The association between dietary trajectories across childhood and blood pressure in early adolescence: The Longitudinal Study of Australian Children

High blood pressure in childhood is an emerging concern and tracks into adulthood, contributing to atherosclerosis, type 2 diabetes and cardiovascular disease. Diet quality is a modifiable factor potentially linked to pediatric BP, but evidence is mixed. Prior studies show inconsistent associations between dietary patterns and BP, and few have incorporated longitudinal changes in diet quality. This study aimed to examine the association between dietary trajectories from ages 4/5 to 10/11 years and BP at age 10/11, and to determine whether any associations are explained by BMI and/or fat mass.

Evidence to date is inconsistent. A 2018 systematic review summarized six cross-sectional studies with mixed results: some linked Western/unhealthy patterns to higher BP and healthier patterns to lower BP, while others showed no association or contradictory findings. Subsequent cross-sectional studies reported slightly lower DBP or SBP with healthier patterns, or no association. Two Dutch longitudinal studies associated higher early-childhood diet quality with lower SBP/DBP at ages 10–12, but did not account for changes in diet during follow-up. One LSAC-based cohort using trajectories from age 2/3 to 10/11 found a never healthy trajectory associated with higher SBP (1.8 mmHg) but not DBP compared with always healthy; however, only 53% attended clinic assessments, likely underrepresenting disadvantaged children. Obesity is strongly associated with higher BP in youth; meta-analytic evidence indicates SBP is 4.5 mmHg higher in children with overweight and 7.5 mmHg higher with obesity versus normal weight. Given diet’s relation to adiposity, clarifying whether BMI or fat mass mediate diet-BP associations is important.

Design and population: Longitudinal Study of Australian Children (LSAC), a nationally representative cohort with biennial data. Included waves: B-cohort waves 1–6 and K-cohort waves 1–4 spanning ages 4/5 to 10/11 (2004–2014). From 7928 children with data at age 10/11, exclusions were applied if parent 1 was not the biological mother (n=194) or parent 2 not the biological father (n=686), if dietary intake was available at fewer than two time points (n=6), if BP was missing at age 10/11 (n=831), or if covariate data were missing (n=1851), yielding N=4360. Data collection: Face-to-face interviews and questionnaires; child-completed audio computer-assisted self-interviews at older ages. Dietary assessment: At each wave, parents (ages 4–9) or children (from age 10) reported prior 24-hour frequency for 11 items. A dietary score (0–14) was computed per wave using a published LSAC scoring system aligned with the 2013 Australian Dietary Guidelines: seven food groups (fruit, vegetables, water, fatty foods, sugary foods, sweetened drinks, milk/alternatives), scoring 0–2 each; higher scores reflect more frequent consumption of recommended items and less frequent consumption of discretionary items. Scores were summed to an overall 0–14 score. The questions and scoring are not formally validated, but have been used in LSAC analyses. Blood pressure: SBP and DBP measured at age 10/11 using A&D UA-767 automated monitor (two measures 1 minute apart; mean used). Device not validated in children. Covariates: Based on literature, included child age, sex, Indigenous status, socio-economic status (SEIFA), maternal education and country of birth; parental BMI (self-reported) and maternal hypertension in pregnancy; child breastfeeding duration, pubertal status, and physical activity (parent-reported). Child anthropometrics: measured weight and height; BMI and BMI z-scores used for weight-status categories; percent body fat measured via Tanita BIA; fat mass index (FMI = fat mass/height²) computed. Statistical analysis: Group-based trajectory modelling of continuous dietary scores by child age (tested 3–5 trajectories; shapes up to cubic), selecting four-trajectory models in both cohorts by BIC; patterns similar across cohorts, so combined-cohort trajectories used. Participant characteristics compared across trajectories via chi-squared tests and ANOVA. Associations of trajectories with SBP and DBP modelled using linear regression, yielding nonstandardized coefficients versus the reference (never healthy). Covariate selection based on associations with both trajectories and BP plus commonly used covariates. Models: Model 1 socio-demographics; Model 2 adds parental factors; Model 3 adds child-related factors; Model 4 adds child BMI; Model 5 adds child FMI. Interaction terms tested for effect modification by child sex and by cohort. Analyses conducted in Stata 16.

