Chronic low-grade inflammation plays a central role in the pathophysiology of gestational diabetes mellitus (GDM). To investigate the ability of different inflammatory blood cell parameters in predicting the development of GDM and pregnancy outcomes, 258 women with GDM and 1,154 women without were included in this retrospective study. First-trimester neutrophil count outperformed white blood cell count and the neutrophil-to-lymphocyte ratio in the predictability for GDM. Subjects were grouped based on tertiles of neutrophil count during their first-trimester pregnancy. The results showed that as the neutrophil count increased, there was a stepwise increase in GDM incidence as well as in glucose and glycosylated hemoglobin levels, HOMA for insulin resistance (HOMA-IR), macrosomia incidence, and newborn weight. Neutrophil count was positively associated with prepregnancy BMI, HOMA-IR, and newborn weight. Additionally, neutrophil count was an independent risk factor for the development of GDM, regardless of the history of GDM. Spline regression showed that there was a significant linear association between GDM incidence and the continuous neutrophil count when it was >5.0 × 109/L. This work suggested that the first-trimester neutrophil count is closely associated with the development of GDM and adverse pregnancy outcomes.

Gestational diabetes mellitus (GDM), one of the most common metabolic disorders in pregnant women, is defined as any degree of glucose intolerance with onset or first diagnosis during pregnancy (1). Over the past few decades, the prevalence of GDM has increased, coinciding with rising rates of obesity and type 2 diabetes (T2DM). In 2010, GDM prevalence in the U.S. was estimated to be 4.6–9.2% (2). In China, GDM prevalence has been reported to be 9.3–18.9% (3). The presence of GDM is associated with higher risk of adverse consequences for both the mother (preeclampsia, cesarean section, development of T2DM after delivery) and infant (macrosomia with consequent shoulder dystocia and birth injury, neonatal hypoglycemia, and childhood obesity) (48). Several traditional factors, including a family or personal history of diabetes, previous adverse pregnancy outcome, glycosuria, and obesity, are associated with GDM, but the exact pathophysiology of GDM remains elusive.

Previous studies have shown that low-grade chronic inflammation plays a crucial role in the pathophysiology of GDM and T2DM (914). The abnormal increase of the inflammatory blood cell parameters, such as white blood cell (WBC) and neutrophil count, neutrophil-to-lymphocyte ratio (NLR), and platelet count, usually serve as simple markers of inflammation, and all have been investigated for their ability to predict GDM in a previous study with a small sample size, but results were inconsistent (1519).

This study investigated the potential correlation of inflammatory blood cell parameters with GDM and adverse pregnancy outcomes. First, we found that the first-trimester neutrophil count outperformed WBC count and NLR as a risk factor and showed better diagnostic predictability for GDM. In addition, our cohort study showed that as the first-trimester neutrophil count increased, the incidence of GDM, blood glucose level, HOMA for insulin resistance (HOMA-IR), and adverse pregnancy outcomes increased in a stepwise manner. The first-trimester neutrophil count was closely associated with prepregnancy BMI, HOMA-IR, and newborn weight and was also an independent risk factor for development of GDM. Finally, a significant linear association between continuous neutrophil count and the incidence of GDM was analyzed by spline regression.

Study Population

From May 2015 to July 2018, 1,781 pregnant women were retrospectively screened at the GDM Care Center of Shanghai Fifth People’s Hospital, Fudan University. The retrospective analysis process followed the procedure described in Fig. 1. Women were excluded from the study for any of the following: 1) any infectious disease 2 weeks before the blood cell test; 2) abnormal liver or renal function; 3) presence of viral infection or positive carrier status (hepatitis B virus, syphilis, and HIV); 4) preexisting diabetes; 5) chronic hypertension; or 6) multiple gestation. Finally, 1,412 women (1,154 without GDM and 258 with GDM) were collected for the analysis.

Figure 1

Flowchart of this study. DM, diabetes.

Figure 1

Flowchart of this study. DM, diabetes.

Close modal

Data Collection and Laboratory Assessments During Pregnancy

At the first visit, gestational age was calculated based on the date of last menstruation or first-trimester ultrasonography. After an overnight fast for 12 h, blood samples were collected for counts of blood cells (XN9000 Automatic Blood Cell Analyzer; Sysmex, Kobe, Japan) and biochemical parameters tests (Cobas 8000 Automatic Biochemical Analyzer; Roche, Basel, Switzerland). Blood pressure and anthropometric parameters were measured, and a questionnaire was also completed. The patient questionnaire obtained information of last menstruation, method of conception, parity, obstetric history, family history of diabetes, previous history of GDM, and prepregnancy weight. Prepregnancy BMI was calculated as the prepregnancy weight in kilograms divided by the square of height in meters. After delivery, details including gestational age at delivery, mode of delivery, newborn weight, and sex of the neonate were recorded by medical staff.

Oral Glucose Tolerance Test

All subjects, with the exception of those diagnosed with overt diabetes or GDM in early pregnancy, underwent routine screening for GDM at 24–28 weeks’ gestation according to a 75-g oral glucose tolerance test (OGTT) (1). OGTT was performed in the morning after an overnight fast of at least 8 h. Diagnosis of GDM was made when fasting blood glucose (FBG) was ≥5.1 mmol/L, the 1-h level was ≥10.0 mmol/L, or the 2-h level was ≥8.5 mmol/L, respectively.

Intervention for GDM

Therapeutic regimen started as soon as the individual was diagnosed with GDM. At first, lifestyle intervention was initiated, and insulin was then supplemented in addition to lifestyle intervention if the goals of glycemic control were not reached (fasting glucose <5.3 mmol/L, 1-h postprandial glucose <7.8 mmol/L, or 2-h postprandial glucose <6.7 mmol/L).

Calculation of HOMA-IR, HOMA of β-Cell Function, and QUICKI

The values for HOMA-IR, HOMA of β-cell function (HOMA-β), and QUICKI were determined from FBG and insulin concentration using the following formula (20): HOMA-IR = (I0 [µIU/mL] × G0 [mmol/L])/22.5; HOMA-β = 20 × (I0 [µIU/mL]/[G0 (mmol/L]) − 3.5; QUICKI = 1/(logI0 [µIU/mL] + logG0 [mg/dL]). I0 is the level of fasting insulin, and G0 is the level of FBG.

Statistical Analysis

To avoid the potential bias due to uneven distribution of covariates between women with or without GDM, a case-control matching method was used to match variables that included prepregnancy BMI, age, and parity. Matching tolerance was 0.5, 2, and 0, respectively. To compare the predictability for GDM among the inflammatory blood cell parameters, logistic regression analysis and receiver operating characteristic curves were performed.

To further validate the association of neutrophil count with GDM and pregnancy outcomes, a cohort including the same subjects as the case-control study was established in which patients were divided into three groups by tertiles of neutrophil count: lowest group (<5.30 × 109/L), middle group (5.30–6.80 × 109/L), and highest group (>6.80 × 109/L). Descriptive statistics for the studied variables are presented as means ± SD for normally distributed variables, median (interquartile range [IQR]) for nonnormally distributed variables, and frequency (percentage) for categorical variables. ANOVA and the Student t test were used to identify the difference in the mean between groups. Bonferroni correction was applied in multiple comparisons. Nonnormally distributed variables were analyzed by Kruskal-Wallis one-way ANOVA or Wilcoxon tests. HOMA-IR, HOMA-β, and QUICKI were log-transformed previously for t tests or ANOVA. Linear correlation between neutrophil count and HOMA-IR and prepregnancy BMI and newborn weight were assessed by simple and multivariate linear regression analysis. Continuous association of neutrophil count with GDM incidence was determined by spline regression analysis. To determine whether neutrophil count was an independent risk factor, logistic regression analysis was performed with GDM classified in a binary manner (presence/absence) as the dependent variable. Neutrophil count and traditional risk factors, including age, previous GDM history, prepregnancy BMI, triglyceride (TG) level, and weight gain before GDM was diagnosed as the possible risk factors, were entered into logistic regression analysis in all mothers, and the same analyses were repeated in the subgroup of mothers with no previous GDM (women without GDM history and nulliparous). All data were analyzed using SPSS 24.0 software (IBM, Armonk, NY). A two-tailed P < 0.05 was considered to indicate statistical significance.

