Associations between the weighted allelic score for 32 SNPs and body mass index/fat mass index and activity measures.

<p>Regression results were adjusted for age. Per (average BMI-increasing) allele effects were obtained by linear regression for all of these continuous outcome variables.</p>a<p>Activity variables were total physical activity, moderate-to-vigorous and minutes of sedentary time.</p>b<p>Coefficients are displayed as sex-specific <i>z</i>-scores for both measures of adiposity and activity levels and have also been rescaled to give more meaningful outcomes relating to the raw units of these variables. The raw-unit difference was computed by multiplying the <i>z</i>-score value by the SD of the variable, taken from <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001618#pmed-1001618-t001" target="_blank">Table 1</a>.</p>c<p>Moderate-to-vigorous activity was log transformed for analysis.</p

Baseline characteristics of children.

<p>Total sample sizes range from 3,121 to 4,098 depending on the availability of the data.</p>a<p>Median and interquartile ranges are displayed for this variable because it is skewed.</p>b<p>Based on parent with highest social class, as defined by the 1991 British Office of Population Censuses and Surveys classification.</p>c<p>Based on highest Tanner scale developmental stage of breasts and pubic hair for females and pubic hair for males.</p><p>DXA, dual energy X-ray absorptiometry.</p

Addressing the causal directions of effect in the association between adiposity and physical activity with the use of allelic scores and Mendelian randomization analysis.

<p>(A) MR analysis to investigate the causal effect of adiposity on levels of physical activity with the use of a weighted allelic score as a genetic instrument. (B) Reciprocal MR analysis to investigate the causal effects of physical activity levels on adiposity using a genome-wide prediction score as a genetic instrument. G, genetic instrument; U, unobserved confounders; X, exposure; Y, outcome.</p

Associations between body mass index/fat mass index and activity levels as tested both by conventional epidemiological approaches and through the application of instrumental variable analysis using the <i>FTO</i> (rs1558902) genetic variant as an instrument.

<p>Regression results were adjusted for age.</p>a<p>Coefficients are displayed as sex-specific <i>z</i>-scores for both measures of adiposity and activity levels and have also been rescaled to give more meaningful outcomes relating to the raw units of these variables. The raw-unit difference was computed by multiplying the <i>z</i>-score value by the SD of the variable, taken from <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001618#pmed-1001618-t001" target="_blank">Table 1</a>.</p>b<p><i>p</i>(DWH) is the <i>p</i>-value of the Durbin form of the DWH test, which examines the difference between the estimates from linear regression and instrumental variable analysis.</p>c<p>Moderate-to-vigorous activity was log transformed for analysis.</p

Associations between body mass index/fat mass index and activity levels as tested both by conventional epidemiological approaches and through the application of instrumental variable analysis using a 31-SNP weighted allelic score (excluding <i>FTO</i>) as an instrument.

<p>Regression results were adjusted for age.</p>a<p>Coefficients are displayed as sex-specific <i>z</i>-scores for both measures of adiposity and activity levels and have been rescaled to give more meaningful outcomes relating to the raw units of these variables. The raw-unit difference was computed by multiplying the <i>z</i>-score value by the SD of the variable, taken from <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001618#pmed-1001618-t001" target="_blank">Table 1</a>.</p>b<p><i>p</i>(DWH) is the <i>p</i>-value of the Durbin form of the DWH test, which examines the difference between the estimates from linear regression and instrumental variable analysis.</p>c<p>Moderate-to-vigorous activity was log transformed for analysis.</p

Associations between measures of adiposity and physical activity levels.

a<p>Model A: adjusted for age.</p>b<p>Model B: adjusted for age, birth weight, gestational age at birth, maternal smoking during pregnancy, maternal education, parental social class, maternal BMI, stage of puberty, total daily dietary intake, and intake of main food groups.</p>c<p>Coefficients are displayed as sex-specific <i>z</i>-scores for both measures of adiposity and activity levels and have also been rescaled to give more meaningful outcomes relating to the raw units of these variables. The raw-unit difference was computed by multiplying the <i>z</i>-score value by the SD of the variable, taken from <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001618#pmed-1001618-t001" target="_blank">Table 1</a>.</p>d<p>Moderate-to-vigorous activity was log transformed for analysis.</p

Participants in ALSPAC and in the analyses presented in this paper.

<p>DXA, dual energy X-ray absorptiometry.</p

Diminishing causal effect of developmental overnutrition across the life course.

<p>Multivariable and IV effect estimates from ALSPAC at ages 7–18 (<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002221#pmed.1002221.t002" target="_blank">Table 2</a>) compared with those obtained when investigating the effect of maternal BMI on offspring ponderal index (kg/m³) at birth in the same cohort.</p

Confounder-adjusted multivariable and genetic IV (MR) associations of maternal pregnancy BMI with offspring BMI and FMI from ages 7 to 18 in ALSPAC (Discovery sample).

<p>Confounder-adjusted multivariable and genetic IV (MR) associations of maternal pregnancy BMI with offspring BMI and FMI from ages 7 to 18 in ALSPAC (Discovery sample).</p

Relationship of maternal pregnancy BMI with offspring BMI at age 7: pooled ALSPAC and Generation-R multivariable and MR analyses.

<p>Relationship of maternal pregnancy BMI with offspring BMI at age 7: pooled ALSPAC and Generation-R multivariable and MR analyses.</p