nested case control study gestational diabetes

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• Published: 22 August 2022

Association between early-pregnancy serum C-peptide and risk of gestational diabetes mellitus: a nested case–control study among Chinese women

• Xue Yang 1 , 2 , 3   na1 ,
• Yi Ye 4 , 5   na1 ,
• Yi Wang 4 , 5 ,
• Ping Wu 4 , 5 ,
• Qi Lu 4 , 5 ,
• Yan Liu 6 ,
• Jiaying Yuan 7 ,
• Xingyue Song 8 , 9 ,
• Shijiao Yan 10 , 11 ,
• Xiaorong Qi 12 ,
• Yi-Xin Wang 13 ,
• Ying Wen 14 ,
• Gang Liu 5 , 15 ,
• Chuanzhu Lv 8 , 9 , 10 ,
• Chun-Xia Yang 1 , 2 ,
• An Pan 4 , 5 ,
• Jianli Zhang 3 &
• Xiong-Fei Pan   ORCID: orcid.org/0000-0002-9350-9230 16 , 17 , 18  

Nutrition & Metabolism volume  19 , Article number:  56 ( 2022 ) Cite this article

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To examine the association of early-pregnancy serum C-peptide with incident gestational diabetes mellitus (GDM) and the predictive ability of maternal C-peptide for GDM.

A nested case–control study of 332 GDM cases and 664 controls was established based on the Tongji-Shuangliu Birth Cohort. The GDM cases and controls were matched at 1:2 on maternal age (± 3 years) and gestational age (± 4 weeks). Multivariable conditional logistic regression was applied to assess the association of C-peptide with risk of GDM. Partial Spearman’s correlation coefficients were estimated for the correlations between C-peptide and multiple metabolic biomarkers. C-statistics were calculated to assess the predictive ability of early-pregnancy C-peptide for GDM.

Of 996 pregnant women, median maternal age was 28.0 years old and median gestational age was 11.0 weeks. After adjustment for potential confounders, the odds ratio of GDM comparing the extreme quartiles of C-peptide was 2.28 (95% confidence interval, 1.43, 3.62; P for trend < 0.001). Partial correlation coefficients ranged between 0.07 and 0.77 for the correlations of C-peptide with fasting insulin, homeostatic model of insulin resistance, leptin, fasting blood glucose, triglycerides, glycosylated hemoglobin, waist–hip ratio, systolic blood pressure, and low-density lipoprotein cholesterol ( P  ≤ 0.025), and were − 0.11 and − 0.17 for high-density lipoprotein cholesterol and adiponectin ( P  < 0.001). Serum C-peptide slightly improved the predictive performance of the model with conventional predictive factors (0.66 vs. 0.63; P  = 0.008).

While the predictive value for subsequent GDM should be validated, early-pregnancy serum C-peptide may be positively associated with risk of GDM.

Gestational diabetes mellitus (GDM) is defined as diabetes firstly diagnosed in the second or third trimester of pregnancy [ 1 ]. It is one of the most common metabolic disorders during pregnancy, with a prevalence of about 16.8% worldwide [ 2 ]. In China, the prevalence of GDM could reach up to 14.8% [ 3 ]. Since GDM is associated with a higher risk of type 2 diabetes in both the mothers [ 4 ] and offspring [ 5 ], its early detection and prevention has strong implications for the control of metabolic diseases.

C-peptide is a short 31-animo-acid protein that is secreted from pancreatic islet β cells into circulation in equimolar amounts with insulin. It is a constant biomarker to measure the β cell function because it has a longer half-life compared to insulin and is subject to negligible hepatic extraction before release to circulation [ 6 ]. Recent evidence indicates that C-peptide is an active peptide hormone with important physiologic functions and exerts metabolic effects [ 7 ]. While accumulated evidence suggests a link between C-peptide and type 2 diabetes [ 8 , 9 ], fewer studies explored the effects of C-peptide on subsequent risk of GDM. Most previous studies on this topic utilized case–control or cross-sectional designs, and inherent limitations such as a lack of temporal associations made causal inference less reliable. To date, only three prospective studies assessed the association between early-pregnancy fasting plasma C-peptide and subsequent risk of GDM [ 10 , 11 , 12 ], and all these studies were conducted in European populations and reported an increased GDM risk associated with higher fasting C-peptide. Despite consistent results in previous studies, it is less clear whether the association varies across populations or exists among Chinese women. Understanding the role of C-peptide in GDM development might help to improve early intervention as well as prediction. However, despite a high prevalence of GDM in China, very little work has been undertaken to assess this association in Chinese women.

To expand our knowledge of the potential role of C-peptide in the development of GDM, in this prospective nested case–control study, we aimed to (1) examine the association of fasting serum C-peptide during early pregnancy with subsequent risk of GDM; (2) examine the correlations between C-peptide and major metabolic biomarkers in pregnant women; and (3) assess the ability of C-peptide for predicting GDM among Chinese women.

Materials and methods

Design and population.

The nested case–control study was conducted in the Tongji-Shuangliu Birth Cohort (TSBC) [ 13 ], which was started from March 2017 in the Shuangliu Maternal and Child Health Hospital in Chengdu. Until June 2019, 6143 pregnant women were enrolled during their first prenatal examination (6–17 weeks of pregnancy). Women were included if they met the following criteria: (1) women aged 18–40 years with singleton pregnancy; and (2) gestational age less than 15 weeks. Participants were excluded if they (1) conceived the fetus using assisted reproductive technology, such as in-vitro fertilization and intrauterine insemination; (2) reported severe chronic disease or infectious disease like cancer, tuberculosis, and HIV infection; or (3) refused to sign the written informed consent or had no ability to complete the questionnaire independently. Structured questionnaires were administered at enrollment, and blood samples were obtained for future analyses. The original cohort study was approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology, and informed consent was obtained from all participants.

GDM diagnosis and matching to controls

GDM was diagnosed at 24–28 weeks of pregnancy according to the International Association of Diabetes in Pregnancy Study Groups criteria using the standard 75 g 2-h oral glucose tolerance test (OGTT): (1) fasting plasma glucose ≥ 5.1 mmol/L; and/or (2) 1-h plasma glucose ≥ 10.0 mmol/L; and/or (3) 2-h plasma glucose ≥ 8.5 mmol/L [ 14 ]. A total of 347 GDM women were diagnosed, of whom 14 did not provide sufficient blood samples for C-peptide measurements at enrollment and 1 had data missing for key covariates. We included 332 eligible GDM cases, and matched them individually to 664 pregnant women with normal glucose tolerance at 1:2 on maternal age (± 3 years) and gestational age (± 4 weeks) (Additional file  1 : Figure S1).

Measurement of serum C-peptide and other biomarkers

Measurement of metabolic biomarkers were conducted using fasting blood samples collected at enrollment. Serum C-peptide, insulin, and leptin were measured using the Metabolic Group 1 (hu) Singleplex Assays on the Meso Scale Discovery (MSD) U-PLEX Metabolic Platform (MSD, Rockville, Maryland, US). The intra- and inter-assay coefficients of variation for C-peptide were 3.7% and 10.3%, separately. Fasting blood glucose (FBG) was measured using the Glucose Assay Kit (Sichuan Maccura Biotechnology, Chengdu, China) by the method of GOD-PAP (glucose oxidase-phenol and 4 aminophenazone). Glycosylated hemoglobin (HbA1c) was measured using a DCA Vantage Analyzer (Siemens Healthcare Diagnostics, Marburg, Hessen, Germany). Serum high-sensitivity C-reactive protein (hs-CRP) and adiponectin were tested using the R&D enzyme-linked immunosorbent assays (R&D Systems, Minneapolis, Minnesota, US). Total cholesterol, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured via the Mindray BS-200 chemistry Analyzer (Mindray Medical International, Shenzhen, China). The homeostatic model of insulin resistance (HOMA-IR) was used to estimate insulin resistance and calculated based on the following formula: HOMA-IR = fasting blood glucose (mmol/L) × fasting insulin (mIU/L)/22.5 [ 15 ]. Missing values for FBG (n = 11), HbA1c (n = 17), and serum lipids (n = 5) were imputed using median values by GDM status in the study.

Measurement of covariates

Data of sociodemographic information, history of disease and reproduction, and lifestyle and behaviors were obtained via questionnaire interviews at enrollment. Anthropometric measurements were conducted at enrollment per standard protocols. Pre-pregnancy body mass index (BMI) was calculated according to the formula: BMI = weight (kilogram)/height 2 (meter), in which pre-pregnancy weight was self-reported. Waist–hip ratio (WHR) was defined as waistline (cm) divided by hipline (cm). Education level was categorized according to years of education: ≤ 12 years and > 12 years. Smoking status and alcohol consumption were both categorized as never, former, and current. Blood pressure was measured twice using Omron electronic sphygmomanometer (Omron, Kyoto, Japan), and the average value was calculated. Physical activity in metabolic equivalent of task (MET)-hours per week was evaluated using the Chinese version of the Pregnancy Physical Activity Questionnaire [ 16 ], which has been validated among Chinese pregnant women [ 17 ]. Parity was classified into 0 and ≥ 1. Parental history of diabetes and history of GDM were both defined as yes and no.

Statistical analysis

For descriptive analyses, continuous variables were reported as mean and standard deviation (SD) or as median and interquartile range (IQR), and categorical variables as frequency and percentage. Baseline characteristics among C-peptide quartile groups were compared using Kruskal–Wallis test or analysis of variance for continuous variables and chi-square test for categorical variables. In addition, baseline characteristics between GDM cases and controls were compared by univariable conditional logistic regression.

Partial Spearman regression was used to examine the relationship of C-peptide with multiple metabolic biomarkers including WHR, blood pressure, FBG, fasting insulin, HOMA-IR, HbA1c, total cholesterol, TG, LDL-C, HDL-C, hs-CRP, adiponectin, and leptin in early pregnancy among all included pregnant women, with adjustment for maternal age, gestational age, education level, smoking status, alcohol consumption, physical activity, pre-pregnancy BMI, parental history of diabetes, history of GDM, parity, and GDM status.

Multivariable conditional logistic regression models were used to estimate odds ratios (ORs) and their 95% confidence intervals (CIs) between early-pregnancy serum C-peptide and risk of GDM. C-peptide was assessed as a categorical variable (quartiles based on the concentration among the control group), and as continuous variables (on the natural log scale and for each 1-SD change). Covariates were sequentially adjusted for in two models: maternal age (continuous, years), gestational age (continuous, weeks), and education level (≤ 12 years and > 12 years) in Model 1; additionally, smoking status (never, former, and current), alcohol consumption (never, former, and current), physical activity (continuous, MET-hours per week), pre-pregnancy BMI (continuous, kg/m 2 ), parental history of diabetes (yes and no), history of GDM (yes and no), and parity (0 and ≥ 1) in Model 2. In sensitivity analyses, we separately adjusted for insulin (continuous, uIU/mL), HOMA-IR (continuous), and leptin (continuous, ng/mL) in multivariable conditional logistic regression model due to their stronger correlations to C-peptide.

P values for trend were estimated by modeling the median value of each C-peptide quartile as a continuous variable. We used restricted cubic splines with five knots at the 5th (reference), 27.5th, 50th, 72.5th, and 95th centiles to model the non-linear association between C-peptide and GDM. To investigate whether the association was modified by the baseline characteristics, we conducted subgroup analyses by maternal age (< 30 and ≥ 30 years), pre-pregnancy BMI (< 24.0 and ≥ 24.0 kg/m 2 ), and parental history of diabetes (yes and no). Interactions (effect modifications) were assessed via the likelihood ratio test by adding an interaction term of a stratifying variable and C-peptide.

We calculated C-statistics based on logistic regression models to assess the predictive ability of early-pregnancy C-peptide for GDM. Four models were established in our analyses: Model 1 included conventional predictive factors for GDM including maternal age, gestational age, pre-pregnancy BMI, physical activity, parental history of DM, and history of GDM; Model 2 included conventional predictive factors and C-peptide; Model 3 included conventional predictive factors and FBG; Model 4 included conventional predictive factors, FBG, and C-peptide. To compare the discriminative performance, the Delong test was used to compare the C-statistics. Moreover, we used net reclassification improvement (NRI) [ 18 ] and integrated discrimination improvement (IDI) [ 19 ] statistics to measure the utility of C-peptide in GDM prediction.

Data analyses were performed by STATA 15.0 (Stata Corporation, College Station, TX, US). Partial Spearman’s correlation coefficients were visualized by GraphPad Prism 8 (GraphPad Software Inc., San Diego, CA, USA). NRI and IDI were calculated by comparison of predictive models using SAS 9.4 (SAS Institute, Cary, NC, USA). Two-sided P  < 0.05 was considered to indicate statistical significance.

Baseline characteristics of participants

Of 996 pregnant women, the median maternal age (IQR) was 28.0 (25.0–30.0) years and median gestational age was 11.0 (9.0–12.0) weeks. Women with higher C-peptide levels showed higher pre-pregnancy BMI, WHR, blood pressure, FBG, fasting insulin, HOMA-IR, HbA1c, TG, LDL-C, and lower HDL-C. In addition, women with higher C-peptide levels were more likely to be multiparous, poorly educated, and have history of GDM (Table 1 ). Comparisons of baseline characteristics between GDM cases and controls are presented in Additional file 1 : Table S1.

Correlations between serum C-peptide and multiple metabolic biomarkers in early pregnancy

Among 996 pregnant women, we found positive correlations of serum C-peptide with maternal fasting insulin ( β  = 0.77; P  < 0.001), HOMA-IR ( β  = 0.75; P  < 0.001), leptin ( β  = 0.26; P  < 0.001), FBG ( β  = 0.21; P  < 0.001), TG ( β  = 0.14; P  < 0.001), HbA1c ( β  = 0.09; P  = 0.005), WHR ( β  = 0.09; P  = 0.006), systolic blood pressure ( β  = 0.07; P  = 0.021), and LDL-C ( β  = 0.07; P  = 0.025). In contrast, high levels of serum C-peptide were correlated with lower HDL-C ( β  = -0.11; P  < 0.001) and adiponectin ( β  = -0.17; P  < 0.001; Fig.  1 ).

Partial spearman correlations between baseline metabolic traits and C-peptide. HbA1c glycosylated hemoglobin, HDL-C high-density lipoprotein cholesterol, HOMA-IR homoeostatic model assessment‐insulin resistance,  hs-CRP high-sensitivity C-reactive protein, LDL-C low-density lipoprotein cholesterol, TG triglycerides, WHR waist–hip ratio. P values were calculated using partial spearman regression with adjustment for maternal age, gestational age, education, parity, smoking status, alcohol consumption, physical activity, pre-pregnancy BMI, family history of diabetes, history of GDM, and GDM status. There were positive correlations of C-peptide with fasting insulin, homeostatic model of insulin resistance, leptin, fasting blood glucose, triglycerides, glycosylated hemoglobin, waist–hip ratio, systolic blood pressure, and low-density lipoprotein cholesterol, and negative correlations with high-density lipoprotein cholesterol and adiponectin, demonstrating an adverse metabolic profile associated with C-peptide

Association between early-pregnancy C-peptide and risk of GDM

Pregnant women with elevated early-pregnancy C-peptide levels showed a higher risk of GDM, with an OR (95% CI) of 2.28 (1.43, 3.62) for the extreme-quartile comparison, after adjustment for maternal age, gestational age, education level, smoking status, alcohol consumption, physical activity, pre-pregnancy BMI, history of parental diabetes, history of GDM, and parity. ORs of GDM were 2.64 (1.76, 3.96) and 1.33 (1.16, 1.54) for each 1 log ng/mL and each 1 SD ng/mL increase of C-peptide, respectively. There was a linear trend in the association of C-peptide with risk of GDM ( P for trend < 0.001; Table 2 ). Modeling with restricted cubic splines showed little evidence for a non-linear relationship between C-peptide and risk of GDM ( P for overall association < 0.001; P for non-linearity = 0.082; Additional file 1 : Figure S2).

