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SUPPLEMENTAL MATERIAL

Kujala UM, Mäkinen V-P, Heinonen I, et al. Long-term leisure-time physical activity and

serum metabolome.

List of online-only elements

Supplemental Methods

Supplemental Methods 1. Serum NMR metabolomics

Supplemental Methods 2. Statistical significance of metabolic profiles

Supplemental Methods 3. Network visualization

Supplemental Tables

Supplemental Table 1. List of analyzed variables (NMR metabolomics platform)

Supplemental Table 2. Definition of composite metabolic measures for Figure 3A in the main

manuscript

Supplemental Table 3. Spearman correlations between serum metabolome measures and the

mean leisure time MET-index during the follow-up from 1980 to 2005 among the twins

Supplemental Figures

Supplemental Figure 1. Illustration of the hypothesis testing framework for global

metabolome significance in the twin study design (hypothetical data)

Supplemental Figure 2. Spearman correlation structure of the twin dataset, adjusted for age

and gender

Supplemental Figure 3. Illustration of network pruning by successive maximal spanning trees

(hypothetical data)

Supplemental References

2

Supplemental Methods

Supplemental Methods 1. Serum NMR metabolomics

The comprehensive metabolite data analyzed in this study were quantified using a single

high-throughput NMR metabolomics platform employing an optimized measurement and

analysis protocols for serum samples.1-3

This platform has recently been applied in various

large-scale epidemiological and genetic studies.4-11

The NMR metabolomics methodology

provides quantitative information on over 100 primary metabolite measures comprising

lipoprotein subclass distributions with 14 subclasses quantified as well as low-molecular-

weight metabolites such as amino acids and ketone bodies, and detailed molecular

information on serum lipid extracts including lipid species concentrations and the degree of

fatty acid saturation. The complete list of metabolite measures is listed in Supplementary

Table 1.

The fasting state serum samples were stored in a freezer at -80 °C. The frozen samples were

first slowly thawed in a refrigerator (+ 4 °C) overnight prior to measurements. Aliquots of

each sample (300 µl) were mixed with 300 µl of sodium phosphate buffer using a robotic

Gilson Liquid Handler. The NMR-based metabolite quantification is achieved through

measurements of three molecular windows from each serum sample. Two of the spectra

(LIPO and LMWM windows) are acquired from native serum and one spectrum from serum

lipid extracts (LIPID window). The NMR spectra were measured using a Bruker AVANCE III

spectrometer operating at 500 MHz.1,2

Measurements of native serum samples and serum

lipid extracts were conducted at 37°C and at 22°C, respectively.

The LIPO window represents a standard spectrum of human serum displaying broad

overlapping resonances arising from lipid molecules in various lipoprotein particles. The

LIPO data were recorded using 8 transients acquired using a NOESY-presat pulse sequence

with mixing time of 10 ms and water peak suppression. Acquisition time was 2.7 s and the

relaxation delay 3.0 s. The LMWM window includes signals from various low-molecular-

weight molecules. The LMWM spectrum was recorded using a relaxation-filtered pulse

sequence that suppress most of the broad macromolecule and lipid signals to enhance

detection of small solutes. Specifically, a CPMG pulse sequence with a 78 ms T2-filter and

fixed echo delay of 403 µs was applied using 24 transients. Acquisition time was 3.3 s and

the relaxation delay 3.0 s.

Extraction of lipid species from native serum samples was carried out by adding 5 ml of

methanol, 10 ml dichloromethane and 15 ml 0.15 M sodium chloride solution to the serum

samples including buffer.2,4

The organic phase was recovered and evaporated to dryness with

pressurized air. The LIPID window of the serum extracts was acquired with a standard 1D

spectrum using 32 transients. A relaxation delay of 3.0 s and an acquisition time of 3.3 s were

used.