Data and Resource Availability

The data sets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Characteristics of Women With and Without GDM in All Subjects and Matched Case-Control Study

GDM developed in 258 women (18.27%) among the 1,412 subjects, and women with older age, previous GDM history, or GDM family history were more likely to develop GDM (Table 1). Compared with women without GDM, patients with GDM had a much higher level of prepregnancy BMI (P < 0.001), first-trimester WBC count (9.37 ± 2.07 vs. 8.57 ± 2.00 × 109/L, P < 0.001), neutrophil count (7.06 ± 1.76 vs. 6.03 ± 1.70 × 109/L, P < 0.001), and NLR (3.93 ± 1.32 vs. 3.36 ± 1.13, P < 0.001), whereas the difference in lymphocyte count or platelet count was not significant. In addition, patients with GDM had much higher second-trimester TG (P = 0.010), HDL (P = 0.011), FBG (P < 0.001), 1-h blood glucose (BG) (P < 0.001), 2-h BG (P < 0.001), HbA1c (P < 0.001), HOMA-IR (P < 0.001), and weight gain before GDM screening (P = 0.022) and lower QUICKI (P < 0.001) and HOMA-β (P < 0.001) than women without GDM. Unexpectedly, patients with GDM had lower LDL than women without GDM (2.81 ± 1.07 vs. 3.11 ± 0.88 mmol/L, P = 0.001). There was no difference in weight gain during the whole pregnancy between women with and without GDM, and we found most patients with GDM (86.8%) simply needed lifestyle intervention, while only 34 women (13.2%) with GDM required insulin treatment. Obviously, mothers with GDM tended to deliver heavier newborns (3,527.2 ± 562.7 vs. 3,361.6 ± 476.5 g, P < 0.001) and had a higher rate of delivering macrosomic infants than mothers without GDM (21.4% vs. 7.7%, P < 0.001) (Table 1).

Table 1

Characteristics of women with and without GDM in all subjects and in the matched case-control study