After additional adjustment for insulin, the association between early-pregnancy C-peptide and GDM was attenuated (OR, 1.94; 95% CI 1.13, 3.34 for the extreme-quartile comparison). ORs of GDM were 3.45 (1.79, 6.65) and 1.47 (1.12, 1.94) for 1 log ng/mL increase and 1-SD increase of C-peptide, respectively (Additional file 1 : Table S2). In addition, when we separately controlled for HOMA-IR (1.72; 1.00, 2.97 for the extreme-quartile comparison) and leptin (2.11; 1.30, 3.42) in multivariable conditional logistic regression models, pregnant women with higher early-pregnancy serum C-peptide levels still showed increased risks of GDM (Additional file 1 : Table S2). In subgroup analyses, no significant interactions were observed between C-peptide and maternal age, pre-pregnancy BMI, or parental history of diabetes for GDM risk (all P for interaction ≥ 0.089; Additional file 1 : Table S3).

Performance of early-pregnancy C-peptide in GDM prediction

For the GDM prediction analyses, the C-statistic for the base model with conventional predictive factors was 0.63 (0.59, 0.67). Adding C-peptide to the base model only yielded a slight improvement of 0.03 in the C-statistic ( P  = 0.008), while no significant change was observed when FBG was added ( P  = 0.240; Fig.  2 and Additional file 1 : Table S4). Meanwhile, we observed a mild increment of NRI (C-peptide: 19.6, P  = 0.036; FBG: 16.0, P  = 0.018) and IDI (C-peptide: 0.018, P  < 0.001; FBG: 0.023, P  < 0.001; Additional file 1 : Table S4) in two models. Compared to the model with conventional predictive factors and FBG, the model with conventional predictive factors and C-peptide showed a similar predictive ability ( P  = 0.412; Fig.  2 and Additional file 1 : Table S4). In addition, adding C-peptide to the model with conventional predictive factors and FBG yielded a mild increase of 0.01 in the C-statistic (0.66 vs. 0.65; P  = 0.021). For the same comparison, we only noted mild NRI ( P  = 0.003) and IDI ( P  = 0.005; Additional file 1 : Table S4).

Receiver operator characteristic curves for early-pregnancy fasting biomarkers of glucose metabolism in gestational diabetes mellitus prediction. ( A ) Comparation of models based on conventional predictive factors and conventional predictive factors plus C-peptide (Difference: 0.03; P  = 0.008); ( B ) Comparation of models with (1) conventional predictive factors and C-peptide, (2) conventional predictive factors and FBG, (3) conventional predictive factors, FBG, and C-peptide. Conventional predictive factors included maternal age, gestational age, pre-pregnancy body mass index, physical activity, parental history of diabetes mellitus, and history of GDM. AUC area under receiver operator characteristic curve, CI confidence interval, FBG fasting blood glucose, GDM gestational diabetes mellitus

Our study documented positive associations between early-pregnancy serum C-peptide and risk of developing GDM among pregnant women in China. This finding was also supported by significant correlations between C-peptide and metabolic biomarkers in pregnant women. C-peptide alone was at least comparable to FBG when added to conventional predictive factors for predicting GDM. Our findings suggest that early-pregnancy C-peptide could be an important risk factor for GDM, although the predictive value for subsequent GDM needs to be confirmed in future large prospective studies.

In our study, higher C-peptide was linearly associated with incident GDM in Chinese pregnant women. Consistent with our finding, in the Omega study among 804 Swedish pregnant women free of pre-existing diabetes, the risk of GDM among women with C-peptide ≥ 3.00 ng/mL showed a 2.28-fold increase risk compared to those with a concentration < 1.45 ng/mL [ 10 ]. Meanwhile, the Camden study among 574 Austrian women showed that higher levels of fasting C-peptide before 16 weeks of pregnancy were associated with an increased risk of GDM, and the adjusted OR was 1.85 per 1 ng/mL increase [ 11 ]. In addition, in another prospective cohort study among 1,368 British pregnant women, those with C-peptide ≥ 0.54 nmol/L experienced a 4.43-fold higher risk of subsequent GDM [ 12 ]. A small case–control study in 82 pregnant women (42 GDM cases and 40 cases of normal pregnant women) also found elevated C-peptide in the GDM group in the third trimester in China [ 20 ]. The slight differences in the strength of association between previous studies and ours might be attributable to variations in study populations, study designs, different sample size, diagnosis criteria of GDM, gestational age of C-peptide measurement, and statistical modelling. The evidence based on clinical and experimental studies demonstrates that C-peptide could stimulate glucose transport [ 21 ], dampen the metabolic effects of insulin at high serum concentrations [ 22 ], promote lipids accumulation in adipocytes [ 23 ] and vascular walls [ 24 ], and accelerate central obesity [ 25 ]. The mechanistic evidence is supported by our finding that serum C-peptide was correlated positively with metabolic profiles including high levels of FBG, fasting insulin, HOMA-IR, HbA1c, TG, LDL-C, leptin, and WHR, but negatively with HDL-C and adiponectin. Our study has been the first prospective one to address this topic among Chinese women and further consolidate previous findings.

Since the diagnosis of GDM is often recommended for the late second or early third trimester of pregnancy according to established guidelines [ 14 ], only a small window of intervention is possible to minimize the adverse effect of GDM. A number of studies predicted the development of GDM using basic characteristics and easily available clinical biomarkers [ 26 , 27 ], which might facilitate recognition of women with high risk of subsequent GDM and targeted intervention for GDM at an earlier stage. Lamain–de Ruiter et al. [ 28 ] reviewed prediction models for the risk of GDM, and these models were mostly based on traditional clinical risk factors and showed limited discriminative capability. In our study, early-pregnancy C-peptide compared well with FBG when added to conventional predictive factors for predicting GDM, and slightly improved the prediction based on conventional predictive factors and FBG. One predictive model based on both clinical and biochemical predictors including fasting plasma glucose, TG, and HbA1c at early pregnancy had an C-statistic of 0.72, which was slightly higher than the van Leeuwen [ 29 ] and the Teede [ 30 ] prediction models just based on clinical factors in the same population [ 31 ]. Recently, a GDM prediction model in Chinese population based on clinical and biochemical predictors also achieved effective discriminate power (C-statistic = 0.77) [ 26 ]. The prediction performance of our model was slightly weakener compared to above-mentioned ones, which could be attributed to differences in the study populations, methods of modeling, predictors for modeling, gestational age of predictors measurement, and diagnosed criteria for GDM. Of note, inclusion of biomarkers of glucose metabolism contributed to the optimization of GDM prediction models. However, due to the under-recognition of C-peptide in glucose metabolism, few studies assessed the accuracy of C-peptide prediction for GDM. One conducted in Vienna reported that C-peptide performed well for prediction of GDM, especially for GDM with a need of pharmacotherapy (C-statistic = 82.2%) [ 11 ]. Thus, the potential predictive ability of C-peptide in early pregnancy for subsequent GDM should be further assessed in future studies.

Although the mechanisms of GDM were not yet fully defined, C-peptide might be involved in the GDM development by the pathways of insulin resistance, lipid metabolism, and inflammation. First, the metabolic effects of insulin could be enhanced by C-peptide at low hormone concentrations and reduced at high concentrations [ 22 ]. Thus, high C-peptide level is considered a marker of decreased insulin sensitivity, which is one of the main metabolic abnormalities underlying GDM [ 32 ]. Second, C-peptide has been reported to promote the lipid accumulation via the pathway of stimulating peroxisome proliferator-activated receptor-γ (PPAR-γ). C-peptide could regulate the expression of PPAR-γ regulated genes involved in metabolic control and inflammation [ 33 ]. PPAR-γ, as a requisite transcription factor in the differentiation of adipose tissue, could enhance the lipids accumulation in adipocyte and then lead to lipid metabolism disorder [ 23 ]. Of note, dyslipidemia such as increased triglycerides was reported to associate with insulin resistance and damaged β cell function independent of overweight or obesity status [ 34 ]. Third, C-peptide was reported to show proinflammatory effects in different body tissues. Animal studies demonstrated that elevated C-peptide promoted inflammatory cell infiltration in ApoE-deficient mice [ 24 ]. Moreover, chronic subclinical inflammation is considered a part of insulin resistance syndrome, which has a central role in the development of GDM [ 35 ]. Our finding could have key clinical implications. C-peptide might be a useful biomarker for GDM in early pregnancy, and could be screened routinely in addition to other glucose metabolism biomarkers. Women with elevated C-peptide in their early pregnancy would need to be monitored for potential risk of future GDM. However, future basic and clinical studies are warranted to elucidate the mechanisms by which elevated early-pregnancy C-peptide increases the risk of GDM.

To our knowledge, this has been the first prospective study to examine the association between early-pregnancy C-peptide and the risk of subsequent GDM among Chinese pregnant women. The prospective design allowed us to better elucidate the temporal relationship. Well-matched controls guaranteed the comparability of baseline characteristics of cases and controls to generate unbiased estimates. Despite the strengths mentioned, certain limitations must be acknowledged. First, how the dynamics of C-peptide levels during pregnancy affect risk of GDM could not be addressed because C-peptide was only measured once in our study. Of note, mild insulin resistance develops physiologically to adapt to fetal growth [ 36 ], so future studies could be directed to the impact of C-peptide changes on GDM across different trimesters. Second, our study was not aimed to reveal the mechanisms of C-peptide in the development of GDM, so the prospective association should not be interpreted as an indication of a causal link between C-peptide and GDM. Third, our study had a moderate sample size and the study population was from a district of western China, which restricts the generalizability of our findings. The identified association between C-peptide and risk of GDM and the predictive model should be further validated in other large populations.

In conclusion, we observed a positive association between higher early-pregnancy C-peptide and increased risk of subsequent GDM among Chinese pregnant women. C-peptide was also correlated with unfavorable metabolic profiles in Chinese pregnant women. Our findings highlight the role of C-peptide in the development of GDM and the potential of using early-pregnancy C-peptide as a routine biomarker for predicting the risk of GDM.

Availability of data and materials

The datasets analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

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Acknowledgements

We thank the participants of the Tongji-Shuangliu Birth Cohort for their support, and doctors and nurses from the Shuangliu Maternal and Child Health Hospital for their collaboration in this project.

This study was supported by the Natural National Scientific Foundation of China (Grant Number 81773517), and the Independent Innovation Foundation of Huazhong University of Science and Technology (Grant Number 2019kfyXKJC052). Y Liu was funded by Sichuan Health Research Grant (No 19PJ182) from the Sichuan Health Commission. The funders had no role in the study design, data analyses, results interpretation, or manuscript writing.

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Xue Yang and Yi Ye should be considered joint first authors

Authors and Affiliations

Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, China

Xue Yang & Chun-Xia Yang

Non-Communicable Diseases Research Center, West China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu, 610041, Sichuan, China

Wenjiang Institute of Women’s and Children’s Health, Wenjiang Maternal and Child Health Hospital, Chengdu, 611130, Sichuan, China

Xue Yang & Jianli Zhang

Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China

Yi Ye, Yi Wang, Ping Wu, Qi Lu & An Pan

Ministry of Education and Ministry of Environmental Protection Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China

Yi Ye, Yi Wang, Ping Wu, Qi Lu, Gang Liu & An Pan

Department of Obstetrics and Gynecology, Shuangliu Maternal and Child Health Hospital, Chengdu, 610200, Sichuan, China

Department of Science and Education, Shuangliu Maternal and Child Health Hospital, Chengdu, 610200, Sichuan, China

Jiaying Yuan

Department of Emergency, Hainan Clinical Research Center for Acute and Critical Diseases, The Second Affiliated Hospital of Hainan Medical University, Haikou, 571199, Hainan, China

Xingyue Song & Chuanzhu Lv

Key Laboratory of Emergency and Trauma of Ministry of Education, Hainan Medical University, Haikou, 571199, Hainan, China

Research Unit of Island Emergency Medicine, Chinese Academy of Medical Sciences, Hainan Medical University, Haikou, 571199, Hainan, China

Shijiao Yan & Chuanzhu Lv

School of Public Health, Hainan Medical University, Haikou, 571199, Hainan, China

Shijiao Yan

Department of Gynecology and Obstetrics, West China Second Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610041, Sichuan, China

Xiaorong Qi

Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA

Yi-Xin Wang

Department of Communicable Diseases Control and Prevention, Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China

Department of Nutrition and Food Hygiene, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China

Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, 610041, Sichuan, China

Xiong-Fei Pan

NMPA Key Laboratory for Technical Research on Drug Products in Vitro and in Vivo Correlation, West China Second University Hospital, Sichuan University, Chengdu, 610041, Sichuan, China

Shuangliu Institute of Women’s and Children’s Health, Shuangliu Maternal and Child Health Hospital, Chengdu, 610200, Sichuan, China

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XY: Conceptualization, formal analysis, methodology, software, writing—original draft. YY: Data curation, formal analysis, methodology, writing—original draft. YW: Data curation, formal analysis, supervision. PW: Formal analysis, methodology, software. QL: Supervision, writing—review and editing. YL: Writing—review and editing. JY: Writing—review and editing. XS: Writing—review and editing. SY: Methodology, Writing—review and editing. XQ: Methodology, writing—review and editing. Y-X Wang: Methodology, writing—review and editing. YW: Methodology, Writing—review and editing. GL: Methodology, writing—review and editing. CL: Methodology, writing—review and editing. C-XY: Conceptualization, data curation, methodology, writing—review and editing. AP: Conceptualization, data curation, methodology, supervision, writing—review. JZ: Conceptualization, data curation, methodology, writing—review and editing. X-FP: Conceptualization, data curation, methodology, supervision, writing—review. All authors read and approved the final manuscript.

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Correspondence to Jianli Zhang or Xiong-Fei Pan .

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Supplementary Information

Additional file 1.

. Table S1 : Baseline characteristics of GDM patients and matched controls. Table S2 : Association between early-pregnancy C-peptide and risk for GDM in three sensitivity analyses. Table S3 : Association between early-pregnancy C-peptide and GDM risk in subgroup analyses. Table S4 : Summary statistics to assess the early-pregnancy C-peptide in predicting gestational diabetes mellitus. Figure S1 : Flowchart of participants selection in the nested case-control study. Figure S2 : Restricted cubic splines-based modeling for the association between early-pregnancy serum C-peptide and GDM.

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Yang, X., Ye, Y., Wang, Y. et al. Association between early-pregnancy serum C-peptide and risk of gestational diabetes mellitus: a nested case–control study among Chinese women. Nutr Metab (Lond) 19 , 56 (2022). https://doi.org/10.1186/s12986-022-00691-3

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The association between dietary amino acid profile and the risk of type 2 diabetes: Ravansar non-communicable disease cohort study

• Farid Najafi 1 ,
• Parisa Mohseni 2 ,
• Yahya Pasdar 1 ,
• Mahdieh Niknam 3 &
• Neda Izadi 1  

BMC Public Health volume  23 , Article number:  2284 ( 2023 ) Cite this article

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Type 2 diabetes (T2D) is one of the most common chronic diseases and the main risk factors for T2D consist of a combination of lifestyle, unhealthy diet, and genetic factors. Amino acids are considered to be a major component of dietary sources for many of the associations between dietary protein and chronic disease. Therefore, this study amied to determine the association between dietary amino acid intakes and the incidence of T2D.

