Distinct HbA1c trajectories in a type 2 diabetes cohort

Dietary glycemic index and glycemic load predict longitudinal change in glycemic and cardio-metabolic biomarkers among old diabetic adults living in a resource-poor country

This study aims to investigate longitudinal associations between the dietary glycemic index (GI) and glycemic load (GL) and changes in glycemic and cardio-metabolic outcomes. A 28-month retrospective cohort study included 110 Vietnamese diabetic patients, collecting their dietary GI and GL values along with blood biochemical data from baseline 24-h dietary recall and medical records. Latent class growth modelling identified three distinct HbA1c trajectories during the follow-up period, with 51% of patients achieving good glycemic control. The adjusted linear mixed-effect model showed that 1 unit increase in logarithms in dietary GL was associated with a 0.14% increase in the log-HbA1c. Among poorly controlled diabetic patients, baseline GL values were positively correlated with increases in HbA1c; GI showed effects on changes in fasting plasma glucose and the triglyceride-glucose (TyG) index. No significant association was observed in patients with good glycemic control.

The efficacy of self-monitoring of blood glucose (SMBG) intervention package through a subscription model among type-2 diabetes mellitus in Malaysia: a preliminary trial

BACKGROUND

The aim of this study was to determine the effect of a Self-Monitoring Blood Glucose (SMBG) intervention package through a subscription model in improving HbA1c and health parameters among type-2 diabetes mellitus (T2DM) individuals in Malaysia.

METHODS

This is a quasi-experimental study involving a total number of 111 individuals with T2DM (mean age 57.0 ± 11.7 years, 61% men) who were assigned to intervention (n = 51) and control (n = 60) groups. The intervention group participants were the subscribers of SugO365 program which provided a personalized care service based on self-recorded blood glucose values. Subscribers received a Contour® Plus One glucometer which can connect to Health2Sync mobile app to capture all blood glucose readings as well as physical and virtual follow up with dietitians, nutritionists, and pharmacists for 6 months. Outcome measures were body weight, body mass index (BMI), random blood glucose (RBG), glycated haemoglobin (HbA1c) and health-related quality of life (HRQoL, assessed by SF-36 questionnaire). Data were measured at baseline, third and sixth months.

RESULTS

Repeated-measure analysis of covariance showed significant improvement in HbA1c level (ƞp2 = 0.045, p = 0.008) in the intervention (baseline mean 7.7% ± 1.1%; end mean 7.3% ± 1.3%) as compared to control (baseline mean 7.7% ± 0.9%; end mean 8.1% ± 1.6%) group. Similar trend was observed for Role Emotional domain of the quality of life (ƞp2 = 0.047, p = 0.023) in the intervention (baseline mean 62.8 ± 35.1, end mean 86.3 ± 21.3) compared to control (baseline mean group 70.5 ± 33.8; end mean 78.4 ± 27.3) group. Negative association was found in HbA1c changes using Z-score and Physical Function domain (r = - 0.217, p = 0.022).

CONCLUSION

A 6 months SMBG intervention package through a subscription model improved blood glucose control as measured by HbA1c and health-related quality of life, particularly the Role Emotional domain. Elevated HbA1c levels are correlated with decreased physical function.There is a need to further examine the efficacy of SMBG intervention package using a larger sample and a longer period of intervention and to determine its cost efficacy.

Longitudinal HbA1c patterns before the first treatment of diabetes in routine clinical practice: A latent class trajectory analysis

AIMS

To examine the longitudinal heterogeneity of HbA1c preceding the initiation of diabetes treatment in clinical practice.

METHODS

In this population-based study, we used HbA1c from routine laboratory and healthcare databases. Latent class trajectory analysis was used to classify individuals according to their longitudinal HbA1c patterns before first glucose-lowering drug prescription irrespective of type of diabetes.

RESULTS

Among 21,556 individuals initiating diabetes treatment during 2017-2018, 20,733 (96 %) had HbA1c measured (median 4 measurements [IQR 2-7]) in the 5 years preceding treatment initiation. Four classes with distinct HbA1c trajectories were identified, with varying steepness of increase in HbA1c. The largest class (74 % of the individuals) had mean HbA1c above the 48 mmol/mol threshold 9 months before treatment initiation. Mean HbA1c was 52 mmol/mol (95 % CI 52-52) at treatment initiation. In the remaining three classes, mean HbA1c exceeded 48 mmol/mol almost 1.5 years before treatment initiation and reached 79 mmol/mol (95 % CI 78-80), 105 mmol/mol (95 % CI 104-106), and 137 mmol/mol (95 % CI 135-140) before treatment initiation.

CONCLUSION

We identified four distinct longitudinal HbA1c patterns before initiation of diabetes treatment in clinical practice. All had mean HbA1c levels exceeding the diagnostic threshold many months before treatment initiation, indicating therapeutic inertia.

Glycated haemoglobin, HOMA2-B, C-peptide to glucose ratio and type 2 diabetes clusters as predictors for therapy failure in individuals with type 2 diabetes without insulin therapy: A registry analysis

AIM

Some people with type 2 diabetes mellitus (T2D) and declining β-cell function do require insulin over time. Various laboratory parameters, indices of glucose metabolism or phenotypes of T2D (clusters) have been suggested, which might predict future therapy failure (TF), indicating the need for insulin therapy initiation. This analysis evaluated glycated haemoglobin (HbA1c), homeostatic model assessment (HOMA)2-B, C-peptide to glucose ratio (CGR) and diabetes clusters as predictive parameters for the occurrence of glycaemic TF in individuals diagnosed with T2D without previous insulin therapy.

MATERIALS AND METHODS

In total, 159 individuals with T2D [41% female, median age 50 (IQR: 53-69) years, diabetes duration 9 (5-15) years], without insulin therapy were prospectively evaluated for the occurrence of a composite primary endpoint, including HbA1c increasing or remaining >8.0% (64 mmol/mol) 3 months after baseline on non-insulin glucose-lowering agents, insulin initiation or hospital admissions because of acute hyperglycaemic events. Diabetes clusters were formed according to previously described characteristics. Only severe autoimmune diabetes clusters were excluded because of a small amount of glutamate decarboxylase antibody-positive participants. The other clusters were distributed as mild age-related diabetes 33%; severe insulin-deficient diabetes 31%; mild obesity-related diabetes 20%; and severe insulin-resistant diabetes 15%.

RESULTS

During a median observation of 57 months, higher tertiles of HbA1c at baseline, HOMA2-B, as well as a lower CGR were significantly predictive for the occurrence of the primary endpoint. The probability of meeting the primary endpoint was the highest for mild obesity-related diabetes [hazard ratio 3.28 (95% confidence interval 1.75-6.2)], followed by severe insulin-deficient diabetes [hazard ratio 2.03 (95% confidence interval 1.1-3.7)], mild age-related diabetes and the lowest for severe insulin-resistant diabetes. The best performance to predict TF with an area under the curve (AUC) of 0.77 was HbA1c at baseline, followed by HOMA2-B (AUC 0.69) and CGR (AUC 0.64).

CONCLUSION

HbA1c, indices of insulin secretion capacity (HOMA2-B and CGR) and T2D clusters might be applicable tools to guide practitioners in the decision of whether insulin is required in people already diagnosed with T2D. These findings need to be validated in prospective studies.

The relationship between glycated haemoglobin and blood glucose-lowering treatment trajectories in type 2 diabetes: The Fremantle Diabetes Study Phase II

AIMS

To examine the relationships between glycaemia and treatment complexity over 6 years in well-characterized community-based people with type 2 diabetes.

MATERIALS AND METHODS

Fremantle Diabetes Study Phase II participants who had type 2 diabetes with glycated haemoglobin (HbA1c) and blood glucose-lowering therapy (BGLT) data over 6 years were included. Group-based multi-trajectory modelling identified combined HbA1c/BGLT trajectory subgroups for diabetes durations of ≤1.0 year (Group 1; n = 160), >1.0 to 10.0 years (Group 2; n = 382;) and >10.0 years (Group 3; n = 357). Multinomial regression was used to identify baseline associates of subgroup membership.

RESULTS

The optimum numbers of trajectory subgroups were three in Group 1 (low, medium, high) and four in Groups 2 and 3 (low, low/high medium, high). Each low trajectory subgroup maintained a mean HbA1c concentration of <53 mmol/mol (<7.0%) on lifestyle measures, or monotherapy (Group 3). All five medium subgroups had stable HbA1c trajectories at <58 mmol/mol (<7.5%) but required increasing oral BGLT, or insulin (Group 3, high medium). The Group 1 high subgroup showed a falling then increasing HbA1c with steady progression to insulin. The high subgroups in Groups 2 and 3 showed stable HbA1c profiles at means of approximately 64 mmol/mol (8.0%) and 86 mmol/L (10.0%), respectively, on insulin. Non-Anglo Celt ethnicity, central obesity and hypertriglyceridaemia were strongly associated with Group 1 high subgroup membership. Younger age at diagnosis and central obesity were independent associates of the most adverse HbA1c trajectories in Groups 2 and 3.

CONCLUSIONS

These data demonstrate diabetes duration-dependent heterogeneity in glycaemic and treatment profiles and related clinical and laboratory variables, which have implications for management.

