Obesity in childhood and adolescence is a major public health issue in both developed and developing countries [1
]. About 107 million children worldwide, under 20 years of age, were living with overweight or obesity in 2015, and in some countries, the number of children with overweight has doubled since 1980 [2
]. French Polynesia has not been spared from the child obesity pandemic. In 2011, a study showed that 60% of Polynesian adolescents were living with overweight, of whom 28% were obese [3
]. The nutritional transition that occurred over the past few decades has led to unhealthy diets which in conjunction with decreased physical activity might be responsible for the rise of obesity in this population [4
]. These lifestyle changes fueled an epidemiological transition characterized by the decline of infectious diseases and the rise of obesity-related chronic diseases such as diabetes, hypertension, and cancer [5
]. The younger generation of Polynesians seems particularly affected by this phenomenon. In 2009, the country had one of the highest prevalence rates of childhood obesity in the world, with 34% of obese children aged 5 to 14 years, compared to 20% in the United States [5
]. However, only few investigations and obesity interventions focused on this region.
Childhood obesity and overweight-related complications are diverse; the first being its persistence into adulthood. In a systematic review of the consequences of childhood obesity, it is estimated that, compared to normal-weight children, overweight children are at least twice as likely to be overweight in adulthood. One study even reported a ten-fold increase in risk [6
]. A recent study predicting obesity among future adults pointed out that 57% of today’s children will be obese by the time they reach 35 years old [7
]. Other than the risk of adulthood obesity, adolescents with obesity or overweight are prone to obesity-related diseases during their adolescence [2
]. A previous study (“La transition alimentaire et sanitaire en Polynésie française”) conducted among French Polynesian natives of the Austral Islands in 2007 revealed that a significant number of adolescents suffered from prediabetes and hypertension associated with a high rate of obesity [5
]. As childhood obesity can profoundly affect children’s physical health, social, and emotional well-being, and self-esteem [6
], prevention efforts are emerging as a public health priority.
School-based interventions are a common choice for obesity prevention because they are ideal for promoting healthy diets, healthy behaviors, and implementing healthy policies [10
]. Several methods are used to evaluate the effects of such interventions on body changes, but no consensus has yet been reached [12
]. Although changes in body mass index (BMI) and overall body weight can be used to estimate the impact of an obesity-related intervention, studies suggest that body fat percentage is a more physiologically significant measure [12
]. Moreover, studies on weight loss programs have pointed out that body weight loss after these programs was not equal to fat loss, and that almost 25% of weight loss observed post-intervention is due to a reduction in lean tissue [13
]. Those studies also showed that the concurrent analysis of body fat and lean tissue provides an improved interpretation of intervention success compared to inference derived from BMI alone. However, modifications in weight and body fat are not the only measurable effect of obesity-related interventions. In fact, these changes are more relevant health-wise when they affect the metabolic system.
Lately, a major challenge that arose in the obesity research area is the early detection of its related chronic diseases. The emerging field of metabolomics has led to the identification of potential biomarkers for type 2 diabetes (T2D) and insulin resistance (IR) [14
]. Branched-chain amino acids (BCAA), aromatic amino acids (AAA), and acylcarnitines have been identified as early markers of obesity and IR that may improve the prevention of cardiometabolic diseases [15
]. In addition, Gaggini et al., using direct measure of IR through in vivo tracers, reported that the concentration of amino acids was associated with obesity and IR, especially in subjects with obesity, likely due to increased IR and protein catabolism [17
Few studies on obesity-related interventions in adolescents focused on metabolomic changes following weight reduction, especially changes in circulating BCAA/AAA concentrations. To date, no study has examined the changes in circulating levels of these metabolites in relation to specific body composition changes in adolescents characterized through concomitant analysis of fat mass (FM) and fat-free mass (FFM). For this study, we assessed data obtained from a school-based 5-month intervention carried out among French Polynesian adolescents called the “Ressources Alimentaires et Santé aux Australes (RASA)” study. We aimed to examine changes in BCAA, AAA, and IR in relation to FM and FFM changes post intervention. We hypothesized that BCAA and AAA concentrations will be associated with weight changes and body composition changes following the 5-month RASA physical activity and diet intervention in Polynesian adolescents. More specifically, a reduction in BCAA and AAA concentrations should be noted in adolescents who lost fat mass, while an increase in BCAA and AAA concentrations should be observed in those who gained fat mass.
In the present study, we used data from a school-based intervention on obesity among Polynesian adolescents to assess changes in early metabolic markers of obesity and IR. The primary assumption of the interventional studies is that weight loss resulting from such interventions induces metabolic changes in the subjects. We therefore measured changes in levels of BCAA/AAA and HOMA-IR2 index in relation to body composition changes post intervention (fat mass and fat-free mass).