The present nested case-control study was conducted using data from the Ravansar Non-Communicable Disease (RaNCD) Cohort Study. The information required for this study was collected from individuals who participated in the Adult Cohort Study from the start of the study until September 2023. Over a 6-year follow-up period, data from 113 new T2D cases were available. Four controls were then randomly selected for each case using density sampling. Cases and controls were matched for sex and age at the interview. Food frequency questionnaire (FFQ) was used to collect data related to all amino acids including tryptophan, threonine, isoleucine, leucine, lysine, methionine, cysteine, phenylalanine, tyrosine, valine, arginine, histidine, alanine, aspartic acid, glutamic acid, glycine, proline, and serine were also extracted. Binary logistic regression was used to estimate the crude and adjusted odds ratio for the risk of T2D.

Using the univariable model, a significant association was found between T2D risk and branched-chain, alkaline, sulfuric, and essential amino acids in the fourth quartile. Accordingly, individuals in the fourth quartile had a 1.81- to 1.87-fold higher risk of developing new T2D than individuals in the lowest quartile ( P <0.05). After adjustment for several variables, the risk of developing a new T2D was 2.70 (95% CI: 1.16-6.31), 2.68 (95% CI: 1.16-6.21), 2.98 (95% CI: 1.27-6.96), 2.45 (95% CI: 1.02-5.90), and 2.66 (95% CI: 1.13-6.25) times higher, for individuals in the fourth quartile of branched-chain, alkaline, sulfuric, alcoholic, and essential amino acids compared with those in the lowest quartile, respectively.

Conclusions

The results showed that the risk of developing a new T2D was higher for individuals in the fourth quartile of branched-chain amino acids, alkaline, sulfate, and essential amino acids than in the lower quartile.

Peer Review reports

Type 2 diabetes (T2D) is one of the most common chronic diseases and is strongly related to cardiovascular disease (CVD), hypertension, and certain types of cancer [ 1 ]. Previous studies have consistently demonstrated that a combination of lifestyle, diet, and genetic factors could affect the risk of developing T2D [ 2 ]. Amino acids are also considered to be a major component of dietary sources for many of the previously reported associations between dietary protein and chronic disease [ 1 , 3 , 4 ]. There is evidence that branched-chain and aromatic amino acids are associated with higher T2D risk, but there is conflicting evidence regarding other amino acids [ 5 , 6 ]. Findings from a meta-analysis of eight prospective studies illustrated that the higher dietary intake of amino acids including isoleucine, leucine, valine, tyrosine, and phenylalanine was associated with a higher risk of T2D. Results from a previous study demonstrated an inverse association between glycine and glutamine and the risk of developing T2D [ 7 ]. In addition, the results of two studies in Germany and Finland showed an inverse association between glycine and the occurrence of T2D, whereas no association was observed in South Asians and immigrants in the United Kingdom [ 8 , 9 , 10 ]. Furthermore, whereas some studies revealed an inverse association between glutamine and T2D risk [ 8 , 11 , 12 ], this association was not significant in other related studies [ 9 , 10 ]. While studies rarey investigated the relationship between histidine and alanine amino acids and risk of T2D, one study showed a positive association [ 7 ].

Most studies have been conducted in European and American populations, whereas this association has been rarely investigated in Asian populations and conflicting results emerged from these studies. In China, a prospective study examined three branched-chain amino acids (BCAA) and two aromatic amino acids in relation to insulin resistance and the development of T2D and highlighted the predictive value of these markers for the development of T2D [ 13 ]. In this regard, a prior study found a positive association between combined scores of nineteen amino acids and T2D among the Japanese adult population [ 12 ]. In addition, the study by Chen et al. (2019) showed that the intake of alanine, valine, leucine, tyrosine, isoleucine, phenylalanine, lysine, glutamate, and ornithine significantly contributed to the occurrence of T2D [ 14 ]. These diverse findings may suggest the ethnic-specific differences in the association between different amino acid intakes and T2D in Western and Asian countries [ 9 ]. Given the ethnic differences between Asian countries in the interaction between genetic, pathophysiological, cultural, and lifestyle factors affecting T2D [ 15 ], it expects to see a specific pattern of interaction between amino acids and diabetes among Asian populations and in countries such as Iran. As dietary amino acid patterns could related to T2D in different ways, this study aimed to determine the association between dietary amino acid intakes and the incidence of type 2 diabetes in the adult population participating in Ravansar Non-Communicable Disease (RaNCD) Cohort Study.

Study population

The present nested case-control study was conducted using data from the Ravansar Non-Communicable Disease (RaNCD) Cohort Study. The RaNCD cohort study is part of the PERSIAN (Prospective Epidemiological Research Studies in IrAN) Cohort and is a population-based prospective study of a group of individuals aged 35–65 years at different phases. The sample size of the main group is at least 10,000 individuals. More details about this cohort are available elsewhere [ 16 ]. The information required for this study was collected from individuals who participated in the Adult Cohort Study from the start of the study until September 2023. At first, the men and women who were diagnosed with T2D (795), hypertension (HTN) (1,332), cancer (67), cardiovascular disease (1,221), renal failure (4), and also pregnant women (93) at baseline and who had an unusual total energy intake (i.e. <500 or > 3,500 kcal per day for women and i.e. <800 or > 4,200 kcal per day for men) (1,078) [ 17 , 18 ] were excluded from the data. After exclusion, data from 113 new T2D cases over a 6-year follow-up period were available for the study. Four controls were then randomly selected for each case using density sampling. Cases and controls were individually matched for sex and age at the interview (Fig. 1 ). All participants gave written informed consent for this study, which was approved by the Kermanshah University of Medical Sciences Review Board.

Flowchart of the study participants and data preparation

Data collection and measurements

Dietary intake of amino acid (main exposure).

The national Iranian food frequency questionnaire (FFQ) was used to determine participants’ usual food intake at the time of recruitment. The FFQ consisted of questions about the frequency of consumption of 125 food items and the corresponding standard serving sizes (e.g., glass, cup, slice, teaspoon, tablespoon, spatula, cube, etc.). The validity and reproducibility of a food intake frequency questionnaire in the PERSIAN Cohort Study was assessed in the study by Eghtesad et al. [ 19 ]. Participants reported the average frequency and portion size of foods consumed in the past year. To minimize recall bias, the FFQ was administered by trained dietitians, and participants were given sufficient time to recall the consumption of each food. The FFQs were then analyzed using Nutritionist IV software, which is based on U.S. Department of Agriculture Food Composition data (USDA National Nutrient Database for Standard Reference, Release 28, 2015), to determine energy and nutrient intakes. All amino acids including tryptophan, threonine, isoleucine, leucine, lysine, methionine, cysteine, phenylalanine, tyrosine, valine, arginine, histidine, alanine, aspartic acid, glutamic acid, glycine, proline, and serine was converted to grams per day (g/day) to measure the daily intake of each amino acid and then were classified into eight groups based on their chemical structure, including branched-chain (leucine, isoleucine, valine), aromatic (tryptophan, phenylalanine, tyrosine), alkaline (histidine, arginine, lysine), sulfuric (methionine, cysteine), acidic (glutamic acid, aspartic acid), alcoholic (serine, threonine), small amino acids (glycine, alanine), and cyclic side chain (proline). In addition, two groups of essential (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine) and nonessential (alanine, arginine, aspartic acid, cysteine, glutamic acid, glycine, proline, serine, tyrosine) amino acids were added to the model as the main exposure. A quartile was also used for the amino acid groups. A higher quartile indicates an elevated level of dietary amino acid intake, reflecting increased consumption of amino acids.

Diabetes was identified by a fasting plasma glucose (FPG) ≥ 126 mg/dl dL [7 mmol per L] and/or use of diabetes medications (insulin and/or oral hypoglycemic agents) in individuals who did not have the disease at baseline in the cohort study [ 20 ].

To control for confounding factors, variables such as age, gender, education level, smoking status, alcohol consumption, physical activity, socioeconomic status, dietary habits, macronutrients, anthropometric characteristics, underlying diseases (self-reported), sleep habits, etc., were also extracted from the demographic and clinical information section of the Persian cohort questionnaire used in the Ravansar cohort. The details of data collection and measurements have been described in detail elsewhere [ 20 , 21 , 22 , 23 , 24 ].

In brief, participants were classified as current smokers, never smokers, passive smokers, and ex-smokers based on smoking status and intensity of smoking. Physical activity was measured by 24-hour physical activity and a 22-item questionnaire and was classified as low (24-36.5 metabolic equivalent = MET/hours per day), moderate (36.6–44.4 MET/hours per day), and vigorous (≥ 44.5 MET/hours per day) [ 25 ]. Socioeconomic status (SES) was defined using asset data. Total asset scores were calculated using a principal component analysis (PCA), which is the sum of the scores for each asset variable. Dietary habits were assessed using a valid and reliable FFQ questionnaire. Also, healthy and unhealthy patterns were calculated using factor analysis. A BIA device (InBody 770 BIOSPACE, Korea) and a BSM 370 (Biospace Co, Seoul, Korea) were used for weight and height measurements (with 0.5 kg and 0.1 cm accuracy, respectively) and body mass index (BMI) were calculated using weight (kg)/height 2 (m) formula. Subjects were categorized as underweight with a BMI < 18.5 kg/m 2 , normal weight with 18.5 ≤ BMI ≤ 24.9 kg/m 2 , overweight with 25 ≤ BMI ≤ 29.9 kg/m 2 , and obese with a BMI ≥ 30 kg/m 2 [ 26 ]. Waist-to-height ratio (WHtR) was defined as waist circumference (cm) /hip (cm) [ 24 ]. Dyslipidemia was defined as total cholesterol of ≥ 240 mg/dl and/or triglycerides of ≥ 200 mg/dl and/or low-density lipoprotein (LDL) cholesterol of ≥ 160 mg/dl and/or high-density lipoprotein (HDL) cholesterol of < 40 mg/dl and/or taking medication for dyslipidemia [ 27 , 28 ]. For metabolic syndrome, three or more of the following criteria must be met; elevated blood pressure (BP), defined as systolic BP ≥ 130 mmHg and/or diastolic BP ≥ 85 mmHg or medication used to treat hypertension, elevated triglycerides (TG) ≥ 150 mg/dl or medication use to treat hypertriglyceridemia, HDL-C < 50 mg/dl or medication use for low HDL-C, elevated fasting blood glucose (FBS) ≥ 100 or medication use for treatment of diabetes, central obesity (waist circumference (WC) ≥ 91 cm) [ 29 ]. Data on thyroid disease and diabetes in the family were based on self-report. In addition, the time between falling asleep and waking up was defined as sleep duration and categorized as < 6 h, 6–8 h, and > 8 h; and the duration the participant was in bed before actually falling asleep was defined as duration of falling asleep (< 15 min or ≥ 15 min).

Statistical analysis

Mean (standard deviation), median (IQR = interquartile range) (for non-normal distribution), and number (percentage) were used to describe quantitative and qualitative variables. Because intakes of most specific nutrients correlate with total energy intake, a residual adjustment was made for total energy [ 30 , 31 , 32 ]. The Chi-square, T-test, and Mann-Whitney test were used to compare the frequency of categorical variables and the distribution of continuous variables between two groups (case and control). Binary logistic regression was used to estimate the crude and adjusted odds ratio (OR) for the risk of T2D [ 33 ]. To determine an association between dietary amino acids and T2D risk, all variables with a P-value less than 0.2 in the univariable model were included in the multivariable analysis. Models were adjusted for residency, SES, education level, family history of diabetes, BMI, WHtR, physical activity, sleep duration, dietary patterns, comorbidities, systolic and diastolic blood pressure, and daily energy intake. In addition, the restricted cubic spline method was used to assess the non-linear relationship between dietary amino acid and the risk of T2D. Different models with different knots (nknot = 3–7) and quadratic, and cubic terms of the amino acid profile were fitted to the data. Data were analyzed using Stata (version 15) and R (version 4.2.0) software. For all statistical tests, P  < 0.05 was considered statistically significant.

Most people with T2D lived in urban areas (70.80% vs. 53.10%) ( P  = 0.001). The frequency of high SES was significantly lower in diabetics than in the control group (21.24% vs. 27.43%). The frequency of current and former smokers was 15.93% in patients with T2D and 16.70% in the control group. Alcohol consumption was slightly higher in the control group than in the case group (2.88% vs. 2.65%). But there is no difference between the case and control groups based on the findings. The frequency of low physical activity was higher in patients with T2D than in the control group (32.74% vs. 28.98%). Sleep duration of less than 6 h and more than 8 h was significantly higher in the case group than in the control group ( P  = 0.03). The frequency of falling asleep for less than 15 min was also higher in patients with T2D, but this finding was not statistically significant ( P  = 0.27). The frequency of adherence to a healthy dietary pattern was higher in T2D patients than in the control group. The frequency of comorbidities, such as dyslipidemia and metabolic syndrome were significantly higher than in control group ( P  < 0.001). Mean anthropometric indices (BMI, WC, and WHtR) and systolic and diastolic blood pressure were significantly higher in patients with T2D than in non-diabetics ( P  < 0.05). The frequency of family history of diabetes in patients with T2D was significantly higher than in the control group (42.48% vs. 22.79%, respectively) (Table  1 ). In addition, The amount of food group consumption in the case and control groups is shown in Appendix 1 . Based on the results, the median of intake of all amino acids (except for alcholic and proline) was higher in patients with T2D than in the control group, and this result was statistically significant ( P  < 0.05) (Table  2 ).

Using the univariable model, a significant association was found between T2D risk and branched-chain, alkaline, sulfuric, and essential amino acids in the fourth quartile. Accordingly, individuals in the fourth quartile had a 1.81- to 1.87-fold higher risk of developing new T2D than individuals in the lowest quartile ( P  < 0.05). In addition, after adjustment for several variables in different models, the risk of developing T2D increased with the higher intakes of amino acids but was not statistically significant for all amino acids. The risk of developing a new T2D was 2.70 (95% CI: 1.16–6.31), 2.68 (95% CI: 1.16–6.21), 2.98 (95% CI: 1.27–6.96), 2.45 (95% CI: 1.02–5.90), and 2.66 (95% CI: 1.13–6.25) times higher, for individuals in the fourth quartile of branched-chain, alkaline, sulfuric, alcoholic, and essential amino acids compared with those in the lowest quartile, respectively (Table  3 ).

Using a restricted cubic spline, there was no significant nonlinear association between dietary amino acid profiles and risk of T2D after adjustment for age, sex, place of residence, SES, education level, family history of diabetes, BMI, WHtR, physical activity, sleep duration, dietary patterns, comorbidities, systolic and diastolic blood pressure, and daily energy intake.

Amino acids have emerged as novel biomarkers for the risk of T2D. We conducted a nested case-control study in a cohort of 565 diabetic and healthy subjects. The results showed that the mean value of all dietary amino acid groups (except alcoholic and proline) was higher in patients with T2D than in the control group, and this result was statistically significant. A significant association between T2D risk and branched-chain, alkaline, sulfuric, and essential amino acids was found in the fourth quarter. Accordingly, individuals in the fourth quartile had a higher risk of developing new T2D than those in the lowest quartile. In addition, after adjustment for several variables in different models, the risk of T2D increased with increasing dietary amino acids but was not statistically significant for all amino acids. Overall, these amino acids may be useful new markers for identifying individuals at risk for T2D before symptoms become apparent. Insulin resistance may explain or mediate the relationship between these amino acids and the risk of T2D.

