妊娠期糖尿病患者血清中甘氨酸结合型脱氧胆酸降低与不良妊娠结局的相关性

Reduced glycodeoxycholic acid levels are associated with negative clinical outcomes of gestational diabetes mellitus 妊娠期糖尿病患者血清中甘氨酸结合型脱氧胆酸降低与不良妊娠结局的相关性 1 2 equal first-author 1 2 equal 1 2 1 2 1 2 3 4 5 6 corresp Dajing XIA, dxia@zju.edu.cn Dajing XIA, dxia@zju.edu.cn dxia@zju.edu.cn https://orcid.org/0000-0003-1645-9046 Department of Laboratorial Medicine, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou310006, China Department of Laboratorial Medicine, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou310006, China Clinical Prenatal Diagnosis Center, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou310006, China Clinical Prenatal Diagnosis Center, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou310006, China Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian116023, China Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian116023, China Hangzhou HealthBank Medical Laboratory, Hangzhou310051, China Hangzhou HealthBank Medical Laboratory, Hangzhou310051, China Department of Toxicology, School of Public Health, Zhejiang University, Hangzhou310006, China Department of Toxicology, School of Public Health, Zhejiang University, Hangzhou310006, China Department of Gynecologic Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou310006, China Department of Gynecologic Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou310006, China Gestational diabetes mellitus (GDM) is characterized by glycemia and insulin disorders. Bile acids (BAs) have emerged as vital signaling molecules in glucose metabolic regulation. BA change in GDM is still unclear, which exerts great significance to illustrate the change of BAs in GDM. GDM patients and normal pregnant women were enrolled during the oral glucose tolerance test (OGTT) screening period. Fasting serums were sampled for the measurement of BAs. BA metabolism profiles were analyzed in both pregnant women with GDM and those with normal glucose tolerance (NGT). Delivery characteristics, delivery gestational age, and infant birthweight were extracted from medical records. GDM patients presented distinctive features compared with NGT patients, including higher body mass index (BMI), elevated serum glucose concentration, raised insulin (both fasting and OGTT), and increased hemoglobin A1c (HbA1c) levels. Higher homeostasis model assessment of insulin resistance (HOMA-IR) and decreased β-cell compensation (i.e., oral disposition index (DI o )) were also prevalent in this group. Total BAs (TBAs) remained stable, but glycodeoxycholic acid (GDCA) and taurodeoxycholic acid (TDCA) levels declined significantly in GDM. GDCA was inversely correlated with HOMA-IR and positively correlated with DI o . No obvious differences in clinical outcome between the GDM and NGT groups were observed. However, GDM patients with high HOMA-IR and low DI o tended to have a higher cesarean delivery rate and younger delivery gestational age. In conclusion, GDCA provides a valuable biomarker to evaluate HOMA-IR and DI o , and decreased GDCA levels predict poorer clinical outcomes for GDM. 目的 2 探讨胆汁酸代谢异常与妊娠期糖尿病不良妊娠结局的相关性。 创新点 2 首次利用高效液相色谱串联质谱（LC-MS）方法检测到妊娠期糖尿病（GDM）患者在口服糖耐量（OGTT）筛查期间血清胆汁酸谱的异常改变，通过结合差异性胆汁酸谱和GDM患者的分娩方式和分娩孕周及胎儿出生体重，以期为预测GDM所致的不良妊娠结局提供科学依据。 方法 2 本研究对浙江大学医学院附属妇产科医院2019年5月至2019年10月筛查到的GDM患者进行了回顾性分析，根据国际糖尿病与妊娠研究组公布的GDM诊断标准纳入OGTT筛查期间GDM患者67名，正常妊娠对照组48名。收集研究对象空腹状态及服糖后1、2小时的血清，检测血糖及胰岛素水平，计算GDM患者和对照组胰岛素抵抗指数（HOMA-IR）、胰岛细胞功能指数（HOMA-β）、胰岛素分泌指数、胰岛素敏感性指数，用胰岛素分泌指数（Stumvoll ?rst-phase estimate）×胰岛素敏感性指数计算β细胞补偿功能（DIo），同时利用LC-MS检测研究对象的空腹胆汁酸代谢谱，研究胆汁酸代谢的改变与上述指标之间的相关性。收集两组人群的临床资料包括基本资料及分娩方式、分娩孕周和胎儿出生体重，分析差异性胆汁酸与不良妊娠结局的相关性。 结论 2 甘氨酸结合型脱氧胆酸（GDCA）可作为评估HOMA-IR和DIo的有效指标，同时GDCA降低可提示GDM患者不良妊娠结局。 