Metabolic outcomes in very low birthweight and preterm infants in later life

The associations of preterm birth and low birth weight with childhood growth curves between birth and 12 years: a SITAR-based longitudinal analysis

BACKGROUND

Children born preterm grow differently from those born at term.

AIM

To compare growth in length/height, weight, and BMI of preterm- and term-born children, grouped by birth weight (BW) and gestational age (GA).

SUBJECTS AND METHODS

Longitudinal data of 950 children (birth to 12 years) were collected retrospectively. Growth trajectories were modelled using SITAR (Superimposition by Translation and Rotation) by sex, with three groups each for GA and BW.

RESULTS

SITAR summarised growth patterns from birth to 12 years and explained 76-79% of height variance, 90-92% for weight, and 72-75% for BMI. Early preterm and low BW groups were shorter, lighter and thinner on average than their term or normal BW peers, with late preterm and low-normal BW groups intermediate. Effects were larger for BW than GA, e.g. early preterm girls/boys were 0.3/0.8 kg lighter, 0.9/0.9 cm shorter and 0.8/0.8 kg/m2 thinner, while low BW girls/boys were 0.5/1.0 kg lighter, 1.5/1.4 cm shorter and 0.8/0.9 kg/m2 thinner. Moreover, faster growth rates were associated with lower BW.

CONCLUSION

Both BW and GA significantly impacted growth, but low BW more so than early preterm birth. This underscores the need for targeted interventions for low BW children to address potential long-term growth challenges.

Genetic contributions to risk of adverse pregnancy outcomes

Purpose of Review

Adverse pregnancy outcomes (APOs), including hypertensive disorders of pregnancy (HDP), low birthweight (LBW), and preterm birth (PTB), along with peripartum cardiomyopathy (PPCM) are associated with short- and long-term maternal and fetal cardiovascular risks. This review focuses on the genetic contributions to the risk of APOs and PPCM.

Recent Findings

The expansion of genome-wide association studies (GWAS) has led to better understanding of the biologic mechanisms underpinning APO, PPCM, and the predisposition to cardiovascular disease across the life course. Genetic loci known to be involved with the risk of hypertension (FTO, ZNF831) have been associated with the development of overall HDP and preeclampsia. Additionally, four loci significantly associated with type 2 diabetes have been associated with GDM (CDKAL1, MTNR1B, TCF7L2, CDK2NA-CDKN2B). Variants in loci known to affect genes coding for proteins involved in immune cell function and placental health (EBF1, EEFSEC, AGTR2, 2q13) have been implicated in the development of PTB and future cardiovascular risks for both the mother and the offspring. Genetic similarities in rare variants between PPCM and dilated cardiomyopathy have been described suggesting shared pathophysiologic origins as well as predisposition for future risk of heart failure, highlighting the need for the development PPCM genetic counseling guidelines.

Summary

Genetics may inform mechanisms, risk, and counseling for individuals after an APO or PPCM. Through recent advances in genetic techniques and analytic approaches, new insights into the underlying biologic mechanisms and genetic variants leading to these risks have been discovered.

Preterm newborn's postnatal growth patterns: how to evaluate them

OBJECTIVES

There are several factors that influence the postnatal growth of preterm infants. It is crucial to define how to evaluate the growth rate of each preterm child and its individual trajectory, the type of growth curve, either with parameters of prescriptive curves for healthy preterm infants with no morbidities or, in the case of preterm infants and their "bundle of vulnerabilities", growth curves that may represent how they are actually growing, with the aim of directing appropriate nutritional care to each gestational age range.

DATA SOURCES

The main studies with growth curves for growth monitoring and the appropriate nutritional adjustments that prioritized the individual trajectory of postnatal growth rate were reviewed. PubMed and Google Scholar were searched.

DATA SYNTHESIS

The use of longitudinal neonatal data with different gestational ages and considering high and medium-risk pregnancies will probably be essential to evaluate the optimal growth pattern.

CONCLUSIONS

Prioritizing and knowing the individual growth trajectory of each preterm child is an alternative for preterm infants with less than 33 weeks of gestational age. For larger preterm infants born at gestational age >33 weeks, the Intergrowth 21st curves are adequate.