The effect of close and intensive therapeutic monitoring of patients with poorly controlled type 2 diabetes with different glycemic background

Patients with type 2 diabetes who have HbA1c values ≥ 10% have different previous glycemic trends, including new diagnosis of diabetes. We aimed to assess the efficacy of 3 months of intensive and facilitated antihyperglycemic treatment in patients with different glycemic backgrounds. In this observational study, patients with type 2 diabetes and poor glycemic control (indicated by an HbA1c level of > = 10%) were divided into groups based on their previous HbA1c levels (group 1; newly diagnosed type 2 diabetics, group 2; patients with previously controlled but now deteriorated HbA1c levels, group 3; patients whose HbA1c was not previously in the target range but was now above 10%, and group 4; patients whose HbA1c was above 10% from the start). Patients received intensive diabetes management with close monitoring and facilitated hospital visits. For further analysis, patients who were known to have previously had good metabolic control (either did not have diabetes or had previously had an HbA1c value < =7) and patients who had prior poor metabolic control were analyzed separately. Of the 195 participants [female, n = 84 (43.1%)], the median age was 54 years (inter-quantile range [IQR] = 15, min = 29, max = 80) and the median baseline HbA1c was 11.8% (IQR = 2.6%, min = 10%, max = 18.3%). The median duration of diabetes was 10 years (IQR = 9, min = 1, max = 35) when newly diagnosed patients were excluded. The ≥ 20% reduction in HbA1c at month 3 was observed in groups 1 to 4 in 97%, 88.1%, 69.1%, and 55.4%, respectively. The percentage of patients who achieved an HbA1c level of 7% or less was 60.6%, 38.1%, 16.4%, and 6.2% in the groups, respectively. The rate of those who achieved an HbA1c of 7% or less was nearly 50% of patients with type 2 diabetes mellitus who had previously had good metabolic control, whereas successful control was achieved in only 1 in 10 patients with persistently high HbA1c levels. Patients' glycemic history played an important role in determining their HbA1c levels at 3 months, suggesting that previous glycemic management patterns may indicate future success in diabetes control.

Risk factors associated with glycated hemoglobin A1c trajectories progressing to type 2 diabetes

BACKGROUND AND OBJECTIVE

The notion of prediabetes, defined by the ADA as glycated hemoglobin A1c (HbA1c) of 5.7-6.4%, implies increased vascular inflammatory and immunologic processes and higher risk for developing diabetes mellitus and major cardiovascular events. We aimed to determine the risk factors associated with rapid progression of normal and prediabetes patients to type 2 diabetes mellitus (T2DM).

METHODS

Retrospective cohort study in a single 8-hospital health system in southeast Michigan, between 2006 and 2020. All patients with HbA1c <6.5% at baseline and at least 2 other HbA1c measurements were clustered in five trajectories encompassing more than 95% of the study population. Multivariate linear regression analysis was performed to examine the association of demographic and comorbidities with HbA1c trajectories progressing to diabetes.

RESULTS

A total of 5,347 prediabetic patients were clustered based on their HbA1c progression (C1: 4,853, C2: 253, C66: 102, C12: 85, C68: 54). The largest cluster (C1) had a baseline median HbA1c value of 6.0% and exhibited stable HbA1c levels in prediabetic range across all subsequent years. The smallest cluster (C68) had the lowest median baseline HbA1c value and also remained stable across subsequent years. The proportion of normal HbA1c in each of the pre-diabetic trajectories ranged from 0 to 12.7%, whereas 81.5% of the reference cluster (C68) were normal HbA1c at baseline. The C2 (steady rising) trajectory was significantly associated with BMI (adj OR 1.10, 95%CI 1.03-1.17), and family history of DM (adj OR 2.75, 95%CI 1.32-5.74). With respect to the late rising trajectories, baseline BMI was significantly associated with both C66 and C12 trajectory (adj OR 1.10, 95%CI 1.03-1.18) and (adj OR 1.13, 95%CI 1.05-1.23) respectively, whereas, the C12 trajectory was also significantly associated with age (adj OR 1.62, 95%CI 1.04-2.53) and history of MACE (adj OR 3.20, 95%CI 1.14-8.93).

CONCLUSIONS

We suggest that perhaps a more aggressive preventative approach should be considered in patients with a family history of T2DM who have high BMI and year-to-year increase in HbA1c, whether they have normal hemoglobin A1c or they have prediabetes.KEY MESSAGESProgression to diabetes from normal or prediabetic hemoglobin A1c within four years is associated with baseline BMI.A steady rise in HbA1c during a four-year period is associated with age and family history of T2DM, whereas age and personal history of MACE are associated with a rapid rise in HbA1c.A more aggressive preventative approach should be considered in patients with a family history of T2DM who have high BMI and year-to-year increase in HbA1c.

Longitudinal trajectories of glycemic control among U.S. Adults with newly diagnosed diabetes

AIMS

To identify longitudinal trajectories of glycemic control among adults with newly diagnosed diabetes, overall and by diabetes type.

METHODS

We analyzed claims data from OptumLabs® Data Warehouse for 119,952 adults newly diagnosed diabetes between 2005 and 2018. We applied a novel Mixed Effects Machine Learning model to identify longitudinal trajectories of hemoglobin A1c (HbA1c) over 3 years of follow-up and used multinomial regression to characterize factors associated with each trajectory.

RESULTS

The study population was comprised of 119,952 adults with newly diagnosed diabetes, including 696 (0.58%) with type 1 diabetes. Among patients with type 1 diabetes, 52.6% were diagnosed at very high HbA1c, partially improved, but never achieved control; 32.5% were diagnosed at low HbA1c and deteriorated over time; and 14.9% had stable low HbA1c. Among patients with type 2 diabetes, 67.7% had stable low HbA1c, 14.4% were diagnosed at very high HbA1c, partially improved, but never achieved control; 10.0% were diagnosed at moderately high HbA1c and deteriorated over time; and 4.9% were diagnosed at moderately high HbA1c and improved over time.

CONCLUSIONS

Claims data identified distinct longitudinal trajectories of HbA1c after diabetes diagnosis, which can be used to anticipate challenges and individualize care plans to improve glycemic control.

Longitudinal HbA1c trajectory modelling reveals the association of HbA1c and risk of hospitalization for heart failure for patients with type 2 diabetes mellitus

BACKGROUND

Inconsistent conclusions in past studies on the association between poor glycaemic control and the risk of hospitalization for heart failure (HHF) have been reported largely due to the analysis of non-trajectory-based HbA1c values. Trajectory analysis can incorporate the effects of HbA1c variability across time, which may better elucidate its association with macrovascular complications. Furthermore, studies analysing the relationship between HbA1c trajectories from diabetes diagnosis and the occurrence of HHF are scarce.

METHODS

This is a prospective cohort study of the SingHealth Diabetes Registry (SDR). 17,389 patients diagnosed with type 2 diabetes mellitus (T2DM) from 2013 to 2016 with clinical records extending to the end of 2019 were included in the latent class growth analysis to extract longitudinal HbA1c trajectories. Association between HbA1c trajectories and risk of first known HHF is quantified with the Cox Proportional Hazards (PH) model.

RESULTS

5 distinct HbA1c trajectories were identified as 1. low stable (36.1%), 2. elevated stable (40.4%), 3. high decreasing (3.5%), 4. high with a sharp decline (10.8%), and 5. moderate decreasing (9.2%) over the study period of 7 years. Poorly controlled HbA1c trajectories (Classes 3, 4, and 5) are associated with a higher risk of HHF. Using the diabetes diagnosis time instead of a commonly used pre-defined study start time or time from recruitment has an impact on HbA1c clustering results.

CONCLUSIONS

Findings suggest that tracking the evolution of HbA1c with time has its importance in assessing the HHF risk of T2DM patients, and T2DM diagnosis time as a baseline is strongly recommended in HbA1c trajectory modelling. To the authors' knowledge, this is the first study to identify an association between HbA1c trajectories and HHF occurrence from diabetes diagnosis time.

The Impact of Dementia on Diabetes Control: An Evaluation of HbA1c Trajectories and Care Outcomes in Linked Primary and Specialist Care Data

OBJECTIVES

Diabetes self-care may become increasingly challenging as cognition declines. We sought to characterize glycated hemoglobin A1c (HbA1c) trajectories, markers of diabetes-related management, health care utilization, and mortality in people with preexisting type 2 diabetes (T2D) with and without dementia and based on the extent of cognitive impairment at the time of dementia diagnosis.

DESIGN

Retrospective matched cohort study.

SETTING AND PARTICIPANTS

Using a linkage between a primary care (Lambeth DataNet) and a secondary mental healthcare database, up to 5 individuals aged ≥65 y with preexisting T2D without dementia were matched to each individual with dementia based on age, sex, and general practice.

METHODS

Comparisons were made for HbA1c trajectories (linear mixed effects models), markers of diabetes-related management and severity at dementia diagnosis (logistic regression), mortality (Cox regression), and health care utilization (multilevel mixed effects binomial regression).

RESULTS

In 725 incident dementia and 3154 matched comparators, HbA1c trajectories differed by dementia status; HbA1c increased over time for mild dementia and non-dementia, but the increase was greater in the mild dementia group; for those with moderate-severe dementia, HbA1c decreased over time. Despite individuals with dementia having increased health care utilization around the time of dementia diagnosis, they were less likely to have had routine diabetes-related management. Patients with dementia had a higher prevalence of macrovascular complications and diabetes foot morbidity at dementia diagnosis and a higher mortality risk than those without dementia; these relationships were most marked in those with moderate-severe dementia.

