Mark Lewis Evans

Aims: The Hypoglycaemia Awareness Questionnaire–Impaired Awareness subscale (HypoA‐Q/IA) was developed to enhance impaired awareness of hypoglycaemia (IAH) assessment, addressing methodological deficiencies of the established Gold and Clarke measures. An IAH cutpoint (HypoA‐Q/IA ≥ 12) was identified from a clamp study in Type 1 diabetes (T1D). We explored cutpoints in relation to Gold and Clarke measures, and their co‐occurrence, in the larger, real‐world observational Hypo‐METRICS study, in people with T1D (pwT1D) and insulin‐treated Type 2 diabetes (pwT2D). Material and Methods: Hypo‐METRICS recruited pwT1D or pwT2D with ≥ 1 hypoglycaemic event in the previous 3 months. IAH was defined as Gold ≥ 4, Clarke ≥ 4 and/or HypoA‐Q/IA ≥ 12. Bidirectional ROC analysis was performed between HypoA‐Q/IA and Gold/Clarke cutpoints. Results: Questionnaires were completed by 232 pwT1D and 285 pwT2D (male 47% vs. 62%, p < 0.001; median age 49 vs. 63 years, p < 0.001; HbA1c 7.3% vs. 7.4%, p = 0.03). IAH prevalence was 24% by Gold ≥ 4, 16% by Clarke ≥ 4 and 7% by HypoA‐Q/IA ≥ 12. ROC analysis yielded HypoA‐Q/IA cutpoints of ≥ 7 against Gold ≥ 4 (AUC 0.87) and ≥ 9 against Clarke ≥ 4 (AUC 0.89), and cutpoints of Gold ≥ 4 (AUC 0.87) and Clarke ≥ 3 (AUC 0.93) against HypoA‐Q/IA ≥ 12. Of those with IAH by either measure, 24% were identified by both HypoA‐Q/IA ≥ 12 and Gold ≥ 4, and 32% by both HypoA‐Q/IA ≥ 12 and Clarke ≥ 4, with lower overlap in T2D than T1D. Conclusions: HypoA‐Q/IA cutpoints corresponding to legacy IAH cutpoints (≥ 7–9) were lower than the clamp‐derived threshold (≥ 12), suggesting ≥ 12 may identify a subgroup with more severe IAH. These cutpoints enable cross‐study comparison; prospective validation against severe hypoglycaemia risk is needed.

Aims: The continuous glucose monitoring (CGM)‐derived glucose management indicator (GMI) is used for estimating glycated haemoglobin (HbA1c). This study assessed agreement between GMI—calculated using both original and updated (uGMI) models—and laboratory‐measured HbA1c across Dexcom, MiniMed and Abbott sensors. Materials and Methods: Real‐world data from adults with type 1 diabetes using automated insulin delivery were retrospectively analysed. Δ(GMI‐HbA1c) and Δ(uGMI‐HbA1c) were evaluated over 90 days preceding HbA1c ( n = 383) and over 28 days preceding HbA1c ( n = 375). Agreement was measured as directional difference, indicating GMI overestimation or underestimation of HbA1c, and absolute difference, indicating magnitude of agreement between GMI and HbA1c. Agreement was assessed across sensor groups using ANCOVA, adjusting for coefficient of variation and ethnicity. Results: Although mean directional differences were statistically significant for Δ(GMI‐HbA1c) and Δ(uGMI‐HbA1c) across all sensor groups ( p < 0.001), differences were small and not clinically important, 90‐day adjusted means (95% CI)—Δ(GMI‐HbA1c): Dexcom 0.17% (0.09, 0.25), MiniMed −0.15% (−0.28, −0.02), Abbott −0.08% (−0.25, 0.09); Δ(uGMI‐HbA1c): Dexcom 0.22% (0.14, 0.30), MiniMed −0.10% (−0.23, 0.02), Abbott −0.02% (−0.19, 0.15). Mean absolute difference did not significantly differ across sensor groups—Δ(GMI‐HbA1c): 0.42%–0.51% ( p = 0.353), Δ(uGMI‐HbA1c): 0.41%–0.52% ( p = 0.221). Conclusions: GMI/uGMI performed consistently across sensor groups. Where clinically meaningful discrepancies between GMI/uGMI and HbA1c arise, these warrant interpretation within the context of individual clinical and laboratory factors rather than attribution to sensor manufacturer, reinforcing the need for personalised use of GMI alongside HbA1c.

