Using adjuvant pharmacotherapy in the treatment of type 1 diabetes

Exploring the influencing factors of non-insulin drug prescriptions in discharged patients with type 1 diabetes

Objective

The aim of this study was to evaluate the admission indicators and characteristics of individuals diagnosed with type 1 diabetes (T1D) to ascertain potential impact on the choice of glucose control therapy after discharge.

Methods

A total of 398 eligible T1D patients were selected. We conducted multivariable logistic regression analysis to determine the independent influence of predictors on the selection of glucose control therapy after discharge. To explore the influencing factors of different subgroups, we additionally performed subgroup analyses based on gender and age.

Results

Our study revealed that body mass index (BMI) was noteworthy influence factor for prescription of insulin and non-insulin antidiabetic drug (NIAD prescription) in T1D patients of general population [OR = 1.109 (1.033-1.195), p = 0.006], male [OR = 1.166 (1.040-1.318), p = 0.011] and individuals below the age of 30 years [OR = 1.146 (1.020-1.301), p = 0.028]. Diastolic blood pressure (DBP) was a protective factor for NIAD prescription in the general population [OR = 0.971 (0.949-0.992), p = 0.008] and women [OR = 0.955 (0.923-0.988), p = 0.008]. The other risk factor of NIAD prescription in men was dyslipidemia [OR = 4.824 (1.442-22.246), p = 0.020]. Pulse pressure [OR = 1.036 (1.007-1.068), p = 0.016] constituted an additional risk factor of NIAD prescription among individuals below the age of 30 years. The risk factors of NIAD prescription for people aged 30 to 50 years were length of stay [OR = 1.097 (1.014-1.196), p = 0.026] and initial blood glucose [OR = 1.078 (1.007-1.168), p = 0.047]. In the case of individuals aged above 50 years, physicians exhibited a higher tendency to prescribe supplementary non-insulin medications to men [OR = 9.385 (1.501-87.789), p = 0.029].

Conclusions

We identified notable factors that influence discharge prescriptions in patients with T1D. In order to enhance the treatment outcome for the patient, clinicians ought to have a special focus on these indicators or factors.

Impact of Probiotics on the Glycemic Control of Pediatric and Adolescent Individuals with Type 1 Diabetes: A Systematic Review and Meta-Analysis

AIMS

Human recombinant insulin is currently the only therapy for children and adolescents with type 1 diabetes (T1D), although not always efficient for the glycemic control of these individuals. The interrelation between the gut microbiome and the glycemic control of apparently healthy populations, as well as various populations with diabetes, is undeniable. Probiotics are biotherapeutics that deliver active components to various targets, primarily the gastrointestinal tract. This systematic review and meta-analysis examined the effect of the administration of probiotics on the glycemic control of pediatric and adolescent individuals with T1D.

MATERIALS AND METHODS

Randomized controlled trials employing the administration of probiotics in children and adolescents with T1D (with ≥10 individuals per treatment arm), written in English, providing parameters of glycemic control, such as mean glucose concentrations and glycosylated hemoglobin (HbA1c), were deemed eligible.

RESULTS

The search strategy resulted in six papers with contradictory findings. Ultimately, five studies of acceptable quality, comprising 388 children and adolescents with T1D, were included in the meta-analysis. Employing a random and fixed effects model revealed statistically significant negative effect sizes of probiotics on the glycemic control of those individuals, i.e., higher concentrations of glucose and HbA1c than controls.

CONCLUSIONS

Children and adolescents with T1D who received probiotics demonstrated worse glycemic control than controls after the intervention. Adequately powered studies, with extended follow-up periods, along with monitoring of compliance and employing the proper strains, are required to unravel the mechanisms of action and the relative effects of probiotics, particularly concerning diabetes-related complications and metabolic outcomes.

