Future Glycemic Control Research Articles

Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development.

Type 2 diabetes (T2D) is a significant global health challenge. Physicians need to assess whether future glycemic control will be poor on the current trajectory of usual care and usual-care treatment intensifications so that they can consider taking extra treatment measures to prevent poor outcomes. Predicting poor glycemic control from trends in hemoglobin A1c (HbA1c) levels is difficult due to the influence of seasonal fluctuations and other factors. We sought to develop a model that accurately predicts poor glycemic control among patients with T2D receiving usual care. Our machine learning model predicts poor glycemic control (HbA1c≥8%) using the transformer architecture, incorporating an attention mechanism to process irregularly spaced HbA1c time series and quantify temporal relationships of past HbA1c levels at each time point. We assessed the model using HbA1c levels from 7787 patients with T2D seeing specialist physicians at the University of Tokyo Hospital. The training data include instances of poor glycemic control occurring during usual care with usual-care treatment intensifications. We compared prediction accuracy, assessed with the area under the receiver operating characteristic curve, the area under the precision-recall curve, and the accuracy rate, to that of LightGBM. The area under the receiver operating characteristic curve, the area under the precision-recall curve, and the accuracy rate (95% confidence limits) of the proposed model were 0.925 (95% CI 0.923-0.928), 0.864 (95% CI 0.852-0.875), and 0.864 (95% CI 0.86-0.869), respectively. The proposed model achieved high prediction accuracy comparable to or surpassing LightGBM's performance. The model prioritized the most recent HbA1c levels for predictions. Older HbA1c levels in patients with poor glycemic control were slightly more influential in predictions compared to patients with good glycemic control. The proposed model accurately predicts poor glycemic control for patients with T2D receiving usual care, including patients receiving usual-care treatment intensifications, allowing physicians to identify cases warranting extraordinary treatment intensifications. If used by a nonspecialist, the model's indication of likely future poor glycemic control may warrant a referral to a specialist. Future efforts could incorporate diverse and large-scale clinical data for improved accuracy.

Short-term recovery of insulin secretion in response to a meal is associated with future glycemic control in type 2 diabetes patients.

Endogenous insulin secretion could be recovered by improving hyperglycemia in patients with type 2 diabetes. This study aimed to investigate the association between short-term recovery of insulin secretion during hospitalization and clinical background or future glycemic control in patients with type 2 diabetes. A total of 127 patients with type 2 diabetes were included. The recovery of endogenous insulin secretion was determined using the following indices: index A: fasting C-peptide index (CPI) at discharge - fasting CPI on admission; index B: postprandial CPI at discharge - postprandial CPI on admission; and index C: Δ C-peptide immunoreactivity (CPR) (postprandial CPR - fasting CPR) at discharge - ΔCPR on admission. We examined the associations of each index with clinical background and future glycemic control measured by glycosylated hemoglobin and continuous glucose monitoring. Using index A and B, the age was significantly younger, whereas BMI and visceral fat area were significantly higher in the high-recovery group than in the low-recovery group. Changes in glycosylated hemoglobin levels were significantly greater at 6 and 12 months in the high-recovery group in the analysis of index C. The receiver operating characteristic curve analysis identified the index B and index C as indicators to predict glycosylated hemoglobin <7.0% at 6 months after discharge. Furthermore, index C was positively correlated with the time in the target glucose range, and inversely correlated with the standard deviation of glucose at 3 and 12 months after discharge. Short-term recovery of insulin secretion in response to a meal during hospitalization, evaluated with the index-C, might predict future glycemic control.