Previous studies have suggested several diabetes-related amino acids as potential biomarkers for insulin resistance and T2D. Higher levels of branched-chain amino acids have previously been associated with a higher risk of diabetes in European, Hispanic, African, and Asian populations [ 5 , 13 , 34 , 35 ]. Furthermore, a large-scale Mendelian randomization analysis identified genetic instruments reflecting higher levels of circulating branched-chain species that are also associated with diabetes risk, suggesting a causal role of branched-chain amino acid metabolism in the development of diabetes [ 36 ].

In the study by Zheng et al. a meta-analysis of all cohorts comparing participants in the highest quintile with those in the lowest quintile of intake, the hazard ratios (HR) (95% confidence intervals) were 1.13 (95% CI: 1.07–1.19) for leucine, 1.13 (95% CI: 1.07–1.19) for isoleucine, and 1.11 (95% CI: 1.05–1.17) for valine. In a healthy subsample, higher dietary BCAA levels were significantly associated with higher plasma levels of these amino acids [ 1 ]. Wang et al. reported that individuals with the highest quartile of plasma BCAA concentrations had an approximately 3-fold higher risk of T2D than those with the lowest quartile [ 5 ]. A recent nested case-control study in the Framingham Offspring Study found that plasma BCAA levels are associated with fasting insulin levels and may predict future risk of diabetes, particularly in obese individuals and those with elevated fasting glucose levels [ 5 ].

A prospective population-based cohort study in Groningen, the Netherlands, showed that individuals with high circulating BCAA concentrations had a significantly higher risk of T2M. This association remained significant after adjustment for established risk factors such as age, sex, BMI, parental history of T2D, hypertension, alcohol consumption, HOMA-IR, and HOMA-β [ 37 ]. On the other hand, Franini et al. reported that 130 representative subjects from Bosnia who developed T2D after a follow-up period of 9.5 years had increased concentrations of leucine, isoleucine, and valine compared with 412 subjects who were free of T2D [ 8 ].

Several prospective studies have consistently reported an association between circulating BCAA concentrations and the development of T2D [ 13 , 14 , 37 ]. The meta-analysis by Ramzan et al. found a statistically significant positive association between BCAA concentrations and the development of T2D, with valine, leucine, and isoleucine [ 38 ]. Also, consistent with the results of Guasch-Ferre et al. a positive association between BCAAs and the incidence of T2D was demonstrated, with a pooled risk ratio (RR) for isoleucine, leucine, and valine of 1.36 (95% CI: 1.24–1.48), 1.36 (95% CI: 1.17–1.58), and 1.35 (95% CI: 1.19–1.53), respectively [ 7 ]. The study by Tai et al. showed a strong association between insulin resistance and branched-chain and aromatic amino acids and a combination of isoleucine, leucine, phenylalanine, and methionine in South Asian and Chinese men with relatively low body weight [ 39 ].

The study by Lu et al. showed that an increase in 6 essential amino acids (isoleucine, leucine, lysine, phenylalanine, tryptophan, and valine) was associated with a higher risk for prevalent and/or incident T2D. In addition, valine showed a positive predictive value for the risk of diabetes in this Chinese population [ 40 ]. In a nested case-control study of 429 Chinese adults, serum BCAA and aromatic amino acids (valine, leucine, isoleucine, phenylalanine, and tyrosine) were significantly and positively associated with T2D [ 13 ]. In a large-scale cross-sectional study among Japanese, individuals with T2D had higher BCAA levels than non-diabetics [ 41 ].

It is important to note that the effect size can be influenced by factors such as ethnicity, the variables that have been adjusted for, and the methods used for measuring BCAAs. In a study conducted by Lee (2016), adjusted OR were calculated for a one standard deviation (SD) increase in plasma BCAAs, which differs from our approach [ 42 ].

Alqudah et al. showed that plasma concentrations of leucine, lysine, phenylalanine, tryptophan, and glutamate were significantly increased in T2D patients compared with the control group and were positively related to poor glucose management [ 43 ].

Tryptophan is involved in the tryptophan-kynurenine and tryptophan-methoxy indole metabolic pathways, which leads to the production of some active metabolites, including kynurenine, kynurenic acid, and serotonin [ 43 ]. Any disturbance in these metabolic pathways is likely to be associated with the development of T2D [ 44 ].

Several previous studies reported a positive association between phenylalanine and the risk of T2D [ 6 , 7 , 45 ]. Among 1,150 participants in the Framingham Heart Study children cohort with normal fasting blood glucose, the adjusted HR for T2D risk per SD increase in phenylalanine was 1.35 (95% CI: 1.11–1.65), and a Mendelian randomization analysis found a similar relationship with the odds ratio (OR) of 1.60 (95% CI: 1.08–2.40) [ 45 ]. A case-control study reported that serum phenylalanine was independently associated with an increased risk of T2D [ 6 ]. A meta-analysis identified 27 cross-sectional studies and 19 prospective studies involving 8,000 subjects and found a 26% higher risk of T2D per study-specific SD [ 46 ].

In a case-cohort study, baseline lysine was found to be associated with a higher risk of T2D with an HR of 1.26 (95% CI: 1.06–1.51) per SD increment [ 47 ]. Another study also showed that an essential amino acid was elevated in people with T2D [ 43 ]. One study also found that cysteine was increased in T2D compared with controls and was associated with high HbA1c [ 43 ]. Histidine has also been positively associated with T2D, although the evidence is limited [ 7 ].

However, there are conflicting data on the role of BCAA as a mechanism for the observed association. Data from the Nagata et al. study suggest that high BCAA intake may be associated with a lower risk of diabetes [ 48 ].

In the present study, no significant association was found between the risk of T2D and aromatic, non-essential, acidic amino acids, small amino acids, and proline. Also, no association was found between glycine and the risk of T2D. This finding is consistent with the results of a cohort study in the United Kingdom, which reported that glycine was not associated with the incidence of T2D in South Asia [ 49 ]. An inverse association of glycine with T2D has been reported in previous European cohort studies [ 8 , 10 ], whereas a Mendelian randomization analysis in a European population showed no association between genetic variants associated with glycine and T2D [ 14 ].

We found that glutamic acid and aspartic acid were not associated with an increased risk of T2D. In some studies, no association was found [ 5 ]. However, some other cohort studies have reported that high glutamine levels are associated with a lower risk of T2D [ 11 , 50 ]. In addition, one study found that higher concentrations of total glutamate/glutamine were associated with insulin resistance and the development of diabetes in Chinese and Indian participants [ 39 ]. In one study, aspartate and glutamate were increased in individuals with T2D compared with healthy controls [ 43 ]. Existing evidence on the association between glutamate and the development of T2D is conflicting.

In this study, proline was not significantly associated with the risk of diabetes. In contrast, in other studies, proline was associated with an increased risk of developing T2D in all participants [ 46 ]. Another cross-sectional study showed that proline was strongly correlated with hemoglobin A1c and insulin-related variables such as C-peptide, insulin, and HOMA-IR [ 51 ].

Although not yet fully understood, there are several possible mechanisms underlying the association between amino acids and risk for T2D. First, type 2 diabetes begins with insulin resistance in peripheral tissues [ 13 ]. Some observations suggest that BCAA facilitates glucose uptake by skeletal muscle and liver and promotes glycogen synthesis in an insulin-independent manner through phosphatidylinositol 3-kinase (PI3-kinase) or protein kinase C (PKC) rather than the mTOR pathway [ 40 , 52 ]. A recent study by Pedersen et al. showed that altered gut microbiota affects BCAA levels and may contribute to insulin resistance [ 53 ]. In addition, recent studies have indicated interactions between adipose tissue, BCAA metabolism, and glucose homeostasis. Increased BCAA may generate more catabolic intermediates propionyl CoA and succinyl CoA, leading to the accumulation of incompletely oxidized fatty acids and glucose, mitochondrial stress, impaired insulin action, and ultimately disruption of glucose homeostasis [ 13 ].

Our study has strengths and limitations. Strengths of our study include a prospective nested case-control design that allows measurement of exposures before T2D diagnosis, and the prospective nature of the study design minimizes the likelihood of recall and selection bias. Residual confounding is a common and unavoidable issue in observational studies. We sought to minimize the influence of potential confounders by controlling for potentially confounding variables, including important lifestyle risk factors. Racial homogeneity limited the generalizability of our findings to other ethnic groups. Replication of these results in another cohort is needed to increase the validity of our results.

The results showed that the risk of developing T2D increased with increasing dietary amino acids, but was not statistically significant for all amino acids. The risk of developing a new T2D was higher for individuals in the fourth quartile of branched-chain amino acids, alkaline, sulfate, and essential amino acids than in the lower quartile. Therefore, in the detection of people who are at risk for T2D, these amino acids could be useful markers. Further studies in other populations should be conducted to investigate the association and to determine the positive and negative effects of related dietary amino acid patterns on chronic diseases.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Branched-chain amino acids

Body mass index

Blood pressure

Cardiovascular disease

Fasting blood glucose

Food frequency questionnaire

High-density lipoprotein

Hazard ratios

Hypertension

Interquartile range

Low-density lipoprotein

Metabolic equivalents

Principal component analysis

Phosphatidylinositol 3-kinase

Protein kinase C

Ravansar Non-Communicable Disease

Standard deviation

Socioeconomic status

Type 2 diabetes

Triglyceride

Waist circumference

Waist-to-height ratio

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Acknowledgements

RaNCD is part of the PERSIAN national cohort and we would like to thank Professor Reza Malekzadeh Deputy of Research and Technology at the Ministry of Health and Medical Education of Iran and Director of the PERSIAN cohort and also Dr.Hossein Poustchi Executive Director of PERSIAN cohort for all their supports during design and running of RaNCD as well as to all individuals helping us in completing this research project.

This study was supported by the Ministry of Health and Medical Education of Iran and Kermanshah University of Medical Sciences Grant No 700/534 and 4020356. The funding agency did not play any role in the planning, conduct, and reporting or in the decision to submit the paper for publication.

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Najafi, F., Mohseni, P., Pasdar, Y. et al. The association between dietary amino acid profile and the risk of type 2 diabetes: Ravansar non-communicable disease cohort study. BMC Public Health 23 , 2284 (2023). https://doi.org/10.1186/s12889-023-17210-5

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The association between maternal diabetes and neonatal seizures: a nested case–Control study

Associated data.

Publicly available datasets were analyzed in this study. These data can be found here: Centers for Disease Control and Prevention (CDC) National Vital Statistics System (NVSS) database, https://www.cdc.gov/nchs/nvss/index.htm .

We aimed to evaluate the association of pregestational diabetes mellitus (PGDM) and gestational diabetes mellitus (GDM) with neonatal seizures during neonatal hospitalization.

In this nested case–control study, all data were collected from the data files of the National Vital Statistics System (NVSS) 2016–2021. Considering the effect of confounders, we used the propensity-score matching (PSM; case:control = 1:4) method to select the study population. The outcome was considered the occurrence of neonatal seizures. Univariate and multivariate logistic regression analyses were adopted to assess the association of PGDM and GDM with neonatal seizures. We also conducted stratified analyses according to gestational age, birthweight, 5 min Apgar score, and maternal age to explore the potential disparities.

After using the PSM method, a total of 6,674 cases of neonatal seizures and 26,696 controls were included. After adjusting for covariates, PGDM was associated with an increased risk of neonatal seizures [odds ratio (OR) = 1.51, 95% confidence interval (CI): 1.15–1.98], whereas the association between GDM and neonatal seizures is not statistically significant. In addition, the correlation between PGDM and increased risk of neonatal seizures was observed in neonates with a gestational age of 37–42 weeks and ≥42 weeks, with a 5 min Apgar score of ≥7, and with a maternal age of ≤40 years.

PGDM was found to be closely associated with an increased risk of neonatal seizures. The findings of our study indicated that neonatologists should consider monitoring the incidence of neonatal seizures in neonates born to mothers with PGDM.

Introduction

Neonatal seizures are the most common neurological condition in newborns, and, depending on their etiology, can lead to long-term outcomes such as epilepsy, cerebral palsy, developmental disabilities, and psychomotor impairments ( 1 , 2 ). The incidence of neonatal seizure is approximately 1.5–5.5 per 1,000 live births ( 2 ), which is considered a significant cause of neonatal mortality ( 3 ). Therefore, the identification of risk factors associated with neonatal seizures is crucial in reducing neurological morbidity and mortality among infants.

Previous studies have indicated that birth asphyxia may contribute to neonatal seizures and is associated with maternal complications both prior to and during delivery ( 4 , 5 ). Recently, several studies have found a correlation between maternal diabetes, including pregestational diabetes mellitus (PGDM) and gestational diabetes mellitus (GDM), and the risk of neonatal seizure ( 6 , 7 ). A retrospective cohort study assessed the relationship between neonatal complications and PGDM in infants born preterm (<36 weeks gestation), revealing that PGDM was associated with an elevated risk of seizures among neonates born <34 weeks gestation ( 6 ). After adjusting for these variables, Glass et al. found that both PGDM and GDM were risk factors for neonatal seizures, with PGDM having a greater impact ( 8 ). However, Hall et al. reported a relationship between PGDM and the increased risk of neonatal seizures, while no such association was found with GDM ( 9 ). To the best of our knowledge, existing studies on the relationship between maternal diabetes and neonatal seizures remain contentious. In addition, post-term delivery (≥42 weeks gestation) is also at high risk of developing neonatal seizures ( 8 ), but few studies have analyzed the relationship between maternal diabetes and neonatal seizures for post-term infants.

Herein, this study aims to evaluate the association of maternal diabetes and neonatal seizures during neonatal hospitalization in a large cohort of the National Vital Statistics System (NVSS).

Study population

We conducted a nested case–control study with data sources collected from the National Vital Statistics System (NVSS) 2016–2021 data files. The NVSS is the result of a partnership between the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC) and all US states, aiming to collect information on a wide range of maternal and infant demographic and health characteristics for all births ( 10 , 11 ). This study is considered exempt from the review of the Huizhou Central People's Hospital Ethics Committee due to the use of deidentified data.

The inclusion criteria comprised (1) newborns diagnosed with seizures and (2) pregnant women with complete information about PGDM and GDM. The exclusion criteria were as follows: (1) the presence of multiple births and (2) participants with missing demographic information. A total of 20,297,340 participants met the criteria for this study (case: n  = 20,290,666; control: n  = 6,674). We used the propensity-score matching (PSM, case:control = 1:4) method to reduce the effects of gender, gestational age, and method of delivery. In total, 6,674 cases of neonatal seizures and 26,696 controls were ultimately included in this study.

Pregestational diabetes mellitus and gestational diabetes mellitus

GDM is defined as glucose intolerance first detected during pregnancy ( 12 ), while PGDM is a condition where diabetes is diagnosed prior to conception ( 13 ).

Neonatal seizures were diagnosed by clinicians based on clinical criteria as follows: any involuntary repetitive, convulsive movement or behavior, and severe alteration of alertness such as obtundation, stupor, or coma. The primary outcome was the occurrence of neonatal seizures during neonatal hospitalization in this study.

Potential covariates

Potential covariates were extracted as follows: maternal characteristics contain age (years), race, educational level, birthplace, previous cesarean delivery, previous preterm births, number of prenatal care visits, smoking before pregnancy, body mass index (BMI, kg/m 2 ), weight gain (pounds), infertility treatment, pregestational hypertension, gestational hypertension, eclampsia, chorioamnionitis, infection, previous delivery, induction of labor, delivery method, use of anesthesia, steroids, and antibiotics. Infant characteristics contain gestational age, gender, birthweight (g), use of surfactant, and 5 min Apgar score.