Overall, our results indicate that the 5-month intervention on diet and physical activity resulted in lower plasma levels of BCAA and AAA for most adolescents. We had assumed that the residents’ group would experience a greater decline in metabolite concentrations compared to the half-residents/externs. However, contrary to our assumptions, the half-residents/externs experienced greater decreases in concentrations for BCAA and AAA, with some overlapping confidence intervals. Comparison of changes in BCAA and AAA concentrations by intervention group yielded controversial results that do not confirm or refute our original hypothesis. An analysis of specific body changes, i.e., in fat mass and fat-free mass yielded more conclusive results.
Our analysis of metabolic marker changes according to weight changes post intervention showed that both adolescents who gained weight and those who had lost weight had a decrease in BCAA and AAA concentrations while for HOMA-IR2 index, only those who lost weight had a decrease in their index. Adolescents with obesity or overweight at baseline experienced a greater decrease of their metabolic markers compared to those with a normal weight status. However, it should be noted that adolescents with obesity at baseline had higher BCAA and AAA concentrations at baseline. Therefore, body changes in these adolescents might have induced more substantial changes in BCAA and AAA metabolic pathways compared to overweight or normal weight adolescents. Indeed, there is experimental evidence of a metabolic pathway between BCAA and adipose tissue by Herman et al. who demonstrated by using mice that there was an overexpression of the glucose transporter protein type-4 (GLUT4) which coordinates down-regulation of BCAA metabolizing enzymes in adipose tissue, thus explaining the corresponding decrease in circulating BCAA levels [19
These results on weight change effects on metabolic markers partially agree with those of Reinehr et al. who observed a decrease of glutamine, methionine and lysophosphatidylcholines levels in adolescents who experienced weight loss, but no change in adolescents who maintained weight after a 1-year lifestyle intervention [20
]. A review of short-term obesity-related interventions also pointed out that both diet and diet/physical activity interventions resulted in a weight loss and a metabolic profile improvement in adolescents and children [21
]. However, Reinehr et al. raised the lack of direct measure of FM as a limitation in interventional studies [12
]. When comparing our results from weight change analysis and specific body composition (FM and FFM), the findings underscore these limitations.
A total of 77 adolescents lost weight after the 5-month RASA intervention, while 93 lost FM. When metabolic changes were analyzed according to FM and FFM changes, we noted that BCAA concentrations and HOMA-IR2 significantly decreased among participants who lost FM, but not among those who gained FM, in whom HOMA-IR2 increased. Moreover, among the four body composition groups, the FMlost/
group experienced the greatest decrease of BCAA/AAA concentrations. This suggests that FM loss associated with an FFM gain has a greater impact on related metabolic pathways. As our study is the first to evaluate effects of a weight loss intervention on these metabolic markers using concomitant analysis of body changes, it is difficult to compare results with the existing literature. However, previous studies showed that people with higher muscle mass had better lipid and protein metabolism, which might explain the lower levels of amino acids [22
]. In addition, a study evaluating the impact of physical activity on IR in women with obesity revealed that changes in skeletal muscles are strongly implicated in IR pathways [24
]. Skeletal muscle metabolism and oxygen consumption are favored by a narrow mitochondrial network on the surface of the myocyte membrane or myofibrillar septum [25
]. If the amount or function of these mitochondria is decreased, skeletal muscle fatty acids are not catabolized and accumulate into diacylglycerol, long-chain acyl-coenzyme A and ceramide, which may lead to IR [25
]. This might explain why we observed that FM loss combined with FFM gain is associated with an IR improvement and reduced metabolic marker concentrations.