1551-P: Association between Short-Term Postprandial C-Peptide Index Recovery during Hospitalization and Future Glycemic Control in Patients with Type 2 Diabetes

Endogenous insulin secretion could be recovered by improving hyperglycemia in patients with type 2 diabetes (T2D). However, no reports have showed the associations between short-term insulin secretion recovery and future glycemic control. In this study, we investigated whether short-term C-peptide index (CPI) recovery during hospitalization could predict future glycemic control. In this study, 117 patients with T2D were included. The recovery of fasting and postprandial CPI were defined as (fasting CPI at discharge - fasting CPI at admission [index-A]) and (postprandial CPI at discharge - postprandial CPI at admission [index-B]). We compared the clinical background between the low-recovery and high-recovery groups of each index and assessed the association between the indices and metabolic parameters after discharge. Using index-A, age was significantly younger (P = 0.017) and body mass index (BMI) was significantly higher (P = 0.004) in the high-recovery group than that in low-recovery group. Using index-B, the age was significantly younger (P = 0.019) and BMI was significantly higher (P = 0.004) in the high-recovery group. Additionally, visceral fat area (P = 0.043), serum TG (P &amp;lt; 0.001), and γ-GTP levels (P = 0.009) were significantly higher in the high-recovery group. Regarding the association between CPI recovery and future glycemic control, GA levels were significantly lower in the high-recovery group than that in low-recovery group at 6 months (16.7 vs 21.1%, P = 0.045) and 12 months (16.8 vs 21.7%, P = 0.033) after discharge in the analysis using index-B. Furthermore, postprandial CPI (cut-off; 3.79 [AUC 0.698, P = 0.007]) at discharge and index-B (cut-off; 1.66 [AUC 0.657, P = 0.018]) were identified as indicators to predict HbA1c ≤ 7.0% at 6 months after discharge. In conclusion, short-term postprandial CPI recovery during hospitalization may predict future glycemic control. Disclosure A.Enkaku: None. D.Chujo: None. M.Kamigishi: None. S.Inagawa: None. W.Sakai: None. S.Matsukoshi: None. A.Takikawa: None. S.Fujisaka: None. K.Tobe: Other Relationship; MSD K.K., Novo Nordisk Pharma Ltd., Takeda Pharmaceutical Company Limited, Daiichi Sankyo, Mitsubishi Tanabe Pharma Corporation, Suntory Global Corporation, Ltd.,, Taiho Pharmaceutical Co. Ltd., Japan Diabetes Foundation, Japan Association for Diabetes Education and Care.

Glycemic control, depression, diabetes distress among adolescents with type 2 diabetes: effects of sex, race, insurance, and obesity.

To determine (1) differences in depression and distress scores between adolescents with type 1 (T1D) and type 2 diabetes (T2D), (2) how socioeconomic factors, obesity, race, and treatment regimen affect depression and diabetes distress in adolescent T2D, (3) the relationships between depression and diabetes distress scores in adolescents with T2D, and (4) how depression and diabetes distress scores relate to current and future glycemic control in adolescents with T2D. Diabetes distress is a negative emotional reaction to diabetes complications, self-management demands, unresponsive providers, poor interpersonal relationships, and to diabetes itself. It is frequently mistaken for depression and the two are interrelated. Increases in both predict poor glycemic control in adolescents with T1D. Depression (PHQ-9) and diabetes distress (PAID-T) scores from self-administered tests were studied in 364 patients with diabetes between the ages of 13-17. Kruskal-Wallis test was used to assess differences between types of diabetes, sexes, races, and insurance status. Spearman correlations, and robust rank order multivariable regression analysis were used to assess relationships. Medical records were reviewed for follow-up hemoglobin A1c (HbA1c) levels over 3years. HbA1c was significantly lower in females with T2D than with T1D (p = 0.019) but not in males. It, also, did not differ between females and males with T2D. Median PHQ-9 score in females with T2D was significantly greater than in females with T1D (p = 0.007) but did not differ between females and males with T2D. PHQ-9 scores did not differ between males with T2D and T1D. PAID-T scores, however, were higher in males with T2D than in males with T1D but did not differ between females. PHQ-9 scores and PAID-T scores were significantly related in T2D (rs = 0.65, p < 0.001). Neither was related to HbA1c in T2D. As in adolescents with T1D, depression and diabetes distress screening scores are closely related in adolescent T2D. However, unlike T1D, they are not related to glycemic control in T2D. Depression and diabetes distress may be more closely related to weight and lifestyle concerns.

