Excessive pre-pregnancy weight is a known risk factor of pregnancy complications. The purpose of this analysis was to assess the relationship between several categories of maternal weight and the risk of developing hypertension and diabetes in pregnancy, and the relationship of these complications with the results of the newborn. It was carried out in a common cohort of pregnant women and taking into account the influence of disturbing factors. Our analysis was conducted in a prospective cohort of 912 Polish pregnant women, recruited during 2015–2016. We evaluated the women who subsequently developed diabetes with dietary modification (GDM-1) (n = 125) and with insulin therapy (GDM-2) (n = 21), as well as the women who developed gestational hypertension (GH) (n = 113) and preeclampsia (PE) (n = 24), compared to the healthy controls. Odds ratios of the complications (and confidence intervals (95%)) were calculated in the multivariate logistic regression. In the cohort, 10.8% of the women had pre-pregnancy obesity (body mass index (BMI) ≥ 30 kg/m²), and 36.8% had gestational weight gain (GWG) above the range of the Institute of Medicine recommendation. After correction for excessive GWG and other confounders, pre-pregnancy obesity (vs. normal BMI) was associated with a higher odds ratio of GH (AOR = 4.94; p < 0.001), PE (AOR = 8.61; p < 0.001), GDM-1 (AOR = 2.99; p < 0.001), and GDM-2 (AOR = 11.88; p <0.001). The threshold risk of development of GDM-2 occurred at lower BMI values (26.9 kg/m²), compared to GDM-1 (29.1 kg/m²). The threshold point for GH was 24.3 kg/m², and for PE 23.1 kg/m². For GWG above the range (vs. GWG in the range), the adjusted odds ratios of GH, PE, GDM-1, and GDM-2 were AOR = 1.71 (p = 0.045), AOR = 1.14 (p = 0.803), AOR = 0.74 (p = 0.245), and AOR = 0.76 (p = 0.672), respectively. The effect of maternal edema on all the results was negligible. In our cohort, hypertension and diabetes were associated with incorrect birth weight and gestational age at delivery. Conclusions: This study highlights the importance and influence of excessive pre-pregnancy maternal weight on the risk of pregnancy complications such as diabetes and hypertension which can impact fetal outcomes.

Early identification of women at risk of developing pregnancy-induced hypertension (PIH) is very important. The involvement of copper (Cu) and zinc (Zn) in the oxidative balance suggests the possibility of their association with this disease, in which oxidative stress plays a key role. However, it has not been established so far whether the microelement levels in early pregnancy may be risk markers of the disease, as prospective studies are limited in number. In our innovative single-center study, we identified from a prospective cohort of healthy women in the 10–14th week of a single pregnancy: women subsequently developing pregnancy-induced hypertension (n = 121) and matched women remaining normotensive (n = 363). We measured the concentrations of microelements in the serum from 10–14 week, using the inductively coupled plasma mass spectrometry (ICP-MS). The odds ratios of the disease (and 95% confidence intervals) were assessed in logistic regression. In the whole cohort, the odds ratio (OR) of PIH was 1.52 (p = 0.174) for women in the lowest (Q1) quartile of Cu (≤1540.58 µg/L) compared with women in the highest (Q4) quartile (>1937.46 µg/L), but adjusted odds ratio (AOR) was 2.17 (p = 0.019) after adjusted for pre-pregnancy body mass index (BMI) and gestational age at recruitment. The higher levels of Cu in the subgroup of BMI ≥ 25 kg/m² compared to normal BMI were found (1847.64 vs. 1673.36 µg/L; p < 0.0001). In the subgroup of women with the normal pre-pregnancy BMI, the adjusted odds ratio of PIH was AOR = 2.95 (p = 0.040) for Q1 vs. Q4 quartile. Our results suggest that lower Cu levels in early pregnancy may be connected with higher risk of PIH, but BMI affected estimated odds ratios. Zinc levels had no effect on the risk.

Although both incidence and aggressiveness of ovarian malignancy rise with age, the exact reason for this tendency, in particular the contribution of senescent cells, remains elusive. In this project we found that the patient’s age determines the frequency of intraperitoneal metastases of ovarian cancer. Moreover, we documented that senescent human peritoneal mesothelial cells (HPMCs) stimulate proliferation, migration and invasion of ovarian cancer cells in vitro, and that this effect is related to both the activity of soluble agents released to the environment by these cells and direct cell-cell contact. The panel of mediators of the pro-cancerous activity of senescent HPMCs appeared to be cancer cell line-specific. The growth of tumors in a mouse peritoneal cavity was intensified when the cancer cells were co-injected together with senescent HPMCs. This effect was reversible when the senescence of HPMCs was slowed down by the neutralization of p38 MAPK. The analysis of lesions excised from the peritoneum of patients with ovarian cancer showed the abundance of senescent HPMCs in close proximity to the cancerous tissue. Collectively, our findings indicate that senescent HPMCs which accumulate in the peritoneum in vivo may create a metastatic niche facilitating intraperitoneal expansion of ovarian malignancy.

