Trajectories Of Glycemic Control Research Articles

Risk Factors for Unfavorable Glycemic Control Trajectory in Type-2 Diabetes Mellitus: A Community-based Longitudinal Study in Urbanslums of Central India.

Diabetes mellitus (DM) is a chronic disease, which requires optimal glycemic control to prevent its ensuing vascular complications. Pathway to optimal glycemic control in T2DM has a complex socio-behavioral construct, especially in vulnerable populations, like slum dwellers, who have reduced health-care access and lower prioritization of health needs. The study aimed to map trajectories of glycemic control amongst individuals with T2DM living in urban slums and identify key determinants associated with unfavourable glycaemic trajectory. This study was a community-based longitudinal study conducted in an urban slum of Bhopal in Central India. Adult patients diagnosed with T2DM and on treatment for more than one year were included. All 326 eligible participants underwent a baseline interview, which captured sociodemographic, personal behavior, medication adherence, morbidity profile, treatment modality, anthropometric and biochemical measurements (HbA1c). Another 6-month follow-up interview was conducted to record anthropometric measurements, HbA1c and treatment modality. Four mixed effect logistic regression models (through theory-driven variable selections) were created with glycemic status as dependent variable and usage of insulin was considered as random effect. A total of 231 (70.9%) individuals had unfavorable glycemic control trajectory (UGCT), and only 95 (29.1%) had a favorable trajectory. Individuals with UGCT were more likely to be women, with lower educational status, non-vegetarian food preference, consumed tobacco, had poor drug adherence, and were on insulin. The most parsimonious model identified female gender (2.44,1.33-4.37), tobacco use (3.80,1.92 to 7.54), and non-vegetarian food preference (2.29,1.27 to 4.13) to be associated with UGCT. Individuals with good medication adherence (0.35,0.13 to 0.95) and higher education status (0.37,0.16 to 0.86) were found to be protective in nature. Unfavorable glycemic control trajectory seems to be an inescapable consequence in vulnerable settings. The identified predictors through this longitudinal study may offer a cue for recognizing a rational response at societal level and adopting strategy formulation thereof.

A Real-World Precision Medicine Program Including the KidneyIntelX Test Effectively Changes Management Decisions and Outcomes for Patients With Early-Stage Diabetic Kidney Disease.

The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to predict a patient's risk for a progressive decline in kidney function over 5 years. We report the 1-year pre- and post-test clinical impact on care management, eGFR slope, and A1C along with engagement of population health clinical pharmacists and patient coordinators to promote a program of sustainable kidney, metabolic, and cardiac health. The KidneyIntelX in vitro prognostic test was previously validated for patients with type 2 diabetes and diabetic kidney disease (DKD) to predict kidney function decline within 5 years was introduced into the RWE study (NCT04802395) across the Health System as part of a population health chronic disease management program from [November 2020 to April 2023]. Pre- and post-test patients with a minimum of 12 months of follow-up post KidneyIntelX were assessed across all aspects of the program. A total of 5348 patients with DKD had a KidneyIntelX assay. The median age was 68 years old, 52% were female, 27% self-identified as Black, and 89% had hypertension. The median baseline eGFR was 62 ml/min/1.73 m2, urine albumin-creatinine ratio was 54 mg/g, and A1C was 7.3%. The KidneyIntelX risk level was low in 49%, intermediate in 40%, and high in 11% of cases. New prescriptions for SGLT2i, GLP-1 RA, or referral to a specialist were noted in 19%, 33%, and 43% among low-, intermediate-, and high-risk patients, respectively. The median A1C decreased from 8.2% pre-test to 7.5% post-test in the high-risk group (P < .001). UACR levels in the intermediate-risk patients with albuminuria were reduced by 20%, and in a subgroup treated with new scripts for SGLT2i, UACR levels were lowered by approximately 50%. The median eGFR slope improved from -7.08 ml/min/1.73 m2/year to -4.27 ml/min/1.73 m2/year in high-risk patients (P = .0003), -2.65 to -1.04 in intermediate risk, and -3.26 ml/min/1.73 m2/year to +0.45 ml/min/1.73 m2/year in patients with low-risk (P < .001). Deployment and risk stratification by KidneyIntelX was associated with an escalation in action taken to optimize cardio-kidney-metabolic health including medications and specialist referrals. Glycemic control and kidney function trajectories improved post-KidneyIntelX testing, with the greatest improvements observed in those scored as high-risk.

