PLOS ONE
 
Dose response of running on blood biomarkers of wellness in the generally healthy
--Manuscript Draft--
 
Manuscript Number:
PONE-D-23-25168
Article Type:
Research Article
Full Title:
Dose response of running on blood biomarkers of wellness in the generally healthy
Short Title:
Biomarker signature of runners
Corresponding Author:
Bartosz Nogal
InsideTracker
Cambridge, MA UNITED STATES
Keywords:
physical activity, exercise, blood biomarkers, running, generally healthy, mendelian
randomization
Abstract:
Exercise is effective toward delaying or preventing chronic disease, with a large body
of evidence supporting its effectiveness.  However, less is known about the specific
healthspan-promoting effects of exercise on blood biomarkers in the disease-free
population.  In this work, we examine 23,237 generally healthy individuals who self-
report varying weekly running volumes and compare them to 4,428 generally healthy
sedentary individuals, as well as 82 professional endurance athletes.  We estimate the
significance of differences among blood biomarkers for groups of increasing running
levels using analysis of variance (ANOVA), adjusting for age, gender, and BMI.  We
attempt and add insight to our observational dataset analysis via two-sample
Mendelian randomization (2S-MR) using large independent datasets.   We find that
self-reported running volume associates with biomarker signatures of improved
wellness, with some serum markers apparently being principally modified by BMI,
whereas others show a dose-effect with respect to running volume. We further detect
hints of sexually dimorphic serum responses in oxygen transport and hormonal traits,
and we also observe a tendency toward pronounced modifications in magnesium
status in professional endurance athletes.   Thus, our results further characterize blood
biomarkers of exercise and metabolic health, particularly regarding dose-effect
relationships, and better inform personalized advice for training and performance.
Order of Authors:
Bartosz Nogal
Svetlana Vinogradova
Gil Blander
Milena Jorge
Paul Fabian
Ali Torkamani
Additional Information:
Question
Response
Financial Disclosure
Enter a financial disclosure statement that
describes the sources of funding for the
work included in this submission. Review
the submission guidelines for detailed
requirements. View published research
articles from PLOS ONE for specific
examples.
This statement is required for submission
and will appear in the published article if
InsideTracker was the sole funding source.
Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation
the submission is accepted. Please make
sure it is accurate.
Unfunded studies
Enter: The author(s) received no specific
funding for this work.
Funded studies
Enter a statement with the following details:
Initials of the authors who received each
award
•
Grant numbers awarded to each author
•
The full name of each funder
•
URL of each funder website
•
Did the sponsors or funders play any role in
the study design, data collection and
analysis, decision to publish, or preparation
of the manuscript?
•
NO - Include this sentence at the end of
your statement: The funders had no role in
study design, data collection and analysis,
decision to publish, or preparation of the
manuscript.
•
YES - Specify the role(s) played.
•
* typeset
Competing Interests
Use the instructions below to enter a
competing interest statement for this
submission. On behalf of all authors,
disclose any competing interests that
could be perceived to bias this
work—acknowledging all financial support
and any other relevant financial or non-
financial competing interests.
This statement is required for submission
and will appear in the published article if
the submission is accepted. Please make
sure it is accurate and that any funding
sources listed in your Funding Information
later in the submission form are also
declared in your Financial Disclosure
statement.
View published research articles from
PLOS ONE for specific examples.
B.N., S.V., P.F., and G.B. are employees of InsideTracker.
Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation
NO authors have competing interests
Enter: The authors have declared that no
competing interests exist.
Authors with competing interests
Enter competing interest details beginning
with this statement:
I have read the journal's policy and the
authors of this manuscript have the following
competing interests: [insert competing
interests here]
* typeset
Ethics Statement
Enter an ethics statement for this
submission. This statement is required if
the study involved:
Human participants
•
Human specimens or tissue
•
Vertebrate animals or cephalopods
•
Vertebrate embryos or tissues
•
Field research
•
Write "N/A" if the submission does not
require an ethics statement.
General guidance is provided below.
Consult the submission guidelines for
detailed instructions. Make sure that all
information entered here is included in the
Methods section of the manuscript.
BRANY IRB File # 22-12-501-1095
BRANY IRB has determined this research is
exempt from IRB review under category(ies) # (4)(ii), as detailed in 45 CFR 46.104(d)
and BRANY’s
Standard Operating Procedures (category excerpted below).
(4) Secondary research for which consent is not required: Secondary research uses of
identifiable
private information or identifiable biospecimens, with the following criterion met:
(ii) Information, which may include information about biospecimens, is recorded by the
investigator in such a manner that the identity of the human subjects cannot readily be
ascertained directly or through identifiers linked to the subjects, the investigator does
not
contact the subjects, and the investigator will not re-identify subjects
Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation
Format for specific study types
Human Subject Research (involving human
participants and/or tissue)
Give the name of the institutional review
board or ethics committee that approved the
study
•
Include the approval number and/or a
statement indicating approval of this
research
•
Indicate the form of consent obtained
(written/oral) or the reason that consent was
not obtained (e.g. the data were analyzed
anonymously)
•
Animal Research (involving vertebrate
animals, embryos or tissues)
Provide the name of the Institutional Animal
Care and Use Committee (IACUC) or other
relevant ethics board that reviewed the
study protocol, and indicate whether they
approved this research or granted a formal
waiver of ethical approval
•
Include an approval number if one was
obtained
•
If the study involved non-human primates,
add additional details about animal welfare
and steps taken to ameliorate suffering
•
If anesthesia, euthanasia, or any kind of
animal sacrifice is part of the study, include
briefly which substances and/or methods
were applied
•
Field Research
Include the following details if this study
involves the collection of plant, animal, or
other materials from a natural setting:
Field permit number
•
Name of the institution or relevant body that
granted permission
•
Data Availability
Authors are required to make all data
underlying the findings described fully
available, without restriction, and from the
time of publication. PLOS allows rare
exceptions to address legal and ethical
concerns. See the PLOS Data Policy and
FAQ for detailed information.
No - some restrictions will apply
Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation
A Data Availability Statement describing
where the data can be found is required at
submission. Your answers to this question
constitute the Data Availability Statement
and will be published in the article, if
accepted.
Important: Stating ‘data available on request
from the author’ is not sufficient. If your data
are only available upon request, select ‘No’ for
the first question and explain your exceptional
situation in the text box.
Do the authors confirm that all data
underlying the findings described in their
manuscript are fully available without
restriction?
Describe where the data may be found in
full sentences. If you are copying our
sample text, replace any instances of XXX
with the appropriate details.
If the data are held or will be held in a
public repository, include URLs,
accession numbers or DOIs. If this
information will only be available after
acceptance, indicate this by ticking the
box below. For example: All XXX files
are available from the XXX database
(accession number(s) XXX, XXX.).
•
If the data are all contained within the
manuscript and/or Supporting
Information files, enter the following:
All relevant data are within the
manuscript and its Supporting
Information files.
•
If neither of these applies but you are
able to provide details of access
elsewhere, with or without limitations,
please do so. For example:
Data cannot be shared publicly because
of [XXX]. Data are available from the
XXX Institutional Data Access / Ethics
Committee (contact via XXX) for
researchers who meet the criteria for
access to confidential data.
The data underlying the results
presented in the study are available
from (include the name of the third party
•
All relevant data are within the manuscript and its Supporting Information files.
Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation
and contact information or URL).
This text is appropriate if the data are
owned by a third party and authors do
not have permission to share the data.
•
* typeset
Additional data availability information:
Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation
B Nogal 
 
1 
1 
Dose response of running on blood biomarkers of wellness in the 
generally healthy 
Bartek Nogal PhD1ﾅ, Svetlana Vinogradova PhD1ﾅ, Milena Jorge MD,PhD1, Ali Torkamani 
PhD 2,3, Paul Fabian BSc1, and  Gil Blander PhD1* 
1InsideTracker, Cambridge, Massachusetts, United States of America. 
2The Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA, USA.  
3Department of Integrative Structural and Computational Biology, The Scripps Research 
Institute, La Jolla, CA, USA. 
ﾅEqual Contribution:  These authors contributed equally 
* Correspondence and reprint requests:   Gil Blander, gblander@insdietracker.com 
Bartek Nogal 
bnogal@insidetracker.com 
Svetlana Vinogradova 
svinogradova@insidetracker.com 
Ali Torkamani 
atorkama@scripps.edu 
Paul Fabian 
pfabian@insdietracker.com 
Running title:  Biomarker signature of runners 
Main Text
Click here to
access/download;Manuscript;Runners_PONE_v1.docx
B Nogal 2 
Abstract 
Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence 
supporting its effectiveness.  However, less is known about the specific healthspan-promoting 
effects of exercise on blood biomarkers in the disease-free population.  In this work, we examine 
23,237 generally healthy individuals who self-report varying weekly running volumes and 
compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional 
endurance athletes.  We estimate the significance of differences among blood biomarkers for 
groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, 
gender, and BMI.  We attempt and add insight to our observational dataset analysis via two-sample 
Mendelian randomization (2S-MR) using large independent datasets.   We find that self-reported 
running volume associates with biomarker signatures of improved wellness, with some serum 
markers apparently being principally modified by BMI, whereas others show a dose-effect with 
respect to running volume. We further detect hints of sexually dimorphic serum responses in 
oxygen transport and hormonal traits, and we also observe a tendency toward pronounced 
modifications in magnesium status in professional endurance athletes.   Thus, our results further 
characterize blood biomarkers of exercise and metabolic health, particularly regarding dose-effect 
relationships, and better inform personalized advice for training and performance. 
 