CONCLUSIONS AND IMPLICATIONS

Our study has highlighted important differences in the monitoring, management, and control of diabetes in people with dementia. The effects of frailty and the extent of cognitive impairment on the ability to self-manage diabetes and on glycemic control may need to be considered in treatment guidelines and by primary care.

Great diversity in the utilization and reporting of latent growth modeling approaches in type 2 diabetes: A literature review

Introduction

The progression of complications of type 2 diabetes (T2D) is unique to each patient and can be depicted through individual temporal trajectories. Latent growth modeling approaches (latent growth mixture models [LGMM] or latent class growth analysis [LCGA]) can be used to classify similar individual trajectories in a priori non-observed groups (latent groups), sharing common characteristics. Although increasingly used in the field of T2D, many questions remain regarding the utilization of these methods.

Objective

To review the literature of longitudinal studies using latent growth modeling approaches to study T2D.

Methods

MEDLINE (Ovid), EMBASE, CINAHL and Wb of Science were searched through August 25th, 2021. Data was collected on the type of latent growth modeling approaches (LGMM or LCGA), characteristics of studies and quality of reporting using the GRoLTS-Checklist and presented as frequencies.

Results

From the 4,694 citations screened, a total of 38 studies were included. The studies were published beetween 2011 and 2021 and the length of follow-up ranged from 8 weeks to 14 years. Six studies used LGMM, while 32 studies used LCGA. The fields of research varied from clinical research, psychological science, healthcare utilization research and drug usage/pharmaco-epidemiology. Data sources included primary data (clinical trials, prospective/retrospective cohorts, surveys), or secondary data (health records/registries, medico-administrative). Fifty percent of studies evaluated trajectory groups as exposures for a subsequent clinical outcome, while 24% used predictive models of group membership and 5% used both. Regarding the quality of reporting, trajectory groups were adequately presented, however many studies failed to report important decisions made for the trajectory group identification.

Conclusion

Although LCGA were preferred, the contexts of utilization were diverse and unrelated to the type of methods. We recommend future authors to clearly report the decisions made regarding trajectory groups identification.

Data-driven long-term glycaemic control trajectories and their associated health and economic outcomes in Finnish patients with incident type 2 diabetes

Background

Treatments should be customized to patients to improve patients’ health outcomes and maximize the treatment benefits. We aimed to identify meaningful data-driven trajectories of incident type 2 diabetes patients with similarities in glycated haemoglobin (HbA1c) patterns since diagnosis and to examine their clinical and economic relevance.

Materials and methods

A cohort of 1540 patients diagnosed in 2011–2012 was retrieved from electronic health records covering primary and specialized healthcare in the North Karelia region, Finland. EHRs data were compiled with medication purchase data. Average HbA1c levels, use of medications, and incidence of micro- and macrovascular complications and deaths were measured annually for seven years since T2D diagnosis. Trajectories were identified applying latent class growth models. Differences in 4-year cumulative healthcare costs with 95% confidence intervals (CIs) were estimated with non-parametric bootstrapping.

Results

Four distinct trajectories of HbA1c development during 7 years after T2D diagnosis were extracted: patients with “Stable, adequate” (66.1%), “Slowly deteriorating” (24.3%), and “Rapidly deteriorating” glycaemic control (6.2%) as well as “Late diagnosed” patients (3.4%). During the same period, 2.2 (95% CI 1.9–2.6) deaths per 100 person-years occurred in the “Stable, adequate” trajectory increasing to 3.2 (2.4–4.0) in the “Slowly deteriorating”, 4.7 (3.1–6.9) in the “Rapidly deteriorating” and 5.2 (2.9–8.7) in the “Late diagnosed” trajectory. Similarly, 3.5 (95% CI 3.0–4.0) micro- and macrovascular complications per 100 person-years occurred in the “Stable, adequate” trajectory increasing to 5.1 (4.1–6.2) in the “Slowly deteriorating”, 5.5 (3.6–8.1) in the “Rapidly deteriorating” and 7.3 (4.3–11.8) in the “Late diagnosed” trajectory. Patients in the “Stable, adequate” trajectory had lower accumulated 4-year medication costs than other patients.

Conclusions

Data-driven patient trajectories have clinical and economic relevance and could be utilized as a step towards personalized medicine instead of the common “one-fits-for-all” treatment practices.

Effect of Hemoglobin A1c Trajectories on Future Outcomes in a 10-Year Cohort With Type 2 Diabetes Mellitus

BACKGROUND

Hemoglobin A1c (HbA1c) variability may be a predictor of diabetic complications, but the predictive values of HbA1c trajectories remain unclear. We aimed to classify long-term HbA1c trajectories and to explore their effects on future clinical outcomes in a 10-year cohort with type 2 diabetes mellitus (T2DM).

METHODS

A total of 2,161 participants with T2DM from the Beijing Community Diabetes Study were included. The 10-year follow-up was divided into two stages for the present data analysis. Stage I (from 2008 to 2014) was used to identify the HbA1c trajectories and to calculate the adjusted SD of HbA1c (HbA1c-adjSD), or the coefficient of variation of HbA1c (HbA1c-CV). Stage II (from 2014 to 2018) was used to collect the records of the new occurrence of diabetes-related clinical outcomes. Latent growth mixture models were used to identify HbA1c trajectories. Cox proportional hazards models were used to explore the relationship between HbA1c trajectories, HbA1c-adjSD, or HbA1c-CV and the future outcomes.

RESULTS

Three HbA1c trajectories were identified, including low stable (88.34%), gradual decreasing (5.83%), and pre-stable and post-increase (5.83%). Either the risk of death or the chronic complications were significantly higher in the latter two groups compared to the low stable group after adjustment for average HbA1c and other traditional risk factors, the adjusted hazard ratios (HRs) for renal events, composite endpoint, and all-cause death for the pre-stable and post-increase group were 2.83 [95%CI: 1.25-6.41, p = 0.013], 1.85 (95%CI: 1.10-3.10, p = 0.020), and 3.01 (95%CI: 1.13-8.07, p = 0.028), respectively, and the adjusted HR for renal events for the gradual decreasing group was 2.37 (95%CI: 1.08-5.21, p = 0.032). In addition, both univariate and multivariate Cox HR models indicated that participants in the fourth and third quartiles of HbA1c-CV or HbA1c-adjSD were at higher risk of renal events compared to participants in the first quartile.

CONCLUSIONS

HbA1c trajectories, HbA1c-CV, and HbA1c-adjSD could all predict diabetes-related clinical outcomes. HbA1c trajectories could reflect long-term blood glucose fluctuation more intuitively, and non-stable HbA1c trajectories may predict increased risk of renal events, all-cause death, and composite endpoint events, independent of average HbA1c.

Trajectories of glycemic control with clinical pharmacy specialist management of veterans with type 2 diabetes

BACKGROUND

Improved control of glycemic control likely lowers the risk of diabetes complications and clinical pharmacy specialist (CPS) services can improve glycemic control. Though the pattern of control may also matter in terms of outcomes.

OBJECTIVES

The objective of this study was to examine the longitudinal trajectories of HbA1c among a large population of Veterans with type 2 diabetes who received CPS-led diabetes management.

METHODS

This is an observational, multicenter cohort study of Veterans with type-2 diabetes managed by CPSs between 7/1/2013 and 7/1/2017 with baseline glycosylated hemoglobin (HbA1c) level ≥8%. Two years of HbA1c measurements were used to group patients into distinct patterns of HbA1c trajectories over time using group-based trajectory modeling. Characteristics associated with successful HbA1c trajectories and association of assigned trajectories with all-cause and diabetes-related hospitalizations were analyzed using logistic regression.

RESULTS

A total of 4119 Veterans were included and able to be successfully segmented into six distinct HbA1c trajectory groups over time: High Gradually Decreasing (n = 325, 7.9%), Moderate Early Decline (n = 1692, 41.1%), Large Early Decline (n = 231, 5.6%), Uncontrolled Stable (n = 1468, 35.6%), Early Decline/Subsequent Increase (n = 266, 6.5%), and Very Uncontrolled Stable (n = 137, 3.3%). The distinguishing factor between successful and less successful trajectories appears to be the progress made within the first six months of pharmacist management.

CONCLUSIONS

Significant variability exists in the pattern of glycemic control over time of type 2 diabetes patients managed by clinical pharmacy specialists. Limited resources should be first prioritized to managing patients with very elevated HbA1c and into the first six months of CPS management.

A Computational Method for Learning Disease Trajectories From Partially Observable EHR Data

Diseases can show different courses of progression even when patients share the same risk factors. Recent studies have revealed that the use of trajectories, the order in which diseases manifest throughout life, can be predictive of the course of progression. In this study, we propose a novel computational method for learning disease trajectories from EHR data. The proposed method consists of three parts: first, we propose an algorithm for extracting trajectories from EHR data; second, three criteria for filtering trajectories; and third, a likelihood function for assessing the risk of developing a set of outcomes given a trajectory set. We applied our methods to extract a set of disease trajectories from Mayo Clinic EHR data and evaluated it internally based on log-likelihood, which can be interpreted as the trajectories' ability to explain the observed (partial) disease progressions. We then externally evaluated the trajectories on EHR data from an independent health system, M Health Fairview. The proposed algorithm extracted a comprehensive set of disease trajectories that can explain the observed outcomes substantially better than competing methods and the proposed filtering criteria selected a small subset of disease trajectories that are highly interpretable and suffered only a minimal (relative 5%) loss of the ability to explain disease progression in both the internal and external validation.

Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review

Background

Population segmentation permits the division of a heterogeneous population into relatively homogenous subgroups. This scoping review aims to summarize the clinical applications of data driven and expert driven population segmentation among Type 2 diabetes mellitus (T2DM) patients.

Methods

The literature search was conducted in Medline®, Embase®, Scopus® and PsycInfo®. Articles which utilized expert-based or data-driven population segmentation methodologies for evaluation of outcomes among T2DM patients were included. Population segmentation variables were grouped into five domains (socio-demographic, diabetes related, non-diabetes medical related, psychiatric / psychological and health system related variables). A framework for PopulAtion Segmentation Study design for T2DM patients (PASS-T2DM) was proposed.

Results

Of 155,124 articles screened, 148 articles were included. Expert driven population segmentation approach was most commonly used, of which judgemental splitting was the main strategy employed (n = 111, 75.0%). Cluster based analyses (n = 37, 25.0%) was the main data driven population segmentation strategies utilized. Socio-demographic (n = 66, 44.6%), diabetes related (n = 54, 36.5%) and non-diabetes medical related (n = 18, 12.2%) were the most used domains. Specifically, patients’ race, age, Hba1c related parameters and depression / anxiety related variables were most frequently used. Health grouping/profiling (n = 71, 48%), assessment of diabetes related complications (n = 57, 38.5%) and non-diabetes metabolic derangements (n = 42, 28.4%) were the most frequent population segmentation objectives of the studies.

Conclusions

Population segmentation has a wide range of clinical applications for evaluating clinical outcomes among T2DM patients. More studies are required to identify the optimal set of population segmentation framework for T2DM patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01209-w.

Predictors of treatment failure during the first year in newly diagnosed type 2 diabetes patients: a retrospective, observational study

Background

Diabetes patients who fail to achieve early glycemic control may increase the future risk of complications and mortality. The aim of the study was to identify factors that predict treatment failure (TF) during the first year in adults with newly diagnosed type 2 diabetes mellitus (T2DM).

Methods

This retrospective cohort study conducted at a medical center in Taiwan enrolled 4,282 eligible patients with newly diagnosed T2DM between 2002 and 2017. Data were collected from electronic medical records. TF was defined as the HbA1c value >7% at the end of 1-year observation. A subgroup analysis of 2,392 patients with baseline HbA1c ≥8% was performed. Multivariable logistic regression analysis using backward elimination was applied to establish prediction models.

Results

Of all study participants, 1,439 (33.6%) were classified as TF during the first year. For every 1% increase in baseline HbA1c, the risk of TF was 1.17 (95% CI 1.15–1.20) times higher. Patients with baseline HbA1c ≥8% had a higher rate of TF than those with HbA1c <8% (42.0 vs 23.0%, p < 0.001). Medication adherence, self-monitoring of blood glucose (SMBG), regular exercise, gender (men), non-insulin treatment, and enrollment during 2010–2017 predicted a significant lower risk of TF in both of the primary and subgroup models.

Conclusions

Newly diagnosed diabetes patients with baseline HbA1c ≥8% did have a much higher rate of TF during the first year. Subgroup analysis for them highlights the important predictors of TF, including medication adherence, performing SMBG, regular exercise, and gender, in achieving glycemic control.

Association of Glycemic Control Trajectory with Short-Term Mortality in Diabetes Patients with High Cardiovascular Risk: a Joint Latent Class Modeling Study

BACKGROUND

The relationship between risk factor or biomarker trajectories and contemporaneous short-term clinical outcomes is poorly understood. In diabetes patients, it is unknown whether hemoglobin A1c (HbA1c) trajectories are associated with clinical outcomes and can inform care in scenarios in which a single HbA1c is uninformative, for example, after a diagnosis of coronary artery disease (CAD).

OBJECTIVE

To compare associations of HbA1c trajectories and single HbA1c values with short-term mortality in diabetes patients evaluated for CAD DESIGN: Retrospective observational cohort study PARTICIPANTS: Diabetes patients (n = 7780) with and without angiographically defined CAD MAIN MEASURES: We used joint latent class mixed models to simultaneously fit HbA1c trajectories and estimate association with 2-year mortality after cardiac catheterization, adjusting for clinical and demographic covariates.

KEY RESULTS

Three HBA1c trajectory classes were identified: individuals with stable glycemia (class A; n = 6934 [89%]; mean baseline HbA1c 6.9%), with declining HbA1c (class B; n = 364 [4.7%]; mean baseline HbA1c 11.6%), and with increasing HbA1c (class C; n = 482 [6.2%]; mean baseline HbA1c 8.5%). HbA1c trajectory class was associated with adjusted 2-year mortality (3.0% [95% CI 2.8, 3.2] for class A, 3.1% [2.1, 4.2] for class B, and 4.2% [3.4, 4.9] for class C; global P = 0.047, P = 0.03 comparing classes A and C, P > 0.05 for other pairwise comparisons). Baseline HbA1c was not associated with 2-year mortality (P = 0.85; hazard ratios 1.01 [0.96, 1.06] and 1.02 [0.95, 1.10] for HbA1c 7-9% and ≥ 9%, respectively, relative to HbA1c < 7%). The association between HbA1c trajectories and mortality did not differ between those with and without CAD (interaction P = 0.1).

CONCLUSIONS

In clinical settings where single HbA1c measurements provide limited information, HbA1c trajectories may help stratify risk of complications in diabetes patients. Joint latent class modeling provides a generalizable approach to examining relationships between biomarker trajectories and clinical outcomes in the era of near-universal adoption of electronic health records.

Trajectories of glycaemia following acute pancreatitis: a prospective longitudinal cohort study with 24 months follow-up

BACKGROUND

New-onset diabetes is the most common sequela of acute pancreatitis (AP). Yet, prospective changes in glycaemia over time have never been investigated comprehensively in this study population. The primary aim was to determine the cumulative incidence of new-onset prediabetes and new-onset diabetes after AP over 24 months of follow-up in a prospective cohort study. The secondary aim was to identify trajectories of glycaemia during follow-up and their predictors at the time of hospitalisation.

METHODS

Patients with a prospective diagnosis of AP and no diabetes based on the American Diabetes Association criteria were followed up every 6 months up to 24 months after hospital discharge. Incidence of new-onset prediabetes/diabetes over each follow-up period was calculated. Group-based trajectory modelling was used to identify common changes in glycaemia. Multinomial regression analyses were conducted to investigate the associations between a wide array of routinely available demographic, anthropometric, laboratory, imaging, and clinical factors and membership in the trajectory groups.

RESULTS

A total of 152 patients without diabetes were followed up. The cumulative incidence of new-onset prediabetes and diabetes was 20% at 6 months after hospitalisation and 43% over 24 months of follow-up (p trend < 0.001). Three discrete trajectories of glycaemia were identified: normal-stable glycaemia (32%), moderate-stable glycaemia (60%), and high-increasing glycaemia (8%). Waist circumference was a significant predictor of moderate-stable glycaemia. None of the studied predictors were significantly associated with high-increasing glycaemia.

CONCLUSIONS

This first prospective cohort study of changes in glycaemia (determined at structured time points in unselected AP patients) showed that at least one out of five patients develops new-onset prediabetes or diabetes at 6 months of follow-up and more than four out of ten-in the first 2 years. Changes in glycaemia after AP follow three discrete trajectories. This may inform prevention or early detection of critical changes in blood glucose metabolism following an attack of AP and, hence, reduce the burden of new-onset diabetes after acute pancreatitis.

Altered levels of MALAT1 and H19 derived from serum or serum exosomes associated with type-2 diabetes

Environmental, genetic and epigenetic risk factors have been closely related to the development of type-2 diabetes (T2D). It has been reported that the expression in H19 and MALAT1 are related to metabolic diseases. To analyze the relationship between the expression of H19 and MALAT1 lncRNAs with diabetic patients. A study was conducted in subjects with T2D and nondiabetic controls, residents of Mexico City. Anthropometric measurements were made, and serum concentrations of glucose, glycosylated hemoglobin, total cholesterol, triglycerides, high- and low-density lipoprotein cholesterol were analyzed. Total RNA was extracted from serum and serum exosomes. The H19 and MALAT1 expression levels were quantified by RT-qPCR. A significant reduction in the expression of MALAT1 from serum or serum exosomes were found in patients with T2D, metabolic syndrome and low levels of HDL-c. Significant increase in H19 levels was found in diabetic subjects with poor glycemic control. Additionally, the principal component analyzes showed that serum MALAT1 expression was associated with total cholesterol and HDL-c levels, and the exosomes H19 expression was associated with waist circumference. The results obtained suggest that MALAT1 expression levels could be an epigenetic biomarker of diabetes risk or of its comorbidities.

Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms

AIM

To assess the potential of supervised machine-learning techniques to identify clinical variables for predicting short-term and long-term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

We included patients with T2DM from the Groningen Initiative to Analyse Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007 and 2013 and had a minimum follow-up of 2 years. Short- and long-term responses at 6 (±2) and 24 (±2) months after insulin initiation, respectively, were assessed. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5 mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty-four baseline clinical variables were used for the analysis and an elastic net regularization technique was used for variable selection. The performance of three traditional machine-learning algorithms was compared for the prediction of short- and long-term responses and the area under the receiver-operating characteristic curve (AUC) was used to assess the performance of the prediction models.

