Cardiovascular safety in type 2 diabetes with sulfonylureas as second-line drugs: a nation-wide population based comparative safety study

   Objective To assess the real-world cardiovascular (CV) safety for SU, in comparison with dipeptidylpeptidase-4 inhibitors (DPP4i) and thiazolidinediones (TZD) through development of robust methodology for causal inference in a whole nation study.  Research Design and Methods A cohort study was performed including people with type 2 diabetes diagnosed in Scotland before 31 December 2017, who failed to reach HbA1c 48 mmol/mol despite metformin monotherapy and initiated second-line pharmacotherapy (SU/DPP4i/TZD) on or after 1 January 2010. The primary outcome was the composite major adverse cardiovascular events (MACE), including hospitalization for myocardial infarction (MI), ischemic stroke, heart failure, and CV death. Secondary outcomes were each individual endpoint and all-cause death. Multivariable Cox proportional hazards regression and an instrumental variable (IV) approach were used to control confounding in a similar way to the randomization process in a randomized control trial.   Results Comparing SU to non-SU (DPP4i/TZD), the hazard ratio (HR) for MACE was 1.00 (95% CI: 0.91 to 1.09) from the multivariable Cox regression and 1.02 (0.91  - 1.13) and 1.03 (0.91- 1.16) using two different IVs. For all-cause death, the HR from Cox regression and the two IV analyses was 1.03 (0.94 - 1.13), 1.04 (0.93 - 1.17), and 1.03 (0.90 - 1.17).  Conclusion Our findings contribute to the understanding that second-line SU for glucose lowering are unlikely to increase CV risk or all-cause mortality. Given their potent efficacy, microvascular benefits, cost effectiveness and widespread use, this study supports that SU should remain a part of the global diabetes treatment portfolio.Article  </p

The impact of hypoglycaemia on daily functioning among adults with diabetes: a prospective observational study using the Hypo-METRICS Application

Aims/hypothesis: To examine the impact of hypoglycaemia on daily functioning among adults with type 1 diabetes or insulin-treated type 2 diabetes, using the novel Hypo-METRICS application. RESEARCH DESIGN AND METHODS For 70 consecutive days, 594 adults (type 1 diabetes: n=274; type 2diabetes: n=320) completed brief morning and evening Hypo-METRICS ‘check-ins’ about their experiencedhypoglycaemia and daily functioning. Participants wore a blinded glucose sensor for the study duration.Days   and   nights   with   or   without   person-reported   hypoglycaemia   (PRH)   and/or   sensor-detectedhypoglycaemia (SDH) were compared using multilevel regression models.  RESULTS  Participants submitted a mean of 86.3±12.5% morning and 90.8±10.7% evening check-ins.  Forboth types of diabetes, SDH alone had no significant associations to the changes in daily functioning scores.However, daytime and night-time PRH (with or without SDH) were significantly associated with worseningof   energy   levels,   mood,   cognitive   functioning,   negative   affect   and   fear   of   hypoglycaemia   later   thatday/while asleep. In addition, night-time PRH (with or without SDH) was significantly associated withworsening of sleep quality (type 1 and 2 diabetes), and memory (type 2 diabetes). Further, daytime PRH(with or without SDH), was associated with worsening of fear of hyperglycemia while asleep (type 1diabetes), memory (type 1 and 2 diabetes) and social functioning (type 2 diabetes). CONCLUSIONS This prospective, real-world study reveals impact on several domains of daily functioningfollowing PRH, but not following SDH alone.These data suggest that the observed negative impact is mainlydriven   by   subjective   awareness   of   hypoglycaemia   (i.e.,   PRH),   through   either   symptoms   or   sensoralerts/readings and/or the need to take action to prevent or treat them.</p

Associations Between Hypoglycemia Awareness Status and Symptoms of Hypoglycemia Among Adults with Type 1 or Insulin-Treated Type 2 Diabetes Using the Hypo-METRICS Smartphone Application

