The ALSPAC
The ALSPAC is a prospective birth cohort study aiming to investigate environmental and inheritable influences on the health and development of children. It has been previously described in full elsewhere and on the web site www.alspac.bris.ac.uk. Pregnant women with expected delivery dates between 1 April 1991 and 31 December 1992 and living in a defined area of Avon including the city of Bristol were eligible for recruitment to the study. A total of 14,541 women were enrolled, and 13,678 of these had a singleton live birth. Ethical approval for the study was obtained from the ALSPAC Law and Ethics Committee and from local ethics committees. At age 9 years, all children with known addresses who were still participating were invited to a “Focus @ 9” clinic, and 7,121 of the singleton children attended. Of these, 6,868 underwent a full-body dual-energy X-ray absorptiometry (DXA) scan.
DXA measurements
Whole body DXA scans were carried out using a Lunar Prodigy scanner (GE Healthcare Bio-Sciences Corp., USA), and after exclusion of those containing artefacts, movement or skeletal irregularities, there remained 6,775 scans. The total body less head (TBLH) region was used to represent the child’s total bone mass, with the head excluded as bone development here is different from the rest of the skeleton and less likely to be influenced by environmental factors. In contrast to the overall skeleton which primarily comprises cortical bone, the spine subregion which was also analysed has a relatively high proportion of trabecular bone. One operator reanalysed the scans to check and adjust automated placement of body regions; in the case of the spinal region, the upper border comprises the cervicothoracic junction, the lower border the lumbosacral junction, and the lateral borders the bone/soft tissue interface. Since curvature in the image of the spine leads to contamination of the spinal region with the ribs, only images with minor or no curvature are included in the analysis of spinal outcomes. Measurements for TBLH and spine BMC, bone area (BA) and areal BMD were subsequently calculated. For both regions, area-adjusted BMC (ABMC) was also derived as a measure of volumetric BMD by using linear regression to adjust BMC for BA and adding the residuals to the mean BMC for the region. The coefficient of variation for TBLH BMD was 0.84% based on 122 pairs of scans repeated on the same day. At the same time as the DXA scan, the child’s standing height (without shoes) was measured using a Harpenden Stadiometer (Holtain Ltd., UK) and weight (unshod and in light clothing) was measured using a Tanita Body Fat Analyzer (model TBF 305, Tanita UK Limited, UK).
Maternal and paternal smoking
At 18 weeks’ gestation, the mothers were sent a postal questionnaire which asked how many times per day they had smoked in the first trimester and in the last 2 weeks, representing smoking during the second trimester. At 32 weeks’ gestation, another postal questionnaire asked how many cigarettes per day the woman was currently smoking, representing smoking during the third trimester. Variables describing smoking in any trimester and smoking in all trimesters were derived from these responses, with one or more cigarettes smoked per day considered as smoking regularly. A questionnaire completed by the mother’s partner at 18 weeks’ gestation asked if he had smoked regularly at any time in the last 9 months. The mother was also asked if her partner smoked in her 18-week questionnaire, and a positive response from either the partner or the mother was assumed sufficient to indicate that the partner smoked regularly during the pregnancy.
Other variables
Maternal and paternal height, weight and highest educational qualifications, household social class, father’s age and the mother’s parity were obtained from questionnaires administered during pregnancy. Household social class was defined from the highest parental occupation, on a scale from I to V, with I indicating a professional/managerial role and V being unskilled manual. Maternal and paternal body mass index (BMI) were calculated as weight (kg)/height (m)2. The child’s sex was obtained at the time of birth, and the child’s birth weight, gestational age and the mother’s age at delivery were abstracted from obstetric records.
In the questionnaire administered at 18 weeks’ gestation, the mother was asked how many hours per week she spent engaging in strenuous physical activity. The questionnaire also asked the number of hours per week the mother spent in a number of specific types of leisure activity, each of which was assigned a MET score [12], and a weighted activity index was developed by multiplying the MET score by the number of hours of activity per week. Dietary information for the mothers was obtained from a food frequency questionnaire administered at 32 weeks’ gestation which asked how often they consumed each of the 43 food groups. Using nutrient information on standard-sized portions, the mother’s total weekly energy, carbohydrate, fat and protein intakes were derived [13]. Although the main analysis did not adjust for these variables, since the equivalent paternal information was not available, an additional analysis was performed in which the relationships of maternal smoking in pregnancy with offspring bone outcomes were adjusted for maternal physical activity (strenuous activity of 3 h or more per week and weighted activity index) and diet (weekly energy, carbohydrate, fat and protein intake) during pregnancy.
Pubertal stage data for the children were obtained from Tanner stage questionnaires administered to the parents at 116 months and were based on pubic hair development for boys and breast development for girls, or pubic hair development if this was unavailable. For girls, age at menarche was derived from a series of questionnaires administered between the ages of 8 and 17 years which asked if the daughter had started her menstrual periods and, if so, the age she was at her first menstrual period. Where there was disagreement between questionnaires, the age given on the earliest questionnaire was used. Most children (99% of boys and 96% of girls) with pubertal stage information were either pre- or early pubertal (Tanner stage 1 or 2). For this reason, and due to the high proportion of missing pubertal stage data, this has not been adjusted for in the main regression analysis, but an additional analysis was performed which adjusted for pubertal stage and, for girls, whether menarche occurred at age ≤10 years.
Paternity
If, when asked in a questionnaire administered in pregnancy, the mother had not confirmed her partner to be the child’s biological father, all paternal information (smoking status, BMI, age, height and education) was treated as missing.
Statistical analysis
We assessed maternal and paternal smoking associations with offspring bone outcomes separately and also in combined mutually adjusted regression models. We used sex-specific models due to evidence of interactions of maternal and paternal smoking with the child’s sex and adjusted first for the child’s age only, then additionally for the potential confounders of household social class, parity and maternal/paternal age, height, BMI and education. Maternal factors were included in maternal exposure models, paternal factors in paternal exposure models, and both maternal and paternal factors in combined models. To explore mediating relationships, we additionally adjusted for the child’s birth weight and gestational age and then finally included the child’s height and weight as potential mediators. Since there was little change in regression coefficients between the simple age-adjusted model and the model adjusting for all potential confounding factors (full results for all four models available from authors), only the confounder-adjusted model (age and all other potential confounders, model 1) and the two additional models exploring potential mediation by birth weight and gestational age (model 2) and by weight and height at age 9.9 (model 3) are presented. Sex-specific standard deviation (SD) scores of TBLH and spine BMC, BA, BMD and ABMC were used as outcomes.
We used multivariate multiple imputation of missing data to impute data for all children who attended the 9-year clinic and also analysed the complete cases with no missing data on any of the exposures, outcomes or covariates to compare findings from the fully observed data with those from partially imputed data. Multiple imputation was used to increase the efficiency of the model estimates and reduce selection bias, which can be present in complete case analysis when data are not missing completely at random. The multiple imputation method is valid provided that the reasons for missingness in the data can be explained by other observed variables [14]. Detailed methods for this procedure are described in the Electronic supplementary material (ESM). All analyses were carried out in Stata version 11.0 (StataCorp LP, USA).