RESULTS

The elastic net regularization-based generalized linear model, which included baseline HbA1c and estimated glomerular filtration rate, correctly classified short- and long-term HbA1c response after treatment initiation, with AUCs of 0.80 (95% CI 0.78-0.83) and 0.81 (95% CI 0.79-0.84), respectively, and outperformed the other machine-learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (95% CI 0.65-0.73) and 0.72 (95% CI 0.66-0.75) was obtained for predicting short-term and long-term response, respectively.

CONCLUSIONS

Machine-learning algorithm performed well in the prediction of an individual's short-term and long-term HbA1c response using baseline clinical variables.

Innovations in personalised diabetes care and risk management

Type 2 diabetes is associated with an increased risk of developing macro and microvascular complications. Nevertheless, there is substantial heterogeneity between people with type 2 diabetes in their risk of developing such complications. Personalised medicine for people with type 2 diabetes may aid in efficient and tailored diabetes care for those at increased risk of developing such complications. Recently, progress has been made in the development of personalised diabetes care in several areas. Particularly for the risk prediction of cardiovascular disease, retinopathy and nephropathy, innovative methods have been developed for prediction and tailored monitoring or treatment to prevent such complications. For other complications or subpopulations of people with type 2 diabetes, such as the frail elderly, efforts are currently ongoing to develop such methods. In this review, we discuss the recent developments in innovations of personalised diabetes care for different complications and subpopulations of people with type 2 diabetes, their performance and modes of application in clinical practice.

Distinct trajectories of HbA1c in newly diagnosed Type 2 diabetes from the DPV registry using a longitudinal group-based modelling approach

AIM

To identify groups of heterogeneous HbA_1c trajectories over time in newly diagnosed Type 2 diabetes.

METHODS

The study comprised 6355 adults with newly diagnosed Type 2 diabetes (55% men, median age 62 years, baseline BMI 31 kg/m² ) from the Diabetes Patienten Verlaufsdokumentation (DPV) prospective multicentre diabetes registry (Germany, Austria). Individuals were assessed during the first 5 years after diabetes diagnosis if they had ≥ 3 aggregated HbA_1c measurements during follow-up. Latent class growth modelling was used to determine distinct subgroups that followed similar longitudinal HbA_1c patterns (SAS: Proc Traj). Multinomial logistic regression models were used to investigate which variables were associated with the respective HbA_1c trajectory groups.

RESULTS

Four distinct longitudinal HbA_1c trajectory (glycaemic control) groups were found. The largest group (56% of participants) maintained stable good glycaemic control (HbA_1c 42-45 mmol/mol). Twenty-six percent maintained stable moderate glycaemic control (HbA_1c 57-62 mmol/mol). A third group (12%) initially showed severe hyperglycaemia (HbA_1c 97 mmol/mol) but reached good glycaemic control within 1 year. The smallest group (6%) showed stable poor glycaemic control (HbA_1c 79-88 mmol/mol). Younger age at diabetes diagnosis, male sex, and higher BMI were associated with the stable moderate or poor glycaemic control groups. Insulin therapy was strongly associated with the highly improved glycaemic control group.

CONCLUSIONS

Four subgroups with distinct HbA_1c trajectories were determined in newly diagnosed Type 2 diabetes using a group-based modelling approach. Approximately one-third of people with newly diagnosed Type 2 diabetes need either better medication adherence or earlier intensification of glucose-lowering therapy.

Relevant patient characteristics for estimating healthcare needs according to healthcare providers and people with type 2 diabetes: a Delphi survey

Background

Recently, there has been growing interest in providing more tailored, patient-centered care for the treatment of type 2 diabetes mellitus (T2DM). Yet it remains unclear which patient characteristics should be determined to guide such an approach. Therefore, the opinions of healthcare providers (HCP) and people with T2DM about relevant patient characteristics for estimating healthcare needs of people with T2DM were assessed and compared.

Methods

Two separate online Delphi studies were conducted according to the RAND-UCLA Appropriateness Method: one with HCPs (n = 22) from Dutch primary and secondary care and one with people with T2DM treated in Dutch primary care (n = 46). The relevance of patient characteristics for estimating healthcare needs, defined as the number of yearly consultations, was assessed on a 5-point Likert scale. Characteristics with a median of 4 or 5 and an interquartile range ≤ 1.5 were considered relevant with consensus. Participants were also asked to select the top 5 of most relevant patient characteristics. To determine the overall top 5, the mean relative importance score of each characteristic was calculated.

Results

In two Delphi rounds, 28 and 15 patient characteristics were rated by HCPs and people with T2DM, respectively. Both HCPs and people with T2DM found health-related characteristics relevant for estimating healthcare needs of people with T2DM. However, HCPs preferred to estimate healthcare needs using person- and context-related characteristics. They ranked self-efficacy as the most relevant estimator. In contrast, people with T2DM were more in favor of health-related characteristics and ranked HbA1c as the most relevant estimator.

Conclusions

The findings show that there is discrepancy in opinions on relevant patient characteristics for estimating healthcare needs between HCPs and people with T2DM. To achieve more tailored, patient-centered care, it is important that both groups agree on the topics to be discussed during patient consultations.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4371-z) contains supplementary material, which is available to authorized users.

Blood Metabolomic Measures Associate With Present and Future Glycemic Control in Type 2 Diabetes

OBJECTIVE

We studied whether blood metabolomic measures in people with type 2 diabetes (T2D) are associated with insufficient glycemic control and whether this association is influenced differentially by various diabetes drugs. We then tested whether the same metabolomic profiles were associated with the initiation of insulin therapy.

METHODS

A total of 162 metabolomic measures were analyzed using a nuclear magnetic resonance-based method in people with T2D from four cohort studies (n = 2641) and one replication cohort (n = 395). Linear and logistic regression analyses with adjustment for potential confounders, followed by meta-analyses, were performed to analyze associations with hemoglobin A1c levels, six glucose-lowering drug categories, and insulin initiation during a 7-year follow-up period (n = 698).

RESULTS

After Bonferroni correction, 26 measures were associated with insufficient glycemic control (HbA1c >53 mmol/mol). The strongest association was with glutamine (OR, 0.66; 95% CI, 0.61 to 0.73; P = 7.6 × 10-19). In addition, compared with treatment-naive patients, 31 metabolomic measures were associated with glucose-lowering drug use (representing various metabolite categories; P ≤ 3.1 × 10-4 for all). In drug-stratified analyses, associations with insufficient glycemic control were only mildly affected by different glucose-lowering drugs. Five of the 26 metabolomic measures (apolipoprotein A1 and medium high-density lipoprotein subclasses) were also associated with insulin initiation during follow-up in both discovery and replication. The strongest association was observed for medium high-density lipoprotein cholesteryl ester (OR, 0.54; 95% CI, 0.42 to 0.71; P = 4.5 × 10-6).

CONCLUSION

Blood metabolomic measures were associated with present and future glycemic control and might thus provide relevant cues to identify those at increased risk of treatment failure.

Longitudinal trends in HbA1c patterns and association with outcomes: A systematic review

BACKGROUND

This study aimed to review studies that identified patterns of longitudinal HbA1c trends in patients with diabetes and to summarize factors and outcomes associated with distinct trajectory patterns.

METHODS

PubMed and Web of Science were systematically searched for studies examining HbA1c trends among patients with diabetes from database inception through September 2017. Articles were included if they met the following inclusion criteria: (a) longitudinal study of subjects with diabetes only, (b) use of serial measurements of HbA1c , and (c) analysis of the trend of HbA1c using group-based trajectory approaches.

RESULTS

Twenty studies were included, 11 on type 1 diabetes and 9 on type 2 diabetes. These studies identified 2 to 6 HbA1c trajectory patterns. The most commonly identified patterns included stable HbA1c around 7.0% and at levels between 8.0% and 9.9%, which usually captured the HbA1c pattern among the majority of subjects in the study population. Unstable patterns identified included increasing HbA1c trend, decreasing HbA1c trend, and non-linear patterns. These patterns were associated with differential risk of disease outcomes, over and beyond single-point HbA1c measures. Age, gender, ethnicity, diabetes duration, disease management frequency, cardiovascular risk factors, insulin treatment, family environment, and psychosocial factors were the most frequently reported factors associated with membership of specific HbA1c pattern groups.

CONCLUSION

Common patterns of longitudinal HbA1c trends were identified despite heterogeneity among the studies. A better understanding of what underlies these different patterns may provide opportunities to tailor therapies and care for these patients to reduce adverse outcomes.

HbA1c response after insulin initiation in patients with type 2 diabetes mellitus in real life practice: Identifying distinct subgroups

AIMS

To identify subgroups of patients with type 2 diabetes mellitus (T2DM) following distinct trajectories of HbA1c after insulin initiation and explore underlying differences in clinical characteristics.

MATERIALS AND METHODS

A cohort study was conducted in patients with T2DM initiating insulin in 2007-2013 with a follow-up of 2 to 4 years. Data were collected from the Groningen Initiative to Analyze Type 2 Diabetes Treatment (GIANTT) database. The primary outcome was subgroups with different trajectories of HbA1c patterns after insulin initiation, as identified by latent class growth modeling. Differences between subgroups were tested using one-way ANOVA, Kruskal-Wallis or chi-square tests, where appropriate.

RESULTS

From 1459 patients, three subgroups with distinct HbA1c patterns were identified. Group 1 (8%) initially showed a moderate decrease followed by an increase in HbA1c 2 years later, despite receiving more comedication. Group 2 (84%) showed a stable decrease. Group 3 (8%) had a high initial level of HbA1c and a rapid decline within the first year, followed by a slow increase thereafter. Group 1 patients were on average 6-7 years younger than patients in groups 2 and 3 and were more likely to receive sulfonylureas than Group 3 patients. Group 3 patients had a shorter diabetes duration and were less well-controlled for HbA1c, systolic blood pressure and LDL-cholesterol at insulin initiation.

