Reliability of Serum Metabolites over a Two-Year Period: A Targeted Metabolomic Approach in Fasting and Non-Fasting Samples from EPIC
Reliability of Serum Metabolites over a Two- Year Period: A Targeted Metabolomic Approach in Fasting and Non-Fasting Samples from EPIC
Marion Carayol 0 1
Idlir Licaj 0 1
David Achaintre 0 1
Carlotta Sacerdote 0 1
Paolo Vineis 0 1
Timothy J. Key 0 1
N. Charlotte Onland Moret 0 1
Augustin Scalbert 0 1
Sabina Rinaldi 0 1
Pietro Ferrari 0 1
0 1 International Agency for Research on Cancer , Lyon , France , 2 Institute of Community Medicine, Faculty of Health Sciences, University of Tromsø , Tromsø , Norway , 3 Unit of Cancer Epidemiology, AO Citta' della Salute e della Scienza-University of Turin and Center for Cancer Prevention (CPO-Piemonte) , Turin, Italy, 4 Human Genetics Foundation (HuGeF), Turin , Italy , 5 School of Public Health, Imperial College London , London , United Kingdom , 6 Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford , Oxford , United Kingdom , 7 Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center , Utrecht , The Netherlands
1 Editor: Erica Villa, University of Modena & Reggio Emilia , ITALY
Data Availability Statement: This study has been
led by researchers from the International Agency for
Research on Cancer (IARC, Lyon, France).
Participants’ blood samples have initially been
collected in EPIC centers from Turin (Italy) and
Utrecht (The Netherlands). The study dataset has
been centralized at IARC and statistical analyses
carried out under the supervision of Dr. Pietro Ferrari.
The primary responsibility for accessing the data
belongs to Turin and Utrecht EPIC centers that
provided the blood samples. Their use as
anonymized dataset could be made available by
Although metabolic profiles have been associated with chronic disease risk, lack of
temporal stability of metabolite levels could limit their use in epidemiological investigations. The
present study aims to evaluate the reliability over a two-year period of 158 metabolites and
compare reliability over time in fasting and non-fasting serum samples.
Metabolites were measured with the AbsolueIDQp180 kit (Biocrates, Innsbruck, Austria) by
mass spectrometry and included acylcarnitines, amino acids, biogenic amines, hexoses,
phosphatidylcholines and sphingomyelins. Measurements were performed on repeat
serum samples collected two years apart in 27 fasting men from Turin, Italy, and 39
nonfasting women from Utrecht, The Netherlands, all participating in the European Prospective
Investigation into Cancer and Nutrition (EPIC) study. Reproducibility was assessed by
estimating intraclass correlation coefficients (ICCs) in multivariable mixed models.
In fasting samples, a median ICC of 0.70 was observed. ICC values were <0.50 for 48% of
amino acids, 27% of acylcarnitines, 18% of lysophosphatidylcholines and 4% of
phosphatidylcholines. In non-fasting samples, the median ICC was 0.54. ICC values were <0.50 for
71% of acylcarnitines, 48% of amino acids, 44% of biogenic amines, 36% of
contacting the corresponding author, Dr. Pietro
Ferrari, , who will issue a request to
scientists of EPIC Utrecht and Turin.
Competing Interests: The authors have declared
that no competing interests exist.
sphingomyelins, 34% of phosphatidylcholines and 33% of lysophosphatidylcholines.
Overall, reproducibility was lower in non-fasting as compared to fasting samples, with a
statistically significant difference for 19–36% of acylcarnitines, phosphatidylcholines and
A single measurement per individual may be sufficient for the study of 73% and 52% of the
metabolites showing ICCs >0.50 in fasting and non-fasting samples, respectively. ICCs
were higher in fasting samples that are preferable to non-fasting.
Advancement of analytical technologies has made high-throughput metabolic profiling of
biological specimens possible . Mass spectrometry techniques have been increasingly used to
characterize the human metabolome [1,2] and the complex metabolic effects of nutrients or
foods on chronic diseases in large-scale epidemiological studies [3–7], mainly because of their
sensitivity and selectivity. However, most of the epidemiological studies rely on a single
measurement of the metabolome. An eventual lack of temporal stability of the metabolome may
bias relative risks based on a single measurement towards the null and could limit the
significance of the findings [8–10].
