scholarly journals Liraglutide-induced structural modulation of the gut microbiota in patients with type 2 diabetes mellitus

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11128
Author(s):  
Junjie Shang ◽  
Fang Liu ◽  
Bing Zhang ◽  
Kunlun Dong ◽  
Man Lu ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Treatment Group ◽  
Gut Microbiota ◽  
Treatment Response ◽  
Blood Urea Nitrogen ◽  
Baseline Hba1c

Accumulating evidence has suggested the importance of gut microbiota in the development of type 2 diabetes mellitus (T2DM). In the present study, 40 patients with T2DM were treated with liraglutide for 4 months. Feces samples and clinical characteristics were collected from these 40 T2DM patients before and after the liraglutide treatment. The diversity and composition of gut microbiota in the two groups were determined by sequencing the V4 region of bacterial 16S rRNA genes. Meanwhile, blood glucose, insulin, hemoglobin A1c (HbA1c), and lipid metabolism were also measured in the pre- and post-liraglutide-treatment groups. We find that Baseline HbA1c was associated with liraglutide treatment response (R2 = 0.527, β =  − 0.726, p < 0.0001). After adjusted for baseline HbA1c, blood urea nitrogen was associated with liraglutide treatment response. Besides, our results showed reduced gut microbial alpha diversity, different community structure distribution and altered microbial interaction network in patients treated with liraglutide. The liner discriminant analysis (LDA) effect size (LEfSe) analysis showed that 21 species of bacteria were abundant in the pre-liraglutide-treatment group and 15 species were abundant in the post-liraglutide-treatment group. In addition, we also find that Megamonas were significantly correlated with older age, diabetes duration and diabetic retinopathy, Clostridum were significantly correlated with family history of diabetes and Oscillospira were significantly correlated with both diabetic retinopathy and diabetic peripheral neuropathy. Functional analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) and cluster of orthologous groups (COG) annotations enriched three KEGG metabolic pathways and six functional COG categories in the post-liraglutide-treatment group. In conclusion, our research suggests that baseline HbA1c, blood urea nitrogen and gut microbiota are associated with the liraglutide treatment applied on patients with T2DM. These findings may contribute to the beneficial effects of liraglutide against diabetes.

scholarly journals Study of Correlation between Renal Function Test and Severity of Diabetic Retinopathy in Patients with Type 2 Diabetes Mellitus

2021 ◽  
Vol 10 (20) ◽  
pp. 1511-1514
Author(s):  
Archana Ramkrishna Thool ◽  
Nikunj Kishore Dhande ◽  
Sachin Vishwanath Daigavane
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Renal Function ◽  
Diabetic Retinopathy ◽  
Serum Creatinine ◽  
Blood Urea Nitrogen ◽  
Glycosylated Haemoglobin ◽  
Function Test

BACKGROUND Retinopathy and nephropathy are chronic vascular complications of type 2 diabetes mellitus, this eventually leads to end stage renal disease and blindness. Diabetic retinopathy is an important cause of legal blindness in 20 - 70 years. The purpose of the study was to establish association between severity of diabetic retinopathy with systemic levels of glycosylated haemoglobin and renal function test. METHODS This is a cross sectional study conducted among 75 patients with type 2 diabetes mellitus attending the ophthalmology out-patient department (OPD) of Acharya Vinoba Bhave Hospital, Wardha and patients referred from the hospital. Detailed fundus examination and staging of diabetic retinopathy (DR) was done. Glycosylated haemoglobin levels, serum creatinine and blood urea nitrogen were measured. RESULTS Majority of patients were in the age range of 61 to 70 years. Mean ± SD of 3 parameters in patients with no DR, mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR and proliferative diabetic retinopathy (PDR) were as follows: Serum creatinine 1.12 ± 0.41, 1.21 ± 0.53, 1.35 ± 0.49, 1.55 ± 0.22 and 1.70 ± 0.23 respectively. P = 0.007. Blood urea nitrogen (BUN) 28.62 ± 4.20, 31.83 ± 7.20, 37.31 ± 12.57, 44.21 ± 7.53 and 48.76 ± 5.08 respectively. P = 0.0001. HbA1c l, 6.72 ± 0.60, 8.14 ± 0.98, 8.52 ± 2.67, 9.96 ± 1.22 and 12.14 ± 1.96 respectively. P = 0.0001. All the 3 parameters were statistically significant. 40 % of cases had clinically significant macular oedema. CONCLUSIONS Poor glycaemic control as seen by higher levels of glycosylated haemoglobin and deranged renal function is associated with severe form of DR. KEY WORDS Diabetes Mellitus, Diabetic Retinopathy, Serum Creatinine HbA1c and BUN

Angiopoietin 1 in patients with type 2 diabetes mellitus and diabetic retinopathy

2015 ◽  
Author(s):  
Sattar El-Deeb Abd El ◽  
Mohamed Halawa ◽  
Ahmed Saad ◽  
Inas Sabry ◽  
Maram Mahdy ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Angiopoietin 1

Relationship between diabetic retinopathy and cerebral perfusion in type 2 diabetes mellitus

