Baseline factors associated with glycaemic response to treatment with once-weekly dulaglutide in patients with type 2 diabetes

2016 ◽  
Vol 18 (11) ◽  
pp. 1138-1142 ◽  
Author(s):  
Carol Wysham ◽  
Bruno Guerci ◽  
David D'Alessio ◽  
Nan Jia ◽  
Fady T. Botros
Keyword(s):  
Type 2 Diabetes ◽  
Response To Treatment ◽  
Glycaemic Response ◽  
Factors Associated ◽  
Baseline Factors

Predictive factors associated with glycaemic response to exenatide in Chinese patients with type 2 diabetes mellitus

2020 ◽  
Vol 45 (5) ◽  
pp. 1050-1057 ◽  
Author(s):  
Tao Wang ◽  
Fan Zhang ◽  
Xiaotong Wang ◽  
Xizhi Li ◽  
Hongwei Ling ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Predictive Factors ◽  
Chinese Patients ◽  
Glycaemic Response ◽  
Factors Associated

scholarly journals Baseline Factors Associated With Glycemic Control and Weight Loss When Exenatide Twice Daily Is Added to Optimized Insulin Glargine in Patients With Type 2 Diabetes

Diabetes Care ◽  
2012 ◽  
Vol 35 (5) ◽  
pp. 955-958 ◽  
Author(s):  
J. Rosenstock ◽  
S. K. Shenouda ◽  
R. M. Bergenstal ◽  
J. B. Buse ◽  
L. C. Glass ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Weight Loss ◽  
Glycemic Control ◽  
Insulin Glargine ◽  
Factors Associated ◽  
Baseline Factors

scholarly journals Fasting plasma triglycerides predict the glycaemic response to treatment of Type 2 diabetes by gastric electrical stimulation. A novel lipotoxicity paradigm

2013 ◽  
Vol 30 (6) ◽  
pp. 687-693 ◽  
Author(s):  
H. E. Lebovitz ◽  
B. Ludvik ◽  
I. Yaniv ◽  
W. Haddad ◽  
T. Schwartz ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Electrical Stimulation ◽  
Gastric Electrical Stimulation ◽  
Response To Treatment ◽  
Glycaemic Response ◽  
Plasma Triglycerides ◽  
Fasting Plasma

Predictors of glycaemic response and impact on weight and BP of dapagliflozin therapy in type 2 diabetes

2015 ◽  
Author(s):  
Feicong Huang ◽  
Lyndsey McConnell ◽  
Christopher Sainsbury ◽  
Gregory Jones
Keyword(s):  
Type 2 Diabetes ◽  
Glycaemic Response

2209-PUB: Factors Associated with Switching from Sulfonylureas (SUs) to Dipeptidyl Peptidase-4 Inhibitors (DPP-4i) among Individuals with Type 2 Diabetes in the United States

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2209-PUB
Author(s):  
XI TAN ◽  
LINGFENG YANG ◽  
KAMLESH KHUNTI ◽  
RUYA ZHANG ◽  
DIANA ZHANG ◽  
...  
Keyword(s):  
United States ◽  
Type 2 Diabetes ◽  
The United States ◽  
Dipeptidyl Peptidase ◽  
Dipeptidyl Peptidase 4 ◽  
Factors Associated ◽  
Dipeptidyl Peptidase 4 Inhibitors

The Probability of A1C Goal Attainment in Patients with Uncontrolled Type-2 Diabetes in a Large Integrated Delivery System: A Prediction Model

2020 ◽  
Author(s):  
Kevin M Pantalone ◽  
Anita D Misra-Hebert ◽  
Todd M Hobbs ◽  
Sheldon X Kong ◽  
Xinge Ji ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Goal Attainment ◽  
Index Date ◽  
Cleveland Clinic ◽  
Patient Characteristics ◽  
Psychiatric Disease ◽  
Lower Probability ◽  
Factors Associated ◽  
One Year