All subjectsMatched case-control study
Women without GDMWomen with GDMWomen without GDMWomen with GDM
(n = 1,154)(n = 258)P(n = 200)(n = 200)P
Anthropometric parameters      
 Age (years) 27 ± 5 30 ± 5 <0.001 29 ± 5 30 ± 5 0.607 
 Parity       
  Nulliparous 276 (26.1) 86 (37.2) 0.0001 65 (32.2) 65 (32.2) 1.000 
  Parous 783 (73.9) 145 (62.8)  137 (67.8) 137 (67.8)  
 Previous GDM       
  No 871 (75.5) 152 (58.9) <0.001 135 (66.8) 117 (57.9) <0.001 
  Yes 7 (0.6) 20 (7.8)  2 (1.0) 20 (9.9)  
  Nulliparous 276 (23.9) 86 (33.3)  65 (32.2) 65 (32.2)  
 Family history of diabetes      
  No 117 (99.5) 236 (93.7) <0.001 193 (99.5) 188 (94.9) 0.011 
  Yes 6 (0.5) 16 (6.3)  1 (0.5) 10 (5.1)  
 Pregnancy BMI (kg/m222.4 ± 3.2 24.0 ± 4.2 <0.001 23.4 ± 3.5 23.4 ± 3.5 0.921 
First trimester       
 SBP (mmHg) 116 ± 10 117 ± 11 0.446 117 ± 9 116 ± 11 0.359 
 DBP (mmHg) 68 ± 7 69 ± 8 0.228 69 ± 7 68 ± 8 0.980 
 WBCs (×109/L) 8.57 ± 2.00 9.37 ± 2.07 <0.001 8.57 ± 1.92 9.27 ± 2.04 0.001 
 Neutrophils (×109/L) 6.03 ± 1.70 7.06 ± 1.76 <0.001 6.05 ± 1.61 7.00 ± 1.74 <0.001 
 Lymphocytes (×109/L) 1.88 ± 0.51 1.88 ± 0.52 0.906 1.86 ± 0.51 1.86 ± 0.52 0.969 
 NLR 3.36 ± 1.13 3.93 ± 1.32 <0.001 3.45 ± 1.21 3.93 ± 1.34 0.001 
 Platelets (×109/L) 216 ± 53 221 ± 49 0.257 217 ± 53 220 ± 50 0.649 
Second trimester       
 OGTT time (weeks) 25.7 ± 2.1 25.6 ± 2.3 0.669 25.5 ± 1.9 25.6 ± 2.1 0.674 
 OGTT       
  FBG (mmol/L) 3.80 (3.50–4.09) 4.80 (4.14–5.37) <0.001 3.86 (3.57–4.25) 4.78 (4.15–5.33) <0.001 
  1-h BG (mmol/L) 6.52 ± 1.37 9.84 ± 2.00 <0.001 6.68 ± 1.38 9.78 ± 1.91 <0.001 
  2-h BG (mmol/L) 5.99 ± 1.08 8.51 ± 1.98 <0.001 6.17 ± 1.04 8.44 ± 1.90 <0.001 
 HbA1c (%) 4.9 ± 0.4 5.4 ± 0.9 <0.001 4.9 ± 0.3 5.4 ± 0.9 <0.001 
 HbA1c (mmol/mol) 30 36  30 36  
 HOMA-IR* 1.46 (1.00–2.09) 2.47 (1.54–3.45) <0.001 1.64 (1.09–2.19) 2.44 (1.56–3.60) 0.001 
 QUICKI* 0.55 (0.54–0.56) 0.52 (0.50–0.54) <0.001 0.54 (0.53–0.56) 0.52 (0.50–0.54) <0.001 
 HOMA-β* 306.16 (258.91–802.39) 182.90 (83.93–426.71) <0.001 258.91 (241.62–715.71) 165.05 (85.20–396.73) <0.001 
 ALT (units/L) 14.0 (11.0–21.0) 17.0 (11.0–28.0) 0.073 17.0 (12.0–24.0) 17.5 (11.0–28.0) 0.86 
 AST (units/L) 18.0 (15.0–22.0) 19.0 (15.0–24.5) 0.685 19.0 (15.0–23.0) 19.0 (15.0–25.0) 0.76 
 Creatinine (mmol/L) 40 ± 7 40 ± 9 0.713 40 ± 8 40 ± 9 0.845 
 TG (mmol/L) 2.51 (1.98–3.20) 2.86 (2.09–3.65) 0.010 2.62 (2.10–3.21) 2.90 (2.09–3.86) 0.84 
 Cholesterol (mmol/L) 5.47 ± 1.09 5.30 ± 1.27 0.116 5.53 ± 1.22 5.28 ± 1.27 0.106 
 HDL (mmol/L) 1.59 ± 0.35 1.69 ± 0.48 0.011 1.59 ± 0.34 1.64 ± 0.47 0.287 
 LDL (mmol/L) 3.11 ± 0.88 2.81 ± 1.07 0.001 3.15 ± 0.99 2.73 ± 1.10 0.003 
Weight gain (kg)       
 Before GDM diagnosis 5.78 ± 2.61 6.63 ± 4.72 0.022 5.93 ± 2.75 6.74 ± 4.95 0.079 
 Whole pregnancy 13.48 ± 5.04 13.08 ± 5.94 0.386 13.24 ± 5.41 13.14 ± 6.14 0.876 
Treatment       
 Lifestyle intervention NA 224 (86.8) NA NA 172 (86.0) NA 
 Insulin NA 34 (13.2) NA NA 28 (14.0) NA 
Pregnancy outcome       
 Delivery time (weeks) 38.4 ± 1.3 38.2 ± 2.1 0.390 37.7 ± 1.5 37.9 ± 2.4 0.705 
 Fetus sex       
  Male 645 (56.5) 83 (50.3) 0.135 117 (58.2) 69 (51.9) 0.254 
  Female 497 (43.5) 82 (49.7)  84 (41.8) 64 (48.1)  
 Birth length (cm) 49.9 ± 0.9 49.9 ± 1.8 0.834 50.0 ± 0.6 49.7 ± 2.1 0.206 
 Newborn weight (g) 3,361.6 ± 476.5 3,527.2 ± 562.7 <0.001 3,409.5 ± 465.0 3,520.9 ± 581.6 0.061 
 Macrosomia       
  No 1,054 (92.3) 136 (78.6) <0.001 180 (89.6) 107 (77.0) 0.002 
  Yes 88 (7.7) 37 (21.4)  21 (10.4) 32 (23.0)  
All subjectsMatched case-control study
Women without GDMWomen with GDMWomen without GDMWomen with GDM
(n = 1,154)(n = 258)P(n = 200)(n = 200)P
Anthropometric parameters      
 Age (years) 27 ± 5 30 ± 5 <0.001 29 ± 5 30 ± 5 0.607 
 Parity       
  Nulliparous 276 (26.1) 86 (37.2) 0.0001 65 (32.2) 65 (32.2) 1.000 
  Parous 783 (73.9) 145 (62.8)  137 (67.8) 137 (67.8)  
 Previous GDM       
  No 871 (75.5) 152 (58.9) <0.001 135 (66.8) 117 (57.9) <0.001 
  Yes 7 (0.6) 20 (7.8)  2 (1.0) 20 (9.9)  
  Nulliparous 276 (23.9) 86 (33.3)  65 (32.2) 65 (32.2)  
 Family history of diabetes      
  No 117 (99.5) 236 (93.7) <0.001 193 (99.5) 188 (94.9) 0.011 
  Yes 6 (0.5) 16 (6.3)  1 (0.5) 10 (5.1)  
 Pregnancy BMI (kg/m222.4 ± 3.2 24.0 ± 4.2 <0.001 23.4 ± 3.5 23.4 ± 3.5 0.921 
First trimester       
 SBP (mmHg) 116 ± 10 117 ± 11 0.446 117 ± 9 116 ± 11 0.359 
 DBP (mmHg) 68 ± 7 69 ± 8 0.228 69 ± 7 68 ± 8 0.980 
 WBCs (×109/L) 8.57 ± 2.00 9.37 ± 2.07 <0.001 8.57 ± 1.92 9.27 ± 2.04 0.001 
 Neutrophils (×109/L) 6.03 ± 1.70 7.06 ± 1.76 <0.001 6.05 ± 1.61 7.00 ± 1.74 <0.001 
 Lymphocytes (×109/L) 1.88 ± 0.51 1.88 ± 0.52 0.906 1.86 ± 0.51 1.86 ± 0.52 0.969 
 NLR 3.36 ± 1.13 3.93 ± 1.32 <0.001 3.45 ± 1.21 3.93 ± 1.34 0.001 
 Platelets (×109/L) 216 ± 53 221 ± 49 0.257 217 ± 53 220 ± 50 0.649 
Second trimester       
 OGTT time (weeks) 25.7 ± 2.1 25.6 ± 2.3 0.669 25.5 ± 1.9 25.6 ± 2.1 0.674 
 OGTT       
  FBG (mmol/L) 3.80 (3.50–4.09) 4.80 (4.14–5.37) <0.001 3.86 (3.57–4.25) 4.78 (4.15–5.33) <0.001 
  1-h BG (mmol/L) 6.52 ± 1.37 9.84 ± 2.00 <0.001 6.68 ± 1.38 9.78 ± 1.91 <0.001 
  2-h BG (mmol/L) 5.99 ± 1.08 8.51 ± 1.98 <0.001 6.17 ± 1.04 8.44 ± 1.90 <0.001 
 HbA1c (%) 4.9 ± 0.4 5.4 ± 0.9 <0.001 4.9 ± 0.3 5.4 ± 0.9 <0.001 
 HbA1c (mmol/mol) 30 36  30 36  
 HOMA-IR* 1.46 (1.00–2.09) 2.47 (1.54–3.45) <0.001 1.64 (1.09–2.19) 2.44 (1.56–3.60) 0.001 
 QUICKI* 0.55 (0.54–0.56) 0.52 (0.50–0.54) <0.001 0.54 (0.53–0.56) 0.52 (0.50–0.54) <0.001 
 HOMA-β* 306.16 (258.91–802.39) 182.90 (83.93–426.71) <0.001 258.91 (241.62–715.71) 165.05 (85.20–396.73) <0.001 
 ALT (units/L) 14.0 (11.0–21.0) 17.0 (11.0–28.0) 0.073 17.0 (12.0–24.0) 17.5 (11.0–28.0) 0.86 
 AST (units/L) 18.0 (15.0–22.0) 19.0 (15.0–24.5) 0.685 19.0 (15.0–23.0) 19.0 (15.0–25.0) 0.76 
 Creatinine (mmol/L) 40 ± 7 40 ± 9 0.713 40 ± 8 40 ± 9 0.845 
 TG (mmol/L) 2.51 (1.98–3.20) 2.86 (2.09–3.65) 0.010 2.62 (2.10–3.21) 2.90 (2.09–3.86) 0.84 
 Cholesterol (mmol/L) 5.47 ± 1.09 5.30 ± 1.27 0.116 5.53 ± 1.22 5.28 ± 1.27 0.106 
 HDL (mmol/L) 1.59 ± 0.35 1.69 ± 0.48 0.011 1.59 ± 0.34 1.64 ± 0.47 0.287 
 LDL (mmol/L) 3.11 ± 0.88 2.81 ± 1.07 0.001 3.15 ± 0.99 2.73 ± 1.10 0.003 
Weight gain (kg)       
 Before GDM diagnosis 5.78 ± 2.61 6.63 ± 4.72 0.022 5.93 ± 2.75 6.74 ± 4.95 0.079 
 Whole pregnancy 13.48 ± 5.04 13.08 ± 5.94 0.386 13.24 ± 5.41 13.14 ± 6.14 0.876 
Treatment       
 Lifestyle intervention NA 224 (86.8) NA NA 172 (86.0) NA 
 Insulin NA 34 (13.2) NA NA 28 (14.0) NA 
Pregnancy outcome       
 Delivery time (weeks) 38.4 ± 1.3 38.2 ± 2.1 0.390 37.7 ± 1.5 37.9 ± 2.4 0.705 
 Fetus sex       
  Male 645 (56.5) 83 (50.3) 0.135 117 (58.2) 69 (51.9) 0.254 
  Female 497 (43.5) 82 (49.7)  84 (41.8) 64 (48.1)  
 Birth length (cm) 49.9 ± 0.9 49.9 ± 1.8 0.834 50.0 ± 0.6 49.7 ± 2.1 0.206 
 Newborn weight (g) 3,361.6 ± 476.5 3,527.2 ± 562.7 <0.001 3,409.5 ± 465.0 3,520.9 ± 581.6 0.061 
 Macrosomia       
  No 1,054 (92.3) 136 (78.6) <0.001 180 (89.6) 107 (77.0) 0.002 
  Yes 88 (7.7) 37 (21.4)  21 (10.4) 32 (23.0)  

Data are means ± SD, median (IQR), or n (%). Boldface P values are statistically significant (P < 0.05). ALT, alanine aminotransferase; DBP, diastolic blood pressure; NA, not applicable; SBP, systolic blood pressure.

*

Log-transformed for t test.

