A prospective cohort study of early-pregnancy risk factors for gestational diabetes in polycystic ovarian syndrome

Metabolic profiling and early prediction models for gestational diabetes mellitus in PCOS and non-PCOS pregnant women

BACKGROUND

Gestational diabetes mellitus (GDM) is the most common pregnancy complication, significantly affecting maternal and neonatal health. Polycystic ovary syndrome (PCOS) is a common endocrine disorder characterized by metabolic abnormalities, which notably elevates the risk of developing GDM during pregnancy.

METHODS

In this study, we utilized ultra-high-performance liquid chromatography for untargeted metabolomics analysis of serum samples from 137 pregnant women in the early-to-mid-pregnancy. The cohort consisted of 137 participants, including 70 in the PCOS group (36 who developed GDM in mid-to-late pregnancy and 34 who did not) and 67 in the non-PCOS group (37 who developed GDM and 30 who remained GDM-free). The aim was to investigate metabolic profile differences between PCOS and non-PCOS patients and to construct early GDM prediction models separately for the PCOS and non-PCOS groups.

RESULTS

Our findings revealed significant differences in the metabolic profiles of PCOS patients, which may help elucidate the higher risk of GDM in the PCOS population. Moreover, tailored early GDM prediction models for the PCOS group demonstrated high predictive performance, providing strong support for early diagnosis and intervention in clinical practice.

CONCLUSIONS

Untargeted metabolomics analysis revealed distinct metabolic patterns between PCOS patients and non-PCOS patients, particularly in pathways related to GDM. Based on these findings, we successfully constructed GDM prediction models for both PCOS and non-PCOS groups, offering a promising tool for clinical management and early intervention in high-risk populations.

Correlation and predictive value of systemic immune-inflammation index for dyslipidemia in patients with polycystic ovary syndrome

BACKGROUND

Polycystic ovary syndrome(PCOS) is one of the main factors leading to infertility in women of reproductive age, which is often accompanied by metabolic changes such as obesity and chronic low-grade inflammation. Chronic inflammation may play an important role in the occurrence and development of metabolic diseases. Therefore, it is of great significance to explore the relationship between abnormal lipid metabolism and inflammation in PCOS patients. This study aims to analyze the correlation between systemic immune-inflammatory(SII) markers and dyslipidemia in patients with PCOS and their value in early diagnosis.

METHODS

A total of 617 PCOS patients aged 20-35 years (according to the Rotterdam diagnostic criteria) who visited the Reproductive Center of the First Hospital of Lanzhou University from January 2020 to December 2022 were included. According to the presence or absence of dyslipidemia, the patients were divided into normal lipid metabolism group and abnormal lipid metabolism group. The clinical data of the patients were collected and analyzed by SPSS software.

RESULTS

There were 454 patients with normal lipid metabolism and 163 patients with abnormal lipid metabolism. The SII level of the abnormal lipid metabolism group was higher than that of the normal group. As the SII quartile increased, TC, TG and LDL increased, while HDL decreased accordingly. The SII level was positively correlated with TC, TG and LDL, and negatively correlated with HDL (all P < 0.05). Among them, SII had the best predictive efficiency for dyslipidemia of polycyctic ovary syndrome at 489.375 (AUC: 0.718, 95%CI: 0.672-0.764), and SII was still associated with the increased occurrence of PCOS dyslipidemia after excluding confounding factors (P < 0.05).

CONCLUSION

The high level of SII has a correlation with the occurrence of dyslipidemia in PCOS patients, and it has a value in the early diagnosis of PCOS.

Enhancing gestational diabetes mellitus risk assessment and treatment through GDMPredictor: a machine learning approach

BACKGROUND

Gestational diabetes mellitus (GDM) is a serious health concern that affects pregnant women worldwide and can lead to adverse pregnancy outcomes. Early detection of high-risk individuals and the implementation of appropriate treatment can enhance these outcomes.