Gestational diabetes mellitus (GDM) Bile acid Insulin resistance β-Cell compensation 妊娠期糖尿病 胆汁酸 胰岛素抵抗 β细胞补偿功能 1 Introduction 1 Gestational diabetes mellitus (GDM) is a common complication in pregnancy with short- and long-term health risks for both the mother and the developing fetus ( Johns et al., 2018 Johns et al., 2018 ; Zhou et al., 2020 Zhou et al., 2020 ). Women with GDM show an increased incidence of hypertensive disorders during pregnancy, including gestational hypertension, preeclampsia, and eclampsia, and thus may need preterm caesarean section, or even develop subsequent maternal type 2 diabetes (T2DM) ( The HAPO Study Cooperative Research Group, 2009 The HAPO Study Cooperative Research Group, 2009 ; O'Sullivan et al., 2011 O'Sullivan et al., 2011 ; Wei et al., 2017 Wei et al., 2017 ). Offspring born to mothers with GDM are at increased risk of multiple immediate complications, including macrosomia, preterm birth, injury, shoulder dystocia, respiratory distress, and other conditions (Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study Cooperative Research Group, 2010; Mortier et al., 2017 Mortier et al., 2017 ; Moll et al., 2020 Moll et al., 2020 ). Insulin resistance (IR) progresses with advancing gestation to supply adequate energy for the growing fetus. Increased IR promotes endogenous glucose production and the breakdown of fat stores, resulting in raised glycemic index and free fatty acid concentration ( di Cianni et al., 2003 di Cianni et al., 2003 ). The abnormal metabolic adaptations of IR in pregnancy always consequently lead to GDM. Bile acids (BAs) have also emerged as crucial signaling molecules in the glucose metabolism via the nuclear hormone farnesoid X receptor (FXR) and the Takeda G protein receptor 5 (TGR5) signaling pathways ( Shapiro et al., 2018 Shapiro et al., 2018 ; Jia et al., 2019 Jia et al., 2019 ; Jin et al., 2019 Jin et al., 2019 ). Studies on rodents showed that the induction of postprandial BA secretion leads to the stimulation of glycogen storage and the inhibition of hepatic glycolytic and lipogenic gene expression in an FXR-dependent manner ( Watanabe et al., 2004 Watanabe et al., 2004 ; Duran-Sandoval et al., 2005 Duran-Sandoval et al., 2005 ). The activation of FXR represses the enzymes involved in hepatic gluconeogenesis, such as phosphoenolpyruvate carboxy kinase (PEP-CK) and glucose 6-phosphatase ( Potthoff et al., 2011 Potthoff et al., 2011 ; Zhang et al., 2012 Zhang et al., 2012 ). In addition to direct regulation, BAs are also involved in glucose metabolism through insulin secretion. The TGR5 receptor, which is differentially activated by lithocholic acid (LCA), deoxycholic acid (DCA), chenodeoxycholic acid (CDCA), and cholic acid (CA) (in the order of activity), promotes the secretion of glucagon-like peptide-1 (GLP-1) by intestinal L cells acting on pancreatic β‑cells to stimulate insulin secretion ( Katsuma et al., 2005 Katsuma et al., 2005 ). The BA receptor FXR is also differentially activated by CDCA, DCA, LCA, and CA (in the order of activity) ( Cariou et al., 2006 Cariou et al., 2006 ). Although GLP-1 secretion is negatively regulated by FXR, TGR5 activation in L cells rapidly occurs postprandially, whereas FXR activation induces a more delayed response that requires transcriptional activation ( Trabelsi et al., 2015 Trabelsi et al., 2015 ; Kim and Fang, 2018 Kim and Fang, 2018 ). Research on interactions between BAs and insulin has demonstrated that insulin controls BA composition by regulating 12α‑hydroxylase called cyp8b1 through forkhead box-O1 (FoxO1), and the serum insulin level is positively correlated with the concentration of 12α-hydroxylated BAs (CA, DCA, and their conjugated forms) ( Haeusler et al., 2012 Haeusler et al., 2012 ). In healthy subjects, IR is associated with increased 12α-hydroxylated BAs, and the ratios of 12α-hydroxylated/non-12α-hydroxylated BAs are associated with key features of