PLOS ONE
 
Dose response of running on blood biomarkers of wellness in generally healthy
individuals
--Manuscript Draft--
 
Manuscript Number:
PONE-D-23-25168R1
Article Type:
Research Article
Full Title:
Dose response of running on blood biomarkers of wellness in generally healthy
individuals
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
Response to Reviewers:
Editor comments:
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‘Financial Disclosure’ sections do not match.
Thank you - we believe that we now addressed this.  We removed all funding
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anything needs further attention.
 3.  In your Data Availability statement, you have not specified where the minimal data
set underlying the results described in your manuscript can be found.
We updated our Data Availability statement via the submission system to reflect our
opennes to share the minimal dataset upon request from the corresponding author, as
well as the url to the public repository where the gwas summary statistics can be found.
4.  We note that you have included the phrase “data not shown” in your manuscript....
Thank you.  We now addressed this within the text of the manuscript and the “data not
shown” no longer appears
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Captions for Supporting Information is now included at the end of the manuscript.
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We believe we have now addressed the style and formatting requirements.
Reviewer comments:
Reviewer #1: The majority parts of the articles are technically sound. Moreover, the
purpose of the study is very sound since it focused on healthy active population.
Among the few drawbacks of the study the way the study subjects categorized into
groups based on the duration of the activity (>10hours per week and less that 10hours
per week is not appropriate. Moreover, the reliability/validity of the information sources
in relation to the biomarker tests and lifestyle habits of the study subjects didn't
consider the immediate effects of medical services and medication conditions of the
respondents at the time of reporting the volume of exercise and biomarker test results.
Medical services and lifestyle habits specially all the habits in addition to
exercises/running are very important to reach informative decision in this research. So,
the above two points need further explanation or modification.
Response to Reviewer #1: We appreciate the reviewer's feedback and are pleased
that they find the majority of our study technically sound and recognize the importance
of our focus on a healthy, active population.  We also appreciate the reviewer pointing
out an opportunity to improve the clarity around our experimental design as it pertains
subject groupings.
Regarding the categorization of study subjects, we want to clarify that we actually
categorized them into five groups. These groups include professional endurance
runners, high volume amateur runners (>10 hours per week), medium volume amateur
runners (3-10 hours per week), low volume amateur runners (<3 hours per week), and
the sedentary.  We now added a sentence starting on line 125 the explicitly states this
categorization (“The cohort was divided into five groups:…”).  These groupings were
determined based on the respondents' self-reported data.
We acknowledge the potential influence of medication use on our analysis, and we
now address it starting on line 461 (“These factors, such as diet, sleep, and/or
medications were not readily ascertained in this free-living cohort…”) and in the Study
Limitations section (line 595). We noted that unmeasured confounders such as
medications, nutritional supplements, and unreported health conditions may exist.
However, given the nature of our cohort, which primarily consists of self-selected,
generally healthy individuals, the impact of significant medication use is expected to be
limited. We believe that the observed trends in healthier biomarker levels with
increased reported running volume support this assertion.
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Furthermore, we recognize the importance of lifestyle habits beyond exercise in
influencing our results. To address this, we employed statistical genomics, specifically
two-sample Mendelian randomization with physical activity as the exposure. This
analysis allowed us to explore other potential habits and behaviors contributing to
improved biomarker signatures in physically active runners within our cohort. We kindly
refer the reviewer to the "Vigorous physical activity associates with healthier behaviors"
section in the results for a detailed examination of this aspect. Notably, our entire
cohort is composed of health-conscious individuals within the same health advisory
platform, with the primary differentiator being self-reported running activity. We also
controlled for key variables such as age, sex, and BMI in our ANOVA analyses.
We hope these explanations clarify our approach and address the reviewer's concerns
adequately.