 
 
 
B Nogal 
 
3 
3 
Keywords:  physical activity, exercise, blood biomarkers, running, generally healthy, mendelian 
randomization 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
B Nogal 4 
1 
Introduction 
Physical inactivity is one of the leading modifiable behavioral causes of death in the US 1.  
Worldwide, physical inactivity is estimated to account for about 8.3% of premature mortality, an 
effect size that is on the same order as smoking and obesity 2.  At the same time, the potent health 
benefits of exercise have been proven time and time again, with results so consistent across a wide 
variety of chronic diseases that some posit it can be considered a medical intervention 3-5.  
However, since most investigators report the effects of exercise in either diseased populations or 
athletes 6,7, there exists a significant gap in knowledge as to the measurable effects of exercise in 
the generally healthy population who exercise for the purpose of improving their healthspan, which 
can be projected via established measures such as blood biomarkers 8-11.   
It is well established that routine laboratory biomarkers are validated proxies of the state of an 
individual’s overall metabolic health and other healthpan-related parameters 12.  A large body of 
evidence supports the effectiveness of exercise in modifying blood biomarkers toward disease 
mitigation in clinical cohorts as well as athletes, where the effect sizes may be larger 6,13.  Indeed, 
it’s been shown that more favorable changes in response to exercise training occur usually in those 
with more pronounced dyslipidemia  13.  In professional athletes, the sheer volume and/or intensity 
of physical activity may drive large effects in various hematological, lipid, immune, and endocrine 
variables 6. Our aim is to help fill the gap in understanding of the effects of exercise on blood 
biomarkers in the generally healthy, free-living population.  Toward this end, we endeavored to 
explore the effects of vigorous exercise such as running in apparently healthy, mostly non-athletic 
cohort to better understand the landscape of blood biomarker modifications expected in the 
individual who partakes in recreational physical activity for the purpose of maintaining good 
health. 
B Nogal 
 
5 
5 
For this purpose, we leveraged the InsideTracker dataset that includes information on self-reported 
exercise habits combined with blood biomarker and genomics data.  We have previously reported 
on the results of a longitudinal analysis on blood biomarker data from 1032 generally healthy 
individuals who used our automated, web-based personalized nutrition and lifestyle platform 14.  
For the purpose of this investigation, we focused on running as the exercise of choice as it is one 
of the most common (purposeful) physical activity modalities practiced globally by generally 
healthy individuals and would thus be relevant.  Moreover, since this was a cross-sectional study 
based on self-reported exercise habits, we attempted to increase our capacity to infer intervention 
effects, as well as tease out potential confounders, by performing 2S-MR in large independent 
cohorts. 
2 
Methods 
2.1 
Dataset 
We conducted an observational analysis of data from InsideTracker users.  InsideTracker is a 
direct-to-consumer (DTC) company established in 2009 that markets and sells InsideTracker 
(insidetracker.com), a personalized lifestyle recommendation platform. The platform provides 
serum biomarker and genomics testing, and performs integrative analysis of these datasets, 
combined with activity/sleep tracker data toward biomarker and healthspan optimization (of note, 
at the time of this analysis, we did not have sufficient users with activity/sleep tracker data to 
include this data stream in the current study).   New users were continuously added to the 
InsideTracker database from January 2011 to March 2022. 
 
B Nogal 6 
2.2 
Recruitment of participants 
Recruitment of participants aged between 18 and 65 and residing in North America was conducted 
through company marketing and outreach. Participants were subscribing members to the 
InsideTracker platform and provided informed consent to have their blood test data and self-
reported information used in an anonymized fashion for research purposes. Research was 
conducted according to guidelines for observational research in tissue samples from human 
subjects. Eligible participants completed a questionnaire that included age, ethnicity, sex, dietary 
preferences, physical activity, and exposure to sunlight. This study employed data from 23,237 
participants that met our analysis inclusion requirements, namely absence of any chronic disease 
as determined by questionnaire and metabolic blood biomarkers within normal clinical reference 
ranges. The platform is not a medical service and does not diagnose or treat medical conditions, 
so medical history and medication use were not collected.  The Institutional Review Board (IRB) 
determine this work was not subject to a review based on category 4 exemption (“secondary 
research” with de-identified subjects). 
2.3 
Biomarker collection and analysis 
Blood samples were collected and analyzed by Clinical Laboratory Improvement Amendments 
(CLIA)–approved, third-party clinical labs (primarily Quest Diagnostics and LabCorp). 
Participants were instructed to fast for 12 hours prior to the phlebotomy, with the exception of 
water consumption. Results from the blood analysis were then uploaded to the platform via 
electronic integration with the CLIA-approved lab. Participants chose a specific blood panel from 
7 possible offerings, each comprising some subset of the biomarkers available. Due to the variation 
in blood panels offered, the participant sample size per biomarker is not uniform. 
B Nogal 
 
7 
7 
2.3 
Biomarker dataset preparation 
In our raw dataset, occasional outlier values were observed that were deemed implausible (e.g. 
fasting glucose < 65 mg/dL). To remove anomalous outliers in a systematic way, we used the 
Interquartile Range (IQR) method of identifying outliers, removing data points which fell below 
Q1 – 1.5 IQR or above Q3 + 1.5 IQR. 
2.4 
Calculation of polygenic scores 
The variants (SNPs) comprising the polygenic risk scores were derived from publicly available 
GWAS summary statistics (https://www.ebi.ac.uk/gwas/).  Scores were calculated across users by 
summing the product of effect allele doses weighted by the beta coefficient for each SNP, as 
reported in the GWAS summary statistics.  Variant p-value thresholds were generally chosen based 
on optimization of respective PGS-blood biomarker correlation in the entire InsideTracker cohort 
with both blood and genomics datasets (~1000-1500 depending on the blood biomarker at the time 
of analysis).  Genotyping data was derived from a combination of a custom InsideTracker array 
and third party arrays such as 23andMe and Ancestry.  Not all variants for any particular PGS were 
genotyped on every array; proxies for missing SNPs were extracted via the “LDlinkR” package 
using the Utah Residents (CEPH) with Northern and Western European ancestry (CEU) population 
(R2 > 0.8 cut-off).  Only results PGSs for which there was sufficient biomarker-genotyping dataset 
overlap were reported (note that none of the blood biomarker PGSs met this requirement). 
2.5 
Blood biomarker analysis with respect to running volume and polygenic scores 
To estimate significance of differences for blood biomarkers levels among exercise groups, we 
performed 3-way analysis of variance (ANOVA) analysis adjusting for age, gender, and BMI 
B Nogal 8 
(type-II analysis-of-variance tables function ANOVA from ‘car’ R package, version 3.0-12). 
When estimating the effort of reported training volume on biomarkers, we assigned numerical 
values corresponding to 4 levels of running and performed ANOVA analysis with those levels 
treating it as an independent variable.  P-values were adjusted using the Benjamini & Hochberg 
method 15. P-values for interaction plots were calculated with ANOVA including interaction 
between exercise group and polygenic scores category.  When comparing runners (PRO and 
HVAM combined) versus sedentary individuals, we used propensity score matching method to 
account for existing covariates (age and gender): we identified 745 sedentary individuals with 
similar to runners’ age distributions among both males and females. We used ‘MatchIt’ R package 
(version 4.3.3)  implementing nearest neighbor method for matching 16. 
2.6 
Mendelian randomization  
We attempted to add insight around the causality of exercise vs. BMI differences with respect to 
serum marker improvement by performing MR analyses on a subset of biomarker observations 
where BMI featured as a strong covariate and was thus used as the IV in the 2S-MR.   Thus, our 
hypothesis here was that BMI differences were the primary (causal) driver behind the improvement 
behind some biomarkers.  MR uses genetic variants as modifiable exposure (risk factor) proxies 
to evaluate causal relationships in observational data while reducing the effects of confounders 
and reverse causation (Figure 1S).  These SNPs are used as instrumental variables and must meet 
3 basic assumptions: (1) they must be robustly associated with the exposure; (2) they must exert 
their effect on outcome via the exposure, and (3) there must be no unmeasured confounders of the 
associations between the genetic variants and outcome (e.g. horizontal pleiotropy) 17.  Importantly, 
SNPs are proper randomization instruments because they are determined at birth and thus serve as 
proxies of long-term exposures and cannot, in general, be modified by the environment.  If the 3 
B Nogal 
 
