Pregnancy involves significant metabolic and hemodynamic changes. Maternal obesity adds to these physiological adaptations, potentially increasing cardiometabolic complications. This study aimed to describe the trajectories of total cholesterol (TC), triglycerides (TG), glucose, and blood pressure (BP) during pregnancy and analyze their association with pBMI and MGWG. Elevated TC, TG, glucose, and BP are associated with cardiovascular and metabolic diseases, and abnormal levels during pregnancy link to complications like preeclampsia, gestational diabetes, preterm delivery, and adverse fetal outcomes. While pBMI is a known predictor of adverse effects, less is known about the impact of GWG on these biomarkers. Existing research primarily focuses on high-income countries, necessitating investigation in populations with high obesity prevalence like Mexico, where genetic and environmental factors may contribute to pregnancy complications and future cardiovascular disease. The study sought to describe the trajectories of these cardiometabolic risk indicators and analyze their association with pBMI and MGWG in Mexican pregnant women.

The literature review highlights the physiological changes in lipid and glucose metabolism, and the cardiovascular system during normal pregnancy. Increased levels of cholesterol and fatty acids are crucial for fetal development, while insulin resistance leads to changes in maternal glucose concentrations. Studies have shown associations between abnormal levels of these cardiometabolic markers and maternal/fetal complications, including preeclampsia, gestational diabetes, preterm delivery, and birth weight abnormalities. While previous research has explored the influence of pBMI on lipid trajectories, showing differences in pregnant women with obesity, less is known about the effect of GWG. Furthermore, there's a lack of studies analyzing the association of pBMI and MGWG on glucose concentrations throughout pregnancy, despite evidence linking obesity to gestational diabetes risk. Studies have indicated a positive association between pBMI and blood pressure, with some evidence suggesting a positive association between GWG and blood pressure. However, most existing research is from high-income countries, with limited data from Latin American countries with high obesity prevalence.

This prospective cohort study, part of the PRINCESA study, recruited 794 pregnant women in Mexico City (2010-2015). Inclusion criteria were age 18-49 years and at least two measurements of anthropometric and biochemical data. Women with pregnancy complications or active smoking were excluded. pBMI was calculated from the first visit (before 18 weeks). MGWG was calculated as the weight difference between consecutive monthly visits. Cardiometabolic risk indicators (TC, TG, glucose, SBP, DBP, MAP) were measured monthly. Covariates included age, education, marital status, parity, energy intake, and micronutrient intake. Mixed-effects regression models were used to describe the trajectories of these biomarkers and analyze their association with pBMI and MGWG, adjusting for covariates. Model selection (linear, quadratic, or cubic) was based on log-likelihood comparisons. Outliers were removed using standard deviation cut-offs. Data were analyzed using STATA/SE 15.0.

720 women were included (16.6% pre-gestational obesity, 33.2% overweight, 45.8% normal pBMI, 4.4% underweight). Women with pre-gestational obesity had higher TC and TG concentrations at the beginning of pregnancy compared to normal-weight women (p<0.05), but the increase was less throughout pregnancy. By the end of pregnancy, obese women had lower TC and TG than normal-weight women. Obese women had higher glucose concentrations and BP levels throughout pregnancy compared to normal-weight women (p<0.05). MGWG was not significantly associated with any of the biochemical indicators or BP trajectories. Adjusted mixed-effects models showed that pBMI was significantly associated with lipid, glucose, and BP trajectories. When MGWG was added to the model for TC, the association with pBMI became non-significant, suggesting MGWG may be a mediating or confounding factor. For other cardiometabolic indicators, pBMI was an independent predictor of trajectories. Figures illustrate the adjusted trajectories of TC, TG, glucose, SBP, DBP, and MAP by pBMI subgroups.

The study's findings indicate a differential association between pBMI and MGWG with cardiometabolic risk indicators in pregnant Mexican women. pBMI, reflecting pre-pregnancy health, was strongly associated with altered lipid, glucose, and BP trajectories. The lower increase in lipids among obese women may be due to increased placental lipid transfer to the fetus, potentially exposing the fetus to abnormal FA concentrations and possibly inducing fetal adipogenesis. Higher pBMI was linked to higher BP, consistent with mechanisms of weight-related hypertension. The lack of association between MGWG and cardiometabolic indicators suggests pBMI is a more important predictor than MGWG in this context. This longitudinal design allowed modeling of monthly changes, providing a detailed understanding of cardiometabolic risk changes throughout pregnancy. The findings support the need for interventions focusing on achieving a healthy pBMI before pregnancy to mitigate cardiometabolic risks during and after pregnancy.

This study demonstrated that pBMI is a stronger predictor of cardiometabolic risk trajectories during pregnancy compared to MGWG. Obese women showed altered lipid, glucose, and blood pressure profiles. Longitudinal monitoring of these women is needed to assess long-term cardiovascular and metabolic health and explore the impact of maternal risk factors on fetal development. These findings highlight the need for interventions aimed at promoting healthy pBMI before pregnancy and refined clinical risk stratification for pregnant women.

Not all women had all eight measurements; however, mixed-effects models utilized all available data. The sample wasn't probabilistically drawn, limiting generalizability. Results might not be fully extrapolatable to other populations.