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

AimsTo identify longitudinal trajectories of glycemic control among adults with newly diagnosed diabetes, overall and by diabetes type. MethodsWe 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. ResultsThe 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. ConclusionsClaims 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.

1088-P: Glycemic Control Trajectories among Individuals with Gestational Diabetes Are Associated with Risk of Childhood Obesity in the Offspring

Gestational diabetes (GDM) increases offspring risk of childhood obesity, but the role of glycemic control (GC) is unclear. We examined the association of GC trajectories, which profile longitudinal GC patterns from GDM diagnosis to delivery, with childhood obesity risk. Among 26774 individuals (78% from minority groups) enrolled in Kaiser Permanente Northern California (KPNC)'s GDM care program (2007-2017), we derived four GC trajectories: stably optimal, rapidly improving to optimal, slowly improving to near optimal and slowly improving to suboptimal. Offspring weight and height were collected at well-child visits until 2022. We calculated adjusted relative risk (aRR) and 95% CI of childhood obesity (i.e., sex-specific BMI-for-age ≥95th percentile using CDC growth charts) in association with GC trajectories using modified Poisson regression models adjusted for sociodemographic factors, parity, pre-pregnancy BMI, smoking and alcohol use, birthweight, preterm birth and breastfeeding. Obesity prevalence was 21.9% at 2-5 years, 28.6% at 6-11 years and 30.2% at 12-16 years. Compared to offspring of individuals in stably optimal trajectory who achieved GC shortly after GDM diagnosis, their counterparts in rapidly improving to optimal, slowly improving to near optimal and slowly improving to suboptimal trajectories had an increasing gradient of obesity risk at 2-5 years (aRR [95% CI]: 1.04 [0.98, 1.10], 1.06 [0.99, 1.14] and 1.16 [1.07, 1.25], respectively) and 6-11 years (1.08 [1.02, 1.15], 1.07 [0.99, 1.15] and 1.15 [1.07, 1.24], respectively); both P-for-trend&amp;lt;0.001. A similar yet weaker pattern was observed at 12-16 years (0.98 [0.88, 1.10], 1.03 [0.90, 1.18] and 1.13 [0.97, 1.30], respectively; P-for-trend=0.11). These associations were stronger in girls than boys (all P-for-interaction&amp;lt;0.05). Early GC achievement after GDM diagnosis and throughout pregnancy may be associated with lower offspring obesity risk in childhood. Disclosure R.F.Chehab: None. A.Ferrara: None. M.Greenberg: None. C.Lee: None. A.Ngo: None. Y.Zhu: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (P30DK092924); The Permanente Medical Group

Glycemic Control Trajectories and Risk of Perinatal Complications Among Individuals With Gestational Diabetes

