Hepatocyte growth factor/c-MET signaling is associated with gestational diabetes (GDM) and GDM complications: a case control study

Hepatocyte growth factor/c-MET signaling is associated with gestational diabetes (GDM) and GDM complications: a case control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hepatocyte growth factor/c-MET signaling is associated with gestational diabetes (GDM) and GDM complications: a case control study Ye Won Jung, Mina Lee, Young Bok Ko, Soo Youn Song, Heon Jong Yoo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6016485/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Hepatocyte growth factor (HGF)/mesenchymal-epithelial transition factor (c-MET) signaling is involved in glucose homeostasis in pancreatic β cells; however, studies on its relationship with gestational diabetes mellitus (GDM) are lacking. This study aimed to investigate HGF and its receptor, c-MET, in pregnant women with GDM and to analyze the correlation between HGF/c-MET signaling and GDM complications. Methods In total, 44 pregnant women with normal glucose tolerance (NGT) and 32 with GDM were studied. Serum levels of HGF and c-MET were measured using an enzyme-linked immunosorbent assay. The relative mRNA expression of HGF and c- MET was measured using real-time polymerase chain reaction. The protein expression of HGF and c-MET in placental and visceral fat tissues was measured using western blot analysis. Logistic regression and Pearson’s correlation analyses examined the associations of these variables with clinical information. Results Serum sc-MET levels were significantly higher (1472.58 vs. 1651.23 ng/mL, p < 0.001) and cord blood HGF levels were significantly lower (370.76 vs. 254.54 ng/mL, p = 0.042) in the GDM group than in the NGT group. The area under the receiver operating characteristic curve for sc-MET was 0.744 (95% confidence interval: 0.632–0.857, p < 0.001). The cut-off serum sc-MET level for predicting GDM was 1455.26 ng/mL. HGF protein expression in the maternal visceral fat tissue was significantly higher in the GDM group than in the NGT group (2-fold higher, p < 0.05). Serum HGF significantly correlated with cord arterial blood gas analysis (ABGA) base excess ( r =-0.390, p = 0.007) and cord ABGA lactic acid ( r = 0.469, p = 0.001). Cord blood HGF levels were significantly correlated with cord blood glucose levels ( r = 0.439, p = 0.002). Conclusions We found an increase in serum sc-MET, a decrease in cord HGF, and an increase in HGF protein expression in the visceral fat tissue of the GDM group, indicating that HGF/c-MET signaling is related to GDM expression. In addition, maternal serum HGF levels correlated with base excess and lactic acid levels in cord ABGA, and cord blood HGF levels correlated with cord blood glucose levels. These outcomes suggest that HGF/c-MET signaling may have potential application in predicting GDM complications. Hepatocyte Growth Factor MET Receptor Tyrosine Kinase Gestational Diabetes Mellitus Placenta Visceral Fat Blood Gas Analysis Figures Figure 1 Figure 2 Figure 3 Background The prevalence of gestational diabetes mellitus (GDM)—glucose intolerance that develops during pregnancy—is gradually increasing owing to rising rates of obesity and advanced maternal age, which are important risk factors for GDM ( 1 ). Inadequately managed GDM during pregnancy can lead to complications in both the mother and newborn. Mothers with GDM are at a higher risk of excessive weight gain, preeclampsia, cesarean section, and the development of type 2 diabetes or recurrent GDM in subsequent pregnancies ( 2 ). Babies born to mothers with GDM are at higher risk of developing hypoglycemia, hypocalcemia, hyperbilirubinemia, respiratory distress syndrome (RDS), polycythemia, obesity, and type 2 diabetes later in life ( 3 , 4 ). GDM pathophysiology is associated with abnormal tissue insulin sensitivity due to maternal insulin resistance (IR) ( 5 , 6 ). Maternal IR is a hallmark of normal glycemic physiology during pregnancy ( 7 ). During normal pregnancy, β-cells usually compensate for IR; however, when β-cell responsiveness to glucose fails, GDM occurs. In rodents, β-cell expansion failure or the inability of β-cells to compensate for maternal IR developed during pregnancy can lead to GDM development ( 8 , 9 ). Among the several cytokines and growth hormones involved in IR and maintaining glucose homeostasis during pregnancy, hepatocyte growth factor (HGF) is an important component of IR pathophysiology; its level is increased in most common IR conditions ( 10 , 11 ). HGF is secreted by mesenchymal cells and acts as a multifunctional cytokine on cells of mainly epithelial origin ( 12 ). It plays a role in angiogenesis, tumorigenesis, and tissue regeneration by stimulating mitosis, cell motility, and matrix invasion. HGF functions after binding to the mesenchymal-epithelial transition factor (c-MET), a hepatocyte growth factor receptor ( 13 ). c-MET consists of an extracellular α-subunit that binds HGF and a transmembrane β-subunit that possesses tyrosine kinase activity. The HGF/c-MET signaling system is expressed in various organs, such as the liver, pancreas, and placenta, as well as in muscle and adipose tissues ( 14 ). HGF induces phosphorylation of tyrosine residues of activated c-MET (phosphorylated MET and p-MET) ( 15 ). The soluble form of c-MET (sc-MET) is a truncated form of the c-MET membrane receptor. S-MET is smaller than the integral c-MET and can competitively bind to HGF ligands with reduced affinity ( 16 , 17 ). The levels of s-MET linearly correlate with c-MET expression in tumors ( 18 ). Recently, the use of c-MET as a cancer biomarker has increased. c-MET is overexpressed in a variety of carcinomas, including lung, breast, ovarian, kidney, colon, thyroid, liver, and gastric carcinomas ( 19 , 20 ). A previous experimental study investigating the relationship between HGF/c-MET signaling and GDM demonstrated the induction of β-cell apoptosis in pregnant mice lacking pancreatic c-Met ( 21 ). Furthermore, β-cell-specific ablation of c-MET resulted in reduced islet size, decreased insulin secretion, and glucose intolerance ( 22 ). However, most studies on HGF/c-Met signaling associated with GDM have been mouse experiments, and studies in humans are lacking. Moreover, no study has investigated the association between GDM complications and HGF/c-Met signaling. Therefore, herein, we aimed to investigate the association between HGF/c-MET signaling and GDM, the suitability of serum HGF and sc-MET levels as predictive or prognostic biomarkers of GDM, and the relationship between HGF/c-MET signaling, GDM, and obstetric and perinatal outcomes. Methods Patient collection and blood sample assays We included 76 pregnant women aged 20–45 years who delivered at Chung Nam National University Hospital between 2015 and 2020. Participants were divided into two groups: normal glucose tolerance (NGT, n = 44) and gestational diabetes mellitus (GDM, n = 32). GDM was diagnosed using a two-step oral glucose tolerance test (OGTT) according to the American Diabetes Association guidelines, as follows: ( 23 ) Step 1: Perform a 50-g OGTT (non-fasting) with plasma glucose measurement at 1 h and 24–28 weeks of gestation in women not previously diagnosed with diabetes. If the plasma glucose level measured 1 h after the load is ≥ 140 mg/dL, proceed to a 100-g OGTT. Step 2: The 100-g OGTT should be performed when the patient is fasting. GDM was diagnosed when at least two of the following four plasma glucose levels (measured fasting and at 1, 2, and 3 h during OGTT) were met or exceeded (Carpenter–Coustan criteria ( 24 )): Fasting: 95 mg/dL; 1 h: 180 mg/dL; 2 h: 155 mg/dL; 3 h: 140 mg/dL. Cases of multiple pregnancies; structural and genetic fetal abnormalities; history of hypertension in the mother; and systemic diseases in the mother, such as kidney, liver, rheumatic, inflammatory, acute hepatitis, febrile, and connective tissue diseases, were excluded. Blood samples were collected immediately before delivery to measure the serum concentrations of HGF, sc-MET, insulin, and glucose. The umbilical cord blood and placental tissues were collected immediately after delivery. Maternal visceral fat tissues were collected from the participants during cesarean section. Enzyme-linked immunosorbent assay (ELISA) The concentrations of HGF (#ab100534; Abcam), sc-MET (#KHO2031; Invitrogen), and insulin (80-INSHU-E01.1, E10.1; ALPCO) in maternal serum and umbilical cord blood were measured using an ELISA. This assay employed a quantitative sandwich enzyme immunoassay technique following the manufacturer’s instructions. The optical density of each well was measured at 450 nm within 30 minutes. The concentrations of the samples were determined according to the absorbance of the samples and standards at 450 nm using a microplate reader. The ELISA experiments were repeated three times. Data with poor duplicates in one set of experiments were omitted, and the mean concentration of each sample was calculated based on repeated experiments. Real-time polymerase chain reaction (RT-PCR) analysis Sixteen samples (eight NGT and eight GDM) were used for RT-PCR analysis. Total RNA from serum, placental tissues, and visceral fat were extracted using TRIzol reagent (ThermoFisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions, and complementary DNA was synthesized from one manufacturer using M-MLV reverse transcriptase and oligo-dT primers (Invitrogen/ThermoFisher Scientific, Waltham, MA, USA). The cDNA was amplified on a 7500 Fast RT-PCR System (Applied Biosystems, Carlsbad, CA) using 2X SYBR Green Mix (Applied Biosystems). To amplify MET gene expressed sequences (Accession number NM_001324402.2), oligonucleotide primers were: sense primer, 5'-TGCCCAGACCCCTTATATGAAG-3'; antisense primer, 5'- GATATCCGGGACACCAGTTCAG-3,’ HGF sequences (Accession number M73240.1) were amplified using primers: sense primer, 5'- GAATGACACTGATGTTCCTTTGG–3'; antisense primer, 5'- GGATACTGAGAATCCCAACGC-3,' and 18s rRNA sequences were amplified using primers: sense primer, 5'-GTAACCCGTTGAACCCCATT-3'; antisense primer, 5'-CCATCCAATCGGTAGTAGCG − 3. ’ Relative gene expression was calculated using the ΔΔ'. Relative gene expression was calculated as rRNA. Values are expressed as the fold change relative to the control group. Western blot analysis Nine samples (four NGT and five GDM) were used for western blot analysis. For western blot analysis of HGF and c-MET, proteins were extracted from placental and visceral fat tissues. The extracts were lysed using 50 nM Tris, pH 7.4, 150 mM NaCl, 1 mM EDTA, and 0.1% Triton X-100 containing protease and phosphatase inhibitors. Extracts were mixed with a sample buffer containing 3.2% sodium dodecyl sulfate, 15% glycerol, 2.8 M b-mercaptoethanol, and 0.0015% bromophenol blue. Proteins from the collected cells were subjected to electrophoresis on an 8–10% SDS polyacrylamide gel and then transferred to a nitrocellulose membrane. After blocking of nonspecific binding sites, western bolts were cut horizontally to allow the detection of