Statistical analysis

We used the mean ± standard deviation (mean ± SD) to describe the measurement data that conform to a normal distribution pattern, and for comparison between the case group and the control group, we used the t -test. The numerical data with non-normally distributed data were presented as a median and interquartile range [ M ( Q 1, Q 3)], and for comparison between the case group and the control group, the rank sum test was used. The categorical data were expressed as the number of cases and composition ratio [ n (%)], and the Chi-square test was used for comparison between both groups.

Considering the effect of confounders, we used the PSM method in this study ( 14 , 15 ). Participants with neonatal seizures were matched in a 1:4 ratio to those without. Subsequently, a descriptive analysis was conducted on the case and control groups both pre- and post-PSM. After performing PSM, univariate and multivariate logistic regression analyses were used to assess the association between maternal diabetes (contains PGDM and GDM) and neonatal seizure. Model 1 was not adjusted for covariates. Model 2 was adjusted for maternal age, birthplace, previous cesarean delivery, previous preterm births, number of prenatal care visits, smoking before pregnancy, BMI, weight gain, infertility treatment, pregestational hypertension, gestational hypertension, eclampsia, chorioamnionitis, infection, previous delivery, induction of labor, use of steroids and antibiotics, infant birthweight, use of surfactant, and 5 min Apgar score. It is worth mentioning that for investigating the relationship between GDM and neonatal seizures, Model 2 was further adjusted for PGDM based on the original adjusted covariates. In addition, we conducted stratified analyses based on gestational age, birthweight, 5 min Apgar score, and maternal age to explore the potential disparities in the association between maternal diabetes and neonatal seizures. The relationship between maternal diabetes and neonatal seizures was presented using an odds ratio (OR) with a 95% confidence interval (CI). A score of P  < 0.05 was considered statistically significant. All statistical analyses were performed using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA) and R studio (version 4.2.1).

Baseline characteristics

The baseline characteristics of the case and control groups, both pre- and post-PSM, are given in Table 1 . After PSM, a total of 33,370 participants were selected for this study with 6,674 in the case group (neonatal seizure group) and 26,696 in the control group (non-neonatal seizure group). There were significant differences in some characteristics between the case and the control groups, such as maternal age, birthplace, previous cesarean delivery, previous preterm births, number of prenatal care visits, smoking before pregnancy, BMI, weight gain, infertility treatment, PGDM, pregestational hypertension, gestational hypertension, eclampsia, chorioamnionitis, infection, previous delivery, induction of labor, use of steroids and antibiotics, birthweight (g), use of surfactant, and 5 min Apgar score ( P  < 0.05). These may be potential covariates in this study.

Baseline characteristics of the case and control groups before and after PSM.

PSM, propensity-score matching; BMI, body mass index; PGDM, pregestational diabetes mellitus; GDM, gestational diabetes mellitus; SD, standard deviation; M ( Q 1, Q 3), median and interquartile range.

The association of maternal diabetes and neonatal seizures

Table 2 shows an association between maternal diabetes and neonatal seizures. In unadjusted analysis, PGDM was found to be a risk factor for neonatal seizures (OR = 2.27, 95% CI: 1.88–2.73, P  < 0.001). After adjusting for all covariates, PGDM was still associated with an increased risk of neonatal seizures (OR = 1.51, 95% CI: 1.15–1.98, P  = 0.003). In addition, we observed that, after adjusting for covariates, the association between GDM and neonatal seizure was not statistically significant ( P  = 0.405).

The association of maternal diabetes and neonatal seizures.

PGDM, pregestational diabetes mellitus; GDM, gestational diabetes mellitus; OR, odds ratio; CI, confidence interval.

Model 1: not adjusted for covariates;

Subgroup analysis based on gestational age, birthweight, 5 min Apgar score, and maternal age

The results of stratified analyses based on gestational age, birthweight, 5 min Apgar score, and maternal age are displayed in Table 3 . A correlation between PGDM and increased risk of neonatal seizures was observed for neonates with a gestational age of 37–42 weeks (Model 2: OR = 1.82, 95% CI: 1.24–2.66, P  = 0.002) and ≥ 42 weeks (Model 2: OR = 4.97, 95% CI: 1.35–18.34, P  = 0.016). There was no statistically significant association between PGDM or GDM and neonatal seizures in neonates of varying birthweights. Among neonates possessing different Apgar scores, PGDM was a risk factor for neonatal seizure only when the Apgar score was ≥7 (Model 2: OR = 1.65, 95% CI: 1.24–2.18, P  < 0.001). Furthermore, we found that PGDM was associated with an increased risk of neonatal seizures among maternal age ≤40 (Model 2: OR = 1.52, 95% CI: 1.16–2.01, P  = 0.003). Figure 1 also depicts the incidence of neonatal seizures and the OR with a 95% CI based on gestational age, birthweight, Apgar score, and maternal age.

Stratified analyses based on gestational age, birthweight, Apgar score, and maternal age.

Model I adjusted for birthplace, previous cesarean delivery, previous preterm births, number of prenatal care visits, smoking before pregnancy, body mass index (BMI), weight gain, infertility treatment, pregestational hypertension, gestational hypertension, eclampsia, chorioamnionitis, infection, previous delivery, induction of labor, use of steroids and antibiotics, infant birthweight, use of surfactant, and 5 min Apgar score.

Model II adjusted for gestational age, maternal age, birthplace, previous cesarean delivery, previous preterm births, number of prenatal care visits, smoking before pregnancy, BMI, weight gain, infertility treatment, pregestational hypertension, gestational hypertension, eclampsia, chorioamnionitis, infection, previous delivery, induction of labor, use of steroids and antibiotics, infant birthweight, use of surfactant, and 5 min Apgar score.

For stratified analysis of gestational age, Model II was not adjusted for gestational age.

For stratified analysis of birthweight, Model I and Model II were not adjusted for infant birthweight.

For stratified analysis of the 5 min Apgar score, Model I and Model II were not adjusted for the 5 min Apgar score.

For stratified analysis of maternal age, Model II was not adjusted for maternal age.

In the stratified analysis of the relationship between GDM and neonatal seizures, Model I and Model II were adjusted for PGDM.

The incidence of neonatal seizure and the 95% CI of OR based on gestational age ( A ), birthweight ( B ), Apgar score ( C ), and maternal age ( D ).

This nested case–control study analyzed the representative NVSS database to investigate the relationship between maternal diabetes and neonatal seizures. The findings indicated that PGDM was associated with an elevated risk of neonatal seizures.

The risk of neonatal seizures was the highest during the first month after birth ( 16 ), suggesting that multiple obstetric risk factors may influence their occurrence ( 17 , 18 ). Previous studies have assessed the impact of maternal diabetes on neonatal seizures ( 6 , 7 ). However, the conclusions regarding the relationship between maternal diabetes and neonatal seizures are controversial. In recent years, the PSM statistical method has gained widespread popularity because of its ability to balance differences between groups and reduce the influence of confounding variables ( 19 , 20 ). In this study, we included 6,674 cases of neonatal seizures and 26,696 controls by using the PSM method. After performing PSM, a significantly higher proportion of mothers diagnosed with PGDM were observed in the neonatal seizure group compared with those in the non-neonatal seizure group ( P  < 0.001). After adjusting for all covariates, PGDM was considered to be related to an increased risk of neonatal seizures (OR = 1.51, 95% CI: 1.15–1.98), which was in keeping with previous studies ( 6 , 8 ). This study also found that PGDM was a risk factor for neonatal seizures for neonates with a gestational age of ≥37 weeks. This finding is inconsistent with the result reported by Tse et al. ( 6 ). This may be attributed to the presence of different research subjects. In addition, preterm birth was associated with an elevated risk of neonatal seizures ( 21 ). The impact of preterm birth on the risk of neonatal seizures may be greater than that of PGDM in this study, thus rendering the association between PGDM and neonatal seizures insignificant within the preterm population. Similarly, this study found a significant association between PDGM and neonatal seizures for neonates with an Apgar score of ≥7 and a maternal age of ≤40 years. A low 5 min Apgar score and advanced maternal age were considered perinatal risk factors for infantile seizures ( 22 , 23 ). We speculated that neonatal seizure risk may be more influenced by a low Apgar score (<7) and gestational age (>40 years), potentially obscuring the impact of PGDM. Additional samples will be collected from our hospitals in the future to further validate this finding and investigate the potential underlying mechanisms. It is noteworthy that birthplace was a confounding factor in this study. A retrospective cohort study has demonstrated that neonates who are delivered outside of a hospital setting have an increased risk of experiencing seizures when compared with those who are delivered in-hospital, as planned ( 24 ).

To our knowledge, the main causes of neonatal seizures are hypoglycemia, ischemia, structural lesions or abnormalities, and infections ( 9 , 25 ). Notably, hypoglycemia frequently occurs in newborns born to mothers with PGDM ( 26 ). In this study, we found no statistically significant association between GDM and neonatal seizures. It has been widely reported that PGDM is associated with a higher risk of adverse pregnancy outcomes compared with GDM ( 27 , 28 ). Given the critical period of organogenesis during early pregnancy, prolonged exposure to prepregnancy hyperglycemia and intrauterine hyperglycemia may elevate the likelihood of neonatal seizures ( 29 , 30 ). The underlying mechanism regarding the association of PGDM, GDM, and neonatal seizure risk remains unclear. More research is needed in the future to clarify the mechanisms by which PGDM affects neonatal seizure risk.

Some limitations of this study need to be taken into account. First, this study was retrospective in nature, which may have led to selection bias. However, we employed the PSM statistical method to mitigate confounding effects and enhance the reliability of our findings. Second, our investigation solely focuses on the correlation between maternal diabetes and neonatal seizures during hospitalization, with no knowledge of seizure occurrence after discharge; moreover, the observation period in this study was less than 1 month. These factors may result in an underestimation of neonatal seizure incidence. Third, the study recruited participants from the NVSS database, which did not contain records of prenatal brain ultrasound magnetic resonance imaging, electroencephalography or amplitude-integrated electroencephalography, glycosylated hemoglobin during pregnancy information, and the Score for Neonatal Acute Physiology II with Perinatal Extension at admission. Last, it should be noted that the NVSS database lacks information on the specific type of PGDM, thus necessitating further investigation into the association between type 1 and type 2 diabetes and neonatal seizures.

In short, this study revealed an association between PGDM and an elevated risk of neonatal seizures. Therefore, it is recommended that neonatologists closely monitor the incidence of seizures in newborns born to mothers with PGDM.

Data availability statement

Ethics statement.

Ethical approval was not provided for this study on human participants because of the use of deidentified data. The patients/ participants provided their written informed consent to participate in the studies to NVSS, who collected the patient data.

Author contributions

YL designed the study and wrote the manuscript. JL and XL collected, analyzed, and interpreted the data. YL critically reviewed, edited, and approved the manuscript. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Clinical pearls, case study: complicated gestational diabetes results in emergency delivery.

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Ginny Lewis; Case Study: Complicated Gestational Diabetes Results in Emergency Delivery. Clin Diabetes 1 January 2001; 19 (1): 25–26. https://doi.org/10.2337/diaclin.19.1.25

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A.R. is a 33-year-old caucasian woman initially diagnosed with diabetes during a recent pregnancy. The routine glucose challenge test performed between 28 and 29 weeks gestation was elevated at 662 mg/dl. A random glucose completed 1–2 days later was also elevated at 500 mg/dl. A follow-up HbA 1c was elevated at 11.6%. Additional symptoms included a 23-lb weight loss over the past 3–4 weeks with ongoing “flu-like” symptoms, including fatigue, nausea, polyuria, and polydypsia.

A.R. had contacted her obstetrician’s office when her symptoms first appeared and was told to contact her primary care provider for the “flu” symptoms. She had called a nurse triage line several times over the previous 2–3 weeks with ongoing symptoms and was told to rest and take fluids.

She presented to her primary care provider 3 days after the HbA 1c was drawn for ongoing evaluation of hyperglycemia. At that time, she was symptomatic for diabetic ketoacidosis. She was hospitalized and started on an insulin drip.

A.R.’s hospitalization was further complicated with gram-negative sepsis, adult respiratory distress syndrome, and Crohn’s disease with a new rectovaginal fistula. She was intubated as her respiratory status continued to decline and was transferred to a tertiary medical center for ongoing management. She required an emergency Caesarian section at 30 1/7 weeks gestation due to increased fetal distress.

A.R. had no family history of diabetes with the exception of one sister who had been diagnosed with gestational diabetes. Her medical history was significant for Crohn’s disease diagnosed in 1998 with no reoccurrence until this hospitalization. Her pre-pregnancy weight was 114–120 lb. She had gained 25 lb during her pregnancy and lost 23 lb just before diagnosis.

A.R.’s blood glucose levels improved postpartum, and the insulin drip was gradually discontinued. She was discharged on no medications.

At her 2-week postpartum visit, home blood glucose monitoring indicated that values were ranging from 72 to 328 mg/dl, with the majority of values in the 200–300 mg/dl range. A repeat HbA 1c was 8.7%. She was restarted on insulin.

1.  What is the differential diagnosis of gestational diabetes versus type 1 diabetes?

2.  At what point during pregnancy should insulin therapy be instituted for blood glucose control?

3.  How can communication systems be changed to provide for integration of information between multiple providers?

Gestational diabetes is defined as “any degree of carbohydrate intolerance with onset first recognized during pregnancy. This definition applies whether insulin ... is used for treatment and whether or not the condition persists after pregnancy.” 1 Risk assessment is done early in the pregnancy, with average-risk women being tested at 24–28 weeks’ gestation and low-risk women requiring no additional testing. 1 , 2 A.R. met the criteria for average risk based on age and a first-degree family member with a history of gestational diabetes.

Screening criteria for diagnosing diabetes include 1 ) symptoms of diabetes plus casual plasma glucose >200 mg/dl (11.1 mmol/l), or   2 ) fasting plasma glucose >126 mg/dl (7.0 mmol/l), or   3 ) 2-h plasma glucose >200 mg/dl (11.1 mmol/l) during an oral glucose tolerance test (OGTT). 3 For women who do not meet the first two criteria, a glucose challenge test (GCT) measuring a 1-h plasma glucose following a 50-g oral glucose load is acceptable. For those women who fail the initial screen, practitioners can then proceed with the OGTT. 1  

In A.R.’s case, she most likely would have met the first criterion if a casual blood glucose had been measured. She had classic symptoms with weight loss, fatigue, polyuria, and polydypsia. Her 1-h plasma glucose following the glucose challenge was >600 mg/dl, which suggests that her casual glucose would also have been quite high.

Medical nutrition therapy (MNT) is certainly a major part of diabetes management. However, with this degree of hyperglycemia, MNT would not be adequate as monotherapy. Treatment for gestational diabetes includes the use of insulin if fasting blood glucose levels are >95 mg/dl (5.3 mmol/l) or 2-h postprandial values are >120 mg/dl (6.7 mmol/l). 1  

Several days passed from the time of A.R.’s initial elevated blood glucose value and the initiation of insulin therapy. While HbA 1c values cannot be used for diagnostic purposes, in this case they further confirmed the significant degree of hyperglycemia.

Plasma blood glucose values initially improved in the immediate postpartum period. A.R. was sent home without medications but instructed to continue home glucose monitoring.