Reliability studies evaluate the reproducibility of measurements taken on the same
individuals at separate times. Reliability over time improves as the proportion of the total variance due
to the between-subject variance increases. The reliability over time of metabolic profiles has
recently been investigated in metabolites measured in blood samples [11–15]. Overall, fair to
good reliability was reported, particularly in amino acids, hexoses, phosphatidylcholines, and
sphingomyelins [11,12,15], except some acylcarnitines compounds that may yield poorer
reliability [11,12]. Those reliability studies focused on assessing the temporal stability of
metabolites measured in fasting blood samples. However, some epidemiological studies investigating
metabolomics-disease links have used blood samples from non-fasting participants [2,16,17].
As food intake influences the metabolome , it has been hypothesized that the reliability was
weaker in non-fasting than in fasting samples. This motivates the need of a study to compare
the reliability of fasting and non-fasting samples.
The present study aims (1) to evaluate the reliability of metabolic profiling measurements
over time in serum samples collected on average 2 years apart in subjects participating in the
European Prospective Investigation into Cancer and Nutrition (EPIC) study, separately for
fasting and non-fasting measures, and (2) to statistically compare reliability over time in fasting
and non-fasting subgroups.
Materials and Methods
Study population and blood collection
EPIC is a large European cohort involving more than 521,000 subjects from 23 centers in 10
European countries . All participants gave written informed consent to the EPIC study
and the Ethical Committee of the International Agency of Research on Cancer specifically
approved the present study. The EPIC cohort has at present an average follow-up of about
11 years. All participants completed questionnaires on diet, lifestyle, and medical history. In
addition, for about 80% of EPIC participants a blood sample was collected at recruitment .
The EPIC cohort is particularly suitable to carry out epidemiological investigations on the link
between metabolomics and risk of cancer. The current study was based on a sub-sample of
participants who gave blood samples at two different time points: 39 non-fasting women from
Utrecht (the Netherlands) and 27 fasting men from Turin (Italy). All the participants included
in the present study were free of disease (diabetes, stroke, heart and overall cancer) at baseline.
Although fasting and non-fasting individuals were different regarding gender and origin, this
specific population was chosen as information about fasting conditions in these EPIC
participants were known and consistent at both time points.
In Utrecht, women aged 50–65 years old, with no history of diabetes, cardiovascular disease
or cancer, were invited to participate in a breast cancer screening from 1993 to 1999. A second
examination with blood collection was carried out in a subsample of the cohort. Among all
women attending this second examination, 39 who gave non-fasting blood samples in both
occasions at 2.4 years interval (5th-95th: 2.2–3.9) were selected. In Turin, blood donors men
aged 35–64 years were recruited from 1993 to 1998. A second blood drawn was taken in 39
men of which 27 who gave fasting blood samples in both occasions at 1.9 years interval
(5th95th: 1.0–2.8) were selected.
All the collected specimens were treated and banked with the same protocol as previously
detailed for the whole cohort [20,21]. In short, blood samples were drawn in tubes without any
anticoagulant (serum fraction), stored at +4°C until centrifugation (4,000 rpm for 20 minutes),
and then stored in liquid nitrogen at -196°C.
