Should Studies of Diabetes Treatment Stratification Correct for Baseline HbA1c?

Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes

An informed estimate of subject-level variance is a key determinate for accurate estimation of the required sample size for clinical trials. Evaluating completed adult Type 2 diabetes studies submitted to the FDA for accuracy of the variance estimate at the planning stage provides insights to inform the sample size requirements for future studies. From the U.S. Food and Drug Administration (FDA) database of new drug applications containing 14,106 subjects from 26 phase 3 randomized studies submitted to the FDA in support of drug approvals in adult type 2 diabetes studies reviewed between 2013 and 2017, we obtained estimates of subject-level variance for the primary endpoint-change in glycated hemoglobin (HbA1c) from baseline to 6 months. In addition, we used nine additional studies to examine the impact of clinically meaningful covariates on residual standard deviation and sample size re-estimation. Our analyses show that reduced sample sizes can be used without interfering with the validity of efficacy results for adult type 2 diabetes drug trials. This finding has implications for future research involving the adult type 2 diabetes population, including the potential to reduce recruitment period length and improve the timeliness of results. Furthermore, our findings could be utilized in the design of future endocrinology clinical trials.

Glycemic control after aortic valve replacement: A retrospective study

Background

Aortic stenosis (AS) is treated through transcatheter aortic valve implantation (TAVI) or surgical aortic valve replacement (SAVR), with diabetes being prevalent among these patients. Inflammation participates in the pathogenesis of AS, and emerging evidence suggests that TAVI may exert anti-inflammatory effects. Given the established link between diabetes and inflammation, we sought to evaluate the impact of aortic valve replacement (AVR) on glycemic control.

Methods

Data from 10,129 consecutive patients undergoing either TAVI or SAVR between January 2010 and January 2022 were analyzed. Of these, 3,783 with diabetes had available pre- and post-procedural glycated hemoglobin (HbA1c) measurements. Analysis of 1,284 individuals with HbA1c ≥ 7 % was conducted. Propensity-score matching produced two well-matched cohorts of 266 TAVI and SAVR patients, enabling comparison of periprocedural HbA1c.

Results

In the total cohort (n = 1,284), HbA1c decreased from 8.15 ± 1.12 to 7.88 ± 1.38 (p < 0.001). After matching, the TAVI group showed a significant reduction from 8.31 ± 1.31 to 7.86 ± 1.56 (p < 0.001), while a modest decrease from 8.33 ± 1.33 to 8.15 ± 1.61 (p = 0.046) was observed in SAVR group. The TAVI group showed a trend toward a greater percentage change in HbA1c (p = 0.051). Clinically meaningful improvement in HbA1c (≥ 0.3 %) was similar between TAVI (53.1 %) and SAVR (45.6 %) patients (OR = 1.34, 95 % CI 0.93-1.95).

Conclusions

Management of AS through either intervention improved post-procedural glycemia in patients with uncontrolled diabetes. The extent of glycemic improvement was more pronounced with TAVI. Further investigations through controlled and prospective studies could provide more conclusive insights into this matter.

Baseline-dependent improvement in CF studies, plausibility of bias

Background

It has been commonly reported that therapeutic treatments in cystic fibrosis (CF) have ceiling effects, such that their efficacy is diminished for persons with high pre-treatment health (Montgomery et al., 2012 and Newsome et al., 2019). Floor effects have also been reported where decline is of lower magnitude in those with below-average pre-treatment health (Harun et al., 2016; Konstan et al., 2012 and Szczesniak et al., 2017). When measurement error is present, the statistical literature has warned of exaggerated or spurious associations between pre-treatment measures and subsequent change (Chambless and Davis, 2003 and Yanez et al., 1998). Measurement error, equivalently described as day-to-day variation, has been described to occur in CF outcome measurements such as forced expiratory volume in 1 s taken by spirometry (FEV1 pp) (Magaret et al., 2024; Stanojevic et al., 2020 and Thornton et al., 2023).

Methods

We conducted a simulation study to assess the potential for spurious floor or ceiling effects in studies of CF therapeutics. We considered uncontrolled or single-arm studies, and evaluated estimated association between pre-treatment FEV1 pp and treatment-induced change: post-versus pre-treatment.

Results

When day-to-day variation was present in FEV1 pp, at levels equivalent to those reported in large studies measuring spirometry both at home and in clinic, naive analytic approaches found spurious associations of change with baseline (Paynter et al., 2022 and Saiman et al., 2003). Type I error ranged from 31.9% to 98.3% for day-to-day variation as high as 3% to 15% relative to biological variation. Incorporating known day-to-day variation, the regression calibration approach corrected bias and controlled type I error (Chambless and Davis, 2003).

Conclusion

Exaggerated ceiling effects are possible. Further studies could provide meaningful confirmation of ceiling effects in CF, perhaps reducing day-to-day variation by incorporating multiple pre- and post-treatment measurements.

Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance

BACKGROUND

Real-world studies of people with type 2 diabetes (T2D) have shown insufficient dose adjustment during basal insulin titration in clinical practice leading to suboptimal treatment. Thus, 60% of people with T2D treated with insulin do not reach glycemic targets. This emphasizes a need for methods supporting efficient and individualized basal insulin titration of people with T2D. However, no systematic review of basal insulin dose guidance for people with T2D has been found.

OBJECTIVE

To provide an overview of basal insulin dose guidance methods that support titration of people with T2D and categorize these methods by characteristics, effect, and user experience.

METHODS

The review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Studies about basal insulin dose guidance, including adults with T2D on basal insulin analogs published before September 7, 2022, were included. Joanna Briggs Institute critical appraisal checklists were applied to assess risk of bias.