Aims: Experimental hypoglycaemia induces a persistent inflammatory response. We investigated whether real‐life sensor‐detected hypoglycaemia (SDH) is associated with immune parameters in people with Type 1 and Type 2 diabetes and explored potential differences between the groups. Materials and Methods: In a multi‐center observational study, 177 people with Type 1 diabetes and 185 with insulin‐treated Type 2 diabetes were recruited to wear blinded glucose sensors for 10 weeks. SDH metrics derived from these sensors included SDH frequency, SDH duration, and time below range (TBR). At the end of the observation period, fasting blood was drawn to measure levels of circulating inflammatory proteins (Type 1 n = 177, Type 2 n = 185), ex vivo monocyte function (Type 1 n = 59; Type 2 n = 90), and circulating immune cell counts (Type 1 n = 29; Type 2 n = 87). Results: Four circulating inflammatory proteins ( CCL28 , CX3CL1 , IL ‐ 17A , IL ‐ 17C ) were positively associated with SDH frequency, SDH duration and TBR in both groups (all p < 0.05). In Type 1 diabetes, pro‐inflammatory responses of monocytes to pathogenic stimuli were inversely associated with SDH frequency, SDH duration and TBR , particularly Pam3Cys ‐induced IL ‐6 production (all p < 0.05); similar but less pronounced associations were observed in Type 2 diabetes. SDH frequency and TBR were inversely associated with total circulating immune cell, neutrophil and monocyte counts in Type 1 diabetes (all p < 0.05). Conclusions: Exposure to real‐life hypoglycaemia is associated with immune alterations in people with Type 1 and Type 2 diabetes, including increased levels of circulating inflammatory proteins in both diabetes types and reduced monocyte function and immune cell counts, particularly in Type 1 diabetes. Trial Registration: NCT04304963

Background and Aims: People with diabetes and chronic kidney disease (CKD) may be at increased risk of iatrogenic hypoglycaemia. We compared rates of sensor‐detected (SDH) and person‐reported hypoglycaemia (PRH) in people with insulin‐treated Type 2 diabetes (T2D) with and without CKD in the Hypo‐METRICS study. Materials and Method: Insulin‐treated T2D patients with > 1 hypoglycaemic episode within the last 3 months wore a blinded continuous glucose monitor (CGM) and recorded PRH on the bespoke Hypo‐METRICS app for 10 weeks. SDH was defined as per consensus guidelines. The participants were grouped based on estimated glomerular filtration rate (eGFR), that is CKD 1 (eGFR ≥ 90 mL/min; n = 117), CKD 2 (eGFR 60–89 mL/min; n = 140), and CKD 3 (eGFR 30–59 mL/min; n = 56). We used descriptive statistics, Kruskal‐Wallis rank sum tests, and a negative binomial models to control for confounders. Results: In total, 313 participants with T2DM were included. Median age increased with declining eGFR ( p < 0.001). Median (IQR) events per week for SDH < 3.9 mmol/L were 1.72 (0.66–3.43) in CKD 1, 2.12 (0.85–4.55) in CKD 2, and 2.22 (1.09–4.57) in CKD 3 ( p = 0.12). For SDH < 3.0 mmol/L, these rates were 0.20 (0.00–0.50), 0.21 (0.10–0.61), and 0.20 (0.00–0.71), respectively ( p = 0.88). PRH events had medians of 1.05 (0.40–2.12), 1.31 (0.61–2.42), and 1.28 (0.68–2.25) for CKD 1, CKD 2, and CKD 3 ( p = 0.24). Conclusion: In people with insulin‐treated T2DM and CKD stages 2 or 3, the rates of SDH and PRH do not significantly differ from those in people without CKD. Trial Registration: ClinicalTRials.gov : NCT04304963