ABSTRACT Approximately 8% of pregnancies in the United States are affected by gestational diabetes mellitus (GDM) per year. The condition is associated with higher risk of adverse perinatal outcomes, including hypertensive disorders of pregnancy, cesarean delivery (CD), macrosomia, shoulder dystocia, neonatal hypoglycemia, and adverse long-term outcomes, including diabetes and cardiovascular disease. Randomized clinical trials have suggested that glycemic control of GDM with medical nutritional and insulin therapies may improve perinatal outcomes. The American Diabetes Association recommends using self-monitoring of blood glucose (BG) to manage glycemic control during pregnancy. However, large-scale, population-based studies evaluating self-monitoring of BG are limited. In addition, the conventional model for glycemic control status using optimal versus suboptimal is insufficient to capture progressive changes during pregnancy. The aim of this study was to review whether glycemic control trajectories (GCTs) from GDM diagnosis to delivery are associated with the risk of perinatal complications. This was a population-based, cohort study of individuals with GDM who received prenatal care in an integrated health care delivery system between 2007 and 2017. Included were those diagnosed with GDM at 24 to 28 weeks of gestation. Excluded were those who had diabetes before pregnancy. Individuals were asked to self-monitor BG measurements to assess glycemic control 4 times per day at mealtimes. The measurements were collected by nurses or dietitians during weekly counseling calls. Optimal glycemic control was defined as &lt;95 mg/dL for fasting and &lt;140 mg/dL for 1 hour after lunch or dinner. Perinatal complications were the outcomes measured, including CD, preterm birth, shoulder dystocia, neonatal intensive care unit (NICU) admission, NICU stays ≥7 days, stillbirth, large-for-gestational age (LGA), and small-for-gestational age (SGA). To estimate associations between GCTs with the perinatal complications, multivariate Poisson regression models were used. A total of 26,774 individuals were included in the analysis. Four distinct GCTs were identified: trajectory 1 (T1) for stably optimal GCTs (39.3% of individuals), trajectory 2 (T2) for rapidly improving GCTs (34.9%), trajectory 3 (T3) for slowly improving to near-optimal GCTs (15.4%), and trajectory 4 (T4) for slowly improving to suboptimal GCTs (11%). For the T1 group, there were lower risks of CD (adjusted relative risk [aRR], 0.93; 95% confidence internal [CI], 0.89–0.96), shoulder dystocia (aRR, 0.75; 95% CI, 0.61–0.92), and NICU admission (aRR, 0.90; 95% CI, 0.83–0.97) compared with the T2 or reference group (P for trend &lt;0.001), whereas for the T4 group there were higher risks of CD (aRR, 1.18; 95% CI, 1.12–1.24; P for trend &lt;0.001), shoulder dystocia (aRR, 1.41; 95% CI, 1.12–1.78; P for trend &lt;0.001), and NICU admission (aRR, 1.33; 95% CI, 1.20–1.47; P for trend &lt;0.001). There was an increased risk of LGA across GCTs (from T1 [aRR, 0.74; 95% CI, 0.69–0.80] to T4 [aRR, 1.42; 95% CI, 1.31–1.53]; P for trend &lt;0.001; vs T2), but there was a decreased trend of SGA (from T1 [aRR, 1.10; 95% CI, 1.02–1.20] to T4 [aRR, 0.63; 95% CI, 0.53–0.75]; P for trend &lt;0.001). There was not a significant trend across GCTs for the risk of preterm birth and NICU stays ≥7 days. Overall, there was increasing risk of perinatal complications from T1 to T4 GCT, except for SGA. This study highlights the need for early GDM management to prevent perinatal complications.

Glycemic Control Trajectories and Risk of Perinatal Complications Among Individuals With Gestational Diabetes