RESULTS

In total, 35 studies were included, and three categories of dose guidance were identified: paper-based titration algorithms, telehealth solutions, and mathematical models. Heterogeneous reporting of glycemic outcomes challenged comparison of effect between the three categories. Few studies assessed user experience.

CONCLUSIONS

Studies mainly used titration algorithms to titrate basal insulin as telehealth or in paper format, except for studies using mathematical models. A numerically larger proportion of participants seemed to reach target using telehealth solutions compared to paper-based titration algorithms. Exploring capabilities of machine learning may provide insights that could pioneer future research while focusing on holistic development.

Association of Baseline Factors With Glycemic Outcomes in GRADE: A Comparative Effectiveness Randomized Clinical Trial

OBJECTIVE

To describe the individual and joint associations of baseline factors with glycemia, and also with differential effectiveness of medications added to metformin.

RESEARCH DESIGN AND METHODS

Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE) participants (with type 2 diabetes diagnosed for <10 years, on metformin, and with HbA1c 6.8-8.5%; N = 5,047) were randomly assigned to a basal insulin (glargine), sulfonylurea (glimepiride), glucagon-like peptide 1 agonist (liraglutide), or dipeptidyl peptidase 4 inhibitor (sitagliptin). The glycemic outcome was HbA1c ≥7.0%, subsequently confirmed. Univariate and multivariate regression and classification and regression tree (CART) analyses were used to assess the association of baseline factors with the glycemic outcome at years 1 and 4.

RESULTS

In univariate analyses at baseline, younger age (<58 years), Hispanic ethnicity, higher HbA1c, fasting glucose, and triglyceride levels, lower insulin secretion, and relatively greater insulin resistance were associated with the glycemic outcome at years 1 and/or 4. No factors were associated with differential effectiveness of the medications by year 4. In multivariate analyses, treatment group, younger age, and higher baseline HbA1c and fasting glucose were jointly associated with the glycemic outcome by year 4. The superiority of glargine and liraglutide at year 4 persisted after multiple baseline factors were controlled for. CART analyses indicated that failure to maintain HbA1c <7% by year 4 was more likely for younger participants and those with baseline HbA1c ≥7.4%.

CONCLUSIONS

Several baseline factors were associated with the glycemic outcome but not with differential effectiveness of the four medications. Failure to maintain HbA1c <7% was largely driven by younger age and higher HbA1c at baseline. Factors that predict earlier glycemic deterioration could help in targeting patients for more aggressive management.

A study of factors influencing long-term glycemic variability in patients with type 2 diabetes: a structural equation modeling approach

Aim

The present study aims to utilize structural equation modeling (SEM) to investigate the factors impacting long-term glycemic variability among patients afflicted with type 2 diabetes.

Method

The present investigation is a retrospective cohort study that involved the collection of data on patients with type 2 diabetes mellitus who received care at a hospital located in Chengdu, Sichuan Province, over a period spanning from January 1, 2013, to October 30, 2022. Inclusion criteria required patients to have had at least three laboratory test results available. Pertinent patient-related information encompassing general demographic characteristics and biochemical indicators was gathered. Variability in the dataset was defined by standard deviation (SD) and coefficient of variation (CV), with glycosylated hemoglobin variation also considering variability score (HVS). Linear regression analysis was employed to establish the structural equation models for statistically significant influences on long-term glycemic variability. Structural equation modeling was employed to analyze effects and pathways.

Results

Diabetes outpatient special disease management, uric acid variability, mean triglyceride levels, mean total cholesterol levels, total cholesterol variability, LDL variability, baseline glycated hemoglobin, and recent glycated hemoglobin were identified as significant factors influencing long-term glycemic variability. The overall fit of the structural equation model was found to be satisfactory and it was able to capture the relationship between outpatient special disease management, biochemical indicators, and glycated hemoglobin variability. According to the total effect statistics, baseline glycated hemoglobin and total cholesterol levels exhibited the strongest impact on glycated hemoglobin variability.

Conclusion

The factors that have a significant impact on the variation of glycosylated hemoglobin include glycosylated hemoglobin itself, lipids, uric acid, and outpatient special disease management for diabetes. The identification and management of these associated factors can potentially mitigate long-term glycemic variability, thereby delaying the onset of complications and enhancing patients' quality of life.

Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes: a retrospective cohort study

BACKGROUND

Current treatment guidelines do not provide recommendations to support the selection of treatment for most people with type 2 diabetes. We aimed to develop and validate an algorithm to allow selection of optimal treatment based on glycaemic response, weight change, and tolerability outcomes when choosing between SGLT2 inhibitor or DPP-4 inhibitor therapies.

METHODS

In this retrospective cohort study, we identified patients initiating SGLT2 and DPP-4 inhibitor therapies after Jan 1, 2013, from the UK Clinical Practice Research Datalink (CPRD). We excluded those who received SGLT2 or DPP-4 inhibitors as first-line treatment or insulin at the same time, had estimated glomerular filtration rate (eGFR) of less than 45 mL/min per 1·73 m², or did not have a valid baseline glycated haemoglobin (HbA_1c) measure (<53 or ≥120 mmol/mol). The primary efficacy outcome was the HbA_1c value reached 6 months after drug initiation, adjusted for baseline HbA_1c. Clinical features associated with differential HbA_1c outcome on the two therapies were identified in CPRD (n=26 877), and replicated in reanalysis of 14 clinical trials (n=10 414). An algorithm to predict individual-level differential HbA_1c outcome on the two therapies was developed in CPRD (derivation; n=14 069) and validated in head-to-head trials (n=2499) and CPRD (independent validation; n=9376). In CPRD, we further explored heterogeneity in 6-month weight change and treatment discontinuation.

