Dataset Information

A1C variability as an independent risk factor for microalbuminuria in young people with type 1 diabetes.

ABSTRACT: To assess the potential association between A1C variability (A1C-SD) and microalbuminuria in young people with type 1 diabetes.Serially collected samples for A1C measurement were available for 1,232 subjects with childhood-onset type 1 diabetes recruited to the Oxford Regional Prospective Study and the Nephropathy Family Study.The median (range) number of A1C assessments was 4 (2-16). Mean intrapersonal A1C was 9.5% and A1C-SD was 0.91. Mean A1C and A1C-SD values were higher in subjects with microalbuminuria (n = 227) than in those with normoalbuminuria (10.3 vs. 9.4%; 1.12 vs. 0.86, P < 0.001). In a Cox regression model, A1C-SD was independently associated with microalbuminuria (hazard ratio 1.31 [95% CI 1.01-1.35]).In the current study, A1C variability was an independent variable that added to the effect of A1C on the risk for microalbuminuria in youth with type 1 diabetes, a population highly vulnerable to vascular complications.

SUBMITTER: Marcovecchio ML

PROVIDER: S-EPMC3064014 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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A1C variability as an independent risk factor for microalbuminuria in young people with type 1 diabetes.

Marcovecchio M Loredana ML Dalton R Neil RN Chiarelli Francesco F Dunger David B DB

Diabetes care 20110218 4

<h4>Objective</h4>To assess the potential association between A1C variability (A1C-SD) and microalbuminuria in young people with type 1 diabetes.<h4>Research design and methods</h4>Serially collected samples for A1C measurement were available for 1,232 subjects with childhood-onset type 1 diabetes recruited to the Oxford Regional Prospective Study and the Nephropathy Family Study.<h4>Results</h4>The median (range) number of A1C assessments was 4 (2-16). Mean intrapersonal A1C was 9.5% and A1C-SD ...[more]

PMID: 21335371

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Project description:ObjectivesMost type 2 diabetes simulation models utilise equations mapping out lifetime trajectories of risk factors [e.g. glycated haemoglobin (HbA1c)]. Existing equations, using historic data or assuming constant risk factors, frequently underestimate or overestimate complication rates. Updated risk factor time path equations are needed for simulation models to more accurately predict complication rates.Aims(1) Update United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2) risk factor time path equations; (2) compare quality-adjusted life-years (QALYs) using original and updated equations; and (3) compare QALY gains for reference case simulations using different risk factor equations.MethodsUsing pooled contemporary data from two randomised trials EXSCEL and TECOS (n = 28,608), we estimated: dynamic panel models of seven continuous risk factors (high-density lipoprotein cholesterol, low density lipoprotein cholesterol, HbA1c, haemoglobin, heart rate, blood pressure and body mass index); two-step models of estimated glomerular filtration rate; and survival analyses of peripheral arterial disease, atrial fibrillation and albuminuria. UKPDS-OM2-derived lifetime QALYs were extrapolated over 70 years using historical and the new risk factor equations.ResultsAll new risk factor equation predictions were within 95% confidence intervals of observed values, displaying good agreement between observed and estimated values. Historical risk factor time path equations predicted trial participants would accrue 9.84 QALYs, increasing to 10.98 QALYs using contemporary equations.DiscussionIncorporating updated risk factor time path equations into diabetes simulation models could give more accurate predictions of long-term health, costs, QALYs and cost-effectiveness estimates, as well as a more precise understanding of the impact of diabetes on patients' health, expenditure and quality of life.Trial registrationClinicalTrials.gov NCT01144338 and NCT00790205.

Project description:BackgroundCardiovascular disease (CVD) prevention in diabetes requires broad-based treatment of dyslipidemia, hypertension, and hyperglycemia. The independent contribution of all combinations of risk factor control to CVD risk has not been evaluated.ObjectiveTo estimate the independent association of control of glycosylated hemoglobin (A1C), systolic blood pressure (SBP), and low-density lipoprotein cholesterol (LDL-C) with risk of cardiovascular disease hospitalization.DesignNon-concurrent longitudinal cohort study.PatientsThe study included 26,636 patients with type 2 diabetes who were members of an integrated group model HMO with multiple A1C, SBP, and LDL-C measurements.Main measuresPatients were followed for a mean (SD) of 5.6 (2.5) years until they died or disenrolled, or until 31 December 2010. The outcome was a first-observed CVD hospitalization. Using the mean of all A1C, SBP, and LDL-C measures during follow-up, we created dichotomous categories of A1C control (< 7 %), SBP control (< 130 mmHg), and LDL-C control (< 100 mg/dL) to estimate the incidence rate of CVD hospitalization associated with all combinations of risk factor control adjusting for demographic and clinical characteristics.Key resultsPatients with no controlled risk factors (18.2/1,000 person-years, 95 % CI 16.5-20.2) or with only A1C in control (16.9, 15.0-19.0) had the highest rate of CVD hospitalization, whereas those with all three risk factors controlled (7.2, 6.2-8.4) or with SBP and LDL-C in control (6.1, 5.1-7.2) had the lowest rates. Those with only SBP or LDL-C in control, A1C and SBP controlled, or A1C and LDL-C controlled had statistically similar incidence between the highest and lowest rates.ConclusionsMaintaining SBP < 130 mmHg or LDL-C < 100 mg/dL was significantly associated with reduced CVD hospitalization risk, especially when both risk factors were well controlled. Maintaining A1C < 7 % was not independently associated with reduced CVD hospitalization risk.

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A1C variability as an independent risk factor for microalbuminuria in young people with type 1 diabetes.

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A1C variability as an independent risk factor for microalbuminuria in young people with type 1 diabetes.

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