Explore the vast range of titles available.

Introduction Glycemic control in participants with insulin-treated diabetes remains challenging. We assessed safety and efficacy of new flash glucose-sensing technology to replace self-monitoring of blood glucose (SMBG). Methods This open-label randomized controlled study (ClinicalTrials.gov, NCT02082184) enrolled adults with type 2 diabetes on intensive insulin therapy from 26 European diabetes centers. Following 2 weeks of blinded sensor wear, 2:1 (intervention/control) randomization (centrally, using biased-coin minimization dependant on study center and insulin administration) was to control (SMBG) or intervention (glucose-sensing technology). Participants and investigators were not masked to group allocation. Primary outcome was difference in HbA1c at 6 months in the full analysis set. Prespecified secondary outcomes included time in hypoglycemia, effect of age, and patient satisfaction. Results Participants ( n  = 224) were randomized (149 intervention, 75 controls). At 6 months, there was no difference in the change in HbA1c between intervention and controls: −3.1 ± 0.75 mmol/mol, [−0.29 ± 0.07% (mean ± SE)] and −3.4 ± 1.04 mmol/mol (−0.31 ± 0.09%) respectively; p  = 0.8222. A difference was detected in participants aged <65 years [−5.7 ± 0.96 mmol/mol (−0.53 ± 0.09%) and −2.2 ± 1.31 mmol/mol (−0.20 ± 0.12%), respectively; p  = 0.0301]. Time in hypoglycemia <3.9 mmol/L (70 mg/dL) reduced by 0.47 ± 0.13 h/day [mean ± SE ( p  = 0.0006)], and <3.1 mmol/L (55 mg/dL) reduced by 0.22 ± 0.07 h/day ( p  = 0.0014) for intervention participants compared with controls; reductions of 43% and 53%, respectively. SMBG frequency, similar at baseline, decreased in intervention participants from 3.8 ± 1.4 tests/day (mean ± SD) to 0.3 ± 0.7, remaining unchanged in controls. Treatment satisfaction was higher in intervention compared with controls (DTSQ 13.1 ± 0.50 (mean ± SE) and 9.0 ± 0.72, respectively; p  < 0.0001). No serious adverse events or severe hypoglycemic events were reported related to sensor data use. Forty-two serious events [16 (10.7%) intervention participants, 12 (16.0%) controls] were not device-related. Six intervention participants reported nine adverse events for sensor-wear reactions (two severe, six moderate, one mild). Conclusion Flash glucose-sensing technology use in type 2 diabetes with intensive insulin therapy results in no difference in HbA1c change and reduced hypoglycemia, thus offering a safe, effective replacement for SMBG. Trial registration ClinicalTrials.gov identifier: NCT02082184. Funding Abbott Diabetes Care.

Heart rate variability (HRV) is widely recognized as an indicator of general health, particularly time domain measures like the root mean square of successive differences (RMSSD) between consecutive heartbeats. Consumer wearables measuring HRV have potential for wide accessibility meaning that their broad use to capture HRV as a health biomarker is possible. Our objective was to investigate the validity of HRV measured by wearables as a general health indicator. We examined whether resting HRV assessed by wearables across five studies—two using smartwatches, two using heart rate chest straps, and one using a smartring—exhibited expected associations with diverse health domains, including mental, physical, behavioral, functional, and physiological. We focused on resting HRV measures recorded while in primarily stationary conditions, either upon waking or while sleeping, because such measures would theoretically reduce the effects of potential confounders such as movement artifacts, daytime caffeine intake, and postural changes. Wearables measured resting HRV had small-to-moderate associations with more clinically oriented and trait-like (or slow-changing) health measures like Hba1c (average blood glucose, r = −0.21, p = 0.014), depressive symptoms (r = −0.22, p = 0.024), and sleep difficulty (r = −0.11, p = 0.003). Wearable-measured resting HRV can potentially serve as a health biomarker, but further research is needed.

