood glucose index (LBGI) | They are implemented by converting glucose values into risk scores. If the glucose risk score is below 0, then the risk is labeled as LBGI, and if it is above 0, then it is labeled as HBGI. | They can assess the risk of severe hypoglycemia or hyperglycemia in diabetic patients. |
See text for references.
Figure 1Glucose fluctuations-related signaling pathways involved in development of diabetic complications NADPH aggravates the production of ROS and oxidative stress though a vicious feed-forward cycle. ROS overproduction is the vital trigger factor to activate the downstream signaling pathways and finally leads to various kinds of damaging effects in different target cells and tissues.