IR. In T2DM patients, levels of total BAs (TBAs) are nearly twofold compared with healthy subjects, although no disproportionate increases in 12α-hydroxylated BAs are spotted ( Haeusler et al., 2013 Haeusler et al., 2013 ). Our recent results showed that BA metabolic profile changes with advancing gestation. Specifically, unconjugated BAs dominate during the second trimester, whilst conjugated BAs are more prevalent in the third trimester ( Zhu et al., 2019 Zhu et al., 2019 ). Since individual BAs exert distinctive affinity to FXR and TGR5 and they also play different roles in glucose metabolism, it is necessary to clarify whether the BA profile is changed in GDM patients and determine the clinical significance of any such changes. In this paper, it is hypothesized that the BA metabolism profile changes in GDM and a correlation between insulin metabolism and specific BA components exists. In order to verify this hypothesis, we analyzed the fasting BA metabolism profile in GDM and normal pregnant women during the oral glucose tolerance test (OGTT) screening period and identified the key BAs that changed in GDM, thereby laying the foundations for the assessment of IR and β-cell compensation using BAs. 2 Materials and methods 1 2.1 Study design and population 2 A total of 67 GDM patients and 48 patients with normal pregnancy, with a maternal age of 23‒44 years and a gestational age of 21‒29 weeks, were enrolled between May 2019 and October 2019 in Women's Hospital, Zhejiang University School of Medicine (Hangzhou, China). Cases of pre-conceptional diabetes, infections, and abnormal liver or kidney function, and those positive for human immunodeficiency virus (HIV) and hepatitis C antibodies were excluded from the study. Blood samples were obtained from all participants after 8–14 h of fasting. Subsequently, all women underwent a 75-g OGTT. Based on the OGTT data, we defined GDM according to the criteria of the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) ( Weinert, 2010 Weinert, 2010 ). Insulin and glucose levels during the OGTT were used to determine the homeostasis model assessment of insulin resistance (HOMA-IR), homeostasis model assessment β cell function (HOMA-β), insulin secretion (using the Stumvoll first-phase estimate), and insulin sensitivity (using the Matsuda index) values ( Matsuda and DeFronzo, 1999 Matsuda and DeFronzo, 1999 ; Stumvoll et al., 2000 Stumvoll et al., 2000 , 2001 2001 ). The insulin secretion index and Matsuda index were multiplied to calculate the oral disposition index (DI o ), which assesses β‍-cell compensation for IR ( Elbein et al., 2000 Elbein et al., 2000 ). The research was approved by the Ethics Committee of the Women's Hospital, Zhejiang University School of Medicine (IRB-20200015-R). 2.2 Measurement of BAs 2 The procedure for BA analysis was performed according to the previously published methods with minor modifications ( Zhu et al., 2019 Zhu et al., 2019 ). Briefly, commercially available reference standards were obtained from Toronto Research Chemicals (TRC) Inc. (Toronto, Canada) and Sigma-Aldrich (St. Louis, USA). Total volumes of 100 μL serum specimen, standard solutions, and quality control were vortexed with 300 μL of the stable isotope-labeled internal standard (IS) stock solutions. A total of 150 µL supernatant was aspirated by the autosampler for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis after centrifugation for 10 min at 13 000 g . BAs were separated using an ultra-high performance liquid chromatography (UHPLC) BEH C18 column (2.1 mm×100 mm, 1.7 µm; Waters, Massachusetts, USA) with 40 ℃ column temperature. The MS detection of BAs was performed using individually optimized cone voltage and collision energy in multiple reaction monitoring (MRM) mode (Table S1). 