Glycemic control is the cornerstone of gestational diabetes management. Glycemic control trajectories account for differences in longitudinal patterns throughout pregnancy; however, studies on glycemic control trajectories are scarce. To examine whether glycemic control trajectories from gestational diabetes diagnosis to delivery were associated with differential risk of perinatal complications. This population-based cohort study included individuals with gestational diabetes with longitudinal electronic health record data from preconception to delivery who received prenatal care at Kaiser Permanente Northern California (KPNC) and were enrolled in KPNC's telemedicine-based gestational diabetes care program between January 2007 and December 2017. Data analysis was conducted from September 2021 to January 2022. Glycemic control trajectories were derived using latent class modeling based on the American Diabetes Association's recommended self-monitoring of blood glucose measurements. Optimal glycemic control was defined as at least 80% of all measurements meeting the targets at KPNC clinical settings. Multivariable Poisson regression models were used to estimate the associations of glycemic control trajectories with cesarean delivery, preterm birth, shoulder dystocia, large- and small-for-gestational-age, and neonatal intensive care unit admission and stay of 7 days or longer. Among a total of 26 774 individuals (mean [SD] age, 32.9 [5.0] years; 11 196 Asian or Pacific Islander individuals [41.8%], 1083 Black individuals [4.0%], 7500 Hispanic individuals [28.0%], and 6049 White individuals [22.6%]), 4 glycemic control trajectories were identified: stably optimal (10 528 individuals [39.3%]), rapidly improving to optimal (9151 individuals [34.2%]), slowly improving to near-optimal (4161 individuals [15.5%]), and slowly improving to suboptimal (2934 individuals [11.0%]). In multivariable models with the rapidly improving to optimal trajectory group as the reference group, glycemic control trajectories were associated with perinatal complications with a gradient across stably optimal to slowly improving to suboptimal. For individuals in the stably optimal trajectory group, there were lower risks of cesarean delivery (adjusted relative risk [aRR], 0.93 [95% CI, 0.89-0.96]), shoulder dystocia (aRR, 0.75 [95% CI, 0.61-0.92]), large-for-gestational age (aRR, 0.74 [95% CI, 0.69-0.80]), and neonatal intensive care unit admission (aRR, 0.90 [95% CI, 0.83-0.97]), while for patients in the slowly improving to suboptimal glycemic control trajectory group, risks were higher for cesarean delivery (aRR, 1.18 [95% CI, 1.12-1.24]; (P for trend < .001), shoulder dystocia (aRR, 1.41 [95% CI, 1.12-1.78]; P for trend < .001), large-for-gestational-age (aRR, 1.42 [95% CI, 1.31-1.53]; P for trend < .001), and neonatal intensive care unit admission (aRR, 1.33 [95% CI, 1.20-1.47]; P for trend < .001). The risk of small-for-gestational-age was higher in patients in the stably optimal group (aRR, 1.10 [95% CI, 1.02-1.20]) and lower in the slowly improving to suboptimal group (aRR, 0.63 [95% CI, 0.53-0.75]). These findings suggest that slowly improving to near-optimal and slowly improving to suboptimal glycemic control trajectories were associated with increased risk of perinatal complications. Future interventions should help individuals achieve glycemic control early after gestational diabetes diagnosis and throughout pregnancy to decrease the risk of perinatal complications.

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

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. 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. 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. 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. 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.

Three-variate trajectories of metabolic control, body mass index, and insulin dose: Heterogeneous response to initiation of pump therapy in youth with type 1 diabetes.

ObjectiveContinuous subcutaneous insulin infusion (CSII) in youths with type 1 diabetes (T1D) is often associated with lower HbA1c, lower total daily insulin dose (TDD), and lower body mass index (BMI) compared with multiple daily injections (MDI). Individual responses to CSII are diverse. The aim was to identify unique three‐variate patterns of HbA1c, BMI standard deviation score (SDS), and TDD after switching to CSII.MethodsFive thousand one hundred and thirty‐three youths (≤20 years; 48% boys; median age at pump start 12.5 years) with T1D duration ≥3 years at CSII initiation were selected from the multicenter DPV registry. We applied group‐based multitrajectory modeling to identify groups of individuals following similar trajectories. Measurements were aggregated quarterly during a 3‐year follow‐up period. Trajectory variables were changes of HbA1c, BMI‐SDS, and TDD from baseline (delta = quarterly aggregated values at each time point [i] minus the respective baseline value).ResultsFour groups of diverging Delta‐HbA1c, Delta‐BMI‐SDS, and Delta‐TDD patterns were identified. All showed improvements in HbA1c during the first 3 months. Group 1 (12%) was characterized by modest HbA1c increase thereafter, TDD reduction, and stable BMI‐SDS. In Group 2 (39%), increasing HbA1c, decreasing BMI‐SDS, and stable TDD were found. By contrast, sustainably improved HbA1c, increasing BMI‐SDS, and stable TDD were observed in Group 3 (32%). Group 4 (17%) was characterized by increasing levels for HbA1c, BMI‐SDS, and TDD. Between‐group differences in baseline HbA1c, BMI‐SDS, TDD as well as in sex ratio, age at diabetes onset and at pump start were observed.ConclusionsDefinite trajectories of glycemic control, BMI, and TDD over 3 years after CSII initiation were identified in youths with T1D allowing a more personalized treatment recommendation.