CONCLUSIONS

Most patients showed a stable HbA1c response, but one out of six patients showed either a poor response, or a rapid initial response only after insulin initiation. Response patterns were associated with age, diabetes duration and risk-factor controls at the time of insulin initiation.

A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

AIM

To identify, predict and validate distinct glycaemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care, as a first step towards more effective patient-centred care.

METHODS

We conducted a retrospective study in two cohorts, using routinely collected individual patient data from primary care practices obtained from two large Dutch diabetes patient registries. Participants included adult patients newly diagnosed with type 2 diabetes between January 2006 and December 2014 (development cohort, n = 10 528; validation cohort, n = 3777). Latent growth mixture modelling identified distinct glycaemic 5-year trajectories. Machine learning models were built to predict the trajectories using easily obtainable patient characteristics in daily clinical practice.

RESULTS

Three different glycaemic trajectories were identified: (1) stable, adequate glycaemic control (76.5% of patients); (2) improved glycaemic control (21.3% of patients); and (3) deteriorated glycaemic control (2.2% of patients). Similar trajectories could be discerned in the validation cohort. Body mass index and glycated haemoglobin and triglyceride levels were the most important predictors of trajectory membership. The predictive model, trained on the development cohort, had a receiver-operating characteristic area under the curve of 0.96 in the validation cohort, indicating excellent accuracy.

CONCLUSIONS

The developed model can effectively explain heterogeneity in future glycaemic response of patients with type 2 diabetes. It can therefore be used in clinical practice as a quick and easy tool to provide tailored diabetes care.

HbA1c is associated with altered expression in blood of cell cycle- and immune response-related genes

AIMS/HYPOTHESIS

Individuals with type 2 diabetes are heterogeneous in their glycaemic control as tracked by blood HbA_1c levels. Here, we investigated the extent to which gene expression levels in blood reflect current and future HbA_1c levels.

METHODS

HbA_1c levels at baseline and 1 and 2 year follow-up were compared with gene expression levels in 391 individuals with type 2 diabetes from the Hoorn Diabetes Care System Cohort (15,564 genes, RNA sequencing). The functions of associated baseline genes were investigated further using pathway enrichment analysis. Using publicly available data, we investigated whether the genes identified are also associated with HbA_1c in the target tissues, muscle and pancreas.

RESULTS

At baseline, 220 genes (1.4%) were associated with baseline HbA_1c. Identified genes were enriched for cell cycle and complement system activation pathways. The association of 15 genes extended to the target tissues, muscle (n = 113) and pancreatic islets (n = 115). At follow-up, expression of 25 genes (0.16%) associated with 1 year HbA_1c and nine genes (0.06%) with 2 year HbA_1c. Five genes overlapped across all time points, and 18 additional genes between baseline and 1 year follow-up. After adjustment for baseline HbA_1c, the number of significant genes at 1 and 2 years markedly decreased, suggesting that gene expression levels in whole blood reflect the current glycaemic state and but not necessarily the future glycaemic state.

CONCLUSIONS/INTERPRETATION

HbA_1c levels in individuals with type 2 diabetes are associated with expression levels of genes that link to the cell cycle and complement system activation.

Longitudinal trends in HbA1c and associations with comorbidity and all-cause mortality in Asian patients with type 2 diabetes: A cohort study

AIMS

This study examined longitudinal trends in HbA1c in a multi-ethnic Asian cohort of diabetes patients, and the associations of these trends with future risk of acute myocardial infarction (AMI), stroke, end stage renal failure (ESRD) and all-cause mortality.

METHODS

6079 participants with type 2 diabetes mellitus in Singapore were included. HbA1c measurements for the five years previous to recruitment were used to identify patterns of HbA1c trends. Outcomes were recorded through linkage with the National Disease Registry. The median follow-up for longitudinal trends in HbA1c was 4.1years and for outcomes was between 7.0 and 8.3years. HbA1c patterns were identified using latent class growth modeling, and associations with outcomes were analyzed using Cox proportional hazards models.

RESULTS

Four distinct HbA1c patterns were observed; "low-stable" (72·2%), "moderate-stable" (22·0%), "moderate-increase" (2·9%), and "high-decrease" (2·8%). The risk of comorbidities and death was significantly higher in moderate-increase and high-decrease groups compared to the low-stable group; the hazard ratios for stroke, ESRD, and death for moderate increase group were 3.22 (95%CI 1.27-8.15), 4.76 (95%CI 1.92-11.83), and 1.88 (95%CI 1.15-3.07), respectively, and for high-decrease group were 2.16 (95%CI 1.02-4.57), 3.05 (95%CI 1.54-6.07), and 2.79 (95%CI 1.97-3.95), respectively. Individuals in the moderate-increase group were significantly younger, with longer diabetes duration, and greater proportions of Malays and Indians.

CONCLUSIONS

Deteriorating HbA1c pattern and extremely high initial HbA1c are associated with increased risk of long-term comorbidities and death. Therapeutic interventions to alter longitudinal HbA1c trends may be helpful in reducing this risk.

Trajectories of Glycemic Change in a National Cohort of Adults With Previously Controlled Type 2 Diabetes

BACKGROUND

Individualized diabetes management would benefit from prospectively identifying well-controlled patients at risk of losing glycemic control.

OBJECTIVES

To identify patterns of hemoglobin A1c (HbA1c) change among patients with stable controlled diabetes.

RESEARCH DESIGN

Cohort study using OptumLabs Data Warehouse, 2001-2013. We develop and apply a machine learning framework that uses a Bayesian estimation of the mixture of generalized linear mixed effect models to discover glycemic trajectories, and a random forest feature contribution method to identify patient characteristics predictive of their future glycemic trajectories.

SUBJECTS

The study cohort consisted of 27,005 US adults with type 2 diabetes, age 18 years and older, and stable index HbA1c <7.0%.

MEASURES

HbA1c values during 24 months of observation.

RESULTS

We compared models with k=1, 2, 3, 4, 5 trajectories and baseline variables including patient age, sex, race/ethnicity, comorbidities, medications, and HbA1c. The k=3 model had the best fit, reflecting 3 distinct trajectories of glycemic change: (T1) rapidly deteriorating HbA1c among 302 (1.1%) youngest (mean, 55.2 y) patients with lowest mean baseline HbA1c, 6.05%; (T2) gradually deteriorating HbA1c among 902 (3.3%) patients (mean, 56.5 y) with highest mean baseline HbA1c, 6.53%; and (T3) stable glycemic control among 25,800 (95.5%) oldest (mean, 58.5 y) patients with mean baseline HbA1c 6.21%. After 24 months, HbA1c rose to 8.75% in T1 and 8.40% in T2, but remained stable at 6.56% in T3.

CONCLUSIONS

Patients with controlled type 2 diabetes follow 3 distinct trajectories of glycemic control. This novel application of advanced analytic methods can facilitate individualized and population diabetes care by proactively identifying high risk patients.

The Hoorn Diabetes Care System (DCS) cohort. A prospective cohort of persons with type 2 diabetes treated in primary care in the Netherlands

PURPOSE

People with type 2 diabetes (T2D) have a doubled morbidity and mortality risk compared with persons with normal glucose tolerance. Despite treatment, clinical targets for cardiovascular risk factors are not achieved. The Hoorn Diabetes Care System cohort (DCS) is a prospective cohort representing a comprehensive dataset on the natural course of T2D, with repeated clinical measures and outcomes. In this paper, we describe the design of the DCS cohort.

PARTICIPANTS

The DCS consists of persons with T2D in primary care from the West-Friesland region of the Netherlands. Enrolment in the cohort started in 1998 and this prospective dynamic cohort currently holds 12 673 persons with T2D.

FINDINGS TO DATE

Clinical measures are collected annually, with a high internal validity due to the centrally organised standardised examinations. Microvascular complications are assessed by measuring kidney function, and screening feet and eyes. Information on cardiovascular disease is obtained by 1) self-report, 2) electrocardiography and 3) electronic patient records. In subgroups of the cohort, biobanking and additional measurements were performed to obtain information on, for example, lifestyle, depression and genomics. Finally, the DCS cohort is linked to national cancer and all-cause mortality registers. A selection of published findings from the DCS includes identification of subgroups with distinct development of haemoglobin A1c, blood pressure and retinopathy, and their predictors; validation of a prediction model for personalised retinopathy screening; the assessment of the role of genetics in development and treatment of T2D, providing options for personalised medicine.

FUTURE PLANS

We will continue with the inclusion of persons with newly diagnosed T2D, follow-up of persons in the cohort and linkage to morbidity and mortality registries. Currently, we are involved in (inter)national projects on, among others, biomarkers and prediction models for T2D and complications and we are interested in collaborations with external researchers.

TRIAL REGISTRATION

ISRCTN26257579.

Microalbuminuria in Type-2 Diabetes Mellitus; the tip of iceberg of diabetic complications

OBJECTIVE

To determine the prevalence of microalbuminuria and its association with hypertension and other diabetic complications among Type-2 diabetic patients attending at Aga Khan University Hospital Karachi.

METHODS

1280 Type-2 diabetes patients who visited the outpatient department of Aga Khan University Hospital from September 2014 to August 2016 were included in the study. Microalbuminuria was diagnosed if spot urinary microalbumin excretion was confirmed to be more than 20mg/l. Hypertension was diagnosed if BP >140/90 or already on antihypertensive medications. Other demographic, clinical and laboratory data were also recorded.