different proteins within a single experiment, where applicable. At least one molecular weight marker was kept above and below the expected protein size, with a minimum of two molecular weight markers per cut section. Membranes were incubated at 4°C with a polyclonal antibody directed against phosphorylated c-MET (1:1000; #3077; Cell Signaling Technology; Beverley, MA, USA), c-MET (1:1000; #4560; Cell Signaling Technology; Beverley, MA, USA), HGF (1:1000; #MA5-14160; Invitrogen/ThermoFisher Scientific; Waltham, MA, USA),and GAPDH (1:1000; #2118s; Cell Signaling Technology; Beverley, MA, USA). Blots were visualized using alkaline phosphate-linked anti-rabbit or anti-mouse secondary antibodies(1:5000), and images were scanned using an ODYSSEY instrument and Image Studio Software (LI-COR Biosciences; Lincoln, NE, USA). Statistical analysis Between-group comparison of categorical variables was performed using the Chi-square test. For normally distributed data, between-group comparison was performed using an independent samples t-test, while for abnormally distributed data, the Mann–Whitney U test was applied. Multiple logistic regression analysis was performed to determine variables affecting GDM expression. Receiver operating characteristic (ROC) curves were drawn, and the area under the curve (AUC) was calculated to determine the predictive power and cut-off points of the variables with significant GDM predictive ability. Pearson’s correlation analysis was used to analyze the correlation between the biochemical variables of the maternal serum and umbilical cord blood, and the obstetric and perinatal outcomes of GDM. The r coefficient indicates the degree of the association; the closer it is to -1, the higher the negative correlation, and the closer it is to 1, the higher the positive correlation. Statistical significance was inferred from two-sided p -values < 0.05 (* p < 0.05, ** p < 0.01). SPSS statistical software for Windows version 20 (SPSS, Chicago, IL, USA) was used for all statistical analyses. For data visualization, R version 22 was used. Ethics statement This study was conducted in compliance with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of Chung Nam National University Hospital (IRB no. 2020-08-025). Informed consent was obtained from all participants. Results The baseline characteristics and biochemical data of the NGT (n = 44) and GDM (n = 32) groups are presented in Table 1 . Compared to primipara, the multipara rate was significantly higher in the GDM group ( p = 0.012). The maternal body mass index (BMI) before pregnancy and gestational age (GA) at delivery were significantly higher ( p < 0.001), whereas maternal weight (Wt) gain during pregnancy was significantly lower ( p = 0.007) in the GDM group than in the NGT group. Maternal serum HGF levels tended to be higher in the GDM group ( p = 0.153), while maternal serum sc-MET levels were significantly higher ( p < 0.001) than in the NGT group. Among the umbilical cord blood (cord) values, the cord HGF level was significantly lower in the GDM group ( p = 0.042) and the cord arterial blood gas analysis (ABGA) lactic acid level was significantly higher in the GDM group ( p = 0.028). Table 1 Baseline characteristics and biochemical data of NGT and GDM groups Variables NGT (n = 44) GDM (n = 32) p -value Age 34(31-37.5) 33( 30 – 37 ) 0.753 Parity Primipara 32(72.7%) 14(43.8%) 0.012* Multipara 12(27.3%) 18(56.3%) Pre-pregnancy BMI 20.50(19.00-22.95) 25.80(21.43–29.25) < 0.001*** Wt gain (kg) 13.60(10.40-15.45) 6.60(3.23–12.35) 0.007** GA at delivery (weeks) 38.6 (37–41) 37.3 ( 31 – 39 ) < 0.001*** Mode of delivery (n) Vaginal delivery 16(36.4%) 9(28.1%) 0.457 Cesarean section 28(63.6%) 23(71.9%) Neonatal weight (g) 3140(2860–3345) 3025(2600–3505) 0.643 Placental weight (g) 552.0(473.0-630.0) 495.5(450.5-663.3) 0.173 Baby sex Male 20(45.5%) 19(59.4%) 0.236 female 24(54.5%) 13(40.6%) 1min Apgar score 8.70(8.50–8.91) 8.56(8.21–8.90) 0.441 5min Apgar score 9.75(9.62–9.88) 9.41(9.08–9.75) 0.063 serum HGF (pg/ml) 614.05 (448.57-691.36) 680.42 (418.89-758.14) 0.153 serum s-MET (ng/ml) 1472.58 (1255.85-1726.50) 1651.23 (908.78-2167.09) < 0.001*** serum Insulin (µIU/ml) 8.89(5.84–30.82) 16.57(9.32–20.78) 0.670 serum Glucose (mg/dl) 95.50 (79.75-104.25) 91.00 (77.25–111.00) 0.684 cord HGF (pg/ml) 370.76 (210.71-863.05) 254.54 (154.39–894.90) 0.042* cord c-MET (ng/ml) 758.23 (604.42-1166.97) 918.09 (438.43-1085.65) 0.187 cord Insulin (µIU/ml) 6.84(5.53–8.69) 5.79(4.97–8.64) 0.409 cord Glucose (mg/dl) 208.45 (196.88-246.58) 215.25 (180.75-286.45) 0.265 cord ABGA pH 7.33(7.29–7.36) 7.33(7.30–7.38) 0.177 cord ABGA BE (mmol/L) -0.75(-2.93- 0.85) -4.15(-5.45- -1.43) 0.110 cord ABGA LA (mmol/L) 1.55(1.38-2.00) 2.70(1.40–4.23) 0.028* Values are presented as median (interquartile range) or as number (percentage) * p < 0.05; ** p < 0.01, *** p < 0.001 NGT, normal glucose tolerance; GDM, gestational diabetes mellitus; BMI, body mass index (calculated as weight in kilograms divided by the square of height in meters); Wt gain, Maternal body weight gain during pregnancy; GA, Gestational age: HGF, hepatocyte growth factor; s-MET, soluble tyrosine-protein kinase; serum, maternal serum; cord, umbilical cord blood ; cord ABGA, umbilical cord arterial blood gas analysis; BE, base excess; LA, lactic acid Logistic regression analysis was performed to determine the effects of serum HGF and c-MET levels on GDM (Table 2 ). Multivariate analysis was performed after adjusting for confounding factors that were identified as significant variables in the bivariate analysis (parity, pre-pregnancy BMI, Wt gain, and GA at delivery). In the multiple logistic regression analysis, the factor most strongly associated with GDM was the serum sc-MET level (odds ratio [OR], 9.063; p = 0.027). Other factors significantly associated with GDM included parity (OR, 0.067; p = 0.005), pre-pregnancy BMI (OR, 1.489; p = 0.003), GA at delivery (OR, 0.260; p = 0.015), and serum HGF levels (OR, 0.007; p = 0.017). Wt gain was not significantly associated with GDM in the multivariate analysis (OR, 0.907; p = 0.184). Table 2 Association of different variables with GDM by univariate logistic regression analysis and the significant risk factors influencing GDM by multivariate logistic regression analysis Crude Adjusted OR (95% CI) p -value OR (95% CI) p -value Parity 0.292 (0.111–0.764) 0.012* 0.067 (0.010–0.441) 0.005** Pre-pregnancy BMI 1.301 (1.118–1.514) 0.001** 1.489 (1.149–1.928) 0.003** Wt gain (kg) 0.876 (0.792–0.969) 0.01* 0.907 (0.785–1.047) 0.184 GA at delivery(weeks) 0.435 (0.250–0.755) 0.003** 0.260 (0.088–0.772) 0.015* Serum HGF (pg/L) 0.224 (0.028–1.776) 0.179 0.007 (0.000-0.420) 0.017* Serum s-MET (ng/L) 11.826 (2.658–52.621) 0.001** 9.063 (1.284–63.957) 0.027* * p < 0.05; ** p < 0.01, *** p < 0.001, Nagelkerke R 2 = 0.70 OR, odds ratio; CI, confidence interval; pre-pregnancy BMI, pre-pregnancy body mass index (calculated as weight in kilograms divided by the square of height in meters); Wt gain, maternal weight gain during pregnancy; GA, gestational age; HGF, hepatocyte growth factor; s-MET, soluble tyrosine-protein kinase The predictive value ​​of serum sc-MET levels was further evaluated using a ROC curve (Fig. 1 ). The ROC AUC of s-MET was 0.744 (95% CI, 0.632–0.857, p < 0.001). The optimal threshold value was 0.471 (sensitivity 0.773, specificity 0.625). The cut-off value of the serum sc-MET level for predicting GDM was 1455.26 ng/mL. The RT-PCR analysis of HGF and c- MET in the serum, placenta, and visceral fat is shown in Fig. 2 . The relative mRNA expression of HGF and c- MET in the serum tended to be higher in the GDM group than in the NGT group (Fig. 2 A). The relative mRNA expression of HGF and c- MET in the placenta decreased more in the GDM group than in the NGT group (Fig. 2 B). The relative mRNA expression of HGF and c- MET in visceral fat tended to increase more in the GDM than in the NGT group (Fig. 2 C). Western blot analysis was performed to examine the expression of HGF and c-MET/phosphorylated c-MET proteins in the placenta and visceral fat, and the results are shown in Fig. 3 . Phosphorylated c-MET and HGF protein expression in the placenta was not significantly different between the NGT and GDM groups (Fig. 3 A). In contrast, the expression of HGF protein in maternal visceral fat tissue was more than two-fold higher in the GDM group than in the NGT group ( p = 0.010, Fig. 3 B). The obstetric and perinatal outcomes in the NGT and GDM groups are shown in Table 3 . The rates of preterm delivery ( p = 0.019), RDS ( p = 0.032), and hypocalcemia ( p = 0.016) were significantly higher in the GDM group. Table 3 Obstetrical and perinatal outcomes of NGT and GDM groups Variables NGT (n = 44) GDM (n = 32) p -value Hypertensive disorder (n) 2 (4.5%) 3 (9.4%) 0.434 Premature rupture of membrane (n) 5 (11.4%) 3 (9.4%) 0.781 Preterm delivery (n) 2(4.5%) 8(25%) 0.019* RDS of newborn (n) 0 9(28.1%) 0.032* Hypoglycemia of newborn (n) 0 1(3.1%) 0.522 Neonatal jaundice (n) 2(4.5%) 6(18.8%) 0.094 Hypocalcemia of newborn (n) 0 4 (12.5%) 0.016* Values are presented as median (interquartile range) or as number (percentage) * p < 0.05; ** p < 0.01, *** p < 0.001 NGT, normal glucose tolerance; GDM, gestational diabetes mellitus Pearson’s correlation analysis was performed with maternal serum and cord blood values, and perinatal outcomes that differed between the NGT and GDM groups (Table 4 ). Serum sc-MET levels significantly correlated with preterm delivery ( r = 0.289, p = 0.011). Serum HGF levels significantly correlated with cord blood sc-MET ( r =-0.299, p = 0.044), cord ABGA base excess ( r =-0.390, p = 0.007), and cord ABGA lactic acid ( r = 0.469, p = 0.001) levels. Cord blood c-MET levels were significantly correlated with serum HGF ( r =-0.299, p = 0.044), serum glucose ( r =-0.303, p = 0.041), cord blood HGF ( r = 0.482, p < 0.001), and cord glucose ( r = 0.426, p = 0.002) levels. Cord glucose levels significantly correlated with cord blood c-MET ( r = 0.426, p = 0.002) and HGF ( r = 0.436, p = 0.002) levels. Hypocalcemia in newborns was significantly correlated with RDS ( r = 0.236, p = 0.034). Table 4 Correlation analysis between biochemical data and perinatal outcomes that differ between the NGT group and the GDM group. ss-MET s-HGF s-Ins s-Glu cc-MET c-HGF c-Ins c-Glu c-pH c-BE c-LA PD RDS Hypo Ca ss-MET 1 s-HGF − .135 1 s-Ins .092 − .051 1 s-Glu .159 .144 .340 ** 1 cc-Met − .171 − .299 * − .115 − .303 * 1 c-HGF − .152 − .008 − .181 − .244 .482 ** 1 c-Ins − .208 .048 − .052 − .075 − .034 − .119 1 c-Glu − .070 − .219 − .118 − .188 .426 ** .436 ** − .197 1 c-pH − .211 − .064 − .030 − .093 .035 − .004 − .070 − .352 1 c-BE − .170 − .390 ** .065 − .079 .150 .278 − .067 − .083 .673 ** 1 c-LA .111 .469 ** − .103 .191 − .326 − .420 .129 − .085 − .472 ** − .876 ** 1 PD .289 * .146 .071 .107 − .213 − .103 − .077 − .100 − .193 .045 .072 1 RDS .122 − .009 .032 .148 − .208 − .237 − .064 − .245 − .117 − .055 .062 .464 ** 1 HypoCa .016 − .135 .124 − .097 .000 .057 − .089 .209 − .019 − .116 .039 .216 .236 * 1 * p < 0.05; ** p < 0.01 ss-Met, maternal serum s-MET; s-HGF, maternal serum HGF; s-IR, serum homeostasis model assessment of insulin resistance; s-β, serum homeostasis model assessment of β cell function; cc-Met, umbilical cord blood c-MET; c-HGF, umbilical cord blood HGF; c-IR, serum homeostasis model assessment of insulin resistance; c- β, serum homeostasis model assessment of β cell function; c-pH, umbilical cord ABGA pH; c-BE, umbilical cord ABGA base excess; c-LA, umbilical cord ABGA lactic acid; ABGA, arterial blood gas analysis; PD, preterm delivery; RDS, neonatal respiratory distress syndrome; HypoCa, hypocalcemia of newborn Discussion We found significantly higher serum sc-MET levels in the GDM group than in the NGT group. Sc-MET had a significant effect on GDM even after adjusting for confounding variables. In this study, there were no significant differences in the mRNA expression of HGF and c-MET in the serum, placenta, and visceral fat. The protein expression of HGF and c-MET was also not significantly different in the placenta; however, HGF was significantly increased only in visceral fat. In addition, maternal serum HGF levels were negatively correlated with cord ABGA base excess and positively correlated with ABGA lactic acid. Cord blood HGF levels were significantly decreased in the GDM group and showed a significant positive correlation with cord blood glucose levels. Knocking out c-MET in the pancreas reportedly induces apoptosis of β-cell mass and impairs glucose tolerance ( 21 ); however, we found that serum sc-MET increased in the GDM group. This may be due to the different pathogenesis of DM and GDM, or to the timing of sample collection. GDM is a complex process affected by the physiological changes during pregnancy and in the placenta. Therefore, we compared HGF and c-MET expression not only in serum, but also in placenta and visceral fat, and found that HGF protein expression increased only in visceral fat. Adipose tissues secrete adipokines, which can alter insulin sensitivity, ( 25 ) and impairments in adipokine signaling may interfere with pancreatic β-cell adaptation and induce GDM ( 26 ). Changes in adipose tissue mass regulate serum HGF, and higher-than-normal amounts of HGF are secreted in obese individuals ( 27 ). In 2015, Dishi et al. showed that being overweight or obese may modify the association between serum HGF levels in early pregnancy and subsequent GDM risk ( 28 ). Another study showed that serum HGF levels increased proportionally with BMI in individuals with obesity ( 29 ). Recent studies have investigated the mechanisms by which HGF affects metabolic acidosis. Decreased base excess and increased lactic acid levels reflect metabolic acidosis and early prognosis in neonates. Neonatal acidemia is correlated with an increased risk of admission to neonatal intensive care units, hypoxic-ischemic encephalopathy, and RDS ( 30 , 31 ). HGF is involved in normal fetal lung development ( 32 ) and alveolar regeneration after acute lung injury; ( 33 ) ( 34 ) in fetal lung development, it regulates mesenchymal-epithelial interactions ( 35 ). Lower concentrations of pulmonary HGF are associated with more severe lung disease in preterm infants ( 36 ). It is well known that one of the major complications in mothers with GDM is neonate hypoglycemia ( 3 , 4 ). Umbilical cord blood glucose levels have been reported to be associated with neonatal blood glucose levels ( 37 , 38 ). Although the mechanism by which increased maternal c-MET levels decrease cord blood HGF levels through the placenta is not known, this study suggests that decreased cord blood HGF levels decrease neonate glucose levels ( 39 ). Therefore, to study the role of HGF/c-MET in GDM complications, further studies on the transmission of HGF/c-MET signaling through the placenta during pregnancy are needed. Sc-MET could be a meaningful predictive biomarker of GDM manifestation. Although the exact mechanism of c-MET overexpression in GDM and whether it is the cause or effect of IR or glucose intolerance remains unclear, meaningful results regarding its potential use as a biomarker for GDM are essential. Therefore, investigating the relevant factors in various tissues, such as the placenta and umbilical cord, synthesizing the results, and inferring the underlying mechanisms is necessary. HGF within adipose tissues may play an important role in GDM development. We hypothesized that increased maternal serum HGF levels may decrease fetal pulmonary HGF levels and interfere with fetal lung maturation. Thus, maternal serum HGF levels can be used to predict respiratory complications in patients with GDM. However, further studies are needed because the mechanism of HGF/c-MET signaling within adipose tissues has not yet been elucidated. This study had several limitations. First, serum samples were collected just before delivery rather than during early pregnancy, which limits the determination of the value of HGF and s-MET as predictive biomarkers of GDM. Second, the reliability of the correlation analysis was low because the number of individuals with perinatal complications was small. However, the strength of this study is that it explored the possibility of s-MET as a predictive factor for the manifestation of GDM and played a pioneering role by comprehensively studying the relationship between the complications of GDM and HGF/c-Met signaling. In addition, we attempted to reveal the relationship between GDM and HGF/c-Met signaling from various perspectives by investigating the placenta and visceral fat. Conclusions Serum sc-MET levels were significantly elevated, and cord blood HGF were significantly decreased in the GDM group, while HGF protein concentrations were significantly increased in visceral fat tissues. In addition, elevated maternal serum HGF levels were shown to affect base excess and lactic acid levels in cord ABGA, and a significant positive correlation was observed between the cord blood HGF levels and glucose levels. Therefore, changes in HGF/c-MET signaling are associated with GDM, and HGF and c-MET have the potential to be used as predictors of GDM development and complications. Abbreviations ABGA, arterial blood gas analysis AUC, area under the curve BMI, body mass index c-MET, mesenchymal-epithelial transition factor ELISA, enzyme-linked immunosorbent assay GA, gestational age GDM, gestational diabetes mellitus HGF, hepatocyte growth factor IR, insulin resistance NGT, normal glucose tolerance OGTT, oral glucose tolerance test OR, odds ratio RDS, respiratory distress syndrome ROC, receiver operating characteristic RT-PCR, real-time polymerase chain reaction Wt, weight Declarations Ethics approval and consent to participate: This study was conducted in compliance with the principles of the Declaration of Helsinki and was approved by the IRB of Chung Nam National University Hospital (IRB no. 2020-08-025). Informed consent was obtained from all participants. Consent for publication: Not applicable. Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This study was supported by the research fund of Chungnam National University Hospital. And this work was supported by research fund of Chungnam National University. Authors' contributions: YWJ, ML, YEK, and HJK contributed to conceptualization and YWJ, OSK, and HJK were responsible for data curation and YWJ, ML, OSK, and HJK conducted the investigation and YWJ, ML, OSK, BHK, and HJK prepared the original draft and YWJ, ML, YBK, SYS, HJY, YEK, OSK, BHK, and HJK participated in writing, review, and editing and BHK and HJK supervised the study, with all authors having read and agreed to the published version of the manuscript. Acknowledgements: Not applicable. References Di Cianni G, Miccoli R, Volpe L, Lencioni C, Del Prato S. Intermediate metabolism in normal pregnancy and in gestational diabetes. Diab/Metab Res Rev. 2003;19(4):259–70. Kim C, Newton KM, Knopp RH. Gestational Diabetes and the Incidence of Type 2 Diabetes: A systematic review. Diabetes Care. 2002;25(10):1862–8. Perkins JM, Dunn JP, Jagasia SM. Perspectives in Gestational Diabetes Mellitus: A Review of Screening,Diagnosis, and Treatment. Clin Diabetes. 2007;25(2):57–62. Devlieger R, Casteels K, Van Assche FA. Reduced adaptation of the pancreatic B cells during pregnancy is the major causal factor for gestational diabetes: Current knowledge and metabolic effects on the offspring. Acta Obstet Gynecol Scand. 2008;87(12):1266–70. Plows JF, Stanley JL, Baker PN, Reynolds CM, Vickers MH. The Pathophysiology of Gestational Diabetes Mellitus. Int J Mol Sci. 2018;19(11):3342. Xiang AH, Peters RK, Trigo E, Kjos SL, Lee WP, Buchanan TA. Multiple metabolic defects during late pregnancy in women at high risk for type 2 diabetes. Diabetes. 1999;48(4):848–54. Hunter SJ, Garvey WT. Insulin action and insulin resistance: diseases involving defects in insulin receptors, signal transduction, and the glucose transport effector system 1. Am J Med. 1998;105(4):331–45. Sorenson RL, Brelje TC. Adaptation of Islets of Langerhans to Pregnancy: β-Cell Growth, Enhanced Insulin Secretion and the Role of Lactogenic Hormones. Horm Metab Res. 1997;29(06):301–7. Ernst S, Demirci C, Valle S, Velazquez-Garcia S, Garcia-Ocaña A. Mechanisms in the adaptation of maternal β-cells during pregnancy. Diabetes Manag (Lond). 2011;1(2):239–48. Otonkoski T, Beattie GM, Rubin JS, Lopez AD, Baird A, Hayek A. Hepatocyte Growth Factor/Scatter Factor Has Insulinotropic Activity in Human Fetal Pancreatic Cells. Diabetes. 1994;43(7):947–53. Garcia-Ocana A, Takane KK, Syed MA, Philbrick WM, Vasavada RC, Stewart AF. Hepatocyte growth factor overexpression in the islet of transgenic mice increases beta cell proliferation, enhances islet mass, and induces mild hypoglycemia. J Biol Chem. 2000;275(2):1226–32. Nakamura T, Mizuno S. The discovery of Hepatocyte Growth Factor (HGF) and its significance for cell biology, life sciences and clinical medicine. Proceedings of the Japan Academy, Series B. 2010;86(6):588–610. Bottaro DP, Rubin JS, Faletto DL, Chan AM-L, Kmiecik TE, Vande Woude GF, et al. Identification of the Hepatocyte Growth Factor Receptor As the c- met Proto-Oncogene Product. Science. 1991;251(4995):802–4. Organ SL, Tsao MS. An overview of the c-MET signaling pathway. Ther Adv Med Oncol. 2011;3(1 Suppl):S7–19. Bottaro DP, Rubin JS, Faletto DL, Chan AM-L, Kmiecik TE, Vande Woude GF, et al. Identification of the hepatocyte growth factor receptor as the c-met proto-oncogene product. Science. 1991;251(4995):802–4. Yang JJ, Yang JH, Kim J, Ma SH, Cho LY, Ko K-P, et al. Soluble c-Met protein as a susceptible biomarker for gastric cancer risk: A nested case-control study within the Korean Multicenter Cancer Cohort. Int J Cancer. 2013;132(9):2148–56. Li L, An JN, Lee J, Shin DJ, Zhu SM, Kim JH, et al. Hepatocyte growth factor and soluble cMet levels in plasma are prognostic biomarkers of mortality in patients with severe acute kidney injury. Kidney Res Clin Pract. 