Introduction Published evaluations of sensor glucose monitoring use in insulin treated type 2 diabetes are limited. The aim of this study was to assess the impact of flash glucose-sensing technology as a replacement for self-monitoring of blood glucose (SMBG) over a 12-month period in participants with type 2 diabetes who were on intensive insulin therapy. Methods An open-label, randomized, controlled study in adults with type 2 diabetes on intensive insulin therapy from 26 European diabetes centers aimed at assessing flash glucose sensing technology was conducted. Participants ( N  = 224) were randomized (1:2 respectively) to a control group ( n  = 75) that used SMBG (FreeStyle Lite™) or to an intervention group ( n  = 149) which used sensor glucose data (FreeStyle Libre™ Flash Glucose Monitoring System) for self-management over 6 months. All intervention group participants who completed the 6-month treatment phase continued into an additional 6-month open-access phase. Results A total of 139 intervention participants completed the 6-month treatment phase and continued into the open-access phase. At 12 months (end of open-access period), time in hypoglycemia [sensor glucose <3.9 mmol/L (70 mg/dL)] was reduced by 50% compared to baseline [−0.70 ± 1.85/24 h (mean ± standard deviation); p  = 0.0002]. Nocturnal hypoglycemia [2300 to 0600 hours, <3.9 mmol/L (70 mg/dL)] was reduced by 52%; p  = 0.0002. There was no change in time in range [sensor glucose 3.9–10.0 mmol/L (70–180 mg/dL)]. SMBG testing fell from a mean of 3.9 (median 3.9) times/day at baseline to 0.2 (0.0), with an average frequency of sensor scanning of 7.1 (5.7) times/day at 12 months, and mean sensor utilization was 83.6 ± 13.8% (median 88.3%) during the open-access phase. During this 6-month extension period no device-related serious adverse events were reported. Nine participants reported 16 instances of device-related adverse events (e.g. infection, allergy) and 28 participants (20.1%) experienced 134 occurrences of anticipated skin symptoms/sensor-insertion events expected with device use (e.g. erythema, itching and rash). Conclusion The use of flash glucose-sensing technology for glycemic management in individuals with type 2 diabetes treated by intensive insulin therapy over 12 months was associated with a sustained reduction in hypoglycemia and safely and effectively replaced SMBG. Trial Registration ClinicalTrials.gov identifier, NCT02082184.

To appraise the Diabetes Self-Management Questionnaire (DSMQ)'s measurement of diabetes self-management as a statistical predictor of glycaemic control relative to the widely used SDSCA. 248 patients with type 1 diabetes and 182 patients with type 2 diabetes were cross-sectionally assessed using the two self-report measures of diabetes self-management DSMQ and SDSCA; the scales were used as competing predictors of HbA1c. We developed a structural equation model of self-management as measured by the DSMQ and analysed the amount of variation explained in HbA1c; an analogue model was developed for the SDSCA. The structural equation models of self-management and glycaemic control showed very good fit to the data. The DSMQ's measurement of self-management showed associations with HbA1c of -0.53 for type 1 and -0.46 for type 2 diabetes (both P < 0.001), explaining 21% and 28% of variation in glycaemic control, respectively. The SDSCA's measurement showed associations with HbA1c of -0.14 (P = 0.030) for type 1 and -0.31 (P = 0.003) for type 2 diabetes, explaining 2% and 10% of glycaemic variation. Predictive power for glycaemic control was significantly higher for the DSMQ (P < 0.001). This study supports the DSMQ as the preferred tool when analysing self-reported behavioural problems related to reduced glycaemic control. The scale may be useful for clinical assessments of patients with suboptimal diabetes outcomes or research on factors affecting associations between self-management behaviours and glycaemic control.

Inflammation is a probable biological pathway underlying the relationship between diabetes and depression, but data on differences between diabetes types and symptom clusters of depression are scarce. Therefore, this cross-sectional study aimed to compare associations of a multimarker panel of biomarkers of inflammation with depressive symptoms and its symptom clusters between people with type 1 diabetes (T1D) and type 2 diabetes (T2D). This cross-sectional study combined data from five studies including 1260 participants ( n  = 706 T1D, n  = 454 T2D). Depressive symptoms were assessed using the Center for Epidemiological Studies-Depression Scale (CES-D). Serum levels of 92 biomarkers of inflammation were quantified with proximity extension assay technology. After quality control, 76 biomarkers of inflammation remained for statistical analysis. Associations between biomarkers and depressive symptom scores and clusters (cognitive-affective, somatic, anhedonia) were estimated with multivariable linear regression models. Nine biomarkers were positively associated with depressive symptoms in the total sample (CCL11/eotaxin, CCL25, CDCP1, FGF-21, IL-8, IL-10RB, IL-18, MMP-10, TNFRSF9; all p  < 0.05) without interaction by diabetes type. Associations differed for eight biomarkers ( p interaction  < 0.05). TNFβ was inversely associated with depressive symptoms in T1D, whereas three biomarkers (GDNF, IL-18R1, LIF-R) were positively associated with depressive symptoms in T2D. For the remaining four biomarkers (CD6, CD244, FGF-5, IFNγ) associations were not significant in either subgroup. Biomarker associations were more pronounced with somatic and anhedonia than with cognitive-affective symptoms. These results indicate that different proinflammatory pathways may contribute to depression in T1D and T2D and that there may be a symptom specificity in the link between subclinical inflammation and depression.

