International Journal of Sport Nutrition and Exercise Metabolism, 2014, 24, 215  -226 http://dx.doi.org/10.1123/ijsnem.2013-0048 © 2014 Human Kinetics, Inc.

www.IJSNEM-Journal.com ORIGINAL RESEARCH

Serum Metabolites Related to Cardiorespiratory Fitness, Physical Activity Energy Expenditure, Sedentary Time and Vigorous Activity Angelika Wientzek, Anna Floegel, Sven Knüppel, Matthaeus Vigl, Dagmar Drogan, Jerzy Adamski, Tobias Pischon, and Heiner Boeing The aim of our study was to investigate the relationship between objectively measured physical activity (PA) and cardiorespiratory fitness (CRF) and serum metabolites measured by targeted metabolomics in a population-based study. A total of 100 subjects provided 2 fasting blood samples and engaged in a CRF and PA measurement at 2 visits 4 months apart. CRF was estimated from a step test, whereas physical activity energy expenditure (PAEE), time spent sedentary and time spend in vigorous activity were measured by a combined heart rate and movement sensor for a total of 8 days. Serum metabolite concentrations were determined by flow injection analysis tandem mass spectrometry (FIA-MS/MS). Linear mixed models were applied with multivariable adjustment and p-values were corrected for multiple testing. Furthermore, we explored the associations between CRF, PA and two metabolite factors that have previously been linked to risk of Type 2 diabetes. CRF was associated with two phosphatidylcholine clusters independently of all other exposures. Lysophosphatidylcholine C14:0 and methionine were significantly negatively associated with PAEE and sedentary time. CRF was positively associated with the Type 2 diabetes protective factor. Vigorous activity was positively associated with the Type 2 diabetes risk factor in the mutually adjusted model. Our results suggest that CRF and PA are associated with serum metabolites, especially CRF with phosphatidylcholines and with the Type 2 diabetes protective factor. PAEE and sedentary time were associated with methionine. The identified metabolites could be potential mediators of the protective effects of CRF and PA on chronic disease risk. Keywords: physical fitness, methionine, phosphatidylcholines Physical activity (PA) can be described by the amount of physical activity energy expenditure (PAEE) but also by the time spent in different PA-intensities, such as moderate or vigorous activity or sedentary time (Caspersen et al., 1985). PA presents a behavior that influences cardiorespiratory fitness (CRF). CRF can be considered a phenotype since it refers basically to the ability to perform dynamic, moderate to high intensity exercise for prolonged periods and is influenced by the individuals’ activity or inactivity, individual lifestyle, genes, and environmental influences (Bouchard & Rankinen, 2001; Bouchard et al., 2011; Caspersen et al., 1985). CRF and PA are two intensively studied traits that are known to have risk reducing effects on overweight, Wientzek, Floegel, Knüppel, Vigl, Drogan, and Boeing are with the Dept. of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. Pischon is with the Dept. of Molecular Epidemiology, Max-Delbrück-Center for Molecular Medicine (MDC), Berlin-Buch, Germany. Adamski is with the Institute of Experimental Genetics, Technical University of Munich, Munich, Germany. Address author correspondence to Angelika Wientzek at [email protected].

obesity and chronic diseases (Simmons et al., 2008). The health enhancing working principles of CRF and PA are subjects of scientific investigations. Previous studies showed that CRF and PA are linked to changes in several well-known clinical markers (Banfi et al., 2012) but also cancer relevant biomarkers and DNA repair systems (Rundle, 2005). Despite the intensive research, the metabolic pathways between CRF, habitual PA and chronic disease development, are not completely understood. Metabolomics, which identifies substrates and products of metabolic reaction chains by using a wide spectrum of metabolites (Oresic, 2009), seems to be a promising way to enhance our understanding of the role of CRF and PA in the pathophysiology of chronic diseases (Walsh et al., 2011). Studies conducted so far focused mainly on exercise induced metabolomic changes (Krug et al., 2012) or investigated only a few biomarkers (Chorell et al., 2012). In the European Prospective Investigation into Cancer and Nutrition Study (EPIC; Riboli & Kaaks, 1997) several metabolites, measured by a targeted metabolomic approach, were linked to higher risk of Type 2 diabetes indicating that changes in metabolism predict the development of chronic diseases (Floegel et

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216    Wientzek et al.

al., 2013a). In the same study population, PA was shown to be associated with lower risk of Type 2 diabetes among healthy individuals, (InterActConsortium, 2012) as well as lower risk of mortality in individuals with Type 2 diabetes (Sluik et al., 2012). Therefore, the aim of the current study was to investigate the association between objectively measured CRF, PAEE, time spent in sedentary or vigorous activities and serum metabolite concentrations measured by targeted metabolomics in 100 participants of the EPIC-Potsdam study. In addition, we studied the association between CRF and PA and two metabolite factors that have previously been linked to risk of Type 2 diabetes in the same population.

Material and Methods Study Design EPIC-Potsdam located in Germany includes 27 548 men and women who were recruited from the general population and were mainly 35–65 years of age at time of enrolment from 1994 to 1998 (Boeing et al., 1999a, 1999b; Riboli & Kaaks, 1997). Out of this EPIC-Potsdam study center 407 participants were reinvited to participate in a PA questionnaire validation study. The randomly selected participants had to be younger than 64 years, had to have a valid telephone number, had to live within a 5 km radius of the study center, and had to have a blood pressure below 180 mmHg (systolic) and 110 mmHg (diastolic). Exclusion criteria were severe cardiologic illness, current use of beta-blockers, or a physical handicap, which disables to walk unaided for a minimum of 10 min. In addition, to determine eligibility to engage in a submaximal exercise test (step test) participants completed a general questionnaire on chest pain and safety of exercising (Rose, 1962) and the Physical Activity Readiness Questionnaire (Shephard, 1988). The center obtained ethical approval from the ethics committee of the Medical Association of the State of Brandenburg, Germany, before participant recruitment. Written informed consent was obtained from all participants. 208 (51%) of the invited subjects met the inclusion criteria and agreed to participate in the study (12 (3%) were excluded after telephone interview, 176 (43%) declined participation and 11 (3%) did not respond). Those attended the study center at two occasions 4 months apart. Both occasions included a study protocol with long term PA measurement, a submaximal exercise test and a fasting blood sample collection. The first visit in the study center took place between October 2007 and March 2008 and the second visit was between February and July 2008. Blood samples and additional subject information on age, sex, body mass index (BMI), and waist circumference were collected at both visits by trained staff in a standardized operating procedure. Highest school degree was assessed at baseline (1994–1998) and additional covariates such as smoking status and alcohol intake were taken from the most recent source, either the fourth (2008)

or third (2005) follow up, respectively. Finally, out of the 208 participants (125 women and 83 men aged between 44 and 63 years), who provided two fasting blood samples and engaged in the CRF and PA assessment, 50 men and 50 women were randomly selected for metabolomic measurements and included into the present analysis.

