By Ellen Feldman, MD
Altru Health System, Grand Forks, ND
SUMMARY POINTS
• This study evaluated the effect of multiple modifiable and nonmodifiable risk factors and a measure of C-reactive protein (CRP) on the development of metabolic disturbance during puberty and beyond.
• Data from a large-scale European longitudinal study of 7,105 children (initially between ages 2 to 9 years) were examined three times over seven years.
• An innovative statistical technique (latent transitional analysis) estimated the probability of metabolic disturbance based on abdominal circumference, dyslipidemia, blood glucose, and hypertension in the study group.
• An increased risk for metabolic disturbance was associated with multiple factors, including having at least one type of media in the bedroom, entering puberty early, higher CRP, higher maternal body mass index, and familial hypertension, while a decreased risk was associated with membership in a sports club and having a higher “well-being” score.
SYNOPSIS: An innovative statistical model examining the development of metabolic disturbances in a large sample of youths finds that having media in a bedroom (associated with higher risk) and belonging to a sports club (associated with lower risk) are among the modifiable risk factors in this population.
SOURCE: Börnhorst C, Russo P, Veidebaum T, et al. The role of lifestyle and non-modifiable risk factors in the development of metabolic disturbances from childhood to adolescence. Int J Obes (Lond) 2020;44:2236-2245.
Metabolic syndrome (MetS), first described in adults in 2001 by the National Cholesterol Education Program, implies the presence of at least three of the following factors: central obesity, hyperglycemia, hypertriglyceridemia, low levels of high-density lipoprotein (HDL) and hypertension.1 In recognition that MetS in adults may have roots developed in early life, medical research has turned to examining the early years of development to better understand this relationship.
However, with at least 40 different definitions of MetS in children, not only is there poor consensus on how to diagnose it, but also how to prevent or address MetS in this population.2,3
Citing the concern that MetS in children leads to a higher likelihood of MetS, type 2 diabetes mellitus (T2DM), and cardiovascular disease in later life, and noting studies that indicate that the risk is mitigated by remission of metabolic disturbances, Börnhorst et al set out to investigate risk factors for MetS in children during the transition to adolescence.3 The group used data from a large multicenter population-based study spanning eight European countries: Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants (IDEFICS). This longitudinal study examined diet- and lifestyle-related conditions in children and families. IDEFICS began with children from ages 2 to 9 years in 2007 and includes periodic reassessment of parent and child interviews, physical exams, and blood draws.4
Börnhorst et al examined data from participants at three points over seven years — initially in 2007/2008, then again in 2009/2010, and finally in 2013/2014. Although more than 16,000 children participated in IDEFICS between 2007 and 2014, 7,105 members of this population, all of whom participated at each time point, were eligible for inclusion in this study. Metabolic outcomes included measurements of abdominal girth, diastolic and systolic blood pressure, lipid panel measurements, and blood glucose values.
Börnhorst et al identified multiple factors to measure, including C-reactive protein (CRP), an acute phase inflammatory protein, and modifiable and nonmodifiable items. Lifestyle or modifiable factors included a psychosocial wellbeing score, frequency of consumption of fresh fruits and vegetables, frequency of consumption of processed food, belonging to a sports club as a measure of physical activity, and having a media device in the bedroom as a measure of sedentary behavior. Nonmodifiable risk factors included educational level of parents, familial history of hypertension and T2DM, maternal body mass index (BMI), and age of puberty.
Latent transition analysis, an innovative statistical approach, enabled the team to generate an odds ratio (OR) and evaluate age-dependent associations between multiple risk factors and five metabolic health statuses:
- Metabolically healthy
- Abdominal obesity
- Dyslipidemia
- Hypertension
- A combination of more than one of the above
Results
Notably, the percentage of children qualifying for inclusion in the “metabolically healthy” category was greater than 50% at all time points, while less than 7% of the children fell into either the dyslipidemia or hypertension category at any one point. In contrast, the prevalence of abdominal obesity rose from 15.2% at baseline to 17.5% at the end of the study, and the percentage of children in the combined metabolic disturbance group rose from 5.6% at baseline to 10.6% by the end of the study.
Of the modifiable and nonmodifiable risk factors examined, no significant association with any metabolic disturbance in the study population was found with breastfeeding, familial diabetes, fruit/vegetable consumption, or processed food consumption. Tables 1, 2, and 3 display the results for most of the other risk factors, controlled for multi-variables. Note that age-related changes were found for two categories — entering puberty (age-related decrease in risk) and having
media in the bedroom leading to an age related increase in risk. (See Tables 1 and 3 for details.) The OR for selected risk factors, as well as the respective confidence intervals (CI), are displayed in each table.