RESULTS

Microalbuminuria was diagnosed in 404(31.56%) patients and among these albuminuric patients 335(82.9%) had hypertension. They were also dyslipidemic, having raised triglyceride levels, lower HDL levels, with more prevalence of background diabetic retinopathy and peripheral neuropathy. They also showed higher HbA1C levels and longer duration of diabetes.

CONCLUSION

The prevalence of the microalbuminuria in our patients with Type-2 diabetes is 31.56% and is not only an early sign of diabetic nephropathy but also a host of other diabetic complications and should be dealt early with strict control of their hyperglycemia and hypertension to help prevent the future complications.

An alternative approach to modelling HbA1c trajectories in patients with type 2 diabetes mellitus

AIMS

Time-dependent HbA1c trajectories in health economic models of type 2 diabetes mellitus (T2DM) are typically informed by the UK Prospective Diabetes Study (UKPDS). However, this approach may not accurately predict HbA1c progression in patients who do not conform to the demographic profile of the original UKPDS cohort. This study aimed to develop an alternative mathematical model (MM) to simulate HbA1c progression in T2DM.

MATERIALS AND METHODS

A systematic literature review identified studies, published between 2005 and 2015, that reported HbA1c in adult T2DM patients over a minimum duration of 18 months. Pooled data from eligible studies were used to develop an alternative MM equation for HbA1c progression, which was then contrasted with the UKPDS 68 progression equation in illustrative scenarios.

RESULTS

A total of 68 studies were eligible for data extraction (mean follow-up time 4.1 years). HbA1c progression was highly heterogeneous across studies, varying with baseline HbA1c, treatment group and patient age. The MM equation was fitted with parameters for mean baseline HbA1c (8.3%), initial change in HbA1c (-0.62%) and upper quartile of maximum observed HbA1c (9.3%). Differences in HbA1c trajectories between the MM and UKPDS approaches altered the timing of therapy escalation in illustrative scenarios.

CONCLUSIONS

The MM represents an alternative approach to simulate HbA1c trajectories in T2DM models, as UKPDS data may not adequately reflect the heterogeneity of HbA1c profiles observed in clinical studies. However, the choice of approach should ultimately be determined by the characteristics of individual patients under consideration and the clinical face validity of the modelled trajectories.

Longitudinal Trajectories of Metabolic Control From Childhood to Young Adulthood in Type 1 Diabetes From a Large German/Austrian Registry: A Group-Based Modeling Approach

OBJECTIVE

Worsening of glycemic control in type 1 diabetes during puberty is a common observation. However, HbA_1c remains stable or even improves for some youths. The aim is to identify distinct patterns of glycemic control in type 1 diabetes from childhood to young adulthood.

RESEARCH DESIGN AND METHODS

A total of 6,433 patients with type 1 diabetes were selected from the prospective, multicenter diabetes patient registry Diabetes-Patienten-Verlaufsdokumentation (DPV) (follow-up from age 8 to 19 years, baseline diabetes duration ≥2 years, HbA_1c aggregated per year of life). We used latent class growth modeling as the trajectory approach to determine distinct subgroups following a similar trajectory for HbA_1c over time.

RESULTS

Five distinct longitudinal trajectories of HbA_1c were determined, comprising group 1 = 40%, group 2 = 27%, group 3 = 15%, group 4 = 13%, and group 5 = 5% of patients. Groups 1-3 indicated stable glycemic control at different HbA_1c levels. At baseline, similar HbA_1c was observed in group 1 and group 4, but HbA_1c deteriorated in group 4 from age 8 to 19 years. Similar patterns were present in group 3 and group 5. We observed differences in self-monitoring of blood glucose, insulin therapy, daily insulin dose, physical activity, BMI SD score, body-height SD score, and migration background across all HbA_1c trajectories (all P ≤ 0.001). No sex differences were present. Comparing groups with similar initial HbA_1c but different patterns, groups with higher HbA_1c increase were characterized by lower frequency of self-monitoring of blood glucose and physical activity and reduced height (all P < 0.01).

CONCLUSIONS

Using a trajectory approach, we determined five distinct longitudinal patterns of glycemic control from childhood to early adulthood. Diabetes self-care, treatment differences, and demographics were related to different HbA_1c courses.

Predictors of HbA1c levels in patients initiating metformin

OBJECTIVE

The aim was to assess demographic and clinical factors as predictors of short (6 months) and long term (18 months) HbA1c levels in diabetes patients initiating metformin treatment.

RESEARCH DESIGN AND METHODS

We conducted a cohort study including type 2 diabetes patients who received their first metformin prescription between 2007 and 2013 in the Groningen Initiative to Analyze Type 2 Diabetes Treatment (GIANTT) database. The primary outcome was HbA1c level at follow-up adjusted for baseline HbA1c; the secondary outcome was failing to achieve the target HbA1c level of 53 mmol/mol. Associations were analyzed by linear and logistic regression. Multiple imputation was used for missing data. Additional analyses stratified by dose and adherence level were conducted.

RESULTS

The cohort included 6050 patients initiating metformin. Baseline HbA1c at target consistently predicted better HbA1c outcomes. Longer diabetes duration and lower total cholesterol level at baseline were predictors for higher HbA1c levels at 6 months. At 18 months, cholesterol level was not a predictor. Longer diabetes duration was also associated with not achieving the target HbA1c at follow-up. The association for longer diabetes duration was especially seen in patients starting on low dose treatment. No consistent associations were found for comorbidity and comedication.

CONCLUSIONS

Diabetes duration was a relevant predictor of HbA1c levels after 6 and 18 months of follow-up in patients initiating metformin treatment. Given the study design, no causal inference can be made. Our study suggests that prompt treatment intensification may be needed in patients who have a longer diabetes duration at treatment initiation.

The relationship between estimated glomerular filtration rate trajectory and all-cause mortality in type 2 diabetes: the Fremantle Diabetes Study

OBJECTIVE

To investigate the association between estimated GFR (eGFR) and all-cause mortality, including the contribution of temporal eGFR changes, in well-characterised community-based patients with type 2 diabetes.

DESIGN

Longitudinal observational study.

METHODS

Participants from the Fremantle Diabetes Study Phase 1 were assessed between 1993 and 1996 and followed until end-December 2012. Cox proportional hazards modelling was used to assess the relationship between baseline eGFR category (Stage 1-5) and all-cause death, and between eGFR trajectories assigned by semiparametric group-based modelling (GBM) and all-cause death in patients with five post-baseline annual eGFR measurements.

RESULTS

In the full cohort (1296 patients; mean±s.d. age 64.1±11.3years, 48.6% males), 738 (56.9%) died during 12.9±6.1years of follow-up. There was a U-shaped relationship between all-cause death and eGFR category. With Stage 3 (45-59mL/min/1.73m(2)) as reference, the strongest association was for eGFR ≥90mL/min/1.73m(2) (hazard ratio (95% CI) 2.01 (1.52-2.66); P<0.001). GBM identified four linear trajectories ('low', 'medium', 'high', 'high/declining') in 532 patients with serial eGFR measurements. With medium trajectory as reference, eGFR trajectory displaced baseline eGFR category as an independent predictor of death, with low and high/declining trajectories associated with more than double the risk (2.03 (1.30-3.18) and 2.24 (1.31-3.83) respectively, P≤0.003) and associated median reductions in survival of 6.5 and 8.7years respectively.

CONCLUSION

There is a nonlinear relationship between eGFR and death in type 2 diabetes, which is at least partially explained by a sub-group of patients with an initially high but then rapidly declining eGFR.

Self-reported hypoglycaemia in patients with type 2 diabetes treated with insulin in the Hoorn Diabetes Care System Cohort, the Netherlands: a prospective cohort study

BACKGROUND

Our aim was to study the prevalence of self-reported hypoglycaemic sensations and its association with mortality in patients with type 2 diabetes (T2D) treated with insulin in usual care.

METHODS

Demographics, clinical characteristics and mortality data were obtained from 1667 patients with T2D treated with insulin in the Hoorn Diabetes Care System Cohort (DCS), a prospective cohort study using clinical care data. Self-reported hypoglycaemic sensations were defined as either mild: events not requiring help; or severe: events requiring help from others (either medical assistance or assistance of others). The association between hypoglycaemic sensations and mortality was analysed using logistic regression analysis.

RESULTS

At baseline, 981 patients (59%) reported no hypoglycaemic sensations in the past year, 612 (37%) reported only mild sensations and 74 (4%) reported severe hypoglycaemic sensations. During a median follow-up of 1.9 years, 98 patients (5.9%) died. Reporting only mild hypoglycaemic sensations was associated with a lower mortality risk (OR 0.48, 95% CI 0.28 to 0.80), while reporting severe sensations was not significantly associated with mortality (OR 0.76, 95% CI 0.33 to 1.80), compared with reporting no hypoglycaemic sensations, and adjusting for demographic and clinical characteristics. Sensitivity analyses showed an OR of 1.38 (95% CI 0.31 to 6.11) for patients reporting severe hypoglycaemic sensations requiring medical assistance.

CONCLUSIONS

Self-reported hypoglycaemic sensations are highly prevalent in our insulin-treated T2D population. Patients reporting hypoglycaemic sensations not requiring medical assistance did not have an increased risk of mortality, suggesting that these sensations are not an indicator of increased short-term mortality risk in patients with T2D.

