Glycemic Variability Patterns Strongly Correlate With Partial Remission Status in Children With Newly Diagnosed Type 1 Diabetes

Comparison of Metabolic Control in Children and Adolescents Treated with Insulin Pumps

BACKGROUND

While insulin pumps remain the most common form of therapy for youths with type 1 diabetes (T1DM), they differ in the extent to which they utilize data from continuous glucose monitoring (CGM) and automate insulin delivery.

METHODS

The aim of the study was to compare metabolic control in patients using different models of insulin pumps. This retrospective single-center study randomly sampled 30 patients for each of the following treatments: Medtronic 720G without PLGS (predictive low glucose suspend), Medtronic 640G or 740G with PLGS and Medtronic 780G. In the whole study group, we used CGM systems to assess patients' metabolic control, and we collected lipid profiles. In three groups of patients, we utilized CGM sensors (Guardian 3, Guardian 4, Libre 2 and Dexcom G6) to measure the following glycemic variability proxy values: time in range (TIR), time below 70 mg/dL (TBR), time above 180 mg/dL (TAR), coefficient of variation (CV) and mean sensor glucose.

RESULTS

Medtronic 640G or 740G and 780G users were more likely to achieve a target time in the target range 70-180 mg/dL (≥80%) [Medtronic 720G = 4 users (13.3%) vs. Medtronic 640G/740G = 10 users (33.3%) vs. Medtronic 780G = 13 users (43.3%); p = 0.0357)] or low glucose variability [Medtronic 720G = 9 users (30%) vs. Medtronic 640G/740G = 18 users (60%) vs. Medtronic 780G = 19 users (63.3%); p = 0.0175)].

CONCLUSIONS

Any integration between the insulin pump and CGM was associated with better glycemic control. More advanced technologies and artificial intelligence in diabetes help patients maintain better glycemia by eliminating various factors affecting postprandial glycemia.

Approaches to Measuring Beta Cell Reserve and Defining Partial Clinical Remission in Paediatric Type 1 Diabetes

CONTEXT

Type 1 diabetes (T1D) results from the autoimmune T-cell mediated destruction of pancreatic beta cells leading to insufficient insulin secretion. At the time of diagnosis of T1D, there is residual beta cell function that declines over the subsequent months to years. Recent interventions have been approved to preserve beta cell function in evolving T1D.

OBJECTIVE

The aim of this review is to summarise the approaches used to assess residual beta cell function in evolving T1D, and to highlight potential future directions.

METHODS

Studies including subjects aged 0 to 18 years were included in this review. The following search terms were used; "(type 1 diabetes) and (partial remission)" and "(type 1 diabetes) and (honeymoon)". References of included studies were reviewed to determine if additional relevant studies were eligible.

RESULTS

There are numerous approaches to quantifying beta cell reserve in evolving T1D. These include c-peptide measurement after a mixed meal or glucagon stimuli, fasting c-peptide, the urinary c-peptide/creatinine ratio, insulin dose-adjusted haemoglobin A1c, and other clinical models to estimate beta cell function. Other biomarkers may have a role, including the proinsulin/c-peptide ratio, cytokines, and microRNA. Studies using thresholds to determine if residual beta cell function is present often differ in values used to define remission.

CONCLUSIONS

As interventions are approved to preserve beta cell function, it will become increasingly necessary to quantify residual beta cell function in research and clinical contexts. In this report, we have highlighted the strengths and limitations of the current approaches.