Abstract 10371: Trajectories of Glycemic Control and Early Markers of Cardiovascular Disease in Canadian Adolescents with Type 1 Diabetes: The Cardea Study

Introduction: Adequate glycemic control can prevent or delay complications of diabetes. Little is known about the role of glycemic control trajectories on early markers of CVD in youth. Hypothesis: Poor glycemic control trajectory will be associated with increased vascular ageing and altered myocardial structure. Methods: Cross-sectional study of 100 youth with T1D recruited at Sainte-Justine Hospital, Canada. Trajectories of glycated hemoglobin (Hb A1c ) were estimated with group-based trajectory modelling, using 3 to 6 values retrieved from patients’ medical records over the preceding year. Vessel stiffness was assessed by pulse-wave velocity (PWV) and distensibility by velocity time integral (VTI) and acceleration during a brachial flow-mediated dilation test. Cardiac magnetic resonance assessed ejection fraction, left ventricular mass indexed by height (LVMH), papillary mass and wall thickness. Linear regression models were estimated between Hb A1c trajectory group and each CVD marker, with age, sex, ethnicity and duration of diabetes at baseline as covariates. Results: Mean age was 16.4 yrs (SD=1.3) and median diabetes duration 6.1 yrs (IQR=3.4-10.4). Forty-eight percent were girls, 63% were White and 43% had overweight/obesity. Three trajectories of Hb A1c were identified ( Figure ). Compared with the optimal trajectory, belonging to the high Hb A1c trajectory was associated with a lower ejection fraction (beta [95% CI]: -2.1% [-6.2; 2.0]), LVMH (-6.3 g/m [-15.4; 2.8]) and papillary mass (-1.2 g [-2.2; -0.2]). Youth in the high Hb A1c trajectory displayed higher PWV (0.2 m/s [-0.4; 0.8]) and lower VTI (-2.9 cm/s 2 [-8.4; 2.7]) and acceleration (-63.6 cm/s 2 [-188.7; 61.5]) compared with the optimal trajectory group. Conclusions: Poor glycemic control is linked to increased vascular ageing and a lower myocardial mass in youth with T1D, which is consistent with our prior finding that youth with T1D exhibit a lower myocardial mass than healthy controls.

860-P: Glycemic Control Trajectories and Associated Risk Factors among Women with Gestational Diabetes (GDM)

860-P: Glycemic Control Trajectories and Associated Risk Factors among Women with Gestational Diabetes (GDM)

Improvement in A1c Levels in Early Adulthood in the T1D Exchange: Impact of Racial, Socioeconomic, and Clinical Factors.

Glycemic control in adolescents with type 1 diabetes is poor; yet, it typically improves during early adulthood. Factors related to improvement of glycemic control are unclear. This work examines how demographic and clinical variables may affect trajectories of glycemic control over time. This retrospective, observational study comprised 1775 participants ages 18 to 30 years at enrollment in the T1D Exchange clinic registry. Latent class trajectory modeling was used to determine subgroups following a similar glycated hemoglobin A1c (HbA1c) trajectory over time. Five distinct trajectories of HbA1c classes were identified: "low-decline" and "moderate-decline" groups had low or moderate HbA1c with a gradual decline, the "high-stable" group had high HbA1c and remained stable, and the "very high-rapid decline" and "very high-slow decline" groups had very high HbA1c with rapid or gradual decline. Compared with the "high-stable" group, the "low-decline" and "moderate-decline" groups were more likely to be male (P = .009), White non-Hispanic (P = .02), nonsmokers (P < .001), check self-monitoring blood glucose (SMBG) more frequently (P < .001), and have higher education (P < .001), lower body mass index (P = .02), and lower daily insulin dose (P < .001). Compared with the "very high-rapid decline" and "very high-slow decline" groups, the "low-decline" and "moderate-decline" groups were more likely to be male (P = .02), have higher education (P < .001), use insulin pumps (P = .01), be nonsmokers (P < .001), and have a higher number of SMBG checks per day at enrollment (P < .001). We determined 5 distinct patterns of glycemic control from early adulthood into adulthood. Further evaluation into the modifiable factors associated with a declining HbA1c trajectory would aid in the development of targeted interventions.

