Lesson 11: Child and Adolescent – Growth Curve Considerations, Perception, and Weight Change Goals

Table of Contents

Introduction

In the last lesson I discussed how to define obesity and various anthropometric considerations with its definition. While it is defined by a person’s body mass index (“BMI”), this doesn’t encapsulate additional information about the amount of body fat vs. lean body mass or the body fat distribution. In this lesson I will extend this conversation to include changes over time, which are evaluated by examining growth curves, and I will emphasize additional insights that can be obtained from seeing how the growth measurements change longitudinally. I will also show that discussing growth curves can be useful for clearing up any misperceptions of a child’s BMI status. Lastly I will discuss recommendations regarding weight change over time when managing childhood overweight and obesity.

Growth curve considerations

One of the primary methods of assessing growth in a child is by examining the child’s growth curve as this indicates how the height and weight parameters have changed over time. This provides considerably more useful information than just looking at height/weight/BMI at a single point in time as the trend over time helps to indicate if any changes should be made to help ensure good health in the future. I will discuss some of the findings in the literature that have implications regarding growth curves here.

Caveats with accuracy

Besides obvious concerns regarding measurement errors when taking a child’s height and weight, there are a couple of other things to consider when assessing growth and any recent changes over time:

  • A 2021 study demonstrated that since puberty has more recently started earlier than when the data for growth curves was initially collected, children are also taller at a given age than when the growth curves were initially conceived, and as a result more children are categorized as having overweight or obesity since taller children generally have a higher BMI than shorter children.(Telford, 2021) In this specific study looking at data from before the year 2000 this increased the prevalence of overweight and obesity by ~2%, but the main point is that children who are unusually short or tall may not be appropriately monitored with standard BMI metrics.
  • Some evidence based on measurements throughout a calendar year has indicated that children gain weight over the summer, but a cross-sectional study of children aged 5-16 years indicated with monthly measurements that this pattern may vary based on the child’s weight status and may differ from what is determined when only looking at two points in time.(Bhutani, 2018) This can be important if considering the impact of any intervention that runs over the summer; it is possible that the results may be skewed by changes that would naturally occur when children are on summer break from school.

How to evaluate the level of obesity based on the growth curve

In the last lesson I showed that obesity is defined as having a BMI ≥95th percentile (“%ile”) for a given age and gender, and I mentioned various classes of obesity can be defined by absolute BMI values (ie, ≥35 = class I obesity, ≥40 = class II obesity), or by BMI values relative to the 95th %ile (≥120% of the 95th %ile = class I obesity, ≥140% of the 95th %ile = class II obesity). The question then becomes if defining higher levels of obesity in relation to the 95th %ile is appropriate.

A 2021 study noted that the BMI z-score (this refers to the BMI standard deviation) is inappropriate to use when the BMI is >97th %ile as this is the highest %ile used in the CDC estimation of the LMS parameter (the method used to create the growth curves); extrapolating beyond this compresses z-scores into a narrow range.(Bramante, 2021) The authors thus assessed other BMI metrics and their association with adiposity and cardiometabolic risk in children and adolescents in a cross-sectional study of 371 children aged 8-17 years. Of all of the evaluated metrics, the BMI %ile relative to the 95th %ile (“%BMIp95”) had the highest correlation with visceral fat % (r = 0.814) and waist circumference (r = 0.941) and the 3rd highest correlation with bodyfat percentage (r = 0.859). While all of the evaluated metrics had relatively low correlations with many of the risk factors and biomarkers that were assessed, this does indicate that the %BMIp95 is likely the best way to evaluate the level of obesity when looking at a growth curve.

A separate study evaluated variability in various types of BMI measurements over a ~1 year time span and assessed how this variability correlates with mean BMI.(Freedman, 2022a) The authors noted that using the % of the 50th %ile of the BMI expressed on a log scale (“log %50th”) yielded individual variability that was most independent of the mean BMI, but this correlated very highly (r ~0.97) with the %BMIp95. As %BMIp95 will be much more trivial to calculate with a growth curve than the log %50th, this further supports that %BMIp95 can and likely should be used.

