A principal components approach to parent-to-newborn body composition associations in South India

Background: size at birth is influenced by environmental factors, like maternal nutrition and parity, and by genes. Birth weight is a composite measure, encompassing bone, fat and lean mass. These may have different determinants. The main purpose of this paper was to use anthropometry and principal components analysis (PCA) to describe maternal and newborn body composition, and associations between them, in an Indian population. We also compared maternal and paternal measurements (body mass index (BMI) and height) as predictors of newborn body composition.Methods: weight, height, head and mid-arm circumferences, skinfold thicknesses and external pelvic diameters were measured at 30 ± 2 weeks gestation in 571 pregnant women attending the antenatal clinic of the Holdsworth Memorial Hospital, Mysore, India. Paternal height and weight were also measured. At birth, detailed neonatal anthropometry was performed. Unrotated and varimax rotated PCA was applied to the maternal and neonatal measurements.Results: rotated PCA reduced maternal measurements to 4 independent components (fat, pelvis, height and muscle) and neonatal measurements to 3 components (trunk+head, fat, and leg length). An SD increase in maternal fat was associated with a 0.16 SD increase (?) in neonatal fat (p < 0.001, adjusted for gestation, maternal parity, newborn sex and socio-economic status). Maternal pelvis, height and (for male babies) muscle predicted neonatal trunk+head (? = 0. 09 SD; p = 0.017, ? = 0.12 SD; p = 0.006 and ? = 0.27 SD; p < 0.001). In the mother-baby and father-baby comparison, maternal BMI predicted neonatal fat (? = 0.20 SD; p < 0.001) and neonatal trunk+head (? = 0.15 SD; p = 0.001). Both maternal (? = 0.12 SD; p = 0.002) and paternal height (? = 0.09 SD; p = 0.030) predicted neonatal trunk+head but the associations became weak and statistically non-significant in multivariate analysis. Only paternal height predicted neonatal leg length (? = 0.15 SD; p = 0.003).Conclusion: principal components analysis is a useful method to describe neonatal body composition and its determinants. Newborn adiposity is related to maternal nutritional status and parity, while newborn length is genetically determined. Further research is needed to understand mechanisms linking maternal pelvic size to fetal growth and the determinants and implications of the components (trunk v leg length) of fetal skeletal growt

Regression models for linking patterns of growth to a later outcome:Infant growth and childhood overweight

Abstract Background Regression models are widely used to link serial measures of anthropometric size or changes in size to a later outcome. Different parameterisations of these models enable one to target different questions about the effect of growth, however, their interpretation can be challenging. Our objective was to formulate and classify several sets of parameterisations by their underlying growth pattern contrast, and to discuss their utility using an expository example. Methods We describe and classify five sets of model parameterisations in accordance with their underlying growth pattern contrast (conditional growth; being bigger v being smaller; becoming bigger and staying bigger; growing faster v being bigger; becoming and staying bigger versus being bigger). The contrasts are estimated by including different sets of repeated measures of size and changes in size in a regression model. We illustrate these models in the setting of linking infant growth (measured on 6 occasions: birth, 6 weeks, 3, 6, 12 and 24 months) in weight-for-height-for-age z-scores to later childhood overweight at 8y using complete cases from the Norwegian Childhood Growth study (n = 900). Results In our expository example, conditional growth during all periods, becoming bigger in any interval and staying bigger through infancy, and being bigger from birth were all associated with higher odds of later overweight. The highest odds of later overweight occurred for individuals who experienced high conditional growth or became bigger in the 3 to 6 month period and stayed bigger, and those who were bigger from birth to 24 months. Comparisons between periods and between growth patterns require large sample sizes and need to consider how to scale associations to make comparisons fair; with respect to the latter, we show one approach. Conclusion Studies interested in detrimental growth patterns may gain extra insight from reporting several sets of growth pattern contrasts, and hence an approach that incorporates several sets of model parameterisations. Co-efficients from these models require careful interpretation, taking account of the other variables that are conditioned on

Life course variations in the associations between FTO and MC4R gene variants and body size

The timing of associations between common genetic variants for weight or body mass index (BMI) across the life course may provide insights into the aetiology of obesity. We genotyped variants in FTO (rs9939609) and near MC4R (rs17782313) in 1240 men and 1239 women born in 1946 and participating in the MRC National Survey of Health and Development. Birth weight was recorded and height and weight were measured or self-reported repeatedly at 11 time-points between ages 2 and 53 years. Hierarchical mixed models were used to test whether genetic associations with weight or BMI standard deviation scores (SDS) changed with age during childhood and adolescence (2–20 years) or adulthood (20–53 years). The association between FTO rs9939609 and BMI SDS strengthened during childhood and adolescence (rate of change: 0.007 SDS/A-allele/year; 95% CI: 0.003–0.010, P < 0.001), reached a peak strength at age 20 years (0.13 SDS/A-allele, 0.08–0.19), and then weakened during adulthood (−0.003 SDS/A-allele/year, −0.005 to −0.001, P = 0.001). MC4R rs17782313 showed stronger associations with weight than BMI; its association with weight strengthened during childhood and adolescence (0.005 SDS/C-allele/year; 0.001–0.008, P = 0.006), peaked at age 20 years (0.13 SDS/C-allele, 0.07–0.18), and weakened during adulthood (−0.002 SDS/C-allele/year, −0.004 to 0.000, P = 0.05). In conclusion, genetic variants in FTO and MC4R showed similar biphasic changes in their associations with BMI and weight, respectively, strengthening during childhood up to age 20 years and then weakening with increasing adult age. Studies of the aetiology of obesity spanning different age groups may identify age-specific determinants of weight gain

Life course body mass index and risk of knee osteoarthritis at the age of 53 years: evidence from the 1946 British birth cohort study

Introduction: The authors examined how body mass index (BMI) across life is linked to the risk of midlife knee osteoarthritis (OA), testing whether prolonged exposure to high BMI or high BMI at a particular period has the greatest influence on the risk of knee OA. Methods: A population-based British birth cohort of 3035 men and women underwent clinical examination for knee OA at age 53 years.Heights and weights were measured 10 times from 2 to 53 years. Analyses were stratified by gender and adjusted for occupation and activity levels. Results: The prevalence of knee OA was higher in women than in men (12.9% (n=194) vs 7.4% (n=108)). In men, the association between BMI and later knee OA was evident at 20 years (p=0.038) and remained until 53 years (OR per z-score 1.38 (95% CI 1.11 to 1.71)). In women, there was evidence for an association at 15 years (p=0.003); at 53 years, the OR was 1.89 (95% CI 1.59 to 2.24) per z-score increase in BMI. Changes in BMI from childhood in women and from adolescence in men were also positively associated with knee OA. A structured modelling approach to disentange the way in which BMI is linked to knee OA suggested that prolonged exposure to high BMI throughout adulthood carried the highest risk and that there was no additional risk conferred from adolescence once adult BMI had been accounted for. Conclusion: This study suggests that the risk of knee OA accumulates from exposure to a high BMI through adulthood. <br/

Examining the effects of an eHealth intervention from infant age 6 to 12 months on child eating behaviors and maternal feeding practices one year after cessation:The Norwegian randomized controlled trial Early Food for Future Health

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