Five heterogeneous HbA1c trajectories from childhood to adulthood in youth with type 1 diabetes from three different continents: A group-based modeling approach.

Only a fraction of youth meet established targets for glycemic control; many experience deteriorating control over time. We compared trajectories of hemoglobin A1c (HbA1c) among youth from three trans-continental type 1 diabetes (T1D) registries and identified clinical variables associated with the odds of following increasing vs stable trajectories. Analyses included longitudinal data from 15 897 individuals age 8 to 18 with T1D for at least 2 years and HbA1c measurements in at least 5 years during the observation period. Cohorts were selected from Australasian Diabetes Data Network (ADDN; Australia), German/Austrian/Luxembourgian Diabetes-Patienten-Verlaufsdokumentation initiative (DPV; Germany/Austria/Luxembourga), and the T1D Exchange Clinic Network (T1DX; US) clinic registries. Group-based trajectory modeling and multivariable logistic regression identified unique HbA1c trajectories and their predictors. Five heterogeneous trajectories of glycemic control in each registry were identified: low, intermediate, high stable; intermediate and high increasing. The overall HbA1c level for each trajectory group tended to be lowest in the DPV, higher in the ADDN, and highest in the T1DX. The absolute level of HbA1c and the proportion of individuals within each trajectory varied across registries: 17% to 22% of individuals followed an increasing trajectory. Compared with maintaining a stable trajectory, following an increasing trajectory was significantly associated with ethnic minority status, lower height z-score, higher BMI z-score, insulin injection therapy, and the occurrence of severe hypoglycemia; however, these factors were not consistent across the three registries. We report the first multinational registry-based comparison of glycemic control trajectories among youth with T1D from three continents and identify possible targets for intervention in those at risk of an increasing HbA1c trajectory.

Trajectories of hemoglobin A1c and body mass index z-score over four decades among 2 to 18 year olds with type 1 diabetes.

To determine trajectories of glycemic control and body mass index (BMI) z-score in a large pediatric sample with type 1 diabetes (T1D) over a 38-year period, and to evaluate sex differences and temporal changes in the prevalence of these trajectories. We conducted a longitudinal, retrospective study of 7002 2 to 18 year olds with T1D followed between 1978 and 2016 at a single center. Group-based modeling was used to identify trajectories for hemoglobin A1c (HbA1c) and BMI z-score. Multinomial logistic regression identified predictors of membership to less favorable glycemic trajectories. Group-based modeling yielded 5 HbA1c trajectories. A total of 86% of the sample fell within 3 trajectories that were largely stable across childhood and adolescence, and 14% fell within 2 trajectories characterized by marked deterioration beginning in pre-adolescence. Girls were more likely to be in the HbA1c trajectory with the highest starting HbA1c and significant deterioration during adolescence, and in the highest two BMI z-score trajectories. Patients with non-white race had the highest odds of belonging to a less favorable HbA1c trajectory. Prevalence of the high stable HbA1c trajectory decreased and prevalence of the low stable HbA1c trajectory increased over the study period. A minority of youth with T1D experienced deterioration of glycemic control during adolescence. Girls were more likely to belong to the worst HbA1c trajectory and to BMI z-score trajectories in the overweight/obese range, which may increase cardiometabolic risk. Addressing racial/ethnic disparities in glycemic control should remain a priority. Advances in T1D management correlated with favorable shifts in HbA1c trajectory prevalence.