Objective CRF and PA Measurement Methods Cardiorespiratory Fitness Measurement.  An 8 min

ramped step test (200-mm step; Reebok, Lancaster, UK) was performed for individual calibration of heart rate (HR) response to exercise and to determine CRF via a submaximal exercise test with VO2max estimation. Participants stepped up and down following a rhythmic voice which progressively accelerated from 60 steps/min to 132 steps/min. The exercise was immediately followed by a 2 min recovery phase. When subjective symptoms occurred or the participants’ HR reached 85% of the agedependent maximal HR (Tanaka, Monahan, & Seals), the test was terminated and the VO2max estimation was based on the registered data. VO2max was calculated individually by extrapolating a regression line for the relationship between workload (determined by step height and step frequency) and HR until the estimated age-related HR (Tanaka et al., 2001). The used regression equation is valid for adults and predicts VO2max in liters/kg/min (Brage et al., 2005). Finally, the obtained values from both step tests conducted at the two study visits were weighted for test duration. The weighted mean VO2max was used for the analysis as the measure of CRF. Physical Activity Measurement.  A validated combined HR and movement sensor (Actiheart, CamNtech, Cambridge, UK), which was attached to the chest via two standard ECG electrodes, was used to measure PA (Brage et al., 2005). Following the step test as described above, the Actiheart sensor was initialized for long-term recording of PA with a frequency of 32 Hz. The device summarized the data into 1 min epochs providing a PA value for every min. Since the sensor is waterproof, the participants were instructed to wear it constantly for a minimum of four days. Data collected by the Actiheart were processed at the Cambridge MRC center. This included HR trace processing using a robust Gaussian Process regression method to handle potential measurement noise (Stegle et al., 2008) and estimation of the activity intensity (J/min/kg) for each min by both HR and acceleration (Brage et al., 2007) using a branched equation framework (Brage et al., 2004). Next, the identification of nonwear periods from the combination of missing or nonphysiological HR and prolonged periods of inactivity was performed. According to the activity intensity cut-off limit of 1.5MET/h, defined for time spent sedentary, each 1-min epoch fulfilling this criterion was allocated into the sedentary time intensity category. The same procedure was used for the

Metabolomics, Fitness, and Physical Activity  217

vigorous time category where epochs above an intensity of 6 MET/h were allocated. Finally, we averaged daily estimates of time spent sedentary and time spent vigorous from the two four day measurements, weighted by the probability of wear. Serum Metabolite Concentrations.  Monovette tubes

with coagulation activator were used for the blood withdrawal. Serum was fractionated by centrifugation at 2 700 g for 10 min, and stored in a freezer at –80 °C until analysis. The AbsoluteIDQ p150 kit (BIOCRATES Life Sciences AG, Innsbruck, Austria) was used to determine serum concentrations of 163 metabolites by flow injection analysis tandem mass spectrometry (FIAMS/MS). Thereby, a total of 163 metabolites including 41 acylcarnitines (Cx:y), 14 amino acids, 1 hexose, 92 glycerophospholipids (lysophosphatidylcholines (lysoPC), diacyl- and acyl-alkyl-phosphatidylcholines (PC aa and PC ae), and 15 sphingolipids (SMx:y) were quantified in a one-step analysis. The samples were prepared as described by manufacturer, the assay procedures were described in detail by Römisch-Margl (Romisch-Margl et al., 2012), and measurement of serum metabolites in these cohort samples has been described previously (Floegel et al., 2011). The median coefficients of variation were 7.3% within-plate and 11.3% between-plate, respectively (Floegel et al., 2011). For the present analysis, we included 127 metabolites that showed robust measurements, which has previously been evaluated by our group (Floegel et al., 2011). The lipid nomenclature referred to the Lipid Maps comprehensive classification system (Fahy et al., 2007). Lipid side chains were abbreviated as Cx:y, where x and y refer to the number of carbons in the side chain and the number of double bonds respectively. OH (hydroxy-), M (methyl-) or DC (dicarboxy-) indicated substitutions of side chains. The technology was restricted as it did not decode the distribution of carbon atoms and double bonds across different side chains in complex lipids. Phospholipid side chains were further differentiated according to type of bonding. For alkyl an a and for ether an e were used where one or two letters implied the number of fatty acids bond to glycerol (a = acyl, e = alkyl, aa = diacyl, ae = acyl-alkyl). The prefix ‘lyso’ indicated a single fatty acid residue. All acylcarnitines were abbreviated in respect to the fatty acid that was bond (e.g., C2 = acetyl-carnitine). C0 represented free DL-carnitine. Amino acids were presented with three letter abbreviations.

Statistical Analysis The serum metabolite concentrations were not normally distributed as indicated by Kolmogorov-Smirnov test, but right-skewed (alpha = .05). Therefore, all concentrations were log-transformed. To make the metabolite estimates comparable we performed a Z-transformation (mean = 0 and standard deviation = 1). Main exposures were CRF, expressed by VO2max in ml/kg/min, PA expressed as PAEE (kJ/kg/day), time spent sedentary (hr/day), and

vigorous activity (hr/day). Covariates were age, sex, and BMI (kg/m2), waist circumference (cm), educational attainment (no degree, vocational training, trade/technical school, university degree), smoking status (never, former, current £20 cigarettes/d, current >20 cigarettes/d), and alcohol consumption (nonconsumers, women: 0–6 g/d, 6–12 g/d, >12 g/d, men: 0–12 g/d, 12–24 g/d, >24 g/d). Initial descriptive and univariate analyses were performed. Thereby, continuous variables of exposures and outcomes were characterized by arithmetic mean and standard deviation (SD). For further analysis, we also explored the association between CRF, PA and two metabolite factors that were previously retrieved from a principal component analysis (PCA) and have recently been linked to risk of Type 2 diabetes in this population (Floegel et al., 2013a). The metabolite factors with the corresponding individual metabolite factor loadings are presented in the Supplementary Table 1. We explored the correlations between CRF, PAEE, sedentary time and vigorous time using Spearman correlation coefficients and 95% confidence intervals (95% CI) according to Fisher’s z-transformation. We examined the relationship between the exposures and the metabolites and reported them as beta coefficients with 95% CI. To account for divergence between the two metabolite measurement time points we used linear mixed models with a Supplemental Table 1  Metabolites and Their Factor Loadings for Factor 1 and Factor 2 According to Floegel et al. (2013) Factor 1 Factor loading

