AIMS: The aim of this study was to identify subgroups of type 2 diabetes mellitus patients with distinct hemoglobin A1c (HbA1c) trajectories. Subgroup characteristics were determined and the prevalence of microvascular complications over time was investigated. STUDY DESIGN AND SETTING: Data from a cohort of 5,423 type 2 diabetes patients from a managed primary care system were used [mean follow-up 5.7 years (range 2-9 years)]. Latent class growth modeling was used to identify subgroups of patients with distinct HbA1c trajectories. Multinomial logistic regression analyses were conducted to determine which characteristics were associated with different classes. RESULTS: Four subgroups were identified. The first and largest subgroup (83 %) maintained good glycemic control over time (HbA1c ≤53 mmol/mol), the second subgroup (8 %) initially showed severe hyperglycemia, but reached the recommended HbA1c target within 2 years. Patients within this subgroup had significantly higher baseline HbA1c levels but were otherwise similar to the good glycemic control group. The third subgroup (5 %) showed hyperglycemia and a delayed response without reaching the recommended HbA1c target. The fourth subgroup (3.0 %) showed deteriorating hyperglycemia over time. Patients within the last two subgroups were significantly younger, had higher HbA1c levels and a longer diabetes duration at baseline. These subgroups also showed a higher prevalence of retinopathy and microalbuminuria. CONCLUSION: Four subgroups with distinct HbA1c trajectories were identified. More than 90 % reached and maintained good glycemic control (subgroup one and two). Patients within the two subgroups that showed a more unfavorable course of glycemic control were younger, had higher HbA1c levels and a longer diabetes duration at baseline.
AIMS: The aim of this study was to identify subgroups of type 2 diabetes mellituspatients with distinct hemoglobin A1c (HbA1c) trajectories. Subgroup characteristics were determined and the prevalence of microvascular complications over time was investigated. STUDY DESIGN AND SETTING: Data from a cohort of 5,423 type 2 diabetespatients from a managed primary care system were used [mean follow-up 5.7 years (range 2-9 years)]. Latent class growth modeling was used to identify subgroups of patients with distinct HbA1c trajectories. Multinomial logistic regression analyses were conducted to determine which characteristics were associated with different classes. RESULTS: Four subgroups were identified. The first and largest subgroup (83 %) maintained good glycemic control over time (HbA1c ≤53 mmol/mol), the second subgroup (8 %) initially showed severe hyperglycemia, but reached the recommended HbA1c target within 2 years. Patients within this subgroup had significantly higher baseline HbA1c levels but were otherwise similar to the good glycemic control group. The third subgroup (5 %) showed hyperglycemia and a delayed response without reaching the recommended HbA1c target. The fourth subgroup (3.0 %) showed deteriorating hyperglycemia over time. Patients within the last two subgroups were significantly younger, had higher HbA1c levels and a longer diabetes duration at baseline. These subgroups also showed a higher prevalence of retinopathy and microalbuminuria. CONCLUSION: Four subgroups with distinct HbA1c trajectories were identified. More than 90 % reached and maintained good glycemic control (subgroup one and two). Patients within the two subgroups that showed a more unfavorable course of glycemic control were younger, had higher HbA1c levels and a longer diabetes duration at baseline.
Authors: Wonsuk Oh; Michael S Steinbach; M Regina Castro; Kevin A Peterson; Vipin Kumar; Pedro J Caraballo; Gyorgy J Simon Journal: IEEE J Biomed Health Inform Date: 2021-07-27 Impact factor: 7.021
Authors: Simone P Rauh; Femke Rutters; Brian L Thorsted; Michael L Wolden; Giel Nijpels; Amber A W A van der Heijden; Iris Walraven; Petra J Elders; Martijn W Heymans; Jacqueline M Dekker Journal: BMJ Open Date: 2016-09-19 Impact factor: 2.692
Authors: Amber Awa van der Heijden; Simone P Rauh; Jacqueline M Dekker; Joline W Beulens; Petra Elders; Leen M 't Hart; Femke Rutters; Nienke van Leeuwen; Giel Nijpels Journal: BMJ Open Date: 2017-06-06 Impact factor: 2.692
Authors: Dorijn F L Hertroijs; Arianne M J Elissen; Martijn C G J Brouwers; Nicolaas C Schaper; Sebastian Köhler; Mirela C Popa; Stylianos Asteriadis; Steven H Hendriks; Henk J Bilo; Dirk Ruwaard Journal: Diabetes Obes Metab Date: 2017-11-24 Impact factor: 6.577
Authors: Sridharan Raghavan; Wenhui G Liu; Seth A Berkowitz; Anna E Barón; Mary E Plomondon; Thomas M Maddox; Jane E B Reusch; P Michael Ho; Liron Caplan Journal: J Gen Intern Med Date: 2020-04-24 Impact factor: 5.128