In this population-based study, strong associations between FFMI and serum metabolite concentrations were found in KORA S4 and reproduced in KORA F4. With higher FFMI, BCAA serum concentrations and the ratio of BCAAs to glucogenic amino acids increased. Free carnitine levels were also positively associated with FFMI, and for the various PCs we found a decrease in chain length and/or saturation of the fatty acid residues in combination with higher concentrations of PC aa in expense of PC ae and lysoPC. In obese subjects of KORA S4, however, these associations were lacking.
The human serum metabolome is currently characterised in many studies using different analytic approaches 
, including the method applied here. To the best of our knowledge, the association of FFMI/fat free mass and serum metabolites was not explored before in an epidemiologic setting. Rather, metabolomics signatures of exercise (before, during and afterwards) in human plasma were investigated showing that subjects who were in better shape exhibited more lipolysis during and after exercise than did the less fit participants 
. This leads to the hypothesis that a sedentary lifestyle leads to derangements of skeletal muscle metabolism that favour the development of obesity and metabolic diseases. In fact, our observation that associations found between FFMI and serum metabolites were largely missing in obese subjects at baseline (S4) fits well to the hypothesis of a deranged skeletal muscle metabolism. As observed by Lewis et al. 
, a decreased capability to stimulate lipid oxidation during exercise could be reflected in the missing associations between FFMI and the various lipid metabolites in obese subjects. For the non-obese participants, we find evidence for an enhanced ß-oxidation with higher FFMI which is reflected in the carnitine and PC metabolite results.
In contrast to a study with physical activity intervention, it was not clear how strong associations between FFMI and serum concentrations of amino acids, acylcarnitines, glycerophospholipids, and sphingolipids could be in a cross-sectional study with collection of serum samples after short-term fasting of at least eight hours. However, our results fit well to biological pathways that are related to skeletal muscle metabolism, namely amino acid metabolism and fatty acid metabolism (reflected by acylcarnitines and glycerophospholipids).
BCAAs are described as a preferred substrates for muscle tissue, and BCAA supplementation can suppress protein degradation 
. During short-term fasting, glucose released from glycogen stores of the liver is provided as fuel for tissues that need glucose (e.g. brain). Muscle protein degradation provides amino acids that are used by the liver for gluconeogenesis 
, with alanine being the most important one. BCAAs are a major source of nitrogen for muscle synthesis of glucogenic amino acids such as alanine 
. This has been shown in studies with 1–3 days of starvation. Levels of BCAAs rise in fasting state parallel to an increased protein degradation. In this state, the only source of BCAAs is appearance from protein degradation, which is a key process for maintenance of protein quality and repair process of tissues. Thus, during overnight fasting, BCAA serum concentrations are increased proportional to the muscle mass of the body. Furthermore, alanine concentration levels rise with increasing protein degradation during starving 
. Thus, our findings of increasing serum concentrations of BCAAs and alanine that are strongly associated with FFMI are in line with the expectations.
Several other studies reported that acylcarnitines are also found in circulation 
. Acylcarnitines are intra-cellularly synthesized for the transport of fatty acids into the mitochondria for β-oxidation. In the present analyses an increase of acylcarnitine concentrations could be observed which argues for an upregulated β-oxidation of fatty acids in subjects with higher muscle mass. One explanation for the origin of propionylcarnitine is oxidative degradation (β-oxidation, γ-oxidation) of BCAAs, releasing propionylcarnitine units. Another source of propionyl residues may be the intestinal production by gut microbiota; however a relationship with plasma propionylcarnitine or FFMI is not established.
Our phospholipid results point towards a decrease in chain length and degree of saturation of the lipid side chains. The concentrations of lysoPC and PC ae decrease with increasing FFMI and PC aa C38:3 is the only PC positively associated with higher FFMI. Considering the GGM results, PC aa C38:3 is central in one cluster of PC metabolites, while the other cluster consists of long chain PCs. Overall, our results implicate that FFMI is associated with lower plasma concentrations of very long chain PCs (GGM cluster 2), while the concentration of PC aa C38:3 increased. Thus, a higher FFMI might indicate a higher activity of enzymes involved in fatty acid oxidation, especially in the oxidation of very-long chain fatty acids. In addition, an altered activity of enzymes involved in chain elongation (ELOVL) or desaturation (FADS) could contribute to these findings. Earlier studies using metabolomics and genetic data could demonstrated that genetic effects (mediated by the expression of enzymes) are reflected in the serum metabolic profile, thus lending credibility to the approach of identifying enzyme activities by means of serum metabolites or ratios of serum metabolites 
. The shift from PC ae and lysoPC towards PC aa could also be explained by modifications in expression or activity of relevant enzymes 
. However, analyses of the association between selected SNPs which are known to be associated with anthropometric characteristics did not reveal any significant relationship with metabolite ratios.