Metabolite

Factor 2 Factor loading

Metabolite

0.82

PC aa C42:0

0.55

C3

0.79

PC aa C42:1

0.66

Phenylalanine

0.8

PC ae C32:1

0.61

Tryptophan

0.78

PC ae C32:2

0.66

Tyrosine

0.7

PC ae C34:2

0.68

Valine

0.72

PC ae C34:3

0.66

Isoleucine

0.71

PC ae C36:2

0.59

PC aa C32:1

0.71

PC ae C36:3

0.7

PC aa C36:1

0.85

PC ae C40:5

0.65

PC aa C36_3

0.76

PC ae C40:6

0.76

PC aa C38:3

0.82

PC ae C42:3

0.72

PC aa C40:4

0.85

PC ae 42:4

0.71

PC aa C40:5

0.87

PC ae C42:5

0.44

Hexose

0.76

PC ae C44:4

0.78

PC ae C44:5

0.83

PC ae C44:6

0.54

SM C16:1

0.57

SM OH C22:2

0.41

LysoPC C17:0

218    Wientzek et al.

random intercept and maximum likelihood as estimation method. The subjects were assigned as the higher level with two measurement occasions. Fixed effect parameters were further compared. We calculated three models. The first model was adjusted for sex, age and measurement occasion. The second model was additionally adjusted for BMI, waist circumference, educational attainment, alcohol consumption and smoking status. Finally, in the third model we adjusted the investigated exposure mutually for the remaining exposures: for example CRF was adjusted for PAEE, sedentary time and vigorous time. To take into account the high number of metabolites and the exploratory approach of our analyses we corrected for multiple testing in the third model using the false discovery rate method (FDR; Benjamini Y, 1995). Thereafter, we again used the linear mixed model for investigating the association between metabolite factors that were previously linked to Type 2 diabetes risk and CRF, PAEE, time spent sedentary and vigorous time. We applied the same adjustments as in the single metabolite models. All statistical analyses were performed with SAS Enterprise Guide version 4.3 (SAS release 9.2, SAS Institute, Cary, NC, USA).

Results The study population had a mean age of 56 years and a mean BMI of 26.8 kg/m2. All characteristics of the participants are presented in Table 1. Thirty three percent of the participants were nonsmokers, 44% former smokers Table 1  Means, Standard Deviations, Minimum and Maximum of Exposures and Confounders in the Study Population (N = 100) Mean

SD

Min

Max

Age (years)

56.15

4.06

47.74

63.85

BMI (kg/m2)

26.77

4.05

19.5

41.66

CRF (ml/kg/min)

29.13

3.3

22.81

39.07

PAEE (kJ/kg/day)

38.86

12.37

15.54

74.93

Sedentary time (hr/day)

16.68

2.09

11.74

20.63

Vigorous time (hr/day)

0.11

0.14

0

0.70

Note. CRF = cardiorespiratory fitness; PAEE = physical activity energy expenditure; BMI = body mass index.

and 13% current smokers. Forty four percent attained a university degree 42% reported vocational training and 14% attended a commercial or technical school or had no degree. The participants consumed on average 9.86 g of alcohol per day. The correlations of CRF and PA variables are shown in Table 2. The linear mixed models analysis results are presented in Supplementary Tables S2a-d. CRF was significantly associated with 19 metabolites in Model 1 and 25 metabolites in Model 2 (Table S2a). Among them were diacyl-, acyl-alkyl- and lyso-phosphatidylcholines, hexose as well as the amino acids glutamine, valine, histidine, phenylalanine and methionine. After applying adjustments for the other exposures (PAEE, sedentary time and vigorous time) and correcting for multiple testing only associations with a cluster of phosphatidylcholines remained significant. A one ml/kg/min increase in CRF was associated with an 8–11% SD increase in phosphatidylcholines. PAEE was significantly associated with nine metabolites in Model 1 and seven in Model 2, (methionine, phenylalanine, arginine, two phosphatidylcholines, nonayl-L-carnitine (C9), and sphingomyelin C18:1; Table S2b). PAEE was significantly associated with nineteen metabolites in the third model. After correction for multiple testing only five remained significant. A one kg/kJ/day increase in PAEE was associated with a 0.5% SD decrease of methionine, 7% SD decrease of lyso-phosphatidylcholine C14:0 and diacyl-phosphatidylcholine C34:4, and a 8% SD decrease of diacylphosphatidylcholine C32:2, and C34:3. Sedentary time was associated with 13 metabolites in Model 1 and 10 metabolites in Model 2 (Table S2c). In Model 3, six metabolites showed significant associations after correction for multiple testing. The highest negative magnitudes were found for diacyl-phosphatidylcholine C32:2 and C34:3, where an increase in one hr/day in sedentary time was associated with a 46% SD decrease in the metabolites. Also negative associations were found for diacyl-phosphatidylcholine, C34:2, C34:4 and lysophosphatidylcholine C14:0 and ranged from 38% to 39% SD decrease of the respective metabolites. Vigorous time was associated with four metabolites in model 1 and seven metabolites in Model 2 (Table S2d). Model 3 revealed nine positively associated metabolites

Table 2  Spearman Correlation Coefficients and 95%CI Between CRF, PAEE, Sedentary Time, and Vigorous Time (N = 100) CRF (ml/kg/min) PAEE (kJ/kg/day)

CRF (ml/kg/min)

PAEE (kJ/kg/day)

Vigorous time (hr/day)

Sedentary time (hr/day)

1

0.39 (0.20–0.54)

0.31 (0.12;0.47)

–0.30 (-0.47;–0.11)

1

0.46 (0.29;0.60)

–0.93 (-0.96;–0.90)

1

–0.33 (-0.49;–0.14)

Vigorous time (hr/day) Sedentary time (hr/day) Abbreviations: CRF, cardiorespiratory fitness; PAEE, physical activity energy expenditure

1

Supplemental Table 2a Linear Mixed Models Results Between Serum Metabolites and Cardiorespiratory Fitness (CRF; ml/kg/min; N = 100) Metabolites PC aa_C36:6 PC aa_C42:1 PC ae_C38:0 PC ae_C42:3 PC aa_C36:0 PC aa_C38:6 PC ae_C42:2 PC aa_C36:5 PC aa_C32:0 PC aa_C38:0 PC aa_C38:5 PC aa_C40:6 PC aa_C42:6 PC ae_C36:0 PC ae_C40:6 PC ae_C40:1 PC ae_C40:3 PC aa_C30:0 PC aa_C32:2 SM C16:1 PC aa C28:1 PC aa C34:4 lysoPC a C28:1 PC aa C34:3 SM OH C14:1 PC ae C36:1 PC ae C34:0 PC ae C38:2 PC ae C38:6 PC ae C44:3 PC ae C44:4 PC ae C32:1 Glutamine Valine PC ae C42:4 PC ae C34:3 SM C16_0 Histidine Phenylalanine lysoPC a C18:2 lysoPC a C18:1 Methionine C18:1 Hexose