2.3 Statistical methods 2 MS data were analyzed using Analyst software v1.6.0 (SCIEX, Framingham, USA). The Student's t -test or Mann-Whitney U -test was used to evaluate differences among groups for continuous variables. One-way analysis of variance (ANOVA) or Kruskal-Wallis test was conducted to compare the differences for categorical variables. Data of BAs were log-transformed for orthogonal partial least squares discrimination analysis (OPLS-DA) using SIMCA-P (Version 13.0, Umetrics, Sweden). All statistical analyses were performed using the GraphPad Prism 7.0 software. P values of < 0.05 were considered statistically significant. 3 Results 1 3.1 Baseline characteristics of the study population 2 Compared with normal glucose tolerance (NGT), GDM patients had a higher body mass index (BMI) ( P < 0.001). The biochemical indicators included triglyceride (TG), total cholesterol (TCH), high-density lipoprotein (HDL), low-density lipoprotein (LDL), glycated albumin (GAL), and hemoglobin A1c (HbA1c). Both HbA1c and TG were elevated significantly in the GDM group ( P =0.013 and P =0.047, respectively). Albeit there was no statistical significance in insulin secretion, the levels of fasting insulin (FIN) and insulin 1 and 2 h post-OGTT were raised in the GDM group compared with the NGT group ( P < 0.001, P =0.005, and P < 0.001, respectively). In addition, GDM patients had a deficiency in insulin sensitivity (3.33–6.30 for GDM vs. 5.84–10.33 for NGT, P < 0.001), and poorer β‍-cell compensation (3628–5186 for GDM vs. 6584–9121 for NGT, P < 0.001) than the NGT group ( Table 1 Table 1 ). All of the significantly changed parameters in Table 1 Table 1 were used for OPLS-DA calculations to evaluate differences between GDM and NGT patients. A distinct clustering pattern was observed between samples from GDM and NGT individuals ( Fig. 1 Fig. 1 a). The variable importance in projection (VIP) scores for the mentioned indicators in Table 1 Table 1 showed that the DI o index, Matsuda index, HOMA-IR, Stumvoll Phase I, and insulin levels in OGTT contributed significantly as a principal component to separating the two groups ( Fig. 1 Fig. 1 b). The data are described as median (IQR). The differences between the NGT and GDM groups were compared using the Kruskal-Wallis test for continuous variables and the Fisher’s exact test for categorical variables. Biochemical indicators, including fasting glucose and insulin, 1/2-h-glucose and insulin, TG, TCH, HDL, LDL, GAL, and HbA1c, were also compared between GDM and NGT groups. Insulin and glucose levels during the OGTT were used to estimate insulin secretion (using the Stumvoll first-phase estimate) and insulin sensitivity (using the Matsuda index). These indices were multiplied to calculate the DI o , which assesses β-cell compensation for insulin resistance. The insulin resistance index and β‑cell function were calculated using fasting glucose concentration (mmol/L)×FIN concentration (μU/mL)/22.5 and 20×FIN concentration (μU/mL)/(fasting glucose concentration (mmol/L)-3.5), respectively. P < 0.05 was considered to be statistically significant. NGT: normal glucose tolerance; GDM: gestational diabetes mellitus; BMI: body mass index; OGTT: oral glucose tolerance test; FIN: fasting insulin; DI o : oral disposition index; HOMA-IR: homeostasis model assessment of insulin resistance; HOMA-β: homeostasis model assessment β cell function; AUC: area under the curve; TG: triglyceride; TCH: total cholesterol; HDL: high-density lipoprotein; LDL: low-density lipoprotein; GAL: glycated albumin; HbA1c: hemoglobin A1c; IQR: interquartile range. Parameter NGT ( n =48) GDM ( n =67) P Age (year) 30 (28‒33) 33 (29‒36) 0.003 Gestational age (year) 24 (24‒25) 25 (24‒26) 0.423 BMI (kg/m 2 ) 22.26 (21.17‒23.07) 24.44 (22.19‒26.58) <0.001 Fasting glucose (mg/dL) 78.0 (74.9‒82.1) 85.0 (78.1‒94.0) <0.001 