9 
9 
above mentioned assumptions hold, MR-estimate effects of exposure on outcomes are not likely 
to be significantly affected by reverse causation or confounding.  In the 2S-MR performed here, 
where GWAS summary statistics are used for both exposure and outcome from independent 
cohorts, reverse causation and horizontal pleiotropy can readily be assessed, and weak instrument 
bias and the likelihood of false positive findings are minimized as a result of the much larger 
samples sizes 17.  Indeed, the bias in the 2S-MR using non-overlapping datasets as performed here 
is towards the null 17.  Furthermore, to maintain the SNP-exposure associations and linkage 
disequilibrium (LD) patterns in the non-overlapping populations we used GWAS datasets from 
the MR-Base platform that were derived from ancestrally similar populations (“ukb”:  analysis of 
UK Biobank phenotypes, and “ieu”: GWAS summary datasets generated by many different 
European consortia).  To perform the analysis we used the R package “TwoSampleMR” that 
combines the effects sizes of instruments on exposures with those on outcomes via a meta-analysis.  
We used “TwoSampleMR” package functions for allele harmonization between exposure and 
outcome datasets, proxy variant substitution when SNPs from exposure were not genotyped in the 
outcome data (Rsq>0.8 using the 1000G EUR reference data integrated into MR-Base), and 
clumping to prune instrument SNPs for LD (the R script used for MR analyses is available upon 
request).  We used 5 different MR methods that were included as part of the “TwoSampleMR” 
package to control for bias inherent to any one technique 18.  For example, the multiplicative 
random effects inverse variance-weighted (IVW) method is a weighted regression of instrument-
outcome effects on instrument-exposure effects with the intercept is set to zero.  This method 
generates a causal estimate of the exposure trait on outcome traits by regressing the, for example, 
SNP-BMI trait association on the SNP-biomarker measure association, weighted by the inverse of 
the SNP-biomarker measure association, and constraining the intercept of this regression to zero.  
B Nogal 10 
This constraint can result in unbalanced horizontal pleiotropy whereby the instruments influence 
the outcome through causal pathways distinct from that through the exposure (thus violating the 
second above-mentioned assumption).  Such unbalanced horizontal pleiotropy distorts the 
association between the exposure and the outcome, and the effect estimate from the IVW method 
can be exaggerated or attenuated. However, unbalanced horizontal pleiotropy can be readily 
assessed by the MR Egger method (via the MR Egger intercept), which provides a valid MR causal 
estimate that is adjusted for the presence of such directional pleiotropy, albeit at the cost of 
statistical efficiency.  Finally, to ascertain the directionality of the various causal relationships 
examined, we also performed each MR analysis in reverse where possible. 
3 
Results 
Study population characteristics 
 
Table 1 shows the demographic characteristics of the study population.  We observed a 
significant trend toward younger individuals reporting higher running volume, with more than 
75% of the professional (PRO) group falling between the ages of 18 and 35 (Table 1S).  Significant 
differences were also observed in the distribution of males and females within study groups (Table 
1).  Moreover, higher running volume associated with significantly lower body mass index (BMI).  
Thus, moving forward, combined comparisons of blood biomarkers as they relate to running 
volume were adjusted for age, gender, and BMI. 
Endurance exercise exhibits a modest association with clusters of blood biomarker features  
In order to begin to understand the most important variables that may associate with endurance 
exercise in the form of running, we performed a principal component analysis (PCA), dividing the 
B Nogal 
 
11 
11 
cohort into two most divergent groups in terms of exercise volume:  PRO/high volume amateur 
(HVAM) and sedentary (SED) groups.  Using propensity matching, PRO and amateur athletes 
who reported running >10h per week were combined into the PRO-HVAM group to balance out 
the sample size between the exercising and non-exercising groups.  Using this approach, we did 
not observe a significant separation between these groups (data not shown).  However, dividing 
this dataset further into males and females yielded a modest degree of separation, with 
hematological, inflammation, and lipid features, as well as BMI explaining some of the variance 
(Figure 1 A through D).  We hypothesized that there may more subtle relationships between 
running volume and the blood biomarker features that contributed to distinguishing the endurance 
exercise and sedentary groups, thus we next performed ANOVA analyses stratified by running 
volume as categorized in Table 1. 
Significant trends in glycemic, hematological, blood lipid, and inflammatory serum traits with 
increasing running volumes  
Weighted ANOVA analyses adjusted for age, gender, and BMI showed significant differences 
among groups for multiple blood biomarkers (Table 2 and 2S, Figures 2 and 3).  We observed a 
trend toward lower HbA1c, hsCRP, RDW, WBC, ferritin, gamma-glutamyl transferase (GGT), 
and LDL.  HDL, hemoglobin (Hb), transferrin saturation (TS), alanine aminotransferase (ALT), 
aspartate aminotransferase (AST), vitamin B12, folate, 25-hydroxy vitamin D, and creatine kinase 
(CK) tended to be higher with increasing reported training volume, particularly in PRO runners 
(Tables 2 and 2S, Figures 2 and 2S, Figure 3).   Hct and Hb were higher only in PRO males, 
whereas increased running volume associated with upward trend in these biomarkers in females 
(Figure 3 A and B).  Increased running volume was associated with markedly lower Fer in males, 
whereas female runners did not exhibit varying levels, and SED females showed increased levels 
B Nogal 12 
(Figure 3 C).  The low ferritin observed in male and female runners was not clinically significant.  
ALT positively associated with running volume in females only (Figure 2S).  Serum and RBC 
magnesium (Mg) were both significantly lower in PRO runners relative to all other groups (Table 
2 and Figure 3 D and E).   Increasing levels of endurance exercise also appeared to be associated 
with higher sex-hormone binding globulin (SHBG), particularly in PRO male runners (Figure 3 
F).  
Endurance exercise correlates with lower BMI across categories of genetic risk  
Using publicly available GWAS summary statistics, we constructed blood biomarker polygenic 
risk scores (PGSs) to explore potential genetic risk-mitigating effects of endurance exercise.  Since 
only a subset of the individuals in our cohort were genotyped, we aggregated the groups into 2 
categories—PRO-HVAM and sedentary—to increase statistical power.  This across-group sample 
size increase generally did not sufficiently power the ANOVA analysis to detect statistically 
significant trends (data not shown), though the BMI polygenic risk was suggestively mitigated for 
both males and female PRO-HVAM runners across categories of genetic risk (Figure 4 B).  
Increased running volume is associated with lower BMI which may drive biomarker changes  
We observed a significant downward trend in the BMI with increased running volume for both 
males and females, and, although some of the biomarker differences between sedentary and 
exercising individuals remained significant after adjustment for BMI, their significance was 
attenuated (Figure 4 A, p-value attenuation data not shown). Thus, we hypothesized that BMI may 
be driving a significant portion of the observed variance in some of the biomarkers across the 
groups.  Thus, to explore causal relationships between weight and biomarker changes, we 
performed 2S-MR with BMI-associated single-nucleotide polymorphisms (SNPs) as the 
B Nogal 
 