857-P: A Risk Stratification Approach to Diabetes Care Delivery Services

Diabetes self-management education and support (DSMES) is known to improve diabetes treatment outcomes, but this limited resource is not always targeted toward the highest risk patients in current models of care. Predictive modeling, which relies on large data sets to predict outcomes, is one tool which can be used to direct resources to patients with the greatest need for these services. University of Pittsburgh Medical Center (UPMC) diabetes care providers along with members of the clinical analytics team created a predictive model, including patient age, HbA1c, and insulin use among other factors, to assess individual patient risk of future poor glycemic control. The model was applied to patients who received DSMES via diabetes care and education specialists (DCES) from December 2018 to December 2020 to evaluate the impact of DSMES according to risk strata. Patients were categorized into high, medium and low risk groups based on clinical data prior their initial DSMES encounter. HbA1c change was calculated using the HbA1c prior to DSMES and most recent HbA1c. Of 1,247 patients eligible for retrospective assessment, 50% were categorized as low risk (n=621), 4% (n=55) medium, and 46% high risk (n=571). Mean HbA1c change after DSMES was -0.54% for low risk, -0.72% for medium risk, and -1.19% for the high risk group. Mean HbA1c after DSMES was 7.6% for low risk, 9.7% for medium risk and 9.6% for high risk patients. 57.5% of low, 59% of medium and 46.6% of high risk patients were seen by an endocrinologist. In this analysis, high risk patients who received DSMES had larger improvement in HbA1c and were less likely to be seen by an endocrinologist. To optimize utilization of diabetes care regarding DCES, predictive modeling can be used to target resources to the highest risk patients. Disclosure M. Zupa: None. J. S. Krall: Research Support; Self; Becton, Dickinson and Company, Sanofi. K. Collins: None. J. Ng: Research Support; Self; Sanofi-Aventis. L. M. Siminerio: Advisory Panel; Self; Abbott Diabetes, Bayer U. S., Research Support; Self; Becton, Dickinson and Company. Funding National Institute of Diabetes and Digestive and Kidney Diseases (5T32DK007052-45)

618-P: Machine Learning in Predicting Poor Glycemic Outcomes

With 1.5 million Americans diagnosed with diabetes mellitus (DM) annually, 84.1 million with prediabetes, and 245 billion in medical costs, efforts to characterize those at highest risk are warranted. Predictive modeling is a methodology that can proactively identify patients and allocate resources like a DM education intervention, known to ensure effective, cost-saving care. Members of the University of Pittsburgh Medical Center (UPMC) endocrine division partnered with the Clinical Data Analytics Department to develop a predictive model using clinical data from patient electronic medical records (EMR), Medical and socioeconomic profiles were used to predict future glycemic control from any patient with a type 2 DM diagnosis or any patient with two HbA1c readings ≥6.5% within 1 year. SHapley Additive exPlanations (SHAP), atheoretic approach used to explain output of a machine learning model, was used to analyze variables of 38,173 patients meeting inclusion criteria. Prior elevated HbA1C levels, younger age at presentation, and previous insulin therapy provided the highest prediction of future poor glycemic control. Predictive modeling is a useful method to foretell future glycemic control. Identifying patients at “high risk” for poor outcomes can allow for efficient deployment early in the disease course for DE services proven to improve outcomes with the potential to reduce health care costs. Disclosure H. Alquadan: None. L.M. Siminerio: Research Support; Self; Becton, Dickinson and Company. J.T. Krall: Research Support; Self; Becton, Dickinson and Company. J. Ng: None.

Future glycemic control of children diagnosed with type 1 diabetes mellitus at toddler and preschool/school age.