2018 ◽  
Vol 71 (1) ◽  
pp. 49-53
Author(s):  
N. Zherdiova ◽  
◽  
N. Medvedovska ◽  
B Mankovsky ◽  
◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Cerebral Perfusion

scholarly journals Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus

2021 ◽  
Vol 9 (1) ◽  
pp. e001443
Author(s):  
Jingjing Zuo ◽  
Yuan Lan ◽  
Honglin Hu ◽  
Xiangqing Hou ◽  
Jushuang Li ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Permutation Test ◽  
Linear Regression Models ◽  
Characteristic Analysis ◽  
Network Biomarkers ◽  
Validation Set

IntroductionDespite advances in diabetic retinopathy (DR) medications, early identification is vitally important for DR administration and remains a major challenge. This study aims to develop a novel system of multidimensional network biomarkers (MDNBs) based on a widely targeted metabolomics approach to detect DR among patients with type 2 diabetes mellitus (T2DM) efficiently.Research design and methodsIn this propensity score matching-based case-control study, we used ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system for serum metabolites assessment of 69 pairs of patients with T2DM with DR (cases) and without DR (controls). Comprehensive analysis, including principal component analysis, orthogonal partial least squares discriminant analysis, generalized linear regression models and a 1000-times permutation test on metabolomics characteristics were conducted to detect candidate MDNBs depending on the discovery set. Receiver operating characteristic analysis was applied for the validation of capability and feasibility of MDNBs based on a separate validation set.ResultsWe detected 613 features (318 in positive and 295 in negative ESI modes) in which 63 metabolites were highly relevant to the presence of DR. A panel of MDNBs containing linoleic acid, nicotinuric acid, ornithine and phenylacetylglutamine was determined based on the discovery set. Depending on the separate validation set, the area under the curve (95% CI), sensitivity and specificity of this MDNBs system were 0.92 (0.84 to 1.0), 96% and 78%, respectively.ConclusionsThis study demonstrates that metabolomics-based MDNBs are associated with the presence of DR and capable of distinguishing DR from T2DM efficiently. Our data also provide new insights into the mechanisms of DR and the potential value for new treatment targets development. Additional studies are needed to confirm our findings.

scholarly journals Gut microbiota influence in type 2 diabetes mellitus (T2DM)

Gut Pathogens ◽  
2021 ◽  
Vol 13 (1) ◽  
Author(s):  
A. L. Cunningham ◽  
J. W. Stephens ◽  
D. A. Harris
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Gut Microbiota ◽  
Potential Role ◽  
Evidence Base ◽  
Human Metabolism ◽  
Altered Glucose

AbstractA strong and expanding evidence base supports the influence of gut microbiota in human metabolism. Altered glucose homeostasis is associated with altered gut microbiota, and is clearly associated with the development of type 2 diabetes mellitus (T2DM) and associated complications. Understanding the causal association between gut microbiota and metabolic risk has the potential role of identifying susceptible individuals to allow early targeted intervention.

scholarly journals Adherence to medication among Tunisian adults with type 2 diabetes

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
A Amara ◽  
R Ghammem ◽  
N Zammit ◽  
S BenFredj ◽  
J Maatoug ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
University Hospital ◽  
Health Concern ◽  
Eligibility Criteria ◽  
Cross Sectional ◽  
Adherence To Medication

Abstract Introduction Diabetes mellitus is a growing public health concern. Despite compelling evidence about the effectiveness of medications, studies have indicated that less than 50% of patients achieved therapeutic targets. The aim of this study was to assess the adherence to type 2 diabetes mellitus treatment and its determinants. Methods A cross-sectional study was conducted between April and June 2017 in the Endocrinology and internal medicine departments of Farhat Hached University Hospital in Sousse, Tunisia. A convenient sample of patients who fulfilled the eligibility criteria was recruited. A pre-tested questionnaire was used to gather information. This was followed by assessing patients' adherence to diabetes medications using the eight-item Morisky Medication Adherence Scale (MMAS-8). Results A total of 330 patients with Type 2 Diabetes Mellitus participated in this study. The mean ±SD age of patients was 58.96±10.3 with female predominance (60.3%). More than half of participants were with high cardiovascular risk. In most cases (70.6 %), participants were moderate adherent. Results showed that patients become non-adherent as the disease gets older (p = 0.001). In addition patients with health insurance were significantly more adherent comparing to those who did not have it (p = 0.01). Regarding self-care practices and other metabolic risk factors' effects, our data revealed that exercising 30 minutes below than 5 times in week and poor self-management of diet were associated with low adherence (p &lt; 10-3). On the other hand, patients who have started insulin therapy were less adherent than those who had not yet (0.01). Patients with diabetic retinopathy or maculopathy were significantly more prone to be non- adherent, with respective percentage of 39.1% and 37.5%. Conclusions This study provides insights into the determinants of non-adherence, ultimately guiding the effective interventions through development of structured long-term policies not yet implemented. Key messages In most cases (70.6 %), participants were moderate adherent. Patients with diabetic retinopathy or maculopathy were significantly more prone to be non- adherent.

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