2021;40(4):596–610. Lv H, Shan B, Tian Z, Li Y, Zhang Y, Wen S. Soluble c-Met is a reliable and sensitive marker to detect c-Met expression level in lung cancer. Biomed Res Int. 2015;2015:626578. Sierra JR, Tsao M-S. c-MET as a potential therapeutic target and biomarker in cancer. Therapeutic Adv Med Oncol. 2011;3(1suppl):S21–35. Abou-Bakr A, Elbasmi A. c-MET overexpression as a prognostic biomarker in colorectal adenocarcinoma. Gulf J Oncolog. 2013;1(14):28–34. Demirci C, Ernst S, Alvarez-Perez JC, Rosa T, Valle S, Shridhar V, et al. Loss of HGF/c-Met Signaling in Pancreatic β-Cells Leads to Incomplete Maternal β-Cell Adaptation and Gestational Diabetes Mellitus. Diabetes. 2012;61(5):1143–52. Dai C, Huh C-G, Thorgeirsson SS, Liu Y. β-Cell-Specific Ablation of the Hepatocyte Growth Factor Receptor Results in Reduced Islet Size, Impaired Insulin Secretion, and Glucose Intolerance. Am J Pathol. 2005;167(2):429–36. Committee ADAPP. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes—2024. Diabetes Care. 2023;47(Supplement1):S20–42. Carpenter MW, Coustan DR. Criteria for screening tests for gestational diabetes. Am J Obstet Gynecol. 1982;144(7):768–73. Šimják P, Anderlová K, Cinkajzlová A, Pařízek A, Kršek M, Haluzík M. The possible role of endocrine dysfunction of adipose tissue in gestational diabetes mellitus. Minerva Endocrinol. 2020;45(3):228–42. Moyce B, Dolinsky V. Maternal β-Cell Adaptations in Pregnancy and Placental Signalling: Implications for Gestational Diabetes. Int J Mol Sci. 2018;19(11):3467. Bell LN, Ward JL, Degawa-Yamauchi M, Bovenkerk JE, Jones R, Cacucci BM, et al. Adipose tissue production of hepatocyte growth factor contributes to elevated serum HGF in obesity. Am J Physiology-Endocrinology Metabolism. 2006;291(4):E843–8. Dishi M, Hevner K, Qiu C, Fida NG, Abetew DF, Williams MA et al. Early Pregnancy Maternal Hepatocyte Growth Factor and Risk of Gestational Diabetes. Br J Med Med Res. 2015;9(1). Rehman J, Considine RV, Bovenkerk JE, Li J, Slavens CA, Jones RM, et al. Obesity is associated with increased levels of circulating hepatocyte growth factor. J Am Coll Cardiol. 2003;41(8):1408–13. Edwards MO, Kotecha SJ, Kotecha S. Respiratory distress of the term newborn infant. Paediatr Respir Rev. 2013;14(1):29–37. Silverman WA, Andersen DH. A controlled clinical trial of effects of water mist on obstructive respiratory signs, death rate and necropsy findings among premature infants. Pediatrics. 1956;17(1):1–10. Lassus P, Janer J, Haglund C, Karikoski R, Andersson LC, Andersson S. Consistent expression of HGF and c-met in the perinatal lung. Biol Neonate. 2006;90(1):28–33. Yanagita K, Matsumoto K, Sekiguchi K, Ishibashi H, Niho Y, Nakamura T. Hepatocyte growth factor may act as a pulmotrophic factor on lung regeneration after acute lung injury. J Biol Chem. 1993;268(28):21212–7. Yamada T, Hisanaga M, Nakajima Y, Mizuno S, Matsumoto K, Nakamura T, et al. Enhanced expression of hepatocyte growth factor by pulmonary ischemia–reperfusion injury in the rat. Am J Respir Crit Care Med. 2000;162(2):707–15. Sato N, Takahashi H. Hepatocyte growth factor promotes growth and lumen formation of fetal lung epithelial cells in primary culture. Respirology. 1997;2(3):185–91. Lassus P, Heikkilä P, Andersson LC, von Boguslawski K, Andersson S. Lower concentration of pulmonary hepatocyte growth factor is associated with more severe lung disease in preterm infants. J Pediatr. 2003;143(2):199–202. Hay WW Jr. Placental-fetal glucose exchange and fetal glucose metabolism. Trans Am Clin Climatol Assoc. 2006;117:321 – 39; discussion 39–40. Wang Y, Liu H, Zhang L, Wang X, Wang M, Chen Z, et al. Umbilical artery cord blood glucose predicted hypoglycemia in gestational diabetes mellitus and other at-risk newborns. BMC Endocr Disorders. 2023;23(1):277. Arimitsu T, Kasuga Y, Ikenoue S, Saisho Y, Hida M, Yoshino J, et al. Risk factors of neonatal hypoglycemia in neonates born to mothers with gestational diabetes. Endocr J. 2023;70(5):511–7. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigure1.pdf Supplementaryfigure2.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6016485","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415233665,"identity":"f231850a-9777-40e2-86c1-cf42b8540424","order_by":0,"name":"Ye Won Jung","email":"","orcid":"","institution":"Chungnam National University Sejong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"Won","lastName":"Jung","suffix":""},{"id":415233666,"identity":"21f3c3a0-9dda-4335-ab3e-a552acb2951e","order_by":1,"name":"Mina Lee","email":"","orcid":"","institution":"Chungnam National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mina","middleName":"","lastName":"Lee","suffix":""},{"id":415233667,"identity":"6622b6d8-3828-48ed-9468-df13f0049945","order_by":2,"name":"Young Bok Ko","email":"","orcid":"","institution":"Chungnam National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Young","middleName":"Bok","lastName":"Ko","suffix":""},{"id":415233668,"identity":"8541febc-f6a2-446f-97a2-3af9c4fd039a","order_by":3,"name":"Soo Youn Song","email":"","orcid":"","institution":"Chungnam National University Sejong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Soo","middleName":"Youn","lastName":"Song","suffix":""},{"id":415233669,"identity":"27eb68e1-8df7-40d3-91ac-3f40398ebd5b","order_by":4,"name":"Heon Jong Yoo","email":"","orcid":"","institution":"Chungnam National University Sejong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Heon","middleName":"Jong","lastName":"Yoo","suffix":""},{"id":415233670,"identity":"0e251991-9de7-48af-84b6-fb9d85918ad8","order_by":5,"name":"Yea Eun Kang","email":"","orcid":"","institution":"Chungnam National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yea","middleName":"Eun","lastName":"Kang","suffix":""},{"id":415233671,"identity":"598292c0-4bbd-4131-926b-dcc5b6febd35","order_by":6,"name":"Ok Soon Kim","email":"","orcid":"","institution":"Chungnam National University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ok","middleName":"Soon","lastName":"Kim","suffix":""},{"id":415233672,"identity":"1200b3a5-06a3-42c2-8fe8-6726ba61cb27","order_by":7,"name":"Byung Hun Kang","email":"","orcid":"","institution":"Chungnam National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Byung","middleName":"Hun","lastName":"Kang","suffix":""},{"id":415233673,"identity":"c7189527-4186-4674-b7b1-460eab0a69cd","order_by":8,"name":"Hyun Jin Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACCWYGNgYJnho5IJsNIkKcFpljxiRoAau0YU5sIFqLZDt32gOLHLb0/tk9Zg8YamwYJGcfwK9Fmpl3u4HEGZncGXfOmBswHEtjkOZLwK9Fjpl3m4RkD1vuBokcMwnGhsMMcjwEHAbR8o853YBoLdIgLRI8zAlwLdKEtEg2g7UcM5xxI61MIuFYGo9kDwEtEufPbpMGRqU8/4zkbRIfamzkJM4Q0AICzPCoSGBgIOQsCGD8QJSyUTAKRsEoGLEAAHiEMOpDHbp2AAAAAElFTkSuQmCC","orcid":"","institution":"Chungnam National University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hyun","middleName":"Jin","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2025-02-12 15:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6016485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6016485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77024623,"identity":"9223d0fa-54a9-4406-a366-5ceeca679cc2","added_by":"auto","created_at":"2025-02-24 11:10:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104809,"visible":true,"origin":"","legend":"\u003cp\u003eExploring the potential of maternal serum s-MET as a biomarker for GDM (A) Area under the ROC- AUC of s-MET was 0.744 (95% CI 0.632– 0.857, p \u0026lt;0.001***) (B) The optimal threshold value is 0.471 (sensitivity 0.773, specificity 0.625)\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6016485/v1/8eeccdcd5ef6af475eb25d37.jpeg"},{"id":77022231,"identity":"aa681746-02a6-4017-ad5c-dc6036df1549","added_by":"auto","created_at":"2025-02-24 11:02:19","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56817,"visible":true,"origin":"","legend":"\u003cp\u003eRT-PCR analysis. Relative mRNA expression of c-MET and HGF in (A) serum, (B) placenta, and (C) visceral fat between NGT and GDM groups (NGT group n=8, GDM group n=8)\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6016485/v1/e42b45228a37c79f60f74a3b.jpeg"},{"id":77024625,"identity":"73942baa-4bdf-4859-a50c-bbbb88074d19","added_by":"auto","created_at":"2025-02-24 11:10:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140137,"visible":true,"origin":"","legend":"\u003cp\u003eWestern blot analysis. Comparison of p-MET/MET protein and HGF/GAPDH protein expression between NGT and GDM groups in (A) placenta (NGT group n=4, GDM group n=5), and (B) visceral fat (NGT group n=4, GDM group n=4)\u003c/p\u003e\n\u003cp\u003e*p \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6016485/v1/e292c143b2f9ef390a7e3231.jpg"},{"id":77026256,"identity":"49acb491-d099-4d1f-8bc8-a089821c8728","added_by":"auto","created_at":"2025-02-24 11:26:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1171085,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6016485/v1/9c54e2e6-d9c1-4716-b68a-152060a06960.pdf"},{"id":77022230,"identity":"8bf6a5e8-e814-4704-88fb-0750f9fd305d","added_by":"auto","created_at":"2025-02-24 11:02:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":145997,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6016485/v1/ff3a60ae53cd189a09643c2e.pdf"},{"id":77022232,"identity":"4685d3bc-ba46-4c90-afd9-9b4a2f4a321c","added_by":"auto","created_at":"2025-02-24 11:02:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":150041,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6016485/v1/d7808f6dc2ba07e8a0562bb9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hepatocyte growth factor/c-MET signaling is associated with gestational diabetes (GDM) and GDM complications: a case control study","fulltext":[{"header":"Background","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe prevalence of gestational diabetes mellitus (GDM)\u0026mdash;glucose intolerance that develops during pregnancy\u0026mdash;is gradually increasing owing to rising rates of obesity and advanced maternal age, which are important risk factors for GDM (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Inadequately managed GDM during pregnancy can lead to complications in both the mother and newborn. Mothers with GDM are at a higher risk of excessive weight gain, preeclampsia, cesarean section, and the development of type 2 diabetes or recurrent GDM in subsequent pregnancies (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Babies born to mothers with GDM are at higher risk of developing hypoglycemia, hypocalcemia, hyperbilirubinemia, respiratory distress syndrome (RDS), polycythemia, obesity, and type 2 diabetes later in life (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGDM pathophysiology is associated with abnormal tissue insulin sensitivity due to maternal insulin resistance (IR) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Maternal IR is a hallmark of normal glycemic physiology during pregnancy (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). During normal pregnancy, β-cells usually compensate for IR; however, when β-cell responsiveness to glucose fails, GDM occurs. In rodents, β-cell expansion failure or the inability of β-cells to compensate for maternal IR developed during pregnancy can