Background The main objective of this study was to compare future glycemic control in children diagnosed with type 1 diabetes mellitus (T1DM) at toddler age and preschool/school age. In addition, we aimed to examine risk factors known to be associated with future glycated hemoglobin A1c (HbA1c) levels in children diagnosed with T1DM. Methods This is a retrospective cohort study of 85 patients diagnosed with T1DM at toddler age (group 1; 0-2.9 years; n = 36) or preschool/school age (group 2; 5-6.9 years; n = 49) who were followed up at the University Children's Hospital in Zurich for at least 10 consecutive years or until the age of 15 years. Results The mean HbA1c level in the first year after diagnosis had a highly predictive value about glycemic control in the following 6 years. In addition, a longer duration of T1DM was associated with higher HbA1c values. HbA1c values did not differ significantly within 11 years after diagnosis between children in the two age groups. Neither was a difference found when comparing the two groups in respect to their chronological age, although a trend was noted (p = 0.09). This trend is very likely due to a longer duration of diabetes in group 1. Conclusions HbA1c level in the first year predicts glycemic control for the next 6 years and deterioration of HbA1c values can be noted with longer duration of T1DM. Moreover, our study demonstrated similar future glycemic control in patients diagnosed with T1DM at toddler age and preschool/school age.

MON-149 The Impact of Diabetes Educational Videos in Diabetes Attitudes in a Brazilian Population

The purpose of this research is show the application of diabetes educational videos and the impact of them in Diabetes Attitudes, analyzing the results of the modified Diabetes Attitude Questionnaire ( ATT-19) before and after the videos. The ATT-19 is a questionnaire based on the measurement of psychological adjustment for diabetes (DM), developed in response to the needs of evaluation of psychological and emotional aspects about the disease. Consists of 19 items that include six factors: stress associated with DM, receptivity to treatment, confidence in treatment, personal effectiveness, health perception and social acceptance. It was a transversal study, with active intervention during The World Diabetes Day of The International Diabetes Federation that occurred at one Brazilian Military Hospital. They were outpatient and participated without identification. To be eligible, patients had to be diabetic and accept to participate watching the videos and answered the two questionnaires. The videos were a construction of the Service and showed to the patients three own made videos of almost two minutes each: Video of the Glycemic Control using the Glucometers with capillary blood finger sample and explaining how easy and safe it is; Video of the Glycemic Control using the Continuous Glucose Monitoring System instead of a routine finger stick; Video of the insulin injection with different pens. The videos were shown personally to each patient by the health professional, using smart cell phones and by televisions when they finished the two questionnaires to fix more the messages to home. They receive too the link of the videos to access them whatever they want and were challenged to show the videos to anybody that they imagine. All patients were able to demonstrate their ability to use a mobile phone and were with good vision. The patients with difficulty to write received help of the Health Professional that assisted them. The study enrolled 31 diabetic participants, 18 women and 13 men, ages between 47 and 80 years. Were excluded 05 patients that did not answer the two questionnaires or not complete them. Analyzing by Test T student the results suggest positive psychological influence of the application of the videos in questions 1 (p=0, 08), 2 (p=0, 09) and statistical significance in 13 (p=0, 05), appointing reduction of stress associated with diabetes and improvement in personal effectiveness. The majority of the patients (88%) after the videos admitted that they achieved new knowledge about diabetes and liked the intervention used to teach and help them. The intervention seems to be a very good tool to diabetes educators as an adjunct to usual diabetes care . The diabetics who have seen the videos improved their Diabetes Attitudes, this probably will reflect in their lifestyle, adherence and future glycemic control. More studies with great number of patients are necessary to confirm it.

Diabetes distress in young adults with early-onset Type 1 diabetes and its prospective relationship with HbA1c and health status.

