1. Introduction
Overweightness (including obesity) has reached epidemic proportions. Approximately 39% of the adult human population is overweight (BMI (body mass index) ≥ 25 to < 30 kg/m
2) or obese (BMI ≥ 30 kg/m
2) [
1]. The global prevalence of overweightness (BMI > +1 standard deviation above the median) among adolescents aged 10 to 19 years has increased steadily over the last 40 years, from 4.3% in 1975 to 17.3% in 2016 [
1,
2]. Current research suggests that overweightness and obesity contribute to the increasing risk of chronic diseases [
1]. The global burden of disease study estimated that in 2015, roughly 7% of deaths from any cause and roughly 5% of disability-adjusted life-years globally were due to high BMI [
3]. Metabolic dysregulation, in addition to inflammation and insulin resistance, may mediate the link between overweightness and many chronic diseases, like Type 2 diabetes or cardiovascular diseases. There is mounting evidence that these links are already present in adolescents [
1,
4,
5], implying an increase in risk of future incidence of chronic diseases. Therefore, it is important to find the metabolic changes already present in adolescence, and to understand the link between overweightness and disease progression in later life.
Being overweight is known to be related to metabolic changes—for example, through body fat functioning as an endocrine organ, producing adipokines like leptin or visfatin [
6,
7]. Additionally, past studies have shown that overweightness is likely to be a causal influence on the metabolome phenotype [
8]. A recent review [
7] that summarizes the current knowledge of the metabolomic signature of adult obesity concluded that many metabolite groups are altered, including sexual steroids, amino acids, and acylcarnitines, among others. Interestingly, only a few epidemiological studies have explored the relationship between body composition and the metabolome in adolescents [
9,
10]. Cho et al. [
9] quantitatively measured the global metabolic repertoire in adolescents, and showed that endogenous metabolites and inflammation-related metabolites are related to body composition. Saner et al. [
10] investigated metabolomic profiles in obese children and adolescents (ages 6 to 18), and found associations in post-pubertal males of several metabolites, including fatty acids, triglycerides, isoleucine, leucine, and glycoprotein with obesity measures. However, overall evidence is scarce calling for more studies profiling the adiposity metabolome, preferably by untargeted methods.
It is well-known that the body composition of adolescents is sex-specific [
5]. While BMI tends to be comparable between males and females, body fat in females is physiologically higher starting in late puberty (Tanner stages IV and V). In addition, a sexual dimorphism in metabolism is well recognized [
11]. Thus, investigating sex differences may reveal pathophysiologically relevant variations, with potential implications for overweightness- or obesity-related health conditions.
We decided to investigate two different measures of body composition to increase our confidence in the metabolite–body composition associations that are present for both measures. We used BMI, as it is the most widely used measure for body composition in observational studies [
5,
12]. It is well-understood that BMI is a good marker for body composition on the population level [
13]. Specifically, in an adolescent population it has been demonstrated that BMI categories correctly identify children with excess body fat in roughly 85% of cases [
5]. However, it has well-documented shortcomings regarding body fat distribution [
14,
15]. To address these shortcomings, we also used body fat percentage, as estimated with skinfold measurements.
Here, we explored the a priori, sex-stratified relationship between these two measures of body composition, BMI and BF, and the urine metabolome cross-sectionally among adolescents.
3. Discussion
The current study explores the sex-specific cross-sectional associations of two measures of body composition, BMI and BF, and the urine metabolome and urine metabolite patterns (ICs) in adolescent boys and girls. Approximately 2.4% of the urine metabolome was associated with body composition in boys; no association was seen in girls. Our results underscore the presence of changes in the urine metabolome in relation to body composition already in adolescence. To our knowledge, this is the first study to relate two measures of body composition to the urine metabolome in adolescents. Our results strongly suggests sex-specificity in associations.
