Inflammatory diets are increasingly recognised as a modifiable determinant of mental illness. However, there is a dearth of studies in early life and across the full mental well-being spectrum (mental illness to positive well-being) at the population level. This is a critical gap given that inflammatory diet patterns and mental well-being trajectories typically establish by adolescence. We examined the associations of inflammatory diet scores with mental well-being in 11–12-year-olds and mid-life adults. Throughout Australia, 1759 11–12-year-olds (49 % girls) and 1812 parents (88 % mothers) contributed cross-sectional population-based data. Alternate inflammatory diet scores were calculated from a twenty-six-item FFQ, based on the prior literature and prediction of inflammatory markers. Participants reported negatively and positively framed mental well-being via psychosocial health, quality of life and life satisfaction surveys. We used causal inference modelling techniques via generalised linear regression models (mean differences and risk ratios (RR)) to examine how inflammatory diets might influence mental well-being. In children and adults, respectively, a 1 sd higher literature-derived inflammatory diet score conferred between a 44 % (RR 95 % CI 1·2, 1·8) to 57 % (RR 95 % CI 1·3, 2·0) and 54 % (95 % CI 1·2, 2·0) to 86 % (RR 95 % CI 1·4, 2·4) higher risk of being in the worst mental well-being category (i.e. <16th percentile) across outcome measures. Results for inflammation-derived scores were similar. BMI mediated effects (21–39 %) in adults. Inflammatory diet patterns were cross-sectionally associated with mental well-being at age 11–12 years, with similar effects observed in mid-adulthood. Reducing inflammatory dietary components in childhood could improve population-level mental well-being across the life course.

This study investigates how dietary patterns and scores are associated with subsequent BMI and waist:height ratio (WHtR), and how BMI and WHtR are associated with subsequent dietary patterns or scores, from 2–3 to 10–11 and 4–5 to 14–15 years of age. In the Longitudinal Study of Australian Children, height, weight and waist circumference were measured biennially in children, yielding BMI z-score and WHtR. Parents, latterly children, reported frequency of child consumption of 12–16 food/drink items during the previous 24 h. At each wave, we empirically derived dietary patterns using factor analyses, and dietary scores based on the 2013 Australian Dietary Guidelines. We used structural-equation modelling to investigate cross-lagged associations (n 1972–2882) between diet and body composition measures in univariable and multivariable analyses. Dietary scores/patterns did not consistently predict WHtR and BMI z-score in the next wave, nor did BMI z-score and WHtR consistently predict diet in the next wave. The few associations seen were weak and often in the opposite direction to that hypothesised. The largest effect, associated with each standard deviation increase in BMI in wave 5 of the K cohort (age 12–13 years), was a 0·06 standard deviation estimated mean increase in dietary score (higher quality diet) in the subsequent wave (95 % CI 0·02, 0·11, P=0·003). Associations between dietary patterns/scores and body composition were not strongly evident in either direction. Better quantitative childhood dietary tools feasible for large-scale administration are needed to quantify how dietary patterns, energy intake and anthropometry co-develop.

This study aimed to derive and compare longitudinal trajectories of dietary scores and patterns from 2–3 to 10–11 years and from 4–5 to 14–15 years of age. In waves two to six of the Baby (B) Cohort and one to six of the Kindergarten (K) Cohort of the population-based Longitudinal Study of Australian Children, parents or children reported biennially on the study child’s consumption of twelve to sixteen healthy and less healthy food or drink items for the previous 24 h. For each wave, we derived a dietary score from 0 to 14, based on the 2013 Australian Dietary Guidelines (higher scores indicating healthier diet). We then used factor analyses to empirically derive dietary patterns for separate waves. Using group-based trajectory modelling, we generated trajectories of dietary scores and empirical patterns in 4504 B and 4640 K Cohort children. Four similar trajectories of dietary scores emerged for the B and K Cohorts, containing comparable proportions of children in each cohort: ‘never healthy’ (8·8 and 11·9 %, respectively), ‘moderately healthy’ (24·0 and 20·7 %), ‘becoming less healthy’ (16·6 and 27·3 %) and ‘always healthy’ (50·7 and 40·2 %). Deriving trajectories based on dietary patterns, rather than dietary scores, produced similar findings. For ‘becoming less healthy’ trajectories, dietary quality appeared to worsen from 7 years of age in both cohorts. In conclusion, a brief dietary measure administered repeatedly across childhood generated robust, nuanced dietary trajectories that were replicable across two cohorts and two methodologies. These trajectories appear ideal for future research into dietary determinants and health outcomes.