A 1:1 case-control matching procedure was performed to avoid the potential bias of covariates that were not evenly distributed between women with and without GDM. After matching for age, pregnancy BMI, and parity, there were no differences in TG and weight gain before GDM screening between women with and without GDM. There remained a significantly higher WBC count (9.27 ± 2.04 vs. 8.57 ± 1.92 × 109/L, P < 0.001), neutrophil count (7.00 ± 1.74 vs. 6.05 ± 1.61 × 109/L, P < 0.001), and NLR (3.93 ± 1.34 vs. 3.45 ± 1.21, P = 0.001) as well as higher glucose level (P < 0.001) and HOMA-IR (P = 0.001) and lower HOMA-β (P < 0.001) and QUICKI (P < 0.001) in women with GDM compared with control subjects (Table 1).

Higher Neutrophil Count Outperformed WBC Count and NLR as an Independent Risk Factor and Diagnostic Predictive Factor for GDM Development and Incidence of Macrosomia

To compare the predictive capability of metric WBC count, neutrophil count, and NLR as risk factors for GDM development, logistic regression analysis with enter selection was performed separately in a matched case-control study. We found neutrophil count had the highest odds ratio (OR) value as an independent risk factor for the development of GDM (OR 3.60; 95% CI 2.02–6.41 in the highest tertile vs. the lowest tertile; P < 0.001), regardless of GDM history (OR 3.70; 95% CI 2.05–6.66; P < 0.001) compared with WBC count (OR 2.40; 95% CI 1.38–4.17; P = 0.002 in all mothers; OR 2.75; 95% CI 1.56–4.86; P < 0.001 in mothers without GDM history) and NLR (OR 2.77; 95% CI 1.58–4.88; P < 0.001 in all mothers; OR 2.45; 95% CI 1.40–4.30; P = 0.002 in mothers without GDM history) (Table 2). Furthermore, we also found neutrophil count and combined basal factors (age, previous GDM history, prepregnancy BMI, and TG) had the highest area under receiver operating characteristic curve for predicting GDM compared with WBC count and NLR (0.787, 0.776, and 0.774, respectively) (Supplementary Fig. 1).

Table 2

Logistic regression analysis to determine the risk factors for development of GDM in matched case-control study

In all mothers (n = 400)In mothers without GDM history (n = 309)
OR (95% CI)POR (95% CI)P
Tertiles of neutrophils (×109/L)     
 Lowest Reference  Reference  
 Middle 1.70 (0.93–3.09) 0.083 1.87 (1.02–3.44) 0.044 
 Highest 3.60 (2.02–6.41) <0.001 3.70 (2.05–6.66) <0.001 
GDM history     
 No Reference    
 Yes 12.55 (2.80–56.19) 0.001   
 Nulliparous 1.01 (0.62–1.60) 0.982   
Tertiles of WBCs (×109/L)     
 Lowest Reference  Reference  
 Middle 1.67 (0.96–2.90) 0.069 1.53 (0.86–2.70) 0.148 
 Highest 2.40 (1.38–4.17) 0.002 2.75 (1.56–4.86) <0.001 
GDM history     
 No Reference    
 Yes 12.50 (2.81–55.57) 0.001   
 Nulliparous 1.01 (0.62–1.62) 0.982   
Tertiles of NLR     
 Lowest Reference  Reference  
 Middle 1.52 (0.87–2.68) 0.069 1.86 (1.06–3.25) 0.003 
 Highest 2.77 (1.58–4.88) <0.001 2.45 (1.40–4.30) 0.002 
GDM history     
 No Reference    
 Yes 14.38 (3.24–63.92) <0.001   
 Nulliparous 1.02 (0.63–1.66) 0.945   
In all mothers (n = 400)In mothers without GDM history (n = 309)
OR (95% CI)POR (95% CI)P
Tertiles of neutrophils (×109/L)     
 Lowest Reference  Reference  
 Middle 1.70 (0.93–3.09) 0.083 1.87 (1.02–3.44) 0.044 
 Highest 3.60 (2.02–6.41) <0.001 3.70 (2.05–6.66) <0.001 
GDM history     
 No Reference    
 Yes 12.55 (2.80–56.19) 0.001   
 Nulliparous 1.01 (0.62–1.60) 0.982   
Tertiles of WBCs (×109/L)     
 Lowest Reference  Reference  
 Middle 1.67 (0.96–2.90) 0.069 1.53 (0.86–2.70) 0.148 
 Highest 2.40 (1.38–4.17) 0.002 2.75 (1.56–4.86) <0.001 
GDM history     
 No Reference    
 Yes 12.50 (2.81–55.57) 0.001   
 Nulliparous 1.01 (0.62–1.62) 0.982   
Tertiles of NLR     
 Lowest Reference  Reference  
 Middle 1.52 (0.87–2.68) 0.069 1.86 (1.06–3.25) 0.003 
 Highest 2.77 (1.58–4.88) <0.001 2.45 (1.40–4.30) 0.002 
GDM history     
 No Reference    
 Yes 14.38 (3.24–63.92) <0.001   
 Nulliparous 1.02 (0.63–1.66) 0.945   

The boldface P values are statistically significant (P < 0.05).

Besides, neutrophil count was also an independent risk factor for the incidence of macrosomia (OR 4.09; 95% CI 1.04–16.13 in the highest tertile vs. the lowest tertile; P = 0.044) corrected by prepregnancy BMI and weight gain during the whole pregnancy, rather than WBC count and NLR (Supplementary Table 1).

Comparison of Parameters in the First Trimester, the Second Trimester, and at Delivery Among Three Groups Categorized by Tertiles of Neutrophil Count in the Cohort Study

Subjects were divided into three groups according to tertiles of neutrophil count in the first trimester: lowest group (<5.30 × 109/L), middle group (5.30–6.80 × 109/L), and highest group (>6.80 × 109/L). There was a step-wise increase in the incidence of GDM (9.1%, 14.7%, and 28.2%; P < 0.001), level of prepregnancy BMI, alanine aminotransferase, 1-h BG (6.68 ± 1.60 vs. 6.99 ± 1.77 vs. 7.60 ± 2.29 mmol/L; P < 0.001), 2-h BG (6.07 ± 1.29 vs. 6.39 ± 1.45 vs. 6.87 ± 1.81 mmol/L; P < 0.001), HbA1c (4.9 ± 0.3 vs. 5.0 ± 0.3 vs. 5.1 ± 0.5%; P < 0.001), and HOMA-IR (P < 0.001) across the lowest, middle, and highest groups, respectively (Table 3). Likewise, macrosomia, neonatal weight, placental weight, and the incidence of cesarean delivery increased as neutrophil count increased (Table 4).

Table 3

Comparison of parameters in the first trimester and the second trimester among three groups categorized by tertiles of neutrophil count in the cohort study