One-hour glucose OGTT (mg/dL) 137.5 (110.5‒150.9) 185.2 (165.1‒197.5) <0.001 Two-hour glucose OGTT (mg/dL) 116.2 (104.3‒130.5) 159.9 (142.0‒172.3) <0.001 FIN (μU/mL) 6.2 (4.9‒8.5) 8.9 (6.5‒11.9) <0.001 One-hour insulin OGTT (μU/mL) 40.5 (30.3‒63.3) 58.6 (39.7‒91.5) 0.005 Two-hour insulin OGTT (μU/mL) 45.7 (31.1‒57.7) 67.9 (46.5‒109.0) <0.001 Insulin sensitivity (Matsuda index) 7.78 (5.84‒10.33) 4.58 (3.33‒6.30) <0.001 Insulin secretion (Stumvoll) 981.5 (865.1‒1264.0) 1054.0 (636.4‒1321.0) 0.683 DI o 7572 (6584‒9121) 4409 (3628‒5186) <0.001 HOMA-IR 1.20 (0.92‒1.71) 1.87 (1.28‒2.64) <0.001 HOMA-β 157.7 (130.6‒234.5) 150.3 (111.8‒194.0) 0.694 AUC (insulin/glucose) 5.15 (4.31‒7.22) 5.83 (4.11‒9.31) 0.136 TG (mmol/L) 2.00 (1.76‒2.52) 2.32 (1.89‒2.88) 0.046 TCH (mmol/L) 5.96 (5.25‒6.63) 5.68 (5.06‒6.55) 0.216 HDL (mmol/L) 1.96 (1.63‒2.24) 1.79 (1.56‒2.00) 0.094 LDL (mmol/L) 2.97 (2.54‒3.61) 2.74 (2.34‒3.13) 0.073 GAL (%) 12.2 (11.6‒13.0) 12.3 (11.7‒12.9) 0.311 HbA1c (%) 4.9 (4.8‒5.1) 5.1 (4.9‒5.3) 0.013 3.2 BA metabolism profiles in GDM and NGT 2 Since glucose and insulin metabolism discriminations exist between the GDM and NGT groups and BAs are known to participate in the glycemic homeostasis via direct and indirect pathways, we postulated that GDM and NGT could be distinguished by BA profiles. The two cohorts, however, could not be separated by OPLS-DA (data not shown), even though TBAs decreased in the GDM group ( Fig. 2 Fig. 2 a). This finding suggested that certain types of BAs may be more intimately involved in glycemic regulation. Indeed, the levels of 12α-hydroxylated BAs decreased obviously ((759.57±64.38) nmol/L vs. (994.74±106.30) nmol/L, P =0.048), whereas non-12α‍-hydroxylated BA levels remained stable in the GDM group ( Fig. 2 Fig. 2 b). Furthermore, among individual 12α-hydroxylated BAs, taurodeoxycholic acid (TDCA) and glycodeoxycholic acid (GDCA) in fasting serum decreased significantly in the GDM group (TDCA: (38.96±7.80) nmol/L vs. (68.98±11.98) nmol/L, P =0.030; GDCA: (156.68±17.95) nmol/L vs. (226.50±26.69) nmol/L, P =0.026), while there was no obvious change in concentrations of non-12α-hydroxylated BAs ( Fig. 2 Fig. 2 c). 3.3 Correlation between individual BAs and insulin indexes 2 In order to further describe the relationship between BA components and the apparently changed insulin-related indicators, their correlation was analyzed. Fasting GDCA was found to positively correlate with insulin sensitivity in both NGT and GDM groups, and this coefficient correlation decreased in the GDM group. The level of GDCA also positively correlated with DI o in the GDM group, but negatively with HOMA-IR and FIN. No correlation between TDCA and insulin-related indicators was shown in the NGT or GDM group, although TDCA was markedly reduced in the GDM group. Among the non-12α-hydroxylated individual BAs, CDCA correlated positively with HOMA-IR and FIN, whilst tauroursodeoxycholic acid (TUDCA) correlated negatively with DI o ( Fig. 3 Fig. 3 ). 3.4 Clinical outcomes for GDM patients with higher HOMA-IR and lower DI o 2 Results showed that GDCA declined in the GDM group, and this decline correlated with insulin sensitivity, IR, DI o , and FIN. In order to assess the relationship between GDCA and clinical outcomes including delivery gestational age, fetus birth weight, and cesarean delivery rate, GDM patients were subdivided into four quartiles (Q1: < 25%, Q2: 25%–50%, Q3: > 50%–75%, and Q4: > 75%) according to the Matsuda index, HOMA-IR, DI o , and FIN, respectively. No apparent change in GDCA occurred among the Q1 and Q4 groups divided by Matsuda index and FIN (Figs. 4 4 a and 4 4 b), but it increased in the DI o Q4 group and decreased in the HOMA-IR Q4 group (Figs. 4 4 c and 4 4 d). With regard to clinical outcomes, no variations were shown for GDM