The impact of the HbA1c level of type 2 diabetics on the structure of haemoglobin

This study explores the impact of HbA₁c levels on the structure of haemoglobin (Hb) in patients with type 2 diabetes. Seventy-four diabetic patients were classified into the following two groups based on their level of HbA₁c: group A, patients with good glycaemic control (HbA₁c < 7.0%, n = 36); group B, patients with persistent hyperglycaemia (HbA₁c ≥ 9.0%, n = 38). Thirty-four healthy people served as controls (group H). Hb structure was examined by Fourier transform infrared spectroscopy (FTIR), and diabetic erythrocytes were modelled to estimate the impact of glucose on these cells and Hb. Increasing glucose concentrations altered both erythrocyte parameters and the Hb secondary structure. Group B differed significantly from group H (p < 0.05): in the former, the ordered Hb secondary structure had a strong tendency to transform into a disordered secondary structure, decreasing structural stability. We presumed here that high HbA₁c levels might be a factor contributing to Hb structural modifications in diabetic patients. FTIR spectral analysis can provide a novel way to investigate the pathogenesis of type 2 diabetes mellitus.

Metabolic memory and all-cause death in community-based patients with type 2 diabetes: the Fremantle Diabetes Study

AIMS

To validate the findings, in a usual care setting, of glycaemic intervention trials, which have shown that tight control in patients with recently diagnosed type 2 diabetes protects against death during post-study monitoring, but that it may be deleterious in long-duration diabetes with vascular complications.

METHODS

A subset of 531 patients with type 2 diabetes from the community-based observational Fremantle Diabetes Study Phase 1, who attended ≥5 annual reviews (mean follow-up 15.9 years), were categorized by baseline diabetes duration [<1 year (Group 1); 1 to <5 years (Group 2); and ≥5 years (Group 3)]. Glycated haemoglobin (HbA1c) trajectories over the first 5 years were determined [low, medium and high; equivalent to mean HbA1c ≤6.6% (<49 mmol/mol), 6.7-8.0% (50-64 mmol/mol) and ≥8.0% (>64 mmol/mol), respectively]. Kaplan-Meier analysis was used to assess survival by duration and HbA1c trajectory. Cox proportional hazards modelling identified predictors of all-cause death.

RESULTS

There was greater mortality in patients with a medium versus those with a low trajectory in Group 1: hazard ratio (HR) 1.99 [95% confidence interval (CI) 1.003-3.94; p = 0.049], and in patients with a high versus a low trajectory in Group 2: HR 2.02 (95% CI 1.11-3.71; p = 0.022). In Group 3, both medium [HR 0.57 (95% CI 0.35-0.92; p = 0.022)] and high [HR 0.56 (95% CI 0.32-0.96); p = 0.035] trajectories were independently and inversely associated with death.

CONCLUSIONS

In community-based patients with newly or recently diagnosed type 2 diabetes, poor glycaemic control was an adverse prognostic indicator. Tight control was independently associated with death in patients with diabetes duration ≥5 years. These data parallel intervention trial findings and support individualization of HbA1c targets.

Real-world evidence of suboptimal blood pressure control in patients with type 2 diabetes

AIMS

In order to eventually improve blood pressure (BP) management, the aim of this study was to identify subgroups of type 2 diabetes mellitus (T2DM) patients with distinct trajectories of SBP levels. Identifying subgroups with distinct SBP trajectories helps to better understand the course of SBP levels in T2DM patients and its associated consequences. Subgroup characteristics were determined and the prevalence of complications and mortality rates over time in the different subgroups was investigated.

METHODS

Five thousand, seven hundred and eleven T2DM patients with at least two SBP follow-up measurements were selected from a prospective T2DM cohort of 9849 T2DM patients. The mean follow-up period was 5.7 years (range 2-9 years). Latent Class Growth Modeling, as currently the most flexible cluster analysis available, was performed to identify subgroups of patients with distinct SBP trajectories. Subgroup characteristics were determined by multinomial logistic regression analyses.

RESULTS

Four subgroups with distinct SBP trajectories were identified. The largest subgroup (85.6%) showed adequate SBP control (at or around 140 mmHg) over time. The second subgroup (5.6%) were hypertensive in the first years, responded slowly to BP management and eventually reached SBP control. The third subgroup (3.4%) showed deteriorating hypertension during the first 4 years, then showed insufficient response to BP management. The fourth subgroup (5.4%) showed deteriorating hypertension over time. Patients within subgroups 2-4 were significantly older, comprised more women, used more antihypertensive medication and had a higher prevalence of retinopathy, microalbuminuria and cardiovascular disease (CVD) mortality.

CONCLUSION

More than 85% reached and maintained adequate SBP control. Subgroups with a more unfavourable course of SBP control also showed higher rates of microvascular complications and CVD mortality over time. This study identified important subgroups to target in order to improve BP management in T2DM patients.

Time to insulin initiation and long-term effects of initiating insulin in people with type 2 diabetes mellitus: the Hoorn Diabetes Care System Cohort Study

OBJECTIVE

The aim of this study was to assess the time to insulin initiation in type 2 diabetes mellitus (T2DM) patients treated with oral glucose-lowering agents and to determine the baseline characteristics associated with time to insulin initiation. This was evaluated in T2DM patients with HbA1c levels consistently ≥7.0% during total follow up and in those with fluctuating HbA1c levels around 7.0%.

DESIGN AND METHODS

Prospective, observational study was performed, comprising 2418 persons with T2DM aged ≥40 years who entered the Diabetes Care System between 1998 and 2012 with a minimum follow up of at least 3 years, following the first HbA1c level ≥7.0%. Cox regression analyses were performed to assess the determinants of time to insulin initiation. Data related to long-term effects of insulin initiation were studied at baseline and at the end of follow up using descriptive summary statistics.

RESULTS

Two-thirds of the patients initiated insulin during follow up. The time to insulin varied from 1.2 years (range 0.3-3.1) in patients with HbA1c levels consistently ≥7.0% to 5.4 years (range 3.0-7.5) in patients with fluctuating HbA1c levels around 7.0%. Longer diabetes duration (hazard ratio (HR) 1.04 95% CI 1.03-1.05) and lower age (HR 1.00 95% CI 0.99-1.00) at baseline were associated with a shorter time to initiation. More insulin initiators had retinopathy compared with patients that remained on oral glucose-lowering agents during follow up.

CONCLUSION

The time to insulin initiation was short, and most of the patients with HbA1c levels consistently ≥7.0% were initiating insulin. Longer diabetes duration and younger age shortened the time to insulin.

Profiling Patients' Healthcare Needs to Support Integrated, Person-Centered Models for Long-Term Disease Management (Profile): Research Design

Background:

This article presents the design of PROFILe, a study investigating which (bio)medical and non-(bio)medical patient characteristics should guide more tailored chronic care. Based on this insight, the project aims to develop and validate ‘patient profiles’ that can be used in practice to determine optimal treatment strategies for subgroups of chronically ill with similar healthcare needs and preferences.

Methods/Design:

PROFILe is a practice-based research comprising four phases. The project focuses on patients with type 2 diabetes. During the first study phase, patient profiles are drafted based on a systematic literature research, latent class growth modeling, and expert collaboration. In phase 2, the profiles are validated from a clinical, patient-related and statistical perspective. Phase 3 involves a discrete choice experiment to gain insight into the patient preferences that exist per profile. In phase 4, the results from all analyses are integrated and recommendations formulated on which patient characteristics should guide tailored chronic care.

Discussion:

PROFILe is an innovative study which uses a uniquely holistic approach to assess the healthcare needs and preferences of chronically ill. The patient profiles resulting from this project must be tested in practice to investigate the effects of tailored management on patient experience, population health and costs.

The Importance of Weight Change Experiences for Performance of Diabetes Self-Care: A Patient-Centered Approach to Evaluating Clinical Outcomes in Type 2 Diabetes

INTRODUCTION

The aim of this study was to examine the influence of weight change experiences over time on motivation to perform diabetes self-care behaviors using data from a study of canagliflozin (an agent that inhibits sodium glucose co-transporter 2) versus glimepiride in dual therapy with metformin and background diet/exercise.

METHODS

Weight and motivation for performing healthy behaviors were collected at baseline and over time. The motivation questionnaire enabled categorization into two groups: those performing or not performing health behaviors. Four distinct patterns of weight change were determined: losing weight, gaining weight, and two patterns for fluctuating weight. The relationships between these patterns and motivation for weight loss, following a diet, and exercise were examined using logistic regression models.

RESULTS

Of 1182 subjects, more than half were already performing behaviors to lose weight, diet, and exercise at baseline. Among those who were not, 52% (246/474) started taking action to lose weight after baseline, 54% (241/448) started following a diet, and 42% (232/556) started exercising. Weight change patterns were significantly related to performance of healthy behaviors at follow-up (week 36). Compared to the weight gain pattern, those who experienced a continuous weight loss pattern from baseline to week 36 were 2.2 (95% confidence interval 1.49, 3.37) times more likely to perform the healthy behaviors. Baseline behavior and confidence were also predictive of performing healthy behaviors.

CONCLUSION

The current work highlights the importance of weight change patterns for performance of diabetes self-care. Tracking weight patterns over time, assessing confidence for performance of healthy behaviors, and being aware of the relationship between weight changes and diabetes self-care behaviors are viable, concrete ways to practice patient-centered care.

FUNDING

Janssen Global Services, LLC.