METHODS: A comprehensive systematic search was performed in Web of Science, PubMed/MEDLINE, Cochrane, SCOPUS and Embase from inception until June 2019. All clinical trials investigating the effects of fasting and energy-restricted diets on leptin and adiponectin in adults were included.
RESULTS: Twelve studies containing 17 arms and a total of 495 individuals (intervention = 249, control = 246) reported changes in serum leptin concentrations, and 10 studies containing 12 arms with a total of 438 individuals (intervention = 222, control = 216) reported changes in serum adiponectin concentrations. The combined effect sizes suggested a significant effect of fasting and energy-restricted diets on leptin concentrations (WMD: -3.690 ng/ml, 95% CI: -5.190, -2.190, p ≤ 0.001; I2 = 84.9%). However, no significant effect of fasting and energy-restricted diets on adiponectin concentrations was found (WMD: -159.520 ng/ml, 95% CI: -689.491, 370.451, p = 0.555; I2 = 74.2%). Stratified analyses showed that energy-restricted regimens significantly increased adiponectin (WMD: 554.129 ng/ml, 95% CI: 150.295, 957.964; I2 = 0.0%). In addition, subsequent subgroup analyses revealed that energy restriction, to ≤50% normal required daily energy intake, resulted in significantly reduced concentrations of leptin (WMD: -4.199 ng/ml, 95% CI: -7.279, -1.118; I2 = 83.9%) and significantly increased concentrations of adiponectin (WMD: 524.04 ng/ml, 95% CI: 115.618, 932.469: I2 = 0.0%).
CONCLUSION: Fasting and energy-restricted diets elicit significant reductions in serum leptin concentrations. Increases in adiponectin may also be observed when energy intake is ≤50% of normal requirements, although limited data preclude definitive conclusions on this point.
PATIENTS AND METHODS: A total of 120 men, aged 40-70 years, with TD (serum total testosterone [TT] ≤ 12 nmol/L) were randomised to receive either i.m. TU (1000 mg) or placebo. In all, 58 and 56 men in the placebo and treatment arm, respectively, completed the study. Participants were seen six times in the 48-week period and the following data were collected: physical examination results, haemoglobin, haematocrit, TT, lipid profile, fasting blood glucose, sex hormone-binding globulin, liver function test, prostate- specific antigen (PSA) and adverse events.
RESULTS: The mean (sd) age of the participants was 53.4 (7.6) years. A significant increase in serum TT (P < 0.001), PSA (P = 0.010), haematocrit (P < 0.001), haemoglobin (P < 0.001) and total bilirubin (P = 0.001) were seen in the treatment arm over the 48-week period. Two men in the placebo arm and one man in the treatment arm developed myocardial infarction. Common adverse events observed in the treatment arm included itching/swelling/pain at the site of injection, flushing and acne. Overall, TU injections were well tolerated.
CONCLUSIONS: TU significantly increases serum testosterone in men with TD. PSA, haemoglobin and haematocrit were significantly elevated but were within clinically safe limits. There was no significant adverse reaction that led to the cessation of treatment.
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.
METHODS: Data of 328 eligible housewives who participated in the MyBFF@Home study was used. Intervention group of 169 subjects were provided with an intervention package which includes physical activity (brisk walking, dumbbell exercise, physical activity diary, group exercise) and 159 subjects in control group received various health seminars. Physical activity level was assessed using short-International Physical Activity Questionnaire. The physical activity level was then re-categorized into 4 categories (active intervention, inactive intervention, active control and inactive control). Physical activity, blood glucose and lipid profile were measured at baseline, 3rd month and 6th month of the study. General Linear Model was used to determine the effect of physical activity on glucose and lipid profile.
RESULTS: At the 6th month, there were 99 subjects in the intervention and 79 control group who had complete data for physical activity. There was no difference on the effect of physical activity on the glucose level and lipid profile except for the Triglycerides level. Both intervention and control groups showed reduction of physical activity level over time.
CONCLUSION: The effect of physical activity on blood glucose and lipid profile could not be demonstrated possibly due to physical activity in both intervention and control groups showed decreasing trend over time.