CRF (Model1*) 0.05(–0.01;0.11) 0.08(0.02;0.13) 0.05(0.00;0.11) 0.10(0.05;0.15) 0.04(–0.01;0.10) 0.03(–0.03;0.09) 0.08(0.02;0.13) 0.02(–0.04;0.08) 0.04(–0.02;0.10) 0.03(–0.03;0.09) 0.02(–0.04;0.08) 0.01(–0.05;0.07) 0.04(–0.01;0.10) 0.05(–0.01;0.11) 0.04(–0.02;0.10) 0.06(0.00;0.12) 0.05(0.00;0.10) 0.04(–0.02;0.09) 0.05(–0.01;0.10) 0.03(–0.02;0.09) 0.05(–0.01;0.10) 0.01(–0.05;0.07) 0.06(0.00;0.11) 0.04(–0.01;0.10) 0.05(–0.01;0.11) 0.04(–0.02;0.09) 0.04(–0.02;0.09) 0.08(0.03;0.13) 0.01(–0.05;0.07) 0.06(0.01;0.12) 0.08(0.02;0.13) 0.06(0.01;0.12) 0.07(0.02;0.13) 0.03(–0.02;0.08) 0.07(0.01;0.13) 0.08(0.02;0.13) 0.06(0.00;0.12) 0.06(0.00;0.12) 0.03(–0.02;0.09) 0.09(0.03;0.14) 0.07(0.02;0.13) 0.01(0.00;0.01) –0.06(-0.11;–0.01) –0.06(–0.12;0.00)

CRF (Model 2†) 0.08(0.03;0.13) 0.08(0.02;0.14) 0.07(0.01;0.13) 0.10(0.05;0.15) 0.07(0.01;0.12) 0.06(0.00;0.12) 0.07(0.01;0.13) 0.04(–0.01;0.10) 0.04(–0.01;0.10) 0.05(–0.01;0.11) 0.04(–0.02;0.10) 0.04(–0.02;0.10) 0.05(–0.01;0.11) 0.04(–0.01;0.10) 0.07(0.00;0.13) 0.04(–0.01;0.10) 0.07(0.01;0.13) 0.04(–0.01;0.10) 0.06(0.01;0.12) 0.07(0.01;0.12) 0.08(0.02;0.15) 0.03(–0.03;0.09) 0.10(0.04;0.16) 0.05(–0.01;0.10) 0.08(0.02;0.15) 0.06(0.00;0.12) 0.05(–0.01;0.11) 0.08(0.02;0.13) 0.03(–0.03;0.09) 0.06(0.01;0.11) 0.07(0.01;0.13) 0.05(–0.01;0.12) 0.08(0.02;0.14) 0.07(0.01;0.12) 0.07(0.01;0.13) 0.06(0.01;0.12) 0.06(0.00;0.12) 0.06(0.00;0.12) 0.07(0.01;0.12) 0.06(0.01;0.11) 0.04(–0.01;0.09) 0.01(0.00;0.01) –0.04(–0.09;0.01) –0.03(–0.09;0.03)

CRF (Model 3‡) 0.11(0.05;0.17) 0.11(0.05;0.18) 0.11(0.05;0.18) 0.10(0.04;0.16) 0.10(0.04;0.17) 0.10(0.04;0.17) 0.10(0.03;0.16) 0.08(0.03;0.14) 0.09(0.02;0.15) 0.09(0.02;0.16) 0.09(0.03;0.15) 0.09(0.02;0.15) 0.09(0.03;0.16) 0.09(0.03;0.15) 0.10(0.03;0.17) 0.08(0.02;0.14) 0.08(0.02;0.14) 0.08(0.01;0.14) 0.07(0.01;0.13) 0.08(0.01;0.15) 0.09(0.01;0.16) 0.07(0.01;0.13) 0.08(0.01;0.15) 0.07(0.01;0.13) 0.08(0.01;0.16) 0.08(0.01;0.15) 0.07(0.00;0.15) 0.07(0.00;0.13) 0.07(0.00;0.14) 0.07(0.01;0.13) 0.08(0.00;0.15) 0.07(0.00;0.14) 0.06(–0.01;0.13) 0.05(–0.01;0.12) 0.06(–0.01;0.13) 0.05(–0.01;0.12) 0.06(–0.01;0.14) 0.05(–0.02;0.12) 0.04(–0.02;0.10) 0.04(-0.02;0.10) 0.04(–0.02;0.10) 0.00(0.00;0.01) –0.03(–0.10;0.03) –0.03(–0.09;0.04)

FDR§ for Model 3‡ 0.0145 0.0206 0.0206 0.0222 0.0279 0.0279 0.0301 0.0317 0.0317 0.0317 0.0317 0.0317 0.0317 0.0317 0.0317 0.0443 0.0464 0.0475 0.0475 0.0513 0.0525 0.0532 0.0538 0.0584 0.0584 0.0667 0.0684 0.0684 0.0684 0.0684 0.0684 0.0805 0.1002 0.1029 0.1029 0.1090 0.1159 0.1411 0.1411 0.1439 0.1463 0.1465 0.1906 0.2511

Note. Only significantly associated metabolites shown. Abbreviations: a = acyl; aa = diacyl; ae = acyl-alkyl; lysoPC = lysophosphatidylcholine; PC = phosphatidylcholine; SM = sphingomyelin. Bold: p < .05. *Model 1: age, sex, and occasion †Model 2: Model 1 + BMI, waist circumference, educational attainment (no degree, vocational training, commercial/technical school, university degree), alcohol intake beverages (FUP3; nonconsumers, women: >0–6 g/d, 6–12 g/d, >12 g/d, men: >0–12 g/d, 12–24 g/d, >24g/d), smoking status (FUP4)(never, former, current ≤20 cigarettes/d, current >20 cigarettes/d) ‡Model 3: a) Model 2 + PAEE, sedentary time, vigorous time; b) Model 2 + CRF, sedentary time, vigorous time, c) Model 2 + CRF, PAEE, vigorous time, d) Model 2 + CRF, PAEE, sedentary time §FDR: False discovery rate corrected p-value.

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220    Wientzek et al.