patients as a whole compared with the NGT group. However, when the GDM subgroup was divided by HOMA-IR and DI o , it was found that GDM patients with elevated HOMA-IR and decreased GDCA (HOMA-IR Q4 group) had increased cesarean delivery rates. Patients with GDM and low DI o presented a younger gestational age before delivery compared with patients with NGT ( Table 2 Table 2 ). The data are described as median (IQR) for gestational age and infant birth weight. Patients with GDM were subdivided into four quartiles (Q1, Q2, Q3, and Q4) according to the levels of HOMA-IR and DI o . Clinical outcomes, including cesarean delivery rate, delivery gestational age, and infant birth weight, were compared between women in the NGT group, GDM group, and GDM subgroups (Q1 and Q4). NS : no statistically significant difference when compared to the NGT group; * P < 0.05; *** P < 0.001, compared to the NGT group; P # represents the difference between Q1 and Q4 groups, and P < 0.05 was considered to be statistically significant. NGT: normal glucose tolerance; GDM: gestational diabetes mellitus; HOMA-IR: homeostasis model assessment of insulin resistance; DI o : oral disposition index; IQR: interquartile range. Group n Missing data Cesarean delivery rate (%) Gestational age (year) Infant birth weight (g) NGT 44/48 4 36.4 (16/44) 39.3 (39.0‒41.1) 3300 (2970‒3635) All-GDM 59/67 8 54.2 (32/59) NS 39.0 (38.0‒39.6) NS 3400 (3090‒3670) NS GDM Q1 (HOMA-IR) 17/17 0 41.2 (7/17) NS 39.0 (38.4‒39.3) NS 3200 (2950‒3430) NS GDM Q4 (HOMA-IR) 12/17 5 91.7 (11/12) *** 38.6 (37.4‒39.4) NS 3430 (3098‒3980) NS P # 0.008 0.718 0.134 GDM Q1 (DI o ) 15/17 2 66.7 (10/15) NS 38.4 (37.0‒39.3) * 3400 (2870‒3810) NS GDM Q4 (DI o ) 16/17 1 43.8 (7/16) NS 39.0 (38.3‒39.4) NS 3235 (3065‒3428) NS P # 0.285 0.073 0.445 4 Discussion 1 In the present work, it was established that the abnormalities of insulin-related indicators were the main factors to distinguish GDM and NGT. Combining BA metabolism profiles, the correlation between specific BAs and insulin-related indicators was clarified. Overall, GDM patients showed higher levels of BMI and HbAc1 during the OGTT screening period. Furthermore, levels of FIN and insulin at 1 and 2 h post the 75-g oral glucose intake increased significantly in GDM patients. In terms of BA metabolism profile, TDCA and GDCA were the most significantly changed BAs in GDM patients. Moreover, GDCA negatively correlated with the HOMA-IR and positively correlated with the β‑cell compensation index. In the clinical outcomes assay, no significant differences between GDM and NGT were observed, while GDM patients in the HOMA-IR Q4 group had elevated rates of cesarean section delivery, and patients in the DI o Q1 group showed younger delivery gestational ages. Therefore, our findings suggest that fasting GDCA levels during OGTT may be a useful indicator to evaluate IR and β-cell compensation, further assisting with the prediction of adverse clinical outcomes of GDM. GDM shares a similar pathogenesis with T2DM, where β‑cell function is insufficient to maintain the glycemic homeostasis during pregnancy. Insufficience of β‑cell function combined with reduced insulin sensitivity finally results in increased serum glucose ( Johns et al., 2018 Johns et al., 2018 ). In addition to glucose and insulin metabolic disorders, lipid metabolism is also altered in GDM, since levels of total TGs, TCH, and LDL gradually increase throughout such pregnancies ( Wang et al., 2019 Wang et al., 2019 ). Our results were consistent with previous reports on clinical characteristics of GDM, including advanced age, higher BMI, TG, and HbA1c. Variations in BA metabolism have been partially proved in T2DM patients, as TBA almost doubled in T2DM patients compared with healthy subjects, suggesting its role in the development of