13 
13 
instrumental variables (IVs) for a subset of the healthspan-related biomarkers where BMI 
explained a relatively large portion of the variance in our analysis.   In general, these blood 
biomarkers associated with inflammation (hsCRP and RDW), lipid metabolism (Tg and HDL), 
glycemic control (HbA1c and Glu), as well as Alb and SHBG. We used GWAS summary statistics 
and found that most of these BMI-blood biomarker relationships examined directionally aligned 
with our study (except for LDL), and some were indicative of causal relationships in the BMI-
biomarker direction even after considering directional pleiotropy (Table 3S).   We entertained the 
possibility of reverse causality and thus repeated the 2S-MR using each of the biomarker levels as 
the exposure and BMI as the outcome, and the results were generally not significant (except for 
WBC – see Table 4S).  Of note, to estimate the direct causal effects of running on blood 
parameters, we attempted to find an instrumental variable for to approximate running as the 
exposure from publicly available GWAS summary statistics.  Toward this end, we found that 
increasing levels of vigorous physical activity did associate with lower hsCRP, HbA1C, higher 
HDL, and possibly higher SHBG (although the explained variance (R2) in this exposure was just 
0.001009, the F statistic was 37.7, thus meeting the criteria of F > 10 for minimizing weak 
instrument bias) (Figures 5 and 3S; Table 5S). 
Vigorous physical activity associates with healthier behaviors 
We hypothesized that those who exercise regularly may also partake in other healthful lifestyle 
habits that may be contributing to more optimal blood biomarker signatures of wellness.  However, 
our dataset did not allow for systematic accounting of other lifestyle habits across all running 
groups.  Thus, we again leveraged the potential of the 2S-MR approach to inform potential 
confounding  associations between modifiable exposures and found that vigorous physical activity 
such as running is at least suggestively associated with several behaviors associated with improved 
B Nogal 14 
health (Figure 4S).  Our analysis showed that those who participate in increasing levels of vigorous 
physical activity may be less likely to eat processed meat (IVW p = 0.0000013), sweets (IVW p = 
0.32), and nap during the day (IVW p = 0.13), while increasing their intake of oily fish (IVW p = 
0.029), salad/raw vegetable intake (IVW p = 0.00016), and fresh fruit (IVW p = 0.0027) (Table 
6S).  Furthermore, following our assessment of reverse causality, we found evidence for the 
bidirectionality in the causal relationship between vigorous activity and napping during the day 
and salad/raw vegetable intake, perhaps suggesting some degree of confounding due to population 
stratification (Table 7S). The suggestive positive effect of fresh fruit and processed meat intake on 
vigorous physical activity appeared to violate MR assumption (3) (Figure 1S) (horizontal 
pleiotropy p-values 0.051 and 0.17, respectively – Figure 5S). 
4 
Discussion 
In this report, we describe the variance in wellness-related blood biomarkers among self-reported 
recreational runners, PRO runners, and individuals who do not report any exercise.  Overall, we 
find that 1) recreational running as an exercise appears to be an effective intervention toward 
modifying several biomarkers indicative of improved metabolic health, 2) an apparent dose-
response relationship between running volume and BMI may itself be responsible for a proportion 
of the apparent metabolic benefits, and 3) both PRO-level status and gender appear to associate 
with heterogeneous physiological responses, particularly in iron and magnesium metabolism, as 
well as some hormonal traits. 
4.1 
Self-reported running improves glycemia and lipidemia  
We did not observe distinct clusters corresponding to self-reported high-volume/PRO runners and 
the sedentary upon dimension reduction.  This is, perhaps, not unexpected due, in part, to the self-
B Nogal 
 
15 
15 
selected healthspan-oriented nature of our cohort, where even the sedentary subset of individuals 
tends to exhibit blood biomarker levels in the normal clinical reference ranges.  Furthermore, the 
measurement of running volume via self-report may be vulnerable to overestimation, which may 
have contributed to the blending of sedentary and exercise groups with respect to the serum 
markers measured, resulting in only marginal separation between the groups 19,20.  However, we 
did observe significant individual blood biomarker variance with respect to reported running 
volumes when the dataset was subjected to ANOVA, even after adjustment for age, sex, and BMI.  
From among glycemic control blood biomarkers, we were able to detect a relatively small exercise 
effect in both fasting glucose and HbA1c in this generally healthy cohort, where the average 
measures of glycemia were below the prediabetic thresholds in even the sedentary subset of the 
cohort. Larger exercise intervention effects on metabolic biomarkers may be expected in cohorts 
that include individuals with more clinically significant baseline values 21.   
Similarly, blood lipids improved with higher self-reported running volume, and this result has been 
reported before in multiple controlled endurance exercise trials 22.  The literature indicates that 
HDL and Tg are two exercise-modifiable blood lipid biomarkers, with HDL being the most widely 
reported to be modified by aerobic exercise 23,24.  Although the mechanism behind this is not 
entirely clear, it likely involves the modification of lecithin acyltransferase and lipoprotein lipase 
activities following exercise training 25.  We observed a similar trend in our blood biomarker 
analysis, with HDL exhibiting an upward trend with increasing reported running volume.  While 
we also found Tg and LDL to decrease with increasing exercise volume, these trends were less 
pronounced.  Reports generally suggest that, in order to reduce LDL more consistently, the 
intensity of aerobic exercise must be high enough 23.  In the case of Tg, baseline levels may have 
B Nogal 16 
a significant impact on the exercise intervention effect, with individuals exhibiting higher baselines 
showing greater improvements 13. 
Importantly, these results suggests that exercise has a significant effect on glycemic control and 
blood lipids even in the self-selected, already healthy individuals who are proactive about 
preventing cardiometabolic disease.  
4.2 
Self-reported running and serum proxies of systemic inflammation  
Chronic low-grade inflammation is one of the major risk factors for compromised cardiovascular 
health and metabolic syndrome (MetS).  While there is no shortage of inflammation-reducing 
intervention studies on CVD patients with clinically high levels of metabolic inflammation, there 
is less emphasis on modifiable lifestyle factors that can help stave off CVD and extend healthspan 
in the generally healthy individual.  Indeed, considering the pathological cardiovascular processes  
begin shortly after birth, prevention in asymptomatic individuals may be a more appropriate 
strategy toward decreasing the burden of CVD on the healthcare system 26.   
Toward this end, increasing self-reported running volume appeared to associate with improved 
markers of inflammation, as shown by the lower levels of hsCRP, WBC, as well as ferritin.  Of 
note, while the acute-phase protein, ferritin, is often used in the differential diagnosis of iron 
deficiency anemia, the biomarker’s specificity appears to depend on the inflammatory state of the 
individual, as it associates with hsCRP and inflammation more than iron stores, particularly in 
those with higher BMI 27.   Although serum ferritin and iron is reported to be lower in male and 
female elite athletes 28, the observed overall negative association of ferritin with increased running 
volume in our cohort may be an indication of lower levels of inflammation rather than 
compromised iron stores, particularly since the average ferritin level across all groups was above 
B Nogal 
 
17 
17 
the clinical iron deficiency thresholds.  Moreover, increased levels of ferritin have been associated 
with insulin resistance and lower levels of adiponectin in the general population, both indicators 
of increased systemic inflammation 29.  Here, exercising groups with lower levels of ferritin also 
exhibited glycemic and blood lipid traits indicative of improved metabolic states, further 
supporting ferritin’s role as an inflammation proxy.  Finally, Hb, TS and iron tended to be higher 
in those who run for exercise compared to the SED group (with the TIBC lower), again suggesting 
that runners, including the PRO group, were iron-sufficient in this cohort.   
4.3 
PRO athletes exhibit distinct biomarker signatures  
PRO athletes exhibited lower serum and RBC Mg, which may be indication of the often-reported 
endurance athlete hypomagnesaemia 30.  While the serum Mg was still within normal clinical 
reference range for both PRO female and male athletes, RBC Mg, a more sensitive biomarker of 
Mg status 31, was borderline low in female PRO athletes and might suggest suboptimal dietary 
intakes and/or much higher volume of running training compared to the other running groups (i.e. 
>>10h /week).  Indeed, this group also had elevated baseline CK and AST, which suggests a much 
higher training intensity and/or volume.  Moreover, PRO level athletes had adequate iron status 
and serum B12 and folate in the upper quartile of the normal reference range, suggesting that these 
athletes’ general nutrition status may have been adequate.  These observations suggest that elite 
endurance runners may need to pay particular attention to their magnesium status. 
Further, we observed higher levels of SHBG in PRO male runners, a biomarker whose levels 
positively correlate with various indexes of insulin sensitivity 32.  However, since the average 
SHBG levels in the SED group were not clinically low in both sexes, the observed increase in 
SHBG levels induced by running in males may be a catabolic response, as cortisol levels in this 
B Nogal 18 
group were also higher.  Indeed,  Popovic et al have shown that endurance exercise may increase 
SHBG, cortisol, and total testosterone levels at the expense of free testosterone levels 33.  This 
could perhaps in part be explained by higher exercise-induced adiponectin levels, which have been 
shown to increase SHBG via cAMP kinase (AMPK) activation 34.  However, since our data is 
observational, we cannot rule out overall energy balance as a significant contributor to SHBG 
levels.  For example, caloric restriction (CR) has been shown to result in higher SHBG and cortisol 
levels 32. 
Finally, regarding the abovementioned PRO group elevated AST and CK biomarkers, evidence 
suggests that normal reference ranges in both CK and AST in well-recovered athletes should be 
adjusted up, as training and competition have a profound, non-pathological, impact on the activity 
of these enzymes 35,36.  Indeed, the recommendation appears to be not to use reference intervals 
derived from the general population with hard-training (particularly competitive) athletes 36. 
4.4 
Effect of BMI on blood biomarkers 
Since the current study is a cross-sectional analysis of self-reported running, we could not rule out 
the possibility that factors other than exercise were the driving force behind the observed 
biomarker variance among the groups examined.  While factors such as diet, sleep, and/or 
medication use were not readily ascertained in this free-living cohort at the time of this study, BMI 
was readily available to evaluate this biomarker’s potential relative contribution to the observed 
mean biomarker differences among self-reported runner groups.  
Multiple studies have attempted to uncouple the effects of exercise and BMI reduction on blood 
biomarker outcomes, with mixed results 37.  For example, it is relatively well-known that acute 
bouts of exercise improve glucose metabolism, but long-term effects are less well described 38.  
B Nogal 
 