At her 2-week postpartum visit, whole blood glucose values were again indicating progressive hyperglycemia, and insulin was restarted. A.R.’s postpartum weight was 104 lb—well below her usual pre-pregnancy weight of 114–120 lb. Based on her ethnic background, weight loss, abrupt presentation with classic diabetes symptoms, and limited family history, she was reclassified as having type 1 diabetes.

In immune-mediated, or type 1, diabetes, b-cell destruction can be variable, with a slower destruction sometimes seen in adults. 3 Presentation of type 1 diabetes can also vary with modest fasting hyperglycemia that can quickly change to severe hyperglycemia and/or ketoacidosis in the presence of infection or other stress. 3 A.R. may have had mild hyperglycemia pre-pregnancy that increased in severity as the pregnancy progressed.

The final issue is communication among multiple health care providers. A.R. was part of a system that uses primary care providers, specialists, and triage nurses. She accessed all of these providers as instructed. However, the information did not seem to be clearly communicated among these different types of providers. A.R. called triage nurses several times with her concerns of increased fatigue, nausea, and weight loss. The specialist performed her glucose challenge with follow-up through the primary care office. It seems that if all of these providers had the full information about this case, the diagnosis could have been made more easily, and insulin could have been initiated more quickly.

1.  Hyperglycemia diagnosed during pregnancy is considered to be gestational diabetes until it is reclassified in the postpartum period. Immune-mediated diabetes can cause mild hyperglycemia that is intensified with the increased counterregulatory hormone response during pregnancy.

2.  Insulin therapy needs to be instituted quickly for cases in which MNT alone is inadequate.

3.  The GCT is an appropriate screening test for an average-risk woman with no symptoms of diabetes. In the face of classic symptoms of diabetes, a casual plasma glucose test can eliminate the need for the glucose challenge.

4.  As part of the health care industry, we need to continue to work on information systems to track patient data and share data among multiple providers. Patients can become lost in an ever-expanding system that relies on “protocols” and does not always allow for individual differences or for cases with unusual presentation.

Ginny Lewis, ARNP, FNP, CDE, is a nurse practitioner at the Diabetes Care Center of the University of Washington School of Medicine in Seattle.

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• Published: 20 November 2023

Associations of early pregnancy serum uric acid levels with risk of gestational diabetes and birth outcomes: a retrospective cohort study

• Ting-Ting Pang 1 ,
• Zi-Xing Zhou 2 ,
• Peng-Sheng Li 2 ,
• Hui-Ting Ma 2 ,
• Xiu-Yin Shen 2 ,
• Ying-Chun Wan 2 ,
• Xiao-Ling Guo 2 ,
• Zheng-Ping Liu 2 &
• Geng-Dong Chen 2  

BMC Endocrine Disorders volume  23 , Article number:  252 ( 2023 ) Cite this article

Metrics details

Previous evidence suggests that higher blood uric acid (UA) levels are associated with adverse cardiovascular outcomes during pregnancy and subsequent birth outcomes. However, it has been relatively unclear whether these associations persist in normotensive pregnant women.

The study was based on a retrospective analysis of 18,250 mother-infant pairs in a large obstetric center in China. Serum UA concentrations in early pregnancy (median: 17.6, IQR: 16.3, 18.6 gestational weeks) were assessed. Hyperuricemia was defined as ≥ one standard deviation (SD) of the reference value for the corresponding gestational age. Outcomes of gestational diabetes mellitus (GDM), preterm birth (PB), low birth weight (LBW), macrosomia, small for gestational age (SGA) and large for gestational age (LGA) were extracted from the medical records.

The mean maternal UA level was 0.22 ± 0.05 mmol/L, and 2,896 (15.9%) subjects had hyperuricemia. After adjustment for several covariates, UA was associated with several adverse outcomes. The ORs (95%CI) per one SD increase in serum UA concentration were 1.250 (1.136, 1.277) for GDM, 1.137 (1.060, 1.221) for PB, 1.134 (1.051, 1.223) for LBW, and 1.077 (1.020, 1.137) for SGA, respectively. Similar adverse associations were found between hyperuricemia and GDM, PB (ORs: 1.394 and 1.385, P < 0.001), but not for LBW, macrosomia, SGA, and LGA. Adverse associations tended to be more pronounced in subjects with higher BMI for outcomes including PB, LBW, and SGA (P interaction = 0.001–0.028).

Higher UA levels in early pregnancy were associated with higher risk of GDM, PB, LBW, and SGA in normotensive Chinese women.

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Uric acid (UA) is one of the major factors that were associated with an increased risk of cardiovascular disease [ 1 , 2 ]. Evidence suggests that higher UA levels also contribute to other chronic diseases such as type 2 diabetes mellitus [ 3 ], metabolic syndrome [ 4 ], and non-alcoholic fatty liver disease [ 5 ]. During pregnancy, higher UA levels are known to be associated with an increased risk of gestational hypertension, pre-eclampsia/eclampsia [ 6 , 7 , 8 ], and subsequent adverse birth outcomes [ 9 ]. However, the associations between UA and other complications, such as gestational diabetes mellitus (GDM) and adverse birth outcomes, have been relatively unclear in normotensive subjects. In addition, previous studies in this area have produced inconsistent results. Several studies have suggested that hyperuricemia is associated with an increased risk of GDM [ 10 , 11 , 12 , 13 ] or adverse birth outcomes [ 14 , 15 , 16 ]. One study showed that both high and low UA levels contribute to adverse fetal growth [ 17 ]. Marginal or null associations with these outcomes have been reported for UA in several other studies [ 18 , 19 , 20 , 21 , 22 ]. The small sample size of most studies, racial heterogeneity, and different trimesters studied may partially explain the heterogeneity of these findings. However, it should be recognized that gestational cardiovascular complications (hypertension, pre-eclampsia, eclampsia) share many common mechanisms with GDM, and independently lead to adverse birth outcomes. Therefore, without eliminating the influence of these patients, the associations of UA with other outcomes in the general population may be overestimated. In addition, with the potential function of antioxidant, UA has also been suggested to be beneficial for degenerative diseases such as osteoporosis [ 23 ], and showed U-shaped associations for several diseases [ 24 , 25 , 26 ]. A U-shaped rather than linear association of UA with fetal growth has been reported [ 17 ]. The functional forms of UA with pregnancy outcomes have been inconsistent or poorly investigated in other studies and warrant further investigation.

Therefore, we aimed to perform a retrospective analysis based on medical record data from a large obstetric center in southern China to better illustrate these problems.

The study was based on retrospective data from the Southern Medical University Affiliated Maternal & Child Health Hospital of Foshan from January 1, 2012 to July 31, 2018. The hospital is the largest obstetric center in Foshan City, Guangdong Province, China, covering a large population of more than 7.7 million people. Singleton pregnant women were included in the analyses if they had serum UA measured at antenatal care visits during the first 20 weeks of pregnancy and delivered a live birth at the hospital. A total of 27,582 subjects provided matching information on exposures and outcomes during the same pregnancy. Subjects were further excluded if they met the following criteria: (a) with UA concentrations detected after 20 weeks’ gestation (7,107 subjects); (b) a history of serious medical conditions: including gestational hypertension/pre-eclampsia/eclampsia (608 subjects), type 1 or type 2 diabetes (47 subjects), malignancy (15 subjects), and thyroid dysfunction (836 subjects); (c) missing core data (407 subjects) or suspicious outliers (> 3SD or <-3 SD, 312 subjects). Finally, a total of 18,250 mother-infant pairs were included in the analyses (Fig.  1 ).

Flow characteristic of the study

Data collection

Serum UA concentration data were obtained from clinical laboratory records or medical records. Blood samples were collected during routine obstetric examinations and measured immediately by the clinical laboratory at the hospital without freezing. UA was detected by the uric acid enzyme colorimetric method using an automated biochemical analyzer (AU5800, Beckman Coulter, Inc, USA). Commercial kits were purchased from Ningbo Ruiyuan Biotechnology Co., Ltd, China. The intra- and inter-assay coefficients of variation were less than 4% and 15%, respectively. To reduce the possibility of influence from pregnancy complications, we restricted the data to UA measured before the first 20 weeks of gestation. Hyperuricemia in early pregnancy is defined as serum UA concentrations ≥ 1 SD of reference values [ 27 ] at the gestational ages (6 ~ 12, > 12 ~ 13, > 13 ~ 14, > 14 ~ 15, > 15 ~ 16, > 16 ~ 17, > 17 ~ 18, > 18 ~ 19, and > 19 ~ 20 gestational weeks) in our study, as shown in Supplemental Table  1 .

Information including maternal age, body mass index (BMI), gestational week of UA measurement, parity, time of last menstrual period and delivery, history of related diseases were obtained from medical records. The outcomes of GDM, PB, LBW, and macrosomia were diagnosed by professional obstetricians with the same criteria, and extracted from medical records. GDM was assessed by the oral glucose tolerance test at 24 to 28 weeks’ gestation, and diagnosed if subjects met any items of the following criteria: fasting blood glucose ≥ 5.1 mmol/L; one-hour blood glucose after oral glucose ingestion ≥ 10.0 mmol/L; two-hour blood glucose after oral glucose ingestion ≥ 8.5 mmol/L. PB was defined as delivery at ≥ 28 and < 37 weeks; gestation. Neonatal birth weight < 2500 g was defined as LBW, and ≥ 4000 g was defined as macrosomia. Based on the latest criteria (WS/T 800—2022) promulgate by the National Health Commission of the People’s Republic of China [ 28 ], appropriate for gestational age (AGA) was defined as neonates whose birth weights were between 10th to 90th percentile by gestational age. Small for gestational age (SGA) and (LGA) was defined as neonates whose birth weights were < 10th percentile and > 90th percentile by gestational age, respectively.

Statistical analyses

Continuous variables were presented as mean ± standard deviation (SD) or median (interquartile range) and tested by Student’s t-test or non-parametric test. Categorical variables were presented as frequencies (percentages) and tested by chi-squared test. Logistic regression analyses were performed to examine the associations between UA and outcomes, including GDM, PB, LBW, macrosomia, SGA, and LGA. UA was analyzed as both a categorical (hyperuricemia versus normal) and continuous variable (per one SD increase). Two different analysis models were performed, model 1 as a univariate model without any adjustment and model 2 adjusted for maternal age, BMI, parity, and gestational week of UA measurement. Stratified analyses were performed for different groups of maternal age (< 35 or ≥ 35 years), BMI (< 24, overweight: ≥24 and < 28, obesity: ≥28 kg/m 2 ) [ 29 ], parity (0 or ≥ 1), gestational week of UA measurement (< 17 or ≥ 17 weeks). Analyses were performed with SPSS software (version 21.0, Chicago, IL, USA). Generalized additive regression models (GAMs) were used to explore the functional forms of the association between UA and related outcomes and were performed using the R software (version 4.3.1, Vienna, Australia). A two-sided P < 0.05 was considered as statistically significant.

A total of 18,250 pregnant women aged 29.2 ± 4.53 years were included in the analyses, of whom 2,896 (15.9%) had hyperuricemia. As shown in Table  1 , maternal serum UA concentrations were measured at a median of 17.6 (IQR: 16.3, 18.6) weeks, with a mean value of 0.22 ± 0.05 mmol/L. The incidence was highest for LGA (13.2%), followed by SGA (9.2%), GDM (7.6%), PB (5.0%), LBW (4.4%), and lowest for macrosomia (2.4%). Subjects with hyperuricemia tended to have higher BMI (26.6 vs. 25.8 kg/m 2 , P < 0.001) and serum UA concentration (0.30 vs. 0.21 mmol/L, P < 0.001); higher incidence of LGA (15.0% vs. 12.9%, P = 0.007), GDM (9.8% vs. 7.2%, P < 0.001), CS (45.8% vs. 42.7%, P = 0.002), and preterm birth (6.1% vs. 4.8%, P = 0.004); and have a lower delivery gestational age (38.7 vs. 38.9 weeks’ gestation, P < 0.001).

After adjustment for several potential covariates, higher UA levels in early pregnancy were associated with several adverse outcomes (Table  2 ). The ORs (95%CI) per one SD increase in UA levels were 1.205 (1.136, 1.277) for GDM, 1.137 (1.060, 1.221) for PB, 1.134 (1.051, 1.223) for LBW, and 1.077 (1.020, 1.137) for SGA, respectively. Similarly, hyperuricemia (vs. normal) was associated with a 39.4% (OR: 1.394, 95%CI: 1.211–1.606) higher risk of GDM, and a 38.5% (OR:1.385, 95%CI: 1.168–1.643) higher risk of PB, but not with LBW, macrosomia, SGA, and LGA. In the analysis of GAMs, when the equivalent degrees of freedom (edf) value was closer to 1.0, the relationship between uric acid and outcome tended to be linear. When the edf value was further from 1.0, the relationship between uric acid and outcome tended to be non-linear. A p-value of less than 0.05 indicates a statistical association between uric acid and outcome in the GAMs model. Serum UA levels showed linear relationships with GDM (edf = 1.11, P < 0.001), PB (edf = 1.00, P < 0.001), LBW (edf = 1.00, P = 0.002), and SGA (edf = 1.00, P = 0.007) based on GAMs (Fig.  2 ). The association between UA and LGA tended to be non-linear (edf = 2.62, P = 0.008). No significant associations were found between UA and macrosomia (edf = 1.00, P = 0.115).

The relationships between early pregnant serum uric acid levels and outcomes of gestational diabetes mellitus ( A ), preterm birth ( B ), low birth weight ( C ), macrosomia ( D ), small for gestational age ( E ), and large for gestational age ( F ) based on the generalised additive regression models (n = 18,250). The covariates were maternal age, BMI, parity, and gestational weeks of uric acid measurement. Dotted lines represented the 95% confidence intervals. When the equivalent degrees of freedom (edf) value was close to 1.0, the relationship between uric acid and outcome tended to be linear. When the edf value was further from 1.0, the relationship between uric acid and outcome tended to be non-linear. A-p value of less than 0.05 indicates a statistical association between uric acid and outcome in the GAMs model

No significant interactions were observed between higher serum UA levels (both hyperuricemia or per one SD increase in UA concentration) and the gestational age of UA measurement on the risk of adverse outcome, as shown in Table  3 . As shown in Table  4 , significant interactions were observed between higher serum UA levels (per one SD increase in UA concentration) and the BMI groups (< 24, ≥ 24 and < 28, ≥28 kg/m 2 ) on the risk of PB (P interaction = 0.001), and LBW (P interaction = 0.008). Significant interactions were observed between hyperuricemia and the BMI groups (< 24, ≥ 24 and < 28, ≥28 kg/m 2 ) on the risk of SGA (P interaction = 0.028). No significant interactions were found between higher serum UA levels and age (< 35 and ≥ 35 years) or parity (0 and ≥ 1) on the risk of all outcomes, and the p-values of the interaction ranged from 0.097 to 0.826 and from 0.133 to 0.986, respectively (data not shown).

In this study based on retrospective data from an 18,250 mother-infants cohort in southern China, we observed that higher serum UA (both as a categorical and continuous variable) in early pregnancy was associated with higher risk of GDM, PB, LBW, and SGA. The dose-response models tended to be linear for most outcomes. Results tended to be more pronounced in subjects with higher BMI for the outcomes of PB, LBW, and SGA, with significant interactions being found for these associations.