METHODS: A cross-sectional observational study was designed. Forty normotensive (median age 47 +/- 6 yrs.) and twenty untreated hypertensive Malay men (median age 50 +/- 7 yrs.) without clinical evidence of cardiovascular complications were selected. Pulse wave velocity measured using the automated Complior machine was used as an index of arterial stiffness. Other measurements obtained were blood pressure, body mass index, fasting insulin, cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, glucose and creatinine level.
RESULTS: The blood pressure and pulse wave velocity (PWV) were significantly higher in the hypertensives compared to the normotensives (blood pressure 169/100 mm Hg +/- 14/7 vs. 120/80 mm Hg +/- 10/4, p < 0.001; PWV 11.69 m/s +/- 1.12 vs. 8.83 m/s +/- 1.35, p < 0.001). Other variables such as body mass index, fasting insulin, cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides and haematocrit were comparable among the two groups. Within each group, there was a significant positive correlation between pulse wave velocity and systolic blood pressure (r = 0.76, p < 0.001 in normotensives; r = 0.73, p < 0.001 in hypertensives) and mean arterial pressure (r = 0.74, p < 0.001 in normotensives; r = 0.73, p < 0.001 in hypertensives). No correlation was noted between pulse wave velocity and diastolic blood pressure, age, body mass index, fasting insulin level, cholesterol, HDL-cholesterol, LDL-cholesterol or triglyceride levels.
CONCLUSION: Arterial stiffness as determined by PWV is increased in newly diagnosed untreated hypertensive subjects even before clinically evident cardiovascular disease. However, arterial stiffness is not correlated with the fasting insulin level in normotensives and newly diagnosed hypertensives.
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.
Methods: 53 women with GDM (30 managed with diet only (GDM-diet) and 23 treated with insulin (GDM-insulin)) and 43 pregnant women with normal glucose tolerance (NGDM) were studied, with GIP and GLP-1 levels measured at 24-28 weeks (E1), prior (E2) and after (E3) delivery, and postpuerperium (E4).
Results: Basal GIP was shown to be low in GDM groups compared to NGDM in E1, and in E4 for GDM-diet. GLP-1 was low in GDM groups during pregnancy and afterwards. At E1, serum GIP and GLP-1 were inversely associated with GDM and participants with lower levels of GIP (<0.23 ng/mL) and GLP-1 (<0.38 ng/mL) had a 6 (95% CI 2.5-14.5)- and 7.6 (95% CI 3.0-19.1)-fold higher risk of developing GDM compared with the higher level, respectively. In the postpuerperium, when there is a drop in β-cell function, participants with previous GDM (pGDM) presented lower GLP-1 (in both GDM subgroups) and lower GIP in GDM-diet subgroup compared to controls.
Conclusion: There is an independent, inverse association between fasting incretins and higher risk of GDM. Furthermore, lowered levels of these peptides may play an important role in the abnormality of glucose regulation following pregnancy.
METHODS: All English-language medical literature published from inception till October 2014 which met the inclusion criteria were reviewed and analyzed.
RESULTS: A total of nine papers were included, reviewed and analyzed. The total sample size was 4276 patients. All studies used either of the two DPP4 inhibitors - Vildagliptin or Sitagliptin, vs sulphonylurea or meglitinides. Patients receiving DPP4 inhibitors were less likely to develop symptomatic hypoglycemia (risk ratio 0.46; 95% CI, 0.30-0.70), confirmed hypoglycemia (risk ratio 0.36; 95% CI, 0.21-0.64) and severe hypoglycemia (risk ratio 0.22; 95% CI, 0.10-0.53) compared with patients on sulphonylureas. There was no statistically significant difference in HbA1C changes comparing Vildagliptin and sulphonylurea.
CONCLUSION: DPP4 inhibitor is a safer alternative to sulphonylurea in Muslim patients with type 2 diabetes mellitus who fast during the month of Ramadan as it is associated with lower risk of symptomatic, confirmed and severe hypoglycemia, with efficacy comparable to sulphonylurea.