lead to GDM development (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Among the several cytokines and growth hormones involved in IR and maintaining glucose homeostasis during pregnancy, hepatocyte growth factor (HGF) is an important component of IR pathophysiology; its level is increased in most common IR conditions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHGF is secreted by mesenchymal cells and acts as a multifunctional cytokine on cells of mainly epithelial origin (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). It plays a role in angiogenesis, tumorigenesis, and tissue regeneration by stimulating mitosis, cell motility, and matrix invasion. HGF functions after binding to the mesenchymal-epithelial transition factor (c-MET), a hepatocyte growth factor receptor (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). c-MET consists of an extracellular α-subunit that binds HGF and a transmembrane β-subunit that possesses tyrosine kinase activity. The HGF/c-MET signaling system is expressed in various organs, such as the liver, pancreas, and placenta, as well as in muscle and adipose tissues (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). HGF induces phosphorylation of tyrosine residues of activated c-MET (phosphorylated MET and p-MET) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The soluble form of c-MET (sc-MET) is a truncated form of the c-MET membrane receptor. S-MET is smaller than the integral c-MET and can competitively bind to HGF ligands with reduced affinity (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The levels of s-MET linearly correlate with c-MET expression in tumors (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Recently, the use of c-MET as a cancer biomarker has increased. c-MET is overexpressed in a variety of carcinomas, including lung, breast, ovarian, kidney, colon, thyroid, liver, and gastric carcinomas (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA previous experimental study investigating the relationship between HGF/c-MET signaling and GDM demonstrated the induction of β-cell apoptosis in pregnant mice lacking pancreatic c-Met (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Furthermore, β-cell-specific ablation of c-MET resulted in reduced islet size, decreased insulin secretion, and glucose intolerance (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, most studies on HGF/c-Met signaling associated with GDM have been mouse experiments, and studies in humans are lacking. Moreover, no study has investigated the association between GDM complications and HGF/c-Met signaling. Therefore, herein, we aimed to investigate the association between HGF/c-MET signaling and GDM, the suitability of serum HGF and sc-MET levels as predictive or prognostic biomarkers of GDM, and the relationship between HGF/c-MET signaling, GDM, and obstetric and perinatal outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePatient collection and blood sample assays\u003c/p\u003e\u003cp\u003eWe included 76 pregnant women aged 20\u0026ndash;45 years who delivered at Chung Nam National University Hospital between 2015 and 2020. Participants were divided into two groups: normal glucose tolerance (NGT, n\u0026thinsp;=\u0026thinsp;44) and gestational diabetes mellitus (GDM, n\u0026thinsp;=\u0026thinsp;32). GDM was diagnosed using a two-step oral glucose tolerance test (OGTT) according to the American Diabetes Association guidelines, as follows: (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStep 1: Perform a 50-g OGTT (non-fasting) with plasma glucose measurement at 1 h and 24\u0026ndash;28 weeks of gestation in women not previously diagnosed with diabetes. If the plasma glucose level measured 1 h after the load is \u0026ge;\u0026thinsp;140 mg/dL, proceed to a 100-g OGTT.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStep 2: The 100-g OGTT should be performed when the patient is fasting. GDM was diagnosed when at least two of the following four plasma glucose levels (measured fasting and at 1, 2, and 3 h during OGTT) were met or exceeded (Carpenter\u0026ndash;Coustan criteria (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)): Fasting: 95 mg/dL; 1 h: 180 mg/dL; 2 h: 155 mg/dL; 3 h: 140 mg/dL.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCases of multiple pregnancies; structural and genetic fetal abnormalities; history of hypertension in the mother; and systemic diseases in the mother, such as kidney, liver, rheumatic, inflammatory, acute hepatitis, febrile, and connective tissue diseases, were excluded. Blood samples were collected immediately before delivery to measure the serum concentrations of HGF, sc-MET, insulin, and glucose. The umbilical cord blood and placental tissues were collected immediately after delivery. Maternal visceral fat tissues were collected from the participants during cesarean section.\u003c/p\u003e \u003cp\u003eEnzyme-linked immunosorbent assay (ELISA)\u003c/p\u003e \u003cp\u003eThe concentrations of HGF (#ab100534; Abcam), sc-MET (#KHO2031; Invitrogen), and insulin (80-INSHU-E01.1, E10.1; ALPCO) in maternal serum and umbilical cord blood were measured using an ELISA. This assay employed a quantitative sandwich enzyme immunoassay technique following the manufacturer\u0026rsquo;s instructions. The optical density of each well was measured at 450 nm within 30 minutes. The concentrations of the samples were determined according to the absorbance of the samples and standards at 450 nm using a microplate reader. The ELISA experiments were repeated three times. Data with poor duplicates in one set of experiments were omitted, and the mean concentration of each sample was calculated based on repeated experiments.\u003c/p\u003e \u003cp\u003eReal-time polymerase chain reaction (RT-PCR) analysis\u003c/p\u003e \u003cp\u003eSixteen samples (eight NGT and eight GDM) were used for RT-PCR analysis. Total RNA from serum, placental tissues, and visceral fat were extracted using TRIzol reagent (ThermoFisher Scientific, Waltham, MA, USA) according to the manufacturer\u0026rsquo;s instructions, and complementary DNA was synthesized from one manufacturer using M-MLV reverse transcriptase and oligo-dT primers (Invitrogen/ThermoFisher Scientific, Waltham, MA, USA). The cDNA was amplified on a 7500 Fast RT-PCR System (Applied Biosystems, Carlsbad, CA) using 2X SYBR Green Mix (Applied Biosystems). To amplify \u003cem\u003eMET\u003c/em\u003e gene expressed sequences (Accession number NM_001324402.2), oligonucleotide primers were: sense primer, 5'-TGCCCAGACCCCTTATATGAAG-3'; antisense primer, 5'- GATATCCGGGACACCAGTTCAG-3,\u0026rsquo; \u003cem\u003eHGF\u003c/em\u003e sequences (Accession number M73240.1) were amplified using primers: sense primer, 5'- GAATGACACTGATGTTCCTTTGG\u0026ndash;3'; antisense primer, 5'- GGATACTGAGAATCCCAACGC-3,' and 18s rRNA sequences were amplified using primers: sense primer, 5'-GTAACCCGTTGAACCCCATT-3'; antisense primer, 5'-CCATCCAATCGGTAGTAGCG \u0026minus;\u0026thinsp;3. \u0026rsquo; Relative gene expression was calculated using the ΔΔ'. Relative gene expression was calculated as rRNA. Values are expressed as the fold change relative to the control group.\u003c/p\u003e \u003cp\u003eWestern blot analysis\u003c/p\u003e \u003cp\u003eNine samples (four NGT and five GDM) were used for western blot analysis. For western blot analysis of HGF and c-MET, proteins were extracted from placental and visceral fat tissues. The extracts were lysed using 50 nM Tris, pH 7.4, 150 mM NaCl, 1 mM EDTA, and 0.1% Triton X-100 containing protease and phosphatase inhibitors. Extracts were mixed with a sample buffer containing 3.2% sodium dodecyl sulfate, 15% glycerol, 2.8 M b-mercaptoethanol, and 0.0015% bromophenol blue. Proteins from the collected cells were subjected to electrophoresis on an 8\u0026ndash;10% SDS polyacrylamide gel and then transferred to a nitrocellulose membrane. After blocking of nonspecific binding sites, western bolts were cut horizontally to allow the detection of different proteins within a single experiment, where applicable. At least one molecular weight marker was kept above and below the expected protein size, with a minimum of two molecular weight markers per cut section. Membranes were incubated at 4\u0026deg;C with a polyclonal antibody directed against phosphorylated c-MET (1:1000; #3077; Cell Signaling Technology; Beverley, MA, USA), c-MET (1:1000; #4560; Cell Signaling Technology; Beverley, MA, USA), HGF (1:1000; #MA5-14160; Invitrogen/ThermoFisher Scientific; Waltham, MA, USA),and GAPDH (1:1000; #2118s; Cell Signaling Technology; Beverley, MA, USA). Blots were visualized using alkaline phosphate-linked anti-rabbit or anti-mouse secondary antibodies(1:5000), and images were scanned using an ODYSSEY instrument and Image Studio Software (LI-COR Biosciences; Lincoln, NE, USA).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBetween-group comparison of categorical variables was performed using the Chi-square test. For normally distributed data, between-group comparison was performed using an independent samples t-test, while for abnormally distributed data, the Mann\u0026ndash;Whitney U test was applied. Multiple logistic regression analysis was performed to determine variables affecting GDM expression. Receiver operating characteristic (ROC) curves were drawn, and the area under the curve (AUC) was calculated to determine the predictive power and cut-off points of the variables with significant GDM predictive ability. Pearson\u0026rsquo;s correlation analysis was used to analyze the correlation between the biochemical variables of the maternal serum and umbilical cord blood, and the obstetric and perinatal outcomes of GDM. The \u003cem\u003er\u003c/em\u003e coefficient indicates the degree of the association; the closer it is to -1, the higher the negative correlation, and the closer it is to 1, the higher the positive correlation. Statistical significance was inferred from two-sided \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). SPSS statistical software for Windows version 20 (SPSS, Chicago, IL, USA) was used for all statistical analyses. For data visualization, R version 22 was used.\u003c/p\u003e\u003cp\u003eEthics statement\u003c/p\u003e\u003cp\u003eThis study was conducted in compliance with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of Chung Nam National University Hospital (IRB no. 2020-08-025). Informed consent was obtained from all participants.