Direction of causality between body fat and insulin resistance in children--a longitudinal study (EarlyBird 51)

Moderating effect of bone maturation on the relationship between body fat and insulin resistance

OBJECTIVES

The aim of the present study was to analyze the moderating effect of maturation on the relationship between body fat and insulin resistance, in children from 9 to 12 years.

METHODS

Maturity offset, homeostasis model assessment index of insulin resistance (HOMA-IR), and body fat percentage (BF%) were estimated in 127 children. Skeletal maturation indicator (SMI) was estimated in 105 participants. A moderation analysis based on a linear regression and on Johnson-Neyman technique was performed using a 95% confidence level.

RESULTS

BF%, insulin levels, and HOMA-IR values were lower in late maturers than on-time maturers (p<0.05). SMI moderated the relationship between BF% and HOMA-IR index in 11.1% of boys and in 32.5% of girls (p<0.05). Also, the highest values of HOMA-IR index were observed at -1 year to achieve the peak height velocity, in girls (p<0.05).

CONCLUSIONS

Maturation process has a moderation effect on the relationship BF% and insulin resistance, particularly in early maturers; furthermore, it was independently associated with HOMA-IR.

Determinants of insulin resistance in children exposed to gestational diabetes in utero

BACKGROUND

The evolution of increased adiposity and cardiometabolic risk in offspring exposed to maternal gestational diabetes (GDM) is not well understood.

OBJECTIVE

(a) To evaluate the impact of in utero exposure to GDM and maternal weight status on homeostasis model assessment of insulin resistance (HOMA-IR) in the offspring longitudinally from 1 to 3 years of age and (b) to compare body mass index (BMI) and HOMA-IR in GDM and non-GDM exposed offspring at 1 and 3 years of age.

METHODS

A prospective cohort of children born to mothers with and without GDM underwent metabolic characterization between birth and 3 years of age.

RESULTS

In the overall cohort, weight gain between birth and 3 years of age was positively associated with HOMA-IR (β = 0.1491, P = .02), independent of maternal weight status. HOMA-IR was not different between GDM and non-GDM exposed children from 1 to 3 years of age; however, BMI z score was greater in GDM exposed children at 3 years of age. Among non-GDM exposed children, male sex predicted a 35.1% lower HOMA-IR (P = .03). In GDM exposed offspring, a 1 unit increase in maternal insulin sensitivity predicted a 20.8% decrease in HOMA-IR (P = .002).

CONCLUSIONS

Overall, weight gain in the first 3 years of life was positively associated with HOMA-IR, while insulin sensitivity of mothers with GDM negatively predicted HOMA-IR in the offspring. Our findings indicate the need to target weight trajectories in early life, as well as maternal factors during gestation to improve metabolic outcomes in the offspring, particularly those exposed to GDM.

A longitudinal study of serum insulin and insulin resistance as predictors of weight and body fat gain in African American and Caucasian children

BACKGROUND

The influence of insulin and insulin resistance (IR) on children's weight and fat gain is unclear.

OBJECTIVE

To evaluate insulin and IR as predictors of weight and body fat gain in children at high risk for adult obesity. We hypothesized that baseline IR would be positively associated with follow-up body mass index (BMI) and fat mass.

SUBJECTS/METHODS

Two hundred and forty-nine healthy African American and Caucasian children aged 6-12 years at high risk for adult obesity because of early-onset childhood overweight and/or parental overweight were followed for up to 15 years with repeated BMI and fat mass measurements. We examined baseline serum insulin and homeostasis model of assessment-IR (HOMA-IR) as predictors of follow-up BMI Z-score and fat mass by dual-energy X-ray absorptiometry in mixed model longitudinal analyses accounting for baseline body composition, pubertal stage, sociodemographic factors and follow-up interval.