The prevalence of hyperuricemia in our study was 15.9% (subjects), which was relatively higher than that of nonpregnant Chinese adults in a study of 5,939 subjects (14.1%) [ 30 ] and a study of 11,601 subjects (11.15%) in Shanghai City [ 31 ]. Serum UA concentrations increased during pregnancy and are affected by gestational age, which may partly explain why the prevalence of hyperuricemia was higher than that in nonpregnant subjects, although we tried to adjust hyperuricemia for gestational age. However, the prevalence of hyperuricemia in our study was relatively lower when compared with another study of 404 normotensive singleton women (103 subjects, 25.4%) [ 15 ]. The prevalence of hyperuricemia was barely mentioned in the previous study, moreover, hyperuricemia was common defined as ≥ 1 SD values at corresponding age, was sample-based, and UA was measured at different trimesters. These increased the difficulty in comparing the prevalence of hyperuricemia among different populations, and these issues should be further considered in the future.

The positive associations found in our study were consistent with several previous studies. Higher UA levels (or hyperuricemia) in the first 20 weeks of pregnancy were associated with a 2.34- and 1.11- fold increased risk of GDM in 1000 Chinese [ 11 ] and 5507 Israeli women [ 12 ], respectively. Similar results have been observed in a retrospective cohort study of 23,843 Chinese with UA measured before 24 weeks of gestation [ 32 ] and other studies [ 10 , 13 ]. However, several studies also reported marginal or null associations between UA and GDM [ 18 , 20 , 22 ]. Adverse associations with PB, SGA, or BW were also found in several studies with small sample size of less than 500 [ 14 , 15 , 16 ], which were consistent with ours. In a retrospective study of 11,580 Chinese, higher UA in late pregnancy (the corresponding gestational weeks at delivery) was associated with a higher risk of LBW, SGA, but also associated with a lower risk of PB [ 33 ]. Marginal or null significant results were found in a cross-sectional study of 885 Germans [ 19 ] and in a prospective cohort of 1541 Americans [ 21 ]. In another prospective cohort of 1291 Americans, both too high and too low UA levels were detrimental to fetal growth [ 17 ]. The UA measured in different trimesters or periods of pregnancy may partly explain the heterogeneity of these results, as UA levels tend to be influenced by the course of pregnancy [ 27 ]. In our study, linear rather than U-shaped relationships were observed for UA and most gestational or birth outcomes. Highlighting the associations with adverse outcomes of UA in early pregnancy instead of late pregnancy, may provide a better opportunity for pregnant women to improve their UA status and possibly partially prevent the occurrence of adverse outcomes. In addition, the longer time between UA measurement (early versus late pregnancy) and outcomes increases the ability to avoid the possibility of causal inversion. Our study included a large number of subjects, which may reduce the possibility of false-negative results that may have occurred in previous studies with much smaller sample sizes. Our results, along with several, but not all, studies, underscore the importance of UA in early pregnancy as an indicator of GDM and adverse birth outcomes, even in normotensive subjects.

Several mechanisms may contribute to the adverse associations of UA. Higher UA might affect GDM by increasing the risk of insulin resistance during pregnancy [ 34 ], through several potential pathways, including endothelial dysfunction [ 35 ], decreased production of nitric oxide [ 36 , 37 ], and induction of metabolic inflammation [ 38 ]. Apart from the mechanisms mentioned above, elevated UA might inhibit amino acid uptake in the placenta [ 39 ], and attenuate trophoblast invasion and integration into endothelial cell monolayers [ 40 ], leading to poor placental development. Most of these mechanisms have also been implicated in the development of adverse gestational cardiovascular complications. Because adverse associations were observed in pregnant women without the occurrence of cardiovascular complications, other potential mechanisms may exist, and need to be well characterized by further studies.

More pronounced adverse associations were found in subjects with higher BMI for outcomes of PB, LBW, and SGA. Indeed, obesity is one of the most important pathways for adverse outcomes [ 41 ]. Moreover, obesity also leads to higher UA levels [ 42 , 43 ], although we tried to attenuate its influence by adjusting for BMI. Our results suggest that clinicians should make more precise considerations for subjects with both high UA levels and obesity in early pregnancy.

Our study has several strengths. The study was based on a large sample of 18,250 subjects, which provided more precise estimates of the associations. Serum UA data were measured and collected in early pregnancy, which established the temporal sequence of the observed associations. We examined the functional forms of the dose-response associations, and potential interactions from multiple variables, including age, BMI, UA measurement times, and parity. Our results provided further evidence for obstetric clinicians to identify higher UA levels and potential high-risk patients.

There are also several limitations to the study that deserve careful consideration. First, serum levels of UA were measured and recorded only once during the pregnancy in subjects without gestational cardiovascular complications, so we were unable to analyze the dynamic course of UA and its influence on outcomes during the early pregnancy. Second, the study was based on data from an obstetric center instead of a community, which might have weakened the representativeness of the sample, but this could partly be counteracted because the obstetric center was the largest one in the areas and received patients from different regions of the city. In addition, it would be of very difficult to collect medical record information from the community for a retrospective study. Third, although we adjusted for several potential covariates, there may still be residual confounding.

Conclusions

In conclusion, this retrospective cohort study found that higher UA levels in early pregnancy were associated with higher risk of GDM, PB, LBW, and SGA in normotensive Chinese women.

Data availability

The data that support the findings of this study are available from the Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding author upon reasonable request and with permission of Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University.

Abbreviations

Gestational diabetes mellitus

• Preterm birth

Low birth weight

Small for gestational age

Large for gestational age

Body mass index

Standard deviation

Confidence interval

Generalised additive regression models

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Acknowledgements

We appreciated the support from the “Deng Feng Project” of Government of Foshan City. We appreciated the generous help of our colleagues including: Hao-jing Liu, Zheng-yuan Ren, Shao-bing Huang, Dong Wang, Qing Yu, and Jin-ping Feng.

This work was supported by National Natural Science Foundation of China (No.82103855, G.D.C.,), Basic and Applied Basic Research Foundation of Guangdong Province (No.2019A1515110163, G.D.C.,) and the Foundation of Bureau of Science and Technology of Foshan City (No.2220001004104, G.D.C.,). The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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Zi-Xing Zhou, Peng-Sheng Li, Hui-Ting Ma, Xiu-Yin Shen, Ying-Chun Wan, Xiao-Ling Guo, Zheng-Ping Liu & Geng-Dong Chen

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GDC and ZPL came up with the idea and designed the study; TTP, and ZXZ contribute to the primary data collection; GDC, PSL, HTM, XYS, YCW, contribute to the analyses of data; TTP wrote the original draft, which was revised by XLG, GDC and ZPL; GDC and ZPL supervised the study. GDC and ZPL administrate the project; All authors approved the final manuscripts.

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Ethical approval was waived by the Ethics Committee of Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University in view of the retrospective nature of the study and all the procedures being performed were part of the routine care. The Affiliated Foshan Maternity & Child Healthcare Hospital provided administrative permissions for the research team to access and use the data included in this research. Data were extracted from medical records, and the consent to participate was unavailable due to the retrospective design of the study and difficulty in reconnection. The requirement for informed consent was waived by the Ethics Committee of Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University because of the retrospective nature of the study. The private information was well protected. All the experiment protocols for involving human in this study were conducted in accordance with the Declaration of Helsinki.

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. Distribution of serum uric acid concentrations (mmol/L) of different gestational age.

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Pang, TT., Zhou, ZX., Li, PS. et al. Associations of early pregnancy serum uric acid levels with risk of gestational diabetes and birth outcomes: a retrospective cohort study. BMC Endocr Disord 23 , 252 (2023). https://doi.org/10.1186/s12902-023-01502-3

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Does gestational diabetes mellitus increase the risk of cardiovascular disease? A Mendelian randomization study

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In recent years, epidemiological studies have revealed the relationship between gestational diabetes mellitus (GDM) and cardiovascular disease (CVD). In this study, we utilized Mendelian randomization (MR) to investigate the potential causal impact of GDM on cardiovascular disease for the first time.

 We retrieved summary statistics from published genome-wide association studies. MR was first performed using significant SNPs extracted from the eighth data release of the FinnGen study. Next, a replication analysis for coronary artery disease (CAD) was conducted in another European ancestry population to validate our findings. Finally, mediation analysis was carried out to assess potential mediation effects.

Our data analysis revealed that genetically predicted GDM was significantly associated with increased CAD risk (OR 1.10, 95% CI 1.02–1.18, p  0.006). Replication analysis confirmed a significant genetic association between GDM and CAD (OR 1.07, 95% CI 1.02–1.12, p  0.003) in another European ancestry population. Mediation analysis indicated no significant mediation effect by type 2 diabetes mellitus (T2DM) on the GDM–CAD relationship (mediation effect β [95% CI]: 0.005 [−0.003, −0.017]).

Women with a prior history of GDM face an elevated risk of future CAD. This increased risk of CAD cannot be solely attributed to the subsequent onset of diabetes. Regular CAD risk assessment and primary prevention strategies are of paramount importance for women with a history of GDM.

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Liang contributed to literature search and the design of the study. Liang analyzed and interpreted the data. Liang wrote the manuscript. Sun formatted the figures and tables, and revised the manuscript. Sun helped perform the analysis with constructive discussions. The final manuscript was approved by all the authors.

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Liang, W., Sun, F. Does gestational diabetes mellitus increase the risk of cardiovascular disease? A Mendelian randomization study. J Endocrinol Invest (2023). https://doi.org/10.1007/s40618-023-02233-x

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Case-Control Studies

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A Nested Case-Control Study

Retrospective and prospective case-control studies.

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Suppose a prospective cohort study were conducted among almost 90,000 women for the purpose of studying the determinants of cancer and cardiovascular disease. After enrollment, the women provide baseline information on a host of exposures, and they also provide baseline blood and urine samples that are frozen for possible future use. The women are then followed, and, after about eight years, the investigators want to test the hypothesis that past exposure to pesticides such as DDT is a risk factor for breast cancer. Eight years have passed since the beginning of the study, and 1.439 women in the cohort have developed breast cancer. Since they froze blood samples at baseline, they have the option of analyzing all of the blood samples in order to ascertain exposure to DDT at the beginning of the study before any cancers occurred. The problem is that there are almost 90,000 women and it would cost $20 to analyze each of the blood samples. If the investigators could have analyzed all 90,000 samples this is what they would have found the results in the table below.

Table of Breast Cancer Occurrence Among Women With or Without DDT Exposure

While 1,439 breast cancers is a disturbing number, it is only 1.6% of the entire cohort, so the outcome is relatively rare, and it is costing a lot of money to analyze the blood specimens obtained from all of the non-diseased women. There is, however, another more efficient alternative, i.e., to use a case-control sampling strategy. One could analyze all of the blood samples from women who had developed breast cancer, but only a sample of the whole cohort in order to estimate the exposure distribution in the population that produced the cases.

If one were to analyze the blood samples of 2,878 of the non-diseased women (twice as many as the number of cases), one would obtain results that would look something like those in the next table.

Odds of Exposure: 360/1079 in the cases versus 432/2,446 in the non-diseased controls.

Totals Samples analyzed = 1,438+2,878 = 4,316

Total Cost = 4,316 x $20 = $86,320

With this approach a similar estimate of risk was obtained after analyzing blood samples from only a small sample of the entire population at a fraction of the cost with hardly any loss in precision. In essence, a case-control strategy was used, but it was conducted within the context of a prospective cohort study. This is referred to as a case-control study "nested" within a cohort study.

Rothman states that one should look upon all case-control studies as being "nested" within a cohort. In other words the cohort represents the source population that gave rise to the cases. With a case-control sampling strategy one simply takes a sample of the population in order to obtain an estimate of the exposure distribution within the population that gave rise to the cases. Obviously, this is a much more efficient design.

It is important to note that, unlike cohort studies, case-control studies do not follow subjects through time. Cases are enrolled at the time they develop disease and controls are enrolled at the same time. The exposure status of each is determined, but they are not followed into the future for further development of disease.

As with cohort studies, case-control studies can be prospective or retrospective. At the start of the study, all cases might have already occurred and then this would be a retrospective case-control study. Alternatively, none of the cases might have already occurred, and new cases will be enrolled prospectively. Epidemiologists generally prefer the prospective approach because it has fewer biases, but it is more expensive and sometimes not possible. When conducted prospectively, or when nested in a prospective cohort study, it is straightforward to select controls from the population at risk. However, in retrospective case-control studies, it can be difficult to select from the population at risk, and controls are then selected from those in the population who didn't develop disease. Using only the non-diseased to select controls as opposed to the whole population means the denominator is not really a measure of disease frequency, but when the disease is rare , the odds ratio using the non-diseased will be very similar to the estimate obtained when the entire population is used to sample for controls. This phenomenon is known as the r are-disease assumption . When case-control studies were first developed, most were conducted retrospectively, and it is sometimes assumed that the rare-disease assumption applies to all case-control studies. However, it actually only applies to those case-control studies in which controls are sampled only from the non-diseased rather than the whole population.  

The difference between sampling from the whole population and only the non-diseased is that the whole population contains people both with and without the disease of interest. This means that a sampling strategy that uses the whole population as its source must allow for the fact that people who develop the disease of interest can be selected as controls. Students often have a difficult time with this concept. It is helpful to remember that it seems natural that the population denominator includes people who develop the disease in a cohort study. If a case-control study is a more efficient way to obtain the information from a cohort study, then perhaps it is not so strange that the denominator in a case-control study also can include people who develop the disease. This topic is covered in more detail in EP813 Intermediate Epidemiology.

Students usually think of case-control studies as being only retrospective, since the investigators enroll subjects who have developed the outcome of interest. However, case-control studies, like cohort studies, can be either retrospective or prospective. In a prospective case-control study, the investigator still enrolls based on outcome status, but the investigator must wait to the cases to occur.

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Content ©2016. All Rights Reserved. Date last modified: June 7, 2016. Wayne W. LaMorte, MD, PhD, MPH

A nested case-control study of first-trimester maternal vitamin D status and risk for spontaneous preterm birth

Affiliation.

• 1 Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Gilling School of Global Pulic Health, University of North Carolina, Chapel Hill, North Carolina 27599-7516, USA. [email protected]
• PMID: 21500145
• PMCID: PMC4372898
• DOI: 10.1055/s-0031-1276731

We assessed if first-trimester vitamin D deficiency is more prevalent in women who experienced a spontaneous preterm birth compared with women who delivered at term. We conducted a nested case-control study of pregnant women who had previously given blood for first-trimester combined screening for trisomy 21 and subsequently delivered at a tertiary hospital between November 2004 and July 2009. From an overall cohort of 4225 women, 40 cases of spontaneous preterm birth (≥ 23 (0/7) and ≤ 34 (6/7) weeks) were matched by race/ethnicity with 120 women delivering at term (≥ 37 (0/7) weeks) with uncomplicated pregnancies. Banked maternal serum was used to measure maternal 25-hydroxyvitamin D [25(OH)D]. The prevalence of first-trimester maternal vitamin D deficiency [25(OH)D < 50 nmol/L] was comparable among women who subsequently delivered preterm compared with controls (7.5% versus 6.7%, P = 0.90). The median 25(OH)D level for all subjects was 89 nmol/L (interquartile range, 73 to 106 nmol/L). Seventy-three percent (117/160) of the cohort had sufficient vitamin D levels [25(OH)D ≥ 75 nmol/L]. In a cohort of pregnant women with mostly sufficient levels of first-trimester serum 25(OH)D, vitamin D deficiency was not associated with spontaneous preterm birth.

© Thieme Medical Publishers.

