Hemoglobin A1C Variability as a Risk Factor for Diabetes Complications

血红蛋白 A1C 变异是糖尿病并发症的危险因素

基本信息

批准号：
10664333
负责人：
PAUL R CONLIN
金额：
$ 25.22万
依托单位：
BOSTON VA RESEARCH INSTITUTE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10664333
关键词：
Address Affect Age Anticoagulation Benefits and Risks Blood Pressure Cardiovascular Diseases Caring Characteristics Clinical Clinical Practice Guideline Clinical Trials Complex Complications of Diabetes Mellitus Development Diabetes Mellitus Elderly Equilibrium Event Glycosylated hemoglobin A Goals Health Healthcare Systems Hyperglycemia Hypoglycemia Intuition Kidney Diseases LDL Cholesterol Lipoproteins Link Measures Mediating Mediator of activation protein Methods Microvascular Dysfunction Monitor Myocardial Infarction Organ Outcome Patients Pattern Pharmaceutical Preparations Process Provider Quality Control Quality of Care RHOA gene Research Retinal Diseases Risk Risk Factors Sampling Selection Bias Statistical Methods Stroke Techniques Testing Time United States Department of Veterans Affairs Variant adverse outcome base blood glucose regulation cholesterol control design diabetes risk health management individual patient individualized medicine interest macrovascular disease medication compliance mortality mortality risk novel point of care population health prevent risk stratification standard measure systematic review

项目摘要

Persistent hyperglycemia predicts the development of microvascular complications (e.g. retinopathy and nephropathy) in patients with diabetes. However, the relationship between glucose control and organ damage is complex. Reducing hemoglobin A1c (A1c) prevents or delays microvascular complications but cardiovascular disease (CVD) and mortality are inconsistently affected. Thus, there is a need to develop new quality measures beyond A1c alone to better identify and treat patients at risk for complications and mortality. A1c variability, as measured by fluctuations in A1c over time, is a strong candidate. Several studies show a significant relationship between increased A1c variability and microvascular disease and CVD. While A1c variability carries important risk information, variance measures such as standard deviation, may not be clinically intuitive. Thus, our goal is to develop a new quality measure of A1c variability – A1c time in range (TIR) – that helps clinicians and patients control A1c in a way that balances long-term benefits and risks. We will define A1c TIR as the percentage of days a patient's A1c levels are in a specific target range, based on their clinical characteristics and clinical practice guidelines. We will study A1c TIR in a generalizable nationwide sample of over 365,000 patients from the Department of Veterans Affairs and Kaiser Permanente. We will apply advanced statistical methods that stringently control for selection bias by using an instrumental variable design, including process quality controls. These methods allow us to draw causal inferences between TIR and risk of new diabetes complications. We will study the predictors of A1c TIR, which we hypothesize will be affected by provider practice patterns, individual patient-level characteristics and medications. We will test the hypothesis that higher A1c TIR confers lower risk of diabetes complications. We will also study the converse – A1c time out-of-range (TOR), with interest in deviations both above (TOR [high]) and below the range (TOR [low]) to determine if either is uniquely associated with micro- or macrovascular complications. Then we will investigate clinical factors, hypoglycemic events and rapid declines in A1c, as mediators of the relationship between TIR and adverse outcomes. Each is linked to mortality and early worsening of diabetes complications, respectively. This study will advance diabetes care by developing a novel quality measure that identifies patients in a risk-stratified way and helps clinicians tailor diabetes treatment based on a patient's unique goals of care. Such a new measure will be used by clinicians at the point-of-care and by healthcare systems for population health management.

持续高血糖可预测微血管并发症的发生（例如视网膜病变和然而，糖尿病患者的血糖控制与器官损伤之间的关系。降低血红蛋白 A1c (A1c) 可以预防或延缓微血管并发症，但这一过程很复杂。心血管疾病（CVD）和死亡率受到的影响不一致，因此需要开发新的治疗方法。超越 A1c 的质量措施可以更好地识别和治疗有并发症和死亡风险的患者。通过 A1c 随时间的波动来衡量的 A1c 变异性是一个强有力的候选者。 A1c 变异性增加与微血管疾病和 CVD 之间存在显着关系。变异性携带重要的风险信息，标准差等方差度量可能不因此，我们的目标是开发一种新的 A1c 变异性质量测量方法——A1c 时间范围。 (TIR) – 帮助患者以平衡长期利益和风险的方式控制 A1c。将 A1c TIR 定义为患者 A1c 水平处于特定目标范围内的天数百分比，基于我们将以可概括的方式研究 A1c TIR。来自退伍军人事务部和 Kaiser Permanente 的全国超过 365,000 名患者的样本。我们将应用先进的统计方法，通过使用仪器严格控制选择偏差变量设计，包括过程质量控制，这些方法使我们能够在之间进行因果推论。 TIR 和新糖尿病并发症的风险我们将研究 A1c TIR 的预测因素。受到提供者实践模式、患者个体特征和药物的影响，我们将进行测试。假设较高的 A1c TIR 会降低糖尿病并发症的风险。相反 – A1c 时间超出范围 (TOR)，关注高于 (TOR [高]) 和低于 (TOR [高]) 的偏差范围（TOR [低]）以确定其中任一者是否与微血管或大血管并发症唯一相关。然后我们将研究临床因素、低血糖事件和 A1c 快速下降，作为 TIR 与不良后果之间的关系均与死亡率和糖尿病早期恶化有关。这项研究将通过开发一种新的质量措施来推进糖尿病护理。以风险分层的方式识别患者，并帮助根据患者的情况制定糖尿病治疗方案农民将在护理点和医疗保健中使用这种新措施。人口健康管理体系。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Association of hemoglobin A1c stability with mortality and diabetes complications in older adults with diabetes.

DOI：
10.1136/bmjdrc-2022-003211
发表时间：
2023-04
期刊：
BMJ OPEN DIABETES RESEARCH & CARE
影响因子：
4.1
作者：
Conlin, Paul R.;Zhang, Libin;Li, Donglin;Nelson, Richard E.;Prentice, Julia C.;Mohr, David C.
通讯作者：
Mohr, David C.

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