Abstract Aims The association of glycemic variability with microvascular disease complications in type 2 diabetes (T2D) has been under-studied and remains unclear. We investigated this relationship using both Action to Control Cardiovascular Risk in Diabetes (ACCORD) and the Veteran Affairs Diabetes Trial (VADT). Methods In ACCORD, fasting plasma glucose (FPG) was measured 1 to 3 times/year for up to 84 months in 10 251 individuals. In the VADT, FPG was measured every 3 months for up to 87 months in 1791 individuals. Variability measures included coefficient of variation (CV) and average real variability (ARV) for fasting glucose. The primary composite outcome was time to either severe nephropathy or retinopathy event and secondary outcomes included each outcome individually. To assess the association, we considered variability measures as time-dependent covariates in Cox proportional hazard models. We conducted a meta-analysis across the 2 trials to estimate the risk of fasting glucose variability as well as to assess the heterogenous effects of FPG variability across treatment arms. Results In both ACCORD and the VADT, the CV and ARV of FPG were associated with development of future microvascular outcomes even after adjusting for other risk factors, including measures of average glycemic control (ie, cumulative average of HbA1c). Meta-analyses of these 2 trials confirmed these findings and indicated FPG variation may be more harmful in those with less intensive glucose control. Conclusions This post hoc analysis indicates that variability of FPG plays a role in, and/or is an independent and readily available marker of, development of microvascular complications in T2D.
more »
« less
Association of Both Short-term and Long-term Glycemic Variability With the Development of Microalbuminuria in the ACCORD Trial
Both long- and short-term glycemic variability have been associated with incident diabetes complications. We evaluated their relative and potential additive effects on incident renal complications in the Action to Control Cardiovascular Risk in Diabetes trial. A marker of short-term glycemic variability, 1,5-anhydroglucitol (1,5-AG), was measured in 4,000 random 12-month postrandomization plasma samples (when hemoglobin A1c [HbA1c] was stable). Visit-to-visit fasting plasma glucose coefficient of variation (CV-FPG) was determined from 4 months postrandomization until the end point of microalbuminuria or macroalbuminuria. Using Cox proportional hazards models, high CV-FPG and low 1,5-AG were independently associated with microalbuminuria after adjusting for clinical risk factors. However, only the CV-FPG association remained after additional adjustment for average HbA1c. Only CV-FPG was a significant risk factor for macroalbuminuria. This post hoc analysis indicates that long-term rather than short-term glycemic variability better predicts the risk of renal disease in type 2 diabetes.
The relative and potential additive effects of long- and short-term glycemic variability on the development of diabetic complications are unknown. We aimed to assess the individual and combined relationships of long-term visit-to-visit glycemic variability, measured as the coefficient of variation of fasting plasma glucose, and short-term glucose fluctuation, estimated by the biomarker 1,5-anhydroglucitol, with the development of proteinuria. Both estimates of glycemic variability were independently associated with microalbuminuria, but only long-term glycemic variability remained significant after adjusting for average hemoglobin A1c. Our findings suggest that longer-term visit-to-visit glucose variability improves renal disease prediction in type 2 diabetes.
- PAR ID:
- 10503018
- Publisher / Repository:
- American Diabetes Association
- Date Published:
- Journal Name:
- Diabetes
- Volume:
- 72
- Issue:
- 12
- ISSN:
- 0012-1797
- Page Range / eLocation ID:
- 1864 to 1869
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Background: The glycemia risk index (GRI) is a composite metric developed and used to estimate quality of glycemia in adults with diabetes who use continuous glucose monitor (CGM) devices. In a cohort of youth with type 1 diabetes (T1D), we examined the utility of the GRI for evaluating quality of glycemia between clinic visits by analyzing correlations between the GRI and longitudinal glycated hemoglobin A1c (HbA1c) measures.
Method: Using electronic health records and CGM data, we conducted a retrospective cohort study to analyze the relationship between the GRI and longitudinal HbA1c measures in youth (T1D duration ≥1 year; ≥50% CGM wear time) receiving care from a Midwest pediatric diabetes clinic network (March 2016 to May 2022). Furthermore, we analyzed correlations between HbA1c and the GRI high and low components, which reflect time spent with high/very high and low/very low glucose, respectively.
Results: In this cohort of 719 youth (aged = 2.5-18.0 years [median = 13.4; interquartile range [IQR] = 5.2]; 50.5% male; 83.7% non-Hispanic White; 68.0% commercial insurance), baseline GRI scores positively correlated with HbA1c measures at baseline and 3, 6, 9, and 12 months later (r = 0.68, 0.65, 0.60, 0.57, and 0.52, respectively). At all time points, strong positive correlations existed between HbA1c and time spent in hyperglycemia. Substantially weaker, negative correlations existed between HbA1c and time spent in hypoglycemia.