The NMR spectral data were analyzed for absolute metabolite quantification in an automated

fashion. For each metabolite a ridge regression model was applied for quantification in order

to overcome the problems of heavily overlapping spectral data. In the case of the lipoprotein

lipid data, quantification models have been calibrated using high performance liquid

chromatography methods12

and individually cross-validated against NMR-independent lipid

3

data. The 14 lipoprotein subclasses were as follows: chylomicrons and extremely large VLDL

particles (particle diameters from 75 nm upwards), very large VLDL (average particle

diameter 64.0 nm), large VLDL (53.6 nm), medium-size VLDL (44.5 nm), small VLDL (36.8

nm), and very small VLDL (31.3 nm); intermediate-density lipoprotein (IDL) (28.6 nm);

large LDL (25.5 nm), medium-size LDL (23.0 nm), and small LDL (18.7 nm); very large

HDL (14.3 nm), large HDL (12.1 nm), medium-size HDL (10.9 nm), and small HDL (8.7

nm). In addition, mean particle diameters of VLDL, LDL and HDL fractions were calculated

on the basis of the corresponding subclass distributions. Low-molecular-weight metabolites

as well as lipid extract measures were quantified in mmol/L based on regression modeling

calibrated against a set of manually fitted metabolite measures. The calibration data was

quantified based on iterative lineshape fitting analyses using PERCH NMR software

(PERCH Solutions Ltd., Kuopio, Finland). Absolute quantification cannot be directly

established for the lipid extract measures due to experimental variation in the lipid extraction

protocol. Therefore, serum extract metabolites are scaled via the total cholesterol as

quantified also from the native serum LIPO spectrum.

Supplemental Methods 2: Statistical significance of metabolic profiles

The large number of metabolites and small number of twin pairs presents a challenge for

statistical analyses. In particular, there is low power to detect clinically meaningful univariate

differences beyond a multiple testing threshold. We therefore reduced the global metabolic

variance into one summary measure, and performed a single test with this measure. Principal

component analysis and other linear decomposition methods are popular means to reduce

data dimensionality, but the paired study design and low number of samples make it difficult

to create a robust multivariate model. Instead, we developed a simpler mathematical approach

to reduce the data to one dimension.

Supplemental Figure 1 is an illustration of the “global metabolism test” we devised for the

twin dataset. Our goal was to determine whether the overall metabolic profile differed

significantly between active and inactive twins. We also wanted to integrate data from the

population cohorts to see if the metabolic differences were consistent across datasets.

Definition and testing of the null hypothesis

Our null hypothesis was that an active twin's metabolic profile and the inactive co-twin's

profile were not different. The twins were selected based on their physical activity records

(not randomly picked from a population), which could lead to sampling bias. We therefore

required that the differences, if any exist, must also be consistent with independent data, or

the null hypothesis stands.

Independent data was obtained from two sources. In the twin set, we calculated the

correlation coefficient between leisure time physical activity and each metabolite, in the

active and inactive groups separately. The internal reference profile was defined as the mean

correlation over the two groups. Although the individuals are the same, the profile is

mathematically independent of the pair-wise differences. The external reference was

calculated by subtracting the mean concentrations in the inactive group from the mean of the

active group in the pooled population cohorts.

Both the magnitude and direction of metabolite differences are of importance. We looked for

a simple statistic that is positive when both the activity-related metabolic differences and

reference profile are in the same direction, and negative otherwise. We also wanted to

emphasize those metabolites that show large differences between active and inactive

4

individuals to prevent random jitter around zero from obscuring the directionality. Vector

multiplication fulfills both criteria: if x denotes the row vector of multivariate differences

between active and their inactive co-twins, and y is the reference, then s = xyT yields a

suitable test statistic.

Statistical significance was tested empirically by permutation analysis.14-16

First, activity

status was permuted within families to create all the possible 216

= 65532 combinations of

activity swaps (see Supplemental Figure 1 for a hypothetical example). The test statistic was

then re-calculated for each permuted set to obtain the null distribution. The final P-value was

estimated by a Gaussian approximation.

