61st EASD Annual Meeting of the European Association for the Study of Diabetes SO 077 Hi Hypo :) I would like to know you better 854 - Detecting hypoglycaemia with glycaemia risk index in adults with type 1 diabetes using continuous glucose monitoring and multiple daily injection therapy: a multicentric real-world study

Background and aims: The glycemia risk index (GRI) is a novel composite score to evaluate the quality of glucose monitoring profiles, weighted according to the risk of both hypo- and hyperglycemia. Aim of this multicentric, retrospective study was to investigate the relationship between GRI and the most common sensor-derived glucose metrics in a population in multiple daily injection (MDI) therapy and with 2nd generation intermittently-scanned continuous glucose monitoring (isCGM) sensors in a real-world setting. Materials and methods: The analysis included 90-day intervals from 539 routine visits of 367 patients (M/F=203/164; mean±SD: age 42±16 years, diabetes duration 21±14 years, HbA1c 7.5±1.0%) with type 1 diabetes (T1D), in MDI therapy and with isCGM sensors, who attended the outpatient diabetes clinics at the Hospitals of Verona (from January 2023 to December 2023) and Bergamo (from January 2023 to April 2023). Patients’ 90-day-long isCGM tracings were stratified into three groups based on time in range (TIR) (groups 1 to 3: >70%, 70 to 50% and ≤50%, respectively) and then in subgroups according to clinical guideline-based thresholds for time below range (TBR) (<4% vs ≥4%). GRI was computed for each subgroup and correlation and regression analyses were performed to assess the relationship between GRI and the main metrics. Results: Pairwise comparisons between TIR-based groups showed significantly higher GRI as TIR decreased among groups (all p<0.001); after further stratifications based on TBR thresholds within TIR groups 1 and 2, the subgroups with TBR≥4% vs <4% displayed significantly higher GRI (31.0±8.4 vs 19.8±6.9 and 51.7±9.7 vs 43.7±8.4 for groups 1 and 2, respectively, both p<0.001), although without any significant differences in TIR (79.0±5.8% vs 80.4±7.0%, p 0.289, and 60.6±5.6% vs 59.6±5.8%, p 0.221, for groups 1 and 2, respectively). Pearson’s correlations and unadjusted linear regression analyses showed negative relationships between GRI and TIR and positive ones with time above range (TAR) and the coefficient of variation (CV) (all p<0.001), but none with TBR; however, when adjusted for TIR and CV, regression analyses unveiled significant opposite associations of GRI with TAR and TBR (coeff. -1.28 and 1.28, respectively, both p<0.001). Conclusion: This real-world, retrospective analysis suggested that GRI may be useful for identifying patients at higher risk of hypoglycemia within groups with similar TIR. Further studies are needed, particularly in patients with different combinations of sensors and insulin administration methods.