Body Mass Index From Early to Late Childhood and Cardiometabolic Measurements at 11 to 12 years – AAP News


Posted: July 7, 2020 at 5:57 pm

Principal Findings

Childhood overweight and obesity from early childhood are associated with a higher MetS risk score, higher arterial stiffness, and increased cIMT at ages 11 to 12 years. When looking at BMI at the 5 biennial time points separately (ie, ages 23 to 1011 years), associations with cardiometabolic scores at 11 to 12 years strengthened with age. Growth trajectory analyses revealed that cumulative exposure to high BMI carried the greatest cardiometabolic risk and revealed a gradient of risk across the series of BMI trajectories.

Previous studies examining BMI and cardiometabolic health have tended to rely on a single BMI time point in childhood and focused on cardiometabolic outcomes in adulthood.24 Through our findings, we extend these studies by measuring BMI over time and cardiometabolic phenotypes in midchildhood. Our results are in keeping with previous studies but provide additional important insights that suggest BMI from as early as 2 to 3 years of age is predictive of preclinical cardiometabolic phenotypes by ages 11 to 12 years.

Authors of several studies have evaluated the trajectory patterns of childhood BMI,8,2832 with typically 3 to 4 distinct trajectories being defined. Most individuals follow a relatively stable trajectory throughout childhood compared with their peers. Higher BMI trajectories have previously been associated with a higher fasting insulin concentration at age 14 years33 and higher blood pressure values at age 18 years, as well as obesity, increased cIMT, and left ventricular mass in adulthood.28,31,32 In line with these studies, BMI trajectories in our sample were relatively stable, and a consistently high BMI trajectory was associated with worse cardiometabolic phenotypes at 11 to 12 years of age. Given that trajectories were relatively stable over time and cardiometabolic phenotypes were only measured at one time point, it is possible that our longitudinal associations reflect associations that emerge in midchildhood. To establish exactly when these associations emerge requires repeated measures of both BMI and cardiometabolic phenotypes throughout childhood.

From a clinical perspective, our data suggest that BMI from 2 to 3 years onward is generally relatively stable among the majority of children and is associated with subsequent preclinical cardiometabolic phenotypes. In terms of intervention efforts that are focused on childhood obesity, our data provide unique evidence that early-life BMI measurements predict cardiometabolic risk later in childhood. The magnitude of associations is also likely to translate into clinically important differences for children in the consistently high BMI trajectories. Compared with children in the low trajectory, those in the always-very-high trajectory had close to a 1 SD high MetS risk score (SMD of 0.92 [95% CI 0.62 to 1.23]). In one of our previous studies,14 a 1 SD higher continuous MetS risk score was associated with an elevated risk of type 2 diabetes and higher cIMT in adulthood, highlighting the clinical significance.

When we dichotomized cardiometabolic health measures, the adverse associations with consistently high BMI were also marked. Growth patterns have been associated with differential cardiometabolic risk by early adolescence, with children with a normal peakearly rebound pattern or without any BMI decline after infancy having higher insulin resistance and metabolic risk scores.8 Because our methodologic approach was used to generate summary BMI trajectory patterns and was designed to reveal empirical typical groupings of patterns rather than individuals with early adiposity rebound, our findings are not directly comparable. Notwithstanding, we observed effects early in life when BMI was considered across the 5 biennial time points separately. Infant BMI was not included because length was not collected in LSAC wave 1. However, when we adjusted estimates for small for gestational age, the results were essentially unchanged.

Despite the strong associations we observed between groups, the amount of variance in cardiometabolic phenotypes explained (R2) was relatively small for both the time-point and trajectory analyses. However, at the population level, the small amount of variance explained is still likely to be meaningful, and this is likely to increase as the pathogenesis of cardiometabolic disease develops over the life course with cumulative risk factor exposure.

Our findings have public health implications because they highlight the subclinical effects of obesity in childhood. This highlights the importance of early interventions when trajectories are likely to be more malleable and adverse cardiometabolic phenotypes are reversible.4 The 2017 World Health Organization Commission on Ending Childhood Obesity report argued that multisectoral action is urgently needed to address the obesogenic environment.34 Such action requires systems-based approaches and policy implementation. Until this is realized, we must continue to try to curb the obesity pandemic at all levels (eg, family, child care, and school) throughout childhood to promote healthy weight and healthy eating, sleep, screen, and activity behaviors in the hope of setting healthy weight trajectories in childhood that track into adolescence and adulthood.35

The study cohort is not completely population representative. Compared with the original population-based sample (n = 5107), those who did not take part (n = 3233) were largely comparable to CheckPoint participants (n = 1874) at baseline (ie, 2004). The exception was that compared with CheckPoint families, those lost to follow-up came from more socioeconomically disadvantaged families (baseline Socio-Economic Indexes for Areas mean of 1019 [SD of 61] vs 1003 [SD of 59]), were more likely to be of indigenous background (2% vs 6%) and have parents whose home language was not English (11% vs 16%).10 However, after applying survey weights, which accounted for nonresponse and loss to follow-up over the 6 waves of the LSAC from 2004 to 2015, the associations were largely unchanged.

Because of the young age of the study population (1112 years), it is not possible to evaluate the effects of BMI on actual cardiovascular disease or events. Instead, their cardiometabolic health was evaluated by using preclinical phenotypes (MetS risk scores, cIMT, and PWV) known to be associated with conventional cardiovascular risk factors in adulthood and used to predict overall cardiovascular morbidity.3638 Physical activity and dietary intake both reveal complex relationships with BMI and cardiometabolic health. We chose not to treat them as potential confounders in these analyses for several reasons: (1) neither could be adequately measured at or before baseline, (2) our previous work in this cohort has revealed that an inflammatory diet is not related to cardiovascular function and structure in children,39 and (3) in crosslagged wave-on-wave analyses, dietary scores and/or patterns did not consistently predict weight-to-height ratio and BMI z score or vice versa in subsequent waves.40 Finally, blood samples were collected after a semifast (median time of 4.2hours) rather than a traditional 8-hour fast. However, previous data suggest that a random sampling or fasting for a 3-hour period is sufficient for reliable glucose measurements,41 and current guidelines recommend that nonfasting blood samples can be routinely used for the assessment of plasma lipid profiles.42

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