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe baseline characteristics and biochemical data of the NGT (n\u0026thinsp;=\u0026thinsp;44) and GDM (n\u0026thinsp;=\u0026thinsp;32) groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared to primipara, the multipara rate was significantly higher in the GDM group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). The maternal body mass index (BMI) before pregnancy and gestational age (GA) at delivery were significantly higher (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas maternal weight (Wt) gain during pregnancy was significantly lower (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) in the GDM group than in the NGT group. Maternal serum HGF levels tended to be higher in the GDM group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.153), while maternal serum sc-MET levels were significantly higher (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than in the NGT group. Among the umbilical cord blood (cord) values, the cord HGF level was significantly lower in the GDM group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) and the cord arterial blood gas analysis (ABGA) lactic acid level was significantly higher in the GDM group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics and biochemical data of NGT and GDM groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNGT (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGDM (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(31-37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33(\u003cspan additionalcitationids=\"CR31 CR32 CR33 CR34 CR35 CR36\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePrimipara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14(43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMultipara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(56.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePre-pregnancy BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.50(19.00-22.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.80(21.43\u0026ndash;29.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWt gain (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.60(10.40-15.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.60(3.23\u0026ndash;12.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGA at delivery (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.6 (37\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.3 (\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35 CR36 CR37 CR38\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eMode of delivery (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaginal delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCesarean section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(71.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNeonatal weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3140(2860\u0026ndash;3345)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3025(2600\u0026ndash;3505)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePlacental weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e552.0(473.0-630.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e495.5(450.5-663.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBaby sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19(59.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13(40.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e1min Apgar score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.70(8.50\u0026ndash;8.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.56(8.21\u0026ndash;8.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e5min Apgar score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.75(9.62\u0026ndash;9.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.41(9.08\u0026ndash;9.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eserum HGF (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e614.05\u003c/p\u003e \u003cp\u003e(448.57-691.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e680.42\u003c/p\u003e \u003cp\u003e(418.89-758.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eserum s-MET (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1472.58\u003c/p\u003e \u003cp\u003e(1255.85-1726.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1651.23\u003c/p\u003e \u003cp\u003e(908.78-2167.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eserum Insulin (\u0026micro;IU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.89(5.84\u0026ndash;30.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.57(9.32\u0026ndash;20.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eserum Glucose (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.50\u003c/p\u003e \u003cp\u003e(79.75-104.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.00\u003c/p\u003e \u003cp\u003e(77.25\u0026ndash;111.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ecord HGF (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e370.76\u003c/p\u003e \u003cp\u003e(210.71-863.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e254.54\u003c/p\u003e \u003cp\u003e(154.39\u0026ndash;894.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ecord c-MET (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e758.23\u003c/p\u003e \u003cp\u003e(604.42-1166.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e918.09\u003c/p\u003e \u003cp\u003e(438.43-1085.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ecord Insulin (\u0026micro;IU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.84(5.53\u0026ndash;8.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.79(4.97\u0026ndash;8.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ecord Glucose (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208.45\u003c/p\u003e \u003cp\u003e(196.88-246.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e215.25\u003c/p\u003e \u003cp\u003e(180.75-286.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ecord ABGA pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.33(7.29\u0026ndash;7.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.33(7.30\u0026ndash;7.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ecord ABGA BE (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.75(-2.93- 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.15(-5.45- -1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ecord ABGA LA (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55(1.38-2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.70(1.40\u0026ndash;4.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues are presented as median (interquartile range) or as number (percentage)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNGT, normal glucose tolerance; GDM, gestational diabetes mellitus; BMI, body mass index (calculated as weight in kilograms divided by the square of height in meters); Wt gain, Maternal body weight gain during pregnancy; GA, Gestational age: HGF, hepatocyte growth factor; s-MET, soluble tyrosine-protein kinase; serum, maternal serum; cord, umbilical cord blood ; cord ABGA, umbilical cord arterial blood gas analysis; BE, base excess; LA, lactic acid\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLogistic regression analysis was performed to determine the effects of serum HGF and c-MET levels on GDM (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Multivariate analysis was performed after adjusting for confounding factors that were identified as significant variables in the bivariate analysis (parity, pre-pregnancy BMI, Wt gain, and GA at delivery). In the multiple logistic regression analysis, the factor most strongly associated with GDM was the serum sc-MET level (odds ratio [OR], 9.063; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027). Other factors significantly associated with GDM included parity (OR, 0.067; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), pre-pregnancy BMI (OR, 1.489; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), GA at delivery (OR, 0.260; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), and serum HGF levels (OR, 0.007; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017). Wt gain was not significantly associated with GDM in the multivariate analysis (OR, 0.907; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.184).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of different variables with GDM by univariate logistic regression analysis and the significant risk factors influencing GDM by multivariate logistic regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.292 (0.111\u0026ndash;0.764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067 (0.010\u0026ndash;0.441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.301 (1.118\u0026ndash;1.514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.489 (1.149\u0026ndash;1.928)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWt gain (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876 (0.792\u0026ndash;0.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.907 (0.785\u0026ndash;1.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA at delivery(weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.435 (0.250\u0026ndash;0.755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.260 (0.088\u0026ndash;0.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum HGF (pg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.224 (0.028\u0026ndash;1.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007 (0.000-0.420)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum s-MET (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.826 (2.658\u0026ndash;52.621)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.063 (1.284\u0026ndash;63.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Nagelkerke R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.70\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOR, odds ratio; CI, confidence interval; pre-pregnancy BMI, pre-pregnancy body mass index (calculated as weight in kilograms divided by the square of height in meters); Wt gain, maternal weight gain during pregnancy; GA, gestational age; HGF, hepatocyte growth factor; s-MET, soluble tyrosine-protein kinase\u003c/p\u003e \u003cp\u003eThe predictive value ​​of serum sc-MET levels was further evaluated using a ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e The ROC AUC of s-MET was 0.744 (95% CI, 0.632\u0026ndash;0.857, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The optimal threshold value was 0.471 (sensitivity 0.773, specificity 0.625). The cut-off value of the serum sc-MET level for predicting GDM was 1455.26 ng/mL.