According to our results, BCAA and AAA changes were more consistent and noticeable compared to HOMA-IR2 after a 5-month intervention, which might indicate that they are more sensitive as metabolic markers. On this matter, Tabák et al. pointed out that changes in traditional biomarkers of T2D (insulin-resistance, fasting, and 2 h post-load plasma glucose) predict early stage only 2 years before its diagnosis, but BCAA and AAA changes were noted 12 years before diagnosis [27
]. Therefore, BCAA and AAA are viewed as metabolic markers that can help predict the severity of obesity and its related complications at a much earlier stage than traditional indicators [16
While this study generated strong findings, it also has some limitations. Of the total group of adolescents, 6.6% were excluded from our study. Since this exclusion was not due to the intervention, there is no apparent selection bias. One concern about this study is the representativeness of the population. The RASA intervention focused only a local college, therefore adolescents in other colleges may have different metabolic characteristics from those in this study. Furthermore, Polynesians are a population with a specific genetics profile and environmental factors that might limit generalization to other populations. Another notable limitation is the fact that we have no information about the diet of adolescents outside the school and no details about other physical activities performed every week. Added to the lack of a control group, these two elements make it hard to attribute the observed results solely to the intervention. Additionally, even if we controlled for gender and age, the most cited confounding factors in the literature, puberty stage, is also an important confounder that should be accounted for when studying the metabolism of adolescents. Hormonal changes in adolescents can have a strong influence on the outcomes of such interventions. Another methodological limitation to consider is the measure of body composition by bioimpedance. While the method is based on the relative stability of hydration in lean, the electrical conduction of body water depends on the amount of electrolytes, which in turn varies with age. This variability could lead to errors in the measure of body composition, especially in children and young adults. However, other tools for measuring body composition such as dual-energy X-ray absorptiometry (DXA) or magnetic resonance imaging (MRI) can be expensive, time-consuming, and not widely available for field studies. Studies have been conducted to determine the variability of results with different measurement tools and several authors have reported a strong correlation between body composition estimated by bioimpedance and that estimated by DXA [28
Another issue is the length of the intervention. The 5-month RASA intervention could be viewed as short since it is recommended to carry out intervention programs for about 9 to 12 months in order to obtain relevant metabolic results [29
]. Finally, the presence of several non-significant changes and overlapping of confidence intervals point out that our post hoc analysis lacks statistical power for stratified analysis. Despite these limits, this study is novel as no interventional study in adolescents had measured BCAA and AAA metabolite changes in relation to concomitant changes in FM and FFM changes. This study also provides information on a unique pediatric population that may be at high risk of developing T2D due to a very high prevalence of overweight. It also provides relatively new markers for assessing metabolic changes following obesity intervention in children and adolescents.
Conceptualization, M.L., E.S., C.M.I.G. and P.A.; methodology, M.L., E.S., P.A. and M.P.P.; validation, M.L. and P.A.; formal analysis, M.P.P. and E.A.-L.-S.; investigation, M.L., E.S., C.M.I.G. and P.A.; resources, M.L., E.S. and P.A.; data curation, E.A.-L.-S.; writing—original draft preparation, M.P.P. and M.L.; writing—review and editing, M.P.P., M.L. and P.A.; supervision, M.L.; project administration, M.L. and P.A; funding acquisition, M.L., E.S. and P.A. All authors have read and agreed to the published version of the manuscript.
This research was funded by the French and French Polynesian Governments (CDP2008/2013-396), the Establishment for Health Prevention of French Polynesia (0381/10/EPAP/afh), and the Lepercq Foundation. The funding sources were not involved in data collection, data analysis, manuscript writing, or publication.
Institutional Review Board Statement
This study (RASA Intervention Research Program) was approved by the Ethics Committee of French Polynesia (CEPF/N °46 on 5 November 2009) and the Ethics Committee of the Centre de Recherche du Centre Hospitalier de Québec (CRCHUQ).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to their containing of information that could compromise the privacy of research participants.
The authors express their gratitude to all the study participants and acknowledge the contributions of all their collaborators for their valuable help.
Conflicts of Interest
The authors declare no conflict of interest.
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Adjusted mean differences of BCAA/AAA concentrations and HOMA-IR2 index according to intervention-induced weight changes in adolescents enrolled in the RASA study. Abbreviations: AAA, aromatic amino acids; BCAA, branched-chain amino acids, FM, fat mass; FFM, fat-free mass; HOMA-IR2, homeostasis model assessment of insulin resistance. We performed a multivariate analysis of variance using PROC GLM procedure (a method of least squares to fit general linear models). We assessed the changes in BCAA, AAA concentrations, and HOMA-IR2 index according to changes in weight after the intervention. Models were adjusted for baseline score of BCAA/AAA concentrations, sex, and age, intervention group, and IOTF weight status.
Adjusted mean differences in (a) plasma BCAA concentrations; (b) plasma AAA concentrations, and (c) HOMA-IR2 indices according to body composition change categories (FM and FFM) among adolescents participating in the RASA study. Abbreviations: AAA, aromatic amino acids; BCAA, branched-chain amino acids, FM, fat mass; FFM, fat-free mass; HOMA-IR2, homeostasis model assessment of insulin resistance, (+), gain; (−), loss. We used SAS software PROC GLM CONTRAST to assess changes in BCAA/AAA concentrations and HOMA-IR2 index according to the four body composition change categories created. Models were adjusted for baseline BCAA/AAA concentrations, sex, age, and weight status. We also tested for linear trends across body composition change categories using the same procedure.
Baseline characteristics according to attendance status of adolescents in school-based RASA intervention, Austral Islands, French Polynesia.
