BMI Trajectories During the First 2 Years, and Their Associations With Infant Overweight/Obesity: A Registered Based Cohort Study in Taizhou, China

Infant BMI trajectories as early risk markers of poor psychosocial health in preadolescence

BACKGROUND

Common mental disorders often emerge during childhood and adolescence, and their prevalence is disproportionately elevated among those affected by obesity. Early life growth patterns may provide a useful target for primordial prevention; however, research is lacking. Therefore, this study aimed to identify distinct body mass index (BMI) trajectories during the first year of life and to assess their associations with psychosocial outcomes in preadolescence (9-13 years).

METHODS

Data were obtained from n = 1778 Greek children (9-13 years). Infant anthropometric data were obtained from paediatric health records and BMI trajectories during the first year of life were estimated using group-based trajectory modelling. Preadolescent emotional functioning, self-esteem, body image dissatisfaction and dieting behaviours were self-reported via validated questionnaires. Associations were estimated using binary and ordinal logistic regression, adjusted for key confounders.

RESULTS

Four BMI trajectories were identified: low (26.7%), average (41.8%), high (25.2%), and very high (6.4%). Children belonging to the very high trajectory had greater odds of body image dissatisfaction (OR: 1.62, 95%CI: 1.11, 2.38), dieting behaviour (OR: 1.49, 95%CI: 1.01, 2.20) and restrained eating (OR: 1.69 95%CI: 1.14, 2.52) than children belonging to the average trajectory. Body image dissatisfaction was also greater in children belonging to the high trajectory (OR: 1.40, 95%CI: 1.11, 1.76). However, infant BMI trajectories did not significantly predict childhood emotional functioning or self-esteem status.

CONCLUSION

Infants with BMI growth in the high reference ranges had poorer psychosocial outcomes in preadolescence. Whilst further research is needed to replicate these findings, monitoring early infant growth trajectories may allow for early stratification of infants at risk of poor psychosocial outcomes.

Growth and development of syphilis-exposed and -unexposed uninfected children during their first 18 months of life in Suzhou, China: a nested case-control study with propensity score matching

Background

With the successful implementation of Prevention of Mother-to-Child Transmission (PMTCT) policies, the proportion of infants with exposure to both syphilis and antibiotic medication in utero has increased in China, but there is limited evidence about the early growth and development of such infants.

Methods

We conducted a retrospective nested case-control study based on data from the China PMTCT program conducted in Suzhou from 2016 to 2021. Propensity score matching (PSM) was employed to extract 826 syphilis-exposed but uninfected (SEU) infants and 1,652 syphilis-unexposed uninfected (SUU) infants from a total of 712,653 infants. Maternal characteristics were collected through questionnaires, such as parity, age, education level, smoking and drinking habits during pregnancy. Infantile characteristics were retrieved from medical records or via questionnaires, such as gestational age, gender, mode of delivery, Apgar scores, birth weight and length, outdoor time, vitamin D intake, and feed pattern. Mixed effects models, adjusting for potential influencing factors, were used to investigate the early infantile growth pattern of SEU and SUU infants. All statistical analysis were conducted using R (version 4.2.0).

Results

Length and weight were slightly higher in SEU infants than in the SUU infants at some time points (months 0 and 18 for length, p-values <0.05; months 0, 6, and 18 for weight, p < 0.05). In the mixed effects model, SEU group was found to be associated with higher weight [exponentiated beta exp.(β) = 1.15, 95% Confidence Interval (CI) = 1.06, 1.25], length [exp(β) = 1.42, 95% CI = 1.14, 1.77], and BMI z-score [exp(β) = 1.09, 95% CI = 1.00, 1.19].

Conclusion

With the effective prevention of congenital syphilis under the PMTCT program, SEU infants have non-inferior growth patterns during their first 18 months of life compared with SUU controls in Suzhou, China.

Modelling individual infancy growth trajectories to predict excessive gain in BMI z-score: a comparison of growth measures in the ABCD and GECKO Drenthe cohorts

BACKGROUND

Excessive weight gain during childhood is a strong predictor for adult overweight, but it remains unknown which growth measures in infancy (0-2 years of age), besides predictors known at birth, are the strongest predictors for excessive weight gain between 2 and 5-7 years of age.

METHODS

The Amsterdam Born Children and their Development (ABCD) study formed the derivation cohort, and the Groningen Expert Center for Kids with Obesity (GECKO) Drenthe study formed the validation cohort. Change (Δ) in body mass index (BMI) z-score between 2 and 5-7 years was the outcome of interest. The growth measures considered were weight, weight-for-length (WfL), and body mass index (BMI). Formats considered for each growth measure were values at 1, 6, 12, and 24 months, at the BMI peak, the change between aforementioned ages, and prepeak velocity. 10 model structures combining different variable formats and including predictors at birth were derived for each growth measure, resulting in 30 linear regression models. A Parsimonious Model considering all growth measures and a Birth Model considering none were also derived.