Concentrations in serum samples for 158 endogenous metabolites were determined at IARC
by ultra-performance liquid chromatography (LC) (1290 Series HPLC; Agilent, Les Ulis,
France) hyphenated to a tandem mass spectrometer (MS/MS) (QTrap 5500; AB Sciex, Les Ulis,
France) using the AbsoluteIDQ p180 kit (Biocrates, Innsbruck, Austria). The AbsoluteIDQ
p180 Kit (Biocrates, Innsbruck, Austria) is a combined flow injection (FIA) and LC-MS/MS
assay. The assay quantifies up to 188 metabolites from five analyte groups: acylcarnitines,
amino acids, biogenic amines, hexoses (sum of hexoses), phosphatidylcholines (PCs), and
sphingomyelins (SMs). The method combines the derivatization with 5% Phenylisothiocyanate
reagent and extraction of analytes using 5mM ammonium acetate in methanol and the
selective mass spectrometrical detection using MRM pairs. Isotope-labeled internal standards are
partially integrated in the kit plate filter for metabolite quantification. Samples were analyzed
using an LC/MS (QTrap 5500; AB Sciex, Les Ulis,France) method (for analysis of amino acids
and biogenic amines) followed by FIA-MS (analysis of lipids, acylcarnitines and hexose). The
limit of detection for the individual metabolites was set to three times the values of the
bufferonly- containing samples. The MetIQ software package (BIOCRATES) allows an automation
of the assay workflow, from sample registration to data processing. The AbsoluteIDQ p180 Kit
validated by the manufacturer according to the Food and Drug Administration guideline
‘Guidance for industry–Bioanalytical Method Validation, May 2011’. For analytical
specifications, refer to the AbsoluteIDQ p180 Kit manuals.
Measurements were made following the procedure recommended by the kit manufacturer.
The kit was first tested on EPIC orphan samples, and 158 metabolites out of the proposed 188
were analytically validated. Repeated samples from the same subject were measured within the
same analytical batch. Two different quality controls were inserted in each analytical batch.
Intra- and inter-batch coefficients of variation ranged from 4% to 15% for the vast majority of
analytes. Overall, 4.9% and 1.8% of total values were below the limit of detection (LOD) and
the limit of quantification (LOQ), respectively. These values were imputed as LOD and LOQ
values, respectively. However, reliability was not assessed when the percentage of values below
LOD or LOQ for a given metabolite was greater than 50%, separately in fasting and non-fasting
samples, which allowed 140 metabolites to be assessed for reliability.
Samples were defined as fasting samples if time since last food or drink at blood collection was
more than six hours. Samples that have been collected less than six hours since last food or
drink were considered as non-fasting samples.
Body weight and height were measured at baseline according to standardized procedures
previously described . Briefly weight was measured to the nearest 0.1kg and height was
measured to the nearest 0.1, 0.5, or 1.0 cm depending on the center, without shoes. The body
mass index (BMI) was calculated as body weight in kilograms divided by squared height in
Occupational, recreational and household physical activities were recorded by the EPIC
physical activity questionnaire previously described . Total physical activity was estimated
as a categorical index (inactive, moderately inactive, moderately active, active) by
cross-tabulation of the level of occupational activity (nonworker, sedentary, standing, manual, heavy
manual and unknown) with combined recreational and household activities (in quartiles of
Other variables such as age (years), smoking status (current, former, never) and time
difference between the two blood drawn (years), were also considered in analysis.
Concentration levels of biomarkers were log-transformed to approximate normality. Estimates
of the intraclass correlation coefficient (ICC) for each metabolite were computed to assess
reliability over time  as the ratio of between-subject variance component over total variability
in mixed models. For each metabolite, the measurement yijk, with i = 1,..,nk indexing study
participant, j = 1,2 blood collection occasion and k = 1,2 center, is modelled through a
randomeffect model as
with α expressing the overall intercept, and β a vector of fixed-effect coefficients to capture the
role of confounding variables, notably participants’ age, BMI and the time difference between
sample collections. The terms ti(k) and eij(k) are center-specific random-effects study
participants and residuals, respectively, whose variance estimates, σ2Bk and σ2Wk, estimate
betweenand within-subject variability. In this way the center-specific ICC estimates are computed as
s2BksþBsk2Wk. The random-effects models were fitted by using the SAS PROC MIXED procedure
which uses a restricted maximum likelihood function for parameter estimation.
The reproducibility of metabolites was assessed according to fasting status, i.e. comparing
fasting samples in Turin and non-fasting samples in Utrecht. In order to control for differences
of age, BMI and the time difference between sample collections across the two centers, these
data were included as fixed effects in the models, whereby smoking status and physical activity
had no virtually influence over variance of any of the metabolites. Reliability was considered as
excellent with ICCs 0.75, good with 0.50 ICCs 0.74, weak with ICCs<0.50.