19 
19 
Indeed, whether exercise without significant weight-loss is effective toward preventing metabolic 
disease (and the associated blood biomarker changes) is inconclusive.  From the literature, it 
appears that, for endurance exercise to have significant effect on most blood biomarkers, the 
volume of exercise needs to be very high, and this typically results in significant reduction in 
weight.  Thus, in practice, it is difficult to demonstrably uncouple the effects of significant exercise 
and the associated weight-loss, and the results may depend on the blood biomarker in question.  
Indeed, there is evidence that exercise without weight-loss does improve markers of insulin 
sensitivity but not chronic inflammation, with the latter apparently requiring a reduction in 
adiposity in the general population 39-41.    
In our study of apparently healthy individuals, we observed a downward trend in BMI with 
increasing self-reported running volume, and, although this study was not longitudinal and we are 
thus unable to claim weight-loss, our 2S-MR analysis using BMI as the exposure nonetheless 
suggests this biomarker to be responsible for a significant proportion of the modification of some 
blood biomarkers.   
4.5.1 Serum markers of systemic inflammation 
Through our 2S-MR analyses, we show that BMI is causally associated with markers of systemic 
inflammation, including RDW, folate, and hsCRP 27,42,43.  Similar analyses have reported that 
genetic variants that associate with higher BMI were associated with higher CRP levels, but not 
the other way around 44.  The prevailing mechanism proposed to explain this relationship appears 
to be the pathological nature of overweight/obesity-driven adipose tissue that results in secretion 
of proinflammatory cytokines such as IL-6 and TNFa, which then stimulate an acute hepatic 
response, resulting in increased hsCRP levels (among other effects) 45.  Thus, our 2S-MR analyses 
B Nogal 20 
and those of others 44 would indicate that the primary factor behind the lower systemic 
inflammation in our cohort may be the exercise-associated lower BMI and not running exercise 
per se, though the lower hsCRP in runners remained significant after adjustment for BMI in our 
analysis.   
Indeed, although a major driver behind reduced systemic inflammation may be a reduction in BMI 
in the general population, additive effects of other lifestyle factors such as exercise cannot be 
excluded. For example, a large body of cross-sectional investigations does indicate that physically 
active individuals exhibit CRP levels that are 19-35% lower than less active individuals, even 
when adjusted for BMI as was the case in the current analysis 41.  Further, it’s been reported that 
physical activity at a frequency of as little as 1 day per week is associated with lower CRP in 
individuals who are otherwise sedentary, while more frequent exercise further reduces 
inflammation 41.   
Significantly, our entire cohort of self-selected apparently healthy individuals did not exhibit 
clinically high hsCRP, with average BMI also below the overweight thresholds.  Because all 
subjects were voluntarily participating in a personalized wellness platform intended to optimize 
blood biomarkers that included hsCRP, it is possible that some individuals from across the study 
groups (both running and sedentary) in our cohort partook in some form of inflammation-reducing 
dietary and/or lifestyle-based intervention.  Thus, that we detected a significant difference in 
hsCRP between exercising and non-exercising individuals in this self-selected already generally 
healthy cohort may be suggestive of the potential for additional preventative effect of scheduled 
physical activity on low-grade systemic inflammation in the generally healthy individual.   
4.5.2 Blood lipids 
B Nogal 
 
21 
21 
Controlled studies that tightly track exercise and the associated adiposity reduction have reported 
that body fat reduction (and not improvement in fitness as measured via VO2max) is a predictor of 
HDL, LDL, and Tg 46.  Similarly, though BMI is an imperfect measure of adiposity, our 2S-MR 
analysis suggests that this biomarker is causally associated with improved levels of HDL and Tg, 
though not LDL.  This latter finding replicates a report by Hu et al. who, using the Global Lipids 
Genetics Consortium GWAS summary statistics, applied a network MR approach that revealed 
causal associations between BMI and blood lipids, where Tg and HDL, but not LDL, were found 
to trend toward unhealthy levels with increasing adiposity 47.  On the other hand, others 
implemented a robust BMI genetic risk score and demonstrated a causal association of adiposity 
with peripheral artery disease and a multiple linear regression showed a strong association with 
HDL, TC, and LDL, among other metabolic parameters 48.  In our cohort, given the lack of 
evidence for a causal BMI-LDL association and the overall healthy levels of BMI, the observed a 
significant improvement in LDL may be a result of marked running intensity and/or volume, 
possibly combined with the aforementioned additional wellness program intervention variables.  
4.5.3 Hormonal traits 
As described above, we observed a trend toward increased plasma cortisol and SHBG in runners, 
particularly PRO level athletes.  The effects on cortisol are consistent with a report by Houmanrd 
et al, who found male distance runners to exhibit higher levels of baseline cortisol 49.  With respect 
to the effects of BMI on baseline cortisol levels, this observation is generally supported by our 2S-
MR analyses with evidence for a consistent effect of increased cortisol with decreasing BMI.  
However, this association was suggestive at best, indicating that the higher levels of cortisol 
exhibited in the PRO runners with significant lower adiposity are not likely to be solely explained 
by their lower BMI.  Indeed, the relationship between BMI and cortisol appears to be complex, 
B Nogal 22 
with some reports suggesting a U-shaped relationship, where the glucocorticoid’s levels associate 
negatively up to about a BMI of 30 kg/m2, then exhibiting a positive correlation into obesity 
phenotypes 50.  MR statistical models generally do not account for such non-linearity and would 
require a more granular demographical treatment, which is not possible using only GWAS 
summary statistics data in the context of 2S-MR 17,51.  
4.6 
Behavioral traits associated with increase physical activity 
The combination of the body of the literature that describes the effects of endurance training on 
blood biomarkers, and our own analysis that included markers such as CK and AST, makes us 
cautiously assured that most of the abovementioned blood biomarker signatures are indeed a result 
of the interplay between self-reported running and the associated lower BMI.  However, as this is 
a self-report-based analysis and we were unable to track other subject behaviors in this free-living 
cohort, we acknowledge that multiple behaviors that associate with exercise may be influencing 
our results.   
Toward this end, our exploratory 2S-MR analyses revealed potentially causal relationships 
between vigorous exercise and multiple dietary habits that have been shown to improve the 
biomarkers we examined.  Indeed, diets that avoid processed meat and sweets while providing 
ample amounts of fresh fruits, as well as oily fish have been shown to be anti-inflammatory, and 
improve glycemic control and dyslipidemia 52,53.  That physically active individuals are also more 
likely to make healthier dietary choices adds insight to the potential confounders in ours and 
others’ observational analyses, and this similar associations have previously been reported 54-56. 
For example, using a calculated healthy eating motivation score, Naughton et al. showed that those 
who partake in more than 2 hours of vigorous physical activity are almost twice as likely to be 
B Nogal 
 