- Four dietary trajectories identified in the combined cohort (N=4360): never healthy 4.3% (n=187); moderately healthy 23.1% (n=1005); becoming less healthy 14.2% (n=621); always healthy 58.4% (n=2547). - At follow-up (mean age 10.4 years; 47.6% girls), mean SBP was 97.5 mmHg (SD 10.9) and mean DBP 58.6 mmHg (SD 8.3). - Compared with never healthy, in fully adjusted Model 3 (socio-demographics + parental + child-related covariates): • Becoming less healthy: SBP -2.19 mmHg (95% CI -3.93, -0.45); DBP -1.54 (95% CI -2.89, -0.20). • Always healthy: SBP -2.19 mmHg (95% CI -3.78, -0.59); DBP -1.71 (95% CI -2.95, -0.47). • Moderately healthy: not significantly associated with SBP or DBP. - After adding BMI (Model 4) or FMI (Model 5), associations attenuated; the always healthy trajectory remained significantly associated with lower DBP: • Model 4 (adds BMI): always healthy DBP -1.34 mmHg (95% CI -2.54, -0.14); SBP -1.37 (-2.82, 0.07). • Model 5 (adds FMI): always healthy DBP -1.23 mmHg (95% CI -2.43, -0.03); SBP -1.17 (-2.64, 0.29). - No evidence of effect modification by sex or cohort (interaction p-values > 0.20). - Children in the always healthy trajectory more often had higher maternal education, normal parental BMI, were more physically active, breastfed ≥6 months, non-Indigenous, and had normal BMI.

The study demonstrates that children whose diets most closely tracked recommended guidelines across childhood had slightly lower SBP and DBP at age 10/11 compared with those with persistently least healthy diets, even after accounting for a broad set of socio-demographic, parental, and child factors. Adjustment for BMI or fat mass attenuated associations, suggesting partial mediation through adiposity, but the association with lower DBP for the always healthy trajectory persisted, indicating additional pathways beyond adiposity (e.g., dietary sodium/potassium balance or other cardiometabolic effects). The finding that a becoming less healthy trajectory from age 8/9 was still associated with lower BP at 10/11 suggests early-life diet quality may confer short-term benefits, though continued follow-up is needed to determine impacts of worsening diet into adolescence. The magnitude of BP differences (approximately 1–2 mmHg) is modest for individuals but meaningful at the population level, given established links between small BP reductions and lower cardiovascular event rates. Results generally align with prior studies linking less healthy patterns to higher BP, while extending the literature by accounting for longitudinal changes in diet quality and assessing the roles of BMI and fat mass.

Children with dietary trajectories consistently aligned with the Australian Dietary Guidelines across childhood have slightly lower diastolic blood pressure in early adolescence compared with peers with persistently less healthy diets. These associations are only partly explained by BMI or fat mass. Encouraging and enabling healthy dietary habits from early childhood may help attenuate increases in BP and reduce future cardio-metabolic disease risk. Future research should track diet and BP into later adolescence and adulthood, evaluate mediating mechanisms (including nutrient-specific effects), and utilize validated dietary and BP assessment tools.

- Dietary questions and the scoring/trajectory system were not validated and captured only prior 24-hour frequency of select items, limiting nutrient quantification and comprehensive alignment with dietary guidelines (e.g., cereals and meat groups not included) and precluding energy intake adjustment. - Several covariates were self-reported via simplified, non-validated instruments (e.g., physical activity, pubertal status), potentially introducing measurement error. - BP device (A&D UA-767) was not validated for pediatric use; measurement error may have biased estimates, likely non-differential. - Mean arterial pressure was not assessed. - Body fat estimated via BIA may be influenced by hydration status and other factors. - Substantial exclusions due to loss to follow-up or missing data (~57% excluded from eligible sample) may introduce selection bias; excluded participants tended to have poorer health/behaviors, possibly underestimating associations. Differences in key metrics between included and excluded participants were small but present.