FINDINGS

Among 10 253 patients initiating SGLT2 inhibitors and 16 624 patients initiating DPP-4 inhibitors in CPRD, baseline HbA_1c, age, BMI, eGFR, and alanine aminotransferase were associated with differential HbA_1c outcome with SGLT2 inhibitor and DPP-4 inhibitor therapies. The median age of participants was 62·0 years (IQR 55·0-70·0). 10 016 (37·3%) were women and 16 861 (62·7%) were men. An algorithm based on these five features identified a subgroup, representing around four in ten CPRD patients, with a 5 mmol/mol or greater observed benefit with SGLT2 inhibitors in all validation cohorts (CPRD 8·8 mmol/mol [95% CI 7·8-9·8]; CANTATA-D and CANTATA-D2 trials 5·8 mmol/mol [3·9-7·7]; BI1245.20 trial 6·6 mmol/mol [2·2-11·0]). In CPRD, predicted differential HbA_1c response with SGLT2 inhibitor and DPP-4 inhibitor therapies was not associated with weight change. Overall treatment discontinuation within 6 months was similar in patients predicted to have an HbA_1c benefit with SGLT2 inhibitors over DPP-4 inhibitors (median 15·2% [13·2-20·3] vs 14·4% [12·9-16·7]). A smaller subgroup predicted to have greater HbA_1c reduction with DPP-4 inhibitors were twice as likely to discontinue SGLT2 inhibitors than DPP-4 inhibitors (median 26·8% [23·4-31·0] vs 14·8% [12·9-16·8]).

INTERPRETATION

A validated treatment selection algorithm for SGLT2 inhibitor and DPP-4 inhibitor therapies can support decisions on optimal treatment for people with type 2 diabetes.

FUNDING

BHF-Turing Cardiovascular Data Science Award and the UK Medical Research Council.

Exploring a Suitable Marker of Glycemic Response to Dulaglutide in Patients with Type 2 Diabetes: A Retrospective Study

INTRODUCTION

Previous studies suggested that β-cell function markers such as fasting and postprandial serum C-peptide and C-peptide increment (FCPR, PCPR, and ΔCPR, respectively) may be useful in estimating glycemic response to glucagon-like peptide-1 receptor agonists. However, it remains elusive whether baseline glycemic control confounds these markers. Here we aimed to identify the least confounded β-cell function markers and investigate whether these markers could predict glycemic response to dulaglutide.

METHODS

We evaluated FCPR, PCPR, and ΔCPR levels in patients with type 2 diabetes who initiated dulaglutide treatment after a standardized meal tolerance test (MTT). We first investigated the confounding effects of baseline HbA1c on β-cell function markers using Pearson's correlation test. Then, we evaluated the association between each β-cell function marker and glycemic response (HbA1c change 0-6 months) to dulaglutide using generalized linear model and logistic regression analysis with adjustment for baseline HbA1c.

RESULTS

In 141 patients, baseline HbA1c was significantly inversely correlated with PCPR and ΔCPR (P < 0.01 for both) but not with FCPR (r = 0.02; P = 0.853), suggesting that FCPR was the marker least confounded by baseline glycemic control. Of all patients, 59 continued dulaglutide for at least 6 months without initiating any additional glucose-lowering medications. Mean ± SE HbA1c change 0-6 months was - 1.16 ± 0.17% (P < 0.001 vs. baseline). The β-cell function markers were significantly associated with HbA1c change 0-6 months in the generalized linear model. FCPR was also a significant predictor for achieving a reduction in HbA1c of at least 1% (P = 0.044) with an area under the receiver operating characteristic curve of 0.83 (sensitivity = 0.81 and specificity = 0.79).

CONCLUSION

Fasting and meal-induced C-peptide levels are associated with glycemic response to dulaglutide, among which FCPR is least confounded by baseline glycemic control, suggesting its utility as a marker for glycemic response to dulaglutide.

Variability in Test Interval Is Linked to Glycated Haemoglobin (HbA1c) Trajectory over Time

AIMS

We previously showed that the glycated haemoglobin (HbA1c) testing frequency links to diabetes control. Here, we examine the effect of variability in test interval, adjusted for the frequency, on change in HbA1c (ΔHbA1c). Materials & Methods. HbA1c results were collected on 83,872 people with HbA1c results at baseline and 5 years (±3 months) later and ≥6 tests during this period. We calculated the standard deviation (SD) of test interval for each individual and examined the link between deciles of SD of the test interval and ΔHbA1c level, stratified by baseline HbA1c.

RESULTS

In general, less variability in testing frequency (more consistent monitoring) was associated with better diabetes control. This was most evident with moderately raised baseline HbA1c levels (7.0-9.0% (54-75 mmol/mol)). For example, in those with a starting HbA1c of 7.0-7.5% (54-58 mmol/mol), the lowest SD decile was associated with little change in HbA1c over 5 years, while for those with the highest decile, HbA1c rose by 0.4-0.6% (4-6 mmol/mol; p < 0.0001). Multivariate analysis showed that the association was independent of the age/sex/hospital site. Subanalysis suggested that the effect was most pronounced in those aged <65 years with baseline HbA1c of 7.0-7.5% (54-58 mmol/mol). We observed a 6.7-fold variation in the proportion of people in the top-three SD deciles across general practices.

CONCLUSIONS

These findings indicate that the consistency of testing interval, not the just number of tests/year, is important in maintaining diabetes control, especially in those with moderately raised HbA1c levels. Systems to improve regularity of HbA1c testing are therefore needed, especially given the impact of COVID-19 on diabetes monitoring.

Comparing Real-World Effectiveness of Dulaglutide and Insulin as the First Injectable for Patients with Type 2 Diabetes: An Australian Single-Site Retrospective Chart Review

AIM

To compare the real-world effectiveness of once-weekly dulaglutide 1.5 mg with insulin in injectable-naïve patients with type 2 diabetes mellitus (T2DM).