Can trajectories of glycemic control be predicted by depression, anxiety, or diabetes-related distress in a prospective cohort of adults with newly diagnosed type 1 diabetes? Results of a five-year follow-up from the German multicenter diabetes cohort study (GMDC-Study)

AimsThe longitudinal association between glycemic control with depression, anxiety or diabetes-related distress in type 1 diabetes is poorly understood. Therefore, we examined long-term trajectories of HbA1c in a new-onset cohort of adults with type 1 diabetes, and analyzed associations with depression, anxiety, and diabetes-related distress. MethodsWe included 313 newly diagnosed adults with type 1 diabetes in a prospective multicenter cohort study. Depression, anxiety, and diabetes-related distress were assessed starting with the diabetes diagnosis and at five annual surveys. HbA1c-measurements started with the one-year follow-up. HbA1c trajectories were analyzed applying Growth mixture modeling, while prediction of membership in the trajectories classes was analyzed using multiple regression, and one-way ANOVA/Chi2 to identify differences between classes. ResultsAverage HbA1c increased constantly: follow-up at 1-year 6.5% (48 mmol/mol), 2-years 6.9% (52 mmol/mol), 3-years 7.1% (54 mmol/mol), 4-years 7.1% (54 mmol/mol), and 5-years 7.4% (57 mmol/mol). HbA1c trajectories included one 'good control' and three 'poor control' (52% of patients) classes. At the five-year follow-up, mean HbA1c was 6.3% (45 mmol/mol) in the 'good control' class, and ranging from 7.9% (63 mmol/mol) to 9.0% (75 mmol/mol) in the three 'poor control' classes. Classes were neither predicable, nor differentiated by depression, anxiety, or diabetes-related distress. ConclusionsWe identified distinct trajectories of glycemic control. Depression and anxiety were highly prevalent but they neither predicted 'poor'/'good' glycemic control trajectories nor were they associated with glycemic control at any assessment point.

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

Individualized diabetes management would benefit from prospectively identifying well-controlled patients at risk of losing glycemic control. To identify patterns of hemoglobin A1c (HbA1c) change among patients with stable controlled diabetes. 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. The study cohort consisted of 27,005 US adults with type 2 diabetes, age 18 years and older, and stable index HbA1c <7.0%. HbA1c values during 24 months of observation. 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. 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.

Featured Article: Trajectories of Glycemic Control Over Adolescence and Emerging Adulthood: An 11-Year Longitudinal Study of Youth With Type 1 Diabetes

To identify trajectories of glycemic control over adolescence and emerging adulthood and to test whether demographic and psychosocial variables distinguished these trajectories. We enrolled 132 youth with type 1 diabetes when they were average age 12 and followed them for 11 years. We used group-based trajectory modeling to identify distinct patterns of glycemic control, and examined whether age 12 demographic and psychosocial variables distinguished the subsequent trajectories. We identified 5 trajectories of glycemic control: stable on target, stable above target, volatile late peak, stable high, and inverted U. Parent social status and household structure distinguished the more problematic trajectories from the stable on target group. Friend conflict, psychological distress, unmitigated communion, and self-care behavior at age 12 distinguished problematic glycemic control trajectories from the stable on target group. These results can be used to identify youth who are at risk for deteriorating glycemic control over adolescence.

