Early and ongoing stable glycaemic control is associated with a reduction in major adverse cardiovascular events in people with type 2 diabetes: A primary care cohort study

Optimizing Cardiovascular Outcomes in Type 2 Diabetes: Early Initiation of Dapagliflozin and Sitagliptin From a Cardiologist's Perspective

INTRODUCTION

Cardiovascular (CV) disease (CVD) risk is greater in patients with diabetes mellitus and is the major contributor to disability and premature mortality compared to those who do not have diabetes. The clinical implications of CVD in people with type 2 diabetes mellitus (T2DM) have increased the emphasis on concurrent treatment to prevent the onset of CVD through personalized management for glycemic control and CVD risk management.

METHODS

Key opinion leaders, comprising 98 cardiologists from across India, participated in seven advisory board meetings held in various cities to explore the challenges and strategies for the early initiation of fixed-dose combinations (FDCs) of sodium-glucose co-transporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase-4 inhibitors (DPP4i) with a focus on the combination of dapagliflozin and sitagliptin in addressing the CVD risks in patients with T2DM and high risk for CV complications. The expert group discussed the available literature evidence from the clinical trials, systematic reviews, and real-world studies on the benefits of FDC of SGLT2i and DPP4i and FDC of dapagliflozin and sitagliptin to provide rational and practical guidance for its optimal use in addressing the CVD risks in patients with T2DM.

RESULTS

The expert group emphasized the importance of timely glycemic control and early initiation of combination therapy of FDC of SGLT2i + DPP4i in T2DM with CVD risks. Addressing multiple pathophysiological aspects of T2DM is crucial, and considering combination therapy with SGLT2i and DPP4i may be pertinent in this context. Combining dapagliflozin and sitagliptin in FDC to target multiple pathophysiological pathways for T2DM appears to have several glycemic and extra-glycemic benefits.

CONCLUSION

This practical guidance document provides valuable insights from leading cardiologists that would support clinicians in selecting the synergistic combination SGLT2i + DPP4i (dapagliflozin + sitagliptin) FDC as an appropriate treatment choice in early intensive therapy in managing people with T2DM and CVD risk for better patient outcomes. The expert opinion in this guidance builds on the established guideline recommendations on FDC of SGLT2i and DPP4i.

Inflammation factors mediate the association between heavy metal and Homa-IR index: An integrated approach from the NHANES (2011∼2016)

INTRODUCTION

The interplay between heavy metals exposure and insulin resistance (IR), specifically through the mediation of inflammation factors, is crucial for understanding metabolic disturbances. This study utilizes data from the NHANES (2011∼2016) to investigate these relationships in a large, diverse U.S.

POPULATION

METHODS

The study analyzed the associations between heavy metals (cadmium (Cd), lead (Pb), mercury (Hg), manganese (Mn)) and the Homeostatic Model Assessment for Insulin Resistance (Homa-IR) index. The analyses included descriptive statistics, Pearson's correlations, linear and non-linear regression models, and advanced statistical models such as Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR). Inflammation factors were assessed for their mediating role in these associations.

RESULTS

The findings highlighted significant positive correlations between specific heavy metals and the Homa-IR index. Both linear and non-linear associations were evident, with certain metals showing a more pronounced impact in the presence of high inflammation markers. It was found that the Homa-IR index was negatively associated with Pb (β (95 %CI) = -0.0126 (-0.0238 ∼ -0.0015), P = 0.0268) and Hg (β (95 %CI) = -0.0090 (-0.0180 ∼ -0.0001), P = 0.0487). The WQS regression indicated an overall positive relationship between heavy metal mixtures (Estimate: 0.0050, P < 0.05) and the Homa-IR index where Cu had the highest weights (0.7741), while BKMR analyses detailed the varying effects of individual metals at different exposure levels. In the mediation analysis, it can be found that monocyte (Mono) mediated the association between Pb and Homa-IR index (direct effect:0.0546, indirect effect:0.0082) and neutrophil (Neu) (direct effect:0.0521, indirect effect:0.0047) can mediate the association between Hg and Homa-IR index.

CONCLUSIONS

This study confirms that exposure to heavy metals is associated with increased insulin resistance and that inflammation significantly mediates this relationship. Understanding these pathways is essential for developing targeted interventions to mitigate the metabolic consequences of environmental exposures.

Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective

Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an interpretable predictive machine learning (ML) model for IR in the non-diabetic populations based on levels of heavy metal exposure. A total of 4354 participants from the NHANES (2003-2020) with complete information were randomly divided into a training set and a test set. Twelve ML algorithms, including random forest (RF), XGBoost (XGB), logistic regression (LR), GaussianNB (GNB), ridge regression (RR), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), AdaBoost (AB), Gradient Boosting Decision Tree (GBDT), Voting Classifier (VC), and K-Nearest Neighbour (KNN), were constructed for IR prediction using the training set. Among these models, the RF algorithm had the best predictive performance, showing an accuracy of 80.14%, an AUC of 0.856, and an F1 score of 0.74 in the test set. We embedded three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) in RF model for model interpretation. Urinary Ba, urinary Mo, blood Pb, and blood Cd levels were identified as the main influencers of IR. Within a specific range, urinary Ba (0.56-3.56 µg/L) and urinary Mo (1.06-20.25 µg/L) levels exhibited the most pronounced upwards trend with the risk of IR, while blood Pb (0.05-2.81 µg/dL) and blood Cd (0.24-0.65 µg/L) levels showed a declining trend with IR. The findings on the synergistic effects demonstrated that controlling urinary Ba levels might be more crucial for the management of IR. The SHAP decision plot offered personalized care for IR based on heavy metal control. In conclusion, by utilizing interpretable ML approaches, we emphasize the predictive value of heavy metals for IR, especially Ba, Mo, Pb, and Cd.

Economic Evaluation of Once-Weekly Insulin Icodec from Italian NHS Perspective

Background

Icodec, once-weekly basal insulin, aims to simplify therapy management by reducing injection frequency for diabetic patients. The efficacy and safety of icodec were evaluated in the ONWARDS clinical development program. This study evaluates icodec economic and quality of life impact from the Italian National Healthcare System (NHS) perspective.

Materials and Methods

A pharmacoeconomic study was developed to assess the once-weekly insulin icodec value, highlighting its potential to decrease needle use while improving adherence and quality of life. In the base case, a differential cost and cost-utility analysis over one year compared to once-daily insulin degludec were developed. Based on the comparison with degludec, a scenario analysis was planned between icodec and the mix of basal insulins available on the market. Economic evaluations included drug and administration costs, needles, and impact on adherence. The cost-utility analysis measured the utility associated with the weekly injection compared to the daily ones, resulting in an incremental cost-effectiveness ratio (ICER), measured as Δ€/ΔQALY (Quality Adjusted Life Years). To assess the robustness of the results, a deterministic one-way sensitivity analysis and a probabilistic sensitivity analysis were carried out.

Results

At an annual cost 25% higher than degludec, considering the economic benefits generated by the needle use reduction (-€51.10) and adherence improvement (-€54.85), once-weekly icodec grants no incremental cost and even potential savings per patient. Furthermore, icodec reported a utility advantage (0.023). It achieved a dominant incremental cost-effectiveness ratio (ICER) compared to degludec. The comparison with the mix of basal insulins also reported a cost-effectiveness profile. Sensitivity tests conducted confirmed the robustness of the findings, highlighting the key drivers of the analysis.

Conclusion

Icodec represents a new therapeutic option to simplify basal insulin treatment. It also improves the patient's management and his quality of life, without increasing the economic burden for the Italian NHS, while guaranteeing an excellent cost-effectiveness profile.

Phenotype execution and modeling architecture to support disease surveillance and real-world evidence studies: English sentinel network evaluation

Objective

To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes.

Method

We curated 3 phenotypes: Type 2 diabetes mellitus (T2DM), excessive alcohol use, and incident influenza-like illness (ILI) using CQL to define clinical and administrative logic. We defined our phenotypes with valuesets, using SNOMED's hierarchy and expression constraint language, and CQL, combining valuesets and adding temporal elements where needed. We compared the count of cases found using PhEMA with our existing approach using convenience datasets. We assessed our new approach against published desiderata for phenotypes.

Results

The T2DM phenotype could be defined as 2 intensionally defined SNOMED valuesets and a CQL script. It increased the prevalence from 7.2% to 7.3%. Excess alcohol phenotype was defined by valuesets that added qualitative clinical terms to the quantitative conceptual definitions we currently use; this change increased prevalence by 58%, from 1.2% to 1.9%. We created an ILI valueset with SNOMED concepts, adding a temporal element using CQL to differentiate new episodes. This increased the weekly incidence in our convenience sample (weeks 26-38) from 0.95 cases to 1.11 cases per 100 000 people.

Conclusions

Phenotypes for surveillance and research can be described fully and comprehensibly using CQL and intensional FHIR valuesets. Our use case phenotypes identified a greater number of cases, whilst anticipated from excessive alcohol this was not for our other variable. This may have been due to our use of SNOMED CT hierarchy. Our new process fulfilled a greater number of phenotype desiderata than the one that we had used previously, mostly in the modeling domain. More work is needed to implement that sharing and warehousing domains.