23 
23 
motivated to eat healthy 56.  Indeed, upon closer examination, the genetic instruments used to 
approximate vigorous physical activity as the exposure in this work included variants in the genes 
DPY19L1, CADM2, CTBP2, EXOC4, and FOXO3 57.  Of these, CADM2 encodes proteins that are 
involved in neurotransmission in brain regions well known for their involvement in executive 
function, including motivation, impulse regulation and self-control 58.  Moreover, variants within 
this locus have been associated with obesity-related traits 59.  Thus, it is likely that the improved 
metabolic outcomes seen here with our self-reported runners are a composite result of both these 
individuals exercise and dietary habits.  Importantly, the above suggests that a holistic wellness 
lifestyle approach is in practice the most likely to be most effective toward preventing 
cardiometabolic disease.  Nonetheless, the focus of this work – exercise in the form of running – 
is known to significantly improve cardiorespiratory fitness (CRF),  which has been shown to be 
an independent predictor of CVD risk and total mortality, outcomes that indeed correlate with 
dysregulated levels in many of the blood biomarkers examined in this work 7. 
4.7 
Study limitations 
This study is based on self-reported running and thus has several limitations.  First, it is generally 
known that subjects tend to overestimate their commitment to exercise when self-reporting, 
although in our cohort is a self-selected health-oriented population that is possibly less likely to 
over-report their running volume.  Furthermore, although the robust increasing trend in baselines 
for muscle damage biomarkers (CK, AST) that have been shown to be associated with participation 
in sports and exercise provides indirect evidence that the running groups were indeed participating 
in increasing volumes of strenuous physical activity, we cannot confirm whether the reported 
running was performed overground or on a treadmill, which may result in some heterogeneity in 
physiological responses , nor can we ascertain the actual training volume of PRO-level runners.   
B Nogal 24 
We also cannot exclude the possibility that the running groups also participated in other forms of 
exercise (such as strength training) or partook in other wellness program interventions that may 
have influenced their blood biomarkers and/or BMI via lean muscle accretion.  Toward this end, 
we have attempted to shed light on potential behavioral covariates related to vigorous physical 
activity via 2S-MR.   Finally, while this cohort is generally healthy, we cannot exclude the potential 
for unmeasured confounders such as medications, nutritional supplements, and unreported health 
conditions.   
2S- MR enables the assessment of causal relationships between modifiable traits and is less prone 
to the so-called “winner’s curse” that more readily affects one-sample MR analyses 17,51.  Because 
2S-MR uses GWAS summary statistics for both exposure and outcome, it is possible to increase 
statistical power because of the increased sample sizes.  However, horizontal pleiotropy is still a 
concern that can skew the results.  Currently, there is no gold standard MR analysis method, thus 
we used different techniques (IVW, MR-Egger, and median-based estimations – all of which are 
based on different assumptions and thus biases) to evaluate the consistency among these estimators 
and only reported associations as ‘causal’ if there was cross-model consistency.  Nonetheless, an 
exposure such as BMI is a complex trait that is composed of multiple sub-phenotypes (such as 
years of education) that could be driving the causal associations.  
 
5 
Conclusion 
Running is one of the most common forms of vigorous exercise practiced globally, thus making it 
a compelling target of research studies toward understanding its applicability in chronic disease 
prevention.  Our cross-sectional study offers insight into the biomarker signatures of self-reported 
B Nogal 
 
25 
25 
running in generally healthy individuals that suggest improved insulin sensitivity, blood lipid 
metabolism, and systemic inflammation.  Furthermore, using 2S-MR in independent datasets we 
provide additional evidence that some biomarkers are readily modified BMI alone, while others 
appear 
to 
respond 
to 
the 
combination 
of 
varying 
exercise 
and 
BM 
I.  Our additional bi-directional 2S-MR analyses toward understanding the causal relationships 
between partaking in vigorous physical activity and other healthy behaviors highlight the inherent 
challenge in disambiguating exercise intervention effects in cross sectional studies of free-living 
populations, where healthy behaviors such as exercising and healthy dietary habits co-occur.   
Overall, our analysis shows that the differences between those who run and the sedentary in our 
cohort are likely a combination of the specific physiological effects of exercise, the associated 
changes in BMI, and lifestyle habits associated with those who exercise, such as food choices and 
baseline activity level.   Looking ahead, the InsideTracker database is continuously augmented 
with blood chemistry, genotyping, and activity tracker data, facilitating further investigation of the 
effects of various exercise modalities on phenotypes related to healthspan, including longitudinal 
analyses and more granular dose-response dynamics. 
Data Availability Statement 
The full set of biomarker change correlations has been made available in the Supplementary 
Information files. Specific components of the raw dataset are available upon reasonable request 
from the corresponding author.  2S-MR analysis was performed using publicly available datasets 
via the TwoSampleMR R package. 
Ethics statement 
B Nogal 26 
This study was submitted to The Institutional Review Board (IRB), which determined this work 
was not subject to a review based on category 4 exemption (“secondary research” with de-
identified subjects). 
Author contributions 
BN performed the 2S-MR analyses, calculated PGSs, and wrote the manuscript; SV performed 
blood biomarker and blood biomarker X PGS interaction analysis; PF calculated PGSs; MJ, AT, 
and GB provided guidance.  All authors have read and agreed to the published version of the 
manuscript. 
Funding 
InsideTracker was the sole funding source. 
Conflict of interest 
InsideTracker is a direct-to-consumer blood biomarker and genomics company providing its 
users with nutritional and exercise recommendations toward improving wellness.  B.N., S.V., 
P.F., and G.B. are employees of InsideTracker. 
 
 
 
 
 
B Nogal 
 
27 
27 
Acknowledgments 
InsideTracker is the sole funding source.  We thank Michelle Cawley and Renee Deehan for their 
assistance with background subject matter research and insightful conversations. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
B Nogal 28 
References 
1. 
Lavie CJ, Ozemek C, Carbone S, Katzmarzyk PT, Blair SN. Sedentary Behavior, Exercise, 
and Cardiovascular Health. Circ Res. Mar 2019;124(5):799-815. 
doi:10.1161/CIRCRESAHA.118.312669 
2. 
Carlson SA, Adams EK, Yang Z, Fulton JE. Percentage of Deaths Associated With 
Inadequate Physical Activity in the United States. Prev Chronic Dis. Mar 29 2018;15:E38. 
doi:10.5888/pcd18.170354 
3. 
Antonicelli R, Spazzafumo L, Scalvini S, et al. Exercise: a "new drug" for elderly patients 
with chronic heart failure. Aging (Albany NY). May 2016;8(5):860-72. 
doi:10.18632/aging.100901 
4. 
Sgro P, Emerenziani GP, Antinozzi C, Sacchetti M, Di Luigi L. Exercise as a drug for 
glucose management and prevention in type 2 diabetes mellitus. Curr Opin Pharmacol. Aug 
2021;59:95-102. doi:10.1016/j.coph.2021.05.006 
5. 
Vina J, Sanchis-Gomar F, Martinez-Bello V, Gomez-Cabrera MC. Exercise acts as a drug; 
the pharmacological benefits of exercise. Br J Pharmacol. Sep 2012;167(1):1-12. 
doi:10.1111/j.1476-5381.2012.01970.x 
6. 
Lee EC, Fragala MS, Kavouras SA, Queen RM, Pryor JL, Casa DJ. Biomarkers in Sports and 
Exercise: Tracking Health, Performance, and Recovery in Athletes. J Strength Cond Res. Oct 
2017;31(10):2920-2937. doi:10.1519/JSC.0000000000002122 
7. 
Lin X, Zhang X, Guo J, et al. Effects of Exercise Training on Cardiorespiratory Fitness and 
Biomarkers of Cardiometabolic Health: A Systematic Review and Meta-Analysis of Randomized 
Controlled Trials. J Am Heart Assoc. Jun 26 2015;4(7)doi:10.1161/JAHA.115.002014 
B Nogal 
 