METHODS

A non-interventional, non-randomised, observational, single-site retrospective chart review enrolled 150 patients, 75 receiving dulaglutide or insulin. Data were collected from electronic medical records of patients with T2DM who were initiated on insulin between October 2010 and May 2017, and patients initiated on dulaglutide between May 2018 and October 2019. A doubly robust approach was used to adjust for potential selection bias with augmented inverse probability weights used to estimate the average treatment effect.

RESULTS

HbA1c favoured dulaglutide with an average change of - 1.6% vs - 0.8% for insulin, with an average treatment effect difference of 0.8% (95% confidence interval (CI) 0.4-1.2%) at 3 months. At 6 months, 58.7% of the dulaglutide group reached a target HbA1c of ≤ 7% compared with 20.0% of the insulin group: average treatment effect difference of 21.3% (95% CI 2.7-43.1). The dulaglutide group lost 2.4 kg compared to the insulin group which gained 2.0 kg: average difference of 4.4 kg (95% CI 2.6-7.3) at 6 months. The incidence of hypoglycaemic events was 12 (16.0%) occurrences in the dulaglutide group compared to 33 (44.0%) in the insulin group.

CONCLUSION

Once-weekly dulaglutide demonstrated greater HbA1c reduction, weight loss and reduced hypoglycaemia compared to insulin, in a real-world practice setting.

Liraglutide-induced structural modulation of the gut microbiota in patients with type 2 diabetes mellitus

Accumulating evidence has suggested the importance of gut microbiota in the development of type 2 diabetes mellitus (T2DM). In the present study, 40 patients with T2DM were treated with liraglutide for 4 months. Feces samples and clinical characteristics were collected from these 40 T2DM patients before and after the liraglutide treatment. The diversity and composition of gut microbiota in the two groups were determined by sequencing the V4 region of bacterial 16S rRNA genes. Meanwhile, blood glucose, insulin, hemoglobin A1c (HbA1c), and lipid metabolism were also measured in the pre- and post-liraglutide-treatment groups. We find that Baseline HbA1c was associated with liraglutide treatment response (R₂ = 0.527, β =  − 0.726, p < 0.0001). After adjusted for baseline HbA1c, blood urea nitrogen was associated with liraglutide treatment response. Besides, our results showed reduced gut microbial alpha diversity, different community structure distribution and altered microbial interaction network in patients treated with liraglutide. The liner discriminant analysis (LDA) effect size (LEfSe) analysis showed that 21 species of bacteria were abundant in the pre-liraglutide-treatment group and 15 species were abundant in the post-liraglutide-treatment group. In addition, we also find that Megamonas were significantly correlated with older age, diabetes duration and diabetic retinopathy, Clostridum were significantly correlated with family history of diabetes and Oscillospira were significantly correlated with both diabetic retinopathy and diabetic peripheral neuropathy. Functional analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) and cluster of orthologous groups (COG) annotations enriched three KEGG metabolic pathways and six functional COG categories in the post-liraglutide-treatment group. In conclusion, our research suggests that baseline HbA1c, blood urea nitrogen and gut microbiota are associated with the liraglutide treatment applied on patients with T2DM. These findings may contribute to the beneficial effects of liraglutide against diabetes.

Introduction of a community health worker diabetes coach improved glycemic control in an urban primary care clinic

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Community health worker (CHW) care benefits diabetes management.

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Propensity score weighting was used to assess diabetes intervention effectiveness.

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CHW coaching reduced hemoglobin A1c by at least 1.0% for most participants.

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Most participants with diabetes working with the CHW met glycemic control.

The burden of diabetes is higher in urban areas and among racial and ethnic minorities. The purpose of this research was to evaluate the effectiveness of extending a diabetes intervention program (DIP) by engaging a team, including a community health worker (CHW), to provide care for patients to meet glycemic control, specifically in a predominantly urban, minority patient population. The DIP enrolled diabetic patients from an internal medicine clinic. A CHW facilitated the collection of glucose meter readings. The CHW coached patients on glycemic control while the CHW’s registered nurse partner titrated the patient’s recommended insulin dose. Subsequent HbA1c values for participants were compared to those seen at the same clinic who were not enrolled. The DIP was deployed for nine months. One hundred forty-four patients were enrolled in the DIP and 348 patients constituted the comparator group. Ninety-three DIP participants had pre- and post-intervention HbA1c values and were compared to 348 non-DIP participants. Propensity score weighted adjusted analyses suggest that participants were more likely to reduce their HbA1c values by at least 1.0% and have HbA1c values of less than 8.0% (64 mmol/mol) than non-participants (adjusted odds ratio = aOR = 1.47, 95% CI 1.26–1.71, and aOR = 1.23, 95% CI 1.06–1.43, respectively). CHW coaches as part of a team in a clinical setting improved glycemic control in a predominantly urban, minority patient population.

Oral glucose tolerance test does not affect degree of hemoglobin glycation as measured by routine assay

BACKGROUND

Hemoglobin A1c (HbA1c) is an accurate index of fluctuation in glycemia over the 2-3 months prior to quantitative assessment. During this time, hemoglobin (Hb) slowly glycates until it shows the properties of advanced glycation end-products. Glycation kinetics is intensified by prolonged glucose exposure. In subjects undergoing oral glucose tolerance testing (OGTT), immediately after ingestion, glucose is ostensibly transported by the glucose transporter 1 (GLUT1) to erythrocyte corpuscular hemoglobin. The earliest significant measurable level of hemoglobin glycation associated with this transportation is still not clear.

SUBJECTS AND METHODS

We attempted to explore the early impact of short-term glucose load on HbA1c levels, because it is now known that transmembrane GLUT1-mediated glucose transport occurs immediately. A total of 88 participants (46 patients and 42 clinically healthy controls) underwent fasting plasma glucose quantitation during an OGTT. HbA1c, revealed by a monoclonal anti-glycation epitope antibody and adiponectin, was quantitated before (T0) and 2 hours (T120) after 80 g glucose ingestion.