RESULTS

The derivation cohort consisted of 3139 infants of which 373 (11.9%) had excessive gain in BMI z-score (> 0.67). The validation cohort contained 2201 infants of which 592 (26.9%) had excessive gain. Across the 3 growth measures, 5 model structures which included measures related to the BMI peak and prepeak velocity (derivation cohort area under the curve [AUC] range = 0.765-0.855) achieved more accurate estimates than 3 model structures which included growth measure change over time (0.706-0.795). All model structures which used BMI were superior to those using weight or WfL. The AUC across all models was on average 0.126 lower in the validation cohort. The Parsimonious Model's AUCs in the derivation and validation cohorts were 0.856 and 0.766, respectively, compared to 0.690 and 0.491, respectively, for the Birth Model. The respective false positive rates were 28.2% and 20.1% for the Parsimonious Model and 70.0% and 74.6% for the Birth Model.

CONCLUSION

Models' performances varied significantly across model structures and growth measures. Developing the optimal model requires extensive testing of the many possibilities.

A Pilot Study Exploring Temporal Development of Gut Microbiome/Metabolome in Breastfed Neonates during the First Week of Life

Purpose

Exclusive breastfeeding promotes gut microbial compositions associated with lower rates of metabolic and autoimmune diseases. Its cessation is implicated in increased microbiome-metabolome discordance, suggesting a vulnerability to dietary changes. Formula supplementation is common within our low-income, ethnic-minority community. We studied exclusively breastfed (EBF) neonates' early microbiome-metabolome coupling in efforts to build foundational knowledge needed to target this inequality.

Methods

Maternal surveys and stool samples from seven EBF neonates at first transitional stool (0-24 hours), discharge (30-48 hours), and at first appointment (days 3-5) were collected. Survey included demographics, feeding method, medications, medical history and tobacco and alcohol use. Stool samples were processed for 16S rRNA gene sequencing and lipid analysis by gas chromatography-mass spectrometry. Alpha and beta diversity analyses and Procrustes randomization for associations were carried out.

Results

Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria were the most abundant taxa. Variation in microbiome composition was greater between individuals than within (p=0.001). Palmitic, oleic, stearic, and linoleic acids were the most abundant lipids. Variation in lipid composition was greater between individuals than within (p=0.040). Multivariate composition of the metabolome, but not microbiome, correlated with time (p=0.030). Total lipids, saturated lipids, and unsaturated lipids concentrations increased over time (p=0.012, p=0.008, p=0.023). Alpha diversity did not correlate with time (p=0.403). Microbiome composition was not associated with each samples' metabolome (p=0.450).

Conclusion

Neonate gut microbiomes were unique to each neonate; respective metabolome profiles demonstrated generalizable temporal developments. The overall variability suggests potential interplay between influences including maternal breastmilk composition, amount consumed and living environment.

Association Between Trajectory Patterns of Body Mass Index Change Up to 10 Months and Early Gut Microbiota in Preterm Infants

Recent research suggests that gut microbiota plays an important role in the occurrence and development of excessive weight and obesity, and the early-life gut microbiota may be correlated with weight gain and later growth. However, the association between neonatal gut microbiota, particularly in preterm infants, and excessive weight and obesity remains unclear. To evaluate the relationship between gut microbiota and body mass index (BMI) growth trajectories in preterm infants, we examined microbial composition by performing 16S rDNA gene sequencing on the fecal samples from 75 preterm infants within 3 months after birth who were hospitalized in the neonatal intensive care unit of Hunan Children’s Hospital from August 1, 2018 to October 31, 2019. Then, we collected their physical growth information during 0–10 months. Latent growth mixture models were used to estimate growth trajectories of infantile BMI, and the relationship between the gut microbiota and the BMI growth trajectories was analyzed. The results demonstrated that there were 63,305 and 61 operational taxonomic units in the higher BMI group (n = 18), the lower BMI group (n = 51), and the BMI catch-up group (n = 6), respectively. There were significant differences in the abundance of the gut microbiota, but no significant differences in the diversity of it between the lower and the higher BMI group. The BMI growth trajectories could not be clearly distinguished because principal component analysis showed that gut microbiota composition among these three groups was similar. The three groups were dominated by Firmicutes and Proteobacteria in gut microbiota composition, and the abundance of Lactobacillus in the higher BMI group was significantly different from the lower BMI group. Further intervention experiments and dynamic monitoring are needed to determine the causal relationship between gut microbiota differences and the BMI change.