MATERIALS AND METHODS: A total of 100 type 2 diabetes participants with stage 3-4 CKD were recruited. Blood for glycated hemoglobin (HbA1c ), serum 25(OH)D, renal and lipid profiles were drawn at enrollment. Correlation and regression analyses were carried out to assess the relationship of serum 25(OH)D, HbA1c and other metabolic traits.
RESULTS: A total of 30, 42, and 28% of participants were in CKD stage 3a, 3b and 4, respectively. The proportions of participants based on ethnicity were 51% Malay, 24% Chinese and 25% Indian. The mean (±SD) age and body mass index were 60.5 ± 9.0 years and 28.3 ± 5.9 kg/m2 , whereas mean HbA1c and serum 25(OH)D were 7.9 ± 1.6% and 37.1 ± 22.2 nmol/L. HbA1c was negatively correlated with serum 25(OH)D (rs = -0.314, P = 0.002), but positively correlated with body mass index (rs = 0.272, P = 0.006) and serum low-density lipoprotein cholesterol (P = 0.006). There was a significant negative correlation between serum 25(OH)D and total daily dose of insulin prescribed (rs = -0.257, P = 0.042). Regression analyses showed that every 10-nmol/L decline in serum 25(OH)D was associated with a 0.2% increase in HbA1c .
CONCLUSIONS: Lower serum 25(OH)D was associated with poorer glycemic control and higher insulin use among multi-ethnic Asians with type 2 diabetes and stage 3-4 CKD.
METHODS: This is a cross-sectional study within the baseline data from the impact evaluation of the Enhanced Primary Health Care (EnPHC) intervention on 40 public clinics in Malaysia. Patients aged 30 and above, diagnosed with T2D, had a clinic visit for T2D between 01 Nov 2016 and 30 April 2017 and had at least one HbA1c, SBP and LDL-C measurement within 1 year from the date of visit were included for analysis. Multilevel linear regression adjusting for patient and clinic characteristics was used to quantify variation at the clinic and patient levels for each outcome.
RESULTS: Variation in intermediate clinical outcomes in T2D lies predominantly (93% and above) at the patient level. The strongest predictors for poor disease control in T2D were the proxy measures for disease severity including duration of diabetes, presence of microvascular complications, being on insulin therapy and number of antihypertensives. Among the three outcomes, HbA1c and LDL-C results provide greatest opportunity for improvement.
CONCLUSION: Clinic variation in HbA1c, SBP and LDL-C accounts for a small percentage from total variation. Findings from this study suggest that standardised interventions need to be applied across all clinics, with a focus on customizing therapy based on individual patient characteristics.
RESEARCH DESIGN AND METHODS: Multinational, prospective cohort study to assess the prevalence of newborns free from major congenital malformations or perinatal or neonatal death (primary end point) following treatment with insulin detemir (detemir) versus other basal insulins.
RESULTS: Of 1,457 women included, 727 received detemir and 730 received other basal insulins. The prevalence of newborns free from major congenital malformations or perinatal or neonatal death was similar between detemir (97.0%) and other basal insulins (95.5%) (crude risk difference 0.015 [95% CI -0.01, 0.04]; adjusted risk difference -0.003 [95% CI -0.03, 0.03]). The crude prevalence of one or more congenital malformations (major plus minor) was 9.4% vs. 12.6%, with a similar risk difference before (-0.032 [95% CI -0.064, 0.000]) and after (-0.036 [95% CI -0.081, 0.009]) adjustment for confounders. Crude data showed lower maternal HbA1c during the first trimester (6.5% vs. 6.7% [48 vs. 50 mmol/mol]; estimated mean difference -0.181 [95% CI -0.300, -0.062]) and the second trimester (6.1% vs. 6.3% [43 vs. 45 mmol/mol]; -0.139 [95% CI -0.232, -0.046]) and a lower prevalence of major hypoglycemia (6.0% vs. 9.0%; risk difference -0.030 [95% CI -0.058, -0.002]), preeclampsia (6.4% vs. 10.0%; -0.036 [95% CI -0.064, -0.007]), and stillbirth (0.4% vs. 1.8%; -0.013 [95% CI -0.024, -0.002]) with detemir compared with other basal insulins. However, differences were not significant postadjustment.
