Abstract

The interest in intermittent energy restriction (IER) diets as a weight-loss approach is increasing. Different IER protocols exist, including time-restricted eating (TRE), alternate-day fasting (ADF), and the 5:2 diet. This meta-analysis compared the effects of these IER diets with continuous energy restriction (CER) on anthropometrics and cardiometabolic risk markers in healthy adults. Twenty-eight trials were identified that studied TRE (k = 7), ADF (k = 10), or the 5:2 diet (k = 11) for 2-52 wk. Energy intakes between intervention groups within a study were comparable (17 trials), lower in IER (5 trials), or not reported (6 trials). Weighted mean differences (WMDs) were calculated using fixed- or random-effects models. Changes in body weight [WMD: -0.42 kg; 95% confidence interval (CI): -0.96 to 0.13; P = 0.132] and fat mass (FM) (WMD: -0.31 kg; 95% CI: -0.98 to 0.36; P = 0.362) were comparable when results of the 3 IER diets were combined and compared with those of CER. All IER diets combined reduced fat-free mass (WMD: -0.20 kg; 95% CI: -0.39 to -0.01; P = 0.044) and waist circumference (WMD: -0.91 cm; 95% CI: -1.76 to -0.06; P = 0.036) more than CER. Effects on body mass index [BMI (kg/m2)], glucose, insulin, homeostatic model assessment for insulin resistance (HOMA-IR), serum lipid and lipoprotein concentrations, and blood pressure did not differ. Further, TRE reduced body weight, FM, and fat-free mass more than CER, whereas ADF improved HOMA-IR more. BMI was reduced less in the 5:2 diet compared with CER. In conclusion, the 3 IER diets combined did not lead to superior improvements in anthropometrics and cardiometabolic risk markers compared with CER diets. Slightly greater reductions were, however, observed in fat-free mass and waist circumference. To what extent differences in energy intakes between groups within studies may have influenced these outcomes should be addressed in future studies.

Abstract

AIMS OF THE STUDY The Eversense® CGM System is the first and only continuous glucose monitoring system (CGMS) that uses a fully subcutaneous implanted sensor. This study aimed to evaluate effectiveness, safety and patient-reported outcomes in patients using the Eversense® CGM System in a realistic clinical setting, assessed at a single Swiss diabetes centre (Luzerner Kantonsspital) with prolonged follow-up. METHODS This was a prospective and retrospective observational study that included patients with type 1 diabetes mellitus in whom at least one Eversense® glucose sensor was implanted between 2017 and 2022. The primary endpoint was the change in HbA1c levels from the baseline (before implantation of the sensor) to 6 ± 2 and 12 ± 2 months and the last follow-up (newest available value) after implantation. The secondary outcome measures were the number of premature sensor breakdowns, adverse events related to the implantation procedure (infection, bleeding, difficulties with implantation or explantation) and patient-related outcomes (assessed with a questionnaire). RESULTS A total of 33 patients participated in this study. The median follow-up time was 50 (IQR 22.3-58.5) months. In total, 178 sensor implantations were performed. Valid HbA1c results were available for 26 participants. Compared to the baseline values, HbA1c levels at 6 and 12 months and the last follow-up changed by -0.25%, -0.45 and -0.2 (p = 0.278, 0.308 and 0.296, respectively). We recorded 16 (9%) premature sensor breakdowns, all occurring between 2019 and 2020. Apart from one late-onset infection and four complicated sensor removals, no major complications were assessed. The results of the questionnaire showed a subjective improvement in hypoglycaemia rates, a better perception of hypoglycaemia and the impression of better diabetes management. Common issues with the device reported by the patients were technical errors (connection problems) and problems with the removal procedure. CONCLUSIONS The use of the Eversense® CGM System resulted in changes in HbA1c of between -0.2% and -0.45%. The rate of premature sensor breakdown was low. Major complications following sensor implantation or removal were absent, apart from one case of infection and four cases of complicated removal. Patient-reported outcomes with the Eversense® CGM System showed a subjective positive impact on hypoglycaemia rates, greater confidence in managing hypoglycaemia and diabetes in general, and easy handling of the transmitter and mobile app. Technical issues must be considered but are nowadays, with the use of the newest sensor generation, very rare.

