Effects of blood glucose on vaspin secretion in patients with gestational diabetes mellitus

Liu, Yan; Gong, Miao; Liu, Suxin; Pan, Yixing; Huo, Yan

doi:10.1080/09513590.2020.1792438

Cited by 8 publications

(14 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar findings were observed for 2-h postprandial blood glucose for the extra meal in the afternoon (at 5 pm). No differences in serum triglyceride and total cholesterol concentrations were found after two weeks of intervention [87].…”

Section: Evidence For the Use Of Complete And Balanced Nutritional Sumentioning

confidence: 75%

“…Liu et al [87] evaluated the effects of a DSF on fasting blood glucose, 2-h postprandial blood glucose, triglycerides and total cholesterol in a 2-week randomized, controlled trial on 40 GDM pregnant women. All participants received individualized dietary counseling in which the total calories were calculated to achieve an ideal body weight with 30-38 kcal/kg body weight.…”

Section: Evidence For the Use Of Complete And Balanced Nutritional Sumentioning

confidence: 99%

See 1 more Smart Citation

The Role of Dietary Carbohydrates in Gestational Diabetes

et al. 2020

View full text Add to dashboard Cite

Gestational diabetes (GDM) is hyperglycemia that is recognized for the first time during pregnancy. GDM is associated with a wide range of short- and long-term adverse health consequences for both mother and offspring. It is a complex disease with a multifactorial etiology, with disturbances in glucose, lipid, inflammation and gut microbiota. Consequently, its management is complex, requiring patients to self-manage their diet, lifestyle and self-care behaviors in combination with use of insulin. In addition to nutritional recommendations for all pregnant women, special attention to dietary carbohydrate (CHO) amount and type on glucose levels is especially important in GDM. Dietary CHO are diverse, ranging from simple sugars to longer-chain oligo- and poly- saccharides which have diverse effects on blood glucose, microbial fermentation and bowel function. Studies have established that dietary CHO amount and type can impact maternal glucose and nutritional recommendations advise women with GDM to limit total intake or choose complex and low glycemic CHO. However, robust maternal and infant benefits are not consistently shown. Novel approaches which help women with GDM adhere to dietary recommendations such as diabetes-specific meal replacements (which provide a defined and complete nutritional composition with slowly-digested CHO) and continuous glucose monitors (which provide unlimited monitoring of maternal glycemic fluctuations) have shown benefits on both maternal and neonatal outcomes. Continued research is needed to understand and develop tools to facilitate patient adherence to treatment goals, individualize interventions and improve outcomes.

show abstract

Section: Evidence For the Use Of Complete And Balanced Nutritional Sumentioning

confidence: 75%

Section: Evidence For the Use Of Complete And Balanced Nutritional Sumentioning

confidence: 99%

The Role of Dietary Carbohydrates in Gestational Diabetes

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In this study, researchers investigated changes in serum vaspin levels in pregnant women with GDM after glucose loading during OGTT. The study involved 30 patients with GDM and 30 age-matched pregnant women with normal glucose tolerance (NGT) [ 113 ]. Liu et al also analyzed the effect of blood glucose on serum vaspin secretion in pregnant women with GDM [ 84 ].…”

Section: Biomoleculesmentioning

confidence: 99%

“…Liu et al also analyzed the effect of blood glucose on serum vaspin secretion in pregnant women with GDM [ 84 ]. The results of the study indicate that during OGTT, vaspin concentration was higher in women in the GDM group than in the NGT group; furthermore, after 1 and 2 h of the test, vaspin concentration was significantly higher than baseline vaspin concentration [ 113 ].…”

Section: Biomoleculesmentioning

confidence: 99%

Novel Biomolecules in the Pathogenesis of Gestational Diabetes Mellitus

Ruszała

Niebrzydowska

Pilszyk

et al. 2021

IJMS

View full text Add to dashboard Cite

Gestational diabetes mellitus (GDM) is one of the most common metabolic diseases in pregnant women. Its early diagnosis seems to have a significant impact on the developing fetus, the course of delivery, and the neonatal period. It may also affect the later stages of child development and subsequent complications in the mother. Therefore, the crux of the matter is to find a biopredictor capable of singling out women at risk of developing GDM as early as the very start of pregnancy. Apart from the well-known molecules with a proven and clear-cut role in the pathogenesis of GDM, e.g., adiponectin and leptin, a potential role of newer biomolecules is also emphasized. Less popular and less known factors with different mechanisms of action include: galectins, growth differentiation factor-15, chemerin, omentin-1, osteocalcin, resistin, visfatin, vaspin, irisin, apelin, fatty acid-binding protein 4 (FABP4), fibroblast growth factor 21, and lipocalin-2. The aim of this review is to present the potential and significance of these 13 less known biomolecules in the pathogenesis of GDM. It seems that high levels of FABP4, low levels of irisin, and high levels of under-carboxylated osteocalcin in the serum of pregnant women can be used as predictive markers in the diagnosis of GDM. Hopefully, future clinical trials will be able to determine which biomolecules have the most potential to predict GDM.

show abstract

“…To study the nature of infectious diseases. In order to study the nature of the infectious disease, Liu et al [15] have analyzed the numerical value of the basic reproduction number of COVID-19. Fanelli and Piazza [16] have analyzed and forecasted the nature of COVID-19 spreading in China, Italy, and France until March 2020 by developing mathematical modelling.…”

Section: Introductionmentioning

confidence: 99%

Clustering of COVID-19 data for knowledge discovery using c-means and fuzzy c-means

Afzal¹,

Ansari²,

Alshahrani³

et al. 2021

Results in Physics

View full text Add to dashboard Cite

In this work, the partitioning clustering of COVID-19 data using c-Means (cM) and Fuzy c-Means (Fc-M) algorithms is carried out. Based on the data available from January 2020 with respect to location, i.e., longitude and latitude of the globe, the confirmed daily cases, recoveries, and deaths are clustered. In the analysis, the maximum cluster size is treated as a variable and is varied from 5 to 50 in both algorithms to find out an optimum number. The performance and validity indices of the clusters formed are analyzed to assess the quality of clusters. The validity indices to understand all the COVID-19 clusters' quality are analysed based on the Zahid SC (Separation Compaction) index, Xie-Beni Index, Fukuyama–Sugeno Index, Validity function, PC (performance coefficient), and CE (entropy) indexes. The analysis results pointed out that five clusters were identified as a major centroid where the pandemic looks concentrated. Additionally, the observations revealed that mainly the pandemic is distributed easily at any global location, and there are several centroids of COVID-19, which primarily act as epicentres. However, the three main COVID-19 clusters identified are 1) cases with value <50,000, 2) cases with a value between 0.1 million to 2 million, and 3) cases above 2 million. These centroids are located in the US, Brazil, and India, where the rest of the small clusters of the pandemic look oriented. Furthermore, the Fc-M technique seems to provide a much better cluster than the c-M algorithm.

show abstract

Effects of blood glucose on vaspin secretion in patients with gestational diabetes mellitus

Cited by 8 publications

References 16 publications

The Role of Dietary Carbohydrates in Gestational Diabetes

The Role of Dietary Carbohydrates in Gestational Diabetes

Novel Biomolecules in the Pathogenesis of Gestational Diabetes Mellitus

Clustering of COVID-19 data for knowledge discovery using c-means and fuzzy c-means

Contact Info

Product

Resources

About