Supplemental Table 2b  Linear Mixed Models Results Between Serum Metabolites and Physical Activity Energy Expenditure (PAEE; kJ/kg/day; N = 100) Metabolites

PAEE (Model 1*)

PAEE (Model 2†)

PAEE (Model 3‡)

FDR§ for Model 3‡

Methionine

0.003(0.00;0.00)

0.003(0.00;0.00)

–0.01(–0.01;0.00)

0.0094

PC aa C32:2

0.010(0.00;0.02)

0.006(–0.01;0.02)

–0.08(–0.12;–0.04)

0.0094

PC aa C34:3

0.007(–0.01;0.02)

0.003(–0.01;0.02)

–0.08(–0.12;–0.04)

0.0094

PC aa C34:4

0.001(–0.01;0.02)

–0.004(–0.02;0.01)

–0.07(–0.12;–0.03)

0.0271

lysoPC a C14:0

0.010(0.00;0.02)

0.005(–0.01;0.02)

–0.07(–0.11;–0.03)

0.0281

PC aa C34:2

0.003(–0.01;0.02)

0.003(–0.01;0.02)

–0.07(–0.12;–0.02)

0.0658

PC aa C30:0

0.006(–0.01;0.02)

0.001(–0.01;0.02)

–0.06(–0.10;–0.02)

0.1150

C0

–0.004(–0.02;0.01)

–0.002(–0.02;0.01)

–0.05(–0.10;–0.01)

0.1697

Valilne

0.008(0.00;0.02)

0.010(0.00;0.02)

–0.05(–0.10;–0.01)

0.1697

Isoleucine

0.005(–0.01;0.02)

0.004(–0.01;0.02)

–0.05(–0.09;–0.01)

0.1697

PC aa C32:1

0.006(–0.01;0.02)

–0.001(–0.01;0.01)

–0.05(–0.10;–0.01)

0.1697

PC aa C34:1

0.001(–0.01;0.02)

–0.006(–0.02;0.01)

–0.05(–0.10;–0.01)

0.1697

PC aa C36:6

0.006(–0.01;0.02)

0.001(–0.01;0.02)

–0.05(–0.10;–0.01)

0.1697

PC aa C32:3

0.00(–0.01;0.01)

0.004(–0.01;0.02)

–0.05(–0.10;–0.01)

0.2156

PC aa C36:3

0.004(–0.01;0.02)

0.005(–0.01;0.02)

–0.05(–0.10;0.00)

0.2162

Tryptophan

0.003(–0.01;0.02)

0.003(–0.01;0.02)

–0.05(–0.09;0.00)

0.2365

PC aa C36:2

–0.001(–0.01;0.01)

0.002(–0.01;0.02)

–0.05(–0.10;0.00)

0.2365

0.016(0.00;0.03)

0.010(0.00;0.03)

–0.05(–0.10;0.00)

0.2542

Histidine PC aa C36:1

–0.004(–0.02;0.01)

–0.007(–0.02;0.01)

–0.05(–0.09;0.00)

0.2542

Phenylalanine

0.012(0.00;0.03)

0.017(0.00;0.03)

–0.03(–0.07;0.01)

0.4735

PC aa C40:6

–0.015(–0.03;0.00)

–0.016(–0.03;0.00)

–0.03(–0.07;0.02)

0.5582

C10

–0.015(–0.03;0.00)

–0.014(–0.03;0.00)

0.03(–0.03;0.08)

0.6187

PC ae C38:4

–0.015(–0.03;0.00)

–0.008(–0.02;0.01)

0.02(–0.03;0.07)

0.6381

PC ae C38:5

–0.017(–0.03;0.00)

–0.015(–0.03;0.00)

0.02(–0.03;0.07)

0.6722

0.013(0.00;0.03)

0.017(0.00;0.03)

–0.01(–0.06;0.04)

0.7391

Arginine lysoPC a C28:1 SM C18:1

0.010(0.00;0.02)

0.016(0.00;0.03)

–0.01(–0.06;0.04)

0.7391

–0.016(–0.03;0.00)

–0.01(–0.03;0.01)

–0.01(–0.06;0.04)

0.7391

0.010(0.00;0.02)

0.019(0.00;0.03)

0.00 (–0.05;0.05)

0.7748

PC aa C38:4

–0.018(–0.03;0.00)

–0.017(–0.03;0.0)

0.00 (–0.05;0.04)

0.7748

PC ae C36:4

–0.015(–0.03;0.00)

–0.012(–0.03;0.0)

0.00 (–0.05;0.05)

0.7748

C9

Note. Only significantly associated metabolites shown. a = acyl; aa = diacyl; ae = acyl-alkyl; lysoPC = lysophosphatidylcholine; PC = phosphatidylcholine; SM = sphingomyelin. Bold: p < .05. *Model 1: age, sex, and occasion †Model

2: Model 1 + BMI, waist circumference, educational attainment (no degree, vocational training, commercial/technical school, university degree), alcohol intake beverages (FUP3; nonconsumers, women: >0–6 g/d, 6–12 g/d, >12 g/d, men: >0–12 g/d, 12–24 g/d, >24g/d), smoking status (FUP4)(never, former, current ≤20 cigarettes/d, current >20 cigarettes/d) ‡Model 3: a) Model 2 + PAEE, sedentary time, vigorous time; b) Model 2 + CRF, sedentary time, vigorous time, c) Model 2 + CRF, PAEE, vigorous time, d) Model 2 + CRF, PAEE, sedentary time §FDR: False discovery rate corrected p-value.

including ornithine, phenylalanine, proline, valine, isoleucine, hexose, lyso-phosphatidylcholine C18:0 and diacyl-phosphatidylcholine C36:2 and one negative association with acyl-alkyl-phosphatidylcholine C42:5. The magnitude of associations was between 154% and 215% SD increase of the respective metabolites with one

hr/day increase in vigorous activity. After adjustment for multiple testing none of these metabolites remained significant. Five metabolites were associated with both, PAEE and sedentary time, independently of all other exposures, after correction for multiple testing: methionine, diacyl-

Metabolomics, Fitness, and Physical Activity  221

Supplemental Table 2c  Linear Mixed Models Results Between Serum Metabolites and Sedentary Time (hr/day; N = 100) Metabolites

Sedentary Time (Model 1*) Sedentary time (Model 2†) Sedentary time (Model 3‡) –0.05(–0.06;–0.03)

FDR§ for Model 3‡

Methionine

–0.02(–0.03;–0.02)

–0.02(–0.03;–0.02)

PC aa C32:2

–0.11(–0.18;–0.03)

–0.08(–0.16;0.00)

–0.46(–0.68;–0.24)

0.0021

PC aa C34:3

–0.09(–0.17;–0.01)

–0.06(–0.15;0.02)

–0.46(–0.68;–0.24)

0.0021

0.0000

lysoPC a C14:0

–0.10(–0.18;–0.03)

–0.07(–0.15;0.01)

–0.39(–0.61;–0.16)

0.0142

PC aa C34:4

–0.07(–0.15;0.02)

–0.02(–0.10;0.06)

–0.38(–0.61;–0.15)

0.0166

PC aa C34:2

–0.05(–0.13;0.03)

–0.05(–0.14;0.04)

–0.38(–0.64;–0.13)

0.0353

PC aa C30:0

–0.08(–0.16;0.00)

–0.04(–0.13;0.04)

–0.34(–0.57;–0.10)

0.0527

PC aa C32:1

–0.09(–0.17;0.00)

–0.03(–0.11;0.05)

–0.30(–0.54;–0.06)