The treatment of diabetes requires substantial self-management. Digital tools can help reduce the burden of self-management and may improve glycemic control. This study aims to determine whether the use of a digital diabetes logbook increased the likelihood of achieving optimal glycemic control (glycated hemoglobin [HbA ] ≤6.5%) after 3 months, based on a secondary analysis of randomized controlled trial (RCT) data. A secondary objective was to evaluate the long-term impact of the logbook on mean blood glucose levels over 3 and 12 months using observational, real-world data (RWD). Data from 342 participants with type 1 or type 2 diabetes enrolled in the mySugr PRO-RCT were analyzed. A robust logistic regression was performed to examine the likelihood of achieving optimal glycemic control, defined as an HbA1c value ≤6.5% at the 3-month follow-up. The dependent variable was the dichotomous outcome indicating whether this threshold was met. The primary independent variable was group allocation, with baseline HbA1c included as a covariate. For the analysis of RWD, a total of 2861 participants with type 1 or type 2 diabetes were identified using propensity score matching to align their characteristics with those of the RCT participants closely. One-sample t tests were conducted to analyze changes in mean blood glucose separately for each diabetes type, from baseline to 3 months of app use, and from baseline to 12 months of app use (in a subcohort of 1176 participants). The RCT data showed that the likelihood of achieving optimal glycemic control was nearly doubled in the intervention group compared with the control group (odds ratio 2.24, 95% CI 1.12-4.47; P=.02). RWD indicated that mean blood glucose levels significantly improved over 3 months of app use in both groups (type 1: -16.3 mg/dL; 95% CI -20.6 to -12.4; P<.001 and type 2: -27.3 mg/dL, 95% CI -28.7 to -25.9; P<.001). Participants with an estimated HbA >8.5% at baseline (before app use) showed the greatest reductions in mean blood glucose (type 1: -82.2 mg/dL; 95% CI -102.0 to -61.8; P<.001; type 2: -104.6 mg/dL, 95% CI -109.1 to -100.3; P<.001). Long-term analyses revealed a sustained reduction in mean blood glucose over a 12-month period, with a mean decrease of -19.8 mg/dL (95% CI -21.8 to -17.9; P<.001) after 12 months of app use in the total RWD sample. The secondary analysis of the RCT demonstrated a significant increase in the likelihood of achieving optimal glycemic control after 3 months of using the mySugr logbook. This finding was supported by observational, real-world data, which showed significant reductions in mean blood glucose after 3 and 12 months of app use-particularly among individuals with elevated baseline HbA1c levels. German Clinical Trials Register DRKS00022923; https://drks.de/search/en/trial/DRKS00022923/details.

Introduction: Depression is a common and serious complication of diabetes. Treatment approaches addressing the specific demands of affected patients are scarce. Objective: The aim of this work was to test whether a stepped care approach for patients with diabetes and depression and/or diabetes distress yields greater depression reduction than treatment-as-usual. Methods: Two-hundred and sixty patients with diabetes and elevated depressive symptoms (CES-D ≥16) and/or elevated diabetes distress (PAID ≥40) were randomized to stepped care for depression or diabetes treatment-as-usual. The primary outcome was the rate of meaningful depression reduction at the 12-month follow-up according to the HAMD (score <9 or reduction by ≥50%). Secondary outcomes were changes in depression scores (HAMD/CES-D), diabetes distress (PAID), diabetes acceptance (AADQ), well-being (WHO-5), quality of life (EQ-5D/SF-36), self-care behavior (SDSCA/DSMQ), HbA 1c , and biomarkers of inflammation. Results: One-hundred and thirty-one individuals were assigned to stepped care and 129 to treatment-as-usual. Overall, 15.4% were lost to follow-up. Meaningful depression reduction was observed in 80.2 versus 51.2% in stepped care versus treatment-as-usual (p < 0.001, intention-to-treat analysis). Of the secondary measures, the HAMD (∆ –3.2, p < 0.001), WHO-5 (∆ 1.5, p = 0.007), and AADQ (∆ –1.0, p = 0.008) displayed significant treatment effects, while effects on CES-D (∆ –2.3, p = 0.065), PAID (∆ –3.5, p = 0.109), and SDSCA (∆ 0.20, p = 0.081) were not significantly different. Both groups showed comparable changes in EQ-5D/SF-36, DSMQ, HbA 1c , and biomarkers of inflammation (all p ≥ 0.19). Conclusions: The stepped care approach improved depression, well-being, and acceptance. The results support that increasing treatment intensity on demand is effective and can help provide more optimal treatment. The inclusion of diabetes-specific interventions may be beneficial for patients with diabetes and elevated depression.