METHODS

We conducted a study on a cohort of 3467 pregnant women during their pregnancy, with a total of 5649 clinical and biochemical records collected. We utilized this dataset as our training dataset to develop a web server called GDMPredictor. The GDMPredictor utilizes advanced machine learning techniques to predict the risk of GDM in pregnant women. We also personalize treatment recommendations based on essential biochemical indicators, such as A1MG, BMG, CysC, CO2, TBA, FPG, and CREA. Our assessment of GDMPredictor's effectiveness involved training it on the dataset of 3467 pregnant women and measuring its ability to predict GDM risk using an AUC and auPRC.

RESULTS

GDMPredictor demonstrated an impressive level of precision by achieving an AUC score of 0.967. To tailor our treatment recommendations, we use the GDM risk level to identify higher risk candidates who require more intensive care. The GDMPredictor can accept biochemical indicators for predicting the risk of GDM at any period from 1 to 24 weeks, providing healthcare professionals with an intuitive interface to identify high-risk patients and give optimal treatment recommendations.

CONCLUSIONS

The GDMPredictor presents a valuable asset for clinical practice, with the potential to change the management of GDM in pregnant women. Its high accuracy and efficiency make it a reliable tool for doctors to improve patient outcomes. Early identification of high-risk individuals and tailored treatment can improve maternal and fetal health outcomes http://www.bioinfogenetics.info/GDM/ .

Investigating the causal impact of polycystic ovary syndrome on gestational diabetes mellitus: a two-sample Mendelian randomization study

Introduction

Determining the causal relationship between polycystic ovary syndrome (PCOS) and gestational diabetes mellitus (GDM) holds significant implications for GDM prevention and treatment. Despite numerous observational studies suggesting an association between PCOS and GDM, it remains unclear whether a definitive causal relationship exists between these two conditions and which specific features of PCOS contribute to increased incidence of GDM.

Methods

The causal relationship between polycystic ovary syndrome (PCOS), its characteristic indices, and gestational diabetes mellitus (GDM) was investigated using a two-sample Mendelian randomization study based on publicly available statistics from genome-wide association studies (GWAS). The inverse-variance weighted method was employed as the primary analytical approach to examine the association between PCOS, its characteristic indices, and GDM. MR Egger intercept was used to assess pleiotropy, while Q values and their corresponding P values were utilized to evaluate heterogeneity. It is important to note that this study adopts a two-sample MR design where PCOS and its characteristic indices are considered as exposures, while GDM is treated as an outcome.

Results

The study results indicate that there is no causal relationship between PCOS and GDM (all methods P > 0.05, 95% CI of OR values passed 1). The IVW OR value was 1.007 with a 95% CI of 0.906 to 1.119 and a P value of 0.904. Moreover, the MR Egger Q value was 8.141 with a P value of 0.701, while the IVW Q value was also 8.141 with a P value of 0.774, indicating no significant heterogeneity. Additionally, the MR Egger intercept was 0.0004, which was close to zero with a P value of 0.988, suggesting no pleiotropy. However, the study did find a causal relationship between several other factors such as testosterone, high-density lipoprotein, sex hormone-binding globulin, body mass index, waist-hip ratio, apolipoprotein A-I, number of children, diabetes illnesses of mother, father and siblings, hemoglobin A1c, fasting insulin, fasting blood glucose, years of schooling, and GDM based on the IVW method.

Conclusion

We observed no association between genetically predicted PCOS and the risk of GDM, implying that PCOS itself does not confer an increased susceptibility to GDM. The presence of other PCOS-related factors such as testosterone, high-density lipoprotein, and sex hormone-binding globulin may elucidate the link between PCOS and GDM. Based on these findings, efforts aimed at preventing GDM in individuals with PCOS should prioritize those exhibiting high-risk features rather than encompassing all women with PCOS.