29 
29 
8. 
Li X, Ploner A, Wang Y, et al. Clinical biomarkers and associations with healthspan and 
lifespan: Evidence from observational and genetic data. EBioMedicine. Apr 2021;66:103318. 
doi:10.1016/j.ebiom.2021.103318 
9. 
Mailliez A, Guilbaud A, Puisieux F, Dauchet L, Boulanger E. Circulating biomarkers 
characterizing physical frailty: CRP, hemoglobin, albumin, 25OHD and free testosterone as best 
biomarkers. Results of a meta-analysis. Exp Gerontol. Oct 1 2020;139:111014. 
doi:10.1016/j.exger.2020.111014 
10. 
Hirata T, Arai Y, Yuasa S, et al. Associations of cardiovascular biomarkers and plasma 
albumin with exceptional survival to the highest ages. Nat Commun. Jul 30 2020;11(1):3820. 
doi:10.1038/s41467-020-17636-0 
11. 
Erema VV, Yakovchik AY, Kashtanova DA, et al. Biological Age Predictors: The Status Quo 
and Future Trends. Int J Mol Sci. Dec 1 2022;23(23)doi:10.3390/ijms232315103 
12. 
Hartmann A, Hartmann C, Secci R, Hermann A, Fuellen G, Walter M. Ranking Biomarkers 
of Aging by Citation Profiling and Effort Scoring. Front Genet. 2021;12:686320. 
doi:10.3389/fgene.2021.686320 
13. 
Trejo-Gutierrez JF, Fletcher G. Impact of exercise on blood lipids and lipoproteins. J Clin 
Lipidol. Jul 2007;1(3):175-81. doi:10.1016/j.jacl.2007.05.006 
14. 
Westerman K, Reaver A, Roy C, et al. Longitudinal analysis of biomarker data from a 
personalized nutrition platform in healthy subjects. Sci Rep. Oct 2 2018;8(1):14685. 
doi:10.1038/s41598-018-33008-7 
15. 
Fox J WS. An R Companion to Applied Regression. Third ed. Sage, Thousand Oaks CA; 
2019. 
B Nogal 30 
16. 
Ho D, Imai K, King G, Stuart EA. MatchIt: Nonparametric Preprocessing for Parametric 
Causal Inference. Journal of Statistical Software. 06/14 2011;42(8):1 - 28. 
doi:10.18637/jss.v042.i08 
17. 
Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian 
randomization investigations. Wellcome Open Res. 2019;4:186. 
doi:10.12688/wellcomeopenres.15555.2 
18. 
Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal 
inference across the human phenome. Elife. May 30 2018;7doi:10.7554/eLife.34408 
19. 
Bogl LH, Pietilainen KH, Rissanen A, Kaprio J. Improving the accuracy of self-reports on 
diet and physical exercise: the co-twin control method. Twin Res Hum Genet. Dec 
2009;12(6):531-40. doi:10.1375/twin.12.6.531 
20. 
Yuen HK, Wang E, Holthaus K, et al. Self-reported versus objectively assessed exercise 
adherence. Am J Occup Ther. Jul-Aug 2013;67(4):484-9. doi:10.5014/ajot.2013.007575 
21. 
Ostman C, Smart NA, Morcos D, Duller A, Ridley W, Jewiss D. The effect of exercise 
training on clinical outcomes in patients with the metabolic syndrome: a systematic review and 
meta-analysis. Cardiovasc Diabetol. Aug 30 2017;16(1):110. doi:10.1186/s12933-017-0590-y 
22. 
Fikenzer K, Fikenzer S, Laufs U, Werner C. Effects of endurance training on serum lipids. 
Vascul Pharmacol. Feb 2018;101:9-20. doi:10.1016/j.vph.2017.11.005 
23. 
Mann S, Beedie C, Jimenez A. Differential effects of aerobic exercise, resistance training 
and combined exercise modalities on cholesterol and the lipid profile: review, synthesis and 
recommendations. Sports Med. Feb 2014;44(2):211-21. doi:10.1007/s40279-013-0110-5 
B Nogal 
 
31 
31 
24. 
Tambalis K, Panagiotakos DB, Kavouras SA, Sidossis LS. Responses of blood lipids to 
aerobic, resistance, and combined aerobic with resistance exercise training: a systematic review 
of current evidence. Angiology. Oct-Nov 2009;60(5):614-32. doi:10.1177/0003319708324927 
25. 
Calabresi L, Franceschini G. Lecithin:cholesterol acyltransferase, high-density 
lipoproteins, and atheroprotection in humans. Trends Cardiovasc Med. Feb 2010;20(2):50-3. 
doi:10.1016/j.tcm.2010.03.007 
26. 
Kemper HC, Snel J, Verschuur R, Storm-van Essen L. Tracking of health and risk indicators 
of cardiovascular diseases from teenager to adult: Amsterdam Growth and Health Study. Prev 
Med. Nov 1990;19(6):642-55. doi:10.1016/0091-7435(90)90061-n 
27. 
Khan A, Khan WM, Ayub M, Humayun M, Haroon M. Ferritin Is a Marker of 
Inflammation rather than Iron Deficiency in Overweight and Obese People. J Obes. 
2016;2016:1937320. doi:10.1155/2016/1937320 
28. 
Nabhan D, Bielko S, Sinex JA, et al. Serum ferritin distribution in elite athletes. J Sci Med 
Sport. Jun 2020;23(6):554-558. doi:10.1016/j.jsams.2019.12.027 
29. 
Ku BJ, Kim SY, Lee TY, Park KS. Serum ferritin is inversely correlated with serum 
adiponectin level: population-based cross-sectional study. Dis Markers. 2009;27(6):303-10. 
doi:10.3233/DMA-2009-0676 
30. 
Pollock N, Chakraverty R, Taylor I, Killer SC. An 8-year Analysis of Magnesium Status in 
Elite International Track & Field Athletes. J Am Coll Nutr. Jul 2020;39(5):443-449. 
doi:10.1080/07315724.2019.1691953 
31. 
Arnaud MJ. Update on the assessment of magnesium status. Br J Nutr. Jun 2008;99 
Suppl 3:S24-36. doi:10.1017/S000711450800682X 
B Nogal 32 
32. 
Simo R, Saez-Lopez C, Barbosa-Desongles A, Hernandez C, Selva DM. Novel insights in 
SHBG regulation and clinical implications. Trends Endocrinol Metab. Jul 2015;26(7):376-83. 
doi:10.1016/j.tem.2015.05.001 
33. 
Popovic B, Popovic D, Macut D, et al. Acute Response to Endurance Exercise Stress: 
Focus on Catabolic/anabolic Interplay Between Cortisol, Testosterone, and Sex Hormone 
Binding Globulin in Professional Athletes. J Med Biochem. Mar 2019;38(1):6-12. 
doi:10.2478/jomb-2018-0016 
34. 
Simo R, Saez-Lopez C, Lecube A, Hernandez C, Fort JM, Selva DM. Adiponectin 
upregulates SHBG production: molecular mechanisms and potential implications. 
Endocrinology. Aug 2014;155(8):2820-30. doi:10.1210/en.2014-1072 
35. 
Mougios V. Reference intervals for serum creatine kinase in athletes. Br J Sports Med. 
Oct 2007;41(10):674-8. doi:10.1136/bjsm.2006.034041 
36. 
Banfi G, Morelli P. Relation between body mass index and serum aminotransferases 
concentrations in professional athletes. J Sports Med Phys Fitness. Jun 2008;48(2):197-200.  
37. 
Ross R, Janiszewski PM. Is weight loss the optimal target for obesity-related 
cardiovascular disease risk reduction? Can J Cardiol. Sep 2008;24 Suppl D:25D-31D. 
doi:10.1016/s0828-282x(08)71046-8 
38. 
Ross R. Does exercise without weight loss improve insulin sensitivity? Diabetes Care. 
Mar 2003;26(3):944-5. doi:10.2337/diacare.26.3.944 
39. 
Cerqueira E, Marinho DA, Neiva HP, Lourenco O. Inflammatory Effects of High and 
Moderate Intensity Exercise-A Systematic Review. Front Physiol. 2019;10:1550. 
doi:10.3389/fphys.2019.01550 
B Nogal 
 