Table 1. Nonmodifiable Risk Factors (Controlled for Multivariables) |
||||
|
Abdominal Obesity |
Dyslipidemia |
Hypertension |
Combined Metabolic Disturbance |
|
|
Lower parental educational level |
OR 1.14 (95% CI, 1-1.29)* |
OR 1.01 (95% CI, 0.91-1.12) |
OR 1.12 (95% CI, 0.99-1.26) |
OR 1.25 (95% CI, 1.05-1.49)* |
|
Lower age entering puberty |
OR 2.43 (95% CI, 1.60-3.69)* (Decreasing by a factor of 0.25 yearly from ages 8 to 13 years) |
OR 1.62 (95% CI, 1.09-2.42)* (Decreasing by a factor of 0.14 yearly from ages 8 to 13 years) |
OR 1.05 (95% CI, 0.76-1.73) |
OR 2.46 (95% CI, 1.53-3.96)* (Decreasing by a factor of 0.29 yearly from ages 8 to 13 years) |
|
Higher maternal body mass index |
OR 1.29 (95% CI, 1.25-1.34)* |
OR 1.09 (95% CI, 1.07-1.11)* |
OR 1.10 (95% CI, 1.07-1.12)* |
OR 1.47 (95% CI, 1.39-1.55)* |
|
Familial dyslipidemia |
OR 0.96 (95% CI, 0.63-1.46) |
OR 1.24 (95% CI, 1.01-1.52)* |
OR 0.98 (95% CI, 0.77-1.26) |
OR 1.08 (95% CI, 0.57-2.03) |
|
Familial hypertension |
OR 2.13 (95% CI, 1.45-3.12)* |
OR 1.26 (95% CI, 1.05-1.52)* |
OR 2.08 (95% CI, 1.67-2.60)* |
OR 3.33 (95% CI, 1.87-5.91)* |
|
OR: odds ratio; CI: confidence interval *Statistically significant results |
||||
Table 2. C-Reactive Protein |
||||
|
Abdominal Obesity |
Dyslipidemia |
Hypertension |
Combined Metabolic Disturbance |
|
|
One standard deviation increase |
OR 1.40 (95% CI, 1.31-1.49)* |
OR 1.16 (95% CI, 1.10-1.22)* |
OR 1.18 (95% CI, 1.12-1.24)* |
OR 1.59 (95% CI, 1.49-1.70) |
|
OR: odds ratio; CI: confidence interval *Statistically significant results |
||||
Table 3. Modifiable Risk Factors |
||||
|
Abdominal Obesity |
Dyslipidemia |
Hypertension |
Combined Metabolic Disturbance |
|
|
Number of media (> 0) in the bedroom |
OR 1.09 (95% CI, 1.00-1.19) |
OR 0.98 (95% CI, 0.90-1.08) |
OR 1.05 (95% CI, 0.96-1.16) |
OR 1.30 (95% CI, 1.18-1.43)* (Increases by a factor of 0.18 per year from ages 8 to 30 years) |
|
No membership in sports clubs |
OR 1.08 (95% CI, 0.99-1.17) |
OR 1.16 (95% CI, 1.07-1.26)* |
OR 1.08 (95% CI, 0.99-1.18) |
OR 1.30 (95% CI, 1.18-1.43)* |
|
Well-being score |
OR 0.90 (95% CI, 0.82-0.98)* |
OR 1.01 (95% CI, 0.92-1.10) |
OR 0.91 (95% CI, 0.92-1.00) |
OR 0.91 (95% CI, 0.82-1.02) |
|
OR: odds ratio; CI: confidence interval *Statistically significant results |
||||
Commentary
This longitudinal, observational study shows a statistical association between nonmodifiable and modifiable risk factors and the development of metabolic disturbances in children over a seven-year period. Additionally, the authors noted an association between higher CRP levels and metabolic disturbances in this same population.
The clinical relevance of this study hinges on understanding the connection between metabolic disturbance in children and teens, and the morbidity associated with MetS in adulthood. Not only have previous studies pointed out the likelihood of persistence of metabolic disturbances over time, but it also bears repeating that several studies have confirmed the mitigation of this risk with correction of the metabolic disturbance.2,4 It follows that identifying and then addressing risk factors for metabolic disturbance at a young age can have profound health implications.
It is interesting to evaluate how best to address the nonmodifiable risk factors in a clinical setting. These include parental educational level, maternal BMI, family history of hypertension or hyperlipidemia, and entering puberty at a young age. Primary care physicians may want to keep in mind that children with such risk factors have a higher risk of developing metabolic disturbances over time. Other studies have consistently confirmed that adverse childhood experiences (ACE) convey ongoing health risks to affected individuals.5 Educating parents and children (as age-appropriate) about these connections is a reasonable first step toward addressing the risk.