Reviewer #2:   How your data is reliable by using A cross-sectional study design? &
How again the Data is reliable by using self-reported running. I understand that
Biomarkers are objective measure, but do you think that Self-report is trustworthy?
Thank you
Response to Reviewer #2: We appreciate the reviewer's questions and concerns
regarding the reliability of our runners data, which is largely derived from self-reported
exercise habits. Cross-sectional studies inherently have limitations when it comes to
establishing causality, and we acknowledge this challenge. To address potential
confounding factors, we conducted additional causal analyses, specifically
investigating the effects of BMI on the biomarkers under examination to begin to
disentangle the relative contributions of known factors. Furthermore, we performed
secondary Mendelian randomization (MR) analyses to identify and account for
potential confounders in our findings. We kindly invite the reviewer to explore the
"Vigorous physical activity associates with healthier behaviors" section in the results for
a comprehensive exploration of these confounding aspects.
Regarding the reliability of self-reported running activity, we recognize that self-reports
can be subject to biases, and individuals may tend to overestimate their exercise
commitment.  To address this drawback, we added language addressing these
limitations in the “Study limitations” section (Line 579:  “First, it is generally known that
subjects tend to overestimate their commitment to exercise …”).  We do note that our
study cohort comprises self-selected individuals who are health-conscious and
possibly less prone to over-report their running volume. Additionally, the robust
increasing trend in baseline levels of muscle damage biomarkers (CK, AST), which are
known to be associated with participation in sports and exercise, provides indirect
evidence that the different running groups in our study were indeed engaging in
increasing volumes of strenuous physical activity.
While self-reporting has its limitations, it remains a valuable method for capturing
individuals' exercise behaviors in large-scale observational studies. We took measures
to mitigate potential biases, and our findings align with established trends in biomarker
responses to physical activity.
Reviewer #3: Upon a meticulous review of the article in question, I wish to commend
the authors for crafting a piece that not only carries immense scientific weight but is
also articulated with great clarity. Such insightful work surely merits publication in your
distinguished journal. It's admirable how the authors have navigated through a myriad
of physiological and biochemical variables (blood biomarkers) across five distinct
participant categories and presented their results with lucidity. The experimental
framework is robust, the statistical evaluations are apt, and the narrative progresses
seamlessly. The references provided are both relevant and adequate. Nevertheless, I'd
like to offer a few observations and suggestions:
Response:  We appreciate the reviewer's positive feedback and kind words about our
manuscript. We eagerly await their observations and suggestions should they see
further opportunities to improve our work based on our responses to the current
suggestions.
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Original Title: “Dose response of running on blood biomarkers of wellness in the
generally healthy.”
Proposed Title: “Dose-response relationship between running and blood biomarkers of
wellness in generally healthy individuals.”
Response:  Thank you – title has been changed.
Page 2, Line 8: The mention of “exposure to sunlight” seems somewhat out of context.
Could the authors clarify its relevance or indicate if it has been discussed elsewhere in
the article?
Response:  Thank you for the suggestion, we removed this as we agree it was not
relevant in this manuscript.
Page 17, Lines 17-18: The text reads: "These observations suggest that elite
endurance runners………to their magnesium status."
Comments: It would be helpful to clarify whether the professional athletes (PRO)
participating in this study are specifically elite endurance runners. Kindly integrate this
distinction into the main text if accurate.
Response:  Thank you for the clarifying suggestion.  We included the pro/elite
endurance runners clarification within the abstract as well as a section heading (lines 7
and 425)
Page 19, Lines 1-2: The assertion: “Indeed whether exercise………..is inconclusive,”
needs to be substantiated with a relevant citation.
Response:  Thank you – citations have been added.
Table 1: Please include standard deviation (SD) values. I also recommend expressing
exercise duration in terms of "h/week" instead of "hr".
Response:  Thank you for the catch – units changed to “h/week” and SDs added to
Table 1.
We are grateful for your valuable feedback, which has contributed to improving the
clarity and accuracy of our manuscript.
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1 
Dose response of running on blood biomarkers of wellness in 
generally healthy individuals 
 