(n = 226)
|Attendance Status||p-Value †|
(n = 69)
(n = 157)
|Age, years ||13.5 (1.6)||14.3 (1.5)||13.2 (1.6)||<0.001|
|(min., max.)||(10, 18) ||(11, 18)||(10, 17)|| |
|Girls, n (%) *||108 (47.79)||37 (53.62)||71 (45.22)||0.24|
|Weight, kg||69.6 (19.1)||72.7 (17.1)||68.2 (19.9)||0.10|
|Height, cm||167 (9.46)||168 (8.96)||16 (9.67)||0.15|
|BMI, kg/m2||24.7 (5.5)||25.5 (4.7)||24.4 (5.8)||0.18|
|Fat-free mass, kg||51.7 (12.4)||53.4 (12.1)||50.9 (12.5)||0.17|
|Fat mass, kg||17.7 (11.2)||18.6 (10.3)||17.3 (11.6)||0.42|
|Fat mass, %||24.9 (10.2)||24.6 (10.5)||23.7 (10.1)||0.52|
|Fasting insulin, pmol/L||108 (92.8)||92.4 (49.1)||115 (112)||0.11|
|Fasting glucose, pmol/L||4.90 (0.38)||4.76 (0.33)||4.95 (0.40)||0.001|
|TG, mmol/L||0.96 (0.62)||0.87 (0.82)||1.00 (0.54)||0.09|
|HDL, mmol/L||1.23 (0.27)||1.26 (0.23)||1.23 (0.29)||0.44|
|TG/HDL||0.87 (0.66)||0.74 (0.48)||0.93 (0.76)||0.06|
|Weight status, N (%) *‡|| || || ||0.51|
|Normal||93 (41.15)||25 (36.23)||68 (43.31)|| |
|Overweight||56 (24.78)||17 (24.64)||39 (24.84)|| |
|Obese||77 (34.07)||27 (39.13)||50 (31.85)|| |
Concentrations of BCAA and AAA (Mean (SD)) (mg/L) at baseline and after a 5-month weight reduction intervention in adolescents enrolled in the RASA study, according to weight and attendance status.
| || || || ||Analysis §|
|IOTF Weight and Attendance Status||Baseline †||Post-Intervention †||Change ‡ |
Mean (95% CI)
|Group × Time |
| || || || || ||p Value|| |
|All (n = 226)¶|| || || || || || |
|BCAA|| || || ||0.95||<0.001||0.48|
|Residents||59.3 (10.8)||56.5 (9.2)||−3.25 (−6.43 to −0.07)|| || || |
|Half-Residents/Ext.||61.6 (10.7)||57.3 (8.9)||−4.53 (−6.39 to −2.68)|| || || |
|AAA|| || || ||0.71||<0.001||0.08|
|Residents||26.1 (4.7)||25.1 (3.1)||−1.16 (−2.16 to −0.15)|| || || |
|Half-Residents/Ext.||27.7 (4.6)||25.3 (3.1)||−2.44 (−3.27 to −1.61)|| || || |
|Normal weight (n = 93)|| || || || || || |
|BCAA|| || || ||0.57||0.11||0.84|
|Residents||59.8 (11.7)||56.2 (7.4)||−2.68 (−8.04 to 2.67)|| || || |
|Half-Residents/Ext.||57.5 (9.5)||54.6 (9.2)||−2.09 (−4.92 to 0.74)|| || || |
|AAA|| || || ||0.30||0.59||0.35|
|Residents||25.0 (3.2)||25.1 (2.6)||0.24 (−1.30 to 1.78)|| || || |
|Half-Residents/Ext.||25.6 (4.5)||24.4 (3.1)||−0.87 (−2.15 to 0.40)|| || || |
|Overweight (n = 56)|| || || || || || |
|BCAA|| || || ||0.52||<0.001||0.26|
|Residents||64.9 (11.9)||57.9 (7.5)||−8.87 (−13.2 to −4.52)|| || || |
|Half-Residents/Ext.||61.4 (9.5)||57.0 (9.3)||−5.73 (−8.71 to −2.75)|| || || |
|AAA|| || || ||0.28||<0.001||0.73|
|Residents||26.9 (3.2)||24.2 (2.6)||−3.08 (−4.46 to −1.69)|| || || |
|Half-Residents/Ext.||27.2 (4.6)||25.1 (3.1)||−2.68 (−4.04 to −1.32)|| || || |
|Obese (n = 77)|| || || || || || |
|BCAA|| || || ||0.62||0.03||0.04|
|Residents||58.5 (11.8)||57.2 (7.4)||−0.25 (−5.70 to 5.20)|| || || |
|Half-Residents/Ext.||65.2 (9.6)||59.8 (9.4)||−6.92 (−10.5 to −3.38)|| || || |
|AAA|| || || ||0.96||<0.001||0.01|
|Residents||27.2 (3.2)||25.8 (2.6)||−1.24 (−3.02 to 0.54)|| || || |
|Half-Residents/Ext.||30.6 (4.6)||26.8 (3.5)||−4.39 (−5.83 to −2.95)|| || || |
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