Publication types

• Research Support, Non-U.S. Gov't
• Case-Control Studies
• Logistic Models
• Pregnancy Complications / blood*
• Pregnancy Trimester, First / blood*
• Premature Birth / blood
• Premature Birth / etiology*
• Statistics, Nonparametric
• Vitamin D / analogs & derivatives
• Vitamin D / blood
• Vitamin D Deficiency / blood
• Vitamin D Deficiency / complications*
• 25-hydroxyvitamin D

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• P30 DK040561/DK/NIDDK NIH HHS/United States
• P30 DK040561-15/DK/NIDDK NIH HHS/United States
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Original research article, the association between maternal diabetes and neonatal seizures: a nested case–control study.

• Department of Pediatrics, Huizhou Central People's Hospital, Huizhou, China

Aim: We aimed to evaluate the association of pregestational diabetes mellitus (PGDM) and gestational diabetes mellitus (GDM) with neonatal seizures during neonatal hospitalization.

Methods: In this nested case–control study, all data were collected from the data files of the National Vital Statistics System (NVSS) 2016–2021. Considering the effect of confounders, we used the propensity-score matching (PSM; case:control = 1:4) method to select the study population. The outcome was considered the occurrence of neonatal seizures. Univariate and multivariate logistic regression analyses were adopted to assess the association of PGDM and GDM with neonatal seizures. We also conducted stratified analyses according to gestational age, birthweight, 5 min Apgar score, and maternal age to explore the potential disparities.

Results: After using the PSM method, a total of 6,674 cases of neonatal seizures and 26,696 controls were included. After adjusting for covariates, PGDM was associated with an increased risk of neonatal seizures [odds ratio (OR) = 1.51, 95% confidence interval (CI): 1.15–1.98], whereas the association between GDM and neonatal seizures is not statistically significant. In addition, the correlation between PGDM and increased risk of neonatal seizures was observed in neonates with a gestational age of 37–42 weeks and ≥42 weeks, with a 5 min Apgar score of ≥7, and with a maternal age of ≤40 years.

Conclusion: PGDM was found to be closely associated with an increased risk of neonatal seizures. The findings of our study indicated that neonatologists should consider monitoring the incidence of neonatal seizures in neonates born to mothers with PGDM.

Introduction

Neonatal seizures are the most common neurological condition in newborns, and, depending on their etiology, can lead to long-term outcomes such as epilepsy, cerebral palsy, developmental disabilities, and psychomotor impairments ( 1 , 2 ). The incidence of neonatal seizure is approximately 1.5–5.5 per 1,000 live births ( 2 ), which is considered a significant cause of neonatal mortality ( 3 ). Therefore, the identification of risk factors associated with neonatal seizures is crucial in reducing neurological morbidity and mortality among infants.

Previous studies have indicated that birth asphyxia may contribute to neonatal seizures and is associated with maternal complications both prior to and during delivery ( 4 , 5 ). Recently, several studies have found a correlation between maternal diabetes, including pregestational diabetes mellitus (PGDM) and gestational diabetes mellitus (GDM), and the risk of neonatal seizure ( 6 , 7 ). A retrospective cohort study assessed the relationship between neonatal complications and PGDM in infants born preterm (<36 weeks gestation), revealing that PGDM was associated with an elevated risk of seizures among neonates born <34 weeks gestation ( 6 ). After adjusting for these variables, Glass et al. found that both PGDM and GDM were risk factors for neonatal seizures, with PGDM having a greater impact ( 8 ). However, Hall et al. reported a relationship between PGDM and the increased risk of neonatal seizures, while no such association was found with GDM ( 9 ). To the best of our knowledge, existing studies on the relationship between maternal diabetes and neonatal seizures remain contentious. In addition, post-term delivery (≥42 weeks gestation) is also at high risk of developing neonatal seizures ( 8 ), but few studies have analyzed the relationship between maternal diabetes and neonatal seizures for post-term infants.

Herein, this study aims to evaluate the association of maternal diabetes and neonatal seizures during neonatal hospitalization in a large cohort of the National Vital Statistics System (NVSS).

Study population

We conducted a nested case–control study with data sources collected from the National Vital Statistics System (NVSS) 2016–2021 data files. The NVSS is the result of a partnership between the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC) and all US states, aiming to collect information on a wide range of maternal and infant demographic and health characteristics for all births ( 10 , 11 ). This study is considered exempt from the review of the Huizhou Central People's Hospital Ethics Committee due to the use of deidentified data.

The inclusion criteria comprised (1) newborns diagnosed with seizures and (2) pregnant women with complete information about PGDM and GDM. The exclusion criteria were as follows: (1) the presence of multiple births and (2) participants with missing demographic information. A total of 20,297,340 participants met the criteria for this study (case: n  = 20,290,666; control: n  = 6,674). We used the propensity-score matching (PSM, case:control = 1:4) method to reduce the effects of gender, gestational age, and method of delivery. In total, 6,674 cases of neonatal seizures and 26,696 controls were ultimately included in this study.

Pregestational diabetes mellitus and gestational diabetes mellitus

GDM is defined as glucose intolerance first detected during pregnancy ( 12 ), while PGDM is a condition where diabetes is diagnosed prior to conception ( 13 ).

Neonatal seizures were diagnosed by clinicians based on clinical criteria as follows: any involuntary repetitive, convulsive movement or behavior, and severe alteration of alertness such as obtundation, stupor, or coma. The primary outcome was the occurrence of neonatal seizures during neonatal hospitalization in this study.

Potential covariates

Potential covariates were extracted as follows: maternal characteristics contain age (years), race, educational level, birthplace, previous cesarean delivery, previous preterm births, number of prenatal care visits, smoking before pregnancy, body mass index (BMI, kg/m 2 ), weight gain (pounds), infertility treatment, pregestational hypertension, gestational hypertension, eclampsia, chorioamnionitis, infection, previous delivery, induction of labor, delivery method, use of anesthesia, steroids, and antibiotics. Infant characteristics contain gestational age, gender, birthweight (g), use of surfactant, and 5 min Apgar score.

Statistical analysis

We used the mean ± standard deviation (mean ± SD) to describe the measurement data that conform to a normal distribution pattern, and for comparison between the case group and the control group, we used the t -test. The numerical data with non-normally distributed data were presented as a median and interquartile range [ M ( Q 1, Q 3)], and for comparison between the case group and the control group, the rank sum test was used. The categorical data were expressed as the number of cases and composition ratio [ n (%)], and the Chi-square test was used for comparison between both groups.

Considering the effect of confounders, we used the PSM method in this study ( 14 , 15 ). Participants with neonatal seizures were matched in a 1:4 ratio to those without. Subsequently, a descriptive analysis was conducted on the case and control groups both pre- and post-PSM. After performing PSM, univariate and multivariate logistic regression analyses were used to assess the association between maternal diabetes (contains PGDM and GDM) and neonatal seizure. Model 1 was not adjusted for covariates. Model 2 was adjusted for maternal age, birthplace, previous cesarean delivery, previous preterm births, number of prenatal care visits, smoking before pregnancy, BMI, weight gain, infertility treatment, pregestational hypertension, gestational hypertension, eclampsia, chorioamnionitis, infection, previous delivery, induction of labor, use of steroids and antibiotics, infant birthweight, use of surfactant, and 5 min Apgar score. It is worth mentioning that for investigating the relationship between GDM and neonatal seizures, Model 2 was further adjusted for PGDM based on the original adjusted covariates. In addition, we conducted stratified analyses based on gestational age, birthweight, 5 min Apgar score, and maternal age to explore the potential disparities in the association between maternal diabetes and neonatal seizures. The relationship between maternal diabetes and neonatal seizures was presented using an odds ratio (OR) with a 95% confidence interval (CI). A score of P  < 0.05 was considered statistically significant. All statistical analyses were performed using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA) and R studio (version 4.2.1).

Baseline characteristics

The baseline characteristics of the case and control groups, both pre- and post-PSM, are given in Table 1 . After PSM, a total of 33,370 participants were selected for this study with 6,674 in the case group (neonatal seizure group) and 26,696 in the control group (non-neonatal seizure group). There were significant differences in some characteristics between the case and the control groups, such as maternal age, birthplace, previous cesarean delivery, previous preterm births, number of prenatal care visits, smoking before pregnancy, BMI, weight gain, infertility treatment, PGDM, pregestational hypertension, gestational hypertension, eclampsia, chorioamnionitis, infection, previous delivery, induction of labor, use of steroids and antibiotics, birthweight (g), use of surfactant, and 5 min Apgar score ( P  < 0.05). These may be potential covariates in this study.

Table 1 . Baseline characteristics of the case and control groups before and after PSM.

The association of maternal diabetes and neonatal seizures

Table 2 shows an association between maternal diabetes and neonatal seizures. In unadjusted analysis, PGDM was found to be a risk factor for neonatal seizures (OR = 2.27, 95% CI: 1.88–2.73, P  < 0.001). After adjusting for all covariates, PGDM was still associated with an increased risk of neonatal seizures (OR = 1.51, 95% CI: 1.15–1.98, P  = 0.003). In addition, we observed that, after adjusting for covariates, the association between GDM and neonatal seizure was not statistically significant ( P  = 0.405).

Table 2 . The association of maternal diabetes and neonatal seizures.

Subgroup analysis based on gestational age, birthweight, 5 min Apgar score, and maternal age

The results of stratified analyses based on gestational age, birthweight, 5 min Apgar score, and maternal age are displayed in Table 3 . A correlation between PGDM and increased risk of neonatal seizures was observed for neonates with a gestational age of 37–42 weeks (Model 2: OR = 1.82, 95% CI: 1.24–2.66, P  = 0.002) and ≥ 42 weeks (Model 2: OR = 4.97, 95% CI: 1.35–18.34, P  = 0.016). There was no statistically significant association between PGDM or GDM and neonatal seizures in neonates of varying birthweights. Among neonates possessing different Apgar scores, PGDM was a risk factor for neonatal seizure only when the Apgar score was ≥7 (Model 2: OR = 1.65, 95% CI: 1.24–2.18, P  < 0.001). Furthermore, we found that PGDM was associated with an increased risk of neonatal seizures among maternal age ≤40 (Model 2: OR = 1.52, 95% CI: 1.16–2.01, P  = 0.003). Figure 1 also depicts the incidence of neonatal seizures and the OR with a 95% CI based on gestational age, birthweight, Apgar score, and maternal age.

Table 3 . Stratified analyses based on gestational age, birthweight, Apgar score, and maternal age.

Figure 1 . The incidence of neonatal seizure and the 95% CI of OR based on gestational age ( A ), birthweight ( B ), Apgar score ( C ), and maternal age ( D ).

This nested case–control study analyzed the representative NVSS database to investigate the relationship between maternal diabetes and neonatal seizures. The findings indicated that PGDM was associated with an elevated risk of neonatal seizures.

The risk of neonatal seizures was the highest during the first month after birth ( 16 ), suggesting that multiple obstetric risk factors may influence their occurrence ( 17 , 18 ). Previous studies have assessed the impact of maternal diabetes on neonatal seizures ( 6 , 7 ). However, the conclusions regarding the relationship between maternal diabetes and neonatal seizures are controversial. In recent years, the PSM statistical method has gained widespread popularity because of its ability to balance differences between groups and reduce the influence of confounding variables ( 19 , 20 ). In this study, we included 6,674 cases of neonatal seizures and 26,696 controls by using the PSM method. After performing PSM, a significantly higher proportion of mothers diagnosed with PGDM were observed in the neonatal seizure group compared with those in the non-neonatal seizure group ( P  < 0.001). After adjusting for all covariates, PGDM was considered to be related to an increased risk of neonatal seizures (OR = 1.51, 95% CI: 1.15–1.98), which was in keeping with previous studies ( 6 , 8 ). This study also found that PGDM was a risk factor for neonatal seizures for neonates with a gestational age of ≥37 weeks. This finding is inconsistent with the result reported by Tse et al. ( 6 ). This may be attributed to the presence of different research subjects. In addition, preterm birth was associated with an elevated risk of neonatal seizures ( 21 ). The impact of preterm birth on the risk of neonatal seizures may be greater than that of PGDM in this study, thus rendering the association between PGDM and neonatal seizures insignificant within the preterm population. Similarly, this study found a significant association between PDGM and neonatal seizures for neonates with an Apgar score of ≥7 and a maternal age of ≤40 years. A low 5 min Apgar score and advanced maternal age were considered perinatal risk factors for infantile seizures ( 22 , 23 ). We speculated that neonatal seizure risk may be more influenced by a low Apgar score (<7) and gestational age (>40 years), potentially obscuring the impact of PGDM. Additional samples will be collected from our hospitals in the future to further validate this finding and investigate the potential underlying mechanisms. It is noteworthy that birthplace was a confounding factor in this study. A retrospective cohort study has demonstrated that neonates who are delivered outside of a hospital setting have an increased risk of experiencing seizures when compared with those who are delivered in-hospital, as planned ( 24 ).

To our knowledge, the main causes of neonatal seizures are hypoglycemia, ischemia, structural lesions or abnormalities, and infections ( 9 , 25 ). Notably, hypoglycemia frequently occurs in newborns born to mothers with PGDM ( 26 ). In this study, we found no statistically significant association between GDM and neonatal seizures. It has been widely reported that PGDM is associated with a higher risk of adverse pregnancy outcomes compared with GDM ( 27 , 28 ). Given the critical period of organogenesis during early pregnancy, prolonged exposure to prepregnancy hyperglycemia and intrauterine hyperglycemia may elevate the likelihood of neonatal seizures ( 29 , 30 ). The underlying mechanism regarding the association of PGDM, GDM, and neonatal seizure risk remains unclear. More research is needed in the future to clarify the mechanisms by which PGDM affects neonatal seizure risk.

Some limitations of this study need to be taken into account. First, this study was retrospective in nature, which may have led to selection bias. However, we employed the PSM statistical method to mitigate confounding effects and enhance the reliability of our findings. Second, our investigation solely focuses on the correlation between maternal diabetes and neonatal seizures during hospitalization, with no knowledge of seizure occurrence after discharge; moreover, the observation period in this study was less than 1 month. These factors may result in an underestimation of neonatal seizure incidence. Third, the study recruited participants from the NVSS database, which did not contain records of prenatal brain ultrasound magnetic resonance imaging, electroencephalography or amplitude-integrated electroencephalography, glycosylated hemoglobin during pregnancy information, and the Score for Neonatal Acute Physiology II with Perinatal Extension at admission. Last, it should be noted that the NVSS database lacks information on the specific type of PGDM, thus necessitating further investigation into the association between type 1 and type 2 diabetes and neonatal seizures.

In short, this study revealed an association between PGDM and an elevated risk of neonatal seizures. Therefore, it is recommended that neonatologists closely monitor the incidence of seizures in newborns born to mothers with PGDM.

Data availability statement

Publicly available datasets were analyzed in this study. These data can be found here: Centers for Disease Control and Prevention (CDC) National Vital Statistics System (NVSS) database, https://www.cdc.gov/nchs/nvss/index.htm .

Ethics statement

Ethical approval was not provided for this study on human participants because of the use of deidentified data. The patients/ participants provided their written informed consent to participate in the studies to NVSS, who collected the patient data.

Author contributions

YL designed the study and wrote the manuscript. JL and XL collected, analyzed, and interpreted the data. YL critically reviewed, edited, and approved the manuscript. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: PGDM, GDM, neonatal seizure, PSM method, NVSS

Citation: Liang Y, Liu J and Lin X (2023) The association between maternal diabetes and neonatal seizures: a nested case–Control study. Front. Pediatr. 11:1145443. doi: 10.3389/fped.2023.1145443

Received: 16 January 2023; Accepted: 13 June 2023; Published: 13 July 2023.

Reviewed by:

© 2023 Liang, Liu and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yanjin Liang [email protected]