This study aimed to determine cross-sectional relationships between diabetes distress and health-related variables, and prospective associations between diabetes distress and future glycaemic control (HbA1c ) and health status among young adults with early-onset Type 1 diabetes. Data were collected from a nationwide cohort study of adults whose Type 1 diabetes onset occurred from 0 to 4years of age during 1993-2002. Questionnaire surveys were conducted in 2012-2013 and 2015-2016 (N=584). Diabetes distress was assessed via the Problem Areas in Diabetes (PAID) scale (0-100 points), depressive symptoms via the Patient Health Questionnaire-9 (PHQ-9) and health status via the 12-Item Short Form Health Survey (SF-12) questionnaire. Multivariable linear regression analyses were applied to cross-sectional and longitudinal data. In the cross-sectional analyses, higher PAID scale total scores (representing higher distress levels) were observed in women than in men and in participants with more severe depressive symptoms. PAID scores were lower in individuals with better physical and mental health. A 1mmol/mol increase in HbA1c was associated with a 0.28-point increase [95% confidence interval (95% CI) 0.20, 0.36] in diabetes distress. In longitudinal analyses adjusting for age, sex, socio-economic index and HbA1c at baseline, a 10-point higher PAID score at baseline was associated with a 1.82mmol/mol higher HbA1c level (95% CI 0.43, 3.20) and a 2.48-point lower SF-12 mental health score (95% CI -3.55, -1.42) three years later. The cross-sectional and longitudinal analyses results suggest that diabetes distress impairs health-related outcomes in young adults with early-onset diabetes.

A risk score of BMI, HbA1c and triglycerides predicts future glycemic control in type 2 diabetes

Introduction: The average patient with type 2 diabetes does not exist, yet evidence-based guidelines are highly standardized, which might result in heterogeneous treatment effects. As a first step towards more effective patient-centered care, the objective of this study was to identify, predict and validate distinct glycaemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care. Methods: We conducted a retrospective study on two cohorts using routinely collected individual patient data in primary care practices from two large Dutch diabetes patient registries. Participants included newly diagnosed, adult patients with type 2 diabetes between January 2006 and December 2014 n = 10,528, development cohort; n = 3,777, validation cohort. Latent growth mixture modeling LGMM identified distinct glycemic 5-year trajectories. Machine learning models were built to predict the trajectories with easily obtainable patient characteristics in daily clinical practice. Results: Three different glycemic trajectories were identified: 1 stable, adequate glycemic control 76.5% of patients; 2 improved glycemic control 21.3% of patients and 3 deteriorated glycemic control 2.2% of patients. Similar trajectories could be discerned in the validation cohort. BMI, HbA1c and triglycerides 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 ROC-AUC of 0.96 in the validation cohort, indicating excellent accuracy. Discussion: This study identified three glycaemic control trajectories in patients with newly diagnosed type 2 diabetes from a diabetes patient registry in the Netherlands. The same trajectories were found in an independent, separate Dutch diabetes patient registry. Future glycaemic control could accurately be identified with only three patient features, i.e. BMI, HbA1c and triglycerides. Conclusions: Patients with type 2 diabetes are a diverse group where current care does not fit all. The developed model could be used in practice as a quick and easy tool to determine patients’ care needs and provided tailored diabetes treatment. Lessons learned: The developed model can effectively explain heterogeneity in future glycaemic response of patients with type 2 diabetes. Limitations: Prescription of glucose lowering drugs and insulin may have influenced the patterns of the trajectories. Suggestions for future research: In further research, we will determine patients’ care preferences per glycaemic control trajectory, using a discrete choice experiment. These preferences shall be paired with the corresponding glycaemic control trajectories, creating patient profiles that we will subsequently test in a feasibility study.

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

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. 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). 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). 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.

