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

ABSTRACT: 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.

SUBMITTER: McCoy RG

PROVIDER: S-EPMC5640500 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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

McCoy Rozalina G RG Ngufor Che C Van Houten Holly K HK Caffo Brian B Shah Nilay D ND

Medical care 20171101 11

<h4>Background</h4>Individualized diabetes management would benefit from prospectively identifying well-controlled patients at risk of losing glycemic control.<h4>Objectives</h4>To identify patterns of hemoglobin A1c (HbA1c) change among patients with stable controlled diabetes.<h4>Research design</h4>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 ...[more]

PMID: 28922296

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Project description:Background: Glycemic variability (GV) may attribute to the pathogenesis of diabetic neuropathy. The aim of this cross-sectional study was to investigate the association between GV and distal symmetric polyneuropathy (DSPN) and cardiovascular autonomic neuropathy (CAN) in a Danish population of young adults with type 1 diabetes. Methods: Young adults between 18 and 24 years with type 1 diabetes were included in this cross-sectional study. CAN was assessed by cardiovascular autonomic reflex tests (CARTs) and heart rate variability (HRV). DSPN was assessed by light pressure, pain and vibration perception, electrochemical skin conductance, sural nerve conduction velocity (SNCV), and amplitude potential (SNAP). GV were obtained by continuous glucose monitoring including coefficient of variation (CV), SD, continuous overall net glycemic action (CONGA), and mean amplitude of glucose excursions (MAGE). Results: The study comprised 133 young adults (43.6% males), mean age of 22 years (SD 1.6). Unadjusted, higher CV was associated with a decreased risk of sural nerve conduction (P = 0.03), abnormal SNAP (P = 0.04) and incidents of definite CAN (P = 0.04). Likewise, higher CONGA was associated with increasing incidents of subclinical DSPN (P = 0.03), abnormal SNAP (P = 0.01), and SNCV (P = 0.02). However, both associations were not statistically significant in the fully adjusted model. Higher MAGE was associated with slightly increasing measures of HRV (P = 0.03) but only when fully adjusted. When correcting for multiple tests significance was lost. A significant association was found between HbA1c and measures of both DSPN (P < 0.02) and HRV (P < 0.03) in fully adjusted models. Conclusions: No significant associations between GV and diabetic neuropathy were found after adjusting for risk factors and multiple tests. This suggests that GV may not be a risk factor for diabetic neuropathy in young adults with type 1 diabetes. However, long-term effects of GV excursions may still play a role in the pathogenic mechanisms leading to neuropathy in later life.

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

Publications

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

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