Conclusions: In youth with T1D, the GRI may be useful for evaluating quality of glycemia between scheduled clinic visits. Additional CGM-derived metrics are needed to quantify risk for hypoglycemia in this population.
-
more » « less
Background: The Glycemia Risk Index (GRI) was developed in adults with diabetes and is a validated metric of quality of glycemia. Little is known about the relationship between GRI and type 1 diabetes (T1D) self-management habits, a validated assessment of youths’ engagement in habits associated with glycemic outcomes.
Method: We retrospectively examined the relationship between GRI and T1D self-management habits in youth with T1D who received care from a Midwest pediatric diabetes clinic network. The GRI was calculated using seven days of continuous glucose monitor (CGM) data, and T1D self-management habits were assessed ±seven days from the GRI score. A mixed-effects Poisson regression model was used to evaluate the total number of habits youth engaged in with GRI, glycated hemoglobin A1c (HbA1c), age, race, ethnicity, and insurance type as fixed effects and participant ID as a random effect to account for multiple clinic visits per individual.
Results: The cohort included 1182 youth aged 2.5 to 18.0 years (mean = 13.8, SD = 3.5) comprising 50.8% male, 84.6% non-Hispanic White, and 64.8% commercial insurance users across a total of 6029 clinic visits. Glycemia Risk Index scores decreased as total number of habits performed increased, suggesting youth who performed more self-management habits achieved a higher quality of glycemia.
Conclusions: In youth using CGMs, GRI may serve as an easily obtainable metric to help identify youth with above target glycemia, and engagement/disengagement in the T1D self-management habits may inform clinicians with suitable interventions for improving glycemic outcomes.
-
Abstract Background Evidence to guide type 2 diabetes treatment individualization is limited. We evaluated heterogeneous treatment effects (HTE) of intensive glycemic control in type 2 diabetes patients on major adverse cardiovascular events (MACE) in the Action to Control Cardiovascular Risk in Diabetes Study (ACCORD) and the Veterans Affairs Diabetes Trial (VADT). Methods Causal forests machine learning analysis was performed using pooled individual data from two randomized trials (n = 12,042) to identify HTE of intensive versus standard glycemic control on MACE in patients with type 2 diabetes. We used variable prioritization from causal forests to build a summary decision tree and examined the risk difference of MACE between treatment arms in the resulting subgroups. Results A summary decision tree used five variables (hemoglobin glycation index, estimated glomerular filtration rate, fasting glucose, age, and body mass index) to define eight subgroups in which risk differences of MACE ranged from − 5.1% (95% CI − 8.7, − 1.5) to 3.1% (95% CI 0.2, 6.0) (negative values represent lower MACE associated with intensive glycemic control). Intensive glycemic control was associated with lower MACE in pooled study data in subgroups with low (− 4.2% [95% CI − 8.1, − 1.0]), intermediate (− 5.1% [95% CI − 8.7, − 1.5]), and high (− 4.3% [95% CI − 7.7, − 1.0]) MACE rates with consistent directions of effect in ACCORD and VADT alone. Conclusions This data-driven analysis provides evidence supporting the diabetes treatment guideline recommendation of intensive glucose lowering in diabetes patients with low cardiovascular risk and additionally suggests potential benefits of intensive glycemic control in some individuals at higher cardiovascular risk.more » « less
-
more » « less
OBJECTIVE Current clinical guidelines for managing type 2 diabetes do not differentiate based on patient-specific factors. We present a data-driven algorithm for personalized diabetes management that improves health outcomes relative to the standard of care.
RESEARCH DESIGN AND METHODS We modeled outcomes under 13 pharmacological therapies based on electronic medical records from 1999 to 2014 for 10,806 patients with type 2 diabetes from Boston Medical Center. For each patient visit, we analyzed the range of outcomes under alternative care using a k-nearest neighbor approach. The neighbors were chosen to maximize similarity on individual patient characteristics and medical history that were most predictive of health outcomes. The recommendation algorithm prescribes the regimen with best predicted outcome if the expected improvement from switching regimens exceeds a threshold. We evaluated the effect of recommendations on matched patient outcomes from unseen data.
RESULTS Among the 48,140 patient visits in the test set, the algorithm’s recommendation mirrored the observed standard of care in 68.2% of visits. For patient visits in which the algorithmic recommendation differed from the standard of care, the mean posttreatment glycated hemoglobin A1c (HbA1c) under the algorithm was lower than standard of care by 0.44 ± 0.03% (4.8 ± 0.3 mmol/mol) (P < 0.001), from 8.37% under the standard of care to 7.93% under our algorithm (68.0 to 63.2 mmol/mol).
CONCLUSIONS A personalized approach to diabetes management yielded substantial improvements in HbA1c outcomes relative to the standard of care. Our prototyped dashboard visualizing the recommendation algorithm can be used by providers to inform diabetes care and improve outcomes.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2337/db23-0374