Aims: We investigated the proportion of sensor‐detected hypoglycaemic (SDH) events progressing to level 2, and associated variables. Materials and Methods: We used data from Hypo‐METRICS, which recruited people with type 1 (pwT1D) and insulin‐treated type 2 diabetes (pwT2D) with ≥ 1 hypoglycaemic event in preceding 3 months, wearing blinded continuous glucose monitoring devices (CGM), additional to usual monitoring modality, and FitBits for 10 weeks. We defined: L1: SDH < 3.9 mmol/L for ≥ 15 min but ≥ 3.0 mmol/L; L2: SDH < 3.9 mmol/L with ≥ 15 min of sensor glucose < 3.0 mmol/L; L2 ratio = L2 /( L1 + L2 ), restricted to participants with both event types during the study period. Associations of selected variables on L2 ratio was assessed using beta regression and purposeful variable selection. Results: We analysed 23 768 SDH events from 212 pwT1D and 213 pwT2D. PwT1D were predominantly female (53% vs. 42% in T2D, p = 0.023) and younger (median age 49 vs. 62 years, p < 0.001), with a higher L2 ratio (16% vs. 14%, p = 0.03). Coefficient of variation (CV), OR = 1.07, 95% CI = 1.06–1.09 ( p < 0.001), and mean sensor glucose, OR = 1.13, 95% CI = 1.08–1.18 ( p < 0.001) were the principal predictors in T1D and T2D respectively; mean L1‐SDH duration, weekly L1‐SDH frequency, personal CGM usage, impaired awareness were not. Progression was higher during sleep than wakefulness (21% vs. 11%, p < 0.001); L2 ratios during sleep did not differ by awareness status. Conclusions: Approximately one sixth of SDH events progressed to L2, with a higher proportion in T1D than T2D. The principal determinants were CV in T1D and mean glucose in T2D. Awareness status was not associated with progression risk during sleep.

Aims/hypothesis The aim of this study was to determine the effect of a novel somatostatin receptor 2 (SSTR2) antagonist, ZT-01, on impaired glucagon counterregulation in hypoglycaemia and its safety in adults with type 1 diabetes. Methods In a randomised crossover phase 1b single-site study, blinded to both participants and researchers, participants, aged 18–65 years with BMI 18.5–27 kg/m² with type 1 diabetes (HbA_1c 42.1–74.9 mmol/mol and C-peptide <200 pmol/l) underwent three separate hyperinsulinaemic euglycaemic–hypoglycaemic clamps. Participants were randomised, by a third party, in equal proportion to treatment order with placebo, 3 mg ZT-01 or 20 mg ZT-01 administered subcutaneously during euglycaemia (plasma glucose target 5.0 mmol/l), and exposed to level 1 (target 3.5 mmol/l) and level 2 (target 2.6 mmol/l) hypoglycaemia induced with variable-rate insulin infusion without addition of dextrose. Glucagon response was the primary endpoint; plasma glucose, other counterregulatory hormones, symptom scores and safety were also determined. Results Twenty-four randomised participants (9 female and 15 male) received at least one administration of placebo or ZT-01 and were included for safety analysis. Twenty-two participants completed at least one glucose clamp and were included for pharmacodynamics analysis. Transient increases in plasma glucagon were observed with ZT-01 administration (by 25.7 ± 2.4 ng/l with 3 mg ZT-01 and by 28.4 ± 2.4 ng/l with 20 mg ZT-01), with levels declining to near the pre-dose values before the start of the level 1 hypoglycaemic period. Mean glucagon levels rose again over baseline in both ZT-01 treatment arms during level 1 (by 15.6 ± 2.3 pg/l with 3 mg ZT-01 and by 14.9 ± 2.4 pg/l with 20 mg ZT-01) and level 2 (by 22.8 ± 2.7 pg/l with 3.0 mg ZT-01 and by 29.6 ± 2.8 pg/l with 20 mg ZT-01) hypoglycaemia. With placebo, glucagon levels were unchanged following dosing and during level 1 hypoglycaemia but rose modestly during level 2 hypoglycaemia (by 8.9 ± 2.5 ng/l). Both frequency and amplitude of the increases in glucagon were higher with ZT-01 vs placebo during level 1 and level 2 hypoglycaemia. There were no drug treatment-related adverse events. Conclusions/interpretation Administration of the SSTR2 antagonist ZT-01 increased glucagon responsiveness during insulin-induced hypoglycaemia in individuals with type 1 diabetes. Trial registration ClinicalTrials.gov NCT05007977 Graphical Abstract The alternative text for this image may have been generated using AI.