Gestational Diabetes Mellitus in the Setting of Polycystic Ovarian Syndrome: A Systematic Review

Gestational diabetes mellitus (GDM) is the most common complication of pregnancy that arises in the 2nd and 3rd trimesters, leading to significant complications for the mother and her neonates, such as an increased rate of pregnancy-induced hypertension and miscarriages, while neonates may have a large birth weight, hypoglycemia, or macrosomnia. Numerous risk factors can lead to GDM; however, a significant one is polycystic ovarian syndrome (PCOS). PCOS is the most common endocrine pathology beginning before puberty, and due to significant hormonal changes, it is not diagnosed until after puberty. PCOS requires at least three of the following symptoms: hyperandrogenism, menstrual irregularities, or polycystic ovary morphology. While it is agreed that women with PCOS are at a significantly increased risk of GDM, no publication to our knowledge has evaluated the full relationship of GDM in the setting of PCOS. This paper aimed to assess this relationship and determine how it may differ for pregnant women with only GDM by determining the prevalence of GDM, the variations within phenotypes, the influence of fertilization methods, specific risk factors, maternal outcomes, and neonatal outcomes. The prevalence of GDM was significantly increased in women with PCOS compared to healthy controls, and some studies have found that phenotype A may be more likely to lead to GDM. Risk factors were similar to pregnant women with only GDM, but with GDM and PCOS specifically, preconception low sex hormone-binding globulin, increased BMI > 25 kg/m2, and preconception impaired glucose tolerance were specific. While maternal outcomes were similar to pregnant women with only GDM, women with GDM and PCOS were even more likely to develop pregnancy-induced hypertension and early miscarriage. Neonates from mothers with GDM and PCOS were more likely to have low birth weights compared to mothers with just GDM who had high birth weights. The evaluation of the relationship between GDM and PCOS allows for illumination of the need to evaluate influences that currently lack research, such as phenotype variation and influences of fertilization method. This also promotes the need to develop predictive algorithms based on risk factors to prevent these adverse outcomes for mothers and neonates.

Is elevated baseline SHBG associated with increased ovulation?

Sexual hormone binding globulin (SHBG) is associated with the endocrine and reproductive systems. We aimed to investigate the role of SHBG in the reproductive process. Therefore, we conducted a secondary analysis of the PCOSAct (Polycystic Ovary Syndrome and Acupuncture Clinical Trial) study, which involved 21 sites in China and a total of 1000 women with PCOS. Out of these, 954 women with SHBG were included in the analysis. Through multivariate analysis of ovulation predictors, we found that age, BMI, estradiol, testosterone, and SHBG all showed a positive predictive value for ovulation (p = 0.0211, 0.0011, 0.0211, 0.0029, 0.0434, respectively). However, the LH to FSH ratio had a negative predictive value (p = 0.0539). Higher quartiles of SHBG were associated with a higher rate of ovulation, and per quartile increased was statistically significant (HR = 1.138, 95%CI [1.054,1.229]). The association remained significant even after adjusting for testosterone (HR = 1.263, 95%CI [1.059, 1.507]). On the other hand, quartiles of testosterone and estradiol did not exhibit any significant tendency toward ovulation. SHBG demonstrated predictive ability for ovulation, conception, pregnancy, and live birth (p < 0.05), and this correlation remained significant after adjusting intervention. Kaplan-Meier curves illustrated that increased levels of SHBG were a factor in high rates of ovulation, conception, and pregnancy. In comparison to other sexual hormones, a higher baseline level of SHBG was related to increased ovulation.

Fetal sex and the development of gestational diabetes mellitus in polycystic ovarian syndrome gravidae

BACKGROUND

Polycystic ovarian syndrome is characterized by elevated androgens and is a well-known risk factor for the occurrence of gestational diabetes mellitus. Androgens (particularly dehydroepiandrosterone-sulfate) are crucial for the development and characteristics of the male reproductive tract during fetal life, and fetal dehydroepiandrosterone-sulfate enters the placenta where it is metabolized and functions as an estrogen substrate. Given this unique sex-specific relationship with androgens and the association of serum dehydroepiandrosterone-sulfate concentration with insulin resistance, we hypothesized that metabolic comorbidities in pregnancy might differ by fetal sex in gravidae with polycystic ovarian syndrome, notably in those with infertility.

OBJECTIVE

This study aimed to evaluate the data in a large population-based database to explore if fetal sex was significantly associated with gestational diabetes mellitus in gravidae with infertility and polycystic ovarian syndrome after controlling for confounders.