T2DM ( Haeusler et al., 2013 Haeusler et al., 2013 ; Zhu et al., 2020 Zhu et al., 2020 ). Intrahepatic cholestasis of pregnancy (ICP) is characterized by increased TBA, and patients with ICP are much more vulnerable to suffering from GDM ( Martineau et al., 2015 Martineau et al., 2015 ). These studies suggest that TBA is likely related to the occurrence of T2DM and GDM. Indeed, in Chinese prospective cohort studies, the incidence of GDM in the group with the highest TBA level (≥4.0 µmol/L) in early pregnancy had a 6.72-fold increased risk of GDM compared with the group presenting the lowest level, and even after adjusting for potential confounders, levels of TBA of ≥2.0 µmol/L still presented an increased risk for developing GDM ( Hou et al., 2016 Hou et al., 2016 ; Kong et al., 2020 Kong et al., 2020 ). In our research, TBA remained stable in the GDM group compared with the NGT group, which may be partially caused by the difference between the TBA detected by enzymatic cycling assay and the TBA consisting of 15 individual BAs detected by MS. This prompts us to pay more attention to individual BA components related to glucose metabolism. Individual BAs have been evidenced to correlate with GDM. Fasting serum levels of glycoursodeoxycholic acid (GUDCA) of ≤0.07 nmol/mL and DCA of ≤0.28 nmol/mL in early pregnancy were independently associated with an increased risk of GDM development ( Li et al., 2018 Li et al., 2018 ). An untargeted metabolomics analysis of pregnant women's fasting serum during the OGTT screening period spotted that CA, ios-DCA, and dehydio-LCA were decreased in GDM patients ( Hou et al., 2018 Hou et al., 2018 ). In a rodent model, increased serum CA concentration combined with diminished BAs receptors, including FXR and TGR5, was correlated with GDM ( Bellafante et al., 2020 Bellafante et al., 2020 ). In the present study, GDCA and TDCA were both clearly decreased in GDM. In contrast to other studies, our previous research indicated that BA metabolism profiles changed periodically with gestational age ( Zhu et al., 2019 Zhu et al., 2019 ), which difference might be caused by dissimilar detection platforms and trimesters when the samples were collected. In women with normal pregnancy, serum GDCA concentration cumulated in a time-dependent manner after 75-g oral glucose intake ( Haslam et al., 2020 Haslam et al., 2020 ). In GDM patients, reduced GDCA combined with increased FIN and fasting glucose levels resulted in a significant negative correlation between GDCA and HOMA-IR. A study has demonstrated that GDCA is indeed associated with insulin secretion and resistance ( van Nierop et al., 2019 van Nierop et al., 2019 ). More importantly, IR can be alleviated by GDCA administration. For example, 40-h fasting induced IR, which had no effect on the BA metabolism profile. However, postprandial GDCA and insulin concentrations changed in a statistically significant positive correlation pattern. Increased GDCA triggered the secretion of insulin in a GLP-1-dependent manner ( van Nierop et al., 2019 van Nierop et al., 2019 ). This is helpful to explain that even if GDCA elevates after glucose intake in GDM patients, the declined GDCA baseline still makes this increase insufficient to promote insulin secretion via GLP-1, finally leading to the failure of glycemic regulation. Polycystic ovary syndrome (PCOS) has been associated with IR, and the HOMA-IR of PCOS patients significantly negatively correlates with GDCA ( Qi et al., 2019 Qi et al., 2019 ; Zhang et al., 2020 Zhang et al., 2020 ; Moghetti and Tosi, 2021 Moghetti and Tosi, 2021 ). Mechanistically, the significantly increased Bacteroides vulgatus presence in the gut microbiota of PCOS individuals causes the reduction of GDCA. The latter combines with GATA-3 in the intestinal group innate lymphoid cell