Joint Modeling of Individual Trajectories, Within-Individual Variability, and a Later Outcome: Systolic Blood Pressure Through Childhood and Left Ventricular Mass in Early Adulthood

Associations between maternal exposure to multiple metals and metalloids and blood pressure in preschool children: A mixture-based approach

BACKGROUND

Exposure to metals during pregnancy can potentially influence blood pressure (BP) in children, but few studies have examined the mixed effects of prenatal metal exposure on childhood BP. We aimed to assess the individual and combined effects of prenatal metal and metalloid exposure on BP in preschool children.

METHODS

A total of 217 mother-child pairs were selected from the Zhuang Birth Cohort in Guangxi, China. The maternal plasma concentrations of 20 metals [e.g. lead (Pb), rubidium (Rb), cesium (Cs), and zinc (Zn)] in early pregnancy were measured by inductively coupled plasmamass spectrometry. Childhood BP was measured in August 2021. The effects of prenatal metal exposure on childhood BP were explored by generalized linear models, restricted cubic spline and Bayesian kernel machine regression (BKMR) models.

RESULTS

In total children, each unit increase in the log10-transformed maternal Rb concentration was associated with a 10.82-mmHg decrease (95% CI: -19.40, -2.24) in childhood diastolic BP (DBP), and each unit increase in the log10-transformed maternal Cs and Zn concentrations was associated with a 9.67-mmHg (95% CI: -16.72, -2.61) and 4.37-mmHg (95% CI: -8.68, -0.062) decrease in childhood pulse pressure (PP), respectively. The log10-transformed Rb and Cs concentrations were linearly related to DBP (P nonlinear=0.603) and PP (P nonlinear=0.962), respectively. Furthermore, an inverse association was observed between the log10-transformed Cs concentration and PP (β =-12.18; 95% CI: -22.82, -1.54) in girls, and between the log10-transformed Rb concentration and DBP (β =-12.54; 95% CI: -23.87, -1.21) in boys, while there was an increasing association between the log10-transformed Pb concentration and DBP there was an increasing in boys (β =6.06; 95% CI: 0.36, 11.77). Additionally, a U-shaped relationship was observed between the log10-transformed Pb concentration and SBP (P nonlinear=0.015) and DBP (P nonlinear=0.041) in boys. Although there was no statistically signiffcant difference, there was an inverse trend in the combined effect of maternal metal mixture exposure on childhood BP among both the total children and girls in BKMR.

CONCLUSIONS

Prenatal exposure to both individual and mixtures of metals and metalloids influences BP in preschool children, potentially leading to nonlinear and sex-specific effects.

Modeling Risk Factors for Intraindividual Variability: A Mixed-Effects Beta-Binomial Model Applied to Cognitive Function in Older People in the English Longitudinal Study of Ageing

Cognitive functioning in older age profoundly impacts quality of life and health. While most research on cognition in older age has focused on mean levels, intraindividual variability (IIV) around this may have risk factors and outcomes independent of the mean value. Investigating risk factors associated with IIV has typically involved deriving a summary statistic for each person from residual error around a fitted mean. However, this ignores uncertainty in the estimates, prohibits exploring associations with time-varying factors, and is biased by floor/ceiling effects. To address this, we propose a mixed-effects location scale beta-binomial model for estimating average probability and IIV in a word recall test in the English Longitudinal Study of Ageing. After adjusting for mean performance, an analysis of 9,873 individuals across 7 (mean = 3.4) waves (2002-2015) found IIV to be greater at older ages, with lower education, in females, with more difficulties in activities of daily living, in later birth cohorts, and when interviewers recorded issues potentially affecting test performance. Our study introduces a novel method for identifying groups with greater IIV in bounded discrete outcomes. Our findings have implications for daily functioning and care, and further work is needed to identify the impact for future health outcomes.

Using linear and natural cubic splines, SITAR, and latent trajectory models to characterise nonlinear longitudinal growth trajectories in cohort studies

BACKGROUND

Longitudinal data analysis can improve our understanding of the influences on health trajectories across the life-course. There are a variety of statistical models which can be used, and their fitting and interpretation can be complex, particularly where there is a nonlinear trajectory. Our aim was to provide an accessible guide along with applied examples to using four sophisticated modelling procedures for describing nonlinear growth trajectories.

METHODS

This expository paper provides an illustrative guide to summarising nonlinear growth trajectories for repeatedly measured continuous outcomes using (i) linear spline and (ii) natural cubic spline linear mixed-effects (LME) models, (iii) Super Imposition by Translation and Rotation (SITAR) nonlinear mixed effects models, and (iv) latent trajectory models. The underlying model for each approach, their similarities and differences, and their advantages and disadvantages are described. Their application and correct interpretation of their results is illustrated by analysing repeated bone mass measures to characterise bone growth patterns and their sex differences in three cohort studies from the UK, USA, and Canada comprising 8500 individuals and 37,000 measurements from ages 5-40 years. Recommendations for choosing a modelling approach are provided along with a discussion and signposting on further modelling extensions for analysing trajectory exposures and outcomes, and multiple cohorts.

RESULTS

Linear and natural cubic spline LME models and SITAR provided similar summary of the mean bone growth trajectory and growth velocity, and the sex differences in growth patterns. Growth velocity (in grams/year) peaked during adolescence, and peaked earlier in females than males e.g., mean age at peak bone mineral content accrual from multicohort SITAR models was 12.2 years in females and 13.9 years in males. Latent trajectory models (with trajectory shapes estimated using a natural cubic spline) identified up to four subgroups of individuals with distinct trajectories throughout adolescence.

CONCLUSIONS

LME models with linear and natural cubic splines, SITAR, and latent trajectory models are useful for describing nonlinear growth trajectories, and these methods can be adapted for other complex traits. Choice of method depends on the research aims, complexity of the trajectory, and available data. Scripts and synthetic datasets are provided for readers to replicate trajectory modelling and visualisation using the R statistical computing software.