Abstract:Aims
To explore the different patterns of C‐peptide decline in patients with and without partial remission of newly diagnosed type 1 diabetes (T1D).
Materials and methods
A total of 298 patients with new‐onset T1D were followed up regularly at 3 months' interval to investigate the loss of C‐peptide. Partial remission was determined by postprandial C‐peptide ≥300 pmol/L or insulin dose‐adjusted A1c ≤ 9 in the absence of C‐peptide. Beta‐cell function was defined as preserved, residual or failed by postprandial C… Show more
“…This is a cross‐sectional study and our inclusive subjects come from a T1D cohort who received clinical treatment at the National Clinical Research Center for Metabolic Diseases, the Second Xiangya Hospital of Central South University from November 2015 to February 2022. This cohort has been described in our previous study (NCT03610984) 23,24 . T1D patients who fulfiled the following criteria were included in this study: (1) aged over 10 years because of the rapidly changing physiological status of younger children; and (2) the duration of diabetes over 6 months due to possible interference of glucotoxicity in the early stage of diabetes, which will affect the evaluation of IR.…”
AimsTo investigate the clinical status of insulin resistance (IR) and its correlation with disease duration in patients with type 1 diabetes (T1D).Materials and MethodsCross‐sectional data from a T1D cohort were obtained (n = 923). IR‐related metabolic disorders including hypertension, obesity, and dyslipidemia were used as outcome variables to explore the cut‐off point for estimated glucose disposal rate (eGDR) by restricted cubic spline (RCS) curve. Regression models were used for multivariate analysis of the clinical factors associated with IR. The correlation between the status of IR and diabetes duration was depicted with the RCS curve.ResultsIR‐related metabolic disorders were observed in 39.4% of patients, with 9.1% meeting the criteria for metabolic syndrome. Specifically, patients with ≥10 years of T1D were more likely to have IR‐related metabolic disorders (54.7% vs. 36.9%, p < 0.05). The presence of IR, defined as an eGDR ≤9.0 mg/kg/min, was observed in 42.2% of patients. Patients with IR had a longer diabetes duration (3.5 vs. 2.7, years, p = 0.003) and higher insulin dose (0.5 vs. 0.4, units per kg per day, p < 0.001). Moreover, the presence of IR showed a gradual increase during 10 years' disease duration and further analysis showed that diabetes duration ≥10 years was a key element behind the development of IR and IR‐related metabolic disorders.ConclusionsThe status of IR is common in T1D patients, especially in those with ≥10 years of disease duration. Therapies targeting balancing glycaemic control and IR are needed to decrease the future risk of cardiovascular diseases in T1D.Clinical trial registration: ClinicalTrials.gov NCT03610984 (cohort study of patients with type 1 diabetes).
“…This is a cross‐sectional study and our inclusive subjects come from a T1D cohort who received clinical treatment at the National Clinical Research Center for Metabolic Diseases, the Second Xiangya Hospital of Central South University from November 2015 to February 2022. This cohort has been described in our previous study (NCT03610984) 23,24 . T1D patients who fulfiled the following criteria were included in this study: (1) aged over 10 years because of the rapidly changing physiological status of younger children; and (2) the duration of diabetes over 6 months due to possible interference of glucotoxicity in the early stage of diabetes, which will affect the evaluation of IR.…”
AimsTo investigate the clinical status of insulin resistance (IR) and its correlation with disease duration in patients with type 1 diabetes (T1D).Materials and MethodsCross‐sectional data from a T1D cohort were obtained (n = 923). IR‐related metabolic disorders including hypertension, obesity, and dyslipidemia were used as outcome variables to explore the cut‐off point for estimated glucose disposal rate (eGDR) by restricted cubic spline (RCS) curve. Regression models were used for multivariate analysis of the clinical factors associated with IR. The correlation between the status of IR and diabetes duration was depicted with the RCS curve.ResultsIR‐related metabolic disorders were observed in 39.4% of patients, with 9.1% meeting the criteria for metabolic syndrome. Specifically, patients with ≥10 years of T1D were more likely to have IR‐related metabolic disorders (54.7% vs. 36.9%, p < 0.05). The presence of IR, defined as an eGDR ≤9.0 mg/kg/min, was observed in 42.2% of patients. Patients with IR had a longer diabetes duration (3.5 vs. 2.7, years, p = 0.003) and higher insulin dose (0.5 vs. 0.4, units per kg per day, p < 0.001). Moreover, the presence of IR showed a gradual increase during 10 years' disease duration and further analysis showed that diabetes duration ≥10 years was a key element behind the development of IR and IR‐related metabolic disorders.ConclusionsThe status of IR is common in T1D patients, especially in those with ≥10 years of disease duration. Therapies targeting balancing glycaemic control and IR are needed to decrease the future risk of cardiovascular diseases in T1D.Clinical trial registration: ClinicalTrials.gov NCT03610984 (cohort study of patients with type 1 diabetes).