CONCLUSIONS: Insulin detemir was associated with a similar risk to other basal insulins of major congenital malformations, perinatal or neonatal death, hypoglycemia, preeclampsia, and stillbirth.
Methods: The Iraqi Anti-Diabetic Medication Adherence Scale (IADMAS) consists of eight items. The face and content validity of the IADMAS were established via an expert panel. For convergent validity, the IADMAS was compared with the Medication Adherence Questionnaire (MAQ). For concurrent validity, the IADMAS was compared with glycosylated hemoglobin. A total of 84 patients with types 2 diabetes were recruited from a diabetes center in Baghdad, Iraq. Test-retest reliability was measured by readministering the IADMAS to the same patients 4 weeks later.
Results: Only 80 patients completed the study (response rate: 95%). Reliability analysis of the IADMAS showed a Cronbach's alpha value of 0.712, whereas that of the MAQ was 0.649. All items in the IADMAS showed no significant difference in the test-retest analysis, indicating that the IADMAS has stable reliability. There was no difference in the psychometric properties of the IADMAS and the MAQ. The sensitivity and specificity of the IADMAS were higher than that of the MAQ (100% vs 87.5% and 33.9% vs 29.7%, respectively).
Conclusion: The IADMAS developed in this study is a reliable and valid instrument for assessing antidiabetic medication adherence among Iraqi patients.
METHODS: We randomized 108 overweight and obese patients with T2D (46 M/62F; age 60 ± 10 years; HbA1c 8.07 ± 1.05%; weight 101.4 ± 21.1 kg and BMI 35.2 ± 7.7 kg/m2) into three groups. Group A met with RDN to develop an individualized eating plan. Group B met with RDN and followed a structured meal plan. Group C did similar to group B and received weekly phone support by RDN.
RESULTS: After 16 weeks, all three groups had a significant reduction of their energy intake compared to baseline. HbA1c did not change from baseline in group A, but decreased significantly in groups B (- 0.66%, 95% CI -1.03 to - 0.30) and C (- 0.61%, 95% CI -1.0 to - 0.23) (p value for difference among groups over time
OBJECTIVES: To compare techniques of blood glucose monitoring and their impact on maternal and infant outcomes among pregnant women with pre-existing diabetes.
SEARCH METHODS: We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (30 November 2016), searched reference lists of retrieved studies and contacted trial authors.
SELECTION CRITERIA: Randomised controlled trials (RCTs) and quasi-RCTs comparing techniques of blood glucose monitoring including SMBG, continuous glucose monitoring (CGM) or clinic monitoring among pregnant women with pre-existing diabetes mellitus (type 1 or type 2). Trials investigating timing and frequency of monitoring were also included. RCTs using a cluster-randomised design were eligible for inclusion but none were identified.
DATA COLLECTION AND ANALYSIS: Two review authors independently assessed study eligibility, extracted data and assessed the risk of bias of included studies. Data were checked for accuracy. The quality of the evidence was assessed using the GRADE approach.