Lowest group (n = 372)Middle group (n = 385)Highest group (n = 348)P
Neutrophil range (×109/L) <5.30 5.30–6.80 >6.80  
Anthropometric and first-trimester parameters    
 Women with GDM 33 (9.1) 56 (14.7) 97 (28.2) <0.001 
 Age (years) 27 ± 5 28 ± 5 27 ± 5 0.316 
 Prepregnancy BMI (kg/m221.7 ± 2.8 22.7 ± 3.5* 23.2 ± 3.6† <0.001 
First trimester     
 SBP (mmHg) 116 ± 10 114 ± 10 117 ± 10 0.084 
 DBP (mmHg) 69 ± 7 68 ± 8 69 ± 8 0.411 
Second trimester     
 OGTT     
  FBG (mmol/L) 3.96 ± 1.52 3.94 ± 0.64 4.12 ± 0.88 0.054 
  1-h BG (mmol/L) 6.68 ± 1.60 6.99 ± 1.77* 7.60 ± 2.29†‡ <0.001 
  2-h BG (mmol/L) 6.07 ± 1.29 6.39 ± 1.45* 6.87 ± 1.81†‡ <0.001 
 HbA1c (%) 4.9 ± 0.3 5.0 ± 0.3* 5.1 ± 0.5†‡ <0.001 
 HbA1c (mmol/mol) 30 31 32  
 HOMA-IR§ 1.37 (0.88–1.82) 1.56 (1.04–2.39) 1.71 (1.19–2.40)† <0.001 
 QUICKI§ 0.54 (0.53–0.56) 0.55 (0.53–0.55) 0.54 (0.53–0.55) 0.090 
 HOMA-β§ 257.04 (230.08–578.52) 332.35 (256.00–830.36) 244.61 (184.80–783.45) 0.209 
 ALT (units/L) 14.0 (11.0–20.0) 15.0 (12.0–22.0) 16.0 (11.0–25.0)‡ 0.006 
 AST (units/L) 18.0 (15.0–21.0) 18.0 (15.0–22.0) 18.0 (15.0–24.0)‡ 0.012 
 Creatinine (mmol/L) 40 ± 8 39 ± 7 39 ± 6 0.516 
 TG (mmol/L) 2.48 (1.98–3.09) 2.50 (1.92–3.34) 2.66 (2.05–3.50) 0.052 
 Cholesterol (mmol/L) 5.44 ± 1.12 5.57 ± 1.12 5.29 ± 1.07‡ 0.014 
 HDL (mmol/L) 1.56 ± 0.31 1.59 ± 0.35 1.58 ± 0.43 0.517 
 LDL (mmol/L) 3.10 ± 0.88 3.21 ± 0.88 2.88 ± 0.89†‡ <0.001 
Weight gain (kg)     
 Before GDM diagnosis 6.08 ± 2.48 5.71 ± 2.54 6.06 ± 3.69 0.158 
 Whole pregnancy 13.82 ± 4.81 13.24 ± 4.50 13.52 ± 5.86 0.295 
Treatment for GDM     
 Lifestyle intervention 32 (19.2) 51 (30.5) 84 (50.3) 0.166 
 Insulin 6 (17.6) 13 (38.2) 15 (44.2)  
Lowest group (n = 372)Middle group (n = 385)Highest group (n = 348)P
Neutrophil range (×109/L) <5.30 5.30–6.80 >6.80  
Anthropometric and first-trimester parameters    
 Women with GDM 33 (9.1) 56 (14.7) 97 (28.2) <0.001 
 Age (years) 27 ± 5 28 ± 5 27 ± 5 0.316 
 Prepregnancy BMI (kg/m221.7 ± 2.8 22.7 ± 3.5* 23.2 ± 3.6† <0.001 
First trimester     
 SBP (mmHg) 116 ± 10 114 ± 10 117 ± 10 0.084 
 DBP (mmHg) 69 ± 7 68 ± 8 69 ± 8 0.411 
Second trimester     
 OGTT     
  FBG (mmol/L) 3.96 ± 1.52 3.94 ± 0.64 4.12 ± 0.88 0.054 
  1-h BG (mmol/L) 6.68 ± 1.60 6.99 ± 1.77* 7.60 ± 2.29†‡ <0.001 
  2-h BG (mmol/L) 6.07 ± 1.29 6.39 ± 1.45* 6.87 ± 1.81†‡ <0.001 
 HbA1c (%) 4.9 ± 0.3 5.0 ± 0.3* 5.1 ± 0.5†‡ <0.001 
 HbA1c (mmol/mol) 30 31 32  
 HOMA-IR§ 1.37 (0.88–1.82) 1.56 (1.04–2.39) 1.71 (1.19–2.40)† <0.001 
 QUICKI§ 0.54 (0.53–0.56) 0.55 (0.53–0.55) 0.54 (0.53–0.55) 0.090 
 HOMA-β§ 257.04 (230.08–578.52) 332.35 (256.00–830.36) 244.61 (184.80–783.45) 0.209 
 ALT (units/L) 14.0 (11.0–20.0) 15.0 (12.0–22.0) 16.0 (11.0–25.0)‡ 0.006 
 AST (units/L) 18.0 (15.0–21.0) 18.0 (15.0–22.0) 18.0 (15.0–24.0)‡ 0.012 
 Creatinine (mmol/L) 40 ± 8 39 ± 7 39 ± 6 0.516 
 TG (mmol/L) 2.48 (1.98–3.09) 2.50 (1.92–3.34) 2.66 (2.05–3.50) 0.052 
 Cholesterol (mmol/L) 5.44 ± 1.12 5.57 ± 1.12 5.29 ± 1.07‡ 0.014 
 HDL (mmol/L) 1.56 ± 0.31 1.59 ± 0.35 1.58 ± 0.43 0.517 
 LDL (mmol/L) 3.10 ± 0.88 3.21 ± 0.88 2.88 ± 0.89†‡ <0.001 
Weight gain (kg)     
 Before GDM diagnosis 6.08 ± 2.48 5.71 ± 2.54 6.06 ± 3.69 0.158 
 Whole pregnancy 13.82 ± 4.81 13.24 ± 4.50 13.52 ± 5.86 0.295 
Treatment for GDM     
 Lifestyle intervention 32 (19.2) 51 (30.5) 84 (50.3) 0.166 
 Insulin 6 (17.6) 13 (38.2) 15 (44.2)  

Data are means ± SD, median (IQR), or n (%).The bold P values are statistically significant (P < 0.05). ALT, alanine aminotransferase; DBP, diastolic blood pressure; SBP, systolic blood pressure.

§

Log-transformed for t test. *Middle group vs. lowest group, P < 0.001. †Highest group vs. lowest group, P < 0.05. ‡Highest group vs. middle group, P < 0.05.

Table 4

Comparison of parameters at delivery among three groups categorized by tertile of neutrophil count in the cohort study

Lowest group (n = 372)Middle group (n = 385)Highest group (n = 348)P
Neutrophil count range (×109/L) <5.30 5.30–6.80 >6.80  
Fetal characteristics     
 Fetus sex     
  Male 213 (58.0) 191 (52.2) 172 (54.6) 0.278 
  Female 154 (42.0) 175 (47.8) 143 (45.4)  
 Birth length (cm) 49.9 ± 0.8 49.8 ± 1.6 49.9 ± 1.1 0.592 
 Newborn weight (g) 3,320.3 ± 478.0 3,369.0 ± 473.1 3,447.9 ± 523.2* 0.003 
 Macrosomia     
  No 343 (93.5) 339 (92.4) 276 (86.0) 0.001 
  Yes 24 (6.5) 28 (7.6) 45 (14.0)  
 Preterm birth     
  No 346 (96.4) 340 (96.9) 279 (95.9) 0.798 
  Yes 13 (3.6) 11 (3.1) 12 (4.1)  
 Placenta weight (g) 619.2 ± 118.9 628.6 ± 111.9 642.7 ± 123.8* 0.035 
 Umbilical cord length (cm) 56.0 ± 10.4 56.5 ± 10.5 56.1 ± 9.9 0.765 
Maternal adverse outcome     
 Hypertension in pregnancy     
  No 99 (99.0) 128 (97.7) 111 (98.2) 0.759 
  Yes 1 (1.0) 3 (2.3) 2 (1.8)  
 Cesarean delivery     
  No 364 (98.9) 360 (97.3) 306 (95.0) 0.009 
  Yes 4 (1.1) 10 (2.7) 16 (5.0)  
 Postpartum hemorrhage     
  No 321 (90.2) 305 (88.4) 248 (86.4) 0.333 
  Yes 35 (9.8) 40 (11.6) 39 (13.6)  
Lowest group (n = 372)Middle group (n = 385)Highest group (n = 348)P
Neutrophil count range (×109/L) <5.30 5.30–6.80 >6.80  
Fetal characteristics     
 Fetus sex     
  Male 213 (58.0) 191 (52.2) 172 (54.6) 0.278 
  Female 154 (42.0) 175 (47.8) 143 (45.4)  
 Birth length (cm) 49.9 ± 0.8 49.8 ± 1.6 49.9 ± 1.1 0.592 
 Newborn weight (g) 3,320.3 ± 478.0 3,369.0 ± 473.1 3,447.9 ± 523.2* 0.003 
 Macrosomia     
  No 343 (93.5) 339 (92.4) 276 (86.0) 0.001 
  Yes 24 (6.5) 28 (7.6) 45 (14.0)  
 Preterm birth     
  No 346 (96.4) 340 (96.9) 279 (95.9) 0.798 
  Yes 13 (3.6) 11 (3.1) 12 (4.1)  
 Placenta weight (g) 619.2 ± 118.9 628.6 ± 111.9 642.7 ± 123.8* 0.035 
 Umbilical cord length (cm) 56.0 ± 10.4 56.5 ± 10.5 56.1 ± 9.9 0.765 
Maternal adverse outcome     
 Hypertension in pregnancy     
  No 99 (99.0) 128 (97.7) 111 (98.2) 0.759 
  Yes 1 (1.0) 3 (2.3) 2 (1.8)  
 Cesarean delivery     
  No 364 (98.9) 360 (97.3) 306 (95.0) 0.009 
  Yes 4 (1.1) 10 (2.7) 16 (5.0)  
 Postpartum hemorrhage     
  No 321 (90.2) 305 (88.4) 248 (86.4) 0.333 
  Yes 35 (9.8) 40 (11.6) 39 (13.6)  