We advise the reader that the results of the present study were exploratory, and therefore should not be overemphasized. Any interpretation we give here in relation to the biological process may only be seen as one of many possible explanations for the reported associations. In fact, many of the reported compounds have not been reported in conjunction with body composition before. A more in-depth investigation of these single compounds is, however, outside of the scope of this study. We found 10 metabolites that related to both measures of body composition in males, and none in females. These metabolites were guanidinosuccinate, isobutyrylglycine (C4), isovalerylglycine, tigloylglycine, nicotinamide N-oxide, glucuronide of C
10H
18O
2 (12), X-21851, X-24469, X-24801, and succinimide. Nicotinamide N-oxide [
1] and tigloylglycine [
2] have been associated with BMI in prior studies. The other eight molecules are reported in association with body composition here for the first time. Additionally, we found 20 metabolites associated with either BMI or BF. When metabolites are significantly associated with both measures of body composition, we should have higher confidence in their association. As both measures have their own unrelated measurement error while measuring different aspects of the same concept (body composition), a significant association with both BMI and BF should indicate that it is more likely related to this underlying concept. The metabolites associated with only BMI or BF, however, were all associated in the same direction with the other body composition measurement. Additional discussion of these metabolites can be found in
Table A5.
In general, our results reinforced the idea of sexual dimorphism in metabolism. The stronger association in males is consistent with previous studies in mice [
3], adults [
4,
5], and adolescents [
6], as well as our own recent findings within this study population [
7]. One potential explanation is that sex hormones might modify the relationship between body composition and the urine metabolome. Specifically, prior studies have shown changes in the type of body composition and overall obesity in relation to sex hormones and displaying sexual dimorphism in their mode of effect [
8,
9,
10,
11]. Furthermore, the sexual dimorphism in the urine metabolome is well-documented [
4,
12,
13,
14,
15]. As sex hormones play an important role in many metabolic pathways, e.g., they have been shown to regulate the liver energy homeostasis [
16], an interaction between sex hormones, body composition, and the urine metabolome is plausible. Another explanation, as was shown for urine cortisol levels [
5], is that sex differences relate to other factors of metabolism, such as enzyme activity. Wang et al. [
17] showed that lipid and lipoprotein metabolism is in fact independent of sex hormone administration, even though there are significant sex differences; however, the mechanism remains to be elucidated. The specific mechanism of sex difference in metabolism might therefore differ for different pathways, and deserves to be studied further. Our results may help to explain sex differences in weight-related health conditions.
We used independent component analysis (ICA) to summarize metabolites into fewer components in the current analysis. We chose ICA because the components are statistically independent, and their interpretation in biological processes allows for the mixture of different pathways and processes that contribute to the living system. Because metabolomics takes a snapshot of these processes and systems, these components hold a large value for understating of processes. In the current study, none of the ICs we retained were associated with body composition. This suggests that body composition influences specific metabolic pathways, and not a mixture of different pathways captured by the ICA.
Guanidinosuccinate is produced by the oxidation of argininosuccinic acid, and was associated with higher measures of body composition in males. The oxidation of guanidinosuccinate occurs favorably with increased levels of urea, and results in a decline of hepatic levels of arginine [
18]. It is well-known that the urea cycle is dysregulated with higher adiposity [
19]; therefore, reduced renal function compared to the average adolescent may partly explain our findings. Guanidinosuccinate may be a marker of the kidneys’ ability to eliminate urea, particularly in males.
Isobutyrylglycine (C4) is a short-chain acylglycine in the catabolism of leucine, isoleucine, and valine. In newborn screenings, elevated levels of this metabolite are used to diagnose isobutyryl-CoA dehydrogenase deficiency [
20]. Since isobutrylglycine levels decrease with higher BMI and BF, isobutyryl-CoA dehydrogenase might be upregulated with elevated measures of body composition. Alternatively, smaller amounts of leucine, isoleucine, and valine might be catabolized in individuals with abnormal adiposity. However, the present association was independent of these metabolites.
Isovalerylglycine is an acyl glycine that is produced in the catabolism of leucine [
18]. Higher BMI and BF are associated with the metabolism of leucine in rats [
21]; however, no study to date exists in humans. This metabolite has also been suggested as a biomarker for cheese consumption [
22]. Although we did not specifically adjust for cheese intake, the fact that we adjusted for macronutrient intake suggests that our finding is independent of cheese intake.
Tigloylglycine is an acylglycine that is an intermediate of the isoleucine catabolism [
18]. Like isovalerylglycine, it was suggested as a biomarker for the consumption of cheese [
18]. Again, we adjusted for nutrition, so an association because of cheese consumption is unlikely. Urinary acylglycine decreases with higher BMI have been documented before [
2]. Similar to other leucine, isoleucine, and valine metabolites, the enzyme metabolizing this compound might be upregulated, or the overarching pathway of branched-chain amino acid (BCAA) catabolism might be dysregulated.