The difference in ICC estimates according to fasting status was tested for statistical
significance using a bootstrap sampling scheme . A total of 300 repetitions provided sufficiently
stable estimates. For each metabolite, P-values were obtained by comparing the ratio of the
mean of the ICC difference between fasting and non-fasting samples over the standard
deviation of the bootstrap distribution to a standardized normal distribution.
All statistical analyses were performed using SAS 9.2 statistical software (SAS Institute Inc,
At study entry, compared to women from Utrecht, men from Turin were younger, more
frequently current smokers and less physically active, as reported in Table 1.
Mean levels, within- and between-variance and ICCs estimates of serum metabolites are
reported in S1 Table and ICCs are represented in Figs 1 and 2. In fasting samples from Turin,
median ICC and ICC range were 0.62 (range: 0–0.89) for acylcarnitines, 0.51 (0.11–0.71) for
amino acids, 0.77 (0.54–0.88) for SMs, 0.71 (0.53–0.78) for biogenic amines, and 0.55 for
hexoses (Fig 1), and 0.63 (0.39–0.79) for lysoPCs and 0.74 (0.43–0.91) for PCs (Fig 2). Out of 140
metabolites, ICC values were higher than 0.70 for 58 metabolites (41%), and higher than 0.50
for 103 metabolites (74%). None of the hexoses, biogenic amines or SMs compounds showed
ICCs<0.5. Among other groups, 48% of amino acids, 27% of acylcarnitines, 18% of lysoPCs
and 4% of PCs compounds had ICCs lower than 0.50.
In non-fasting samples from Utrecht, median ICC and ICC range were respectively, 0.34
(range: 0.28–0.59) for acylcarnitines, 0.56 (0.23–0.79) for amino acids, 0.54 (0.26–0.66) for
SMs, 0.46 (0.06–0.73) for biogenic amines, and 0.58 for hexoses (Fig 1), and 0.62 (0.39–0.77)
for lysoPCs and 0.55 (0.20–0.79) for PCs (Fig 2). Out of 140 metabolites, ICC values were
higher than 0.7 for 12 metabolites (8%), and higher than 0.5 for 73 metabolites (52%).
aFor continuous variables the mean value and the 5th-95th percentiles are provided.
Fig 1. Intra-class correlation coefficients (ICCs) of serum sugars, amino acids, biogenic amines (A),
acylcarnitines and sphingolipids (B) targeted metabolites in 27 fasting men and 39 non-fasting
women. *P-values < 0.05 for difference between fasting and non-fasting ICCs
Fig 2. Intra-class correlation coefficients (ICCs) of serum targeted phosphatidylcholines in 27 fasting
men and 39 non-fasting women according to their ICC values: ICCs < 0.70 (A); ICCs 0.70 (B).
*Pvalues < 0.05 for difference between fasting and non-fasting ICCs
ICCs<0.5 were seen in 71% of acylcarnitines, 48% of amino acids, 44% of biogenic amines,
36% of SMs, 34% of PCs and 33% of lysoPCs compounds.
In both fasting and non-fasting samples, weak reliability over time was observed in some
amino acids, with values ranging from 0.11 to 0.46 for phenylalanine, glutamine, glutamic acid,
valine, lysine, histidine, serine, leucine and isoleucine, and a few acylcarnitines compounds,
with values lower than 0.46 for C3_Dc_C4_Oh, C5, C3, C16 and C18:1.
Comparison of reliability of fasting vs non-fasting samples
Overall, reliability over time was lower when analyzing non-fasting samples (median ICC:
0.54 with 5th -95th percentile values ranging from 0.26 to 0.75) than fasting samples (0.70;
0.37–0.86). This was particularly apparent for acylcarnitines, PCs and SMs (ICC is lower in
non-fasting samples for the majority of them). Reliability of amino acids, biogenic amines,
hexoses and lysoPC was overall not different according to fasting status (Figs 1 & 2). ICCs were
significantly lower in non-fasting vs. fasting samples in 36% of SMs, 19% of acylcarnitines, 20%
of PCs, 17% of biogenic amines, and 0.05% of amino acids (S1 Table).