Data are means ± SD or n (%). The boldface P values are statistically significant (P < 0.05). Macrosomia was defined as birth weight >4,000 g. Preterm birth was defined as delivery <37 weeks of gestation. Gestational hypertension was defined as a systolic blood pressure ≥140 mmHg and/or a diastolic blood pressure ≥90 mmHg on two occasions at least 4 h apart after 20 weeks of gestation in a woman with previously normal blood pressure.

*

Highest group vs. lowest group, P < 0.05.

First-Trimester Neutrophil Count Was Closely Associated With Prepregnancy BMI, HOMA-IR, and Newborn Weight

To investigate the correlation between neutrophil count and insulin resistance or newborn weight, correlation analysis was performed. Simple linear regression analyses were performed to determine the association of neutrophil count during the first trimester with prepregnancy BMI, HOMA-IR, and newborn weight. There was a significant and linear correlation for neutrophil count with prepregnancy BMI [β = 0.29; F(1, 1,085) = 27.51; adjusted R2 = 0.02; P < 0.001] (Fig. 2A), HOMA-IR [β = 0.07; F(1, 426) = 19.88; adjusted R2 = 0.04; P < 0.001] (Fig. 2B), and newborn weight [β = 0.03; F(1, 1,039) = 10.27; adjusted R2 = 0.01; P = 0.001] (Fig. 2C). Multiple linear regression analysis adjusting for confounding factors was performed to analyze the association between neutrophil count and prepregnancy BMI, HOMA-IR, and newborn weight. There was a significant linear association of neutrophil count with prepregnancy BMI (P < 0.001) and HOMA-IR (P = 0.045) (Supplementary Table 2).

Figure 2

Simple linear regression analysis between the first-trimester neutrophil (N) count and prepregnancy BMI, HOMA-IR, and newborn weight. The neutrophil count showed a significant and moderate linear correlation with prepregnancy BMI [β = 0.29; F(1, 1,085) = 27.51; adjusted R2 = 0.02; P < 0.001] (A), HOMA-IR [β = 0.07; F(1, 426) = 19.88; adjusted R2 = 0.04; P < 0.001] (B), and newborn weight [β = 0.03; F(1, 1,039) = 10.27; adjusted R2 = 0.01; P = 0.001] (C).

Figure 2

Simple linear regression analysis between the first-trimester neutrophil (N) count and prepregnancy BMI, HOMA-IR, and newborn weight. The neutrophil count showed a significant and moderate linear correlation with prepregnancy BMI [β = 0.29; F(1, 1,085) = 27.51; adjusted R2 = 0.02; P < 0.001] (A), HOMA-IR [β = 0.07; F(1, 426) = 19.88; adjusted R2 = 0.04; P < 0.001] (B), and newborn weight [β = 0.03; F(1, 1,039) = 10.27; adjusted R2 = 0.01; P = 0.001] (C).

Close modal

Neutrophil Count Was an Independent Risk Factor for the Development of GDM

To determine independent risk factors for the development of GDM, tertiles of neutrophil count, GDM history (divided into no previous GDM, previous GDM, and nulliparous), prepregnancy BMI, age, TG, and weight gain before GDM was diagnosed were entered into logistic regression analysis with enter selection. The risk of developing GDM in the highest tertile neutrophil count increased 3.71-fold compared with the lowest tertile neutrophil count (P < 0.001). Risk of developing GDM in women with a previous history of GDM was significantly higher than in those without (OR 58.16; 95% CI 18.60–181.86; P < 0.001), and women with a higher prepregnancy BMI (OR 1.12; 95% CI 1.04–1.20; P = 0.004), age (OR 1.16; 95% CI 1.09–1.23; P < 0.001), and TG level (OR 1.19; 95% CI 1.03–1.37; P = 0.020) also had a tendency to develop GDM. Furthermore, the independent risk factors in women without a history of GDM (including those with no previous GDM and nulliparous) were also determined, and neutrophil count (OR 3.66; 95% CI 1.78–7.56 in highest tertile vs. in lowest tertile; P < 0.001) remained a risk factor for development of GDM independent of prepregnancy BMI, age, and TG level (Table 5).

Table 5

Logistic regression analysis to determine the risk factors for development of GDM in the cohort study

In all mothers (n = 756)In mothers without GDM history (n = 734)
OR (95% CI)POR (95% CI)P
Tertile of neutrophils (×109/L)     
 Lowest Reference  Reference  
 Middle 1.15 (0.52–2.53) 0.733 1.14 (0.52–2.53) 0.739 
 Highest 3.71 (1.80–7.63) <0.001 3.66 (1.78–7.56) <0.001 
GDM history, n (%)     
 No Reference    
 Yes 58.16 (18.60–181.86) <0.001   
 Nulliparous 4.77 (2.47–9.21) <0.001   
Prepregnancy BMI (kg/m21.12 (1.04–1.20) 0.004 1.09 (1.01–1.17) 0.027 
Age (years) 1.16 (1.09–1.23) <0.001 1.11 (1.05–1.17) <0.001 
TG (mmol/L) 1.19 (1.03–1.37) 0.020 1.23 (1.07–1.41) 0.003 
Weight gain before GDM diagnosis (kg) 1.05 (0.97–1.13) 0.236 1.08 (1.00–1.17) 0.065 
In all mothers (n = 756)In mothers without GDM history (n = 734)
OR (95% CI)POR (95% CI)P
Tertile of neutrophils (×109/L)     
 Lowest Reference  Reference  
 Middle 1.15 (0.52–2.53) 0.733 1.14 (0.52–2.53) 0.739 
 Highest 3.71 (1.80–7.63) <0.001 3.66 (1.78–7.56) <0.001 
GDM history, n (%)     
 No Reference    
 Yes 58.16 (18.60–181.86) <0.001   
 Nulliparous 4.77 (2.47–9.21) <0.001   
Prepregnancy BMI (kg/m21.12 (1.04–1.20) 0.004 1.09 (1.01–1.17) 0.027 
Age (years) 1.16 (1.09–1.23) <0.001 1.11 (1.05–1.17) <0.001 
TG (mmol/L) 1.19 (1.03–1.37) 0.020 1.23 (1.07–1.41) 0.003 
Weight gain before GDM diagnosis (kg) 1.05 (0.97–1.13) 0.236 1.08 (1.00–1.17) 0.065 

The boldface P values are statistically significant (P < 0.05).

