Identifying Developmental Trajectories of Body Mass Index in Childhood

 The study I decided to write on, Identifying developmental trajectories of body mass index in childhood using latent class growth (mixture) modelling: associations with dietary, sedentary and physical activity behaviors: a longitudinal study” (Koning, 2016), is a longitudinal study tracking the correlation between habits and social circumstances and the body mass index to glean whether there is a predictable trend. The study focused on 613 children and included a survey completed by the parents to get a view of their socio-economic status, the BMIs of the parents, the children’s food choices and exercise habits, as well as their status three and six years later. The information was gathered over three weeks in 2006, 2009 and 2012 starting with children between the ages of 4 and 12 which included height, weight, SES, the weight of the parents, exercise habits, food choices and the child’s sedentary activity (such as watching TV or using a computer). The goal was to establish a visible trajectory between childhood lifestyle and the development of an unhealthy BMI. Modeling techniques were employed to track the movement of BMI over time and behaviors associated with them.

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 The study I read was an interesting topic. The idea that there is a predictable model for behaviors and health outcomes seems like it would have solid foundations in mappable trends. The focus of this study was to aid in being able to cite specific attributes of lifestyle to manage the growing trend of unhealthy life outcomes in later life. Interestingly, the paper itself stated that the “results indicate the importance of healthy lifestyle behaviors at a young age, and indicate the maternal BMI is a very important risk factor for the development of childhood overweight” (Koning, 2016). I found this information striking. When it comes to the habits of children, it is easy to assume that there is more significance in food consumed and exercise performed (including sports in this study) than there is in maternal standing. While the study focused on a small clutch of children, the implications scale to the entire world. There were factors that surfaced as “high risk behaviors in early childhood, especially in low SES children, children on non-western ethnicity and children whose mother is overweight” (Koning, 2016). I have read studies before relating the low SES to the availability of nutritious food in low income areas. The concept that the food available is higher in fat, lower in vitamins and minerals and result in a gradual trend to unhealthy makes sense. Being of non-western descent draws curiosity, though. Sadly, there is not more information in this article further detailing the mechanism behind that finding. Still, this is a known problem for the entire world.

 It is stated that “in populations worldwide, the prevalence of childhood overweight and obesity has dramatically increased during the last decades” (Koning, 2016). The study cites an observation in the Netherlands, where from “1980, the prevalence of childhood overweight in 2009 has increased two to three fold and the prevalence of obesity increased four to six fold” (Koning, 2016). The study states the staggering fact that “in 2009 12.8% of the boys and 14.8% of the girls aged 2 to 21 years were classified as overweight and 1.8% of the boys and 2.2% of the girls were classified as obese” (Koning, 2016). These trends are concerning due to the frequency of increase. The monitored behaviors that were tested to result in this unhealthy increase appear to shed light on at least some factors that can be managed. It should come as no surprise that there was a link between childhood overweight and an increased risk of being overweight as an adult. Less obvious is that beyond the cardiovascular risks in adulthood, “elevated blood pressure, type 2 diabetes mellitus, abnormal blood lipids, sleep apnea, and reduced physical fitness” (Koning, 2016), there are problems psychologically. Some of the problems like negative body image are predictable due to the commonality of such distress. The fact that “mental distress, depression and a reduced levels of quality of life and self-esteem” (Koning, 2016) is possibly less apparent. The need for medical treatment due to the healthy affects is visible, which might allow for the psychological effects to be overlooked. Additionally, the management of the problem early on would be the catalyst for numerous helpful outcomes. As it stands, the paper states that “recent developments in statistical techniques make it possible to study the potential heterogeneity in the development of BMI during childhood” (Koning, 2016). The expectation is that there are multiple means to get to the same end of being overweight in the study. While different studies are cited in the paper to support (or refute) their assumption, it is stated that their goal is “to 1) identify distinct BMI trajectories with an exploratory approach and 2) examine associations between dietary, sedentary and physical activity behaviors and these distinct trajectories” (Koning, 2016). So, the study took to the Netherlands.

 The primary schools in the city of Zwolle participated. In 2006, 2009, and 2012, all the schools in the city were invited to participate. The totals for participation were 80%, 79% and 81%, respectively. Letters were sent to the schools for the parents and children to fill out to get reported data. Measurements were also taken in each of the three stated years. “Total response in 2006 was 4,072 children (49%) of whom anthropometric measurements and questionnaires were available, in 2009 this was 3,026 (35%) and in 2012 5,849 (61%)” (Koning, 2016). Having thousands of participants made for a great start to the paper. Some of the losses used for cohorts may have offered some fine-tuning of the numbers, though. There is good reason to follow the idea and give it value, though. The model has merit if it is functionally useful and should scale all the same, if it does hold true. The “BMI was calculated as weight in kilograms divided by height in meters squared” (Koning, 2016). Some modifications were made to adjust for the fact that they were children and are subject to varied growth at times and between genders. The questionnaires that the parents filled out included information on “the child’s age, gender, postal code, ethnicity (assessed by the country of birth of both parents) and socio-economic status (SES) (assessed by educational level parents). Parents’ self-reported weight and height data were used to calculate their BMI” (Koning, 2016).  For the study, information on food consumption was also taken in addition to the children and parents’ information. Things like sugared drink consumption and fruit and vegetable intake was factored in to the final assessments.