0.1069

PC aa C32:3

–0.03(–0.11;0.04)

–0.06(–0.14;0.03)

–0.31(–0.57;–0.06)

0.1167

PC aa C36:3

–0.06(–0.14;0.02)

–0.05(–0.14;0.03)

–0.29(–0.53;–0.05)

0.1167

PC aa C36:6

–0.08(–0.16;0.00)

–0.03(–0.12;0.05)

–0.27(–0.49;–0.04)

0.1167

PC aa C34:1

–0.05(–0.13;0.04)

0.00(–0.08;0.08)

–0.27(–0.50;–0.04)

0.1237

C0

–0.02(–0.10;0.06)

–0.02(–0.10;0.07)

–0.27(–0.51;–0.03)

0.1251

Valilne

–0.08(–0.15;–0.01)

–0.07(–0.15;0.01)

–0.26(–0.50;–0.03)

0.1251

Isoleucine

–0.07(–0.14;0.00)

–0.04(–0.12;0.04)

–0.25(–0.48;–0.02)

0.1251

PC ae C42:5

0.04(–0.04;0.12)

0.05(–0.05;0.14)

0.30(0.03;0.57)

0.1251

Histidine

–0.11(–0.19;–0.03)

–0.08(–0.17;0.01)

–0.27(–0.53;–0.01)

0.1466

PC aa C36:5

–0.07(–0.16;0.01)

–0.02(–0.10;0.05)

–0.22(–0.44;0.00)

0.1648

0.11(0.02;0.19)

0.11(0.01;0.21)

0.27(–0.02;0.56)

0.2148

Phenylalanine

–0.10(–0.18;–0.03)

–0.11(–0.19;–0.03)

–0.21(–0.44;0.02)

0.2204

PC ae C38:5

0.10(0.02;0.18)

0.10(0.01;0.19)

0.19(–0.08;0.46)

0.2953

–0.10(–0.18;–0.02)

–0.11(–0.2;–0.03)

–0.15(–0.40;0.11)

0.3510

0.08(0.00;0.17)

0.09(0.00;0.18)

0.14(–0.13;0.40)

0.3524

C9

–0.06(–0.14;0.02)

–0.12(–0.21;–0.03)

–0.11(–0.38;0.16)

0.3739

PC aa C38:4

0.08(–0.01;0.16)

0.11(0.03;0.19)

0.10(–0.13;0.34)

0.3739

SM C20:2

0.07(0.00;0.14)

0.05(–0.02;0.12)

0.08(–0.13;0.28)

0.4149

PC aa C40:6

0.05(–0.04;0.13)

0.09(0.01;0.18)

0.00(–0.25;0.24)

0.4897

C10

Arginine PC aa C38:0

Note. Only significantly associated metabolites shown. a = acyl; aa = diacyl; ae = acyl-alkyl; lysoPC = lysophosphatidylcholine; PC = phosphatidylcholine; SM = sphingomyelin. Bold: p < .05. *Model 1: age, sex, and occasion †Model

2: Model 1 + BMI, waist circumference, educational attainment (no degree, vocational training, commercial/technical school, university degree), alcohol intake beverages (FUP3; nonconsumers, women: >0–6 g/d, 6–12 g/d, >12 g/d, men: >0–12 g/d, 12–24 g/d, >24g/d), smoking status (FUP4)(never, former, current ≤20 cigarettes/d, current >20 cigarettes/d) ‡Model 3: a) Model 2 + PAEE, sedentary time, vigorous time; b) Model 2 + CRF, sedentary time, vigorous time, c) Model 2 + CRF, PAEE, vigorous time, d) Model 2 + CRF, PAEE, sedentary time §FDR: False discovery rate corrected p-value.

phosphatidylcholines C 32:2, C34:3 and C34:4, and lyso-phosphatidylcholine C14:0. We examined if CRF, or PAEE, or sedentary time or vigorous time were associated with one of the newly established metabolite factors that were linked to risk of Type 2 diabetes in this population previously (Floegel et al., 2013a). In the third model CRF was positively associated with the Type 2 diabetes protective factor (metabolite factor 1), see Supplementary Table S3. A one ml/kg/min

increase in CRF was associated with a 7.5% SD increase of this metabolite factor. Vigorous activity was associated with a 185% SD increase of the Type 2 diabetes Risk Factor 2 with a 1 hr per day increase in vigorous activity was found in the mutually adjusted model. However, this association is characterized by a wide 95% CI (0.26; 3.45) indicating low precision of the estimate. PAEE and sedentary time showed no significant association with any of the two investigated metabolite factors.

222    Wientzek et al.

Supplemental Table 2d Linear Mixed Models Results Between Serum Metabolites and Vigorous Time (hr/day; N = 100) Metabolites Ornitine

Vigorous Time (Model 1*)

Vigorous Time (Model 2†)

Vigorous Time (Model 3‡)

FDR§ for Model 3‡

0.69(-0.50;1.89)

1.52(0.25;2.79)

1.72(0.07;3.37)

0.3622

Phenylalanine

0.97(-0.16;2.10)

1.74(0.58;2.89)

1.54(0.08;3.00)

0.3622

Proline

0.92(-0.31;2.15)

1.32(-0.01;2.66)

2.18(0.47;3.90)

0.3622

Valilne

1.15(0.05;2.24)

1.74(0.55;2.93)

2.15(0.66;3.64)

0.3622

Isoleucine

0.83(-0.24;1.90)

1.23(0.07;2.38)

1.73(0.28;3.18)

0.3622

PC aa C36:2

0.56(-0.62;1.74)

1.02(-0.23;2.28)

1.68(0.09;3.26)

0.3622

PC ae C42:5

–0.72(–1.94;0.50)

–0.88(–2.25;0.49)

–1.85(–3.55;–0.14)

0.3622

lysoPC a C18:0

0.55(–0.65;1.75)

1.50(0.19;2.80)

1.82(0.14;3.51)

0.3622

Hexose

0.34(–0.94;1.62)

0.79(–0.48;2.06)

1.69(0.06;3.31)

0.3622

Histidine

0.77(–0.45;0.20)

1.37(0.06;2.68)

1.63(–0.02;3.28)

0.3983

Methionine

0.09(0.01;0.17)

0.15(0.06;0.23)

0.09(0.00;0.180)

0.3998

PC ae C36:5

–1.29(–2.54;–0.04)

–1.18(–2.52;0.17)

–1.53(–3.25;0.20)

0.3998

1.22(0.01;2.43)

0.96(–0.39;2.31)

0.19(–1.53;1.90)

0.5676

C9

Note. Only significantly associated metabolites shown. a = acyl; aa = diacyl; ae = acyl-alkyl; lysoPC = lysophosphatidylcholine; PC = phosphatidylcholine; SM = sphingomyelin. Bold: p < .05. *Model 1: age, sex, and occasion †Model

2: Model 1 + BMI, waist circumference, educational attainment (no degree, vocational training, commercial/technical school, university degree), alcohol intake beverages (FUP3; nonconsumers, women: >0–6 g/d, 6–12 g/d, >12 g/d, men: >0–12 g/d, 12–24 g/d, >24g/d), smoking status (FUP4)(never, former, current ≤20 cigarettes/d, current >20 cigarettes/d) ‡Model 3: a) Model 2 + PAEE, sedentary time, vigorous time; b) Model 2 + CRF, sedentary time, vigorous time, c) Model 2 + CRF, PAEE, vigorous time, d) Model 2 + CRF, PAEE, sedentary time §FDR: False discovery rate corrected p-value.