Expert Consensus on Dipeptidyl Peptidase-4 Inhibitor-Based Therapies in the Modern Era of Type 2 Diabetes Mellitus Management in India

India has a high prevalence of type 2 diabetes mellitus (T2DM) with unique clinical characteristics compared to other populations. Despite advancements in diabetes therapy, a significant number of patients in India still experience poor glycemic control and complications. Dipeptidyl peptidase-4 (DPP-4) inhibitors continue to be an important component of T2DM treatment due to their favorable efficacy and tolerability profile. Given the current scenario, there is a need to revisit the role of DPP-4 inhibitors in T2DM management in Indian patients. This consensus paper aims to provide guidance on the utilization of DPP-4 inhibitors in T2DM management from an Indian perspective. A consensus group of 100 experts developed recommendations based on an extensive literature review and discussions. The expert group emphasized the importance of timely glycemic control, combination therapy, and targeting the underlying pathophysiology of T2DM. The combinations of DPP-4 inhibitors with metformin and/or sodium-glucose transport protein-2 inhibitors are rationalized in this paper, considering their complementary mechanisms of action. This paper provides valuable insights for clinicians in optimizing the management of T2DM in the Indian population with the use of DPP-4 inhibitors and proposes an algorithm for selecting DPP-4 inhibitor-based therapies.

Glycemic variability evaluated by HbA1c rather than fasting plasma glucose is associated with adverse cardiovascular events

Background

Although studies have shown that glycemic variability is positively associated with an increased risk of cardiovascular disease, few studies have compared hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) variability with adverse cardiovascular events in patients with type 2 diabetes mellitus (T2DM).

Methods

This was a post hoc analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. Cox proportional hazards models were used to explore the relationship between HbA1c or FPG variability and the incidence of major adverse cardiovascular events (MACEs).

Results

In total, 9,547 patients with T2DM were enrolled in this study. During the median 4.6 ± 1.5 years follow-up period, 907 patients developed MACEs. The risk of MACEs increased in the HbA1c variability group in each higher quartile of HbA1c variability (P < 0.01). Compared with those in the first quartile of HbA1c variability, patients in the fourth quartile had a hazard ratio of 1.37 (Model 2, 95% confidence interval: 1.13-1.67) for MACEs. Higher FPG variability was not associated with a higher risk of MACEs in patients with T2DM (P for trend=0.28). A U-shaped relationship was observed between HbA1c and FPG variability, and MACEs. Glucose control therapy modified the relationship between HbA1c and MACEs; participants with higher HbA1c variability receiving intensive glucose control were more likely to develop MACEs (P for interaction <0.01).

Conclusion

In adults with T2DM, the relationship between glycemic variability evaluated using HbA1c and FPG was U-shaped, and an increase in HbA1c variability rather than FPG variability was significantly associated with MACEs. The relationship between HbA1c variability and MACEs was affected by the glucose control strategy, and a higher HbA1c variability was more strongly associated with MACEs in patients receiving an intensive glucose control strategy.

Early treatment with dipeptidyl-peptidase 4 inhibitors reduces glycaemic variability and delays insulin initiation in type 2 diabetes: A propensity score-matched cohort study

AIMS

To examine whether early treatment intensification using dipeptidyl-peptidase 4 inhibitors (DPP4i) delays insulin initiation in Chinese patients diagnosed with type 2 diabetes for less than 5 years.

MATERIALS AND METHODS

In a territory-wide prospective cohort study, patients with type 2 diabetes initiating DPP4i at diabetes duration <2 years (early intensification) and 3-5 years (late intensification) were matched using 1:1 propensity-score matching (n = 908 in each arm). We used Cox regression to compare the risk of insulin initiation between the two groups. We explored the interactive and mediation effects of glycated haemoglobin (HbA1c) variability score (HVS), defined as the percentage of HbA1c varying by ≥0.5% compared with preceding values.

RESULTS

Of 1816 patients (60.7% men, mean age 54.4 ± 11.9 years), 92.4% and 71.9% were treated with metformin and sulphonylureas respectively at DPP4i initiation. Early DPP4i intensification [hazard ratio (HR) 0.71, (95% CI 0.58-0.68)] and low HVS (<50%) (HR = 0.40, 0.33-0.50) were associated with delayed insulin initiation during a median 4.08 years of follow-up. Early intensification with low HVS had the lowest risk versus late intensification with high HVS (HR = 0.30, 0.22-0.40) (pinteraction  = 0.013). HVS mediated 19.5% of the total effect of early DPP4i intensification on delaying insulin initiation. The late and early intensification groups had similar HbA1c at month 0 (8.4 ± 1.3% vs. 8.4 ± 1.5%) and month 3 (7.6 ± 1.2% vs. 7.6 ± 1.3%) after DPP4i initiation. By month 12, HbA1c in the late intensification group deteriorated (7.9 ± 1.4%) but remained stable in the early intensification group (7.6 ± 1.4%, p = 0.001) with persistent between-group difference over 72 months (8.2 ± 1.7% vs. 7.7 ± 1.6%, p = 0.001).

CONCLUSIONS

In type 2 diabetes, early DPP4i intensification delayed insulin initiation, partially explained by reduced glycaemic variability.