The modifiable risk factors identified in this study include having media in the bedroom (as a reflection of sedentary behavior), not belonging to a sports club (as a reflection of physical activity), and having a lower wellness index.
One clear clinical message is that the risk of metabolic disturbance is associated with having media (more than zero) in the bedroom — and that this risk increases with age. This study did not specify the type(s) of media involved and did not compare quantity of media in the bedroom to the risk of having media available out of the bedroom. Both of these are interesting areas for further exploration. However, it still is relevant for primary care physicians to address the findings from this study. According to a 2019 Common Sense Media survey, about one in five U.S. children have a cell phone by 8 years of age, and more than half have a cell phone by 11 years of age.6 Additionally, the pandemic has ushered in an era of more online connectivity and rising screen time among youth.7 The risk of metabolic disturbances is one of a number of risks associated with unsupervised media use among children and can be used as a concrete starting point for a broader discussion about removing media from the bedroom for wellness.
Likewise, encouraging physical activity (or a sports club equivalent) for children at all ages has benefits beyond those examined in this study.
The wellness score was based on a self-completed questionnaire regarding psychosocial stressors, such as stability within the home — a lower score in this area was associated with a higher risk of abdominal obesity. Incorporating community and regional resources (such as therapists, social workers, etc.) into the provider toolbox may support family efforts to develop a healthier and more desired lifestyle. Connection with such providers also may assist in addressing emotional disturbances stemming from ACEs and potentially build resilience.5
It is unclear if the association with higher CRP levels is a manifestation of metabolic disturbance or a contributor to etiology. Future studies in this area will be helpful in broadening our understanding of the role of CRP in metabolic disturbances.
The strength of Börnhorst et al’s work lies in several key areas: the large number of participants and the longitudinal and detailed nature of the study, allowing comprehensive data collection from the participants starting at a young age. This may serve as a reminder of the importance of large-scale, long-term, multi-site studies (in this case spanning multiple countries) when attempting to understand factors involved in population health.
A relative weakness of the study is that the bulk of the lifestyle data was self-reported and that proxy measures were necessary to evaluate both sedentary behavior and physical activity. For example, it is unclear if not belonging to a sports club reflects lower socioeconomic status as well as lower levels of physical activity; if so, this could influence findings. Future studies may precisely measure extent and types of activity to further the understanding of the relationship between movement and metabolic disturbance.
However, the take-home message for clinicians from this study is clear. Although the development of metabolic disturbances in youth appears multifactorial, intervention on a lifestyle level has potential health benefits. Specifically, advising parents to limit media in children’s bedrooms and encourage physical activity may have a significant effect in reducing metabolic disturbances in this age group. With studies suggestive of the likelihood of early metabolic patterns continuing into adulthood, these simple recommendations may help to potentiate lifelong metabolic health.
REFERENCES
- Rezaianzadeh A, Namayandeh S-M, Sadr S-M. National Cholesterol Education Program Adult Treatment Panel III vs. International Diabetic Federation definition of metabolic syndrome, which one is associated with diabetes mellitus and coronary artery disease? Int J Prev Med 2012;3:552-558.
- Al-Hamad D, Raman V. Metabolic syndrome in children and adolescents. Transl Pediatr 2017;6:397-407.
- Mäestu E, Harro J, Veidebaum T, et al. Changes in cardiorespiratory fitness through adolescence predict metabolic syndrome in young adults. Nutr Metab Cardiovasc Dis 2020;30:701-708.
- IDEFICS - identification and prevention of dietary- and lifestyle-induced health effects in children and infants. Leibniz-Institut für Präventionsforschung und Epidemiologie. https://www.ideficsstudy.eu/home.html
- Hughes K, Bellis MA, Hardcastle KA, et al. The effect of multiple adverse childhood experiences on health: A systematic review and meta-analysis. Lancet Public Health 2017;2:e356-e366.
- Kamenetz A. Report: More than half of U.S. children now own a smartphone by age 11. National Public Radio Published Oct. 29, 2019. https://www.npr.org/2019/10/29/774306250/report-more-than-half-of-u-s-children-now-own-a-smartphone-by-age-11
- Wong CW, Tsai A, Jonas JB, et al. Digital screen time during the COVID-19 pandemic: Risk for a further myopia boom? Am J Ophthalmol 2020;223:333-337.
An innovative statistical model examining the development of metabolic disturbances in a large sample of youths finds that having media in a bedroom (associated with higher risk) and belonging to a sports club (associated with lower risk) are among the modifiable risk factors in this population.
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