Abstract

BACKGROUND AND AIMS Sleep disorders are bidirectionally linked with eating behaviors and glucose metabolism, which could be clinically relevant in type 1 diabetes (T1D). We investigated the relationship between dietary habits and sleep quality in individuals with T1D on insulin pumps and continuous glucose monitoring (CGM). METHODS AND RESULTS In a cross-sectional study, dietary habits (7-day food diary, EPIC questionnaire) and sleep quality (Pittsburgh Sleep Quality Index questionnaire) were assessed in 59 men and 58 women with T1D, aged 19-79 years, using CGM and insulin pump. Differences in dietary habits and blood glucose after dinner (6 h) between participants differing in sleep quality, sleep duration, and sleep onset latency were evaluated. Bad Sleepers (n = 81) were twice as prevalent as Good Sleepers (n = 36) and had a significantly higher intake of fat than Good Sleepers (dinner: 30.7 ± 10.7 vs. 24.0 ± 10.5 g, p = 0.004). Short sleepers had a significantly higher usual intake (g/1000 kcal) of coffee and tea (90.4 ± 71.7 vs. 62.0 ± 35.6), alcoholic (47.8 ± 51.1 vs. 28.9 ± 31.5) and carbonated beverages (21.8 ± 38.1 vs. 9.3 ± 17.2) (p < 0.05 for all) than Long Sleepers. Long Sleep Onset Latency was associated with a significantly higher fat intake at dinner (41.8 ± 7.4 vs. 38.1 ± 9.1 % total energy, p = 0.029) than Short Sleep Onset Latency. No significant differences in post-dinner blood glucose levels were detected between participants with good or bad sleep quality. CONCLUSION Sleep disruption is common in T1D and is associated with unhealthy dietary choices, especially at dinner, independently of post-dinner blood glucose control.

Abstract

BACKGROUND & AIMS Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes. METHODS Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome. RESULTS Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease. CONCLUSION GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.

Abstract

AIMS/HYPOTHESIS Continuous glucose monitoring (CGM) is increasingly used in the treatment of type 2 diabetes, but the effects on glycaemic control are unclear. The aim of this systematic review and meta-analysis is to provide a comprehensive overview of the effect of CGM on glycaemic control in adults with type 2 diabetes. METHODS We performed a systematic review using Embase, MEDLINE, Web of Science, Scopus and ClinicalTrials.gov from inception until 2 May 2023. We included RCTs investigating real-time CGM (rtCGM) or intermittently scanned CGM (isCGM) compared with self-monitoring of blood glucose (SMBG) in adults with type 2 diabetes. Studies with an intervention duration <6 weeks or investigating professional CGM, a combination of CGM and additional glucose-lowering treatment strategies or GlucoWatch were not eligible. Change in HbA1c and the CGM metrics time in range (TIR), time below range (TBR), time above range (TAR) and glycaemic variability were extracted. We evaluated the risk of bias using the Cochrane risk-of-bias tool version 2. Data were synthesised by performing a meta-analysis. We also explored the effects of CGM on severe hypoglycaemia and micro- and macrovascular complications. RESULTS We found 12 RCTs comprising 1248 participants, with eight investigating rtCGM and four isCGM. Compared with SMBG, CGM use (rtCGM or isCGM) led to a mean difference (MD) in HbA1c of -3.43 mmol/mol (-0.31%; 95% CI -4.75, -2.11, p<0.00001, I2=15%; moderate certainty). This effect was comparable in studies that included individuals using insulin with or without oral agents (MD -3.27 mmol/mol [-0.30%]; 95% CI -6.22, -0.31, p=0.03, I2=55%), and individuals using oral agents only (MD -3.22 mmol/mol [-0.29%]; 95% CI -5.39, -1.05, p=0.004, I2=0%). Use of rtCGM showed a trend towards a larger effect (MD -3.95 mmol/mol [-0.36%]; 95% CI -5.46 to -2.44, p<0.00001, I2=0%) than use of isCGM (MD -1.79 mmol/mol [-0.16%]; 95% CI -5.28, 1.69, p=0.31, I2=64%). CGM was also associated with an increase in TIR (+6.36%; 95% CI +2.48, +10.24, p=0.001, I2=9%) and a decrease in TBR (-0.66%; 95% CI -1.21, -0.12, p=0.02, I2=45%), TAR (-5.86%; 95% CI -10.88, -0.84, p=0.02, I2=37%) and glycaemic variability (-1.47%; 95% CI -2.94, -0.01, p=0.05, I2=0%). Three studies reported one or more events of severe hypoglycaemia and macrovascular complications. In comparison with SMBG, CGM use led to a non-statistically significant difference in the incidence of severe hypoglycaemia (RR 0.66, 95% CI 0.15, 3.00, p=0.57, I2=0%) and macrovascular complications (RR 1.54, 95% CI 0.42, 5.72, p=0.52, I2=29%). No trials reported data on microvascular complications. CONCLUSIONS/INTERPRETATION CGM use compared with SMBG is associated with improvements in glycaemic control in adults with type 2 diabetes. However, all studies were open label. In addition, outcome data on incident severe hypoglycaemia and incident microvascular and macrovascular complications were scarce. REGISTRATION This systematic review was registered on PROSPERO (ID CRD42023418005).

