PATIENTS & METHODS: DPP4, WFS1 and KCNJ11 gene polymorphisms were genotyped in a cohort study of 662 T2DM patients treated with DPP-4 inhibitors sitagliptin, vildagliptin or linagliptin. Genotyping was performed by Applied Biosystems TaqMan SNP genotyping assay.
RESULTS: Patients with triglyceride levels less than 1.7 mmol/l (odds ratio [OR]: 2.2.; 95% CI: 1.031-4.723), diastolic blood pressure (DBP) less than 90 mmHg (OR: 1.7; 95% CI: 1.009-2.892) and KCNJ11 rs2285676 (genotype CC) (OR: 2.0; 95% CI: 1.025-3.767) were more likely to response to DPP-4 inhibitor treatment compared with other patients, as measured by HbA1c levels.
CONCLUSION: Triglycerides, DBP and KCNJ11 rs2285676 are predictors of the DPP-4 inhibitor treatment response in T2DM patients.
PURPOSE: This study aimed to identify antidiabetic regimens as well as other factors that associated with glycemic control in T2DM patients with different stages of chronic kidney disease (CKD).
PATIENTS AND METHODS: This retrospective, cross-sectional study involved 242 T2DM inpatients and outpatients with renal complications from January 2009 to March 2014 and was conducted in a tertiary teaching hospital in Malaysia. Glycated hemoglobin (A1C) was used as main parameter to assess patients' glycemic status. Patients were classified to have good (A1C <7%) or poor glycemic control (A1C ≥7%) based on the recommendations of the American Diabetes Association.
RESULTS: Majority of the patients presented with CKD stage 4 (43.4%). Approximately 55.4% of patients were categorized to have poor glycemic control. Insulin (57.9%) was the most commonly prescribed antidiabetic medication, followed by sulfonylureas (43%). Of all antidiabetic regimens, sulfonylureas monotherapy (P<0.001), insulin therapy (P=0.005), and combination of biguanides with insulin (P=0.038) were found to be significantly associated with glycemic control. Other factors including duration of T2DM (P=0.004), comorbidities such as anemia (P=0.024) and retinopathy (P=0.033), concurrent medications such as erythropoietin therapy (P=0.047), α-blockers (P=0.033), and antigouts (P=0.003) were also correlated with A1C.
CONCLUSION: Identification of factors that are associated with glycemic control is important to help in optimization of glucose control in T2DM patients with renal complication.
METHODS: As a quasi-experimental design with a control group and pre-tests., we examined 1,598 medical records of T2DM subjects in six healthcare facilities in the state of Pahang, Malaysia. In all study sites, there was a pre and post-intervention assessment of the percentage of appropriate statin therapy prescribing that complied with the clinical guidelines with no potential safety issues. The intervention was an academic detailing program offered to the health care providers in three study sites, while the other three sites served as the control group. A comparison of the overall percentage of appropriate statin therapy prescribing before and after the academic detailing was performed in all intervention and control sites.
RESULTS: Overall, 797 medical records were examined in the pre-intervention phase, and 801 records were evaluated in the post-intervention phase. The academic detailing program was associated with a statistically significant difference in the proportion of appropriate statin therapy prescribing between the post-intervention phase compared to the pre-intervention phase (n = 246, 61.7% versus n = 188, 47.1%), p = 0.001. Whereas, the appropriate statin therapy prescribing in the control study sites experienced a modest change from 53.8% (214/398) to 56.7% (228/402), p = 0.220. The academic detailing showed significant increases in the proportions of appropriate statin therapy prescribing in both hospital and primary care settings.
CONCLUSIONS: The academic detailing program was found to be significantly associated with a positive impact on the overall statin therapy prescribing among patients with T2DM in Malaysian hospital and primary care settings.
PURPOSE: To investigate the association of psychological factors, patients' knowledge, and management among ED patients.
PATIENTS AND METHODS: A total of 93 patients with an age range from 31 to 81 years who have undergone treatment for ED were included in this study.
RESULTS: It was found that the feeling of blame (P=0.001), guilt (P=0.001), anger or bitterness (P=0.001), depression (P=0.001), feeling like a failure (P=0.001), and the feeling of letting down a partner during intercourse (P=0.001) were significantly associated with ED. Age was also found to be significantly associated with patients' psychological scale (P=0.004). In addition, the majority of patients in this study practice the right method of administration of ED therapy. However, no significant correlation was found between patients' knowledge of ED therapy and demographic characteristics.
CONCLUSION: This study concluded that ED does affect psychological well-being of people. In addition, patient's knowledge about ED and its management is also crucial in ensuring that the patient achieves optimal therapeutic outcomes from ED therapy.