RESULTS

Wilcoxon test revealed that the HbA1c values did not significantly vary (P=0.15) during the OGTT, whereas glucose concentration varied strongly between T0 and T120.

DISCUSSION

It is well known that quantitative estimation of HbA1c is informative for clinical care, independently of glucose level. The molecular mechanisms and dynamics by which glucose enters/exits red blood cells are incompletely known and may differ between individuals. We here show, for the first time, that HbA1c levels do not significantly increase during OGTT, supporting the view that non-enzymatic glycation of hemoglobin occurs slowly and that glycation during the 2 hours of an OGTT is insignificant.

Baseline gut microbiome composition predicts metformin therapy short-term efficacy in newly diagnosed type 2 diabetes patients

BACKGROUND

The study was conducted to investigate the effects of metformin treatment on the human gut microbiome's taxonomic and functional profile in the Latvian population, and to evaluate the correlation of these changes with therapeutic efficacy and tolerance.

METHODS

In this longitudinal observational study, stool samples for shotgun metagenomic sequencing-based analysis were collected in two cohorts. The first cohort included 35 healthy nondiabetic individuals (metformin dose 2x850mg/day) at three time-points during metformin administration. The second cohort was composed of 50 newly-diagnosed type 2 diabetes patients (metformin dose-determined by an endocrinologist) at two concordant times. Patients were defined as Responders if their HbA1c levels during three months of metformin therapy had decreased by ≥12.6 mmol/mol (1%), while in Non-responders HbA1c were decreased by <12.6 mmol/mol (1%).

RESULTS

Metformin reduced the alpha diversity of microbiota in healthy controls (p = 0.02) but not in T2D patients. At the species level, reduction in the abundance of Clostridium bartlettii and Barnesiella intestinihominis, as well as an increase in the abundance of Parabacteroides distasonis and Oscillibacter unclassified overlapped between both study groups. A large number of group-specific changes in taxonomic and functional profiles was observed. We identified an increased abundance of Prevotella copri (FDR = 0.01) in the Non-Responders subgroup, and enrichment of Enterococcus faecium, Lactococcus lactis, Odoribacter, and Dialister at baseline in the Responders group. Various taxonomic units were associated with the observed incidence of side effects in both cohorts.

CONCLUSIONS

Metformin effects are different in T2D patients and healthy individuals. Therapy induced changes in the composition of gut microbiome revealed possible mediators of observed short-term therapeutic effects. The baseline composition of the gut microbiome may influence metformin therapy efficacy and tolerance in T2D patients and could be used as a powerful prediction tool.

Precision Medicine in Type 2 Diabetes: Using Individualized Prediction Models to Optimize Selection of Treatment

Despite the known heterogeneity of type 2 diabetes and variable response to glucose lowering medications, current evidence on optimal treatment is predominantly based on average effects in clinical trials rather than individual-level characteristics. A precision medicine approach based on treatment response would aim to improve on this by identifying predictors of differential drug response for people based on their characteristics and then using this information to select optimal treatment. Recent research has demonstrated robust and clinically relevant differential drug response with all noninsulin treatments after metformin (sulfonylureas, thiazolidinediones, dipeptidyl peptidase 4 [DPP-4] inhibitors, glucagon-like peptide 1 [GLP-1] receptor agonists, and sodium-glucose cotransporter 2 [SGLT2] inhibitors) using routinely available clinical features. This Perspective reviews this current evidence and discusses how differences in drug response could inform selection of optimal type 2 diabetes treatment in the near future. It presents a novel framework for developing and testing precision medicine-based strategies to optimize treatment, harnessing existing routine clinical and trial data sources. This framework was recently applied to demonstrate that "subtype" approaches, in which people are classified into subgroups based on features reflecting underlying pathophysiology, are likely to have less clinical utility compared with approaches that combine the same features as continuous measures in probabilistic "individualized prediction" models.

Impact of Pharmacist-Led Drug Therapy Management Services on HbA1c Values in a Predominantly Hispanic Population Visiting an Outpatient Endocrinology Clinic

PURPOSE

To assess the impact pharmacists have on improving glycemic control among predominantly Hispanic diabetic patients visiting an endocrinology clinic in South Texas. Pharmacists were recently integrated into this clinic to be part of a collaborative team.

METHODS

This study follows a retrospective cohort design. All patients received diabetic care from endocrinologists, and some received pharmacist-led drug therapy management (PDTM). Patients with ≥1 PDTM were categorized as the intervention group and those without PDTM as the standard of care (SOC) group. The outcome variables were the mean absolute change in glycosylated hemoglobin (HbA_1c) from baseline and the proportion of patients at goal HbA_1c (<7%) postintervention.

RESULTS

Data were collected from 222 patients (n = 120 SOC patients, n = 102 PDTM patients). The mean age was 61 ± 14 years, 136 (61%) were female, and 197 (89%) were Hispanic. The mean absolute change in HbA_1c was -1.3%. In the adjusted model, the mean absolute change in HbA_1c in the PDTM compared to the SOC group was not significant (-0.1% ± 0.2%; P < .74), and concurrent interventions from registered dieticians (RDs) and licensed professional counselors (LPC) were identified as effect modifiers of the association. The stratum specific analysis identified the greatest decrease in HbA_1c when the three interventions (ie, PDTM, RD, and LPC) coincided (-1.0% ± 0.3%; P < .01). Postintervention, 25% of those who received PDTM achieved an HbA_1c<7% as compared to 19% in the SOC group.

CONCLUSION

The clinical importance of pharmacists is enhanced when integrated with behavioral modifying programs to achieve additional improvement in HbA_1c.