Abstract

BACKGROUND Previous clinical studies showing that cinnamon spice lowers blood glucose concentrations had inconsistent results. OBJECTIVES To determine the effect of daily cinnamon spice supplementation in an amount commonly used for seasoning on glucose concentrations in adults with obesity and prediabetes. METHODS Following a 2-wk run-in period of maintaining a low polyphenol/fiber diet, 18 participants with obesity and prediabetes underwent a 10-wk randomized, controlled, double-blind, crossover trial (mean age 51.1 y; mean fasting plasma glucose 102.9 mg/dL). The participants were randomly assigned to take cinnamon (4 g/d) or placebo for 4-wk, followed by a 2-wk washout period, and then crossed over to the other intervention for an additional 4-wk. Glucose changes were measured with continuous glucose monitoring. Oral glucose tolerance testing immediately following ingestion of cinnamon or placebo was performed at 4-time points to assess their acute effects both at the baseline and end of each intervention phase. Digestive symptom logs were obtained daily. RESULTS There were 694 follow-up days with 66,624 glucose observations. When compared with placebo, 24-h glucose concentrations were significantly lower when cinnamon was administered [mixed-models; effect size (ES) = 0.96; 95 % confidence interval (CI): -2.9, -1.5; P < 0.001]. Similarly, the mean net-area-under-the-curve (netAUC) for glucose was significantly lower than for placebo when cinnamon was given (over 24 h; ES = -0.66; 95 % CI: 2501.7, 5412.1, P = 0.01). Cinnamon supplementation resulted in lower glucose peaks compared with placebo (Δpeak 9.56 ± 9.1 mg/dL compared with 11.73 ± 8.0 mg/dL; ES = -0.57; 95 % CI: 0.8, 3.7, P = 0.027). Glucose-dependent-insulinotropic-polypeptide concentrations increased during oral glucose tolerance testing + cinnamon testing (mixed-models; ES = 0.51; 95 % CI: 1.56, 100.1, P = 0.04), whereas triglyceride concentrations decreased (mixed-models; ES = 0.55; 95 % CI: -16.0, -1.6, P = 0.02). Treatment adherence was excellent in both groups (cinnamon: 97.6 ± 3.4 % compared with placebo: 97.9 ± 3.7 %; ES = -0.15; 95 % CI: -1.8, 0.2, P = 0.5). No differences were found in digestive symptoms (abdominal pain, borborygmi, bloating, excess flatus, and stools/day) between cinnamon and placebo groups. CONCLUSIONS Cinnamon, a widely available and low-cost supplement, may contribute to better glucose control when added to the diet in people who have obesity-related prediabetes. This trial was registered at clinicaltrials.gov as NCT04342624.