Statistical comparisons by bootstrapping

Due to the selected case-control study design, we chose bootstrapping as the main method of

statistical comparisons between active and inactive individuals.16

For the twin study, the

bootstrapping algorithm was applied to the pairs: random sampling with replacement was

applied to the 16 pairs of twins, and the mean difference between an active and inactive twin

was stored. For the population cohorts, the same bootstrapping algorithm was applied for the

age and sex-matched active-inactive pairs. A total of 10000 bootstrap samples were created

for each dataset.

Meta-analysis was performed after the bootstrapping statistics were calculated for each

cohort. First, the initial means and variances of the metabolite differences were calculated

from the bootstrap samples for each cohort separately. The final meta-analyzed mean

difference was the inverse-variance weighted average of the cohort-specific mean differences.

All variables in the bootstrap method were screened for skewed distributions and log-

transformed if absolute skewness exceeded 1. Ratios were not treated differently from the

absolute concentrations since the logarithmic preprocessing successfully converted all

affected variables into well-contained distributions. The preprocessed values were divided by

their respective standard deviation before calculating the pair differences to eliminate scale

effects. The bootstrap sample distributions were also checked if they were incompatible with

the Gaussian approximation, but all were symmetric with well-contained tails.

Multiple testing correction was performed by estimating the minimum number of orthogonal

linear components (PCA) that explained 99% of the observed variance in a dataset. The

highest number (26 components) was observed in the NFBC66 cohort, which was also the

largest cohort by number of individuals, and we used that as a consistent conservative

estimate in all multiple testing corrections using Bonferroni method.

Supplemental Methods 3: Network visualization

A large number of variables is a challenge not only in statistical modeling, but also in the

illustration of results. Data-driven network approaches may provide important insight into the

statistical relationships between gene expression, intermediate metabolites and clinical

phenotypes. We calculated the pruned correlation structure of the dataset to visualize

associations between the observable quantities and hence give insights into the underlying

biological pathways.

Grouping related variables into composite traits

Before the network analysis, we condensed the metabolomics dataset to ensure legibility of

the final figure (the full correlation matrix is shown in Supplemental Figure 2). We grouped

5

the age and gender adjusted variables into blocks according to knowledge of biological or

physical relatedness, and then calculated the first principal component for each block; this

provided us with a single continuous score for each metabolite block. Together with selected

clinical features and gene expression data, we ended up with 24 variables. This limited set of

variables was considered the nodes of a network, and the connections between the nodes

were defined by the statistical associations between the corresponding variables. The

composite measures (blocks) and relative contributions of block members are listed in

Supplemental Table 2.

Pruning of the network topology

Next we calculated the Spearman correlations (i.e. the link weights) between all possible

pairs of the preprocessed variables (i.e. the nodes). Drawing all the resulting 276 correlations

leads to a complicated image that is difficult to interpret visually, so some of them had to be

discarded. The obvious way to do this would be to define a threshold of statistical

significance, but this type of approach will typically lead to a break-up of the network, and is

not able to highlight important structural features such as weak links connecting densely

interconnected modules. Hence we devised a method based on the spanning tree, which is

formally defined as an acyclic fully connected network.17

In other words, we find a set of

links that connects every node to the rest of the network, but without causing a loop within

the set. If branches are seen as links, then a tree is the natural example of such a topology.

A dense graph such as the correlation network may contain a large number of formally

acceptable spanning trees due to the multiple possible ways of selecting the link set.

However, we further select the spanning tree with the largest sum of link weights to extract

the strongest statistical evidence. But one tree alone is usually not so informative, so we

repeat the process for the remainder (Supplemental Figure 3). Consequently, the algorithm

tends to choose the strongest links, while maintaining the integrity of the original network,

and the resulting figure can be directly used for inferring the relevant correlation patterns in

the dataset. The correlations were calculated by standard functions in Octave environment

and the network analysis was performed with the Himmeli18

software package, code version

2011-09-03.

