Methodology: A total of 205 patients who fit eligibility criteria were included in the study. A questionnaire was completed, and blood was drawn to study vitamin B12 levels. Vitamin B12 deficiency was defined as serum B12 level of ≤300 pg/mL (221 pmol/L).
Results: The prevalence of vitamin B12 deficiency among metformin-treated patients with type 2 DM patients was 28.3% (n=58). The median vitamin B12 level was 419 (±257) pg/mL. The non-Malay population was at a higher risk for metformin-associated vitamin B12 deficiency [adjusted odds ratio (OR) 3.86, 95% CI: 1.836 to 8.104, p<0.001]. Duration of metformin use of more than five years showed increased risk for metformin-associated vitamin B12 deficiency (adjusted OR 2.06, 95% CI: 1.003 to 4.227, p=0.049).
Conclusion: Our study suggests that the prevalence of vitamin B12 deficiency among patients with type 2 diabetes mellitus on metformin in our population is substantial. This is more frequent among the non-Malay population and those who have been on metformin for more than five years.
OBJECTIVES: To assess the effects of colesevelam for type 2 diabetes mellitus.
SEARCH METHODS: Several electronic databases were searched, among these The Cochrane Library (Issue 1, 2012), MEDLINE, EMBASE, CINAHL, LILACS, OpenGrey and Proquest Dissertations and Theses database (all up to January 2012), combined with handsearches. No language restriction was used.
SELECTION CRITERIA: We included randomised controlled trials (RCTs) that compared colesevelam with or without other oral hypoglycaemic agents with a placebo or a control intervention with or without oral hypoglycaemic agents.
DATA COLLECTION AND ANALYSIS: Two review authors independently selected the trials and extracted the data. We evaluated risk of bias of trials using the parameters of randomisation, allocation concealment, blinding, completeness of outcome data, selective reporting and other potential sources of bias.
MAIN RESULTS: Six RCTs ranging from 8 to 26 weeks investigating 1450 participants met the inclusion criteria. Overall, the risk of bias of these trials was unclear or high. All RCTs compared the effects of colesevelam with or without other antidiabetic drug treatments with placebo only (one study) or combined with antidiabetic drug treatments. Colesevelam with add-on antidiabetic agents demonstrated a statistically significant reduction in fasting blood glucose with a mean difference (MD) of -15 mg/dL (95% confidence interval (CI) -22 to - 8), P < 0.0001; 1075 participants, 4 trials, no trial with low risk of bias in all domains. There was also a reduction in glycosylated haemoglobin A1c (HbA1c) in favour of colesevelam (MD -0.5% (95% CI -0.6 to -0.4), P < 0.00001; 1315 participants, 5 trials, no trial with low risk of bias in all domains. However, the single trial comparing colesevelam to placebo only (33 participants) did not reveal a statistically significant difference between the two arms - in fact, in both arms HbA1c increased. Colesevelam with add-on antidiabetic agents demonstrated a statistical significant reduction in low-density lipoprotein (LDL)-cholesterol with a MD of -13 mg/dL (95% CI -17 to - 9), P < 0.00001; 886 participants, 4 trials, no trial with low risk of bias in all domains. Non-severe hypoglycaemic episodes were infrequently observed. No other serious adverse effects were reported. There was no documentation of complications of the disease, morbidity, mortality, health-related quality of life and costs.
AUTHORS' CONCLUSIONS: Colesevelam added on to antidiabetic agents showed significant effects on glycaemic control. However, there is a limited number of studies with the different colesevelam/antidiabetic agent combinations. More information on the benefit-risk ratio of colesevelam treatment is necessary to assess the long-term effects, particularly in the management of cardiovascular risks as well as the reduction in micro- and macrovascular complications of type 2 diabetes mellitus. Furthermore, long-term data on health-related quality of life and all-cause mortality also need to be investigated.
MATERIAL AND METHODS: This was a five-year retrospective open cohort study using secondary data from the National Diabetes Registry. The study setting was all public health clinics (n = 47) in the state of Negeri Sembilan, Malaysia. Time to treatment intensification was defined as the number of years from the index year until the addition of another oral antidiabetic drug or initiation of insulin. Life table survival analysis based on best-worst case scenarios was used to determine the time to treatment intensification. Discrete-time proportional hazards model was fitted for the factors associated with treatment intensification.
RESULTS: The mean follow-up duration was 2.6 (SD 1.1) years. Of 7,646 patients, the median time to treatment intensification was 1.29 years (15.5 months), 1.58 years (19.0 months) and 2.32 years (27.8 months) under the best-, average- and worst-case scenarios respectively. The proportion of patients with treatment intensification was 45.4% (95% CI: 44.2-46.5), of which 34.6% occurred only after one year. Younger adults, overweight, obesity, use of antiplatelet medications and poorer HbA1c were positively associated with treatment intensification. Patients treated with more oral antidiabetics were less likely to have treatment intensification.
CONCLUSION: Clinical inertia is present in the management of T2D patients in Malaysian public health clinics. We recommend further studies in lower- and middle-income countries to explore its causes so that targeted strategies can be developed to address this issue.
Methods: A cross-sectional study was conducted at the Universiti Kebangsaan Malaysia Medical Centre (UKMMC) using outpatient population diabetic patients. Demographic data on social and clinical characteristics were collected from participants. Several questionnaires were administered, including the Beck Depression Inventory-II (BDI-II) to measure depressive symptoms, the Generalized Anxiety Disorder-7 (GAD-7) to assess anxiety symptoms, the Big Five Inventory (BFI) to evaluate personality traits, and the WHO Quality of Life-BREF (WHOQOL-BREF) to assess QOL. Multivariate binary logistic regression was performed to determine the predictors of poor glycaemic control.
Results: 300 patients with diabetes mellitus were recruited, with the majority (90%) having type 2 diabetes. In this population, the prevalence of poor glycaemic control (HbA1C ≥ 7.0%) was 69%, with a median HbA1C of 7.6% (IQR = 2.7). Longer duration of diabetes mellitus and a greater number of days of missed medications predicted poor glycaemic control, while older age and overall self-perception of QOL protected against poor glycaemic control. No psychological factors were associated with poor glycaemic control.
Conclusion: This study emphasizes the importance of considering the various factors that contribute to poor glycaemic control, such as duration of diabetes, medication adherence, age, and QOL. These findings should be used by clinicians, particularly when planning a multidisciplinary approach to the management of diabetes.