CXCL12-CXCR4 mediates CD57+ CD8+ T cell responses in the progression of type 1 diabetes

CD57+ CD8+ T cells, also referred as effector memory cells, are implicated in various conditions including tumor immunity, virus immunity, and most recently with autoimmunity. However, their roles in the progression and remission of T1D are still unclear. Here, we noted an increase in peripheral CD57+ CD8+ T cells in a T1D patient harboring an activator of transcription 3 (STAT3) mutation. Our in-depth study on the role of CD57+ CD8+ T cells within a T1D patient cohort revealed that these cells undergo significant compositional shifts during the disease's progression. Longitudinal cohort data suggested that CD57+ CD8+ T cell prevalence may be a harbinger of β-cell function decline in T1D patients. Characterized by robust cytotoxic activity, heightened production of pro-inflammatory cytokines, and increased intracellular glucose uptake, these cells may be key players in the pathophysiology of T1D. Moreover, in vitro assays showed that the CXCL12-CXCR4 axis promotes the expansion and function of CD57+ CD8+ T cells via Erk1/2 signaling. Notably, the changes of serum CXCL12 concentrations were also found in individuals during the peri-remission phase of T1D. Furthermore, treatment with the CXCR4 antagonist LY2510924 reduced the immunological infiltration of CD57+ CD8+ T cells and mitigated hyperglycemia in a STZ-induced T1D mouse model. Taken together, our work has uncovered a novel role of the CXCL12-CXCR4 axis in driving CD57+ CD8+ T cells responses in T1D, and presented a promising therapeutic strategy for delaying the onset and progression of diabetes.

Immunometabolic biomarkers for partial remission in type 1 diabetes mellitus

Shortly after diagnosis of type 1 diabetes mellitus (T1DM) and initiation of insulin therapy, many patients experience a transient partial remission (PR) phase, also known as the honeymoon phase. This phase presents a potential therapeutic opportunity due to its association with immunoregulatory and β cell-protective mechanisms. However, the lack of biomarkers makes its characterization difficult. In this review, we cover the current literature addressing the discovery of new predictive and monitoring biomarkers that contribute to the understanding of the metabolic, epigenetic, and immunological mechanisms underlying PR. We further discuss how these peripheral biomarkers reflect attempts to arrest β cell autoimmunity and how these can be applied in clinical practice.

Glycaemia risk index uncovers distinct glycaemic variability patterns associated with remission status in type 1 diabetes

AIMS/HYPOTHESIS

The aim of this work was to define a unique remission status using glycaemia risk index (GRI) and other continuous glucose monitoring (CGM) metrics in individuals with type 1 diabetes for improved phenotyping.

METHODS

A group of 140 individuals with type 1 diabetes were recruited for a cross-sectional study. The participants were categorised into four groups based on their remission status, which was defined as insulin-dose-adjusted A1c (IDAA1c) <9 or C-peptide ≥300 pmol/l: new-onset (n=24); mid-remission (n=44); post-remission (n=44); and non-remission (individuals who did not experience remission, n=28). Participants in the remission phase were referred to as 'remitters', while those who were not in the remission phase were referred to as 'non-remitters', the latter group including new-onset, post-remission and non-remission participants. Clinical variables such as HbA1c, C-peptide and insulin daily dose, as well as IDAA1C and CGM data, were collected. The patterns of CGM metrics were analysed for each group using generalised estimating equations to investigate the glycaemic variability patterns associated with remission status. Then, unsupervised hierarchical clustering was used to place the participants into subgroups based on GRI and other CGM core metrics.

RESULTS

The glycaemic variability patterns associated with remission status were found to be distinct based on the circadian CGM metrics. Remitters showed improved control of blood glucose levels over 14 days within the range of 3.9-10 mmol/l, and lower GRI compared with non-remitters (p<0.001). Moreover, GRI strongly correlated with IDAA1C (r=0.62; p<0.001) and was sufficient to distinguish remitters from non-remitters. Further, four subgroups demonstrating distinct patterns of glycaemic variability associated with different remission status were identified by clustering on CGM metrics: remitters with low risk of dysglycaemia; non-remitters with high risk of hypoglycaemia; non-remitters with high risk of hyperglycaemia; and non-remitters with moderate risk of dysglycaemia.

CONCLUSIONS/INTERPRETATION

GRI, an integrative index, together with other traditional CGM metrics, helps to identify different glycaemic variability patterns; this might provide specifically tailored monitoring and management strategies for individuals in the various subclusters.