STUDY DESIGN

This study was designed to evaluate the risk for the occurrence and rates of gestational diabetes mellitus among gravidae with infertility and a history of polycystic ovarian syndrome. We used a 2-hospital, single academic institution database comprising more than 30,000 subjects enrolled from September 2011 to June 2021 to identify all gravidae with diagnoses of infertility and polycystic ovarian syndrome at the time of delivery and to compare them with gravidae who lacked these comorbidities. Data on covariates, including but not limited to maternal age, body mass index, fetal sex, race, ethnicity, presence or absence of hypertensive disease, and presence or absence of gestational diabetes were identified. Unadjusted and adjusted odds rations were calculated.

RESULTS

We found a statistically significant association between fetal female sex and the development of gestational diabetes mellitus in gravidae with polycystic ovarian syndrome (odds ratio for female vs male, 2.13; 95% confidence interval, 1.06-4.32; P=.03). After adjusting for potential confounders identified in our univariate analyses, there continued to be a statistically significant association between female fetuses and the development of gestational diabetes mellitus (adjusted odds ratio for female vs male, 2.10; 95% confidence interval, 1.04-4.41; P=.04). In contrast, there was no significant association between fetal sex and the development of gestational diabetes mellitus in our similar analysis of gravidae without infertility and polycystic ovarian syndrome (P=.99).

CONCLUSION

Although the origin of gestational diabetes mellitus is multifactorial, we found that female fetal sex is associated with gestational diabetes mellitus in gravidae with infertility and polycystic ovarian syndrome but not in their comparative controls. Further research on the molecular mechanisms driving the association between female fetuses and the development of gestational diabetes mellitus in the context of maternal polycystic ovarian syndrome is warranted.

Associations of low sex hormone-binding globulin and androgen excess in early pregnancy with fasting and post-prandial hyperglycaemia, gestational diabetes, and its severity

AIMS

We studied whether androgen excess and low sex hormone-binding globulin (SHBG) measured in early pregnancy are independently associated with fasting and post-prandial hyperglycaemia, gestational diabetes (GDM), and its severity.

MATERIALS AND METHODS

This nationwide case-control study included 1045 women with GDM and 963 non-diabetic pregnant controls. We measured testosterone (T) and SHBG from biobanked serum samples (mean 10.7 gestational weeks) and calculated the free androgen index (FAI). We first studied their associations with GDM and secondly with the type of hyperglycaemia (fasting, 1 and 2 h glucose concentrations during the oral glucose tolerance test), early-onset GDM (<20 gestational weeks) and the need for anti-diabetic medication.

RESULTS

After adjustments for gestational weeks at sampling, pre-pregnancy BMI, and age, women with GDM had 3.7% (95% CI 0.1%-7.3%) lower SHBG levels, 3.1% (95% CI 0.1%-6.2%) higher T levels, and 4.6% (95% CI 1.9%-7.3%) higher FAI levels than controls. SHBG was inversely associated with fasting glucose, whereas higher FAI and T were associated with higher post-prandial glucose concentrations. Women with early-onset GDM had 6.7% (95% CI 0.7%-12.7%) lower SHBG levels and women who needed insulin for fasting hyperglycaemia 8.7% (95% CI 1.8%-14.8%) lower SHBG levels than other women with GDM.

CONCLUSIONS

Lower SHBG levels were associated especially with early-onset GDM, higher fasting glucose and insulin treatment, whereas androgen excess was associated with higher post-prandial glucose values. Thus, a low SHBG level may reflect the degree of existing insulin resistance, while androgen excess might impair post-prandial insulin secretion.

Maternal metabolic factors and the association with gestational diabetes: A systematic review and meta-analysis