Depressive symptoms in people with diabetes are associated with increased risk of adverse outcomes. Although successful psychosocial treatment options are available, little is known about factors that facilitate treatment response for depression in diabetes. This prospective study aims to examine the impact of known risk factors on improvement of depressive symptoms with a special interest in the role of diabetes-related distress. 181 people with diabetes participated in a randomized controlled trial. Diabetes-related distress was assessed using the Problem Areas In Diabetes (PAID) scale; depressive symptoms were assessed using the Center for Epidemiologic Studies Depression (CES-D) scale. Multiple logistic and linear regression analyses were used to assess associations between risk factors for depression (independent variables) and improvement of depressive symptoms (dependent variable). Reliable change indices were established as criteria of meaningful reductions in diabetes distress and depressive symptoms. A reliable reduction of diabetes-related distress (15.43 points in the PAID) was significantly associated with fourfold increased odds for reliable improvement of depressive symptoms (OR = 4.25, 95% CI: 2.05-8.79; P<0.001). This result was corroborated using continuous measures of diabetes distress and depressive symptoms, showing that greater reduction of diabetes-related distress independently predicted greater improvement in depressive symptoms (ß = -0.40; P<0.001). Higher age had a positive (Odds Ratio = 2.04, 95% CI: 1.21-3.43; P<0.01) and type 2 diabetes had a negative effect on the meaningful reduction of depressive symptoms (Odds Ratio = 0.12, 95% CI: 0.04-0.35; P<0.001). The reduction of diabetes distress is a statistical predictor of improvement of depressive symptoms. Diabetes patients with comorbid depressive symptomatology might benefit from treatments to reduce diabetes-related distress.

Evidence of the negative impact of depression on glycaemic control is equivocal, and diabetes-related distress has been proposed as potential mediator. 466 diabetes patients were cross-sectionally assessed for depression (Center for Epidemiologic Studies Depression Scale), diabetes-related distress (Diabetes Distress Scale), and glycaemic control (HbA 1c ). We distinguished the associations of depression and diabetes distress with glycaemic control using analysis of variance and multiple regression. Neither patients with depression only nor diabetes distress only differed significantly from controls regarding HbA 1c . However, HbA 1c was substantially increased when both conditions were present (9.2 vs. 8.6 %, P  = 0.01). As in previous studies, we observed a significant association between depression and hyperglycaemia ( P  < 0.01). However, a mediation analysis revealed that this association in fact depended on the presence of diabetes distress ( P  < 0.01). Depression seems to be associated with hyperglycaemia particularly when accompanied by diabetes distress, suggesting that adjusting clinical procedures regarding diabetes distress may facilitate the identification and care of high-risk patients.

OBJECTIVE: To evaluate the efficacy of the group program PREDIAS for diabetes prevention. RESEARCH DESIGN AND METHODS: PREDIAS consists of 12 lessons and aims at lifestyle modification. The control group received written information about diabetes prevention. In this study, a total of 182 persons with an elevated diabetes risk participated (aged 56.3 ± 10.1 years, 43% female, and BMI 31.5 ± 5.3 kg/m²). RESULTS: After 12 months, weight loss was significantly higher (P = 0.001) in PREDIAS than in the control group (-3.8 ± 5.2 vs. -1.4 ± 4.09 kg). There were also significant effects (P = 0.001) on fasting glucose (control group 1.8 ± 13.1 mg/dl vs. PREDIAS -4.3 ± 11.3 mg/dl), duration of physical activity per week (control group 17.9 ± 63.8 min vs. PREDIAS 46.6 ± 95.5 min; P = 0.03), and eating behavior. CONCLUSIONS: PREDIAS significantly modified lifestyle factors associated with an elevated diabetes risk.