<b>Objective:</b> To assess patient characteristics and treatment factors associated with uncontrolled type 2 diabetes (T2D) and the probability of A1C goal attainment. <p><b>Research Design and Methods</b>: Retrospective cohort study using the electronic health record at Cleveland Clinic. Patients with uncontrolled T2D (A1C>9%) were identified on the index date of 12/31/2016 (n=6,973), grouped by attainment (n=1,653 [24.7%) or non-attainment (n=5,320 [76.3%]) of A1C<8% by 12/31/2017, and subgroups compared on a number of demographic and clinical variables. Based on these variables, a nomogram was created for predicting probability of A1C goal attainment. </p> <p><b>Results:</b> For the entire population, median age at index date was 57.7 years (53.3% male), and the majority were white (67.2%). Median A1C was 10.2%. Obesity (50.6%), cardiovascular disease (46.9%) and psychiatric disease (61.1%) were the most common comorbidities. Metformin (62.7%) and sulfonylureas (38.7%) were the most common anti-diabetes medications. Only 1,653 (24%) patients achieved an A1C <8%. Predictors of increased probability of A1C goal attainment were older age, white/non-Hispanic race/ethnicity, Medicare health insurance, lower baseline A1C, higher frequency of endocrinology/primary care visits, DPP-4i use, thiazolidinedione use, metformin use, GLP-1RA use, and fewer classes of anti-diabetes drugs. Factors associated with lower probability included insulin use and longer time in the T2D database (both presumed as likely surrogates for duration of T2D). </p> <p><b>Conclusions:</b> A minority of patients with an A1C>9% achieved an A1C<8% at one year. While most identified predictive factors are non-modifiable by the clinician, pursuit of frequent patient engagement and tailored drug regimens may help improve A1C goal attainment. </p>

Risk Factors Associated With Hyperuricemia in Patients with Diabetes Type 2: About 190 Cases

2020 ◽  
Vol 2 (1) ◽  
pp. 12-16
Author(s):  
Fennoun H ◽  
Haraj NE ◽  
El Aziz S ◽  
Bensbaa S ◽  
Chadli A
Keyword(s):  
Type 2 Diabetes ◽  
Heart Disease ◽  
Glycemic Control ◽  
Ischemic Heart Disease ◽  
Diabetes Type 2 ◽  
Diabetes Type ◽  
Ischemic Heart ◽  
Factors Associated ◽  
Patients With Diabetes

Introduction: Hyperuricemia is common Type 2 diabetes at very high cardiovascular risk. Objective: Evaluate the relationship between hyperuricemia and diabetes type 2, and determine its predictive factors in this population. Patients and Methods: Retrospective study cross including 190 patients with diabetes type 2 hospitalized Service of Endocrinology of CHU Ibn Rushd Casablanca from January 2015 to December 2017. Hyperuricemia was defined as a serum uric acid concentration> 70 mg/L (men) and> 60 mg/L (women). The variables studied were the anthropometric measurements), cardiovascular factors (tobacco, hypertension, dyslipidemia), and degenerative complications (retinopathy, neuropathy, kidney failure, ischemic heart disease). The analyzes were performed by SPSS software. Results: Hyperuricemia was found in 26.5% of patients with a female predominance (76%), an average age of 55.9 years, and an average age of 12.4ans diabetes. The glycemic control was found in 84.6% of cases with mean glycated hemoglobin 8.6%. Factors associated al hyperuricemia were the blood pressure in 86% (p <0.05), dyslipidemia in 76.3% of cases (p <0.001) with hypertriglyceridemia in 48.3% of cases (p <0.02), and a hypoHDLémie 28% (p <0.001). The age, obesity, smoking, and glycemic control were associated significantly n al hyperuricemia. The research of degenerative complications of hyperuricemia has objectified renal impairment (GFR between 15 and 60ml / min) chez47% (p <0.001), it was kind of moderate in 35.8% (p <0.01) and severe in 5.1% (p <0.02), ischemic heart disease was found in 34% of cases (p <0.01). Conclusion: In our study, hyperuricemia in type 2 diabetes is common in female patients, especially with hypertension, dyslipidemia, and renal failure. Other factors such as age, obesity, smoking is not associated with hyperuricemia in type 2 diabetics.

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