In addition, a higher FFMI was found to be associated with an increased concentration of sphingomyelins as compared to hydroxysphingomyelins. Recently, Wang-Sattler et al. 
reported a reduced concentration of SM and an increased concentration of SM (OH) in smokers. This finding was explained as a consequence of smoking on the activity of the peroxisomal enzyme alkylglycerone phosphate synthase (alkyl-DHAP). Following the same line of argumentation, FFMI is probably associated with an increase of the activity of alkyl-DHAP. The negative association of the SM C16
1 ratio with FFMI might be explained by regulatory processes in the fatty acid biosynthesis pathway. In particular, stearoyl-CoA desaturase (SCD), which catalyses the desaturation of C16 and C18 fatty acids, might be a target enzyme/gene affected by greater muscle mass and/or activity.
Strength and Limitations
The participants of our studies represent random samples of the underlying population. In such a population with a heterogeneous metabolic makeup influenced by different environmental factors, genetic predispositions, and lifestyles, the interpretation of metabolic profiling is quite ambitious 
. The metabolite concentrations which are reported in the present paper were identified by means of two slightly different AblsoluteIDQ
™ kits p150 and p180. These two kits are not identical (190 different metabolites were measured with an overlap of 141) and the measurement method of the amino acids slightly differs. However, this technology allows the automated quantification of hundreds of metabolites for many samples at a time which is very helpful for future studies 
. Furthermore, the side chain composition of the different PCs, lysoPCs, sphingomyelins and acylcarnitines carries many information on the different fatty acid pools in the human body, such as n−3 and n−6 polyunsaturated fatty acids or saturated and mono-saturated short and medium chain fatty acids 
. Thus, the kit is well oriented to map out the human lipidome and therefore particular for the purpose of this study.
The bioelectrical impedance analysis measurements did only take place in KORA S4. For the analyses of associations between FFMI and the metabolite concentrations in KORA F4 only the results of the bioelectrical impedance analysis measurements at baseline examination (KORA S4) were available. Assuming that the participants’ body composition is fairly stable in weight-stable subjects, the KORA F4 based analyses were restricted to the weight-stable subjects in order to account for this.
For the current analysis, subjects with a diagnosis of diabetes or hypertension or medication for both diseases were excluded. However, obese subjects are more likely to develop derangements in glucose or lipid metabolism as compared to normal-weight subjects which are in a subclinical state and have not led to a clinical diagnosis yet. An analysis of serum glucose (hexose) and triacylglycerol indicated significantly different means between non-obese and obese subjects. In non-obese subjects, mean ± SD serum glucose concentration was 5.18±0.76 mmol/l and mean serum triacylglycerol concentration was 1.4±0.81 mmol/l; the corresponding values in obese subjects were 5.6±1.11 mmol/l and 1.70±1.05 mmol/l, respectively. However, the higher glucose and triacylglycerol concentrations in obese do not indicate a catabolic status and thus are not sufficient to explain the missing association between FFMI and serum metabolites.
Stratified analyses by gender, age, and sports activity were conducted in order to detect potential confounders and effect modifiers. Similar results were found for men and women separately. P-values increased, as the sample size within the strata (slightly more for men) was smaller, but there was no improvement of the model fit, represented by the adjusted R2
. In addition, the FFMI is independent of height 
thus taking one difference between men and women into account. Furthermore, analysing the whole sample increases the power and we were able to cover a wider range of the FFMI. With respect to sports activity, slight differences were found for the acylcarnitine metabolite group. There, these effects were no longer present within the inactive subjects. However, as sports activity and FFMI are correlated and FFMI covers the long-term sports effect as it represents the muscle mass, sports activity was not included as a covariate in the main analyses.
The GGMs present low levels of linkage between the metabolites which is in line with findings from Krumsiek et al. 
who demonstrated that GGMs omit indirect correlations. Nevertheless, the GGMs complete the linear regression results with the underlying relationships between the metabolites.
We found strong associations between serum amino acids, acylcarnitines, and glycerophospholipids with the FFMI in a population-based sample. These findings were stable as they could be reproduced in a follow-up study of this population. Most interestingly, such associations were largely missing in obese subjects. The latter finding supports the hypothesis that a sedentary lifestyle associated with accumulation of fat tissue may be accompanied by a derangement in skeletal muscle metabolism, especially a limited inducibility of fatty acid oxidation.