Technical details of the network method:

1. Adjust dataset for age and gender.

2. Condense the full dataset into composite scores as described in the previous section.

3. Calculate Spearman correlation coefficients for all pairs of scores. This produces a set of

link weights S0 that connects each composite score (the node in the network) with every other

one.

4. Determine the smallest set of links T1 within S0 that is enough to connect every node to the

network (spanning tree) with the largest sum of correlation magnitudes by the Kruskal

algorithm.

5. Remove set T1 from S0 to produce a new set S1 .

6. Repeat step 4 for set S1 to produce set T2.

7. Put sets T1 and T2 together for the final pruned set T.

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Supplemental Tables

Supplemental Table 1. List of analyzed variables (NMR metabolomics platform)

Albumin, lipoprotein subclasses and derived measures (‘LIPO window’)

Albumin

Phospholipids in chylomicrons and extremely large VLDL

Total lipids in chylomicrons and extremely large VLDL

Concentration of chylomicrons and extremely large VLDL particles

Phospholipids in very large VLDL

Triglycerides in very large VLDL

Total lipids in very large VLDL

Concentration of very large VLDL particles

Total cholesterol in large VLDL

Free cholesterol in large VLDL

Phospholipids in large VLDL

Triglycerides in large VLDL

Cholesterol esters in large VLDL

Total lipids in large VLDL

Concentration of large VLDL particles

Total cholesterol in medium VLDL

Free cholesterol in medium VLDL

Phospholipids in medium VLDL

Triglycerides in medium VLDL

Cholesterol esters in medium VLDL

Total lipids in medium VLDL

Concentration of medium VLDL particles

Total cholesterol in small VLDL

Free cholesterol in small VLDL

Phospholipids in small VLDL

Triglycerides in small VLDL

Total lipids in small VLDL

Concentration of small VLDL particles

Phospholipids in very small VLDL

Triglycerides in very small VLDL

Total lipids in very small VLDL

Concentration of very small VLDL particles

Free cholesterol in IDL

Phospholipids in IDL

Total lipids in IDL

Concentration of IDL particles

Total cholesterol in large LDL

7

Free cholesterol in large LDL

Phospholipids in large LDL

Cholesterol esters in large LDL

Total lipids in large LDL

Concentration of large LDL particles

Total cholesterol in medium LDL

Phospholipids in medium LDL

Cholesterol esters in medium LDL

Total lipids in medium LDL

Concentration of medium LDL particles

Total cholesterol in small LDL

Total lipids in small LDL

Concentration of small LDL particles

Total cholesterol in very large HDL

Free cholesterol in very large HDL

Phospholipids in very large HDL

Triglycerides in very large HDL

Cholesterol esters in very large HDL

Total lipids in very large HDL

Concentration of very large HDL particles

Total cholesterol in large HDL

Free cholesterol in large HDL

Phospholipids in large HDL

Cholesterol esters in large HDL

Total lipids in large HDL

Concentration of large HDL particles

Total cholesterol in medium HDL

Free cholesterol in medium HDL

Phospholipids in medium HDL

Cholesterol esters in medium HDL

Total lipids in medium HDL

Concentration of medium HDL particles

Triglycerides in small HDL

Total lipids in small HDL

Concentration of small HDL particles

Triglycerides in chylomicrons and extremely large VLDL

Triglycerides in VLDL

Triglycerides in IDL

Total cholesterol in IDL

Total cholesterol in LDL

Total cholesterol in HDL

Serum total triglycerides

Serum total cholesterol

8

Mean diameter for VLDL particles

Mean diameter for LDL particles

Mean diameter for HDL particles

Total cholesterol in HDL2*

Apolipoprotein A-I*

Apolipoprotein B*

Apolipoprotein B by apolipoprotein A-I*

Total cholesterol in HDL3*

LDL cholesterol (Friedewald)