Most of the 140 investigated metabolites showed good reliability over time in serum samples
collected on average 2 years apart in participants from the EPIC study. Reliability over time
was overall good for non-fasting samples, with a median ICC value equal to 0.54, but
metabolites analyzed from fasting samples generally exhibited larger reproducibility (ICCmedian equal
to 0.70). Although reproducibility of amino acids, biogenic amines, hexoses and lysoPC did not
generally differ according to fasting status, acylcarnitines, PCs and SMs were generally less
stable over time for non-fasting compared to fasting samples. It may be noticed that weak
reliability over time was observed for some amino acids and acylcarnitines compounds for both
fasting and non-fasting samples.
Previous studies have already investigated the reproducibility of endogenous metabolites
using the same assay as we used (Biocrates kit) over short time periods in fasting samples
[11,12,15]. In 22 healthy German volunteers who donated 3 fasting samples each day, Breier
et al. found good reproducibility of most metabolites over a short time period of 3 consecutive
days (ICCmedian equal to 0.66), except for long chain and unsaturated acylcarnitines . In
100 EPIC German subjects who gave 2 fasting samples 4 months apart, Floegel et al. reported
an overall median ICC of 0.57, with median ICC estimates equal to 0.58 for amino acids, 0.58
for PCs, 0.66 for SMs, 0.76 for hexoses and 0.45 for acylcarnitines . In line with previous
findings , poor reproducibility in hydroxy- (0.11 ICCs 0.45) and monounsaturated
acylcarnitines (0.09 ICCs 0.63) was mainly attributed to low concentrations [11,12]. In
the study by Yu et al. involving 83 repeated fasting samples, most of hydroxy- and unsaturated
acylcarnitines were excluded from the analyses because of low concentrations or poor
reliability . Among those analytically validated with concentrations higher than LOQ/LOD (16
out of 55), the present study exhibited good reliability of acylcarnitines with a median ICC of
0.62 in fasting samples and good reliability of monounsaturated acylcarnitines (0.46 ICCs
0.75). Reproducibility of these 16 acylcarnitine compounds was particularly affected by fasting
status, with a median ICC dropping to 0.34 in non-fasting samples. As acylcarnitines occur in
the process of fatty acid translocation into the inner mitochondrial membrane for β-oxidation,
blood acylcarnitine concentrations reflect the substrate flux through β-oxidation, and then
could be particularly affected by the composition and the time since the last meal. The weak
reproducibility of phenylalanine, glutamine, glutamic acid, valine, lysine, histidine, serine,
leucine and isoleucine amino acids in our study did not confirm previous findings. Amino acids
measured in fasting samples generally showed good reproducibility, ICCs ranging from 0.41 to
0.84 in previous studies [11,12]. Amino acids reproducibility was not particularly influenced by
fasting status, possibly reflecting the tight genetic regulation of amino acids homeostasis .
The main limitation of the present study was the different center origin and gender of
fasting and non-fasting samples that led to the comparison of individuals with general
characteristics differences which may well have driven part of the difference in reliability. To partially
correct for differences in participants’ age, BMI and the time difference between sample
collections, a combined mixed model adjusted for these factors was employed. To our knowledge,
reproducibility studies on metabolomics profiling have not investigated the effects of gender or
country origin on reliability. Yet, the effect of gender on metabolites concentrations seems to
be limited as it was estimated to account for 7.3% of variability . The present study included
subjects from general population (blood donors and women participating to a breast cancer
screening) who were free of disease (diabetes, stroke, heart and overall cancer) at baseline.
However, there is the lack of information on health status of subjects at the second time point.
If participants had developed chronic conditions during the 2-year follow-up, the
within-person variability would increase, leading to underestimation of reliability over time. Only two
time points were available in the EPIC cohort for the assessment of metabolites reliability. To
study the influence of the number of time points on reliability estimates, Sampson et al.
compared ICCs of metabolites calculated from two time points one year apart to those estimated
from three time points four years apart , and the authors found similar distribution of
ICCs. As our study sample was limited in size, no adjustment for multiple testing was adopted
for the comparison of fasting and non-fasting ICC estimates. This strategy was chosen in order
to avoid non detection of differences between fasting and non-fasting ICC estimates, as the
number of metabolites whose reliability was differential with respect to fasting status would
have diminished drastically.