Post-Hypoglycemic hyperglycemia are highly relevant markers for stratification of glycemic variability and partial remission status of pediatric patients with new-onset type 1 diabetes

AIMS

To evaluate whether parameters of post-hypoglycemic hyperglycemia (PHH) correlated with glucose homeostasis during the first year after type 1 diabetes onset and helped to distinguish pediatric patients undergoing partial remission or not.

METHODS

In the GLUREDIA (GLUcagon Response to hypoglycemia in children and adolescents with new-onset type 1 DIAbetes) study, longitudinal values of clinical parameters, continuous glucose monitoring metrics and residual β-cell secretion from children with new-onset type 1 diabetes were analyzed during the first year after disease onset. PHH parameters were calculated using an in-house algorithm. Correlations between PHH parameters (i.e., PHH frequency, PHH duration, PHH area under the curve [PHHAUC]) and glycemic homeostasis markers were studied using adjusted mixed-effects models.

RESULTS

PHH parameters were strong markers to differentiate remitters from non-remitters with PHH/Hyperglycemia duration ratio being the most sensitive (ratio<0.02; sensitivity = 86% and specificity = 68%). PHHAUC moderately correlated with parameters of glucose homeostasis including TIR (R2 = 0.35, p-value < 0.05), coefficient of variation (R2 = 0.22, p-value < 0.05) and Insulin-Dose Adjusted A1c (IDAA1C) (R2 = 0.32, p-value < 0.05) and with residual β-cell secretion (R2 = 0.17, p-value < 0.05). Classification of patients into four previously described glucotypes independently validated PHH parameters as reliable markers of glucose homeostasis and improved the segregation of patients with intermediate values of IDAA1C and estimated C-peptide (CPEPEST). Finally, a combination of PHH parameters identified groups of patients with specific patterns of hypoglycemia.

CONCLUSION

PHH parameters are new minimal-invasive markers to discriminate remitters from non-remitters and evaluate glycemic homeostasis during the first year of type 1 diabetes. PHH parameters may also allow patient-targeted therapeutic management of hypoglycemic episodes.

Determinants and Characteristics of Insulin Dose Requirements in Children and Adolescents with New-Onset Type 1 Diabetes: Insights from the INSENODIAB Study

Aims

New-onset type 1 diabetes mellitus (T1D) in pediatric patients represents a clinical challenge for initial total daily insulin dosing (TIDD) due to substantial heterogeneity in practice and lack of consensus on the optimal starting dose. Our INSENODIAB (INsulin SEnsitivity in New Onset type 1 DIABetes) study is aimed at (1) exploring the influence of patient-specific characteristics on insulin requirements in pediatric patients with new-onset T1D; (2) constructing a predictive model for the recommended TIDD tailored to individual patient profiles; and (3) assessing potential associations between TIDD and patient outcomes at follow-up intervals of 3 and 12 months.

Methods

We conducted a comprehensive analysis of medical records for children aged 6 months to 18 years, hospitalized for new-onset T1D from 2013 to 2022. The study initially involved multivariable regression analysis on a retrospective cohort (rINSENODIAB), incorporating baseline variables. Subsequently, we validated the model robustness on a prospective cohort (pINSENODIAB) with a significance threshold of 5%. The model accuracy was assessed by Pearson's correlation.

Results

Our study encompassed 103 patients in the retrospective cohort and 80 in the prospective cohort, with median TIDD at diagnosis of 1.1 IU/kg BW/day (IQR 0.5). The predictive model for optimal TIDD was established using baseline characteristics, resulting in the following formula: TIDD (IU/d) = ([0.09 × Age2] + [0.68 × %Weight Loss] + [28.60 × Veinous pH] - [1.03 × Veinous bicarbonates] + [0.81 × Weight] - 194.63). Validation of the model using the pINSENODIAB cohort demonstrated a significant Pearson correlation coefficient of 0.74. Notably, no significant correlation was observed between TIDD at diagnosis and partial remission markers (IDAA1C, C-peptide) at 3- and 12-months postdiagnosis time points.