Trajectories of glycemic control are associated with depressive symptoms beyond cognitive function in nondemented very elderly with type 2 diabetes

Trajectories of glycemic control are associated with depressive symptoms beyond cognitive function in nondemented very elderly with type 2 diabetes

VITAMIN B12 AND FOLATE IN RELATION TO THE RATE OF BRAIN ATROPHY IN SUBJECTS AT RISK OF DEMENTIA: A LONGITUDINAL POPULATION BASED STUDY

sion. Subjects averaged 72.8 years of age. Six trajectories of HbA1c were identified, characterizedbyHbA1c level at entry into theDR(Higher/Lower), and trend over time (Stable/Decreasing/Increasing). Both groups with a trajectory of decreasing HbA1c levels had high HbA1c levels at entry into the DR (9.2%, 10.7%), and high, though decreasing, HbA1c levels over time. They had the worst cognitive performance, particularly in overall cognition (p<0.02) and semantic categorization (p<0.01), followed by that of subjects whose HbA1c at entry into the DR was relatively high (7.2%, 7.8%) and increased over time. Subjects with stable HbA1c over time had the lowest HbA1c levels at entry (6.0%, 6.8%) and performed best in cognitive tests. Glycemic control trajectories, which better reflect chronicity of T2D than a single HbA1c measurement, predict cognitive performance. A trajectory of stable HbA1c levels over time is associated with better cognitive function.

Trajectories in glycemic control over time are associated with cognitive performance in elderly subjects with type 2 diabetes.

ObjectiveTo study the relationships of long-term trajectories of glycemic control with cognitive performance in cognitively normal elderly with type 2 diabetes (T2D).MethodsSubjects (n = 835) pertain to a diabetes registry (DR) established in 1998 with an average of 18 HbA1c measurements per subject, permitting identification of distinctive trajectory groups of HbA1c and examining their association with cognitive function in five domains: episodic memory, semantic categorization, attention/working memory, executive function, and overall cognition. Analyses of covariance compared cognitive function among the trajectory groups adjusting for sociodemographic, cardiovascular, diabetes-related covariates and depression.ResultsSubjects averaged 72.8 years of age. Six trajectories of HbA1c were identified, characterized by HbA1c level at entry into the DR (Higher/Lower), and trend over time (Stable/Decreasing/Increasing). Both groups with a trajectory of decreasing HbA1c levels had high HbA1c levels at entry into the DR (9.2%, 10.7%), and high, though decreasing, HbA1c levels over time. They had the worst cognitive performance, particularly in overall cognition (p<0.02) and semantic categorization (p<0.01), followed by that of subjects whose HbA1c at entry into the DR was relatively high (7.2%, 7.8%) and increased over time. Subjects with stable HbA1c over time had the lowest HbA1c levels at entry (6.0%, 6.8%) and performed best in cognitive tests.ConclusionGlycemic control trajectories, which better reflect chronicity of T2D than a single HbA1c measurement, predict cognitive performance. A trajectory of stable HbA1c levels over time is associated with better cognitive function.

Identification and prediction of group-based glycemic control trajectories during the transition to adolescence.

To identify trajectories of glycemic control over a period of 3 years in a pediatric sample of youth diagnosed with Type 1 diabetes transitioning to adolescence. A second aim was to examine a set of modifiable individual and family level baseline predictors of glycemic control group membership. This multisite, prospective study included 239 children and adolescents (ages 9-11 years at baseline) diagnosed with Type 1 diabetes and their caregivers. Glycemic control was based on hemoglobin A1c (HbA1c) collected at 6-month intervals over a period of 3 years. Predictors of glycemic control membership included baseline global executive functioning, diabetes self-management, diabetes-specific family conflict, blood glucose monitoring frequency, and relevant individual and family level covariates. Group-based trajectory analyses were used to describe patterns of glycemic control from baseline to 36 months and 3 trajectories were identified: low risk (42.9%), elevated risk (44.6%), and high risk (12.1%) subgroups. Baseline maternal-reported family conflict, blood glucose monitoring frequency, and gender were significant predictors of glycemic control group membership. Higher levels of baseline family conflict, lower frequency of blood glucose monitoring, and female gender were associated with elevated and high-risk group membership. These findings underscore the importance of examining trajectories of HbA1c across time. These results suggest that problematic trajectories of glycemic control are evident during the transition to adolescence. Furthermore, there are modifiable individual and family level characteristics that predict group membership and hence could be targeted in interventions to ensure adequate glycemic control is maintained over time and that risks for diabetes-related complications are reduced.