Example: Over time, as a child grows taller it can be difficult to determine if their body composition is improving based solely on tracking their weight, as they may lose body fat while still gaining lean body mass and gaining weight overall. Using the %BMIp95 helps to account for this. Thus, using this calculator for an example:

  • Gender = Male, Age = 10 years and 3 months, Height = 55 inches, Weight = 120 pounds

The BMI = 27.9, and you can click the provided link to see the plot. Looking at the graph, you can see that the 95th %ile BMI is ~22.5. Thus, the %BMIp95 = 27.9/22.5 = 124%.

If the child makes mild changes to implement healthier lifestyle habits for 6 months, perhaps the new measurements will be:

  • Gender = Male, Age = 10 years and 9 months, Height = 56 inches, Weight = 124 pounds

Now the BMI is essentially the same at 27.8, and you can click the provided link to see the plot. but the 95th %ile BMI is ~23, and the %BMIp95 = 27.8/23 = 121%. Thus, even though the child has gained 4 pounds in 6 months, he did gain 1 inch in height while his BMI has not changed much, and as BMI normally increases to some degree at this age his BMI has relatively improved. More significant results would likely be possible with additional lifestyle changes, but this shows that he has made legitimate progress.

Rapid weight gain (“RWG”)

Another consideration regarding growth curves is how the weight or BMI changes over time. For example, in Lesson 7 I showed that RWG in early childhood increases the risk of future obesity.

A 2021 analysis took data from a cohort (96.1% of the cohort was white and British) and generated 6 BMI trajectories from childhood to adulthood.(Norris, 2021) There were 2 trajectories that were normal weight throughout, 2 that ended in overweight (with 1 being persistently overweight and 1 increasing from normal weight to overweight with RWG), and 2 that ended in obesity (with 1 being persistently either in the overweight or obesity category and the other increasing from normal weight to obesity with RWG). The two trajectories with rapid weight gain had the shortest final height for the following reasons:

  • Typically having excess weight leads to increased height early in life; these two groups though had normal weight early in life so did not get this additional growth.
  • The RWG in both groups was associated with earlier onset of the peak height velocity, decreasing the total length of time for growth to occur.
  • For the normal to obesity group the peak height velocity additionally was lower in magnitude.

There were large differences in fat mass between the classes but only a small difference in lean body mass (“LBM”, there was a 2-6 kg difference in LBM between the normal weight and the obesity classes). Of importance, the trajectories that had RWG had the greatest cardiometabolic health risks. Thus, RWG leading to a transition to a higher BMI category in adolescence led to worse cardiometabolic health profiles than persistently staying in the higher BMI category from childhood. The authors hypothesized the later transition may have led to greater visceral fat levels, but perhaps persistent overweight/obesity is associated with greater LBM in childhood and that has protective health benefits; more research is needed to evaluate how the timing of RWG can impact overall health.

Adiposity rebound (“AR”)

Another important concept is the AR; typically a child’s BMI decreases until age 5-7 years prior to increasing again (the point in time when this increases is referred to as the AR) but if the increase occurs prior to age 5 years then the AR is said to occur early; when this does occur early this increases the risk of obesity.(Styne, 2017) It is also possible for the BMI to continually increase from infancy such that there is no AR, which further increases the risk.

Note: Since the adiposity rebound is determined by the change in BMI, and not the change in body fat, it is likely more appropriate to refer to this as the “BMI rebound” rather than the “adiposity rebound”, but I will use the latter term to remain consistent with the majority of the literature.