RESULTS

At baseline, 39% were obese (BMI⩾95th percentile for age/sex). Data from 1335 annual visits were examined. Children were followed for an average of 7.2±4.3 years, with a maximum follow-up of 15 years. After accounting for covariates, neither baseline insulin nor HOMA-IR was significantly associated with follow-up BMI (Ps>0.26), BMIz score (Ps>0.22), fat mass (Ps>0.78) or fat mass percentage (Ps>0.71). In all models, baseline BMI (P<0.0001), body fat mass (P<0.0001) and percentage of fat (P<0.001) were strong positive predictors for change in BMI and fat mass. In models restricted to children without obesity at baseline, some but not all models had significant interaction terms between body adiposity and insulinemia/HOMA-IR that suggested less gain in mass among those with greater insulin or IR. The opposite was found in some models restricted to children with obesity at baseline.

CONCLUSIONS

In middle childhood, BMI and fat mass, but not insulin or IR, are strong predictors of children's gains in BMI and fat mass during adolescence.

Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research

Systems biology is an important approach for deciphering the complex processes in health maintenance and the etiology of metabolic diseases. Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults. To achieve this, a better understanding of the physiological processes at anthropometric, cellular and molecular level for any given individual is needed. In this respect, novel omics technologies in combination with sophisticated data modeling techniques are key. Due to the highly complex network of influential factors determining individual trajectories, it becomes imperative to develop proper tools and solutions that will comprehensively model biological information related to growth and maturation of our body functions. The aim of this review and perspective is to evaluate, succinctly, promising data analysis approaches to enable data integration for clinical research, with an emphasis on the longitudinal component. Approaches based on empirical and mechanistic modeling of omics data are essential to leverage findings from high dimensional omics datasets and enable biological interpretation and clinical translation. On the one hand, empirical methods, which provide quantitative descriptions of patterns in the data, are mostly used for exploring and mining datasets. On the other hand, mechanistic models are based on an understanding of the behavior of a system's components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools. Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.

BMI gain and insulin resistance among school-aged children: a population-based longitudinal study in the Brazilian Amazon

Investigation of the determinants of metabolic outcomes associated with non-communicable diseases is increasingly important in developing countries, but such parameters have not been explored extensively during childhood. The present study assessed the impact of weight gain, measured as BMI-for-age Z-scores, on glucose and insulin concentrations, homeostasis model assessment index of insulin resistance (HOMA-IR) values, and systolic and diastolic blood pressure during school years among Amazonian children. A population-based prospective study of 696 children aged >4 to ≤ 10 years with complete anthropometric information at baseline (51% females and 86% of mixed race) was carried out; 411 children had data on metabolic parameters after a median follow-up period of 2.0 years (range 1.7-2.6 years). During follow-up, there was a significant increase in the proportion of overweight children (BMI-for-age Z-score >1) from 10.1 to 15.8% (P= 0.003). In linear regression models adjusted for the child's sex, age, race/ethnicity, baseline household wealth, birth weight and pubertal development stage, for each unit of BMI-for-age Z-score variation during follow-up, an increase of 8.58 (95% CI 7.68, 9.60) pmol/l in fasting plasma insulin concentrations and 1.47 (95% CI 1.30, 1.66) in HOMA-IR values was observed. There was no significant impact of weight gain on glucose concentrations and systolic and diastolic blood pressure. In conclusion, we found evidence that an increase in BMI during a 2-year period affected insulin resistance during school years. Considering the significant increase in overweight in this age group, special attention should be paid to monitoring increases in BMI in children from the Brazilian Amazon.