“…A standard 543.6 kcal MMTT was performed, with 44.4% of calories coming from carbohydrates, 47.7% from fat, and 7.9% from protein. Long-acting insulin and basal rates (for insulin pump users) were used normally on the day before or on the morning of the study, but their morning dose of short-acting insulin or rapid-acting insulin was withheld as previously reported ( 25 ). The MMTT was conducted at least two weeks after the correction of ketoacidosis.…”
Section: Methodsmentioning
confidence: 99%
“…The C-peptide area under curve (AUC-CP) was calculated by the trapezoidal method. Beta-cell function preservation was defined as 2h-CP >200 pmol/L ( 25 ).…”
Section: Methodsmentioning
confidence: 99%
“…The analysis of covariance (ANCOVA) was used to adjust for the course of disease. Generalized Estimating Equations (GEE) was used to analyze repeated measurement data during follow-up ( 25 ). GEE was employed to compare the C-peptide and HbA1c levels in patients with high and low T cells glucose uptake.…”
BackgroundAbnormal intracellular glucose/fatty acid metabolism of T cells has tremendous effects on their immuno-modulatory function, which is related to the pathogenesis of autoimmune diseases. However, the association between the status of intracellular metabolism of T cells and type 1 diabetes is unclear. This study aimed to investigate the uptake of glucose and fatty acids in T cells and its relationship with disease progression in type 1 diabetes.MethodsA total of 86 individuals with type 1 diabetes were recruited to detect the uptake of glucose and fatty acids in T cells. 2-NBDG uptake and expression of glucose transporter 1 (GLUT1); or BODIPY uptake and expression of carnitine palmitoyltransferase 1A(CPT1A) were used to assess the status of glucose or fatty acid uptake in T cells. Patients with type 1 diabetes were followed up every 3-6 months for 36 months, the progression of beta-cell function was assessed using generalized estimating equations, and survival analysis was performed to determine the status of beta-cell function preservation (defined as 2-hour postprandial C-peptide >200 pmol/L).ResultsPatients with type 1 diabetes demonstrated enhanced intracellular glucose uptake of T cells as indicated by higher 2NBDG uptake and GLUT1 expression, while no significant differences in fatty acid uptake were observed. The increased T cells glucose uptake is associated with lower C-peptide and higher hemoglobin A1c levels. Notably, patients with low T cell glucose uptake at onset maintained high levels of C-peptide within 36 months of the disease course [fasting C-petite and 2-hour postprandial C-peptide are 60.6 (95%CI: 21.1-99.8) pmol/L and 146.3 (95%CI: 14.1-278.5) pmol/L higher respectively], And they also have a higher proportion of beta-cell function preservation during this follow-up period (P<0.001).ConclusionsIntracellular glucose uptake of T cells is abnormally enhanced in type 1 diabetes and is associated with beta-cell function and its progression.
“…Continuous glucose monitoring (CGM) and artificial intelligence have been used to direct personalized insulin use and glucose management (13). Different patterns of beta cell function decline have also been observed in patients with T1DM (14). These new developments have critically advanced our understanding of the underlying pathophysiology and molecular mechanisms of T1DM and are instrumental in personalized management of T1DM.…”
Editorial on the Research TopicHeterogeneity of clinical phenotypes in type 1 diabetes and of beta cell deterioration in type 1 diabetes Precision medicine is of particular importance in diabetes as it is a highly heterogeneous syndrome (1) that contains genetic, environmental, behavioral and other etiological and pathophysiological components. Information garnered through many large multicenter cohort studies, multi-integration of bio-information obtained using genomics, epigenomics, proteomics and metabolomics approaches, and the utilization of data-driven classification strategies have drastically altered our understanding of diabetes, especially type 2 diabetes (T2DM), heterogeneity. Indeed, patients with T2DM can be stratified using the clustering method into five subgroups with distinctive characteristics, which has been replicated in cohorts with diverse backgrounds, and this stratification has been proved to be effective for complication prevention and treatment tailoring (2-4). The application of machine learning methods in diabetes classification in more integrative data sources and a soft-clustering strategy to sub-classify patients using genome-wide association studies (GWAS) (5) have all been proved useful. A continuous classification for diabetes has been attempted using the "palette model" concept (6). Newly diagnosed patients with T2DM could be classified into four distinctive subgroups and one subgroup with mixture characteristics (7). On the other hand, while studies in precision medicine have been thriving in T2DM, our understanding of heterogeneity in type 1 diabetes (T1DM) remains limited.
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