METHODS: A total of 220 T2DM patients from the University of Malaya Medical Centre, Malaysia, who had at least one CV complication and who had been taking at least one antidiabetic drug for at least 3 months, were included. The associations of antidiabetics, cardiovascular diseases, laboratory parameters, concurrent medications, comorbidities, demographics, and clinical characteristics with glycemic control were investigated.
RESULTS: Sulfonylureas in combination (P=0.002) and sulfonylurea monotherapy (P<0.001) were found to be associated with good glycemic control, whereas insulin in combination (P=0.051), and combination biguanides and insulin therapy (P=0.012) were found to be associated with poor glycemic control. Stroke (P=0.044) was the only type of CVD that seemed to be significantly associated with good glycemic control. Other factors such as benign prostatic hyperplasia (P=0.026), elderly patients (P=0.018), low-density lipoprotein cholesterol levels (P=0.021), and fasting plasma glucose (P<0.001) were found to be significantly correlated with good glycemic control.
CONCLUSION: Individualized treatment in T2DM patients with CVDs can be supported through a better understanding of the association between glycemic control and CV profiles in T2DM patients.
OBJECTIVE: This study sought to identify demographic, clinical, and genetic factors that may contribute to increased insulin resistance or worsening of glycaemic control in patients with T2DM.
SETTING: This prospective cohort study included 156 patients with T2DM and severe or acute hyperglycaemia who were treated with insulin at any medical ward of the National University of Malaysia Medical Centre.
METHOD: Insulin resistance was determined using the homeostatic model assessment-insulin resistance index. Glycaemic control during the episode of hyperglycaemia was assessed as the degree to which the patient achieved the target glucose levels. The polymerase chain reaction-restriction fragment length polymorphism method was used to identify polymorphisms in insulin receptor substrate (IRS) genes.
MAIN OUTCOME MEASURE: Identification of possible predictors (demographic, clinical, or genetic) for insulin resistance and glycaemic control during severe/acute hyperglycaemia.
RESULTS: A polymorphism in IRS1, r.2963 G>A (p.Gly972Arg), was a significant predictor of both insulin resistance [odds ratios (OR) 4.48; 95 % confidence interval (CI) 1.2-16.7; P = 0.03) and worsening of glycaemic control (OR 6.04; 95 % CI 0.6-64.6; P = 0.02). The use of loop diuretics (P < 0.05) and antibiotics (P < 0.05) may indirectly predict worsening of insulin resistance or glycaemic control in patients with severe/acute hyperglycaemia.
CONCLUSION: Clinical and genetic factors contribute to worsening of insulin resistance and glycaemic control during severe/acute hyperglycaemia in patients with T2DM. Early identification of factors that may influence insulin resistance and glycaemic control may help to achieve optimal glycaemic control during severe/acute hyperglycaemia.
METHODS: We assessed sCD26/DPP-IV levels, active GLP-1 levels, body mass index (BMI), glucose, insulin, A1c, glucose homeostasis indices, and lipid profiles in 549 Malaysian subjects (including 257 T2DM patients with MetS, 57 T2DM patients without MetS, 71 non-diabetics with MetS, and 164 control subjects without diabetes or metabolic syndrome).
RESULTS: Fasting serum levels of sCD26/DPP-IV were significantly higher in T2DM patients with and without MetS than in normal subjects. Likewise, sCD26/DPP-IV levels were significantly higher in patients with T2DM and MetS than in non-diabetic patients with MetS. However, active GLP-1 levels were significantly lower in T2DM patients both with and without MetS than in normal subjects. In T2DM subjects, sCD26/DPP-IV levels were associated with significantly higher A1c levels, but were significantly lower in patients using monotherapy with metformin. In addition, no significant differences in sCD26/DPP-IV levels were found between diabetic subjects with and without MetS. Furthermore, sCD26/DPP-IV levels were negatively correlated with active GLP-1 levels in T2DM patients both with and without MetS. In normal subjects, sCD26/DPP-IV levels were associated with increased BMI, cholesterol, and LDL-cholesterol (LDL-c) levels.
CONCLUSION: Serum sCD26/DPP-IV levels increased in T2DM subjects with and without MetS. Active GLP-1 levels decreased in T2DM patients both with and without MetS. In addition, sCD26/DPP-IV levels were associated with Alc levels and negatively correlated with active GLP-1 levels. Moreover, metformin monotherapy was associated with reduced sCD26/DPP-IV levels. In normal subjects, sCD26/DPP-IV levels were associated with increased BMI, cholesterol, and LDL-c.