Piloya-Were Thereza, Nyangabayaki Catherine, Dunn Timothy C, Malinga Daniel, Nambooze Jemima, Pappenfus Elizabeth, Zhang Lin, Bindal Anila, Beasley Shannon, Sunni Muna, Nathan Brandon M, Liu Sandy, Moran Antoinette
Department of Pediatrics, Makerere University College of Health Sciences, Kampala, Uganda.
Department of Pediatrics, St. Francis Hospital, Kampala, Uganda.
Diabetes Technol Ther. 2025 Aug;27(8):641-650. doi: 10.1089/dia.2024.0537. Epub 2025 Mar 20.
Continuous glucose monitoring (CGM) is unaffordable in sub-Saharan Africa, and providers rely heavily on hemoglobin A1c (A1c) to guide insulin adjustment. The relationship between A1c and mean glucose (MG) varies between individuals and populations. We assessed this relationship in Ugandan youth of age 4-26 years with type 1 diabetes, and evaluated whether calculation of the personalized A1c (pA1c), which only requires a brief initial sensor wear, is clinically useful. CGM data were averaged across three blinded sensor wears (31-41 days). We calculated individual apparent glycation ratios using A1c after the second sensor, and applied these to A1cs collected after the third sensor to determine pA1c. Participants were evaluated for clinical factors that influence red blood cell (RBC) lifespan (malaria, G6PD deficiency, sickle-cell trait, hemolysis, iron deficiency). Patients across the A1c spectrum experienced substantial time in both hyper- and hypoglycemia; average coefficient of variation was 44%. MG was >250 mg/dL (13.9 mmol/L) in 50% of participants, and 55% of participants spent ≥4% time with glucose <70 mg/dL (3.9 mmol/L). There was considerable variability in the A1c-MG relationship. The pA1c more accurately represented MG by significantly reducing variation in this relationship ( = 0.84 vs. 0.40; = 0.92 vs. 0.63), but MG is not useful in individuals with the wide glucose fluctuations seen in this population. Clinical factors did not impact the A1c-MG relationship. Neither the measured A1c nor the calculated pA1c provided reliable guidance for insulin adjustment in this population. No matter how accurately MG is measured or estimated, it is just an average, with limited clinical application in individuals with wide glycemic variation. These measures cannot replace the information available from CGM about glycemic excursion, daily glucose patterns, or percent time in various glucose ranges. Our data suggest that it is essential to find a way to make CGM at least periodically affordable in low-resource settings.
在撒哈拉以南非洲，持续葡萄糖监测（CGM）费用高昂，医疗服务提供者严重依赖糖化血红蛋白（A1c）来指导胰岛素调整。A1c与平均血糖（MG）之间的关系因个体和人群而异。我们评估了乌干达4至26岁1型糖尿病青少年中这种关系，并评估了仅需在初始阶段短暂佩戴传感器即可计算出的个性化A1c（pA1c）在临床上是否有用。CGM数据是在三次盲法佩戴传感器（31至41天）期间进行平均的。我们在第二次佩戴传感器后使用A1c计算个体表观糖化率，并将其应用于第三次佩戴传感器后收集的A1c以确定pA1c。对参与者评估了影响红细胞（RBC）寿命的临床因素（疟疾、葡萄糖-6-磷酸脱氢酶缺乏症、镰状细胞性状、溶血、缺铁）。整个A1c范围的患者在高血糖和低血糖状态下都经历了大量时间；平均变异系数为44%。50%的参与者MG>250mg/dL（13.9mmol/L），55%的参与者有≥4%的时间血糖<70mg/dL（3.9mmol/L）。A1c与MG之间的关系存在相当大的变异性。pA1c通过显著降低这种关系中的变异性更准确地反映了MG（分别为0.84对0.40；0.92对0.63），但MG在该人群中血糖波动较大的个体中并不有用。临床因素并未影响A1c与MG之间的关系。无论是测得的A1c还是计算出的pA1c都不能为该人群的胰岛素调整提供可靠指导。无论MG测量或估计得多么准确，它都只是一个平均值，在血糖变异性较大的个体中临床应用有限。这些测量方法无法替代CGM提供的有关血糖波动、每日血糖模式或处于不同血糖范围的时间百分比的信息。我们的数据表明，必须找到一种方法，使CGM在资源匮乏地区至少能定期负担得起。