Impact of baseline characteristics and beta-cell function on the efficacy and safety of subcutaneous once-weekly semaglutide: A patient-level, pooled analysis of the SUSTAIN 1-5 trials

AIM

To evaluate the impact of relevant patient-level characteristics on the efficacy and safety of subcutaneous, once-weekly semaglutide in subjects with type 2 diabetes.

MATERIALS AND METHODS

Exploratory post hoc analyses of pooled SUSTAIN 1-5 (phase 3a) randomized, controlled trials examined the change from baseline in HbA1c and body weight (BW), and the proportions of subjects achieving the composite endpoint (HbA1c < 7.0% [53 mmol/mol]), without weight gain or severe/blood glucose-confirmed symptomatic hypoglycaemia at week 30 with semaglutide (0.5/1.0 mg) across clinically relevant patient subgroups: baseline HbA1c (≤7.5%, >7.5%-8.0%, >8.0%-8.5%, >8.5%-9.0% and > 9.0%), background medications, diabetes duration and pancreatic beta-cell function.

RESULTS

Mean HbA1c (% point) reductions increased from lowest to highest HbA1c subgroups (-0.9%, -1.2%,-1.5%, -1.7% and -2.3% [effect of subgroup within treatment: P = 0.247] for semaglutide 0.5 mg, and -1.1%, -1.4%, -1.9%, -2.1% and -2.7% [P = 0.045] for semaglutide 1.0 mg), with mean HbA1c ranges at week 30 of 6.3%-7.3% and 6.1%-6.9%, respectively. The corresponding BW reductions generally decreased with increasing baseline HbA1c (-4.4, -3.9, -3.9, -3.3 and -2.9 kg [P = 0.004], and -6.4, -5.9, -5.2, -4.5 and -4.8 kg [P < 0.001], respectively). HbA1c and BW reductions were consistently greater for semaglutide 1.0 mg versus 0.5 mg across background medication, diabetes duration and pancreatic beta-cell function subgroups. Adverse events with semaglutide were consistent with the glucagon-like peptide-1 receptor agonist class, with gastrointestinal events the most common.

CONCLUSIONS

Semaglutide was consistently efficacious across the continuum of diabetes care in a broad spectrum of patient subgroups with a range of clinical characteristics.

Reduction in HbA1c with SGLT2 inhibitors vs. DPP-4 inhibitors as add-ons to metformin monotherapy according to baseline HbA1c: A systematic review of randomized controlled trials

AIMS

This study compared the reduction of glycated haemoglobin (HbA1c) with sodium-glucose cotransporter type-2 inhibitors (SGLT2is) vs. dipeptidyl peptidase-4 inhibitors (DPP-4is) as add-ons to metformin in patients with type 2 diabetes mellitus (T2DM), with a specific focus on HbA1c changes according to baseline HbA1c.

MATERIALS AND METHODS

Electronic databases were scrutinized for randomized controlled trials (RCTs) evaluating the reduction of HbA1c from baseline (Δ HbA1c) with an SGLT2i or DPP-4i in patients with T2DM not well controlled by metformin monotherapy. The endpoint was Δ HbA1c using both indirect and direct comparisons.

RESULTS

Overall, Δ HbA1c was slightly greater with SGLT2is (-0.80±0.20% from 8.03±0.35%; 44 analyses, 29 RCTs, 15 with two doses, n=9321) than with DPP-4is (-0.71±0.23% from 8.05±0.43%; 61 analyses, 59 RCTs, n=17,914; P=0.0354). When the mean baseline HbA1c was<8% ([64mmol/mol] 7.79±0.15% vs. 7.71±0.23%), Δ HbA1c averaged -0.735±0.17% vs. -0.62±0.16% (P=0.0117) with SGLT2is vs. DPP-4is, respectively. However, this difference vanished when the mean baseline HbA1c was≥8% (-0.87±0.22% from 8.27±0.32% with SGLT2is vs. -0.80±0.24% from 8.35±0.33% with DPP-4is; P=0.2756). The relationship between Δ HbA1c and baseline HbA1c was only slightly stronger with SGLT2is (slope: -0.39, r²=-0.43; P<0.0001) than with DPP-4is (slope: -0.26, r²=-0.25; P<0.0001).

CONCLUSION

Because of the small difference in Δ HbA1c whatever the baseline HbA1c level with SGLT2is vs. DPP-4is as add-ons to metformin, choosing between these glucose-lowering agents in clinical practice should be based on other efficacy criteria (such as weight and blood pressure changes, cardiovascular and renal protection) or on safety profiles rather than on HbA1c levels.

Evaluation of the Safety and Efficacy of Teneligliptin at a Higher Dose in Indian Type 2 Diabetes Patients: A Retrospective Analysis

Background and aim

While diabetes mellitus (DM) is occupying the topmost global epidemic position, India is harboring a challenging number of type 2 DM patients in the world. This devastating picture of the health sector in India requires the availability of more cost-effective, context-specific, and safer drugs for DM management. This study aimed to evaluate the safety and efficacy of teneligliptin in Indian patients with type 2 DM inadequately controlled with diet, exercise, and a maximal dose of metformin treatment.

Materials and methods

This was a retrospective, observational, and single-center study conducted at a diabetic clinic in India in type 2 DM patients who have been treated with teneligliptin 40 mg once daily as add-on therapy with diet, exercise, and the maximal tolerable dose of metformin for three months. The study was observational, where the data collection was through self-reporting and an observational study conducted over one year (September 2018 to August 2019). A total of 100 patients were enrolled in the study (male 69% and female 31%). Patients with available data for fasting plasma glucose (FPG), postprandial plasma glucose (2h PPG), glycated hemoglobin (HbA1c), renal function parameters, such as urinary albumin to creatinine ratio (UACR), and electrocardiogram (ECG) at baseline and three months after treatment were enrolled in the study.