Discussion We investigated the associations between CRF, PA and serum metabolites with a targeted metabolomics approach. We could show that several metabolite classes especially a cluster of phosphatidylcholines were associated with CRF, and methionine was associated with PAEE and sedentary time independently of the other exposures. We also found associations with metabolite factors that have previously been linked to risk of Type 2 diabetes (Floegel et al., 2013a). The Type 2 diabetes protective factor was positively associated with CRF independently of PA and the Type 2 diabetes risk factor positively related to vigorous activity independently of the other exposures. The major strengths of our study were the repeated estimates of the targeted metabolomic platform that included quantitative measurements of 127 metabolites of known identity. We further used a combined HR and movement sensor that objectively measured PA. The twofold and objective PA measurement (including weekends and weekdays) is likely to reflect more precisely the habitual PA of an individual than a single measurement (Metzger et al., 2008; Troiano, 2007) and is regarded as more precise than a questionnaire (Neilson et al., 2008). Also CRF was estimated at both occasions using a submaximal step test. The intraclass correlation coefficient between the two time-point measurements equaled 0.825

which indicates high reliability and an averaging of the two time points should have provided an appropriate estimate of CRF. One of the study limitations was the cross-sectional study design. Further, the small sample size of 100 participants results in a low power and may lead to underestimation of some true relationships. Furthermore, the large number of investigated metabolites introduces the possibility of a Type I error. Nevertheless, this problem was addressed by multiple testing corrections. The fact that PAEE and sedentary time in model 3 might be burdened with the problem of multicollinearity should be considered, as the direction of many associations changed from positive to negative after the mutual adjustment and the correlation coefficient between PAEE and sedentary time was high. Therefore, the association directions for these exposures should be interpreted by considering Model 2. Lastly, there is a chance that due to the objective PA measurement some participants may have changed their activity during the measurement period (McCarney et al., 2007). Another limitation may be seen in missing dietary data as a potential confounder. However, we are not aware of a strong impact of diet on the serum metabolites studied here (Floegel et al., 2013b). The investigated amino acids were associated with PA related exposures. Besides methionine, also histidine, phenylalanine, tryptophan, isoleucine, valine, ornithine,

Metabolomics, Fitness, and Physical Activity  223

Supplemental Table 3  Linear Mixed Model Results Between Metabolite Factors and CRF, PAEE, Sedentary Time and Vigorous Time (N = 100) Exposure CRF (ml/kg/min)

Model

Factor

β and 95% CI

p

1

Factor1*

0.053 (-0.003;0.11)

0.0630

2

0.064 (0.002;0.13)

0.0418

3

0.074 (0.01;0.14)

0.0348

–0.050 (–0.11;0.01)

0.0964

2

–0.001 (–0.06;0.06)

0.9678

3

0.010 (–0.08;0.06)

0.7600

–0.003 (–0.02;0.01)

0.6607

0.001 (–0.01;0.02)

0.8958

–0.004 (–0.05;0.05)

0.8754

–0.004 (–0.02;0.01)

0.5578

2

–0.002 (–0.02;0.01)

0.8367

3

–0.003 (–0.08;0.02)

0.1875

0.039 (–0.04;0.01)

0.3395

–0.005 (–0.08;0.10)

0.9035

1

PAEE (kJ/kg/day)

1

Factor2†

Factor1*

2 3 1

Sedentary time (hr/day)

1

Factor2†

Factor1*

2 3

0.061 (–0.20;0.32)

0.6464

–0.020 (–0.11;0.07)

0.6391

2

0.005 (–0.08;0.08)

0.9135

3

–0.117 (–0.37;0.14)

0.3575

0.408 (–0.78;1.60)

0.4976

1

Vigorous time (hr/day)

1

Factor2†

Factor1*

2

0.276 (–1.03;1.59)

0.6758

3

–0.120 (–1.78;1.54)

0.8859

0.463 (–0.79;1.72)

0.4666

2

0.981 (–0.26;2.22)

0.1202

3

1.853 (0.26;3.45)

0.0232

1

Factor2†

Note. CRF = cardiorespiratory fitness; PAEE = physical activity energy expenditure. Model 1: age, sex, and occasion. Model 2: Model 1 + BMI, waist circumference, educational attainment (no degree vocational training, trade/technical school, university degree), alcohol intake beverages (nonconsumers, women: >0–6 g/d, 6–12 g/d, >12 g/d, men: >0–12 g/d, 12–24 g/d, >24g/d), smoking status (never, former, current =20 cigarettes/d, current >20 cigarettes/d), Model 3: Model 2 + CRF or/and PA and/or sedentary time and /or vigorous activity *Factor 1= 0.82 × PC aa C42:0 + 0.79 × PC aa C42:1 + 0.80 × PC ae C32:1 + 0.78 × PC ae C32:2 + 0.70 × PC ae C34:2 + 0.72 × PC ae C34:3 + 0.71 × PC ae C36:2 + 0.71 × PC ae C36:3 + 0.85 × PC ae C40:5 + 0.76 × PC ae C40:6 + 0.82 × PC ae C42:3 + 0.85 × PC ae C42:4 + 0.87 × PC ae C42:5 + 0.76 × PC ae C44:4 + 0.78 × PC ae C44:5 + 0.83 × PC ae C44:6 + 0.54 × SM C16:1 + 0.57 × SM OH C22:2 + 0.41 × lysoPC a C17:0 (Floegel et al., 2013a). Factor 2= 0.55 × C9 + 0.66 × phenylalanine + 0.61 × tryptophan + 0.66 × tyrosine + 0.68 × valine + 0.66 × isoleucine + 0.59 × PC aa C32:1 + 0.70 × PC aa C36:1 + 0.65 × PC aa C36:3 + 0.76 × PC aa C38:3 + 0.72 × PC aa C40:4 + 0.71 × PC aa C40:5 + 0.44 × hexose (Floegel et al., 2013a) †

and proline were associated with PAEE, sedentary time or vigorous time but lost significance after multiple testing correction. Methionine is an essential amino acid. Nevertheless, it can be regenerated by the human body from homocysteine. Since, homocysteine levels do not differ between high-methionine and low-methionine diet in humans, lower methionine levels in sedentary persons might be due to a blocked or weakened methionine regeneration from homocysteine, leading to lower serum methionine and higher homocysteine (Mann et al., 1999; Ward et al., 2001). The magnitude of the association between methionine and the investigated exposures in our study

was lower compared with other metabolites. The narrower 95% CI, however, indicates a more precise estimation of these relationships. Elevated homocysteine level is considered an independent risk factor of cardiovascular diseases (CVD; Clarke et al., 1991; Refsum et al., 1998). Homocysteine levels have been shown to be associated with PA, although the duration, intensity, and mode of exercise, as well as the genotype play a role in these relationships (Joubert & Manore, 2006). As homocysteine is being remethylated into methionine it may be possible that PA helps preventing CVD by promoting this reaction. In our study CRF was positively associated with phosphatidylcholines. Phosphatidylcholines are synthe-