Abstract

BACKGROUND People with type 1 diabetes generally view it easier to exercise when having continuous information of the glucose levels. We evaluated whether patients with type 1 diabetes managed with multiple daily insulin injections (MDI) exercised more after initiating continuous glucose monitoring (CGM) and whether the improved glycemic control and well-being associated with CGM translates into improved blood lipids and markers of inflammation. METHOD The GOLD trial was a randomized cross-over trial over 16 months where patients used either CGM or capillary self-monitoring of blood glucose (SMBG) over six months, with a four-month wash-out period between the two treatment periods. We compared grade of physical activity, blood lipids, apolipoproteins, and high-sensitivity C-reactive protein (hsCRP) levels during CGM and SMBG. RESULTS There were 116 patients with information of physical activity estimated by the International Physical Activity Questionnaire (IPAQ) during both CGM and SMBG. No changes were found during CGM or SMBG, IPAQ scores 3305 versus 3878 (P = .16). In 136 participants with information of blood lipid levels with no change in lipid-lowering medication during the two treatment periods, HbA1c differed by 4.2 mmol/mol (NGSP 0.39%) between SMBG and CGM treatment (P < .001). No significant changes existed in low-density lipoprotein, high-density lipoprotein, triglycerides, total cholesterol, apolipoprotein A1, apolipoprotein B1, or hsCRP, during CGM and SMBG. CONCLUSION Although many patients experience it easier to perform physical activity when monitoring glucose levels with CGM, it does not influence the amount of physical activity in persons with type 1 diabetes. Blood lipids, apolipoprotein, and hsCRP levels were similar during CGM and SMBG.

Abstract

OBJECTIVE  This study evaluated the effects of receiving glucose feedback from continuous glucose monitoring (CGM) by intermittent scanning (unblinded group), and CGM with masked feedback (blinded group) in the subsequent development of gestational diabetes mellitus (GDM). STUDY DESIGN  This was a prospective, single-center, pilot, randomized controlled trial including n = 206 pregnant women in the first trimester of pregnancy with no prior diagnosis of type 1 or type 2 diabetes. The participants were randomized into the unblinded group or blinded group and wore the CGM in the first trimester of pregnancy (9-13 weeks), the second trimester of pregnancy (18-23 weeks), and late-second to early-third trimester (24-31 weeks). The primary outcome was GDM rate as diagnosed by the 75-g oral glucose tolerance test (OGTT) at 24 to 28 weeks. RESULTS  Over 47 months, 206 pregnant women were enrolled at 9 to 13 weeks. The unblinded group had a higher prevalence of women who developed GDM (21.5 vs. 14.9%; p > 0.05), compared to the blinded group. In the unblinded group compared to the blinded group, plasma glucose values were higher at 1 hour (median 7.7 [interquartile range {IQR}: 6.3-9.2] vs. 7.5 [6.3-8.7]) and 2 hours (6.3 [5.8-7.7] vs. 6.2 [5.3-7.2]), but lower at 0 hour (4.2 [4.0-4.5] vs. 4.3 [4.1-4.6]; p > 0.05). All these differences were not statistically significant. CONCLUSION  Glucose feedback from CGM wear in the first to the third trimester of pregnancy without personalized patient education failed to alter GDM rate. KEY POINTS · Continuous glucose monitoring (CGM) is feasible for use in pregnant women.. · No significant difference in gestational diabetes rates with or without CGM feedback.. · Future clinical trials should incorporate CGM education and personalized guidance to enhance study outcomes..