A 2020 review on RWG noted that an early AR may be associated with a normal or even low BMI early in childhood but can lead to a faster rate of fat gain thereafter and is associated with a greater risk of obesity as well as metabolic syndrome later in life.(Arisaka, 2020) On the other hand, a late AR (after turning 7 years old) seems to be associated with delayed pubertal maturation but decreased cardiometabolic risks. A separate commentary on the AR noted that regardless of the BMI prior to an early AR there will be an increased risk of developing overweight later in life, but when the BMI is on the low side prior to an early AR this also associates with decreased future LBM, which is known to associate with increased cardiometabolic health risks.(Rolland-Cachera, 2019)

However, other research demonstrates the importance of considering the BMI at the point of the adiposity rebound to determine future obesity risk. A 2022 study evaluated the growth of >12,000 children from 4 children’s hospitals in the United States; the authors created a model to predict future obesity risk at age 15 years based on the timing of the AR as well as the BMI from ages 2-8 years.(Freedman, 2022b) The model found that the BMI at the time of the AR was more informative than the age of the AR (correlation of r = 0.57 vs. r = -0.44), and the timing of the AR did not provide additional information for future obesity risk if the BMI at age 6 years (or older) was known. I have included a nomogram and figure they created summarizing the risk of obesity based on the age and BMI at the time of the AR below.

Reproduced from: Freedman DS, Goodwin-Davies AJ, Kompaniyets L, Lange SJ, Goodman AB, Phan TT, Rao S, Eneli I, Forrest CB. Interrelationships among age at adiposity rebound, BMI during childhood, and BMI after age 14 years in an electronic health record database. Obesity (Silver Spring). 2022 Jan;30(1):201-208. doi: 10.1002/oby.23315. PMID: 34932881.
Predicted probability of obesity at age 15 years by BMI level at a rebound age of 2, 4, 5, or 7 years. BMI values represent the BMI or mean BMI at the rebound age.
Reproduced from: Freedman DS, Goodwin-Davies AJ, Kompaniyets L, Lange SJ, Goodman AB, Phan TT, Rao S, Eneli I, Forrest CB. Interrelationships among age at adiposity rebound, BMI during childhood, and BMI after age 14 years in an electronic health record database. Obesity (Silver Spring). 2022 Jan;30(1):201-208. doi: 10.1002/oby.23315. PMID: 34932881.

Thus, the AR provides increasingly less value as time goes on. This implies that any increased obesity risk from an early AR manifests at relatively young ages.

Tip: If children see a healthcare provider frequently enough with accurate growth measurements to determine the timing of the AR, and this is determined to be early, this knowledge will likely be useful to spark a discussion about what lifestyle changes can be made at this point in time to improve health and decrease obesity risk in the future. If the impact of an early AR manifests in the early ages then this can be an excellent clue (if it is early) that there is something ongoing that can be improved. As seen in the figure above a concern of developing obesity may be less valid if the BMI is on the low side, though the earlier cited literature suggests this may still portend a deficit in lean body mass and faster fat mass accrual.

Thus, if the AR is early and the BMI is not low, an intervention of some sort may go a long way in helping to prevent the onset of childhood obesity in the future. If the AR is early and the BMI is low this still indicates it is worth considering if any potential healthy lifestyle changes can be made and it will be useful to continue tracking growth moving forward.

Misperception of child weight status

Many families of children with overweight or obesity do not realize that the child has an elevated body weight:

  • A 2018 study including 2,720 children aged 3-11 years from 10 different countries found that among children with overweight or obesity, 89% of those with overweight and 52% of those with obesity were classified as normal weight by their mothers.(Gregori, 2018)
    • There was a greater misperception if the mother had a higher BMI and if there was a higher International Brand Awareness Inventory score (which associates with adoption of more Western-driven habits).
  • A 2019 SR including 135 studies found that 14-83% of parents and 11.1-81.7% of children misperceived the child’s weight status.(Blanchet, 2019) The authors noted that recent studies indicate that when children are perceived as having overweight or obesity, regardless of accuracy, they tend to gain more weight over time than their peers (even compared to peers who do have overweight or obesity but are misclassified as normal weight); this has been attributed to weight-based stigma and unhealthy coping behaviors.
    • However, I do think there is another plausible explanation for why children perceived as having excess weight subsequently gain more weight over time; perhaps the children perceived as having excess weight have already gained significant amounts of weight with a lifestyle conducive to obesity and hence are more prone than other children to continue to gain more weight in the future. Stigma may very well play a role but there could be a biological basis as well.