Development and validation of an insulin resistance model for a population without diabetes mellitus and its clinical implication: a prospective cohort study

Background

Insulin resistance (IR) is associated with diabetes mellitus, cardiovascular disease (CV), and mortality. Few studies have used machine learning to predict IR in the non-diabetic population.

Methods

In this prospective cohort study, we trained a predictive model for IR in the non-diabetic populations using the US National Health and Nutrition Examination Survey (NHANES, from JAN 01, 1999 to DEC 31, 2012) database and the Taiwan MAJOR (from JAN 01, 2008 to DEC 31, 2017) database. We analysed participants in the NHANES and MAJOR and participants were excluded if they were aged <18 years old, had incomplete laboratory data, or had DM. To investigate the clinical implications (CV and all-cause mortality) of this trained model, we tested it with the Taiwan biobank (TWB) database from DEC 10, 2008 to NOV 30, 2018. We then used SHapley Additive exPlanation (SHAP) values to explain differences across the machine learning models.

Findings

Of all participants (combined NHANES and MJ databases), we randomly selected 14,705 participants for the training group, and 4018 participants for the validation group. In the validation group, their areas under the curve (AUC) were all >0.8 (highest being XGboost, 0.87). In the test group, all AUC were also >0.80 (highest being XGboost, 0.88). Among all 9 features (age, gender, race, body mass index, fasting plasma glucose (FPG), glycohemoglobin, triglyceride, total cholesterol and high-density cholesterol), BMI had the highest value of feature importance on IR (0.43 for XGboost and 0.47 for RF algorithms). All participants from the TWB database were separated into the IR group and the non-IR group according to the XGboost algorithm. The Kaplan-Meier survival curve showed a significant difference between the IR and non-IR groups (p < 0.0001 for CV mortality, and p = 0.0006 for all-cause mortality). Therefore, the XGboost model has clear clinical implications for predicting IR, aside from CV and all-cause mortality.

Interpretation

To predict IR in non-diabetic patients with high accuracy, only 9 easily obtained features are needed for prediction accuracy using our machine learning model. Similarly, the model predicts IR patients with significantly higher CV and all-cause mortality. The model can be applied to both Asian and Caucasian populations in clinical practice.

Funding

Taichung Veterans General Hospital, Taiwan and Japan Society for the Promotion of Science KAKENHI Grant Number JP21KK0293.

Remission in Ketosis-Prone Diabetes

Heterogeneous forms of Ketosis-prone diabetes (KPD) are characterized by patients who present with diabetic ketoacidosis (DKA) but lack the typical features and biomarkers of autoimmune T1D. The A-β+ subgroup of KPD provides unique insight into the concept of "remission" since these patients have substantial preservation of beta-cell function permitting the discontinuation of insulin therapy, despite initial presentation with DKA. Measurements of C-peptide levels are essential to predict remission and guide potential insulin withdrawal. Further studies into predictors of remission and relapse can help us guide patients with A-β+ KPD toward remission and develop targeted treatments for this form of atypical diabetes.

Assessment of QRISK3 as a predictor of cardiovascular disease events in type 2 diabetes mellitus

BACKGROUND

The risk of cardiovascular disease (CVD) in diabetes mellitus (DM) patients is two- to three-fold higher than in the general population. We designed a 10-year cohort trial in T2DM patients to explore the performance of QRESEARCH risk estimator version 3 (QRISK3) as a CVD risk assessment tool and compared to Framingham Risk Score (FRS).

METHOD

This is a single-center analysis of prospective data collected from 566 newly-diagnosed patients with type 2 DM (T2DM). The risk scores were compared to CVD development in patients with and without CVD. The risk variables of CVD were identified using univariate analysis and multivariate cox regression analysis. The number of patients classified as low risk (<10%), intermediate risk (10%-20%), and high risk (>20%) for two tools were identified and compared, as well as their sensitivity, specificity, positive and negative predictive values, and consistency (C) statistics analysis.

RESULTS

Among the 566 individuals identified in our cohort, there were 138 (24.4%) CVD episodes. QRISK3 classified most CVD patients as high risk, with 91 (65.9%) patients. QRISK3 had a high sensitivity of 91.3% on a 10% cut-off dichotomy, but a higher specificity of 90.7% on a 20% cut-off dichotomy. With a 10% cut-off dichotomy, FRS had a higher specificity of 89.1%, but a higher sensitivity of 80.1% on a 20% cut-off dichotomy. Regardless of the cut-off dichotomy approach, the C-statistics of QRISK3 were higher than those of FRS.

CONCLUSION

QRISK3 comprehensively and accurately predicted the risk of CVD events in T2DM patients, superior to FRS. In the future, we need to conduct a large-scale T2DM cohort study to verify further the ability of QRISK3 to predict CVD events.