to facilitate interleukin-22 secretion, which in turn improves the PCOS phenotype, including reduced IR and ovarian function recovery ( Qi et al., 2019 Qi et al., 2019 ). It is yet to be established, however, whether variations in the gut microbiome occur in GDM, subsequently decreasing GDCA levels and thereby resulting in the IR associated with the condition. In this study, hyperglycemia in GDM led to severe maternal and fetal outcomes, while the overall clinical outcome for GDM patients was not significantly different from that of NGT patients, which may be attributed to early lifestyle changes and even pharmacotherapy. Moreover, it is worth noting that GDM patients with serious insulin compensation deficiency, as well as high HOMA-IR subgroups, showed worse clinical outcomes. Despite that our study was single-center with a small sample size, significant data were obtained on the relationship between BA metabolism and GDM. It is considered that abnormalities in specific BAs may predict the adverse clinical outcomes of GDM. The greatest limitation of this study was its cross-sectional nature, which conversely provoked the determination of the metabolic profile of BAs in early gestational age to spot potentially valuable biomarkers for earlier GDM screening. In addition, the GLP-1 baseline was not measured in the fasting serum, as variations in the GLP-1 baseline may influence FIN levels. Changes in the levels of GLP-1 and BAs 1 and 2 h after oral glucose intake were not detected either, which limited the possibility to elucidate the dynamic mechanism of BA metabolism regulating insulin secretion through GLP-1. Moreover, the sample size was fairly small, resulting in a failure to establish a cut-off point for GDCA. 5 Conclusions 1 The level of GDCA declined significantly in GDM patients, which was inversely correlated with insulin sensitivity and positively correlated with β-cell compensation. 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Metabolism , 95 : 77 - 83 . https://doi.org/10.1016/j.metabol.2019.04.004 https://doi.org/10.1016/j.metabol.2019.04.004 Characteristics of bile acids metabolism profile in the second and third trimesters of normal pregnancy Zhu W , Wang SY , Dai HJ , et al. , 2020 . Serum total bile acids associate with risk of incident type 2 diabetes and longitudinal changes in glucose-related metabolic traits . J Diabetes , 12 ( 8 ): 616 - 625 . https://doi.org/10.1111/1753-0407.13040 https://doi.org/10.1111/1753-0407.13040 Serum total bile acids associate with risk of incident type 2 diabetes and longitudinal changes in glucose-related metabolic traits Bo ZHU and Zhixin MA designed the study. Lei FANG and Hong ZHANG collected the data. Zhixin MA performed the statistical data analyses. Hongwei KONG conducted the BA measurements. Yuning ZHU and Zhixin MA contributed to drafting the manuscript. Dajing XIA designed, organized, and supervised the project, and revised the manuscript. All authors have reviewed and approved the manuscript to produce the final version. The present work was supported by the National Key R&D Program of China (Nos. 2018YFC1002700 and 2018YFC1002702). We would like to express our gratitude to all the physicians, nurses, technicians, and especially the patients for their support and dedication towards the study. 10.1631/jzus.B2000483 1673-1581（2021）03-0223-10 2020-08-18 2021-01-03 2021-02-22 2021-03-15 2022-05-09 © Zhejiang University Press 2021 Bo ZHU, Zhixin MA, Yuning ZHU, Lei FANG, Hong ZHANG, Hongwei KONG, and Dajing XIA declare that they have no conflict of interest in relation to the study. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). All the participants provided their informed consent to enroll in the trial and the follow-up study. Compliance with ethics guidelines 浙江大学学报（英文版）（B辑：生物医学和生物技术） 3 22