Determination of Risk for Suboptimal Glycemic Control in New-Onset Type 1 Diabetes

Background: Most youth with type 1 diabetes (T1D) do not meet current ADA recommendations for A1C, thus, it is important to identify at diagnosis, those at-risk for later suboptimal glycemic control (A1C ≥9.5%) and complications. Methods: The Risk Index for Poor Glycemic Control (RI-PGC) was administered to 266 parents during children’s (ages &amp;lt;1-19 years) routine new onset T1D visit. The RI-PGC provides a single score (range = 0-9, higher scores = higher risk) based on psychosocial factors (e.g., insurance, child mood, parent stress/anxiety) to classify risk as “Low,” “Moderate,” or “High” in the 1-4 years after diagnosis. Demographic data and DKA occurrence at diagnosis were also collected. Results: Of 266 children with new onset T1D (Mage 9.98 ± 4.42 years; 52% male; 68% Caucasian), 18.8% and 28.6% were Moderate and High Risk, respectively. DKA at onset (n=157; 59%) was significantly correlated with risk score (r=0.888, p&amp;lt;0.001). Children in DKA at onset were more than twice as likely to score high on RI-PGC (p=0.009; Table). Conclusions: We use the RI-PGC in a prevention program to identify risk of future suboptimal glycemic control. These results are consistent with the only other study (Schwartz et al., 2014) to examine risk-most children were Low Risk and more children were High Risk than Moderate Risk. Presence of DKA at onset may be another predictor of future suboptimal glycemic control, DKA episodes, and complications. Risk Classification at T1D New Onset for Later ComplicationsRI-PGC Risk Category (n; %)LowModerateHighDKA at Onset75 (54%)26 (52%)56 (74%)No DKA at Onset65 (46%)24 (48%)20 (26%) Disclosure S. Majidi: None. J.M. Vogeli: None. K.A. Driscoll: None.

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

AimTo 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.MethodsWe 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.ResultsThree 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.ConclusionsThe 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 tracking and bio-psychosocial determinants of glycaemic control in children and adolescents with type 1 diabetes: retrospective cohort study and multilevel analysis

To explore the association between HbA1c 6 months after diagnosis (6 m-HbA1c) and long-term glycaemic control in children with type 1 diabetes, accounting for other bio-psychosocial determinants. This retrospective cohort study, included 155 children (≤16 yr) from the North of Scotland, diagnosed between January 1993 and August 2011, and receiving care between November 2008 and August 2012. Multilevel analysis explored the relationships between 6 m-HbA1c, other persistent or dynamic variables, and HbA1c. Patterns of glycaemic control were identified by cluster-analysis. 6 m-HbA1c was positively associated with diabetic ketoacidosis at diagnosis, shorter duration of partial-remission, female gender, and psychosocial adversity. In multilevel analysis the effects of 6 m-HbA1c on subsequent HbA1c trajectories remained significant after adjusting for patient- and observation-level predictors. An increase in 6 m-HbA1c of 10 mmol/mol (0.9%) was associated with an average increase in HbA1c levels of 5.3 (95% CI: 4.5-6.2) mmol/mol, or 0.48% (0.41 to 0.57%; p < 0.001) over the follow-up period. Coefficients for linear and quadratic growth identified sustained effects of 6 m-HbA1c on glycaemic control (p < 0.001). Higher average levels or accelerated increases in HbA1c were associated with age at diagnosis, falling BMI (in girls > boys), mental health diagnosis, major adverse life-events, single-parenting, child welfare concerns, neighbourhood deprivation, and clinic non-attendance. Cluster-analysis identified groups with poor or deteriorating control, characterized by older age at diagnosis, multiple psychosocial adversities, and maladaptive healthcare use. Early HbA1c predicted future glycaemic control across childhood. Trajectories were further modified by biological factors, exposures to psychosocial adversity, and healthcare use.

Longitudinal association between medication adherence and glycaemic control in Type 2 diabetes

