Longitudinal characterization of determinants associated with obesogenic growth patterns in early childhood

Early life factors that affect obesity and the need for complex solutions

The prevalence of obesity increases with age but is apparent even in early life. Early childhood is a critical period for development that is known to influence future health. Even so, the focus on obesity in this phase, and the factors that affect the development of obesity, has only emerged over the past two decades. Furthermore, there is a paucity of iterative work in this area that would move the field forward. Obesity is a complex condition involving the interplay of multiple influences at different levels: the individual and biological level, the sociocultural level, and the environmental and system levels. This Review provides a brief overview of the evidence for these factors with a focus on aspects specific to early life. By spotlighting the complex web of interactions between the broad range of influences, both causal and risk markers, we highlight the complex nature of the condition. Much work in the early life field remains observational and many of the intervention studies are limited by a focus on single influences and a disjointed approach to solutions. Yet the complexity of obesity necessitates coordinated multi-focused solutions and joined-up action across the first 2,000 days from conception, and beyond.

Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes

BACKGROUND

Body mass index (BMI) trajectories have been used to assess the growth of children with respect to their peers, and to anticipate future obesity and disease risk. While retrospective BMI trajectories have been actively studied, models to prospectively predict continuous BMI trajectories have not been investigated.

MATERIALS AND METHODS

Using longitudinal BMI measurements between birth and age 10 y from a mother-offspring cohort, we leveraged a multi-task Gaussian process approach to develop and evaluate a unified framework for modeling, clustering, and prospective prediction of BMI trajectories. We compared its sensitivity to missing values in the longitudinal follow-up of children, compared its prediction performance to cubic B-spline and multilevel Jenss-Bayley models, and used prospectively predicted BMI trajectories to assess the probability of future BMIs crossing the clinical cutoffs for obesity.

RESULTS

MagmaClust identified 5 distinct patterns of BMI trajectories between 0 to 10 y. The method outperformed both cubic B-spline and multilevel Jenss-Bayley models in the accuracy of retrospective BMI trajectories while being more robust to missing data (up to 90%). It was also better at prospectively forecasting BMI trajectories of children for periods ranging from 2 to 8 years into the future, using historic BMI data. Given BMI data between birth and age 2 years, prediction of overweight/obesity status at age 10 years, as computed from MagmaClust's predictions exhibited high specificity (0.94), negative predictive value (0.89), and accuracy (0.86). The accuracy, sensitivity, and positive predictive value of predictions increased as BMI data from additional time points were utilized for prediction.

CONCLUSION

MagmaClust provides a unified, probabilistic, non-parametric framework to model, cluster, and prospectively predict childhood BMI trajectories and overweight/obesity risk. The proposed method offers a convenient tool for clinicians to monitor BMI growth in children, allowing them to prospectively identify children with high predicted overweight/obesity risk and implement timely interventions.

[A prospective cohort study of association between early childhood body mass index trajectories and the risk of overweight]

OBJECTIVE

To compare the association between body mass index (BMI) trajectories determined by different methods and the risk of overweight in early childhood in a prospective cohort study, and to identify children with higher risk of obesity during critical growth windows of early childhood.

METHODS

A total of 1 330 children from Peking University Birth Cohort in Tongzhou (PKUBC-T) were included in this study. The children were followed up at birth, 1, 3, 6, 9, 12, 18, and 24 months and 3 years of age to obtain their height/length and weight data, and calculate BMI Z-score. Latent class growth mixture modeling (GMM) and longitudinal data-based k-means clustering algorithm (KML) were used to determine the grouping of early childhood BMI trajectories from birth to 24 mouths. Linear regression was used to compare the association between early childhood BMI trajectories determined by different methods and BMI Z-score at 3 years of age. The predictive performance of early childhood BMI trajectories determined by different methods in predicting the risk of overweight (BMI Z-score > 1) at 3 years was compared using the average area under the curve (AUC) of 5-fold cross-validation in Logistic regression models.

RESULTS

In the study population included in this research, the three-category trajectories determined using GMM were classified as low, medium, and high, accounting for 39.7%, 54.1%, and 6.2% of the participants, respectively. The two-category trajectories determined using the KML method were classified as low and high, representing 50. 3% and 49. 7% of the participants, respectively. The three-category trajectories determined using the KML method were classified as low, medium, and high, accounting for 31.1%, 47.4%, and 21.5% of the participants, respectively. There were certain differences in the growth patterns reflected by the early childhood BMI trajectories determined using different methods. Linear regression analysis found that after adjusting for maternal ethnicity, educational level, delivery mode, parity, maternal age at delivery, gestational week at delivery, children' s gender, and breastfeeding at 1 month of age, the association between the high trajectory group in the three-category trajectories determined by the KML method (manifested by a slightly higher BMI at birth, followed by rapid growth during infancy and a stable-high BMI until 24 months) and BMI Z-scores at 3 years was the strongest. Logistic regression analysis revealed that the three-category trajectory grouping determined by the KML method had the best predictive performance for the risk of overweight at 3 years. The results were basically consistent after additional adjustment for the high bound score of the child' s diet balanced index, average daily physical activity time, and screen time.

CONCLUSION

This study used different methods to identify early childhood BMI trajectories with varying characteristics, and found that the high trajectory group determined by the KML method was better able to identify children with a higher risk of overweight in early childhood. This provides scientific evidence for selecting appropriate methods to define early childhood BMI trajectories.