The main strength of our study was that we assessed reliability in a wide spectrum of
metabolites including different classes of compounds with a validated high-throughput technique
that can be applied to future metabolome studies. In particular, our results are likely to be
generalizable for the whole EPIC cohort where metabolome measurements have been undertaken.
Another strength is that our results showed overall good reliability over a 2-year interval which
represents a longer time interval compared to previous investigations involving the same
metabolites spectrum [11,12,15]. Finally, to our knowledge, this is the first study that compares
reliability in fasting and non-fasting samples for the metabolites measured in this assay
The present study showed good reproducibility for most of the Biocrates metabolites over a
2-year period, including PCs, SMs, biogenic amines, hexoses and most of the amino acids and
acylcarnitines in fasting samples from healthy men from the EPIC cohort. More variability
over time was observed in the 140 metabolites when measured in non-fasting samples. Our
results indicate that a single measurement could be sufficient for hexoses and most of the
amino acids, biogenic amines, SMs, and PCs in non-fasting samples, but not for most of the
acylcarnitines. Fasting samples are preferable to non-fasting.
S1 Table. Percentage of samples below the limit of detection (%<LOD), mean
concentrations and their 95% confidence intervals (CIs), between- (B) and within-subject (W)
variance components, intraclass correlation coefficients (ICC) of serum concentrations
Conceived and designed the experiments: SR AS PF TJK NCOM PV CS. Performed the
experiments: DA. Analyzed the data: PF IL MC. Wrote the paper: MC IL PF. Designed the statistical
plan: PF. Reviewed and provided substantial revision to the manuscript: SR AS TJK PV.
1. Dettmer K , Aronov PA , Hammock BD ( 2007 ) Mass spectrometry-based metabolomics . Mass Spectrom Rev 26 : 51 - 78 . PMID: 16921475
2. Dunn WB , Broadhurst DI , Atherton HJ , Goodacre R , Griffin JL ( 2011 ) Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy . Chem Soc Rev 40 : 387 - 426 . doi: 10.1039/b906712b PMID: 20717559
3. Mayers JR , Wu C , Clish CB , Kraft P , Torrence ME , et al. ( 2014 ) Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development . Nat Med 20 : 1193 - 1198 . doi: 10.1038/nm.3686 PMID: 25261994
4. Wang TJ , Larson MG , Vasan RS , Cheng S , Rhee EP , et al. ( 2011 ) Metabolite profiles and the risk of developing diabetes . Nat Med 17 : 448 - 453 . doi: 10.1038/nm.2307 PMID: 21423183
5. Wurtz P , Makinen VP , Soininen P , Kangas AJ , Tukiainen T , et al. ( 2012 ) Metabolic signatures of insulin resistance in 7,098 young adults . Diabetes 61 : 1372 - 1380 . doi: 10.2337/db11-1355 PMID: 22511205
6. Scalbert A , Brennan L , Fiehn O , Hankemeier T , Kristal BS , et al. ( 2009 ) Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research . Metabolomics 5: 435 - 458 . PMID: 20046865
7. Jenab M , Slimani N , Bictash M , Ferrari P , Bingham SA ( 2009 ) Biomarkers in nutritional epidemiology: applications, needs and new horizons . Hum Genet 125 : 507 - 525 . doi: 10.1007/s00439- 009 - 0662 - 5 PMID: 19357868
8. Rappaport SM , Kupper LL ( 2008 ). Quantitative exposure assessment . S. Rappaport, El Cerrito , Calif.
9. Fleiss JL ( 1999 ) Reliability of Measurement. The Design and Analysis of Clinical Experiments : John Wiley & Sons, Inc. pp. 1 - 32 .