Abstract

AIMS/HYPOTHESIS Hypomagnesaemia has been associated with insulin resistance and an increased risk of type 2 diabetes. Whether magnesium supplementation improves insulin sensitivity in people with type 2 diabetes and a low serum magnesium level is unknown. METHODS Using a randomised, double-blind (both participants and investigators were blinded to the participants' treatment sequences), placebo-controlled, crossover study design, we compared the effect of oral magnesium supplementation (15 mmol/day) for 6 weeks with that of matched placebo in individuals with insulin-treated type 2 diabetes (age ≥18 years, BMI 18-40 kg/m2, HbA1c <100 mmol/mol [11.3%], serum magnesium ≤0.79 mmol/l). Participants were recruited from the outpatient clinic and through advertisements. Randomisation to a treatment sequence order was done using a randomisation list. We used block randomisation and the two possible treatment sequences were evenly distributed among the trial population. The primary outcome was the mean glucose infusion rate during the final 30 min of a hyperinsulinaemic-euglycaemic clamp (i.e. M value). Secondary outcomes included variables of glucose control, insulin need, BP, lipid profile and hypomagnesaemia-related symptoms during follow-up. RESULTS We recruited 14 participants (50% women, 100% White, mean ± SD age 67±6 years, BMI 31±5 kg/m2, HbA1c 58±9 mmol/mol [7.4±0.9%]) with insulin-treated type 2 diabetes. Magnesium supplementation increased both mean ± SEM serum magnesium level (0.75±0.02 vs 0.70±0.02 mmol/l, p=0.016) and urinary magnesium excretion (magnesium/creatinine ratio, 0.23±0.02 vs 0.15±0.02, p=0.005), as compared with placebo. The M value of the glucose clamp did not differ between the magnesium and placebo study arms (4.6±0.5 vs 4.4±0.6 mg kg-1 min-1, p=0.108). During the 6 weeks of treatment, continuous glucose monitoring outcomes, HbA1c, insulin dose, lipid profile and BP also did not differ, except for a lower HDL-cholesterol concentration after magnesium compared with placebo (1.14±0.08 vs 1.20±0.09 mmol/l, p=0.026). Symptoms potentially related to hypomagnesaemia were similar for both treatment arms. CONCLUSIONS/INTERPRETATION Despite an albeit modest increase in serum magnesium concentration, oral magnesium supplementation does not improve insulin sensitivity in people with insulin-treated type 2 diabetes and low magnesium levels. TRIAL REGISTRATION EudraCT number 2021-001243-27. FUNDING This study was supported by a grant from the Dutch Diabetes Research Foundation (2017-81-014).

Abstract

AIMS: Although conventional interventions for people at high risk of developing type 2 diabetes are usually conducted face-to-face, such interventions are burdensome for health care providers. We developed a lifestyle intervention program combining lifestyle coaching via a smartphone application augmented by intermittently scanned continuous glucose monitoring without burdening health care providers. Its effectiveness for glycemic control and body weight reduction in people at risk of type 2 diabetes was investigated. MATERIALS AND METHODS For this 12-week randomized unblinded trial with offline recruitment, participants with a hemoglobin A1c level of 5.6% to 6.4% or a fasting blood glucose of 110 to 125 mg/dL and body mass index (BMI) >23 kg/m2 but <40 kg/m2 were randomly assigned to the intervention group (App) and control group (C). The primary endpoint was the difference in time in range of blood glucose between 70 and 140 mg/dL (3.9-7.8 mmol/L) before and after the study period between the 2 groups. RESULTS Among 168 patients (mean age, 48.1 years; mean BMI, 26.6 kg/m2; and male, 80.4%), 82 and 86 were assigned to the App group and C group, respectively. After 12 weeks, time in range of blood glucose at 70 to 140 mg/dL significantly improved in the App group compared with the C group (-2.6 minutes/day vs +31.5 minutes/day, P = .03). Changes in time above range did not differ, whereas time below range (blood glucose <70 mg/dL; +23.5 minutes/day vs -8.9 minutes/day, P = .02) improved in the App group. BMI (-0.26 vs -0.59, P = .017) was reduced in the App group compared with the C group. CONCLUSION Intervention with a smartphone app and intermittently scanned continuous glucose monitoring increased glycemic control accompanied by decreased carbohydrate intake and weight loss. Further trials are needed to confirm whether these interventions can reduce incident type 2 diabetes.