Table 3.
Associations between participant’s size at birth and MRI percent water
| Relative change in MRI percent water, geometric meansa (95 % CI) | ||||
|---|---|---|---|---|
| Complete datab | Imputed datac (n = 491) | |||
| Absolute size vs. rate of growth | n = 455 | |||
| Model 1 | Birthweight (per 1 SD 472.6 g) | 1.03 (1.02–1.05) | 1.03 (1.02–1.05) | |
| Model 2 | Birthweight (per 1 SD 472.6 g) | 1.04 (1.02–1.06) | 1.04 (1.02–1.06) | |
| Gestational age (weeks) | <39 | 1 (ref) | 1 (ref) | |
| 39 | 0.97 (0.92–1.02) | 0.97 (0.92–1.02) | ||
| 40 | 0.97 (0.92–1.02) | 0.97 (0.92–1.02) | ||
| 41+ | 0.96 (0.92–1.01) | 0.96 (0.92–1.01) | ||
| LR test/Wald test p valued | 0.519 | 0.477 | ||
| Which measure best captures linear (skeletal) growth? | n = 356 | |||
| Birth length (per 1 SD 2.3 cm) | 1.00 (0.98–1.02) | 1.01 (0.99–1.03) | ||
| Head circumference (per 1 SD 1.2 cm) | 1.02 (1.00–1.05) | 1.02 (1.00–1.04) | ||
| Linear growth vs. adiposity | n = 361 | |||
| Model 1 | Birthweight (per 1 SD 472.6 g) | 1.03 (1.01–1.05) | 1.03 (1.02-1.05) | |
| Model 2 | Birthweight (per 1 SD 472.6 g) | 1.03 (1.00–1.06) | 1.03 (1.01–1.06) | |
| Head circumference (per 1 SD 1.2 cm) | 1.01 (0.98–1.03) | 1.00 (0.97–1.03) | ||
| LR test/Wald test p valued (n = 353) | 0.671 | 0.917 | ||
| Model 1 | Birthweight (per 1 SD 472.6 g) | 1.03 (1.01–1.05) | 1.03 (1.02–1.05) | |
| Model 2 | Birthweight (per 1 SD 472.6 g) | 1.03 (1.01–1.05) | 1.03 (1.02–1.05) | |
| Ponderal Index (per 1 SD 4.1 g/cm3) | 1.01 (0.99–1.02) | 1.00 (0.99–1.02) | ||
| LR test/Wald test p valued | 0.577 | 0.654 | ||
Abbreviations: MRI Magnetic resonance imaging, LR Likelihood ratio test, ref Reference category
aMRI percent water was log-transformed for the analysis, and exponentiated estimated regression parameters, with 95 % CIs calculated by exponentiating the original 95 % CIs, are presented. Models adjusted for age, BMI z-score and menstrual phase/hormonal contraceptive use at the time of MRI scan. Bold indicates 95 % CI do not cross the null (1.00)
bAnalysis restricted to those with non-missing data for all variables included in each model
cSee Statistical methods section of main text
dLR test performed on the complete record data, while a Wald test was performed on the imputed data (and summarised using Rubin’s rule), to test the null hypothesis that the inclusion of the additional variable in model 2 did not improve the fit to the data