Impact of flash glucose monitoring on glycemic control varies with the age and residual β-cell function of patients with type 1 diabetes mellitus

Residual β-cell function in Brazilian Type 1 diabetes after 3 years of diagnosis: prevalence and association with low presence of nephropathy

BACKGROUND

Persistence of β cell-function in Type 1 diabetes (T1D) is associated with glycaemia stability and lower prevalence of microvascular complications. We aimed to assess the prevalence of residual C- peptide secretion in long-term Brazilian childhood onset T1D receiving usual diabetes care and its association to clinical, metabolic variables and microvascular complications.

METHODS

A cross-sectional observational study with 138 T1D adults with ≥ 3 years from the diagnosis by routine diabetes care. Clinical, metabolic variables and microvascular complications were compared between positive ultra-sensitive fasting serum C-peptide (FCP +) and negative (FCP-) participants.

RESULTS

T1D studied had ≥ 3 yrs. of diagnosis and 60% had FCP > 1.15 pmol/L. FCP + T1D were older at diagnosis (10 vs 8 y.o; p = 0.03) and had less duration of diabetes (11 vs 15 y.o; p = 0.002). There was no association between the FCP + and other clinical and metabolic variable but there was inversely association with microalbuminuria (28.6% vs 13.4%, p = 0.03), regardless of HbA_1c. FCP > 47 pmol/L were associated with nephropathy protection but were not related to others microvascular complications.

CONCLUSION

Residual insulin secretion is present in 60% of T1D with ≥ 3 years of diagnosis in routine diabetes care. FCP + was positively associated with age of diagnosis and negatively with duration of disease and microalbuminuria, regardless of HbA_1c.

Association between residual islet beta-cell function and achieving the target of time in range in inpatients with type 2 diabetes undergoing antidiabetic treatment: An observation study

AIM

To assess whether the beta-cell function of inpatients undergoing antidiabetic treatment influences achieving time in range (TIR) and time above range (TAR) targets.

MATERIALS AND METHODS

This cross-sectional study included 180 inpatients with type 2 diabetes. TIR and TAR were assessed by a continuous glucose monitoring system, with target achievement defined as TIR more than 70% and TAR less than 25%. Beta-cell function was assessed by the insulin secretion-sensitivity index-2 (ISSI2).

RESULTS

Following antidiabetic treatment, logistic regression analysis showed that lower ISSI2 was associated with a decreased number of inpatients achieving TIR (OR = 3.10, 95% CI: 1.19-8.06) and TAR (OR = 3.40, 95% CI: 1.35-8.55) targets after adjusting for potential confounders. Similar associations still existed in those participants treated with insulin secretagogues (TIR: OR = 2.91, 95% CI: 0.90-9.36, P = .07; TAR, OR = 3.14, 95% CI: 1.01-9.80) or adequate insulin therapy (TIR: OR = 2.84, 95% CI: 0.91-8.81, P = .07; TAR, OR = 3.24, 95% CI: 1.08-9.67). Furthermore, receiver operating characteristic curves showed that the diagnostic value of the ISSI2 for achieving TIR and TAR targets was 0.73 (95% CI: 0.66-0.80) and 0.71 (95% CI: 0.63-0.79), respectively.

CONCLUSIONS

Beta-cell function was associated with achieving TIR and TAR targets. Stimulating insulin secretion or exogenous insulin treatment could not overcome the disadvantage of lower beta-cell function on glycaemic control.

Serum Metabolomics Reveals a Potential Benefit of Methionine in Type 1 Diabetes Patients with Poor Glycemic Control and High Glycemic Variability

Glycemic variability (GV) in some patients with type 1 diabetes (T1D) remains heterogeneous despite comparable clinical indicators, and whether other factors are involved is yet unknown. Metabolites in the serum indicate a broad effect of GV on cellular metabolism and therefore are more likely to indicate metabolic dysregulation associated with T1D. To compare the metabolomic profiles between high GV (GV-H, coefficient of variation (CV) of glucose ≥ 36%) and low GV (GV-L, CV < 36%) groups and to identify potential GV biomarkers, metabolomics profiling was carried out on serum samples from 17 patients with high GV, 16 matched (for age, sex, body mass index (BMI), diabetes duration, insulin dose, glycated hemoglobin (HbA1c), fasting, and 2 h postprandial C-peptide) patients with low GV (exploratory set), and another 21 (GV-H/GV-L: 11/10) matched patients (validation set). Subsequently, 25 metabolites were significantly enriched in seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the GV-H and GV-L groups in the exploratory set. Only the differences in spermidine, L-methionine, and trehalose remained significant after validation. The area under the curve of these three metabolites combined in distinguishing GV-H from GV-L was 0.952 and 0.918 in the exploratory and validation sets, respectively. L-methionine was significantly inversely related to HbA1c and glucose CV, while spermidine was significantly positively associated with glucose CV. Differences in trehalose were not as reliable as those in spermidine and L-methionine because of the relatively low amounts of trehalose and the inconsistent fold change sizes in the exploratory and validation sets. Our findings suggest that metabolomic disturbances may impact the GV of T1D. Additional in vitro and in vivo mechanistic studies are required to elucidate the relationship between spermidine and L-methionine levels and GV in T1D patients with different geographical and nutritional backgrounds.

Metabolic syndrome associated with higher glycemic variability in type 1 diabetes: A multicenter cross-sectional study in china

AIMS

The comorbidity of metabolic syndrome (MetS) and type 1 diabetes mellitus (T1DM) is an obstacle to glucose control in patients with T1DM. We compared glycemic profiles using continuous glucose monitoring (CGM) systems in patients with T1DM with or without MetS.

METHODS

This was a multicenter cross-sectional study of patients with T1DM (N = 207) with or without MetS. CGM data were collected from study enrollment until discharge during a 1-week study session. We analyzed baseline HbA1c, average glucose, estimated HbA1c, time in range (TIR), time above range (TAR), time below range (TBR), coefficient of variation (CV), postprandial glucose excursions (PPGE) and other glycemic variability (GV) metrics. Logistic regression was developed to investigate the association between MetS and CGM metrics.

RESULTS

The results showed higher average baseline HbA1c levels, and a higher percentage of patients with baseline HbA1c levels ≥7.5%, in the T1DM with MetS group. Furthermore, MetS was associated with GV, which indicated a higher CV in patients with T1DM with MetS. However, our results showed that TAR, TIR, TBR and other GV metrics were comparable between the two groups. The T1DM with MetS group also had a higher proportion of patients with high CV (≥ 36%) than the group without MetS. In multivariable logistic regression analysis, the presence of MetS was a risk factor for high CV (≥ 36%) in our study participants.

CONCLUSIONS

T1DM patients with MetS in our study had better β-cell function. However, MetS was associated with worse glycemic control characterized by higher GV and HbA1c levels. Efforts should be expanded to improve treatment of MetS in patients with T1DM to achieve better glycemic control.