Continuous Neutrophil Count in the First Trimester Was Closely Associated With the Incidence of GDM

After adjusting for GDM history, prepregnancy BMI, age, and TG, a spline model showed a significant relationship between continuous neutrophil count during the first trimester and GDM incidence. The risk of developing GDM increased when the neutrophil count was >5.0 × 109/L (Fig. 3).

Figure 3

Continuous association of the neutrophil count in the first trimester with the incidence of GDM. Adjusted for GDM history, prepregnancy BMI, age, and TG.

Figure 3

Continuous association of the neutrophil count in the first trimester with the incidence of GDM. Adjusted for GDM history, prepregnancy BMI, age, and TG.

Close modal

This retrospective case-control and cohort study is the first one to confirm the closest association of neutrophil count with development of GDM in a large sample size. Many studies have demonstrated increased inflammatory markers during pregnancy compared with a nonpregnant state characterized by elevated WBC count and neutrophil count (19,21). Nevertheless, pregnant women generally have a steady state of pro- and anti-inflammatory cytokines, although this balance is disturbed in some pathological states, including obesity and insulin resistance (2224). A growing number of studies have described the central role of inflammation in GDM. In their 2004 cohort study, Wolf et al. (17) showed that women who developed GDM had a much higher leukocyte count than those who did not.

Neutrophils, which constitute the largest fraction of WBCs, have been found to be involved in chronic metainflammatory states such as diabetes, nonalcoholic fatty liver disease, and atherosclerosis (2527). Although previous studies have produced inconsistent results, Yilmaz et al. (15) showed that NLR was significantly higher in patients with GDM compared with pregnant women with normal glycemic levels and was a powerful predictor of GDM, whereas Sargın et al. (16) showed no predictive ability of NLR. In our case-control study, after matching the possible confounder factors, we found that neutrophils, WBCs, and NLR were all associated with the development of GDM but that the neutrophil count had the highest OR and possessed the most predictive value. As we know, the WBC count is largely equal to the sum of the neutrophil count and the lymphocyte count, NLR is the ratio of neutrophil count to lymphocyte count. Because there is no difference of lymphocytes, which may dilute the impact of neutrophils on GDM, the WBC count and NLR were inferior to the neutrophil count in the role of GDM development and its outcomes. These results support the important pathological role of innate immune cells in the development of diabetes (28,29). Further analyses of the relationship between neutrophil count and GDM were performed in the cohort study. We found the incidence of GDM increased progressively with the increase of the neutrophil count, which was also an independent factor for GDM development. Moreover, fully adjusted spline regression showed a significant correlation of continuous neutrophil count with GDM incidence, and the risk abruptly increased when the neutrophil count was >5.0 × 109/L. All of these demonstrated a close association of the first-trimester neutrophil count with GDM development.

From a functional perspective, Talukdar et al. (30) and Mansuy-Aubert et al. (31) both revealed that neutrophils contribute to the etiology of chronic inflammation and insulin resistance via secreted neutrophil elastase (NE) by the degradation of insulin receptor substrate 1 (IRS1). Recently, Stoikou et al. (32) reported that patients with GDM had increased neutrophil activity with elevated neutrophil extracellular traps (NETs) and NE levels in vitro. Lou et al. (33) found that high levels of neutrophil gelatinase-associated lipocalin (NGAL) in plasma and subcutaneous adipose tissue were associated with insulin resistance in GDM. Our study showed a significant positive association between neutrophil count and HOMA-IR, supporting the crucial role of neutrophils in insulin resistance. All of these results demonstrate that neutrophils may contribute to GDM development by mediating insulin resistance and that neutrophil-derived NE, NETs, and NGAL may serve as the potential targets.

Another important finding in our study was that an increased neutrophil count was also associated with adverse pregnancy outcomes. A higher neutrophil count was an independent risk factor for macrosomia corrected by prepregnancy BMI and weight gain during the whole pregnancy in the case-control study. Women with the highest tertile of neutrophil count had the highest risk for macrosomia and cesarean delivery in the cohort study. The developmental overnutrition hypothesis suggests that maternal hyperglycemia and obesity predispose offspring to obesity and metabolic dysfunction and may have been transferred from the mother through the placenta (34,35), although the underlying mechanism is elusive. An increased neutrophil count may lead to a rise in NE and NETs in the placenta, as suggested in the Stoikou et al. (32) study; therefore, we hypothesize that neutrophil count may play a crucial role in this programming process via NE and NETs.

Moreover, our study found that patients with GDM had much higher levels of TG and lower levels of LDL, consistent with previous studies (3640). The precise mechanism of lower LDL in women with GDM was unclear. This might be attributed to higher concentration of estrogen and insulin resistance in women with GDM.

There were some limitations of this study. First, all subjects were derived from one center, which may have led to biased results. We also acknowledge that a mechanistic insight into the potentially pathophysiological role of neutrophils in GDM development and offspring metabolic dysfunction is lacking in this clinical study. Further studies using reliable rodent GDM models to delineate the function of neutrophils, especially NE, NETs, and NGAL, are warranted.

Conclusions

This study demonstrated that the first-trimester neutrophil count was closely associated with GDM development and adverse pregnancy outcomes, especially macrosomia. The neutrophil count was an independent risk factor for GDM development when it was >5.0 × 109/L.

This article contains supplementary material online at https://doi.org/10.2337/db20-4567/suppl.12145296.

T.S. and F.M. are co-first authors.

Funding. This study received support from the Natural Science Foundation of China (81700510), the Medical Key Faculty Foundation of Shanghai (ZK2019B15), Health Profession Clinical Funds of the Shanghai Municipal Health Commission (201940295), the Science Foundation of the Fifth People’s Hospital of Shanghai (2019WYZD02, 2018WYZD04), and the Science and Technology Planning Project of Hangzhou City (20170533B43).

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. T.S. wrote the manuscript and researched data. F.M. and H.D. contributed to discussion and reviewed and edited manuscript. H.Z. contributed to data collection. M.Y., R.Z., Z.Y., and X.H. contributed to data collection and database establishment. J.L. and S.Z. reviewed and edited the manuscript. S.Z. researched data. S.Z. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in abstract form at the 79th Scientific Sessions of the American Diabetes Association, San Francisco, CA, 7–11 June 2019.