Gestational diabetes (GDM) is associated with several adverse outcomes for the mother and child. Higher levels of individual lipids are associated with risk of GDM and metabolic syndrome (MetS), a clustering of risk factors also increases risk for GDM. Metabolic factors can be modified by diet and lifestyle. This review comprehensively evaluates the association between MetS and its components, measured in early pregnancy, and risk for GDM. Databases (Cumulative Index to Nursing and Allied Health Literature, PubMed, Embase, and Cochrane Library) were searched from inception to 5 May 2021. Eligible studies included ≥1 metabolic factor (waist circumference, blood pressure, fasting plasma glucose (FPG), triglycerides, and high-density lipoprotein cholesterol), measured at <16 weeks' gestation. At least two authors independently screened potentially eligible studies. Heterogeneity was quantified using I² . Data were pooled by random-effects models and expressed as odds ratio and 95% confidence intervals (CIs). Of 7213 articles identified, 40 unique articles were included in meta-analysis. In analyses adjusting for maternal age and body mass index, GDM was increased with increasing FPG (odds ratios [OR] 1.92; 95% CI 1.39-2.64, k = 7 studies) or having MetS (OR 2.52; 1.65, 3.84, k = 3). Women with overweight (OR 2.17; 95% CI 1.89, 2.50, k = 12) or obesity (OR 4.34; 95% CI 2.79-6.74, k = 9) also were at increased risk for GDM. Early pregnancy assessment of glucose or the MetS, offers a potential opportunity to detect and treat individual risk factors as an approach towards GDM prevention; weight loss for pregnant women with overweight or obesity is not recommended. Systematic review registration: PROSPERO CRD42020199225.

The incidence of gestational diabetes mellitus among women with polycystic ovary syndrome: a meta-analysis of longitudinal studies

BACKGROUND

Previous studies have shown that polycystic ovary syndrome is a predictor of gestational diabetes mellitus, but we do not know exactly how many polycystic ovary syndrome patients may develop gestational diabetes mellitus. Currently, the incidence of gestational diabetes mellitus among women with polycystic ovary syndrome varies greatly across studies, ranged from 4.12% to 59.50%. Besides, many factors have been found to be related to the incidence of gestational diabetes mellitus among women with polycystic ovary syndrome, but the results among different studies are not consistent. The possible causes of inconsistencies between the current estimates were unclear. This review aimed at exploring the pooled incidence of gestational diabetes mellitus among women with polycystic ovary syndrome, summarizing possible causes of the inconsistencies in the current estimates, try to provide a reference for prevention of gestational diabetes mellitus and polycystic ovary syndrome in the future.

METHODS

Systematic searches of different databases (including EMBASE, Web of Science, MEDLINE, The Cochrane Library, CNKI and PubMed) were conducted for studies published until 31 May 2021. Statistical analyses were performed using R software, the pooled incidence of gestational diabetes mellitus among polycystic ovary syndrome patients was combined using random effects model. Cochrane's "Tool to Assess Risk of Bias in Cohort Studies" was used for quality assessment.

RESULTS

Twenty-two longitudinal studies were included. A total of 24,574 women with polycystic ovary syndrome were identified in the 22 articles, of which 4478 were reported with gestational diabetes mellitus. The pooled incidence of gestational diabetes mellitus among women with polycystic ovary syndrome was 20.64%, with a 95% CI of 14.64% to 28.30%. In the meta-regression model, several variables including age, area, quality score and sample size were suggested as significant sources of heterogeneity, accounted for 77.57% of the heterogeneity across studies.

CONCLUSIONS

Evidence in this review suggests that gestational diabetes mellitus were common among women with polycystic ovary syndrome. More research is needed to found effective interventions for preventing gestational diabetes mellitus among women with polycystic ovary syndrome.

Effect of maternal body mass index on the steroid profile in women with gestational diabetes mellitus

OBJECTIVE

To explore the effect of maternal body mass index (BMI) on steroid hormone profiles in women with gestational diabetes mellitus (GDM) and those with normal glucose tolerance (NGT).

METHODS

We enrolled 79 women with NGT and 80 women with GDM who had a gestational age of 24-28 weeks. The participants were grouped according to their BMI. We quantified 11 steroid hormones profiles by liquid chromatography-tandem mass spectrometry and calculated the product-to-precursor ratios in the steroidogenic pathway.

