Statistical analyses

Descriptive characteristics of the study population are presented as the mean and SD for continuous variables and numbers and percentages for categorical variables for boys and girls separately. Since both body composition and bone acquisition differ between boys and girls, especially in adolescence, statistical analyses were stratified by sex. In paper I, combined results are also presented for the main analysis. Statistical differences between groups were tested by an independent samples t-test for continuous variables and by the 𝒳^" test for categorical variables. Since some of the body composition measures were slightly right skewed, correlations between continuous variables were assessed by Spearman’s rank correlation coefficient. ANOVA with Bonferroni correction for multiple comparisons was used to assess differences in the means between several categories.

The normality of variables was checked by a visual inspection of histograms. Scatterplots were used to check for outliers and linearity between exposures and outcome variables. We controlled for homogeneity of variance, and model residuals were checked by visual

inspections of histograms and plots. No assumptions were considered violated for the models presented in the thesis. Cross-product terms with sex and exposure variables were included in the models to formally test for potential sex interactions.

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In paper I, the degree of tracking of birth weight and childhood BMI into adolescence was estimated as odds ratios (ORs) for being overweight/obese at 15-20 years of age. The outcomes were BMI categories at 15-17 and 18-20 years of age dichotomized as underweight/normal weight or overweight/obesity.

Generalized estimating equations (GEE) were used to model our longitudinal data when the outcome was dichotomized [159, pp. 128-140]. Longitudinal data are correlated within the subject and in GEE analysis the dependency of the observations is accounted for by choosing an appropriate “working correlation structure” for the repeated measurements. The model produce a population averaged estimate [159, pp. 57-68, 128-140]. GEE with a logit link function and an unstructured correlation matrix were used to estimate ORs with 95%

confidence intervals (CI). Exposure variables were analysed both as continuous and

categorical variables: birth weight (per 1-SD increase), birth weight SDS, ponderal index in tertiles, BMI (per 1-SD increase), BMI SDS and BMI divided into four categories at 2-4 and 5-7 years of age. In addition, due to the small proportion of obesity in childhood, an

alternative approach with three BMI categories was tested: light overweight and severe overweight/obesity compared to underweight/normal weight.

Potential confounders from MBRN and TFF1 were tested, and both crude and adjusted models are presented.

Paper II

The main outcomes in paper II were body composition measures at 15-20 years of age: FMI SDS, FFMI SDS, and android:gynoid FMR. In addition, WC was dichotomized as central overweight/obesity or not, FMI was dichotomized as < or ≥1.0 SDS and used as outcomes.

Exposure variables in the main analyses were birth weight and ponderal index per 1-SD increase, growth status at birth: being born small, or appropriate or large for GA. In addition, BMI categories at 2.5, 6.0 years of age were dichotomized as underweight/normal weight and overweight/obesity. BMI at 16.5 years of age were divided in four categories: underweight, normal weight, light overweight and severe overweight/obesity. In the analyses of childhood growth in this paper, the BMI gains between birth and age 2.5, and between consecutive ages were used as the exposure variables.

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Linear mixed models [159] were used to assess associations between exposure variables and repeated measures of body composition as continuous outcomes at 15-17 and 18-20 years of age. Our data have a two-level structure were repeated observations are clustered within subjects. To account for the clustering of the observations, we used linear mixed models with only a random intercept on the subject level. In the model, correction for the clustering are carried out by estimating the variance of the intercepts and adding this to the longitudinal regression model. However, only the fixed effects are reported [159, pp. 69-85]. GEE were used in the analysis of binary outcome variables (see 2.5.3 paper I).

A conditional growth model [158, 160] was used to assess the impact of BMI gain in different age intervals related to the outcomes. In a conditional growth model, growth

measures are adjusted for prior body size. Accordingly, standardized residuals were obtained by multiple linear regression analyses of BMI SDS at all target ages regressed on prior BMI SDS. These residuals were used simultaneously in linear mixed models with FMI SDS, FFMI SDS and android:gynoid FMR as outcomes. Standardized beta coefficients are reported for FMI SDS and FFMI SDS. This index of growth is statistically independent of body size at the start of each growth period and adjusts for both catch-up growth and regression to the mean [160]. This approach asks a prospective question: for each child, is he/she growing more than expected, given his or her body size at the start of the growth period, and how is this growth associated with the outcome measure? [158]

Models were adjusted for potential confounding factors; birth weight was adjusted for GA, associations between BMI at age 2.5 and 6.0 were adjusted for height at the same ages, and BMI at age 16.5 was additionally adjusted for height, pubertal maturation and physical activity levels. All conditional growth models were adjusted for GA, pubertal maturation and physical activity levels.

In a subgroup analysis of those with body composition measures from both TFF1 and TFF2 (n=621), we used a conditional growth model to explore the relationship between BMI gain and changes in FMI SDS and FFMI SDS between 15-17 and 18-20 years of age.

Paper III

The main outcomes in paper III were bone measures: total hip and total body standardized BMC and aBMD scores (z-scores) at 15-20 years of age. Exposure variables were birth weight SDS and BMI categories: underweight, normal weight and overweight/obesity at 2.5,

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6.0 and 16.5 years of age. In analyses of childhood growth in this paper, the height (cm/year) and weight (kg/year) growth rate between birth and 2.5, 2.5 to 6.0, and 6.0-16.5 years of age based on the linear spline multilevel model were used as the exposure variables.

Linear mixed models with a random intercept on the subject level (see 2.5.3 paper II) were used to evaluate the relationship between exposure variables and repeated BMC and aBMD z-scores as continuous outcomes. Both crude models and models adjusted for potential confounding factors are presented. Details of covariates used in the separate models, are described in the paper’s method section as well as in the tables in paper II.

In the analyses of growth and in accordance with others [161, 162], the rate of length/height growth was conditioned on earlier body size, and weight gain was conditioned on earlier body size as well as concurrent height growth. Hence, this model asks the same prospective question as the standard conditional growth model used in paper II [158].

Analysis of growth can be challenging, and several models can be used [157, 158, 160]. See section 4.1.9 for a discussion of the choice of growth models.

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3 Results