1.
International Association of Diabetes and Pregnancy Study Groups Consensus Panel
;
Metzger
BE
,
Gabbe
SG
,
Persson
B
, et al
.
International Association of Diabetes and Pregnancy Study Groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy
.
Diabetes Care
2010
;
33
:
676
682
2.
DeSisto
CL
,
Kim
SY
,
Sharma
AJ
.
Prevalence estimates of gestational diabetes mellitus in the United States, Pregnancy Risk Assessment Monitoring System (PRAMS), 2007-2010
.
Prev Chronic Dis
2014
;
11
:
E104
3.
Gao
C
,
Sun
X
,
Lu
L
,
Liu
F
,
Yuan
J
.
Prevalence of gestational diabetes mellitus in mainland China: a systematic review and meta-analysis
.
J Diabetes Investig
2019
;
10
:
154
162
4.
Dabelea
D
,
Hanson
RL
,
Lindsay
RS
, et al
.
Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships
.
Diabetes
2000
;
49
:
2208
2211
5.
Young
RC
.
Risk of stillbirth and infant death stratified by gestational age
.
Obstet Gynecol
2012
;
120
:
1211
1212; author reply 1212
6.
Ben-Haroush
A
,
Yogev
Y
,
Hod
M
.
Epidemiology of gestational diabetes mellitus and its association with type 2 diabetes
.
Diabet Med
2004
;
21
:
103
113
7.
Kaaja
RJ
,
Greer
IA
.
Manifestations of chronic disease during pregnancy
.
JAMA
2005
;
294
:
2751
2757
8.
Savyon
M
.
The risk of overt diabetes mellitus among women with gestational diabetes: a population-based study
.
Diabet Med
2010
;
27
:
852
9.
Kuzmicki
M
,
Telejko
B
,
Zonenberg
A
, et al
.
Circulating pro- and anti-inflammatory cytokines in Polish women with gestational diabetes
.
Horm Metab Res
2008
;
40
:
556
560
10.
Dalfrà
MG
,
Fedele
D
,
Ragazzi
E
, et al
.
Elevations of inflammatory cytokines during and after pregnancy in gestational diabetes
.
J Endocrinol Invest
2009
;
32
:
289
290
11.
Bastard
JP
,
Maachi
M
,
Van Nhieu
JT
, et al
.
Adipose tissue IL-6 content correlates with resistance to insulin activation of glucose uptake both in vivo and in vitro
.
J Clin Endocrinol Metab
2002
;
87
:
2084
2089
12.
Esser
N
,
Legrand-Poels
S
,
Piette
J
,
Scheen
AJ
,
Paquot
N
.
Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes
.
Diabetes Res Clin Pract
2014
;
105
:
141
150
13.
Pantham
P
,
Aye
ILMH
,
Powell
TL
.
Inflammation in maternal obesity and gestational diabetes mellitus
.
Placenta
2015
;
36
:
709
715
14.
Syngelaki
A
,
Visser
GH
,
Krithinakis
K
,
Wright
A
,
Nicolaides
KH
.
First trimester screening for gestational diabetes mellitus by maternal factors and markers of inflammation
.
Metabolism
2016
;
65
:
131
137
15.
Yilmaz
H
,
Celik
HT
,
Namuslu
M
, et al
.
Benefits of the neutrophil-to-lymphocyte ratio for the prediction of gestational diabetes mellitus in pregnant women
.
Exp Clin Endocrinol Diabetes
2014
;
122
:
39
43
16.
Sargın
MA
,
Yassa
M
,
Taymur
BD
,
Celik
A
,
Ergun
E
,
Tug
N
.
Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios: are they useful for predicting gestational diabetes mellitus during pregnancy
?
Ther Clin Risk Manag
2016
;
12
:
657
665
17.
Wolf
M
,
Sauk
J
,
Shah
A
, et al
.
Inflammation and glucose intolerance: a prospective study of gestational diabetes mellitus
.
Diabetes Care
2004
;
27
:
21
27
18.
Christoforaki
V
,
Zafeiriou
Z
,
Daskalakis
G
,
Katasos
T
,
Siristatidis
C
.
First trimester neutrophil to lymphocyte ratio (NLR) and pregnancy outcome
.
J Obstet Gynaecol
2020
;
40
:
59
64
19.
Yang
H
,
Zhu
C
,
Ma
Q
,
Long
Y
,
Cheng
Z
.
Variations of blood cells in prediction of gestational diabetes mellitus
.
J Perinat Med
2015
;
43
:
89
93
20.
Gutch
M
,
Kumar
S
,
Razi
SM
,
Gupta
KK
,
Gupta
A
.
Assessment of insulin sensitivity/resistance
.
Indian J Endocrinol Metab
2015
;
19
:
160
164
21.
Kuvin
SF
,
Brecher
G
.
Differential neutrophil counts in pregnancy
.
N Engl J Med
1962
;
266
:
877
878
22.
Ramsay
JE
,
Ferrell
WR
,
Crawford
L
,
Wallace
AM
,
Greer
IA
,
Sattar
N
.
Maternal obesity is associated with dysregulation of metabolic, vascular, and inflammatory pathways
.
J Clin Endocrinol Metab
2002
;
87
:
4231
4237
23.
Stewart
FM
,
Freeman
DJ
,
Ramsay
JE
,
Greer
IA
,
Caslake
M
,
Ferrell
WR
.
Longitudinal assessment of maternal endothelial function and markers of inflammation and placental function throughout pregnancy in lean and obese mothers
.
J Clin Endocrinol Metab
2007
;
92
:
969
975
24.
Madan
JC
,
Davis
JM
,
Craig
WY
, et al
.
Maternal obesity and markers of inflammation in pregnancy
.
Cytokine
2009
;
47
:
61
64
25.
Zang
S
,
Wang
L
,
Ma
X
, et al
.
Neutrophils play a crucial role in the early stage of nonalcoholic steatohepatitis via neutrophil elastase in mice
.
Cell Biochem Biophys
2015
;
73
:
479
487
26.
Ye
D
,
Yang
K
,
Zang
S
, et al
.
Lipocalin-2 mediates non-alcoholic steatohepatitis by promoting neutrophil-macrophage crosstalk via the induction of CXCR2
.
J Hepatol
2016
;
65
:
988
997
27.
Mócsai
A
.
Diverse novel functions of neutrophils in immunity, inflammation, and beyond
.
J Exp Med
2013
;
210
:
1283
1299
28.
Donath
MY
,
Dinarello
CA
,
Mandrup-Poulsen
T
.
Targeting innate immune mediators in type 1 and type 2 diabetes
.
Nat Rev Immunol
2019
;
19
:
734
746
29.
Wada
J
,
Makino
H
.
Innate immunity in diabetes and diabetic nephropathy
.
Nat Rev Nephrol
2016
;
12
:
13
26
30.
Talukdar
S
,
Oh
DY
,
Bandyopadhyay
G
, et al
.
Neutrophils mediate insulin resistance in mice fed a high-fat diet through secreted elastase
.
Nat Med
2012
;
18
:
1407
1412
31.
Mansuy-Aubert
V
,
Zhou
QL
,
Xie
X
, et al
.
Imbalance between neutrophil elastase and its inhibitor α1-antitrypsin in obesity alters insulin sensitivity, inflammation, and energy expenditure
.
Cell Metab
2013
;
17
:
534
548
32.
Stoikou
M
,
Grimolizzi
F
,
Giaglis
S
, et al
.
Gestational diabetes mellitus is associated with altered neutrophil activity
.
Front Immunol
2017
;
8
:
702
33.
Lou
Y
,
Wu
C
,
Wu
M
,
Xie
C
,
Ren
L
.
The changes of neutrophil gelatinase-associated lipocalin in plasma and its expression in adipose tissue in pregnant women with gestational diabetes
.
Diabetes Res Clin Pract
2014
;
104
:
136
142
34.
Oben
JA
,
Mouralidarane
A
,
Samuelsson
AM
, et al
.
Maternal obesity during pregnancy and lactation programs the development of offspring non-alcoholic fatty liver disease in mice
.
J Hepatol
2010
;
52
:
913
920
35.
Nobili
V
,
Cianfarani
S
,
Agostoni
C
.
Programming, metabolic syndrome, and NAFLD: the challenge of transforming a vicious cycle into a virtuous cycle
.
J Hepatol
2010
;
52
:
788
790
36.
Layton
J
,
Powe
C
,
Allard
C
, et al
.
Maternal lipid profile differs by gestational diabetes physiologic subtype
.
Metabolism
2019
;
91
:
39
42
37.
Chen
Y
,
Du
M
,
Xu
J
,
Chen
D
.
The small dense LDL particle/large buoyant LDL particle ratio is associated with glucose metabolic status in pregnancy
.
Lipids Health Dis
2017
;
16
:
244
38.
Qiu
C
,
Rudra
C
,
Austin
MA
,
Williams
MA
.
Association of gestational diabetes mellitus and low-density lipoprotein (LDL) particle size
.
Physiol Res
2007
;
56
:
571
578
39.
Todoric
J
,
Handisurya
A
,
Leitner
K
,
Harreiter
J
,
Hoermann
G
,
Kautzky-Willer
A
.
Lipoprotein(a) is not related to markers of insulin resistance in pregnancy
.
Cardiovasc Diabetol
2013
;
12
:
138
40.
Butte
NF
.
Carbohydrate and lipid metabolism in pregnancy: normal compared with gestational diabetes mellitus
.
Am J Clin Nutr
2000
;
71
(
Suppl.
):
1256S
1261S
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.