The Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Aurora, Colorado.
Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora.
JAMA Netw Open. 2023 Apr 3;6(4):e237030. doi: 10.1001/jamanetworkopen.2023.7030.
The in utero metabolic milieu is associated with offspring adiposity. Standard definitions of maternal obesity (according to prepregnancy body mass index [BMI]) and gestational diabetes (GDM) may not be adequate to capture subtle yet important differences in the intrauterine environment that could be involved in programming.
To identify maternal metabolic subgroups during pregnancy and to examine associations of subgroup classification with adiposity traits in their children.
DESIGN, SETTING, AND PARTICIPANTS: This cohort study included mother-offspring pairs in the Healthy Start prebirth cohort (enrollment: 2010-2014) recruited from University of Colorado Hospital obstetrics clinics in Aurora, Colorado. Follow-up of women and children is ongoing. Data were analyzed from March to December 2022.
Metabolic subtypes of pregnant women ascertained by applying k-means clustering on 7 biomarkers and 2 biomarker indices measured at approximately 17 gestational weeks: glucose, insulin, Homeostatic Model Assessment for Insulin Resistance, total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides, free fatty acids (FFA), HDL-C:triglycerides ratio, and tumor necrosis factor α.
Offspring birthweight z score and neonatal fat mass percentage (FM%). In childhood at approximately 5 years of age, offspring BMI percentile, FM%, BMI in the 95th percentile or higher, and FM% in the 95th percentile or higher.
A total of 1325 pregnant women (mean [SD] age, 27.8 [6.2 years]; 322 [24.3%] Hispanic, 207 non-Hispanic Black [15.6%], and 713 [53.8%] non-Hispanic White), and 727 offspring with anthropometric data measured in childhood (mean [SD] age 4.81 [0.72] years, 48% female) were included. We identified the following 5 maternal metabolic subgroups: reference (438 participants), high HDL-C (355 participants), dyslipidemic-high triglycerides (182 participants), dyslipidemic-high FFA (234 participants), and insulin resistant (IR)-hyperglycemic (116 participants). Compared with the reference subgroup, women in the IR-hyperglycemic and dyslipidemic-high FFA subgroups had offspring with 4.27% (95% CI, 1.94-6.59) and 1.96% (95% CI, 0.45-3.47) greater FM% during childhood, respectively. There was a higher risk of high FM% among offspring of the IR-hyperglycemic (relative risk, 8.7; 95% CI, 2.7-27.8) and dyslipidemic-high FFA (relative risk, 3.4; 95% CI, 1.0-11.3) subgroups; this risk was of greater magnitude compared with prepregnancy obesity alone, GDM alone, or both conditions.
In this cohort study, an unsupervised clustering approach revealed distinct metabolic subgroups of pregnant women. These subgroups exhibited differences in risk of offspring adiposity in early childhood. Such approaches have the potential to refine understanding of the in utero metabolic milieu, with utility for capturing variation in sociocultural, anthropometric, and biochemical risk factors for offspring adiposity.
宫内代谢环境与后代肥胖有关。根据孕前体重指数(BMI)定义的母亲肥胖和妊娠期糖尿病(GDM)标准可能不足以捕捉到可能参与编程的细微但重要的宫内环境差异。
确定孕妇在怀孕期间的代谢亚群,并研究亚群分类与子女肥胖特征的关联。
设计、地点和参与者:本队列研究纳入了来自科罗拉多大学医院妇产科的健康起跑产前队列(2010-2014 年招募)中的母婴对。对女性和儿童的随访仍在进行中。数据于 2022 年 3 月至 12 月进行分析。
在大约 17 孕周时通过应用 k-均值聚类分析 7 种生物标志物和 2 种生物标志物指数(葡萄糖、胰岛素、胰岛素抵抗稳态模型评估、总胆固醇、高密度脂蛋白胆固醇 [HDL-C]、甘油三酯、游离脂肪酸 [FFA]、HDL-C:甘油三酯比值和肿瘤坏死因子 α)来确定孕妇的代谢亚群。
后代出生体重 z 评分和新生儿脂肪百分比(FM%)。在儿童期(大约 5 岁),后代 BMI 百分位数、FM%、BMI 在第 95 百分位数或更高、FM%在第 95 百分位数或更高。
共有 1325 名孕妇(平均[SD]年龄 27.8[6.2]岁;322[24.3%]西班牙裔、207 名非西班牙裔黑人[15.6%]和 713[53.8%]非西班牙裔白人)和 727 名儿童期有体重测量数据的后代(平均[SD]年龄 4.81[0.72]岁,48%女性)被纳入。我们确定了以下 5 种孕妇代谢亚群:参考(438 名参与者)、高 HDL-C(355 名参与者)、血脂异常高甘油三酯(182 名参与者)、血脂异常高 FFA(234 名参与者)和胰岛素抵抗(IR)高血糖(116 名参与者)。与参考亚群相比,IR 高血糖和血脂异常高 FFA 亚群的后代在儿童期的 FM%分别增加了 4.27%(95%CI,1.94-6.59)和 1.96%(95%CI,0.45-3.47)。IR 高血糖(相对风险,8.7;95%CI,2.7-27.8)和血脂异常高 FFA(相对风险,3.4;95%CI,1.0-11.3)亚群的后代肥胖风险更高;与单纯孕前肥胖、单纯 GDM 或两种情况相比,这种风险更为显著。
在这项队列研究中,一种无监督聚类方法揭示了孕妇的不同代谢亚群。这些亚群在后代早期肥胖风险方面表现出差异。这种方法有可能改善对宫内代谢环境的理解,有助于捕捉与后代肥胖相关的社会文化、人体测量和生化风险因素的变化。