Aims/hypothesis

Gestational diabetes mellitus (GDM) affects 14% of all pregnancies worldwide and is associated with cardiometabolic risk. We aimed to exploit high-resolution wearable device time-series data to create a fine-grained physiological characterisation of the postpartum GDM state in free-living conditions, including clinical variables, daily glucose dynamics, food and drink consumption, physical activity, sleep patterns and heart rate.

Methods

In a prospective observational study, we employed continuous glucose monitors (CGMs), a smartphone food diary, triaxial accelerometers and heart rate and heart rate variability monitors over a 2 week period to compare women who had GDM in the previous pregnancy (GDM group) and women who had a pregnancy with normal glucose metabolism (non-GDM group) at 1–2 months after delivery (baseline) and 6 months later (follow-up). We integrated CGM data with ingestion events recorded with the smartphone app MyFoodRepo to quantify the rapidity of returning to preprandial glucose levels after meal consumption. We inferred the properties of the underlying 24 h rhythm in the baseline glucose. Aggregating the baseline and follow-up data in a linear mixed model, we quantified the relationships between glycaemic variables and wearable device-derived markers of circadian timing.

Results

Compared with the non-GDM group (n=15), the GDM group (n=22, including five with prediabetes defined based on fasting plasma glucose [5.6–6.9 mmol/l (100–125 mg/dl)] and/or HbA_1c [39–47 mmol/mol (5.7–6.4%)]) had a higher BMI, HbA_1c and mean amplitude of glycaemic excursion at baseline (all p≤0.05). Integrating CGM data and ingestion events showed that the GDM group had a slower postprandial glucose decrease (p=0.01) despite having a lower proportion of carbohydrate intake, similar mean glucose levels and a reduced amplitude of the underlying glucose 24 h rhythm (p=0.005). Differences in CGM-derived variables persisted when the five women with prediabetes were removed from the comparison. Longitudinal analysis from baseline to follow-up showed a significant increase in fasting plasma glucose across both groups. The CGM-derived metrics showed no differences from baseline to follow-up. Late circadian timing (i.e. sleep midpoint, eating midpoint and peak time of heart rate) was correlated with higher fasting plasma glucose and reduced amplitudes of the underlying glucose 24 h rhythm (all p≤0.05).

Conclusions/interpretation

We reveal GDM-related postpartum differences in glucose variability and 24 h rhythms, even among women clinically considered to be normoglycaemic. Our results provide a rationale for future interventions aimed at improving glucose variability and encouraging earlier daily behavioural patterns to mitigate the long-term cardiometabolic risk of GDM.

Trial registration

ClinicalTrials.gov no. NCT04642534

Graphical Abstract

中文翻译：

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使用多种可穿戴设备表征的产后妊娠糖尿病的代谢和昼夜节律特征

 目标/假设

妊娠糖尿病 （GDM） 影响全球 14% 的妊娠，并与心脏代谢风险有关。我们旨在利用高分辨率可穿戴设备时间序列数据来创建自由生活条件下产后 GDM 状态的精细生理特征，包括临床变量、每日血糖动态、饮食消耗、身体活动、睡眠模式和心率。

 方法

在一项前瞻性观察研究中，我们在 2 周内采用连续血糖监测器 （CGM）、智能手机食物日记、三轴加速度计以及心率和心率变异性监测器，以比较上次妊娠患有 GDM 的妇女（GDM 组）和妊娠后 1-2 个月（基线）和葡萄糖代谢正常的妇女（非 GDM 组）分娩后 1-2 个月（基线）和 6 个月后（随访）。我们将 CGM 数据与智能手机应用程序 MyFoodRepo 记录的摄入事件相结合，以量化饭后恢复到餐前血糖水平的速度。我们推断了基线葡萄糖中潜在 24 小时节律的特性。将基线和随访数据汇总到线性混合模型中，我们量化了血糖变量与可穿戴设备衍生的昼夜节律时间标志物之间的关系。

 结果

与非 GDM 组 （n=15） 相比，GDM 组 （n=22，包括 5 例根据空腹血糖 [5.6-6.9 mmol/l （100-125 mg/dl）] 和/或 HbA_1c [39-47 mmol/mol （5.7-6.4%）] 定义的糖尿病前期患者具有更高的 BMI、HbA_1c 和基线时血糖波动的平均幅度 （均 p≤0.05）。整合 CGM 数据和摄入事件表明，尽管碳水化合物摄入量比例较低，平均血糖水平相似，潜在葡萄糖 24 小时节律幅度降低，但 GDM 组餐后血糖下降较慢 （p=0.01） （p=0.005）。当从比较中去除 5 名糖尿病前期女性时，CGM 衍生变量的差异仍然存在。从基线到随访的纵向分析显示，两组空腹血糖均显著增加。CGM 衍生的指标显示从基线到随访没有差异。昼夜节律时间较晚 （即 睡眠中点、进食中点和心率峰值时间） 与较高的空腹血糖和潜在葡萄糖 24 小时节律振幅降低相关 （均 p≤0.05）。

结论/解释

我们揭示了 GDM 相关的产后血糖变异性和 24 小时节律的差异，即使在临床上被认为血糖正常的女性中也是如此。我们的结果为未来旨在改善葡萄糖变异性和鼓励早期日常行为模式以减轻 GDM 的长期心脏代谢风险的干预措施提供了理论依据。

 试用注册

ClinicalTrials.gov 没有。NCT04642534

 图形摘要