Minor James M, Rickey Leslie M, Bergenstal Richard M
1 Newark, DE, USA.
2 Yale School of Medicine, New Haven, CT, USA.
J Diabetes Sci Technol. 2017 Sep;11(5):975-979. doi: 10.1177/1932296817700920. Epub 2017 Mar 22.
Diabetes health care relies on the HbA1c (A1c) assay and associated average glucose (AG) to evaluate and control chronic glycemia. However, the A1c assay is plagued with significant noise, lag time, and specificity issues. Current studies support the significant health care advantage of clinical action based on real-time blood glucose (BG) metrics. We seek to improve diabetes management by directly relating such metrics to AG levels as mediated by recently discovered recurrent endocrine cycles.
Several studies collected multiple months of BG data on 111 subjects totaling 261 893 CGM measurements and 29 278 meter readings. These data are a rich source of multiday metrics in terms of the CGM and SMBG daily profiles. The recurrent endocrine patterns expose key metric relationships for monitoring AG related to A1c using CGM and SMBG data. Consequently, day-to-day tracking of AG is expressed as a simple two-parameter function of fasting BG for all studies.
Consequently, when applied to 2518 qualified days of 64 subjects, the function predicts daily AG values with 2% relative standard error. All studies produced compatible results. By restricting one parameter to a constant, the error increased to 3%.
The recurrent endocrine patterns revealed a persistent structure hidden within the multiday fluctuations that becomes a simple meter-compatible equation that accurately measures real-time trending of AG using fasting BG values. This enables a digital health monitoring service and self-monitoring device that reveals immediate disease progression as well as the impact of interventions and medications better than possible with the A1c assay.
糖尿病医疗保健依赖糖化血红蛋白（HbA1c，A1c）检测及相关平均血糖（AG）来评估和控制慢性血糖水平。然而，A1c检测存在显著的噪声、延迟时间和特异性问题。当前研究支持基于实时血糖（BG）指标进行临床干预所具有的显著医疗保健优势。我们试图通过将此类指标与最近发现的反复出现的内分泌周期所介导的AG水平直接关联，来改善糖尿病管理。
多项研究收集了111名受试者数月的BG数据，共计261893次连续血糖监测（CGM）测量值和29278次血糖仪读数。就CGM和自我血糖监测（SMBG）的每日概况而言，这些数据是多日指标的丰富来源。反复出现的内分泌模式揭示了利用CGM和SMBG数据监测与A1c相关的AG的关键指标关系。因此，在所有研究中，AG的日常追踪都表示为空腹血糖的一个简单双参数函数。
因此，当将该函数应用于64名受试者的2518个合格日时，其预测每日AG值的相对标准误差为2%。所有研究都得出了一致的结果。若将其中一个参数设为常数，误差会增至3%。
反复出现的内分泌模式揭示了隐藏在多日波动中的一种持久结构，该结构成为一个简单的与血糖仪兼容的方程，可利用空腹血糖值准确测量AG的实时趋势。这使得一种数字健康监测服务和自我监测设备成为可能，其能比A1c检测更好地揭示疾病的即时进展以及干预措施和药物的影响。