\u003c/p\u003e \u003cp\u003eThe RT-PCR analysis of \u003cem\u003eHGF\u003c/em\u003e and c-\u003cem\u003eMET\u003c/em\u003e in the serum, placenta, and visceral fat is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The relative mRNA expression of \u003cem\u003eHGF\u003c/em\u003e and c-\u003cem\u003eMET\u003c/em\u003e in the serum tended to be higher in the GDM group than in the NGT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The relative mRNA expression of \u003cem\u003eHGF\u003c/em\u003e and c-\u003cem\u003eMET\u003c/em\u003e in the placenta decreased more in the GDM group than in the NGT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The relative mRNA expression of \u003cem\u003eHGF\u003c/em\u003e and c-\u003cem\u003eMET\u003c/em\u003e in visceral fat tended to increase more in the GDM than in the NGT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWestern blot analysis was performed to examine the expression of HGF and c-MET/phosphorylated c-MET proteins in the placenta and visceral fat, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Phosphorylated c-MET and HGF protein expression in the placenta was not significantly different between the NGT and GDM groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In contrast, the expression of HGF protein in maternal visceral fat tissue was more than two-fold higher in the GDM group than in the NGT group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe obstetric and perinatal outcomes in the NGT and GDM groups are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The rates of preterm delivery (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), RDS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), and hypocalcemia (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) were significantly higher in the GDM group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eObstetrical and perinatal outcomes of NGT and GDM groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNGT (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDM (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertensive disorder (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremature rupture of membrane (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreterm delivery (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDS of newborn (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoglycemia of newborn (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatal jaundice (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypocalcemia of newborn (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eValues are presented as median (interquartile range) or as number (percentage)\u003c/p\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eNGT, normal glucose tolerance; GDM, gestational diabetes mellitus\u003c/p\u003e \u003cp\u003ePearson\u0026rsquo;s correlation analysis was performed with maternal serum and cord blood values, and perinatal outcomes that differed between the NGT and GDM groups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Serum sc-MET levels significantly correlated with preterm delivery (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.289, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). Serum HGF levels significantly correlated with cord blood sc-MET (\u003cem\u003er\u003c/em\u003e =-0.299, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), cord ABGA base excess (\u003cem\u003er\u003c/em\u003e =-0.390, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), and cord ABGA lactic acid (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.469, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) levels. Cord blood c-MET levels were significantly correlated with serum HGF (\u003cem\u003er\u003c/em\u003e =-0.299, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), serum glucose (\u003cem\u003er\u003c/em\u003e =-0.303, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), cord blood HGF (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.482, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and cord glucose (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.426, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) levels. Cord glucose levels significantly correlated with cord blood c-MET (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.426, p\u0026thinsp;=\u0026thinsp;0.002) and HGF (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.436, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) levels. Hypocalcemia in newborns was significantly correlated with RDS (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.236, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis between biochemical data and perinatal outcomes that differ between the NGT group and the GDM group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ess-MET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003es-HGF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003es-Ins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003es-Glu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecc-MET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ec-HGF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ec-Ins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ec-Glu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ec-pH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ec-BE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ec-LA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eRDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eHypo\u003c/p\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ess-MET\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003es-HGF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003es-Ins\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003es-Glu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.340\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd 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\u003cp\u003e\u0026minus;\u0026thinsp;.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRDS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.464\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypoCa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.236\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003cp\u003ess-Met, maternal serum s-MET; s-HGF, maternal serum HGF; s-IR, serum homeostasis model assessment of insulin resistance; s-β, serum homeostasis model assessment of β cell function; cc-Met, umbilical cord blood c-MET; c-HGF, umbilical cord blood HGF; c-IR, serum homeostasis model assessment of insulin resistance; c- β, serum homeostasis model assessment of β cell function; c-pH, umbilical cord ABGA pH; c-BE, umbilical cord ABGA base excess; c-LA, umbilical cord ABGA lactic acid; ABGA, arterial blood gas analysis; PD, preterm delivery; RDS, neonatal respiratory distress syndrome; HypoCa, hypocalcemia of newborn\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found significantly higher serum sc-MET levels in the GDM group than in the NGT group. Sc-MET had a significant effect on GDM even after adjusting for confounding variables. In this study, there were no significant differences in the mRNA expression of \u003cem\u003eHGF\u003c/em\u003e and \u003cem\u003ec-MET\u003c/em\u003e in the serum, placenta, and visceral fat. The protein expression of HGF and c-MET was also not significantly different in the placenta; however, HGF was significantly increased only in visceral fat. In addition, maternal serum HGF levels were negatively correlated with cord ABGA base excess and positively correlated with ABGA lactic acid. Cord blood HGF levels were significantly decreased in the GDM group and showed a significant positive correlation with cord blood glucose levels.\u003c/p\u003e \u003cp\u003eKnocking out c-MET in the pancreas reportedly induces apoptosis of β-cell mass and impairs glucose tolerance (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e); however, we found that serum sc-MET increased in the GDM group. This may be due to the different pathogenesis of DM and GDM, or to the timing of sample collection. GDM is a complex process affected by the physiological changes during pregnancy and in the placenta. Therefore, we compared HGF and c-MET expression not only in serum, but also in placenta and visceral fat, and found that HGF protein expression increased only in visceral fat. Adipose tissues secrete adipokines, which can alter insulin sensitivity, (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and impairments in adipokine signaling may interfere with pancreatic β-cell adaptation and induce GDM (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Changes in adipose tissue mass regulate serum HGF, and higher-than-normal amounts of HGF are secreted in obese individuals (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In 2015, Dishi et al. showed that being overweight or obese may modify the association between serum HGF levels in early pregnancy and subsequent GDM risk (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Another study showed that serum HGF levels increased proportionally with BMI in individuals with obesity (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies have investigated the mechanisms by which HGF affects metabolic acidosis. Decreased base excess and increased lactic acid levels reflect metabolic acidosis and early prognosis in neonates. Neonatal acidemia is correlated with an increased risk of admission to neonatal intensive care units, hypoxic-ischemic encephalopathy, and RDS (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). HGF is involved in normal fetal lung development (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and alveolar regeneration after acute lung injury; (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) in fetal lung development, it regulates mesenchymal-epithelial interactions (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Lower concentrations of pulmonary HGF are associated with more severe lung disease in preterm infants (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is well known that one of the major complications in mothers with GDM is neonate hypoglycemia (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Umbilical cord blood glucose levels have been reported to be associated with neonatal blood glucose levels (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Although the mechanism by which increased maternal c-MET levels decrease cord blood HGF levels through the placenta is not known, this study suggests that decreased cord blood HGF levels decrease neonate glucose levels (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Therefore, to study the role of HGF/c-MET in GDM complications, further studies on the transmission of HGF/c-MET signaling through the placenta during pregnancy are needed.\u003c/p\u003e \u003cp\u003eSc-MET could be a meaningful predictive biomarker of GDM manifestation. Although the exact mechanism of c-MET overexpression in GDM and whether it is the cause or effect of IR or glucose intolerance remains unclear, meaningful results regarding its potential use as a biomarker for GDM are essential. Therefore, investigating the relevant factors in various tissues, such as the placenta and umbilical cord, synthesizing the results, and inferring the underlying mechanisms is necessary.\u003c/p\u003e \u003cp\u003eHGF within adipose tissues may play an important role in GDM development. We hypothesized that increased maternal serum HGF levels may decrease fetal pulmonary HGF levels and interfere with fetal lung maturation. Thus, maternal serum HGF levels can be used to predict respiratory complications in patients with GDM. However, further studies are needed because the mechanism of HGF/c-MET signaling within adipose tissues has not yet been elucidated.