In a prospective cohort study examining the association of umbilical cord blood metabolites with cardiovascular outcomes in children, the general characteristics of 763 mothers and their children were analyzed. This study aimed to assess the impact of maternal and fetal factors on metabolic health at ages 6 and 10. The results showed that the participating mothers had significant differences in their characteristics compared to a group without information on cardiovascular risk outcomes. These mothers were, on average, older, more often nulliparous (without previous children), had higher levels of education, and smoked less during pregnancy. Furthermore, alcohol consumption during pregnancy was higher in this group, and folic acid supplement use was also more common among them. These differences highlight the important role of maternal factors in shaping children's metabolic health in later life.
Maternal Characteristics The mothers in this study had specific characteristics that could affect their children's metabolic outcomes. Their average age at enrollment was 31.8 years, with a standard deviation of 3.9 years, indicating a relatively balanced age distribution. Among these mothers, 62.3% (475 individuals) were nulliparous, meaning this pregnancy was their first birthing experience. The pre-pregnancy body mass index of these mothers was reported to be, on average, 22.5 kg/m², with a range of 18.6 to 34.3, indicating diversity in their physical status before pregnancy. In terms of education, 64.8% (491 individuals) had higher education, and this high level of education could be associated with greater awareness of prenatal care.
Regarding behavioral habits, 79.5% of mothers (543 individuals) stated that they never smoked during pregnancy, while 8.6% (59 individuals) smoked until they became aware of their pregnancy and then quit, and 11.9% (81 individuals) continued to smoke throughout pregnancy. This difference in smoking behavior can have various effects on fetal health, as smoking during pregnancy is associated with lower birth weight and metabolic problems. Alcohol consumption was another factor examined: 29% of mothers (197 individuals) did not consume any alcohol during pregnancy, 15.9% (108 individuals) consumed alcohol until they became aware of their pregnancy and then stopped, and 55.1% (375 individuals) continued to consume alcohol throughout pregnancy. This high rate of alcohol consumption is notable compared to other groups and may have affected fetal metabolism. Folic acid supplement use was also very common in this group, with 92.5% of mothers (582 individuals) using this supplement. Folic acid is essential for fetal neurological development, and its widespread use in this study indicates a high level of attention from mothers to medical recommendations. The average daily energy intake of these mothers was 2106 kcal, with a range of 1295 to 3096 kcal, indicating diversity in their diet.
Fetal and Neonatal Characteristics The fetuses and newborns studied also had specific characteristics that could help in a better understanding of their early health. Among the 763 fetuses, 48% (363 individuals) were female, indicating an almost balanced gender distribution. The average gestational age at birth was 40.3 weeks, with a range of 37.1 to 42.4 weeks, indicating that most newborns were born at appropriate times and at term. The average birth weight of these newborns was 3563 grams, with a range of 2621 to 4474 grams, indicating good overall health at birth. This data was used as a basis for subsequent examinations at older ages.
Follow-up at 6 Years Old The follow-up of children at 6 years old provided valuable information about their metabolic and cardiovascular status. Among the 722 children examined, the average age was 5.9 years. The body mass index (BMI) of these children was reported to be, on average, 15.6 kg/m², with a standard deviation of 1.3, indicating slight variability in their relative weight. The total body fat mass of these children was, on average, 4996 grams, with a range of 3220 to 8995 grams. Android fat mass (abdominal region) was 174 grams (range 88 to 464) and gynoid fat mass (pelvic region) was 759 grams (range 415 to 1484). The android-to-gynoid ratio, a measure of body fat distribution, was reported to be, on average, 0.24, with a range of 0.16 to 0.35. The systolic blood pressure of these children was, on average, 102 mmHg (standard deviation 7.9) and diastolic blood pressure was 60.3 mmHg (standard deviation 6.6), which is within the normal range for this age. Insulin levels were, on average, 111.1 pmol/L (range 19.6 to 373.7), total cholesterol 4.2 mmol/L (standard deviation 0.6), HDL 1.3 mmol/L (standard deviation 0.3), LDL 2.3 mmol/L (standard deviation 0.6), and triglycerides 1.0 mmol/L (range 0.4 to 2.2). The prevalence of cardiovascular risk clustering, defined as having three or more risk factors, was 8.2% (37 out of 454) at this age, indicating a small percentage of children with a risk profile.