Introduction: This study examined associations between hypoglycemia awareness status and hypoglycemia symptoms reported in real-time using the novel Hypoglycaemia-MEasurement, ThResholds and ImpaCtS (Hypo-METRICS) smartphone application (app) among adults with insulin-treated type 1 (T1D) or type 2 diabetes (T2D). Methods: Adults who experienced at least one hypoglycemic episode in the previous 3 months were recruited to the Hypo-METRICS study. They prospectively reported hypoglycemia episodes using the app for 10 weeks. Any of eight hypoglycemia symptoms were considered present if intensity was rated between "A little bit" to "Very much" and absent if rated "Not at all." Associations between hypoglycemia awareness (as defined by Gold score) and hypoglycemia symptoms were modeled using mixed-effects binary logistic regression, adjusting for glucose monitoring method and diabetes duration. Results: Of 531 participants (48% T1D, 52% T2D), 45% were women, 91% white, and 59% used Flash or continuous glucose monitoring. Impaired awareness of hypoglycemia (IAH) was associated with lower odds of reporting autonomic symptoms than normal awareness of hypoglycemia (NAH) (T1D odds ratio [OR] 0.43 [95% confidence interval {CI} 0.25-0.73], P = 0.002); T2D OR 0.51 [95% CI 0.26-0.99], P = 0.048), with no differences in neuroglycopenic symptoms. In T1D, relative to NAH, IAH was associated with higher odds of reporting autonomic symptoms at a glucose concentration 70 mg/dL (OR 2.18 [95% CI 1.21-3.94], P = 0.010). Conclusion: The Hypo-METRICS app is sensitive to differences in hypoglycemia symptoms according to hypoglycemia awareness in both diabetes types. Given its high ecological validity and low recall bias, the app may be a useful tool in research and clinical settings. The clinical trial registration number is NCT04304963

A Comparison of the Rates of Clock-Based Nocturnal Hypoglycemia and Hypoglycemia While Asleep Among People Living with Diabetes: Findings from the Hypo-METRICS Study

Introduction Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates whilst asleep to those of clock-based nocturnal hypoglycemia in adults with type 1 (T1D) or insulin-treated type 2 diabetes (T2D). Methods Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00hrs) vs diurnal and whilst asleep vs awake defined by Fitbit sleeping intervals. Paired sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results 574 participants (47% T1D, 45% women, 89% White, median (IQR) age 56 (45-66) years and HbA1c 7.3% (6.8-8.0)) were included. Median sleep duration was 6.1h (5.2-6.8), bedtime and waking time approximately 23:30 and 07:30 respectively. There were higher median weekly rates of SDH and PRH whilst asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH<70 mg/dL (1.7 vs 1.4, p<0.001). Higher weekly rates of SDH whilst asleep than nocturnal SDH were found among people with T2D, especially for SDH<70 mg/dL (0.8 vs 0.7, p<0.001). Conclusion Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia whilst asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia whilst asleep more accurately

A Comparison of the Rates of Clock-Based Nocturnal Hypoglycemia and Hypoglycemia While Asleep Among People Living with Diabetes: Findings from the Hypo-METRICS Study

Introduction Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates whilst asleep to those of clock-based nocturnal hypoglycemia in adults with type 1 (T1D) or insulin-treated type 2 diabetes (T2D). Methods Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00hrs) vs diurnal and whilst asleep vs awake defined by Fitbit sleeping intervals. Paired sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results 574 participants (47% T1D, 45% women, 89% White, median (IQR) age 56 (45-66) years and HbA1c 7.3% (6.8-8.0)) were included. Median sleep duration was 6.1h (5.2-6.8), bedtime and waking time approximately 23:30 and 07:30 respectively. There were higher median weekly rates of SDH and PRH whilst asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH<70 mg/dL (1.7 vs 1.4, p<0.001). Higher weekly rates of SDH whilst asleep than nocturnal SDH were found among people with T2D, especially for SDH<70 mg/dL (0.8 vs 0.7, p<0.001). Conclusion Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia whilst asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia whilst asleep more accurately