Conclusions

In the context of new-onset T1D in pediatric patients, we identified key influencing factors for determining optimal TIDD, including age, percentage of weight loss, weight, veinous pH, and bicarbonates. These findings have paved the way for the development of a dosing algorithm to potentially expedite glycemic control stabilization and facilitate a more individualized approach to treatment regimens.

Influence of the occurrence and duration of partial remission on short-term metabolic control in type 1 diabetes: the DIABHONEY pediatric study

OBJECTIVE

To evaluate the residual effect of partial remission (PR) on immediate post-PR glycemic control according to its occurrence and duration in a cohort of children with type 1 diabetes mellitus (T1DM).

PATIENTS AND METHODS

Values of glycemic control parameters [i.e. HbA_1C, insulin dose-adjusted hemoglobin A_1C (IDAA_1C), glycemic target-adjusted HbA_1C (GTAA_1C)] and data from glucose monitoring devices from 189 pediatric patients with new-onset type 1 diabetes were collected retrospectively from 24 months. Patients were characterized according to their remission status (PR⁺ and PR^-). PR⁺ patients were subdivided into three subgroups regarding PR duration [i.e. short (⩾3-⩽6 months), intermediate (>6-⩽12 months), and long PR (>12-⩽14 months)]. We compared glycemic control data from each PR⁺ subgroup at +6 and +12 months post-PR with PR^- patients at the same postdiagnosis time. Second, PR⁺ subgroups were compared with each other.

RESULTS

PR⁺ patients showed improved glycemic control (i.e. HbA_1C, IDAA_1C, and GTAA_1C) at + 6 months post-PR when compared with nonremitters (PR^-), independently of the PR duration subgroups (p < 0.05). Interestingly, patients in long PR⁺ subgroup exhibited higher positive residual effect than short PR⁺ subgroup with lower GTAA_1C scores (p = 0.02), better time in range (TIR) (p = 0.003), less time in hypoglycemia (10.45 versus 16.13%, p = 0.03) and less glycemic variability (83.1 mg/dl versus 98.84 mg/dl, p = 0.03). No significant differences were found for glucose control between PR⁺ and PR^- patients at +12 months post-PR.

CONCLUSION

This study supports the positive impact of PR occurrence and duration on short-term metabolic control (better HbA_1C levels, IDAA_1C and GTAA_1C scores, TIR, and less glycemic variability) with the residual effect increasing according to PR duration.

Mechanisms and therapeutic strategies of immune checkpoint molecules and regulators in type 1 diabetes

BACKGROUND

Considered a significant risk to health and survival, type 1 diabetes (T1D) is a heterogeneous autoimmune disease characterized by hyperglycemia caused by an absolute deficiency of insulin, which is mainly due to the immune-mediated destruction of pancreatic beta cells.

SCOPE OF REVIEW

In recent years, the role of immune checkpoints in the treatment of cancer has been increasingly recognized, but unfortunately, little attention has been paid to the significant role they play both in the development of secondary diabetes with immune checkpoint inhibitors and the treatment of T1D, such as cytotoxic T-lymphocyte antigen 4(CTLA-4), programmed cell death protein-1(PD-1), lymphocyte activation gene-3(LAG-3), programmed death ligand-1(PD-L1), and T-cell immunoglobulin mucin protein-3(TIM-3). Here, this review summarizes recent research on the role and mechanisms of diverse immune checkpoint molecules in mediating the development of T1D and their potential and theoretical basis for the prevention and treatment of diabetes.

MAJOR CONCLUSIONS

Immune checkpoint inhibitors related diabetes, similar to T1D, are severe endocrine toxicity induced with immune checkpoint inhibitors. Interestingly, numerous treatment measures show excellent efficacy for T1D via regulating diverse immune checkpoint molecules, including co-inhibitory and co-stimulatory molecules. Thus, targeting immune checkpoint molecules may exhibit potential for T1D treatment and improve clinical outcomes.