RESULTS

Women with GDM and BMI<25kg/m² showed higher concentrations of dehydroepiandrosterone (DHEA) (p<0.001), testosterone (T) (p=0.020), estrone (E1) (p=0.010) and estradiol (E2) (p=0.040) and lower Matsuda index and HOMA-β than women with NGT and BMI<25kg/m². In women with GDM, concentrations of E1 (p=0.006) and E2 (p=0.009) declined, accompanied by reduced E2/T (p=0.008) and E1/androstenedione (A4) (p=0.010) in the BMI>25 kg/m² group, when compared to that in the BMI<25 kg/m² group. The values of E2/T and E1/A4 were used to evaluate the cytochrome P450 aromatase enzyme activity in the steroidogenic pathway. Both aromatase activities negatively correlated with the maternal BMI and positively correlated with the Matsuda index in women with GDM.

CONCLUSIONS

NGT women and GDM women with normal weight presented with different steroid hormone profiles. Steroidogenic pathway profiling of sex hormones synthesis showed a significant increase in the production of DHEA, T, E1, and E2 in GDM women with normal weight. Additionally, the alteration of steroid hormone metabolism was related to maternal BMI in women with GDM, and GDM women with overweight showed reduced estrogen production and decreased insulin sensitivity compared with GDM women with normal weight.

Gestational Diabetes Mellitus: A Harbinger of the Vicious Cycle of Diabetes

Gestational diabetes mellitus (GDM), characterized by a transitory form of diabetes induced by insulin resistance and pancreatic β-cell dysfunction during pregnancy, has been identified as one of the major obstacles in achieving improved maternal and child health. Approximately 9-25% of pregnancies worldwide are impacted by the acute, long-term, and transgenerational health complications of this disease. Here, we discuss how GDM affects longstanding maternal and neonatal outcomes, as well as health risks that likely persist into future generations. In addition to the current challenges in the management and diagnosis of and the complications associated with GDM, we discuss current preclinical models of GDM to better understand the underlying pathophysiology of the disease and the timely need to increase our scientific toolbox to identify strategies to prevent and treat GDM, thereby advancing clinical care.

A mid-pregnancy risk prediction model for gestational diabetes mellitus based on the maternal status in combination with ultrasound and serological findings

Although previous studies have proposed predictive models of gestational diabetes mellitus (GDM) based on maternal status, they do not always provide reliable results. The present study aimed to create a novel model that included ultrasound data of maternal fat distribution and serum inflammatory factors. The clinical data of 1,158 pregnant women treated at Tangshan Gongren Hospital and eight other flagship hospitals in Tangshan, including the First Hospital of Tangshan Gongren Hospital group, Ninth Hospital of Tangshan Gongren Hospital group, Tangshan Gongren Hospital group rehabilitation hospital, Tangshan railway central hospital, Tangshan Gongren Hospital group Fengnan hospital, Tangshan Gongren Hospital group Qianan Yanshan hospital, Tangshan Gongren Hospital group Qianxi Kangli hospital and Tangshan Gongren Hospital group Jidong Sub-hospital, were analyzed following the division of subjects into GDM and non-GDM groups according to their diagnostic results at 24-28 weeks of pregnancy. Univariate analysis was performed to investigate the significance of the maternal clinical parameters for GDM diagnosis and a GDM prediction model was established using stepwise regression analysis. The predictive value of the model was evaluated using a Homer-Lemeshow goodness-of-fit test and a receiver operating characteristic curve (ROC). The model demonstrated that age, pre-pregnancy body mass index, a family history of diabetes mellitus, polycystic ovary syndrome, a history of GDM, high systolic pressures, glycosylated hemoglobin levels, triglyceride levels, total cholesterol levels, low-density lipoprotein cholesterol levels, serum hypersensitive C-reactive protein, increased subcutaneous fat thickness and visceral fat thickness were all correlated with an increased GDM risk (all P<0.01). The area under the curve value was 0.911 (95% CI, 0.893-0.930). Overall, the results indicated that the current model, which included ultrasound and serological data, may be a more effective predictor of GDM compared with other single predictor models. In conclusion, the present study developed a tool to determine the risk of GDM in pregnant women during the second trimester. This prediction model, based on various risk factors, demonstrated a high predictive value for the GDM occurrence in pregnant women in China and may prove useful in guiding future clinical practice.

