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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.
While birth cohorts are shaped by underpinning life course frameworks, few if any report how they select them. This review aimed to (1) summarise publicly available frameworks relevant to planning and communicating large new early-life cohorts and (2) help select frameworks to guide and communicate Generation Victoria (GenV), a whole-of-state birth and parent cohort in planning in the state of Victoria, Australia. We identified potential frameworks from prior knowledge, networks and a pragmatic literature search in 2019. We considered for inclusion only frameworks with an existing visual graphic. We summarised each framework’s concept, then judged it on a seven-item matrix (Scope, Dimensions, Outcomes, Life course, Mechanisms, Multi-age, and Visual Clarity) to be of high, intermediate or low relevance to GenV. We presented and evaluated 14 life course frameworks across research and policy. Two, nine and three frameworks, respectively, were ranked as high, intermediate and low relevance to GenV, although none totally communicated its scope and intent. Shonkoff’s biodevelopmental framework was selected as GenV’s primary framework, adapted to include ongoing feedback loops through the life course and influence of an individual’s outcomes on the next generation. Because conceptual simplicity precluded the primary framework from capturing the wide range of relevant exposures, we selected the Australian Institute of Health and Welfare’s person-centred model as a secondary framework. This summary of existing life course frameworks may prove helpful to other cohorts in planning. Our transparent process and focus on visual communication are already assisting in explaining and selecting measures for GenV. The feasibility, comprehension and validity of these frameworks could be further tested at implementation.
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.
With the intention to inform future public health initiatives, we aimed to determine the extent to which typical childhood dietary trajectories predict adolescent cardiovascular phenotypes.
Longitudinal study. Exposure was determined by a 4 d food diary repeated over eight waves (ages 4–15 years), coded by Australian Dietary Guidelines and summed into a continuous diet score (0–14). Outcomes were adolescent (Wave 8, age 15 years) blood pressure, resting heart rate, pulse wave velocity, carotid intima-media thickness, retinal arteriole-to-venule ratio. Latent class analysis identified ‘typical’ dietary trajectories from childhood to adolescence. Adjusted linear regression models assessed relationships between trajectories and cardiovascular outcomes, adjusted for a priori potential confounders.
Community sample, Melbourne, Australia.
Children (n 188) followed from age 4 to 15 years.
Four dietary trajectories were identified: unhealthy (8 %); moderately unhealthy (25 %); moderately healthy (46 %); healthy (21 %). There was little evidence that vascular phenotypes associated with the trajectories. However, resting heart rate (beats/min) increased (β; 95 % CI) across the healthy (reference), moderately healthy (4·1; −0·6, 8·9; P=0·08), moderately unhealthy (4·5; −0·7, 9·7; P=0·09) and unhealthy (10·5; 2·9, 18·0; P=0·01) trajectories.
Decade-long dietary trajectories did not appear to influence macro- or microvascular structure or stiffness by mid-adolescence, but were associated with resting heart rate, suggesting an early-life window for prevention. Larger studies are needed to confirm these findings, the threshold of diet quality associated with these physiological changes and whether functional changes in heart rate are followed by phenotypic change.
To determine which parental health behaviours early in childhood most strongly predict whole-of-childhood dietary trajectories.
Population-based Longitudinal Study of Australian Children (LSAC, waves 1–6; 2004–2014). Exposures were parents’ fruit/vegetable consumption, alcohol, smoking and physical activity at child age 0–1 years (B Cohort) or 4–5 years (K Cohort). Outcomes, from repeated biennial short diet diaries, were group-based trajectories of (i) dietary scores and empirically derived patterns of (ii) healthful and (iii) unhealthful foods consumed, spanning ages 2–3 to 10–11 years (B Cohort) and 4–5 to 14–15 years (K Cohort). We investigated associations of baseline parental health behaviours with child dietary trajectories using multinomial logistic regression.
Of children, 4443 (87·0 %) from the B Cohort and 4620 (92·7 %) from the K Cohort were included in all trajectories. Multivariable analyses included 2719 to 2905 children and both parents.
Children whose primary caregiver reported the lowest fruit/vegetable consumption had markedly higher odds of belonging to the least healthy score and pattern trajectories (K Cohort: OR=8·7, 95 % CI 5·0, 15·1 and OR=8·4, 95 % CI 4·8, 14·7, respectively); associations were weaker (K Cohort: OR=2·3, 95 % CI 1·0, 5·2) for the unhealthiest pattern trajectory. Secondary caregiver fruit/vegetable associations were smaller and inconsistent. Parental alcohol, smoking and physical activity were not predictive in multivariable analyses. Results were largely replicated for the B Cohort.
Low primary caregiver fruit/vegetable consumption increased nearly ninefold the odds of children being in the lowest intake of healthy, but only weakly predicted unhealthy, food trajectories. Healthy and unhealthy food intake may have different determinants.
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.
This study examines potential predictors of ‘precocious talking’ (expressive language ⩾90th percentile) at one and two years of age, and of ‘stability’ in precocious talking across both time periods, drawing on data from a prospective community cohort comprising over 1,800 children. Logistic regression was used to examine the relationship between precocious talking and the following potential predictors: gender, birth order, birth weight, non-English speaking background, socioeconomic status, maternal age, maternal mental health scores, and vocabulary and educational attainment of parents. The strongest predictors of precocity (being female and having a younger mother) warrant further exploration. Overall, however, it appears that precocity in early vocabulary development is not strongly influenced by the variables examined, which together explained just 2·6% and 1% of the variation at 1 ; 0 and 2 ; 0 respectively.
To investigate the prevalence and incidence of overweight and obesity, the frequency of overweight resolution and the influence of parental adiposity during middle childhood.
As part of a prospective cohort study, height and weight were measured in 1997 and 2000/2001. Children were classified as non-overweight, overweight or obese based on standard international definitions. Body mass index (BMI) was transformed into age- and gender-specific Z-scores employing the LMS method and 2000 growth chart data of the Centers for Disease Control and Prevention. Parents self-reported height and weight, and were classified as underweight, healthy weight, overweight or obese based on World Health Organization definitions.
Primary schools in Victoria, Australia.
In total, 1438 children aged 5–10 years at baseline.
The prevalence of overweight and obesity increased between baseline (15.0 and 4.3%, respectively) and follow-up (19.7 and 4.8%, respectively; P < 0.001 for increase in overweight and obesity combined). There were 140 incident cases of overweight (9.7% of the cohort) and 24 of obesity (1.7% of the cohort); only 3.8% of the cohort (19.8% of overweight/obese children) resolved to a healthy weight. The stability of child adiposity as measured by BMI category (84.8% remained in the same category) and BMI Z-score (r = 0.84; mean change = −0.05) was extremely high. Mean change in BMI Z-score decreased with age (linear trend β = 0.03, 95% confidence interval 0.01–0.05). The influence of parental adiposity largely disappeared when children's baseline BMI was adjusted for.
During middle childhood, the incidence of overweight/obesity exceeds the proportion of children resolving to non-overweight. However, for most children adiposity remains stable, and stability appears to increase with age. Prevention strategies targeting children in early childhood are required.
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