Results

There was a significant reduction in fasting blood sugar (P=<0.001), postprandial blood sugar (P=<0.001), and HbA1c (P=<0.001) at the end of the three months treatment in comparison to the baseline level and in the primary outcomes of this study as compared to baseline. The teneligliptin treatment did not cause any significant reduction in body mass index (BMI) before and after treatment. When we compared the secondary outcomes, the indicator of renal function as expressed through the albumin-to-creatinine ratio (ACR; P=0.052), there was a borderline change in ACR from baseline to three months. The mean corrected QT interval at screening baseline was 429.7 ± 8.89 milliseconds while after three months, it was 429.1 ± 8.68 milliseconds, which was statistically insignificant.

Conclusion

The current results demonstrated a high level of efficacy as an add-on therapy of teneligliptin at a high dose with inadequately controlled type 2 DM subjects in India. The study results also indicate the good tolerance of this drug with no critical adverse event in this study design.

Predictive factors associated with three years of response to HbA1c goals with exenatide QW or insulin glargine: Post-hoc analysis of the DURATION-3 study

This post-hoc analysis of the DURATION-3 study aimed to identify factors associated with sustained glycaemic response with exenatide once weekly (QW) or insulin glargine (IG) among patients with type 2 diabetes. Response was defined as achieving treatment target of HbA1c <7.0% (<53 mmol/mol) at Week 26; sustained responders maintained the treatment target for ≥80% of remaining visits, including one during the final 6 months. Of 467 patients, 287 (61.5%) completed 156 weeks of treatment. At Week 26, 175 patients (61.0%) (exenatide QW, n = 95; IG, n = 80) achieved an HbA1c response. At Week 156, 84 of 175 responders (48.0%) had sustained response, with more sustained responders with exenatide QW (22.7% vs 13.9% with IG; P < 0.03). Logistic regression identified three predictors of sustained response: (a) exenatide QW vs IG treatment (odds ratio, 2.584 [95% confidence interval, 1.288-5.187]; P = 0.0075), (b) lower HbA1c at Week 26 (0.139 [0.053-0.366]; P < 0.0001), and (c) lower fasting serum glucose at Week 26 (0.693 [0.541-0.888]; P = 0.0037). A regression model was used to estimate the likelihood of sustained response with either treatment. This analysis provides a helpful tool for predicting sustained response with exenatide QW or IG.

Evaluating associations between the benefits and risks of drug therapy in type 2 diabetes: a joint modeling approach

OBJECTIVE

Precision medicine drug therapy seeks to maximize efficacy and minimize harm for individual patients. This will be difficult if drug response and side effects are positively associated, meaning that patients likely to respond best are at increased risk of side effects. We applied joint longitudinal-survival models to evaluate associations between drug response (longitudinal outcome) and the risk of side effects (survival outcome) for patients initiating type 2 diabetes therapy.

STUDY DESIGN AND SETTING

Participants were randomized to metformin (MFN), sulfonylurea (SU), or thiazolidinedione (TZD) therapy in the A Diabetes Outcome Progression Trial (ADOPT) drug efficacy trial (n=4,351). Joint models were parameterized for 1) current HbA1c response (change from baseline in HbA1c) and 2) cumulative HbA1c response (total HbA1c change).

RESULTS

With MFN, greater HbA1c response did not increase the risk of gastrointestinal events (HR per 1% absolute greater current response 0.82 [95% CI 0.67, 1.01]; HR per 1% higher cumulative response 0.90 [95% CI 0.81, 1.00]). With SU, greater current response was associated with an increased risk of hypoglycemia (HR 1.41 [95% CI 1.04, 1.91]). With TZD, greater response was associated with an increased risk of edema (current HR 1.45 [95% CI 1.05, 2.01]; cumulative 1.22 [95% CI 1.07, 1.38]) but not fracture.

CONCLUSION

Joint modeling provides a useful framework to evaluate the association between response to a drug and the risk of developing side effects. There may be great potential for widespread application of joint modeling to evaluate the risks and benefits of both new and established medications.

Sex and BMI Alter the Benefits and Risks of Sulfonylureas and Thiazolidinediones in Type 2 Diabetes: A Framework for Evaluating Stratification Using Routine Clinical and Individual Trial Data

OBJECTIVE

The choice of therapy for type 2 diabetes after metformin is guided by overall estimates of glycemic response and side effects seen in large cohorts. A stratified approach to therapy would aim to improve on this by identifying subgroups of patients whose glycemic response or risk of side effects differs markedly. We assessed whether simple clinical characteristics could identify patients with differing glycemic response and side effects with sulfonylureas and thiazolidinediones.

RESEARCH DESIGN AND METHODS

We studied 22,379 patients starting sulfonylurea or thiazolidinedione therapy in the U.K. Clinical Practice Research Datalink (CPRD) to identify features associated with increased 1-year HbA_1c fall with one therapy class and reduced fall with the second. We then assessed whether prespecified patient subgroups defined by the differential clinical factors showed differing 5-year glycemic response and side effects with sulfonylureas and thiazolidinediones using individual randomized trial data from ADOPT (A Diabetes Outcome Progression Trial) (first-line therapy, n = 2,725) and RECORD (Rosiglitazone Evaluated for Cardiovascular Outcomes and Regulation of Glycemia in Diabetes) (second-line therapy, n = 2,222). Further replication was conducted using routine clinical data from GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland) (n = 1,977).