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, serum samples were collected just before delivery rather than during early pregnancy, which limits the determination of the value of HGF and s-MET as predictive biomarkers of GDM. Second, the reliability of the correlation analysis was low because the number of individuals with perinatal complications was small. However, the strength of this study is that it explored the possibility of s-MET as a predictive factor for the manifestation of GDM and played a pioneering role by comprehensively studying the relationship between the complications of GDM and HGF/c-Met signaling. In addition, we attempted to reveal the relationship between GDM and HGF/c-Met signaling from various perspectives by investigating the placenta and visceral fat.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSerum sc-MET levels were significantly elevated, and cord blood HGF were significantly decreased in the GDM group, while HGF protein concentrations were significantly increased in visceral fat tissues. In addition, elevated maternal serum HGF levels were shown to affect base excess and lactic acid levels in cord ABGA, and a significant positive correlation was observed between the cord blood HGF levels and glucose levels. Therefore, changes in HGF/c-MET signaling are associated with GDM, and HGF and c-MET have the potential to be used as predictors of GDM development and complications.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABGA, arterial blood gas analysis\u003c/p\u003e\n\u003cp\u003eAUC, area under the curve\u003c/p\u003e\n\u003cp\u003eBMI, body mass index\u003c/p\u003e\n\u003cp\u003ec-MET, mesenchymal-epithelial transition factor\u003c/p\u003e\n\u003cp\u003eELISA, enzyme-linked immunosorbent assay\u003c/p\u003e\n\u003cp\u003eGA, gestational age\u003c/p\u003e\n\u003cp\u003eGDM, gestational diabetes mellitus\u003c/p\u003e\n\u003cp\u003eHGF, hepatocyte growth factor\u003c/p\u003e\n\u003cp\u003eIR, insulin resistance\u003c/p\u003e\n\u003cp\u003eNGT, normal glucose tolerance\u003c/p\u003e\n\u003cp\u003eOGTT, oral glucose tolerance test\u003c/p\u003e\n\u003cp\u003eOR, odds ratio\u003c/p\u003e\n\u003cp\u003eRDS, respiratory distress syndrome\u003c/p\u003e\n\u003cp\u003eROC, receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eRT-PCR, real-time polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eWt, weight\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This study was conducted in compliance with the principles of the Declaration of Helsinki and was approved by the IRB of Chung Nam National University Hospital (IRB no. 2020-08-025). Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by the research fund of Chungnam National University Hospital. And this work was supported by research fund of Chungnam National University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eYWJ, ML, YEK, and HJK contributed to conceptualization and YWJ, OSK, and HJK were responsible for data curation and YWJ, ML, OSK, and HJK conducted the investigation and YWJ, ML, OSK, BHK, and HJK prepared the original draft and YWJ, ML, YBK, SYS, HJY, YEK, OSK, BHK, and HJK participated in writing, review, and editing and BHK and HJK supervised the study, with all authors having read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDi Cianni G, Miccoli R, Volpe L, Lencioni C, Del Prato S. Intermediate metabolism in normal pregnancy and in gestational diabetes. 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Diabetes. 2012;61(5):1143\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai C, Huh C-G, Thorgeirsson SS, Liu Y. β-Cell-Specific Ablation of the Hepatocyte Growth Factor Receptor Results in Reduced Islet Size, Impaired Insulin Secretion, and Glucose Intolerance. Am J Pathol. 2005;167(2):429\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCommittee ADAPP. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes\u0026mdash;2024. Diabetes Care. 2023;47(Supplement1):S20\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarpenter MW, Coustan DR. Criteria for screening tests for gestational diabetes. Am J Obstet Gynecol. 1982;144(7):768\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠimj\u0026aacute;k P, Anderlov\u0026aacute; K, Cinkajzlov\u0026aacute; A, Pař\u0026iacute;zek A, Kršek M, Haluz\u0026iacute;k M. The possible role of endocrine dysfunction of adipose tissue in gestational diabetes mellitus. Minerva Endocrinol. 2020;45(3):228\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoyce B, Dolinsky V. Maternal β-Cell Adaptations in Pregnancy and Placental Signalling: Implications for Gestational Diabetes. Int J Mol Sci. 2018;19(11):3467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBell LN, Ward JL, Degawa-Yamauchi M, Bovenkerk JE, Jones R, Cacucci BM, et al. Adipose tissue production of hepatocyte growth factor contributes to elevated serum HGF in obesity. Am J Physiology-Endocrinology Metabolism. 2006;291(4):E843\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDishi M, Hevner K, Qiu C, Fida NG, Abetew DF, Williams MA et al. Early Pregnancy Maternal Hepatocyte Growth Factor and Risk of Gestational Diabetes. Br J Med Med Res. 2015;9(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRehman J, Considine RV, Bovenkerk JE, Li J, Slavens CA, Jones RM, et al. Obesity is associated with increased levels of circulating hepatocyte growth factor. J Am Coll Cardiol. 2003;41(8):1408\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdwards MO, Kotecha SJ, Kotecha S. Respiratory distress of the term newborn infant. Paediatr Respir Rev. 2013;14(1):29\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilverman WA, Andersen DH. A controlled clinical trial of effects of water mist on obstructive respiratory signs, death rate and necropsy findings among premature infants. Pediatrics. 1956;17(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLassus P, Janer J, Haglund C, Karikoski R, Andersson LC, Andersson S. Consistent expression of HGF and c-met in the perinatal lung. Biol Neonate. 2006;90(1):28\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYanagita K, Matsumoto K, Sekiguchi K, Ishibashi H, Niho Y, Nakamura T. Hepatocyte growth factor may act as a pulmotrophic factor on lung regeneration after acute lung injury. J Biol Chem. 1993;268(28):21212\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamada T, Hisanaga M, Nakajima Y, Mizuno S, Matsumoto K, Nakamura T, et al. Enhanced expression of hepatocyte growth factor by pulmonary ischemia\u0026ndash;reperfusion injury in the rat. Am J Respir Crit Care Med. 2000;162(2):707\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSato N, Takahashi H. Hepatocyte growth factor promotes growth and lumen formation of fetal lung epithelial cells in primary culture. Respirology. 1997;2(3):185\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLassus P, Heikkil\u0026auml; P, Andersson LC, von Boguslawski K, Andersson S. Lower concentration of pulmonary hepatocyte growth factor is associated with more severe lung disease in preterm infants. J Pediatr. 2003;143(2):199\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHay WW Jr. Placental-fetal glucose exchange and fetal glucose metabolism. Trans Am Clin Climatol Assoc. 2006;117:321\u0026thinsp;\u0026ndash;\u0026thinsp;39; discussion 39\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Liu H, Zhang L, Wang X, Wang M, Chen Z, et al. Umbilical artery cord blood glucose predicted hypoglycemia in gestational diabetes mellitus and other at-risk newborns. 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Endocr J. 2023;70(5):511\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatocyte Growth Factor, MET Receptor Tyrosine Kinase, Gestational Diabetes Mellitus, Placenta, Visceral Fat, Blood Gas Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6016485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6016485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHepatocyte growth factor (HGF)/mesenchymal-epithelial transition factor (c-MET) signaling is involved in glucose homeostasis in pancreatic β cells; however, studies on its relationship with gestational diabetes mellitus (GDM) are lacking. This study aimed to investigate HGF and its receptor, c-MET, in pregnant women with GDM and to analyze the correlation between HGF/c-MET signaling and GDM complications.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn total, 44 pregnant women with normal glucose tolerance (NGT) and 32 with GDM were studied. Serum levels of HGF and c-MET were measured using an enzyme-linked immunosorbent assay. The relative mRNA expression of \u003cem\u003eHGF\u003c/em\u003e and c-\u003cem\u003eMET\u003c/em\u003e was measured using real-time polymerase chain reaction. The protein expression of HGF and c-MET in placental and visceral fat tissues was measured using western blot analysis. Logistic regression and Pearson\u0026rsquo;s correlation analyses examined the associations of these variables with clinical information.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSerum sc-MET levels were significantly higher (1472.58 vs. 1651.23 ng/mL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cord blood HGF levels were significantly lower (370.76 vs. 254.54 ng/mL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) in the GDM group than in the NGT group. The area under the receiver operating characteristic curve for sc-MET was 0.744 (95% confidence interval: 0.632\u0026ndash;0.857, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The cut-off serum sc-MET level for predicting GDM was 1455.26 ng/mL. HGF protein expression in the maternal visceral fat tissue was significantly higher in the GDM group than in the NGT group (2-fold higher, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Serum HGF significantly correlated with cord arterial blood gas analysis (ABGA) base excess (\u003cem\u003er\u003c/em\u003e =-0.390, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and cord ABGA lactic acid (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.469, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Cord blood HGF levels were significantly correlated with cord blood glucose levels (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.439, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe found an increase in serum sc-MET, a decrease in cord HGF, and an increase in HGF protein expression in the visceral fat tissue of the GDM group, indicating that HGF/c-MET signaling is related to GDM expression. In addition, maternal serum HGF levels correlated with base excess and lactic acid levels in cord ABGA, and cord blood HGF levels correlated with cord blood glucose levels. These outcomes suggest that HGF/c-MET signaling may have potential application in predicting GDM complications.\u003c/p\u003e","manuscriptTitle":"Hepatocyte growth factor/c-MET signaling is associated with gestational diabetes (GDM) and GDM complications: a case control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-24 11:02:12","doi":"10.21203/rs.3.rs-6016485/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b2e00632-9285-4762-be4d-662a192340c2","owner":[],"postedDate":"February 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-13T03:08:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-24 11:02:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6016485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6016485","identity":"rs-6016485","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}