Aims: Gold and Clarke questionnaires are used to identify impaired awareness of hypoglycaemia (IAH) and severe hypoglycaemic event (SHE) risk in people with diabetes (pwD). We explored their overlap, including Clarke's Hypoglycaemia Awareness Status (Clarke‐HAS) subfactor, and subsection differences in SHE incidence (Gold, Clarke, overlap). Materials and Methods: This post hoc analysis of the Hypo‐METRICS study recruited pwD on insulin (type 1: T1D; type 2: T2D) with ≥ 1 hypoglycaemic event in the previous 3 months. IAH was defined as Gold ≥ 4, Clarke ≥ 4, Clarke‐HAS ≥ 2. Results: Gold and Clarke were completed by 232 pwT1D and 285 pwT2D (age: 47 vs. 62 years, p < 0.001; HbA1c: 56 mmol/mol (7.3%) vs. 57 mmol/mol (7.4%), p = 0.03). IAH prevalence in T1D vs. T2D was 21% vs. 26% ( p = 0.1), 14% vs. 18% ( p = 0.2), and 41% vs. 48% ( p = 0.2) according to Gold, Clarke and Clarke‐HAS. The overlap between Gold ≥ 4, Clarke ≥ 4 was 40% (T1D: 45%; T2D: 37%); between Gold ≥ 4, Clarke‐HAS ≥ 2 it was 45% (T1D: 43%; T2D: 47%). The overlap between Gold ≥ 4, Clarke ≥ 4 was 41% vs. 39% in CGM vs. non‐CGM users (T1D: 50% vs. 35%; T2D: 32% vs. 40%); between Gold ≥ 4, Clarke‐HAS ≥ 2 it was 39% vs. 53% (T1D: 38% vs. 54%; T2D: 40% vs. 53%). In the IAH sample, SHE rate was lower in people found with IAH by Gold vs. Clarke, 9% vs. 53%, and in those assessed by Gold‐Clarke overlap vs. Clarke, 20% vs. 53% (all p < 0.001). Conclusions: Gold and Clarke show moderate consistency (37%–54%) in identifying IAH in insulin‐treated diabetes, with/without CGM use, each associated with different SHE rates, indicating the two questionnaires measure different, independent aspects of IAH.

Background and Aims: In people with insulin‐treated diabetes experiencing hypoglycaemia, the multicentre Hypo‐METRICS study found that 60% of sensor‐detected hypoglycaemic episodes (SDH) were asymptomatic, and over 40% of person‐reported hypoglycaemia (PRH) occurred at glucose levels ≥70 mg/dL (3.9 mmol/L). This subanalysis explored participants' experiences of these episodes to identify possible clinical implications. Methods: Fifty‐eight Austrian participants received a 15‐item questionnaire on their experience of asymptomatic hypoglycaemia and symptoms at glucose levels ≥70 mg/dL (3.9 mmol/L). Results: The response rate was 86% ( n = 50). Among all participants, 56% ( n = 28) reported experiencing hypoglycaemic symptoms at glucose levels ≥70 mg/dL (3.9 mmol/L) “sometimes” or “often.” They attributed this to a combination of a threshold shift due to chronic hyperglycaemia, rapid glucose decline and fear of hypoglycaemia. 68% of all SDH < 70 mg/dL and 59% of those below the clinically critical level of 54 mg/dL were asymptomatic. Conclusion: These results demonstrate that SDH and PRH each capture different, yet equally important, dimensions of the hypoglycaemia experience. Relying on only one source of information inevitably provides an incomplete picture. By integrating the patient's voice, diabetes professionals can provide appropriate support and tailor continuous glucose monitoring (CGM) alarm settings and treatment plans to truly meet individual needs.