Preface

The new millennium sparked ambitious new birth cohorts around the world. One such study was Growing Up in Australia (also known as the Longitudinal Study of Australian Children, LSAC). Australia’s only national child ‘population-representative’ study, LSAC—now celebrating its 15th birthday—has captured environmental, social, developmental, service and economic data biennially since 2004, when it recruited 5000 infants and 5000 4–5 year olds. In size, content and intent, LSAC resembles other millennial cohorts such as the UK’s Millenium Cohort Study and the Growing Up in Scotland, Ireland and New Zealand studies.

What sets LSAC apart is its recent standalone biophysical wave. The Child Health CheckPoint ushers in a new depth and breadth of cross-generational precision phenotyping and biosamples. Its first major tranche of multisystems data were released for use by all researchers in January 2019 (go to https://dataverse.ada.edu.au/dataverse/lsac to access the datasets), with genetic, micronutrient, oral health and other data to accrue over the coming years.

In this Special Issue of BMJ Open, we describe the CheckPoint’s methodology1 and outline the epidemiology and intergenerational concordance of 14 diverse sets of measurements taken simultaneously in nearly 1900 parent–child dyads at child age 11–12 years.2–15 All are relevant to people’s current and future health and in particular to the development of non-communicable diseases.

Why devote an entire Special Issue of BMJ Open to what may seem straightforward information? The simple answer is that these articles report information that researchers often seek, but rarely find, regarding measured phenotypes at the population level.

First, a vital step in population-based research is understanding the distributional properties of one’s measures—not just of central tendency (mean, median, mode), but also the shapes of distributions and the proportions of individuals falling beyond various percentile values. This information helps to plan sample sizes, categorise variables, design analyses and interpret likely generalisability. Quantifying parent–child concordance may help in teasing out meaningful causal effects of environmental exposures.

Second, we describe each measure’s methods for secondary data users’ reference. Understanding what was done is vital to transparency and interpretation. Unfortunately, journal word limits typically preclude detail on protocol when reporting associational research or cohort profiles. This BMJ Open Special Issue provides a peer-reviewed opportunity for redress. Reporting our methods may support future prospective harmonisation, lack of which is one of the biggest barriers to creating a unified evidence base. It may also encourage others to believe that the impossible is possible: multiple precise phenotypic measures can be streamlined and simultaneously incorporated in ‘field’ settings.

Third, the findings are interesting. Examples include:

• Carotid artery intima-media thickness, distensibility and elasticity show only minor parent–child concordance at child aged 11–12 years.2

• Conversely, cross-generational concordance in multiple vascular function markers is established early.3

• Australian parent–child concordance values for the microvasculature align with moderate polygenic heritability reported in smaller studies.4

• Mean lung volumes were larger, but airway size was smaller than international standards.5

• Bone geometry and density show strong parent–child concordance even before the period of peak adolescent bone deposition.6

• Albuminuria is common among Australian children and adults, which is of concern because it predicts risk for kidney and cardiovascular disease and mortality.7

• Our multiple measures of auditory and verbal systems provide important reference values, since they are rarely captured in population settings.8

• Cross-generational concordance across all measures of leanness and adiposity is already evident by late childhood.9

• We report distinct age-specific and sex-specific metabolic profiles, with concentrations and sex differences generally greater in adults than children10.

• Relative telomere length is shorter in adults than children, and modest parent–child concordance diminishes with increasing parent age.11

• While most parents and children show adequate sleep duration, poor-quality (low-efficiency) sleep is common.12

• More parents than children met physical activity guidelines, despite parents having lower mean activity counts, indicating a need to coordinate meaningful definitions across age groups.13

• Small parent–child concordance during a standardised 15-min ‘snack break’ could account for substantial annualised population gradients in energy, fat and sodium intake for 11–12 year olds.14

• Parent–child concordance in health-related quality of life was small in this relatively healthy sample.15

In conclusion, we hope that this BMJ Open Special Issue will be widely used for reference, planning and the science it contains on a wide range of health, molecular and risk phenotypes in older children and their parents.