33 
33 
40. 
Church TS, Earnest CP, Thompson AM, et al. Exercise without weight loss does not 
reduce C-reactive protein: the INFLAME study. Med Sci Sports Exerc. Apr 2010;42(4):708-16. 
doi:10.1249/MSS.0b013e3181c03a43 
41. 
Plaisance EP, Grandjean PW. Physical activity and high-sensitivity C-reactive protein. 
Sports Med. 2006;36(5):443-58. doi:10.2165/00007256-200636050-00006 
42. 
Salvagno GL, Sanchis-Gomar F, Picanza A, Lippi G. Red blood cell distribution width: A 
simple parameter with multiple clinical applications. Crit Rev Clin Lab Sci. 2015;52(2):86-105. 
doi:10.3109/10408363.2014.992064 
43. 
Carmel R, Green R, Rosenblatt DS, Watkins D. Update on cobalamin, folate, and 
homocysteine. Hematology Am Soc Hematol Educ Program. 2003:62-81. 
doi:10.1182/asheducation-2003.1.62 
44. 
Welsh P, Polisecki E, Robertson M, et al. Unraveling the directional link between 
adiposity and inflammation: a bidirectional Mendelian randomization approach. J Clin 
Endocrinol Metab. Jan 2010;95(1):93-9. doi:10.1210/jc.2009-1064 
45. 
Maachi M, Pieroni L, Bruckert E, et al. Systemic low-grade inflammation is related to 
both circulating and adipose tissue TNFalpha, leptin and IL-6 levels in obese women. Int J Obes 
Relat Metab Disord. Aug 2004;28(8):993-7. doi:10.1038/sj.ijo.0802718 
46. 
Katzmarzyk PT, Leon AS, Rankinen T, et al. Changes in blood lipids consequent to aerobic 
exercise training related to changes in body fatness and aerobic fitness. Metabolism. Jul 
2001;50(7):841-8. doi:10.1053/meta.2001.24190 
B Nogal 34 
47. 
Hu X, Zhuang XD, Mei WY, et al. Exploring the causal pathway from body mass index to 
coronary heart disease: a network Mendelian randomization study. Ther Adv Chronic Dis. 
2020;11:2040622320909040. doi:10.1177/2040622320909040 
48. 
Huang Y, Xu M, Xie L, et al. Obesity and peripheral arterial disease: A Mendelian 
Randomization analysis. Atherosclerosis. Apr 2016;247:218-24. 
doi:10.1016/j.atherosclerosis.2015.12.034 
49. 
Houmard JA, Costill DL, Mitchell JB, Park SH, Fink WJ, Burns JM. Testosterone, cortisol, 
and creatine kinase levels in male distance runners during reduced training. Int J Sports Med. 
Feb 1990;11(1):41-5. doi:10.1055/s-2007-1024760 
50. 
Schorr M, Lawson EA, Dichtel LE, Klibanski A, Miller KK. Cortisol Measures Across the 
Weight Spectrum. J Clin Endocrinol Metab. Sep 2015;100(9):3313-21. doi:10.1210/JC.2015-2078 
51. 
O'Donnell CJ, Sabatine MS. Opportunities and Challenges in Mendelian Randomization 
Studies to Guide Trial Design. JAMA Cardiol. Oct 1 2018;3(10):967. 
doi:10.1001/jamacardio.2018.2863 
52. 
Hosseini B, Berthon BS, Saedisomeolia A, et al. Effects of fruit and vegetable 
consumption on inflammatory biomarkers and immune cell populations: a systematic literature 
review and meta-analysis. Am J Clin Nutr. Jul 1 2018;108(1):136-155. doi:10.1093/ajcn/nqy082 
53. 
Djousse L, Arnett DK, Coon H, Province MA, Moore LL, Ellison RC. Fruit and vegetable 
consumption and LDL cholesterol: the National Heart, Lung, and Blood Institute Family Heart 
Study. Am J Clin Nutr. Feb 2004;79(2):213-7. doi:10.1093/ajcn/79.2.213 
54. 
L D. Physical Activity and Dietary Habits of College Students. The Journal of Nurse 
Practitioners. February 2015 2015;11(2):192-198.e2.  
B Nogal 
 
35 
35 
55. 
Shi X, Tubb L, Fingers ST, Chen S, Caffrey JL. Associations of physical activity and dietary 
behaviors with children's health and academic problems. J Sch Health. Jan 2013;83(1):1-7. 
doi:10.1111/j.1746-1561.2012.00740.x 
56. 
Naughton P, McCarthy SN, McCarthy MB. The creation of a healthy eating motivation 
score and its association with food choice and physical activity in a cross sectional sample of 
Irish adults. Int J Behav Nutr Phys Act. Jun 6 2015;12:74. doi:10.1186/s12966-015-0234-0 
57. 
Klimentidis YC, Raichlen DA, Bea J, et al. Genome-wide association study of habitual 
physical activity in over 377,000 UK Biobank participants identifies multiple variants including 
CADM2 and APOE. Int J Obes (Lond). Jun 2018;42(6):1161-1176. doi:10.1038/s41366-018-0120-
3 
58. 
Arends RM, Pasman JA, Verweij KJH, et al. Associations between the CADM2 gene, 
substance use, risky sexual behavior, and self-control: A phenome-wide association study. 
Addict Biol. Nov 2021;26(6):e13015. doi:10.1111/adb.13015 
59. 
Morris J, Bailey MES, Baldassarre D, et al. Genetic variation in CADM2 as a link between 
psychological traits and obesity. Scientific Reports. 2019/05/14 2019;9(1):7339. 
doi:10.1038/s41598-019-43861-9 
 
 
 
 
 
B Nogal 36 
Table 1 Study Population Demographics 
 
Group 
N 
Female, % 
Age, yrs 
Body mass index, kg/m2 
PRO 
82 
53.7% 
33.68 
20.15 
HVAM 
1103 
52.9% 
39.48 
22.57 
MVAM 
6747 
54.2% 
41.49 
23.35 
LVAM 
10877 
34.2% 
41.16 
24.72 
SED 
4428 
48.9% 
44.25 
27.83 
PRO = Professional, HVAM  = high volume amateur (>10 hr), MVAM = medium 
volume amateur (3-10hr), LVAM = low volume amateur (<3 hr), SED = sedentary 
 
 
 
 
 
 
 
 
 
B Nogal 
 
37 
37 
Table 2 Blood Biomarkers Significantly Different Among Sedentary 
Individuals and Those Who Partake in Running for Exercise to 
Various Degrees 
 
Biomarker 
ANOVA p-value  
Trend p-value  
lowest mean 
highest mean 
Alb 
<1e-16 
<0.001 
MVAM 
PRO 
ALT 
<1e-16 
<1e-16 
SED 
PRO 
AST 
<1e-16 
<0.001 
SED 
PRO 
B12 
<0.001 
<0.001 
SED 
PRO 
Chol 
<0.001 
0.005 
PRO 
SED 
CK 
<1e-16 
<1e-16 
SED 
PRO 
Cor 
<0.001 
0.675 
SED 
PRO 
FE 
<0.001 
0.119 
SED 
PRO 
Fer 
<1e-16 
<1e-16 
MVAM 
SED 
Fol 
<1e-16 
<0.001 
SED 
PRO 
FT 
<0.001 
0.013 
SED 
PRO 
GGT 
<1e-16 
<0.001 
PRO 
SED 
Glu 
0.087 
0.184 
PRO 
SED 
Hb 
0.002 
<0.001 
MVAM 
PRO 
HCT 
0.053 
0.055 
MVAM 
PRO 
HDL 
<1e-16 
<0.001 
SED 
PRO 
HbA1c 
<0.001 
0.010 
PRO 
SED 
B Nogal 38 
hsCRP 
<0.001 
0.176 
PRO 
SED 
LDL 
<0.001 
0.006 
PRO 
SED 
Mg 
<0.001 
0.276 
PRO 
SED 
MPV 
0.058 
0.089 
SED 
HVAM 
Na 
<1e-16 
0.622 
HVAM 
SED 
RBC_Mg 
<0.001 
0.773 
PRO 
SED 
RDW 
<1e-16 
0.002 
PRO 
SED 
SHBG 
<1e-16 
0.004 
SED 
PRO 
Tg 
<1e-16 
<1e-16 
PRO 
SED 
WBC 
<1e-16 
<1e-16 
PRO 
SED 
 
 
 
 
 
Figure 1
Click here to access/download;Figure;Figure_1.jpg
Figure 2
Click here to access/download;Figure;Figure_2.jpg
Figure 3
Click here to access/download;Figure;Figure_3.jpg
Figure 4
Click here to access/download;Figure;Figure_4.jpg
Figure 4
Click here to access/download;Figure;Figure_5 .jpg
  
Supporting Information
Click here to access/download
Supporting Information
Supplementary_Materials_PONE.pdf

Dose response of running on blood biomarkers of wellness in generally healthy individuals.
Nogal, Bartek,Vinogradova, Svetlana,Jorge, Milena,Torkamani, Ali,Fabian, Paul,Blander, Gil