Childhood overweight and obesity: age stratification contributes to the differences in metabolic characteristics

OBJECTIVE

The aim of this study was to identify the differential metabolic characteristics of children with overweight and obesity and understand their potential mechanism in different age stratifications.

METHODS

Four hundred seventy-three children were recruited and divided into two age stratifications: >4 years (older children) and ≤4 years (younger children), and overweight and obesity were defined according to their BMI percentile. A one dimensional proton nuclear magnetic resonance (1 H-NMR)-based metabolomics strategy combined with pattern recognition methods was used to identify the metabolic characteristics of childhood overweight and obesity.

RESULTS

Four and sixteen potential biomarkers related to overweight and two and twenty potential biomarkers related to obesity were identified from younger and older children, respectively. Fluctuations in phenylalanine, tyrosine, glutamine, leucine, histidine, and ascorbate co-occurred in children with obesity at two age stratifications. The disturbances in biosynthesis and metabolism of amino acids, lipid metabolism, and galactose metabolism disturbance were mainly involved in children with overweight and obesity.

CONCLUSIONS

The metabolic disturbances show a significant progression from overweight to obesity in children, and different metabolic characteristics were demonstrated in age stratifications. The changes in the levels of phenylalanine, tyrosine, glutamine, leucine, histidine, and ascorbate were tracked with the persistence of childhood obesity. These findings will promote the mechanistic understanding of childhood overweight and obesity.

The association of serum toxic metals and essential elements during early pregnancy with body mass index trajectory of infants during the first years: A prospective study

To the best of our knowledge, prior research has yet to delve into the combined and interactive relationships between maternal exposure to essential elements and toxic metals and infancy's continuous growth and trajectories. This study aims to discern infant growth trajectories in the first year of life and to determine the associations of maternal serum levels of essential elements and toxic metals with growth trajectory. Within a Chinese prospective cohort in 2019 - 2021, 407 mother-infant pairs were included, and the serum levels of five essential elements (zinc, calcium, copper, magnesium and iron) and two toxic metals (cadmium and lead) in early pregnancy were assessed. The growth trajectory of infants was followed until age one year. Raw BMI and height values were transformed to age- and sex-specific BMI and height standard deviation (SD) scores. Latent-class group-based trajectory models and piecewise linear mixed regression were estimated to determine infant growth trajectories and growth velocity, respectively. The individual relationship between maternal metallic element levels and infant growth trajectory was examined using multinomial logistic regression models and linear mixed regression, while joint associations and interactive relationships were explored using Bayesian kernel machine regression (BKMR) following confounder adjustments. Four distinct trajectory patterns based on BMI-z score (low-rapid BMI gain group, normal-stable BMI group, very low-rapid BMI gain group and normal-rapid BMI gain group) and length-for-age (high-stable length group, low-stable length group, normal-rapid length gain group, very low-rapid length gain group) were identified during the first year post-birth, respectively. In single-metal and multiple-metal models, infants born to mothers with higher serum Zn and lower serum Cu levels were associated with a normal-rapid BMI gain trajectory during the first year. Serum Cu exhibited a positive correlation with the rate of BMI change solely in infants aged 6-12 months. Further, the BKMR analysis revealed a statistically significant and negative joint effect of the five essential elements on the likelihood of normal-rapid BMI/length gain trajectory when serum levels of these elements fell below the 70th percentile compared to median levels. In addition, high levels of serum copper and calcium interactively affect the rates of BMI change during 6-12 months old (β: -0.21, 95% CI: -0.44, -0.03, P = 0.04, P-interaction=0.04). In conclusion, maternal trace elements at early pregnancy are linked to infant growth patterns and growth velocity in the first year of life.

Children who appeared or remained overweight or obese predict a higher follow-up blood pressure and higher risk of hypertension: a 6-year longitudinal study in Yantai, China

Few longitudinal studies have examined the effect of weight status change on blood pressure change over time and incidence of hypertension among Chinese children. The longitudinal study enrolled 17,702 Chinese children aged 7 years in Yantai in 2014 as baseline, with a continuous 5 years of follow-up to 2019. Generalized estimating equation model was fitted to examine the main and interaction effects of weight status change and time with blood pressure and the incidence of hypertension. Compared with the participants who remained normal weight, the participants who remained overweight or obese had higher systolic blood pressure (SBP) (β = 2.89, p < 0.001) and diastolic blood pressure (DBP) (β = 1.79, p < 0.001). Significant interactions were identified between weight status change and time with SBP (χ2interaction = 697.77, p < 0.001) and DBP (χ2interaction = 270.49, p < 0.001). The odds ratio (OR) and 95% confidence interval (CI) of hypertension were 1.70 (1.59-1.82) for participants who appeared overweight or obese, 2.26 (2.14-2.40) for participants who remained overweight or obese, compared with the participants who remained normal weight. Those who switched from overweight or obesity to normal weight had almost the same risk of developing hypertension (OR = 1.13, 95% CI: 1.02 to 1.26) as children who remained normal weight. Children who appeared or remained overweight or obese predict a higher follow-up blood pressure and higher risk of hypertension, whereas losing weight could reduce blood pressure and the risk of hypertension. Children who appeared or remained overweight or obese predict a higher follow-up blood pressure and higher risk of hypertension, whereas losing weight could reduce blood pressure and the risk of hypertension.