英国出生队列中 308 名老年参与者的连续血糖监测 (CGM):可变性和相关性

Sophie V. Eastwood, Michele Orini, Andrew Wong, Scott T Chiesa, Joshua King-Robson, Jonathan Scott, Nishi Chaturvedi
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引用次数: 0

摘要

导言和背景高血糖和低血糖时期可能会分别增加常见慢性疾病的风险,并损害认知和身体功能,即使没有糖尿病的人也是如此。老年人发生不良血糖偏移的频率可能更高,部分原因是自主神经功能紊乱和睡眠质量下降。然而,有关非糖尿病老年人的数据却很少。目标与方法1)描述血糖变异性(已完成);2)描述主要是非糖尿病老年人队列(计划中)中血糖变异性与社会人口学和生活方式的相关性。参与者于 2021-2023 年期间从英国出生队列(1946 年全国健康与发展调查研究)中招募。他们佩戴连续血糖监测仪(Freestyle libre Abbott),每小时测量四次循环血糖,持续七天。计算了汇总统计数据和超出范围(4.4-7.8mmol/L)的时间。有关血糖偏离和日变异性的更多信息将使用 R "iglu "软件包收集。对于所有 CGM 摘要和偏移量,未来的分析将研究:与 HbA1c、社会人口统计学、身体组成、体育活动、饮食和饮酒的关联。分析结果将根据同步动态心电图(飞利浦 Actiwatch Spectrum Plus)估算的睡眠/觉醒时间段进行分层。敏感性分析将排除服用低血糖/高血糖药物者和糖尿病患者。与数字足迹的相关性未来的研究可以利用得出的总结性指标,深入了解作为人群风险因素的血糖变异性。这项工作将汇集多种数据源,即来自 CGM、动图和基线队列数据的数据。结果参与者年龄在 75-76 岁之间,45% 为女性,10% 已确诊糖尿病;体重指数中位数(IQR)为 26.8 (24.6-29.2) kg/m2。收集了 308 名参与者的 CGM 数据,中位数(IQR)为 6.9(6.7-7.6)天。记录期间的平均血糖值为 5.7mmol/L (5.3-6.2mmol/L),标准偏差为 1.0mmol/L (0.8-1.3mmol/L),超出范围的时间为 12.8% (6.2-24.7%),16% 的参与者每天高于范围≥1 小时,低于范围≥1 小时。结论与启示血糖仪对这批老年人来说是可行的,并显示出主要是非糖尿病群体的血糖超出范围的时间水平较高。未来的分析将确定,与单独的血糖测量相比,增强的血糖变异性特征描述是否是预测未来疾病风险的更准确工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Continuous glucose monitoring (CGM) for 308 older-age participants in an English birth cohort: variability and correlates
Introduction & BackgroundEpochs of hyperglycaemia and hypoglycaemia may each increase risk of common chronic diseases and impair both cognitive and physical function even in people without diabetes. Older people may have greater frequency of adverse glycaemic excursions, partly due to disordered autonomic function and sleep quality. Data for older, non-diabetic people are however scant. Objectives & Approach1) To describe blood glucose variability (completed) and 2) its socio-demographic and lifestyle correlates in a predominantly non-diabetic cohort of older adults (planned). Participants were recruited during 2021-2023 from an English birth cohort (the 1946 National Survey for Health and Development Study). They wore a continuous glucose monitor (Freestyle libre Abbott), which measured circulating glucose four times/hour, for seven days. Summary statistics and time outside range (4.4-7.8mmol/L) were calculated. Further information on glycaemic excursions and day-to-day variability will be gleaned using the R “iglu” package. For all CGM summary and excursion measures, future analyses will investigate: associations with HbA1c, socio-demographics, body composition, physical activity, diet and alcohol use. Results will be stratified by sleep/ wake time periods estimated from simultaneous actigraphy (Philips Actiwatch Spectrum Plus). Sensitivity analyses will exclude people taking hypo/ hyperglycaemic medications and those with diabetes. Relevance to Digital FootprintsDerived summary measures can be used by future studies to give insights into glycaemic variability as a population-level risk factor. This work will bring together multiple data sources, i.e. from CGM, actigraphy and baseline cohort data. ResultsParticipants were aged 75-76 years, 45% female and 10% had diagnosed diabetes; median (IQR) BMI was 26.8 (24.6-29.2) kg/m2. CGM data from 308 participants was collected, for a median (IQR) of 6.9 (6.7-7.6) days. Average glucose over the recording period was 5.7mmol/L (5.3-6.2mmol/L), standard deviation was 1.0mmol/L (0.8-1.3mmol/L), time outside range was 12.8% (6.2-24.7%) and 16% of participants spent ≥1 hour/day above and ≥1 hour/day below range. Conclusions & ImplicationsCGM was feasible for this cohort of older adults, and demonstrated high levels of time outside range for a predominantly non-diabetic group. Future analysis will determine whether enhanced characterisation of glycaemic variability is a potentially more accurate tool for predicting future disease risk than isolated glucose measurements.
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