OBJECTIVE: This study aimed to evaluate the impact of CMI on medication adherence and glycaemic control among patients with type 2 diabetes in Qatar.
METHODS: We developed and customised CMI for all the anti-diabetic medications used in Qatar. A randomised controlled trial in which the intervention group patients (n = 66) received the customised CMI with usual care, while the control group patients (n = 74) received usual care only, was conducted. Self-reported medication adherence and haemoglobin A1c (HbA1c ) were the primary outcome measures. Glycaemic control and medication adherence parameters were measured at baseline, 3 months, and 6 months in both groups. Medication adherence was measured using the 8-item Morisky Medication Adherence Scale (MMAS-8).
RESULTS: Although the addition of CMI resulted in better glycaemic control, this did not reach statistical significance, possibly because of the short-term follow-up. The median MMAS-8 score improved from baseline (6.6 [IQR = 1.5]) to 6-month follow-up (7.0 [IQR = 1.00]) in the intervention group. In addition, there was a statistically significant difference between the intervention and the control groups in terms of MMAS-8 score at the third visit (7.0 [IQR = 1.0]) vs 6.5 (IQR = 1.25; P-value = .010).
CONCLUSION: CMI for anti-diabetic medications when added to usual care has the potential to improve medication adherence and glycaemic control among patients with type 2 diabetes. Therefore, providing better health communication and CMI to patients with diabetes is recommended.
METHOD: A multicenter cross-sectional observational study was conducted in 388 diabetes patients attending daily diabetes clinics and teaching hospitals in Pakistan's twin city between August 2019 and February 2020. The chi-square test and linear regression were used to detect RLS-related factors in type 2 diabetes mellitus.
RESULTS: The prevalence of RLS found was; 3.1% patients with diabetes were suffering from very severe RLS, 23.5% from severe RLS, 34% from moderate RLS, 21.1% from mild RLS and 18.3% from non-RLS. Gender, age, education, blood glucose fasting (BSF), blood glucose random (BSR) and HBA1c were found to be significant predictors of RLS in patients with diabetes.
CONCLUSION: Policy makers can develop local interventions to curb the growing RLS prevalence by keeping in control the risk factors of RLS in people living with type 2 diabetes.
RESULTS: In this study, L-cells were isolated from a primary intestinal cell line to create suitable target cells for insulin expression studies. The isolated cells displayed L-cell properties and were therefore used as an L-cell surrogate. Next, the isolated L-cells were transfected with the recombinant plasmid consisting of an insulin gene located downstream of the GLP-1 promoter. The secretion tests revealed that an increase in glucose concentration from 5 mM to 25 mM induced insulin gene expression in the L-cells by 2.7-fold. Furthermore, L-cells quickly responded to the glucose stimulation; the amount of insulin protein increased 2-fold in the first 30 minutes and then reached a plateau after 90 minutes.
CONCLUSION: Our data showed that L-cells efficiently produced the mature insulin protein. In addition, the insulin protein secretion was positively regulated with glucose induction. In conclusion, GLP-1 promoter and L-cell could be potential candidates for diabetes gene therapy agents.
METHODS: A total of 1844 (780 males and 1064 females) known diabetics aged ≥ 35 years were identified from the South East Asia Community Observatory (SEACO) health and demographic surveillance site database.
RESULTS: 41.3% of the sample had poor glycaemic control. Poor glycaemic control was associated with age and ethnicity, with older participants (65+) better controlled than younger adults (45-54), and Malaysian Indians most poorly controlled, followed by Malay and then Chinese participants. Metabolic risk factors were also highly associated with poor glycaemic control.
CONCLUSIONS: There is a critical need for evidence for a better understanding of the mechanisms of the associations between risk factors and glycaemic control.
MATERIALS AND METHODS: Data was collected using a self-administered pilot-tested questionnaire. Dentists awareness about link between oral and systemic link was assessed on five point likert scale. Data was entered and analysed using SPSS.
RESULTS: Of the 588 dentists, 500 completed the questionnaire (response rate 85.03%). About 93% of the participants (mean age 25.82 ± 4.21 years) agreed that oral health was associated with systemic health. Most dentists were aware of a connection between periodontal disease and diabetes (84.4%) and heart disease (70.2%). Similarly, 85.6% believed in the negative impact of oral disease on the quality of life of patients. More female than male dentists were aware of the relationship between periodontal disease and adverse pregnancy outcomes, diabetes, and rheumatoid arthritis (P < 0.001). Most dentists (97%) believed that more patients would seek oral care if they were aware of the oral-systemic link. After adjustments, private dentists were 4.65 times more likely than public dentists to believe in improving access to oral care with increased patient awareness of the oral-systemic connection (P = 0.011).
CONCLUSIONS: Most dentists were aware of the oral-systemic link. They believed that patients' access to oral care would improve if they were aware of a connection between oral and systemic health. Therefore, patients should be informed of the oral-systemic link to improve their oral health.