Follow-up at 10 Years Old At 10 years old, 700 children were followed up. The average age of these children was 9.8 years. The body mass index was reported to be, on average, 16.7 kg/m² (range 13.9 to 22.2), indicating a natural increase in weight with growth. Android fat mass increased to 304.2 grams (range 120.8 to 1248.4) and gynoid fat mass increased to 1350.3 grams (range 597.9 to 3087.6). The android-to-gynoid ratio was, on average, 0.23 (range 0.16 to 0.43), showing a slight change compared to 6 years old. Systolic blood pressure at this age was, on average, 103 mmHg (standard deviation 7.63) and diastolic 58 mmHg (standard deviation 6.3), still within the normal range. Insulin levels increased to 170.3 pmol/L (range 35.8 to 535.7), consistent with metabolic changes during growth. Total cholesterol was 4.3 mmol/L (standard deviation 0.6), HDL 1.5 mmol/L (standard deviation 0.3), LDL 2.3 mmol/L (standard deviation 0.6), and triglycerides 1.0 mmol/L (range 0.4 to 2.4). The prevalence of cardiovascular risk clustering increased to 13.6% (59 out of 435) at this age, indicating a gradual growth of this risk with increasing age.
Umbilical Cord Blood Metabolites and Outcomes Analyses showed no significant association between individual metabolite concentrations, metabolite groups, or their ratios with blood pressure, insulin, HDL, LDL, and triglycerides at ages 6 and 10 after FDR correction. However, some findings were notable. For example, higher concentrations of the metabolite Carn.a.C.8.1 were associated with android fat mass percentage at 6 years old (β: 0.14; CI: 0.07-0.22) and Carn.a.C.6.0 at 10 years old (β: 0.16; CI: 0.08-0.23) in the crude model, but this association did not remain significant in the main model that accounted for more confounding factors. This suggests that other factors may have influenced these relationships.
Cardiovascular Risk Clustering The prevalence of cardiovascular risk clustering was 8.2% at 6 years old and 13.6% at 10 years old, and its increase with age is expected. In the crude model at 6 years old, 5 Carn.a metabolites and short-chain, medium-chain, and acylcarnitine groups were associated with an increased chance of risk clustering (p<0.05). The strongest association was observed with the metabolite Carn.a.C.6.0, where for every standard deviation increase in concentration, the chance of risk clustering was 1.95 times higher (CI: 1.39-2.75). However, after adjusting for confounding factors such as maternal age, education, and behavioral habits, this association did not remain significant. At 10 years old, no association was observed between umbilical cord blood metabolites and risk clustering, which may be due to metabolic changes with increasing age.
Discussion and Interpretation Adverse exposures during fetal life can alter maternal and fetal metabolism, predisposing to an unfavorable cardiovascular profile in later life. This study was the first to investigate the association of umbilical cord blood metabolites with cardiovascular outcomes at several childhood ages. Unlike previous studies that focused on body mass index (BMI), this research used the android-to-gynoid ratio, which is a more accurate measure for predicting the risk of cardiovascular diseases in children. The lack of a strong association observed in this study may be due to its healthy and educated population, which likely reduced metabolic effects. Also, umbilical cord blood metabolites are a mixture of maternal, placental, and fetal metabolites and are influenced by factors such as stress, hormone levels, and maternal diet, which may complicate the analyses.
Methodological Considerations One of the strengths of this study is the collection of longitudinal data from birth, which allows for long-term follow-up. The relatively large sample size (763 mothers and children) also contributes to the validity of the results. However, there are also limitations. The study population was predominantly healthy, educated, and from a specific ethnic group (Dutch), which may limit the generalizability of the results to more diverse populations. Blood samples in this study were not fasted and were collected at different times of the day, which could affect the accuracy of metabolite measurements. Also, due to the practical conditions of umbilical cord blood collection, some samples may be a mixture of arterial and venous blood, which affects the stability of metabolites.
Conclusion This cohort study showed no strong and consistent association between umbilical cord blood metabolites and cardiovascular risk clustering at ages 6 and 10. However, there is preliminary evidence that children with an unfavorable cardiovascular risk profile may have higher levels of short-chain and medium-chain Carn.a metabolites at birth. These findings should be interpreted with caution, as they did not remain significant after adjusting for confounding factors. To better understand these relationships, future studies should investigate more diverse populations with a higher prevalence of risk factors such as obesity, gestational diabetes, and hypertension. Such research could help clarify the role of early metabolites in long-term metabolic health and pave the way for preventive interventions. End of content/