HDL cholesterol by LDL cholesterol

Serum lipid extracts (‘Lipid window’)

Esterified cholesterol

Free cholesterol

Omega-3 fatty acids

Omega-6 fatty acids

Omega-7, omega-9 and saturated fatty acids

Total fatty acids

Linoleic acid

Other polyunsaturated fatty acids than linoleic acid

Docosahexaenoic acid

Monounsaturated fatty acids

Total phosphoglycerides

Phosphatidylcholine and other cholines

Sphingomyelins

Ratio of omega-3 fatty acids to total fatty acids

Ratio of omega-6 fatty acids to total fatty acids

Ratio of omega-7, omega-9 and saturated fatty acids to total fatty acids

Average number of methylene groups per fatty acid chain

Ratio of triglycerides to phosphoglycerides

Average number of methylene groups per double bond

Average number of double bonds per fatty acid chain

Ratio of bisallylic groups to double bonds

Ratio of bisallylic groups to total fatty acids

Average fatty acid chain length

Amino acids and other low-molecular-weight metabolites (serum) (‘LWMW

window’)

3-hydroxybutyrate

Acetate

Acetoacetate

Alanine

CH2 groups of mobile lipids

CH3 groups of mobile lipids

Citrate

9

Creatinine

Glucose

Glutamine

Glycerol

Alpha1-acid glycoprotein

Histidine

Isoleucine

Lactate

Leucine

Phenylalanine

Pyruvate

Tyrosine

Urea

Valine

*For computational method see Niemi et al.13

10

Supplemental Table 2. Definition and fit of composite metabolic measures for Figure 3A in

the main manuscript. The NMR metabolomics produces a number of biologically overlapping

measures from each lipoprotein subclass and lipid species. The goal of the network analysis

was to simplify the data representation, and we therefore limited our selection to the two

primary lipoprotein lipids (triglycerides and cholesterol) and excluded all derived measures

or fatty acid ratios.

Composite

measure

Block member Variance

captured

by block

score

Composite

measure

Block member Variance

captured

by block

score

VLDL

subclass

lipids

Largest VLDL-TG 70% Omega-6 FA Omega-6 FA 95%

Very large VLDL-

TG

81% Linoleic acid 95%

Large VLDL-TG 93% Monounsat.

and other FA

Omega-7,9 and

saturated FA

98%

Medium VLDL-TG 96% Monounsaturated FA 98%

Small VLDL-TG 92% Glycemia Glucose 53%

Very small VLDL-

TG

82% Hemoglobin A1c 15%

Large VLDL-C 97% Acetoacetate 25%

Medium VLDL-C 96% 3-hydroxybutyrate 50%

Small VLDL-C 77% Substrates Acetate 13%

IDL and

LDL

subclass

lipids

IDL-TG 57% Citrate 49%

IDL-C 95% Lactate 74%

Large LDL-C 97% Pyruvate 49%

Medium LDL-C 97% Branched-

chain amino

acids

Isoleucine 78%

Small LDL-C 95% Leucine 76%

HDL

subclass

lipids

Very large HDL-

TG

21% Valine 61%

Small HDL-TG 64% Other amino

acids

Alanine 29%

Very large HDL-C 66% Glutamine <1%

Large HDL-C 88% Histidine 66%

Medium HDL-C 29% Phenylalanine 73%

Omega-3

and

polyunsat.

FA

Non-18:2 PUFA 81% Tyrosine 79%

Omega-3 FA 93%

Docosahexaenoic

acid

85%

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Supplemental Table 3. Age- and sex-adjusted Spearman correlations between the key serum

metabolome measures (see Figure 1) and the mean leisure time MET-index during the

follow-up from 1980 to 2005 among the inactive and active members of the twin pairs and

among all the twins.