PGC1α promoter methylation in blood at 5-7 years predicts adiposity from 9 to 14 years (EarlyBird 50)

The early environment, acting via epigenetic processes, is associated with differential risk of cardiometabolic disease (CMD), which can be predicted by epigenetic marks in proxy tissues. However, such measurements at time points disparate from the health outcome or the environmental exposure may be confounded by intervening stochastic and environmental variation. To address this, we analyzed DNA methylation in the peroxisome proliferator-activated receptor γ coactivator 1α promoter in blood from 40 children (20 boys) collected annually between 5 and 14 years of age by pyrosequencing. Body composition was measured annually by dual X-ray absorptiometry, physical activity by accelerometry, and pubertal timing by age at peak high velocity. The effect of methylation on transcription factor binding was investigated by electrophoretic mobility shift assays. Seven cytosine guanine dinucleotide (CpG) loci were identified that showed no significant temporal change or association with leukocyte populations. Modeling using generalized estimating equations showed that methylation of four loci predicted adiposity up to 14 years independent of sex, age, pubertal timing, and activity. Methylation of one predictive locus modified binding of the proadipogenic pre-B-cell leukemia homeobox-1/homeobox 9 complex. These findings suggest that temporally stable CpG loci measured in childhood may have utility in predicting CMD risk.

Insulin resistance as a predictor of gains in body fat, weight, and abdominal fat in nondiabetic women: a prospective study

The purpose was to determine the relationship between insulin resistance (IR) and risk of gaining body fat percentage (BF%), body weight, and abdominal fat over 18 months. A prospective cohort study was conducted using a sample of 226 women. IR was assessed using fasting blood insulin and glucose levels to calculate homeostatic model assessment (HOMA). Participants were divided into High (4th quartile) Moderate (2nd and 3rd quartiles), and Low (1st quartile) HOMA categories. BF% was estimated using plethysmography (Bod Pod), weight was measured in a standard swimsuit, and abdominal fat was indexed using the average of two circumferences taken at the umbilicus. Participants wore accelerometers and completed weighed food logs for 7 consecutive days to control for the effect of physical activity (PA) and energy intake, respectively. On average, women in the High HOMA group decreased in BF% (-0.48 ± 3.60), whereas those in the Moderate (0.40 ± 3.66) and Low HOMA (1.17 ± 3.15) groups gained BF% (F = 5.4, P = 0.0211). Changes in body weight showed a similar dose-response relationship (F = 4.7, P = 0.0317). However, baseline IR was not predictive of changes in abdominal fat (F = 0.8, P = 0.3635). Controlling for several covariates had little effect on gains in BF% and weight, but adjusting for initial BF% and/or initial weight nullified changes in BF% and weight across the IR groups. In conclusion, women with High HOMA tend to gain significantly less BF% and weight than women with low or moderate HOMA. The decreased risk appears unrelated to several covariates, except initial BF% and weight levels, which seem to play key roles in the relationships.

The contribution of parental BMI to the metabolic health of their offspring: a longitudinal cohort study (EarlyBird 55)

OBJECTIVE

The objective of this study was to establish the extent to which parental factors influence the metabolic health of their offspring.

DESIGN

The study was designed as a prospective longitudinal cohort study

SUBJECTS

The study's subjects were 226 healthy trios from a 1995 to 1996 birth cohort randomly recruited in the city of Plymouth, UK MEASUREMENTS: Body mass index (BMI) and metabolic z-score (derived from natural log HOMA-IR, triglycerides, total/high-density lipoprotein cholesterol ratio), measured at nine annual time points, from 5 to 13 years.

RESULTS

As expected, the metabolic z score was closely related to BMI in both genders and at all ages (r = 0.40-0.57, P < 0.001). Accordingly, there were large and significant differences in the metabolic z-score between children categorized as normal weight or overweight/obese. At 13 years, for example, the metabolic z score of the overweight/obese girls was 14-fold greater than that of the normal-weight girls (P < 0.001). However, parental BMI and metabolic status had little effect on these differences. Indeed, mixed effects modelling showed that, as the child's BMI increased, so the influence of parental factors became less relevant. Time-lag analyses confirmed that weight gain preceded metabolic disturbances in the children.

CONCLUSION

The impact of obesity on the metabolic health of contemporary children is a function of their own weight gain, rather than that of their parents, and is therefore potentially preventable.