224    Wientzek et al.

sized from choline in liver based pathways (Li & Vance, 2008). Particularly, acyl-alkyl-phosphatidylcholines belong to the class of plasmalogenes, which were shown to be reduced in neurodegenerative diseases and can prevent polyunsaturated fatty acids (PUFA) oxidation (Wallner & Schmitz, 2011). A role in cardiac failure, Type 2 diabetes, obesity and cancer has also been described previously (Wallner & Schmitz, 2011). Phosphatidylcholine biosynthesis is required for normal very low density lipoproteins (VLDL) and high density lipoproteins (HDL) secretion (Cole et al., 2012). In addition, acyl-alkyl-phosphatidylcholines were found to be positively related to HDL-cholesterol (Floegel et al., 2013a). Treede et al. (Treede et al., 2007) investigated different phosphatidylcholines and could show that they also carry out anti-inflammatory effects. We found two phosphatidylcholine clusters associated with CRF in the current study. Thus, phosphatidylcholines could act as potential mediators of the anti-inflammatory effect and protective attributes of CRF on chronic disease risk. We found lyso-phosphatidylcholine C14:0 negatively associated with PAEE and sedentary time, and lyso-phosphatidylcholine C28:1 borderline significantly, positively associated with CRF. Lyso-phosphatidylcholines are lipids resulting from hydrolysis of one fatty acid residue from phosphatidylcholines. They play an important role in cell signaling, fatty acid, choline and phospatidylglycerol transport and binding. They are also components of oxidized low density lipoproteins (Ox-LDL; Meyer zu Heringdorf & Jakobs, 2007). The latter attributes lyso-phosphatidylcholines a possible role in atherosclerosis and acute chronic inflammation (Schmitz & Ruebsaamen, 2010). Drobnik et al. (Drobnik et al., 2003) found that particular fatty acids side chains of lyso-phosphatidylcholines were decreased in septic patients: C16:0, C18:0, C18:1 and C18:2. Recently published studies elucidated differences between the saturated and unsaturated lyso-phosphatidylcholines. Saturated lyso-phosphatidylcholines but also C18:1 showed proinflammatory effects. Representatives for the polyunsaturated fraction, had no or even antiinflammatory effects (Hung et al., 2012). Furthermore, it was observed that saturated lyso-phosphatidylcholines elicited less neutrophil reactive oxygen species (ROS) production than unsaturated species (Ojala et al., 2007). In our study lyso-phosphatidylcholine C14:0 was negatively associated with sedentary time, indicating that there is a relationship between inactivity and a proinflammatory marker. Further, lyso-phosphatidylcholine C28:1 was positively associated with CRF, which could be an indicator for anti-inflammatory properties of CRF. As CRF and PA are known to influence the immune system (Walsh et al., 2011), lyso-phosphatidylcholines may be a link to the working principle of this pathway. Floegel et al. found two metabolite factors associated with risk of Type 2 diabetes (Floegel et al., 2013a). The first metabolite factor including acyl-alkylphosphatidylcholine species, sphingomyelins, and lysophosphatidylcholines was associated with reduced risk

of Type 2 diabetes, whereas metabolite factor 2 including diacyl-phosphatidylcholines, branched chain and aromatic amino acids, propionyl- carnitine (C3), and hexose was associated with a higher risk. In our study CRF was significantly positively associated with the Type 2 diabetes protective factor after adjustment for the included covariates. Individual metabolites of this metabolite factor included diacyl-phosphatidylcholine C42:1, and acylalkyl-phosphatidylcholines C40:6 and C42:3, which were also individually positively related to CRF. These findings further suggest that CRF may trigger metabolic changes, which are linked to lower risk of chronic diseases. Vigorous activity was positively associated with the Type 2 diabetes risk factor indicating a contradictory finding suggesting that high intensity PA is related to a higher risk of Type 2 diabetes. Nevertheless, the bright 95%CI indicates low precision in the estimation of this relationship. One possible explanation could be the previously discussed problem of participants changing their activity during an objective measurement, in our case engaging in a higher amount of vigorous activity (McCarney et al., 2007). Another explanation is that studies have shown that high intensity PA influences the immune system (Walsh et al., 2011). Since subclinical inflammation underlies Type 2 diabetes pathophysiology (Donath & Shoelson, 2011), it is possible that pro- and anti-inflammatory reactions play a role in the observed relationship. Nevertheless, this finding needs further clarification. In conclusion, our study provides evidence that there is an association between CRF, PA and specific serum metabolites: especially a number of phosphatidylcholines and lyso-phosphatidylcholine C28:1 with CRF, and methionine and lyso-phosphatidylcholine C14:0 with PA. The metabolites associated with CRF and PA, were also shown in other studies to be involved in chronic disease development. Therefore, they may provide a link between CRF, PA and their preventive effect on chronic diseases. This study, although cross-sectional in nature, is one of the first showing a clear connection between a lifestyle factor (PA) and a phenotype (CRF) and serum metabolites obtained by metabolomic profiling in the frame of a population study. Acknowledgments The authors would like to thank all study participants and the study center staff. We are grateful to Ellen Kohlsdorf for data handling and the MRC Cambridge UK Epidemiology Unit Team, for Actiheart data processing. We thank Dr. Werner Römisch-Margl, Dr Cornelia Prehn, Julia Scarpa, Katharina Sckell and Arsin Sabunchi for metabolomics measurements performed at the Helmholtz Zentrum München, Genome Analysis Center, Metabolomics Core Facility. We also thank Silke Feller und Jana Förster for their comments on the manuscript draft. This study was supported in part by a grant from the German Federal Ministry of Education and Research (BMBF Förderkennzeichen 34010 NGFN-plus and BMBF Förderkennzeichen 01GI0922 to D.Z.D (German Center for Diabetes Research DZD e.V.). None of the authors declared a conflict of interest.

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