MAIN RESULTS: This review update includes at total of 10 trials (538) women (468 women with type 1 diabetes and 70 women with type 2 diabetes). The trials took place in Europe and the USA. Five of the 10 included studies were at moderate risk of bias, four studies were at low to moderate risk of bias, and one study was at high risk of bias. The trials are too small to show differences in important outcomes such as macrosomia, preterm birth, miscarriage or death of baby. Almost all the reported GRADE outcomes were assessed as being very low-quality evidence. This was due to design limitations in the studies, wide confidence intervals, small sample sizes, and few events. In addition, there was high heterogeneity for some outcomes.Various methods of glucose monitoring were compared in the trials. Neither pooled analyses nor individual trial analyses showed any clear advantages of one monitoring technique over another for primary and secondary outcomes. Many important outcomes were not reported.1. Self-monitoring versus standard care (two studies, 43 women): there was no clear difference for caesarean section (risk ratio (RR) 0.78, 95% confidence interval (CI) 0.40 to 1.49; one study, 28 women) or glycaemic control (both very low-quality), and not enough evidence to assess perinatal mortality and neonatal mortality and morbidity composite. Hypertensive disorders of pregnancy, large-for-gestational age, neurosensory disability, and preterm birth were not reported in either study.2. Self-monitoring versus hospitalisation (one study, 100 women): there was no clear difference for hypertensive disorders of pregnancy (pre-eclampsia and hypertension) (RR 4.26, 95% CI 0.52 to 35.16; very low-quality: RR 0.43, 95% CI 0.08 to 2.22; very low-quality). There was no clear difference in caesarean section or preterm birth less than 37 weeks' gestation (both very low quality), and the sample size was too small to assess perinatal mortality (very low-quality). Large-for-gestational age, mortality or morbidity composite, neurosensory disability and preterm birth less than 34 weeks were not reported.3. Pre-prandial versus post-prandial glucose monitoring (one study, 61 women): there was no clear difference between groups for caesarean section (RR 1.45, 95% CI 0.92 to 2.28; very low-quality), large-for-gestational age (RR 1.16, 95% CI 0.73 to 1.85; very low-quality) or glycaemic control (very low-quality). The results for hypertensive disorders of pregnancy: pre-eclampsia and perinatal mortality are not meaningful because these outcomes were too rare to show differences in a small sample (all very low-quality). The study did not report the outcomes mortality or morbidity composite, neurosensory disability or preterm birth.4. Automated telemedicine monitoring versus conventional system (three studies, 84 women): there was no clear difference for caesarean section (RR 0.96, 95% CI 0.62 to 1.48; one study, 32 women; very low-quality), and mortality or morbidity composite in the one study that reported these outcomes. There were no clear differences for glycaemic control (very low-quality). No studies reported hypertensive disorders of pregnancy, large-for-gestational age, perinatal mortality (stillbirth and neonatal mortality), neurosensory disability or preterm birth.5.CGM versus intermittent monitoring (two studies, 225 women): there was no clear difference for pre-eclampsia (RR 1.37, 95% CI 0.52 to 3.59; low-quality), caesarean section (average RR 1.00, 95% CI 0.65 to 1.54; I² = 62%; very low-quality) and large-for-gestational age (average RR 0.89, 95% CI 0.41 to 1.92; I² = 82%; very low-quality). Glycaemic control indicated by mean maternal HbA1c was lower for women in the continuous monitoring group (mean difference (MD) -0.60 %, 95% CI -0.91 to -0.29; one study, 71 women; moderate-quality). There was not enough evidence to assess perinatal mortality and there were no clear differences for preterm birth less than 37 weeks' gestation (low-quality). Mortality or morbidity composite, neurosensory disability and preterm birth less than 34 weeks were not reported.6. Constant CGM versus intermittent CGM (one study, 25 women): there was no clear difference between groups for caesarean section (RR 0.77, 95% CI 0.33 to 1.79; very low-quality), glycaemic control (mean blood glucose in the 3rd trimester) (MD -0.14 mmol/L, 95% CI -2.00 to 1.72; very low-quality) or preterm birth less than 37 weeks' gestation (RR 1.08, 95% CI 0.08 to 15.46; very low-quality). Other primary (hypertensive disorders of pregnancy, large-for-gestational age, perinatal mortality (stillbirth and neonatal mortality), mortality or morbidity composite, and neurosensory disability) or GRADE outcomes (preterm birth less than 34 weeks' gestation) were not reported.
AUTHORS' CONCLUSIONS: This review found no evidence that any glucose monitoring technique is superior to any other technique among pregnant women with pre-existing type 1 or type 2 diabetes. The evidence base for the effectiveness of monitoring techniques is weak and additional evidence from large well-designed randomised trials is required to inform choices of glucose monitoring techniques.
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.
PARTICIPANTS: Between 2020 and 2021, 383 children and young people with T1D who were active in the A4D supported programmes were reviewed including information on health coverage, multidisciplinary team management, diabetic ketoacidosis (DKA) on admission and insulin regimen.
RESULTS: Mean HbA1c between 2020 and 2021 for patients in these LMICs arereported for the first time. The average glycaemic index in the five SEAcountries reviewed between 2020 and 2021 were high at 83 mmol/mol (9.7%).
CONCLUSIONS: Government partnership working with non-government organisationsto support T1D from diagnosis to adulthood are the first steps to closing thegaps in many LMICs. Further epidemiological studies are needed to identify the glycaemic outcomes and DKA rates on admission for many of these countries.