Continuous glucose monitoring has facilitated the evaluation of dynamic changes in glucose throughout the day and their effect on fetal growth abnormalities in pregnancy. However, studies of multiple continuous glucose monitoring metrics combined and their association with other adverse pregnancy outcomes are limited. This study aimed to (1) use machine learning techniques to identify discrete glucose profiles based on weekly continuous glucose monitoring metrics in pregnant individuals with pregestational diabetes mellitus and (2) investigate their association with adverse pregnancy outcomes. This study analyzed data from a retrospective cohort study of pregnant patients with type 1 or 2 diabetes mellitus who used Dexcom G6 continuous glucose monitoring and delivered a nonanomalous, singleton pregnancy at a tertiary center between 2019 and 2023. Continuous glucose monitoring data were collapsed into 39 weekly glycemic measures related to centrality, spread, excursions, and circadian cycle patterns. Principal component analysis and k-means clustering were used to identify 4 discrete groups, and patients were assigned to the group that best represented their continuous glucose monitoring patterns during pregnancy. Finally, the association between glucose profile groups and outcomes (preterm birth, cesarean delivery, preeclampsia, large-for-gestational-age neonate, neonatal hypoglycemia, and neonatal intensive care unit admission) was estimated using multivariate logistic regression adjusted for diabetes mellitus type, maternal age, insurance, continuous glucose monitoring use before pregnancy, and parity. Of 177 included patients, 90 (50.8%) had type 1 diabetes mellitus, and 85 (48.3%) had type 2 diabetes mellitus. This study identified 4 glucose profiles: (1) well controlled; (2) suboptimally controlled with high variability, fasting hypoglycemia, and daytime hyperglycemia; (3) suboptimally controlled with minimal circadian variation; and (4) poorly controlled with peak hyperglycemia overnight. Compared with the well-controlled profile, the suboptimally controlled profile with high variability had higher odds of a large-for-gestational-age neonate (adjusted odds ratio, 3.34; 95% confidence interval, 1.15–9.89). The suboptimally controlled with minimal circadian variation profile had higher odds of preterm birth (adjusted odds ratio, 2.59; 95% confidence interval, 1.10–6.24), cesarean delivery (adjusted odds ratio, 2.76; 95% confidence interval, 1.09–7.46), and neonatal intensive care unit admission (adjusted odds ratio, 4.08; 95% confidence interval, 1.58–11.40). The poorly controlled profile with peak hyperglycemia overnight had higher odds of preeclampsia (adjusted odds ratio, 2.54; 95% confidence interval, 1.02–6.52), large-for-gestational-age neonate (adjusted odds ratio, 3.72; 95% confidence interval, 1.37–10.4), neonatal hypoglycemia (adjusted odds ratio, 3.53; 95% confidence interval, 1.37–9.71), and neonatal intensive care unit admission (adjusted odds ratio, 3.15; 95% confidence interval, 1.20–9.09). Discrete glucose profiles of pregnant individuals with pregestational diabetes mellitus were identified through joint consideration of multiple continuous glucose monitoring metrics. Prolonged exposure to maternal hyperglycemia may be associated with a higher risk of adverse pregnancy outcomes than suboptimal glycemic control characterized by high glucose variability and intermittent hyperglycemia.

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使用连续血糖监测数据确定离散血糖曲线及其与不良妊娠结局的关联

连续血糖监测有助于评估全天血糖的动态变化及其对妊娠期胎儿生长异常的影响。然而，对多种连续血糖监测指标的结合及其与其他不良妊娠结局的关联的研究是有限的。本研究旨在 (1) 使用机器学习技术根据每周连续血糖监测指标来识别患有孕前糖尿病的孕妇的离散血糖曲线，以及 (2) 调查其与不良妊娠结局的关联。本研究分析了 1 型或 2 型糖尿病妊娠患者的回顾性队列研究的数据，这些患者使用 Dexcom G6 连续血糖监测，并于 2019 年至 2023 年间在三级中心分娩了非异常单胎妊娠。连续血糖监测数据被分解为 39 个数据。与中心性、分布、偏移和昼夜节律模式相关的每周血糖测量。使用主成分分析和 k 均值聚类来识别 4 个离散组，并将患者分配到最能代表其妊娠期间连续血糖监测模式的组。最后，使用针对糖尿病类型进行调整的多变量逻辑回归来估计血糖谱组与结局（早产、剖宫产、先兆子痫、大于胎龄新生儿、新生儿低血糖和新生儿重症监护室入住）之间的关联。母亲年龄、保险、怀孕前连续血糖监测的使用以及胎次。在 177 名患者中，90 名（50.8%）患有 1 型糖尿病，85 名（48.3%）患有 2 型糖尿病。这项研究确定了 4 种血糖谱：(1) 控制良好； (2) 控制欠佳，变异性高，空腹低血糖，日间高血糖； (3) 控制不佳，昼夜节律变化最小； (4) 控制不佳，夜间出现高血糖峰值。与控制良好的概况相比，具有高变异性的次优控制概况具有更高的大于胎龄新生儿的几率（调整后的优势比，3.34；95％置信区间，1.15-9.89）。昼夜节律变化最小的次优控制者的早产几率较高（调整后的比值比，2.59；95% 置信区间，1.10–6.24）、剖宫产（调整后的比值比，2.76；95% 置信区间，1.09–7.46）、和新生儿重症监护病房入院（调整后的比值比，4.08；95% 置信区间，1.58–11.40）。夜间高血糖峰值控制不佳的先兆子痫（调整后的比值比，2.54；95% 置信区间，1.02–6.52）、大胎龄新生儿（调整后的比值比，3.72；95% 置信区间， 1.37–10.4)，新生儿低血糖（调整后的比值比，3.53；95% 置信区间，1.37–9.71），和新生儿重症监护病房入院（调整后的比值比，3.15；95% 置信区间，1.20-9.09）。通过联合考虑多个连续血糖监测指标，确定了患有孕前糖尿病的孕妇的离散血糖曲线。与以高血糖变异性和间歇性高血糖为特征的次优血糖控制相比，长期暴露于母亲高血糖可能会导致不良妊娠结局的风险更高。