RESULTS

In CPRD, male sex and lower BMI were associated with greater glycemic response with sulfonylureas and a lesser response with thiazolidinediones (both P < 0.001). In ADOPT and RECORD, nonobese males had a greater overall HbA_1c reduction with sulfonylureas than with thiazolidinediones (P < 0.001); in contrast, obese females had a greater HbA_1c reduction with thiazolidinediones than with sulfonylureas (P < 0.001). Weight gain and edema risk with thiazolidinediones were greatest in obese females; however, hypoglycemia risk with sulfonylureas was similar across all subgroups.

CONCLUSIONS

Patient subgroups defined by sex and BMI have different patterns of benefits and risks on thiazolidinedione and sulfonylurea therapy. Subgroup-specific estimates can inform discussion about the choice of therapy after metformin for an individual patient. Our approach using routine and shared trial data provides a framework for future stratification research in type 2 diabetes.

Precision Medicine in Type 2 Diabetes: Clinical Markers of Insulin Resistance Are Associated With Altered Short- and Long-term Glycemic Response to DPP-4 Inhibitor Therapy

OBJECTIVE

A precision approach to type 2 diabetes therapy would aim to target treatment according to patient characteristics. We examined if measures of insulin resistance and secretion were associated with glycemic response to dipeptidyl peptidase 4 (DPP-4) inhibitor therapy.

RESEARCH DESIGN AND METHODS

We evaluated whether markers of insulin resistance and insulin secretion were associated with 6-month glycemic response in a prospective study of noninsulin-treated participants starting DPP-4 inhibitor therapy (Predicting Response to Incretin Based Agents [PRIBA] study; n = 254), with replication for routinely available markers in U.K. electronic health care records (Clinical Practice Research Datalink [CPRD]; n = 23,001). In CPRD, we evaluated associations between baseline markers and 3-year durability of response. To test the specificity of findings, we repeated analyses for glucagon-like peptide 1 (GLP-1) receptor agonists (PRIBA, n = 339; CPRD, n = 4,464).

RESULTS

In PRIBA, markers of higher insulin resistance (higher fasting C-peptide [P = 0.03], HOMA2 insulin resistance [P = 0.01], and triglycerides [P < 0.01]) were associated with reduced 6-month HbA_1c response to DPP-4 inhibitors. In CPRD, higher triglycerides and BMI were associated with reduced HbA_1c response (both P < 0.01). A subgroup defined by obesity (BMI ≥30 kg/m²) and high triglycerides (≥2.3 mmol/L) had reduced 6-month response in both data sets (PRIBA HbA_1c reduction 5.3 [95% CI 1.8, 8.6] mmol/mol [0.5%] [obese and high triglycerides] vs. 11.3 [8.4, 14.1] mmol/mol [1.0%] [nonobese and normal triglycerides]; P = 0.01). In CPRD, the obese, high- triglycerides subgroup also had less durable response (hazard ratio 1.28 [1.16, 1.41]; P < 0.001). There was no association between markers of insulin resistance and response to GLP-1 receptor agonists.

CONCLUSIONS

Markers of higher insulin resistance are consistently associated with reduced glycemic response to DPP-4 inhibitors. This finding provides a starting point for the application of a precision diabetes approach to DPP-4 inhibitor therapy.

Effects of hemoglobin variants HbJ Bangkok, HbE, HbG Taipei, and HbH on analysis of glycated hemoglobin via ion-exchange high-performance liquid chromatography

BACKGROUND

To explore the effects of HbJ Bangkok, HbE, HbG Taipei, and α-thalassemia HbH on the results of HbA1c assessment using ion-exchange high-performance liquid chromatography (IE-HPLC).

METHODS

We enrolled five patients in which the results of the IE-HPLC HbA1c assay were inconsistent with the average levels of FBG. We performed hemoglobin capillary (Hb) electrophoresis using whole-blood samples. We also sequenced the genes encoding Hb using dideoxy-mediated chain termination and analyzed HbA1c using borate affinity HPLC (BA-HPLC) and turbidimetric inhibition immunoassay (TINIA).

RESULTS

Two patients had the HbJ Bangkok variant. Hb genotypes of these patients were β^41-42 /β^{J Bangkok} and β^N /β^{J Bangkok} , and the content of HbJ Bangkok was 93.9% and 52.4%, respectively. The remaining three patients had the following: HbE (β^N /β^E Hb genotype, 23.6% HbE content), HbG Taipei (β^N /β^{G Taipei} Hb genotype, 39.4% HbG Taipei content), and α-thalassemia HbH (6.1% HbH content, 2.8% Hb Bart's content). In the patients with β-thalassemia and HbJ Bangkok variants, the presence of the variants interfered with the results of HbA1c analyses using IE-HPLC and TINIA; in the remaining four patients, there was interference with the results of HbA1c IE-HPLC but not with the TINIA assay. There was no interference with BA-HPLC HbA1c results.

CONCLUSIONS

HbJ Bangkok, HbE, HbG Taipei Hb, and α-thalassemia HbH disease cause varying degrees of interference with the analysis of HbA1c using IE-HPLC. In these patients, we suggest using methods free from such interference for the analysis of HbA1c and other indicators to monitor blood glucose levels.

Co-expression analysis among microRNAs, long non-coding RNAs, and messenger RNAs to understand the pathogenesis and progression of diabetic kidney disease at the genetic level

Diabetic kidney disease (DKD) is a serious disease that presents a major health problem worldwide. There is a desperate need to explore novel biomarkers to further facilitate the early diagnosis and effective treatment in DKD patients, thus preventing them from developing end-stage renal disease (ESRD). However, most regulation mechanisms at the genetic level in DKD still remain unclear. In this paper, we describe our innovative methodologies that integrate biological, computational, and statistical approaches to investigate important roles performed by regulations among microRNAs (miRs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs) in DKD. We conducted fully transparent, rigorously designed experiments. Our robust and reproducible results identified hsa-miR-223-3p as a candidate novel biomarker performing important roles in DKD disease process.