Estimating Type 2 Diabetes Prevalence: A Model of Drug Consumption Data

Observational, cross-sectional prevalence studies are costly and time-consuming. The development of indirect methods estimating prevalence used to obtain faster, less-expensive, and more robust results would be an advantage for several healthcare applications. This study aimed to use the drug dispensing data from community pharmacies to estimate the prevalence of Type 2 Diabetes mellitus (T2DM) in the Portuguese population. A cross-sectional study was conducted using a database of dispensed medicines with an indication for Diabetes mellitus in 2018 and 2021, stratified by geographic region. The methodology was based on a sequential method of acquiring prevalence estimates obtained through exposure to medicines using the daily doses defined per thousand inhabitants per day and adjusted to the rate of adherence to therapy, prescription patterns, and concomitance of antidiabetic drugs. The estimated overall T2DM prevalence in 2018 was 13.9%, and it was 14.2% for 2021. The results show the increased consumption of antidiabetic drugs, with fixed-dose combination antidiabetics and new antidiabetics being particularly important in 2021. This work allowed for the development of a model to obtain the estimated prevalence of T2DM based on drug consumption, using a simple, fast, and robust method that is in line with the available evidence. However, with the recent expanding indications for new antidiabetics, the inclusion of further data in the model needs to be studied.

The safety of sotagliflozin in the therapy of diabetes mellitus type 1 and type 2: A meta-analysis of randomized trials

BACKGROUND

Diabetes mellitus (DM) is a global health problem, and it has become a shocking threat in the contemporary era. The objective of this study was to analyze the safety of sotagliflozin in patients with DM systematically and intuitively.

METHODS

On November 15, 2021, literature retrieval was performed on PubMed, Web of Science, EBSCO, and Cochrane libraries. The meta-analysis results included genital mycotic infection, related-to-acidosis events, and other related adverse events, including diarrhea, severe nocturnal hypoglycemia event, and volume depletion. In addition, a subgroup analysis was also conducted based on different doses of sotagliflozin. Moreover, the patient-treated years analyzed in the study were 12 weeks, 24 weeks, and 52 weeks, respectively, for type 1 diabetes, and were 12 weeks, 22 weeks, and 52 weeks, respectively, for type 2 diabetes.

RESULTS

The results of this meta-analysis illustrated that sotagliflozin could increase the risk of genital mycotic infection for patients with T1D and T2D (RR: 3.49, 95% Cl: 2.54-4.79, p < 0.001; RR: 2.83, 95% Cl: 2.04-3.93, p < 0.001; respectively). In addition, the subgroup analysis showed that the drug doses that could increase the risk of genital mycotic infection were 400 mg and 200 mg (RR: 3.63, 95% Cl: 2.46-5.36, p < 0.001; RR: 3.21, 95% Cl: 1.84-5.62, p < 0.001; respectively) in T1D. Moreover, sotagliflozin could increase the risk of events related to acidosis in the patients of T1D, including acidosis-related adverse events, positively adjudicated diabetic ketoacidosis, acidosis-related event, and diabetic ketoacidosis (RR: 7.49, 95% Cl: 3.20-17.52, p < 0.001; RR: 6.05, 95% Cl: 2.56-14.30, p < 0.001; RR: 4.83, 95% Cl: 3.13-7.45, p < 0.001; RR: 8.12, 95% Cl: 3.06-21.52, p < 0.001; respectively). In the patients of T2D, sotagliflozin could not increase the risk of DKA (RR: 1.30, 95% Cl: 0.34-4.99, p = 0.70). About serious of acidosis-related adverse events, positively adjudicated diabetic ketoacidosis (DKA) and acidosis-related event, the included studies were not reported for T2D patients. As for the other related adverse events, sotagliflozin was found to be a risk factor for diarrhea and volume depletion in T1D patients (RR: 1.44, 95% Cl: 1.09-1.90, p = 0.01; RR: 2.50, 95% Cl: 1.33-4.69, p < 0.01; respectively) and T2D patients (RR: 1.44, 95% Cl: 1.26-1.64, p < 0.001; RR: 1.25, 95% Cl: 1.07-1.45, p < 0.01; respectively).

CONCLUSIONS

This meta-analysis showed that the adverse events of sotagliflozin were tolerable to patients with DM, in terms of the incidence of genital mycotic infection, related-to-acidosis events, diarrhea, volume depletion, and severe nocturnal hypoglycemia events. In addition, the subgroup analysis of sotagliflozin dosage is considered to have great clinical significance for future guidance of sotagliflozin application in patients with DM.

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.