Marie-Anne Burckhardt, Marie Auzanneau, Joachim Rosenbauer, Elisabeth Binder, Jantje Weiskorn, Melanie Hess, Christof Klinkert, Joaquina Mirza, Lara-Sophie Zehnder, Sandra Wenzel, Kerstin Placzek, Reinhard W Holl
{"title":"1 型糖尿病青少年患者中，范围内时间、严格范围内时间与 HbA1c 之间的关系是什么？德国/奥地利/卢森堡/瑞士糖尿病前瞻性随访登记的结果。","authors":"Marie-Anne Burckhardt, Marie Auzanneau, Joachim Rosenbauer, Elisabeth Binder, Jantje Weiskorn, Melanie Hess, Christof Klinkert, Joaquina Mirza, Lara-Sophie Zehnder, Sandra Wenzel, Kerstin Placzek, Reinhard W Holl","doi":"10.1177/19322968241288870","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Time in range (TIR, 70-180 mg/dL) is an established marker of glycemic control. More recently, time in tight range (TTR, 70-140 mg/dL) has been proposed as well. The aim of this study was to examine the relationship between TIR, TTR, and HbA1c in youth and young adults with type 1 diabetes (T1D) in the German/Austrian/Luxembourgian/Swiss Diabetes Prospective Follow-up (DPV) registry.</p><p><strong>Methods: </strong>Data of youth and young adults aged ≤25 years with T1D for >3 months, documented in the DPV registry between 2019 and 2022 were analyzed. The most recent available HbA1c and corresponding continuous glucose monitoring (CGM) profiles in the 12 preceding weeks with at least 80% completeness were included. Associations were investigated using correlation and adjusted regression models.</p><p><strong>Results: </strong>1901 individuals (median age 14.0 years [IQR 10.4-16.9]) were included in the analysis. TIR and TTR correlated strongly, r = 0.965 (95% CI [0.962, 0.968]), <i>P</i> < .001. TTR estimates predicted from TIR were significantly higher in the group with high coefficient of variation (CV group ≥ 36%), <i>P</i> < .001. Correlations between TIR or TTR and HbA1c were both strong, r = -0.764 (95% CI [-0.782, -0.745]) and r = -0.777 (95% CI [-0.795, -0.759]), both <i>P</i> < .001, with no significant difference (<i>P</i> = .312) However, adjusted regression models indicated a slightly better fit for the prediction of HbA1c from TIR compared with TTR.</p><p><strong>Conclusions: </strong>Based on large, real-world data from a multinational registry, TIR and TTR correlated strongly, and both showed a good prediction of HbA1c. TTR estimates predicted from TIR were significantly higher in people with high glucose variability (CV).</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968241288870"},"PeriodicalIF":4.1000,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571145/pdf/","citationCount":"0","resultStr":"{\"title\":\"What is the Relationship Between Time in Range, Time in Tight Range, and HbA1c in Youth and Young Adults With Type 1 Diabetes? 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The most recent available HbA1c and corresponding continuous glucose monitoring (CGM) profiles in the 12 preceding weeks with at least 80% completeness were included. Associations were investigated using correlation and adjusted regression models.</p><p><strong>Results: </strong>1901 individuals (median age 14.0 years [IQR 10.4-16.9]) were included in the analysis. TIR and TTR correlated strongly, r = 0.965 (95% CI [0.962, 0.968]), <i>P</i> < .001. TTR estimates predicted from TIR were significantly higher in the group with high coefficient of variation (CV group ≥ 36%), <i>P</i> < .001. Correlations between TIR or TTR and HbA1c were both strong, r = -0.764 (95% CI [-0.782, -0.745]) and r = -0.777 (95% CI [-0.795, -0.759]), both <i>P</i> < .001, with no significant difference (<i>P</i> = .312) However, adjusted regression models indicated a slightly better fit for the prediction of HbA1c from TIR compared with TTR.