Correlations with the mean MET-index during the

follow-up

Inactive (N=16) Active (N=16) All (N=32)

r r r

Lipoprotein particle concentration

Extremely large VLDL -0.23 0.06 -036*

Very large VLDL -0.20 0.08 -0.32

Large VLDL -0.16 -0.10 -0.32

Medium VLDL -0.13 0.01 -0.29

Small VLDL -0.08 -0.03 -0.17

Very small VLDL 0.09 -0.10 -0.16

IDL -0.08 -0.20 -0.25

Large LDL -0.17 -0.26 -0.32

Medium LDL -0.21 -0.21 -0.33*

Small LDL -0.27 -0.19 -0.34*

Very large HDL -0.18 0.42 0.35*

Large HDL 0.04 0.45 0.39*

Medium HDL 0.32 0.46 0.20

Small HDL 0.34 0.50* 0.07

Lipoprotein particle size

VLDL diameter -0.19 0.09 -0.26

LDL diameter 0.47 -0.27 0.08

HDL diameter -0.03 0.27 0.35*

Apolipoproteins

ApoA1 -0.25 0.53* 0.16

ApoB -0.17 -0.16 -0.26

ApoB / ApoA1 ratio -0.17 -0.27 -0.30

Triglycerides

Total triglycerides -0.09 -0.10 -0.29

Extremely large VLDL-TG -0.30 -0.08 -0.37*

Total VLDL-TG -0.12 -0.08 -0.31

IDL-TG 0.01 -0.13 -0.23

Cholesterol

Total cholesterol -0.18 -0.12 -0.24

IDL-C -0.13 -0.25 -0.27

LDLC -0.16 -0.25 -0.32

Large HDL-C -0.02 0.51* 0.45**

HDL2-C 0.09 0.62* 0.39*

HDL3-C 0.07 0.06 -0.18

HDL-C/LDL-C ratio 0.24 0.25 0.33*

Fatty acids

Omega-3 FA 0.50* 0.59* 0.48**

Omega-3 FA ratio 0.56* 0.41 0.57***

Docosahexaenoic acid 0.66** 0.28 0.50**

Non-18:2 PUFA 0.37 0.39 0.33*

12

Stars indicate statistical significance: *P<0.05, **p<0.01, ***p<0.001.

Omega-6 FA -0.11 0.12 -0.19

Omega-6 FA ratio -0.06 -0.05 0.21

Linoleic acid -0.18 -0.04 -0.25

Omega-7,9 and sat. FA 0.00 0.07 -0.16

Omega-7,9 and sat. FA ratio -0.20 -0.04 -0.31

Monounsaturated FA -0.05 0.04 -0.22

Metabolic substrates

Glucose -0.25 0.19 -0.27

Acetoacetate 0.39 -0.21 -0.10

3-hydroxybutyrate 0.11 0.17 -0.40*

Acetate 0.16 0.04 0.24

Citrate 0.16 -0.43 0.04

Lactate 0.54* 0.03 0.31

Pyruvate 0.47 0.19 0.14

Amino acids

Isoleucine -0.20 -0.24 -0.31

Leucine -0.31 0.02 -0.27

Valine 0.01 -0.24 -0.33

Alanine 0.22 -0.15 0.07

Glutamine -0.43 -0.12 -0.11

Histidine 0.08 0.15 -0.11

Phenylalanine 0.43 -0.30 -0.06

Tyrosine 0.44 -0.21 -0.12

Miscellaneous

α1-acid glycoprotein -0.09 -0.30 -0.32

Creatinine 0.17 -0.24 0.09

Urea 0.00 -0.16 -0.26

Albumin -0.22 0.36 0.18

13

Supplemental Figures

Supplemental Figure 1. Illustration of the hypothesis testing framework for

global metabolome significance in the twin study design (hypothetical data)

14

Supplemental Figure 2. Spearman correlation structure of the twin dataset,

adjusted for age and sex.

The colored pixels denote coefficients with a single test significance of P<0.05.

15

Supplemental Figure 3. Illustration of network pruning by successive

maximal spanning trees (hypothetical data)

16

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