It is clear that a large subset of families with children with overweight or obesity do not realize that their child has an elevated body weight. It has also been shown that misperceptions of child weight status associate with alterations in feeding styles regarding pressuring to eat and restrictive nutritional practices.(Costa, 2022; Gketsios, 2022) More education, principally by looking at growth curves at medical appointments, is likely warranted to help the child achieve the best health outcomes possible.

Weight loss goals

There is no consensus regarding what constitutes meaningful weight loss for health outcomes in childhood obesity.(San Giovanni, 2021) For adolescents who have achieved most of their adult height, aiming to lose at least 1.5 BMI points or preferably at least 5-7% of their body weight will yield significant health benefits.(Cardel, 2020) Recommendations regarding goals for weight change per one review include(Mittal, 2021):

  • 2-5 years old: the goal is to maintain weight until the child is no longer in the overweight or obesity BMI range.
  • 6-11 years old:
    • Children in the overweight range should maintain their weight.
    • Children in the obesity range should lose 0.5-2 kg per month (depending on the severity of obesity) and target a ~10% decrease in body weight.
  • ≥12 years old:
    • Children in the overweight range should maintain or gradually lose weight.
    • Children in the obesity range should lose up to 1 kg per week and target a ~10% reduction in weight.

A 2021 review of 21 international clinical practice guidelines, 5 position statements, and 2 consensus statements regarding pediatric obesity found that these documents generally recommend rates of weight loss between 0.4 kg/month to 0.9 kg/week for children and 0.5-2 kg/month to 0.9 kg/week for adolescents, or between 5-10% of their initial body weight.(Alman, 2021) Overall guidance regarding dietary approaches to achieve this was scarce.

Thus, it seems there is a wide range of rates of weight loss that are typically recommended. In general for adults I recommend losing no more than 1% of your body weight per week; by extension I think this will also be applicable to children and adolescents who weigh ≤90 kg (~200 pounds). If they weigh >90 kg then they should lose no more than 0.9 kg/wk (~2 pounds per week) per the guidance above. This may be slower weight loss than in adults but this makes sense when considering that children and adolescents are still growing (if not in height than at least in skeletal muscle mass and bone mineral content accrual); losing weight too quickly may prevent appropriate growth from occurring.

When starting the process of losing weight, a couple of studies indicate that the initial rate of weight loss is informative for future progress:

  • A 2020 study included 137 children aged 8-12 years with overweight or obesity in a 6-month intervention with subsequent follow-up assessments at 6 and 18 months.(Eichen, 2020) Based on their data they determined the best weight loss cutoff at 4 weeks to indicate continued success was 2.4% weight loss (~4.5 pounds on average). 45 of the children (33% of the sample) achieved this, and this was strongly associated with a 5 or 10% decrease in their BMI standard deviation score by the end of the intervention.
  • A weight management intervention in children with a mean age of 12.2 years included 687 people.(Gross, 2019) Of these, individuals who gained 1% in BMI in the first month had a 2.7% gain by 6 months. People who maintained their BMI at 1 month had no change in their BMI at 6 months. People who lost 5% of their BMI at 1 month lost 8% by 6 months. Of the people who lost 4% of their BMI at 1 month, 84.6% had continued success at 6 months. Therefore, nonresponse to the intervention at 1 month was highly predictive of not having any benefit moving forward.

Thus, the first month of an intervention is important; if there is not much success during this first month then something should be changed about the overall approach as doing more of the same will likely not prove effective.

Tip: While being successful in the first month indicates likely continued progress moving forward (if a person sticks to the healthier lifestyle changes they have made) this does not mean that someone who is not successful in the first month is doomed. It simply means more significant changes need to be made.