Studies have demonstrated that changes in DNA methylation of cancer related genes can be an elementary process accounting for ovarian tumorigenesis. Therefore, we evaluated the possible association of single nucleotide polymorphisms (SNPs) of DNA methyltransferases (DNMTs) genes, including DNMT1, DNMT3B, and DNMT3A, with ovarian cancer development in the Polish population. Using PCR–RFLP and HRM analyses, we studied the prevalence of the DNMT1 rs8101626, rs2228611 and rs759920, DNMT3A rs2289195, 7590760, rs13401241, rs749131 and rs1550117, and DNMT3B rs1569686, rs2424913 and rs2424932 SNPs in patients with ovarian cancer (n = 159) and controls (n = 180). The lowest p values of the trend test were observed for the DNMT1 rs2228611 and rs759920 SNPs in patients with ovarian cancer (p _trend = 0.0118 and p _trend = 0.0173, respectively). Moreover, we observed, in the recessive inheritance model, that the DNMT1 rs2228611 and rs759920 SNPs are associated with an increased risk of ovarian cancer development [OR 1.836 (1.143–2.949), p = 0.0114, p _corr = 0.0342, and OR 1.932 (1.185–3.152), p = 0.0078, p _cor=0.0234, respectively]. However, none of other nine studied SNPs displayed significant contribution to the development of ovarian cancer. Furthermore, haplotype and multifactor dimensionality reduction analysis of the studied DNMT1, DNMT3B, and DNMT3A polymorphisms did not reveal either SNP combinations or gene interactions to be associated with the risk of ovarian cancer development. Our results may suggest that DNMT1 variants may be risk factors of ovarian cancer.

In the face of the obesity epidemic around the world, attention should be focused on the role of maternal obesity in the development of pregnancy. The purpose of this analysis was to evaluate the prediction of preeclampsia (PE) and isolated gestational hypertension (GH) for a number of maternal factors, in order to investigate the importance of pre-pregnancy obesity (body mass index, BMI ≥ 30 kg/m²), compared to other risk factors (e.g., prior PE, pregnancy weight gain (GWG), infertility treatment, interpregnancy interval, family history, the lack of vitamin supplementation, urogenital infection, and socioeconomic factors). In total, 912 women without chronic diseases were examined in a Polish prospective cohort of women with a single pregnancy (recruited in 2015–2016). Separate analyses were performed for the women who developed GH (n = 113) vs. 775 women who remained normotensive, as well as for those who developed PE (n = 24) vs. 775 controls. The probability of each disease was assessed for the base prediction model (age + primiparity) and for the model extended by one (test) variable, using logistic regression. Three measures were used to assess the prediction: area under curve (AUC) of the base and extended model, integrated discrimination improvement (IDI) (the index shows the difference between the value of the mean change in the predicted probability between the group of sick and healthy women when a new factor is added to the model), and net reclassification improvement (NRI) (the index focuses on the reclassification table describing the number of women in whom an upward or downward shift in the disease probability value occurred after a new factor had been added, including results for healthy and sick women). In the GH prediction, AUC increased most strongly when we added BMI (kg/m²) as a continuous variable (AUC = 0.716, p < 0.001) to the base model. The highest IDI index was obtained for prior GH/PE (IDI = 0.068, p < 0.001). The addition of BMI as a continuous variable or BMI ≥ 25 kg/m² improved the classification for healthy and sick women the most (NRI = 0.571, p < 0.001). In the PE prediction, AUC increased most strongly when we added BMI categories (AUC = 0.726, p < 0.001) to the base model. The highest IDI index was obtained for prior GH/PE (IDI = 0.050, p = 0.080). The addition of BMI categories improved the classification for healthy and sick women the most (NRI = 0.688; p = 0.001). After summing up the results of three indexes, the probability of hypertension in pregnancy was most strongly improved by BMI, including BMI ≥ 25 kg/m² for the GH prediction, and BMI ≥ 30 kg/m² for the PE prediction. Main conclusions: Pre-pregnancy BMI was the most likely factor to increase the probability of developing hypertension in pregnancy, compared to other risk factors. Hierarchies of PE and GH risk factors may suggest different (or common) mechanisms of their development.