BCAAs have a well-documented association with higher markers of body composition: increased blood levels of BCAAs correlated with higher levels of body composition [
19]. A recent study by Elliot et al. [
23] reported associations between increased urine levels of leucine, isoleucine, and valine and BMI. Additionally, they reported lower levels of ketoleucine with higher BMI. Ketoleucine is the first metabolic product in the energy use of leucine [
24]. The metabolites we found that decreased with higher measures of body composition are downstream metabolites of BCAAs, which are produced through similar processes as ketoleucine from leucine, namely when their respective BCAA is used for energy in skeletal muscle. As BCAAs are not the first energy source muscles use in response to physical activity, increased blood levels of BCAAs and decreased levels of their energy pathway downstream products are in line with decreased physical activity and overabundance of other energy sources in persons with higher measures of body composition.
Nicotinamide N-oxide is a precursor of nicotinamide adenine dinucleotide (NAD) and a catabolite of nicotinamide [
18,
25]. Increased urine nicotinamide N-oxide is associated with high-fat, diet-induced obesity in mice [
26]. In humans, serum levels of another nicotinamide was positively associated with BMI and waist circumference [
1]. This finding suggests that in individuals with higher measures of body composition, there is a nicotinamide overload, or enzymes catabolizing nicotinamide to nicotinamide N-oxide are overexpressed or hyper-activated. However, our result is independent of nicotinamide, which favors the latter explanation.
Succimide is commonly found in anticonvulsant drugs [
18]. The fact that a common side effect of anticonvulsant drugs are changes in weight [
27] might provide a potential explanation for the association with adiposity.
Additionally, there are no available data on the relationship between the unknown metabolites X-21851, X-24469, and X-24801, or the partially characterized metabolite glucuronide of C10H18O2 (12) and body composition. Besides, since they are without biochemical identities, or only partially characterized, it is difficult to provide explanations. Nevertheless, with the rapidly developing field of metabolomics, the identification of these metabolites should not be far from sight.
The present study has some notable strengths. We investigated the associations between body composition and the urine metabolome using two measures of body composition, in order to achieve a more comprehensive relationship between body composition and alteration in urine metabolites. The sex-specific investigation defined a priori also ensures that sex-specific relationships are well explored. Additionally, we used 24 h urine samples in a comparatively large study population to study the urine metabolome with an untargeted approach. To limit the possibility of false positives that untargeted approaches entail, we controlled for multiple testing by holding the False Discovery Rate (FDR) at 5%.
However, we acknowledge several limitations to the study. First, our participants are all Caucasians (Germans), residing in a large city (Dortmund) and surroundings, mostly from a high socioeconomic background. Thus, the generalizability of our findings is limited. Further, our study sample had very few individuals in the extremes of body composition, namely in the underweight (BMI < 18.5) and the obese (BMI ≥ 30) classifications, our findings may only be generalizable to individuals with normal and overweight body composition status. More associations of metabolites with BF as compared to BMI may also be due to BF having a larger variation in our study sample. Additionally, we cannot rule out residual confounding by either unknown or unmeasured (for example, genetic influences) factors. Lastly, because we only had one measurement of the urine metabolome, we were not able to establish a relationship of body composition and variability in the urine metabolome.
Future research should try to identify the unknown or partially characterized molecules that were associated in this study, as they have potential to help elucidate the biological mechanisms of the relationship between body composition and metabolic function on the pathway to health outcomes. Additionally, more studies are needed that stratify their metabolomic analysis by sex, in order to increase our understanding of the physiological differences in metabolism between males and females. Furthermore, future studies should try to replicate our findings in an independent adolescent population, and try to extend the analysis to a longitudinal design to elucidate the temporal relation of body composition with urine metabolome. Additionally, it would be interesting to evaluate differences between the blood and urine metabolome in a similar study setting, preferably in the same participants. Overall, metabolomics would benefit greatly from more unified data analysis approaches to facilitate meta-analysis of different cohorts. Lastly, a similar analysis carried out in a cohort with a larger proportion of overweight and obese participants would help to disentangle the gradient relationship between body composition and the urine metabolome.