Despite the widespread assumption that adherence drives glycaemic control, there is little published support for this in Type 2 diabetes. The study objective was to determine whether self-reported medication adherence predicts future glycaemic control in Type 2 diabetes, after accounting for baseline control. Medication adherence (4-item Morisky scale), glycaemic control (HbA(1c)%), and other variables were assessed in 287 adult primary care patients prescribed oral medication (40% also on insulin) for Type 2 diabetes. Glycaemic control was reassessed 6 months later. Regression analyses examined concurrent and future glycaemic control as a function of baseline medication adherence after adjustment for baseline glycaemia and other potential confounders. Only half of patients reported high adherence. Cross-sectional adjusted analysis replicated prior reports of an adherence-HbA(1c) association (P = 0.011). Even after adjusting for baseline HbA(1c), each one-point increase in baseline Morisky total score was associated with a 1.8 mmol/mol (or 0.16%) increase in HbA(1c) measured 6 months later. Additionally, baseline endorsement of forgetting to take medication was associated with a 4.7 mmol/mol (or 0.43%) increase in 6-month HbA(1c) (P = 0.005). This effect persisted after adjusting for psychological distress and did not vary by key demographic and medical features. Even after stringent adjustment for baseline glycaemic control, self-reported adherence to diabetes medication predicts long-term glycaemic control. The Morisky scale is an easy-to-use clinical tool to identify patients whose glycaemic control will subsequently worsen, regardless of age, gender and psychological distress.

Association between beta cell function and future glycemic control in patients with type 2 diabetes

The aim of this study was to clarify the association between C-peptide immunoreactivity (CPR), a marker of beta cell function, and future glycemic control in patients with type 2 diabetes. We conducted a retrospective analysis of 513 consecutive patients with type 2 diabetes who were admitted to our hospital between 2000 and 2007 and followed up for 2 years. Serum and urinary CPR levels were measured during admission, and CPR index was calculated as the ratio of CPR to plasma glucose. The associations between these markers at baseline and glycemic control after 2 years were assessed by means of logistic regression models. After 2 years, 167 patients (32.6%) showed good glycemic control (HbA1c <6.9%). Baseline serum and urinary CPR indices were significantly associated with good glycemic control after 2 years, and the postprandial CPR to plasma glucose ratio (postprandial CPR index) showed the strongest association (odds ratio (OR) 1.29, 95% confidence interval (CI) 1.12-1.50, P = 0.001) among CPR indices. Multivariate analyses showed consistent results (OR 1.23, 95%CI 1.03-1.48, P = 0.021). In conclusion, preserved beta cell function at baseline was associated with better glycemic control thereafter in patients with type 2 diabetes.

Prospective Associations Between Emotional Distress and Poor Outcomes in Type 2 Diabetes

OBJECTIVECross-sectional studies link both depressive symptoms (DS) and diabetes-related distress (DRD) to diabetes self-management and/or glycemic control. However, longitudinal studies of these variables are rare, and their results are somewhat conflicting. The study objective was to compare DS and DRD as longitudinal predictors of medication adherence, self-care behavior, and glycemic control in type 2 diabetes.RESEARCH DESIGN AND METHODSPrimary care patients with type 2 diabetes reported DS, DRD, and other variables at baseline were studied. Medication adherence, self-care behaviors (diet, physical activity, and glucose testing), and glycemic control (HbA1c) were assessed 6 months later (n = 253). Cross-sectional and longitudinal regression analyses were used to model behavioral and medical outcomes as a function of baseline confounders, DS, and DRD.RESULTSAdjusted cross-sectional and longitudinal analyses yielded very similar results. In the latter, only DS were significantly associated with future diet behavior (P = 0.049), physical activity (P = 0.001), and glucose testing (P = 0.018). In contrast, only DRD predicted future glycemic control (P < 0.001) and medication adherence (P = 0.011).CONCLUSIONSDistress-outcome associations seem to vary by type of distress under consideration. Only DS predicts future lifestyle-oriented self-management behaviors. In contrast, only DRD predicts glycemic control, perhaps by decreasing medication adherence. Clinical assessment and intervention should encompass both types of distress, unless the goal is to narrowly target a highly specific outcome.