</p><p><strong>Conclusions: </strong>Based on large, real-world data from a multinational registry, TIR and TTR correlated strongly, and both showed a good prediction of HbA1c. TTR estimates predicted from TIR were significantly higher in people with high glucose variability (CV).</p>\",\"PeriodicalId\":15475,\"journal\":{\"name\":\"Journal of Diabetes Science and Technology\",\"volume\":\" \",\"pages\":\"19322968241288870\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2024-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571145/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Diabetes Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/19322968241288870\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Diabetes Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/19322968241288870","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
摘要
目标：血糖控制范围时间（TIR，70-180 mg/dL）是血糖控制的既定指标。最近，又有人提出了 "紧幅时间"（TTR，70-140 毫克/分升）。本研究旨在研究德国/奥地利/卢森堡/瑞士糖尿病前瞻性随访（DPV）登记中 1 型糖尿病（T1D）青年和年轻成人的 TIR、TTR 和 HbA1c 之间的关系：方法：分析了2019年至2022年期间在DPV登记册中记录的年龄≤25岁、罹患T1D超过3个月的青年和年轻成人的数据。研究纳入了至少80%完整的前12周的最新HbA1c和相应的连续血糖监测（CGM）资料。使用相关性和调整回归模型对相关性进行了研究：分析共纳入 1901 人（中位年龄 14.0 岁 [IQR 10.4-16.9]）。TIR和TTR密切相关，r = 0.965 (95% CI [0.962, 0.968])，P < .001。在变异系数高的组别（CV 组≥ 36%）中，根据 TIR 预测的 TTR 估计值明显更高，P < .001。TIR 或 TTR 与 HbA1c 之间的相关性都很强，分别为 r = -0.764 (95% CI [-0.782, -0.745])和 r = -0.777 (95% CI [-0.795, -0.759])，均 P <.001，无显著差异 (P = .312)，但调整后的回归模型显示，与 TTR 相比，TIR 预测 HbA1c 的拟合度稍高：结论：基于跨国登记处的大量真实数据，TIR 和 TTR 具有很强的相关性，两者都能很好地预测 HbA1c。根据 TIR 预测的 TTR 估计值在血糖变异性（CV）高的人群中明显更高。
What is the Relationship Between Time in Range, Time in Tight Range, and HbA1c in Youth and Young Adults With Type 1 Diabetes? Results From the German/Austrian/Luxembourgian/Swiss Diabetes Prospective Follow-Up Registry.
Objectives: Time in range (TIR, 70-180 mg/dL) is an established marker of glycemic control. More recently, time in tight range (TTR, 70-140 mg/dL) has been proposed as well. The aim of this study was to examine the relationship between TIR, TTR, and HbA1c in youth and young adults with type 1 diabetes (T1D) in the German/Austrian/Luxembourgian/Swiss Diabetes Prospective Follow-up (DPV) registry.
Methods: Data of youth and young adults aged ≤25 years with T1D for >3 months, documented in the DPV registry between 2019 and 2022 were analyzed. The most recent available HbA1c and corresponding continuous glucose monitoring (CGM) profiles in the 12 preceding weeks with at least 80% completeness were included. Associations were investigated using correlation and adjusted regression models.
Results: 1901 individuals (median age 14.0 years [IQR 10.4-16.9]) were included in the analysis. TIR and TTR correlated strongly, r = 0.965 (95% CI [0.962, 0.968]), P < .001. TTR estimates predicted from TIR were significantly higher in the group with high coefficient of variation (CV group ≥ 36%), P < .001. Correlations between TIR or TTR and HbA1c were both strong, r = -0.764 (95% CI [-0.782, -0.745]) and r = -0.777 (95% CI [-0.795, -0.759]), both P < .001, with no significant difference (P = .312) However, adjusted regression models indicated a slightly better fit for the prediction of HbA1c from TIR compared with TTR.
Conclusions: Based on large, real-world data from a multinational registry, TIR and TTR correlated strongly, and both showed a good prediction of HbA1c. TTR estimates predicted from TIR were significantly higher in people with high glucose variability (CV).
期刊介绍：
The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.
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