In general I advocate making the “obvious” lifestyle changes first (ie, removing non-nutritious snacks and sugar-sweetened beverages from the home, increasing physical activity, optimizing sleep, working on mindful eating, etc), but if these changes are not yielding desirable results then it makes more sense to keep a 3-day food/drink diary to help determine where the excess calories are coming from and address this directly.

Conclusion

In this lesson I discussed various aspects of growth curves, perception of weight status, and weight change goals, including:

  • Growth curves are very informative not only to assess current BMI status but to also determine future health risks if rapid weight gain or an early adiposity rebound has occurred. Tracking growth regularly can help determine if any intervention is warranted even while the BMI is in the normal range to help decrease the risk of developing obesity in the future.
  • Many caregivers are unaware of their child’s weight status so examining a growth curve can be informative.
  • Goals for weight loss or weight maintenance depend on a child’s age and current BMI level, but if progress is not made in the first month then some sort of adjustment is warranted to help spur improvement moving forward.

In the next two lessons I will discuss more practical aspects of healthy lifestyle changes you can make to help a child with obesity improve their health.

Click here to proceed to Lesson 12

References

  1. Alman KL, Lister NB, Garnett SP, Gow ML, Aldwell K, Jebeile H. Dietetic management of obesity and severe obesity in children and adolescents: A scoping review of guidelines. Obes Rev. 2021 Jan;22(1):e13132. doi: 10.1111/obr.13132. Epub 2020 Sep 7. PMID: 32896058.
  2. Arisaka O, Ichikawa G, Koyama S, Sairenchi T. Childhood obesity: rapid weight gain in early childhood and subsequent cardiometabolic risk. Clin Pediatr Endocrinol. 2020;29(4):135-142. doi: 10.1297/cpe.29.135. Epub 2020 Oct 3. PMID: 33088012; PMCID: PMC7534524.
  3. Bhutani S, Hanrahan LP, Vanwormer J, Schoeller DA. Circannual variation in relative weight of children 5 to 16 years of age. Pediatr Obes. 2018 Jul;13(7):399-405. doi: 10.1111/ijpo.12270. Epub 2018 Apr 17. PMID: 29665291; PMCID: PMC6441331.
  4. Blanchet R, Kengneson CC, Bodnaruc AM, Gunter A, Giroux I. Factors Influencing Parents’ and Children’s Misperception of Children’s Weight Status: a Systematic Review of Current Research. Curr Obes Rep. 2019 Dec;8(4):373-412. doi: 10.1007/s13679-019-00361-1. PMID: 31701349.
  5. Bramante CT, Palzer EF, Rudser KD, Ryder JR, Fox CK, Bomberg EM, Bensignor MO, Gross AC, Sherwood NE, Kelly AS. BMI metrics and their association with adiposity, cardiometabolic risk factors, and biomarkers in children and adolescents. Int J Obes (Lond). 2021 Oct 30. doi: 10.1038/s41366-021-01006-x. Epub ahead of print. PMID: 34718333.
  6. Cardel MI, Atkinson MA, Taveras EM, Holm JC, Kelly AS. Obesity Treatment Among Adolescents: A Review of Current Evidence and Future Directions. JAMA Pediatr. 2020 Jun 1;174(6):609-617. doi: 10.1001/jamapediatrics.2020.0085. PMID: 32202626; PMCID: PMC7483247.
  7. Eichen DM, Mestre ZL, Strong DR, Rhee KE, Boutelle KN. Defining and identifying predictors of rapid response to pediatric obesity treatment. Pediatr Obes. 2020 Jun;15(6):e12621. doi: 10.1111/ijpo.12621. Epub 2020 Feb 25. PMID: 32100412; PMCID: PMC7202991.