MATERIALS AND METHODS: A total of 100 adults with type 2 diabetes were assessed with 6-day continuous glucose monitoring and HbA1c . Area under the curve (AUC) ≥5.6 mmol/L was defined as AUCTOTAL . AUC equal to or greater than each preprandial glucose for 4-h duration was defined as AUCPPH . The total PPH (AUCTPPH ) was the sum of the various AUCPPH. The postprandial contribution to overall hyperglycemia was calculated as (AUCTPPH / AUCTOTAL ) × 100%.
RESULTS: The present study comprised of Malay, Indian, and Chinese type 2 diabetes patients at 34, 34 and 28% respectively. Overall, the mean PPH significantly decreased as HbA1c advanced (mixed model repeated measures adjusted, beta-estimate = -3.0, P = 0.009). Age (P = 0.010) and hypoglycemia (P = 0.006) predicted the contribution difference. In oral antidiabetic drug-treated patients (n = 58), FH contribution increased from 54% (HbA1c 6-6.9%) to 67% (HbA1c ≥10%). FH predominance was significant in poorly-controlled groups (P = 0.028 at HbA1c 9-9.9%; P = 0.015 at HbA1c ≥10%). Among insulin users (n = 42), FH predominated when HbA1c was ≥10% before adjustment for hypoglycemia (P = 0.047), whereas PPH was numerically greater when HbA1c was <8%.
CONCLUSIONS: FH and PPH contributions were equal in well-controlled Malaysian type 2 diabetes patients in real-world practice. FH predominated when HbA1c was ≥9 and ≥10% in oral antidiabetic drug- and insulin-treated patients, respectively. A unique observation was the greater PPH contribution when HbA1c was <8% despite the use of basal and mealtime insulin in this multi-ethnic cohort, which required further validation.
METHODS: A total of 20 healthy volunteers were challenged with 3 test meals, similar in fat content (~31% en) but varying in saturated SFA content and polyunsaturated/saturated fatty acid ratios (P/S). The 3 meals were lauric + myristic acid-rich (LM), P/S 0.19; palmitic acid-rich (POL), P/S 0.31; and stearic acid-rich (STE), P/S 0.22. Blood was sampled at fasted baseline and 2, 4, 5, 6, and 8 hours. Plasma lipids (triacylglycerol [TAG]) and lipoproteins (TC, LDL-C, high density lipoprotein-cholesterol [HDL-C]) were evaluated.
RESULTS: Varying SFA in the test meal significantly impacted postprandial TAG response (p < 0.05). Plasma TAG peaked at 5 hours for STE, 4 hours for POL, and 2 hours for LM test meals. Area-under-the-curve (AUC) for plasma TAG was increased significantly after STE treatment (STE > LM by 32.2%, p = 0.003; STE > POL by 27.9%, p = 0.023) but was not significantly different between POL and LM (POL > LM by 6.0%, p > 0.05). At 2 hours, plasma HDL-C increased significantly after the LM and POL test meals compared with STE (p < 0.05). In comparison to the STE test meal, HDL-C AUC was elevated 14.0% (p = 0.005) and 7.6% (p = 0.023) by the LM and POL test meals, respectively. The TC response was also increased significantly by LM compared with both POL and STE test meals (p < 0.05).
CONCLUSIONS: Chain length of saturates clearly mediated postmeal plasma TAG and HDL-C changes.
METHODS: DIA-RAMADAN (NCT04132934) was a prospective, international, observational study conducted in nine countries. Patients >18 years of age with T2DM (N = 1244) were examined at an inclusion visit (V0) occurring 6-8 weeks before the start of Ramadan. Patients received a diary to report treatment changes, hypoglycaemic events (HEs), and other adverse events. Gliclazide MR was taken once daily for 14-18 weeks. A second visit (V1) was conducted 4-6 weeks after the end of Ramadan. The primary endpoint was the proportion of patients reporting ≥1 symptomatic HE. Changes in HbA1c, fasting plasma glucose (FPG), and body weight were secondary endpoints.
RESULTS: The proportion of patients reporting ≥1 symptomatic HE during Ramadan was low (2.2%) with no reported severe HEs. There was a significant reduction in HbA1c (-0.3%), FPG (-9.7 mg/dL), body weight (-0.5 kg) and body mass index (-0.2 kg/m2) between V0 and V1 (p