Sex hormone binding globulin for prediction of gestational diabetes mellitus in pre-conception and pregnancy: A systematic review

AIM

The purpose of the present study was to assess the relationship of sex hormone binding globulin (SHBG) and gestational diabetes mellitus (GDM).

METHODS

The Cochrane Library, Medline, ScienceDirect, and Web of Science were searched for studies published from the inception of the databases up to February 2019. Our inclusion criteria were published observational full-text articles. All data were analyzed using Review Manager 5.3. Of 208 papers reviewed, 26 studies (n = 6668) were considered for meta-analysis.

RESULTS

The SHBG level was significantly lower in women with GDM compared to healthy women (MD = -11.86; 95% CI: [-13.02, -10.71]). Also, SHBG in women with PCOS and GDM and obesity was significantly lower than women with PCOS without GDM (MD = -38.14; 95% CI: [-56.79, -19.48]) and normal weight women (MD: -58.96; 95% CI: [-79.32, -38.59]). SHBG in the second trimester was lower than that in the first trimester and pre-conception.

CONCLUSIONS

This systematic review showed that the level of SHBG is significantly lower in GDM pregnant women than that in healthy women. The results of this systematic review about the relationship of GDM and SHBG and suggestion to assess this marker in early pregnancy should be considered with caution.

Systemic and periodontal conditions of overweight/obese patients during pregnancy and after delivery: a prospective cohort

OBJECTIVE

To evaluate the systemic and periodontal conditions, as well as the determinants of health in pregnant women with and without obesity/overweight during the second and third trimesters of pregnancy and after delivery.

MATERIALS AND METHODS

In the second trimester (T1), 93 pregnant women were divided into two groups with either excessive weight (G1, n = 53) or normal weight (G2, n = 40) and subsequently examined them in the third trimester of pregnancy (T2) and at least 2 months after delivery (T3). The following variables were analyzed: (a) systemic impairments during pregnancy-arterial hypertension (AH) and gestational diabetes mellitus (GDM); (b) oral hygiene behavior; (c) periodontal conditions; (d) anthropometric data and systemic health condition after pregnancy. The Mann-Whitney test, chi-squared test, ANOVA, and binary logistic regression were adopted (p < 0.05).

RESULTS

G1 showed higher frequency of GDM and AH in T1 and T2, respectively (p = 0.047; p = 0.004). Both groups had worse oral hygiene behaviors after delivery. A higher frequency of periodontitis was found in all periods for G1 (p < 0.05). G2 showed improvement of all periodontal parameters after delivery, whereas G1 showed no difference regarding these parameters between time periods.

CONCLUSION

Pregnant women with excessive weight presented worse systemic and periodontal conditions during pregnancy and after delivery.

CLINICAL RELEVANCE

Low socioeconomic level and overweight/obesity were significant predictors of periodontitis during pregnancy and after delivery.

The impact of self-reported preconception body mass index on gestational abnormal glucose tolerance in a Chinese center

AIMS

To investigate the association between self-reported preconception body mass index (BMI) and the risk of abnormal glucose tolerance (AGT).

METHODS

Data were obtained from a prospective cohort study conducted in China. We recruited 5305 qualified women who registered during 22-24 gestational weeks. Blood glucose was measured by trained professionals, and other health-related information was recorded prospectively. We used logistic regression to evaluate the relationship between preconception BMI with AGT and its subtypes, after controlling for potential confounders.

RESULTS

649 of the 5305 participants (12.2%) were diagnosed with AGT. The prevalences of AGT in underweight, normal weight, overweight and obese population indicated a significant linear increased trend (8.4%, 11.1%, 20.0% and 27.7%, respectively) (p < 0.001), regardless of parity status. After adjustment for maternal age, education and parity, the adjusted odds ratios of AGT for underweight: OR = 0.82 (95% CI: 0.62, 1.06); overweight: OR = 1.92 (95% CI: 1.54, 2.38); obese: OR = 2.82 (95% CI: 1.88, 4.22) compared with normal weight. Stratified analysis showed preconception BMI had a greater impact on primiparous women.

CONCLUSIONS

Our results support an association between self-reported preconception BMI with increased risk of AGT, and it was dependent on parity.