  8. Freedman DS, Goodwin Davies AJ, Phan TT, Cole FS, Pajor N, Rao S, Eneli I, Kompaniyets L, Lange SJ, Christakis DA, Forrest CB. Measuring BMI change among children and adolescents. Pediatr Obes. 2022a Jan 22:e12889. doi: 10.1111/ijpo.12889. Epub ahead of print. PMID: 35064761.
  9. Freedman DS, Goodwin-Davies AJ, Kompaniyets L, Lange SJ, Goodman AB, Phan TT, Rao S, Eneli I, Forrest CB. Interrelationships among age at adiposity rebound, BMI during childhood, and BMI after age 14 years in an electronic health record database. Obesity (Silver Spring). 2022b Jan;30(1):201-208. doi: 10.1002/oby.23315. PMID: 34932881.
  10. Gketsios I, Foscolou A, Vassilakou T, Panagiotakos DB, Kosti RI. Parental Misperceptions of Their Offspring’s Weight and Their Strategies for Child’s Eating Behavior: A Narrative Review of the Recent Evidence. Children (Basel). 2022 Oct 16;9(10):1565. doi: 10.3390/children9101565. PMID: 36291501; PMCID: PMC9600609.
  11. Gregori D, Hochdorn A, Azzolina D, Berchialla P, Lorenzoni G; OBEY-AD Study Consortium. Does Love Really Make Mothers Blind? A Large Transcontinental Study on Mothers’ Awareness About Their Children’s Weight. Obesity (Silver Spring). 2018 Jul;26(7):1211-1224. doi: 10.1002/oby.22214. PMID: 29932519.
  12. Gross AC, Kaizer AM, Kelly AS, Rudser KD, Ryder JR, Borzutzky CR, Santos M, Tucker JM, Yee JK, Fox CK; POWER Work Group. Long and Short of It: Early Response Predicts Longer-Term Outcomes in Pediatric Weight Management. Obesity (Silver Spring). 2019 Feb;27(2):272-279. doi: 10.1002/oby.22367. PMID: 30677263; PMCID: PMC6352906.
  13. Mittal M, Jain V. Management of Obesity and Its Complications in Children and Adolescents. Indian J Pediatr. 2021 Dec;88(12):1222-1234. doi: 10.1007/s12098-021-03913-3. Epub 2021 Oct 5. PMID: 34609654; PMCID: PMC8491444.
  14. Norris T, Mansukoski L, Gilthorpe MS, Hamer M, Hardy R, Howe LD, Hughes AD, Li L, O’Donnell E, Ong KK, Ploubidis GB, Silverwood RJ, Viner RM, Johnson W. Distinct Body Mass Index Trajectories to Young-Adulthood Obesity and Their Different Cardiometabolic Consequences. Arterioscler Thromb Vasc Biol. 2021 Apr;41(4):1580-1593. doi: 10.1161/ATVBAHA.120.315782. Epub 2021 Mar 4. PMID: 33657884; PMCID: PMC7610444.
  15. Rolland-Cachera MF, Cole TJ. Does the age at adiposity rebound reflect a critical period? Pediatr Obes. 2019 Jan;14(1). doi: 10.1111/ijpo.12467. Epub 2018 Sep 25. PMID: 30253063.
  16. San Giovanni CB, Sweeney B, Skelton JA, Kelsey MM, Kelly AS. Aversion to Off-label Prescribing in Clinical Pediatric Weight Management: The Quintessential Double Standard. J Clin Endocrinol Metab. 2021 Jun 16;106(7):2103-2113. doi: 10.1210/clinem/dgab276. PMID: 33901290; PMCID: PMC8600003.
  17. Styne DM, Arslanian SA, Connor EL, Farooqi IS, Murad MH, Silverstein JH, Yanovski JA. Pediatric Obesity-Assessment, Treatment, and Prevention: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab. 2017 Mar 1;102(3):709-757. doi: 10.1210/jc.2016-2573. PMID: 28359099; PMCID: PMC6283429.
  18. Telford RD, Olds TS, Telford RM, Cunningham RB. The effect of height on estimates of the change in BMI-based prevalence of childhood obesity. Int J Obes (Lond). 2021 Nov;45(11):2506-2510. doi: 10.1038/s41366-021-00916-0. Epub 2021 Aug 18. PMID: 34408256.
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