 The analysis of data allowed for distinct subgroups to be classified. Logistic regression models were also carried out. The link between behaviors in 2006 and 2012 were what was being teased out. There were interesting findings with an increased accuracy from previous studies that showed that children on the increasing track for BMI raise from overweight and obese children at 8.7% to 22.3% from 2006 to 2012.  The predicted track for lowering BMI went from 6.5% to 1.7%, respectively. It turned out that the children at the most risk “scored higher odds in 2012 for drinking more than the recommended amount of sugared drinks (OR, 1.2; 95% CI 0.8 to 1.8), participating in organized sports less than recommended (OR, 1.3; 95% CI 0.8 to 2.0), and more than recommended use of use of screen time (Or, 1.3; 95% CI 0.9 to 1.8)” (Koning, 2016) as compared to the children who were on a decreasing BMI trajectory. Over the six years, two groups of children seemed to appear. There were those with increasing BMIs and those with stable decreasing.  They were 32% and 68%, respectively. Interestingly, “the increasing BMI SDS trajectory consisted statistically significantly of more participants of lower and middle SES and more of non-western ethnicity” (Koning, 2016). A danger also apparently came from the BMI of the parents, too. An increased risk was related to the status of the BMI of both parents. Maternal smoking also apparently contributes. Quite an interesting article, though not without a few flaws.

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 It is stated that “a potential limitation is that only three measurements of BMI were used to obtain BMI developmental trajectories” (Koning, 2016). This could be a factor as many aspects of child development and body assessment would offer more of a detailed picture. The groundwork is of benefit, though more precision is always more helpful. Additionally, “at the follow up in 2012, children were involved up to 12.9 years, meaning that pubertal status could possibly have influenced” (Koning, 2016) outcomes. I think the changes involved in puberty stand as a real confounding aspect. Weight gain before a growth spurt might be a factor, much like the size of body parts pre and post pubertal influences. Still, the study had some hits and was intriguing enough to make me want to keep learning more. The psychological aspects would be of great interest to me. The nature of the influence between mind and body is something that is eternally interesting. It is also likely there is much more to know. Placebo can do a lot of good and a lot of harm. As for the study, it appears that the “results indicate the importance of healthy lifestyle behaviors at a young age, and as confirmed in other studies investigating developmental trajectories in childhood, indicate that maternal BMI is a very important risk factor for the childhood overweight” (Koning, 2016). The influence over a lifetime of healthy habits should come as no surprise. It’s practically woven into the consciousness of the world. Critical times when exercise is necessary would be interesting to sus out. I have read studies indicating there are influences in utero and pre-puberty that can be used to change the metabolism throughout the lifetime. Although, the study failed to “find statistically significant results for the associations between the trajectories and the health related behaviors, except for the participation in organized sports at follow-up” (Koning, 2016). Even the lack of finding makes a bold statement. There is clearly a lot more to discover relating to health and healthy living. Creating a method to predict outcomes is always useful, though it appears to be quite tricky when it comes to the human body. The health implications are obvious, and it should follow that there are financial stakes, as well. A healthier population is good for everyone. That includes the lesser known mental stress, which affects more than a single person at a time. There is no doubt a link between childhood activity and a healthy adult outcome, though the state of the home, mental resilience, encouraging influences and outlets for energy can also come into play. I’ve never been to the Netherlands, so I can’t speak with authority on what exactly the larger context of these peoples’ lives come into play. I would suspect that there are many other factors involved in the overall wellbeing of a person. I think that is why there are failings in the predictive model seen here. BMI takes a very limited view of a very complex subject. Access to food, homogeneity of population, country of origin, traditions, access to recreational areas, proximity to other children, stability of the home, and even safety of the neighborhoods can all play a part. There will need to be more studies done, including those on the circumstances surrounding the gestation period. Still, every step forward is a step in the right direction. One can only hope we keep working toward a better future for all. 

References

Maaike Koning, Trynke Hoekstra, Elske de Jong, Tommy L. S. Visscher, Jacob C. Seidell, & Carry M. Renders. (2016). Identifying developmental trajectories of body mass index in childhood using latent class growth (mixture) modelling: associations with dietary, sedentary and physical activity behaviors: a longitudinal study. BMC Public Health, Vol 16, Iss 1, Pp 1-12 (2016), (1), 1. https://doi-org.ezproxy.fau.edu/10.1186/s12889-016-3757-7