The Risks and Benefits of Implementing Glycemic Control Guidelines in Frail Older Adults with Diabetes Mellitus

To determine the hypo- and hyperglycemic outcomes associated with implementing the American Geriatrics Society (AGS) guideline for a glycosylated hemoglobin (HbA1c) level of less than 8% in frail older adults with diabetes mellitus (DM). Guideline implementation. Program of All-Inclusive Care for the Elderly. All participants in the before (October 2002-December 2004, n=338), early (January 2005-June 2006, n=289) and late (July 2006-December 2008, n=385) periods of guideline implementation with a diagnosis of DM and at least one HbA1c measurement. Clinician education in 2005 with annual monitoring of the proportion of each clinician's patients with DM with HbA1c less than 8%. Hypoglycemia (blood glucose<50 mg/dL), hyperglycemia (blood glucose>400 mg/dL), and severe hypoglycemia (emergency department (ED) visit for hypoglycemia). Participants in the before, early, and late periods were similar in age, race and ethnicity, comorbidities, and functional dependence. Antihyperglycemic medication use was greater in the late period, with more participants using metformin (28% before, 42% late, P<.001) and insulin (23% before, 34% late, P<.001) and achieving the AGS glycemic target of HbA1c of less than 8% (74% before, 84% late, P<.001). Episodes of hyperglycemia (per 100 person-years) were dramatically lower in the late period (159 before, 46 late, P<.001), and episodes of hypoglycemia were similar (10.1 before, 9.3 late, P=.50). There were more episodes of severe hypoglycemia in the early period (1.1 before, 2.9 early, P=.03). Implementing the AGS glycemic control guideline for frail older adults led to fewer hyperglycemic episodes but more severe hypoglycemic episodes requiring ED visits in the early implementation period. Future glycemic control guideline implementation efforts should be coupled with close monitoring for severe hypoglycemia in the early guideline implementation period.

The utility of hemoglobin A1c at diagnosis for prediction of future glycemic control in children with type 1 diabetes

IntroductionWe evaluated the relationships of hemoglobin A1c (A1c) at diagnosis of type 1 diabetes (T1DM) to future glycemic control and to a series of clinical variables in children with T1DM. Materials and methodsPatients <18 years old diagnosed with T1DM during a one year period who had an A1c at diagnosis and at least one follow-up visit at our center were eligible for inclusion. Baseline variables examined included age, race, gender, symptom duration, admission acuity, anthropometrics, bicarbonate, and A1c. Annual anthropometric and A1c data were also obtained from clinic visits through 4 years after diagnosis. ResultsWe identified 120 children (53 males). Mean age at diagnosis was 7.6±3.9 years. Mean A1c at diagnosis was 10.9±1.9%. A1c at diagnosis correlated with age at diagnosis, symptom duration, and A1c at 3-years, with trends towards correlations at 6 weeks and 4 years. A1c at 1 year correlated highly with A1c at subsequent visits. No other baseline variables correlated with subsequent glycemic control. ConclusionsIn children with newly diagnosed diabetes, A1cs at diagnosis and one year post diagnosis are related to subsequent glycemic control. Children with high A1cs particularly at one year post diagnosis may benefit from targeted intensification of resources.

Islet antigen specific IL-10 + immune responses but not CD4 +CD25 +FoxP3 + cells at diagnosis predict glycemic control in type 1 diabetes

The immune phenotype of the partial remission phase or “honeymoon phase” of type 1 diabetes is not well defined. We compared flow cytometry and cytokine production by ELISPOT assays in children with newly diagnosed type 1 diabetes and children in the partial remission phase of type 1 diabetes. Newly diagnosed children had higher levels of FoxP3 expression in CD4 CD25 double positive cells (56.1% +/− 24.9 vs. 24.9% +/− 24.6 p = 0.03) and higher mean numbers of IL-10 producing cells (7.3 cells/2 × 10 5 cells +/− 6.6 vs. 0.86 cells/2 × 10 5 +/− 0.36 p = 0.0043) compared to partial remission patients. Higher FoxP3 expression at diagnosis predicted worse future glycemic control while higher mean numbers of IL-10 cells were associated with better future glucose control. These data provide an immune phenotype of the honeymoon phase and suggest that analyzing IL-10 and FoxP3 at diagnosis may identify patients that will experience better glucose control.