10. Rosner B , Willett WC , Spiegelman D ( 1989 ) Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error . Stat Med 8 : 1051 - 1069 . PMID: 2799131
11. Breier M , Wahl S , Prehn C , Fugmann M , Ferrari U , et al. ( 2014 ) Targeted metabolomics identifies reliable and stable metabolites in human serum and plasma samples . PLoS One 9: e89728. doi: 10.1371/ journal.pone.0089728 PMID: 24586991
12. Floegel A , Drogan D , Wang-Sattler R , Prehn C , Illig T , et al. ( 2011 ) Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach . PLoS One 6: e21103. doi: 10.1371/journal.pone.0021103 PMID: 21698256
13. Nicholson G , Rantalainen M , Maher AD , Li JV , Malmodin D , et al. ( 2011 ) Human metabolic profiles are stably controlled by genetic and environmental variation . Mol Syst Biol 7 : 525 . doi: 10.1038/msb.2011. 57 PMID: 21878913
14. Sampson JN , Boca SM , Shu XO , Stolzenberg-Solomon RZ , Matthews CE , et al. ( 2013 ) Metabolomics in epidemiology: sources of variability in metabolite measurements and implications . Cancer Epidemiol Biomarkers Prev 22 : 631 - 640 . doi: 10.1158/ 1055 - 9965 . EPI-12-1109 PMID: 23396963
15. Yu Z , Kastenmuller G , He Y , Belcredi P , Moller G , et al. ( 2011 ) Differences between human plasma and serum metabolite profiles . PLoS One 6: e21230. doi: 10.1371/journal.pone.0021230 PMID: 21760889
16. Cross AJ , Moore SC , Boca S , Huang WY , Xiong X , et al. ( 2014 ) A prospective study of serum metabolites and colorectal cancer risk . Cancer 120 : 3049 - 3057 . doi: 10.1002/cncr.28799 PMID: 24894841
17. Moore SC , Matthews CE , Sampson JN , Stolzenberg-Solomon RZ , Zheng W , et al. ( 2014 ) Human metabolic correlates of body mass index . Metabolomics 10 : 259 - 269 . PMID: 25254000
18. Medina S , Domínguez-Perles R , Ferreres F , Tomás-Barberán FA , Gil-Izquierdo Á ( 2013 ) The effects of the intake of plant foods on the human metabolome . TrAC Trends in Analytical Chemistry 52 : 88 - 99 .
19. Riboli E , Hunt KJ , Slimani N , Ferrari P , Norat T , et al. ( 2002 ) European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection . Public Health Nutr 5 : 1113 - 1124 . PMID: 12639222
20. Leenders M , Ros MM , Sluijs I , Boshuizen HC , van Gils CH , et al. ( 2013 ) Reliability of selected antioxidants and compounds involved in one-carbon metabolism in two Dutch cohorts . Nutr Cancer 65 : 17 - 24 . doi: 10.1080/01635581.2013.741754 PMID: 23368909
21. Palli D , Berrino F , Vineis P , Tumino R , Panico S , et al. ( 2003 ) A molecular epidemiology project on diet and cancer: the EPIC-Italy Prospective Study. Design and baseline characteristics of participants . Tumori 89 : 586 - 593 . PMID: 14870823
22. Haftenberger M , Lahmann PH , Panico S , Gonzalez CA , Seidell JC , et al. ( 2002 ) Overweight, obesity and fat distribution in 50- to 64-year-old participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) . Public Health Nutr 5 : 1147 - 1162 . PMID: 12639224
23. Cust AE , Smith BJ , Chau J , van der Ploeg HP , Friedenreich CM , et al. ( 2008 ) Validity and repeatability of the EPIC physical activity questionnaire: a validation study using accelerometers as an objective measure . International Journal of Behavioral Nutrition and Physical Activity 5: 33. doi: 10.1186/1479- 5868-5-33 PMID: 18513450
24. Dunn G ( 1989 ) Design and analysis of reliability studies: The statistical evaluation of measurement errors: Edward Arnold Publishers .
25. Efron B , Tibshirani RJ ( 1994 ) An introduction to the bootstrap : CRC press.
26. McBride KL , Belmont JW , O'Brien WE , Amin TJ , Carter S , et al. ( 2007 ) Heritability of plasma amino acid levels in different nutritional states . Mol Genet Metab 90 : 217 - 220 . PMID: 17005426