Bartek Nogal1¶ , Svetlana Vinogradova1¶, Milena Jorge1, Ali Torkamani2,3, Paul Fabian1, 
Gil Blander1* 
1InsideTracker, Cambridge, Massachusetts, United States of America 
 
2The Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA, 
United States of America 
 
3Department of Integrative Structural and Computational Biology, The Scripps Research 
Institute, La Jolla, CA, United States of America 
 
* Corresponding author 
E-mail:  gblander@insidetracker.com (GB) 
 
¶These authors contributed equally to this work 
 
 
 
 
 
Manuscript
Click here to access/download;Manuscript;Manuscript.docx
 
 
 
1 
Abstract 
1 
Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence 
2 
supporting its effectiveness.  However, less is known about the specific healthspan-promoting 
3 
effects of exercise on blood biomarkers in the disease-free population.  In this work, we examine 
4 
23,237 generally healthy individuals who self-report varying weekly running volumes and 
5 
compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional 
6 
endurance runners.  We estimate the significance of differences among blood biomarkers for 
7 
groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, 
8 
gender, and BMI.  We attempt and add insight to our observational dataset analysis via two-sample 
9 
Mendelian randomization (2S-MR) using large independent datasets.   We find that self-reported 
10 
running volume associates with biomarker signatures of improved wellness, with some serum 
11 
markers apparently being principally modified by BMI, whereas others show a dose-effect with 
12 
respect to running volume. We further detect hints of sexually dimorphic serum responses in 
13 
oxygen transport and hormonal traits, and we also observe a tendency toward pronounced 
14 
modifications in magnesium status in professional endurance athletes.   Thus, our results further 
15 
characterize blood biomarkers of exercise and metabolic health, particularly regarding dose-effect 
16 
relationships, and better inform personalized advice for training and performance. 
17 
Introduction 
18 
Physical inactivity is one of the leading modifiable behavioral causes of death in the US [1].  
19 
Worldwide, physical inactivity is estimated to account for about 8.3% of premature mortality, an 
20 
effect size that is on the same order as smoking and obesity [2].  At the same time, the potent health 
21 
 
 
benefits of exercise have been proven time and time again, with results so consistent across a wide 
22 
variety of chronic diseases that some posit it can be considered a medical intervention [3-5].  
23 
However, since most investigators report the effects of exercise in either diseased populations or 
24 
athletes [6, 7], there exists a significant gap in knowledge as to the measurable effects of exercise 
25 
in the generally healthy population who exercise for the purpose of improving their healthspan, 
26 
which can be projected via established measures such as blood biomarkers [8-11].   
27 
It is well established that routine laboratory biomarkers are validated proxies of the state of an 
28 
individual’s overall metabolic health and other healthpan-related parameters [12].  A large body 
29 
of evidence supports the effectiveness of exercise in modifying blood biomarkers toward disease 
30 
mitigation in clinical cohorts as well as athletes, where the effect sizes may be larger [6, 13].  
31 
Indeed, it’s been shown that more favorable changes in response to exercise training occur usually 
32 
in those with more pronounced dyslipidemia  [13].  In professional athletes, the sheer volume 
33 
and/or intensity of physical activity may drive large effects in various hematological, lipid, 
34 
immune, and endocrine variables [6]. Our aim is to help fill the gap in understanding of the effects 
35 
of exercise on blood biomarkers in the generally healthy, free-living population.  Toward this end, 
36 
we endeavored to explore the effects of vigorous exercise such as running in apparently healthy, 
37 
mostly non-athletic cohort to better understand the landscape of blood biomarker modifications 
38 
expected in the individual who partakes in recreational physical activity for the purpose of 
39 
maintaining good health. 
40 
For this purpose, we leveraged the InsideTracker dataset that includes information on self-reported 
41 
exercise habits combined with blood biomarker and genomics data.  We have previously reported 
42 
on the results of a longitudinal analysis on blood biomarker data from 1032 generally healthy 
43 
individuals who used our automated, web-based personalized nutrition and lifestyle platform [14].  
44 
 
 
 
3 
For the purpose of this investigation, we focused on running as the exercise of choice as it is one 
45 
of the most common (purposeful) physical activity modalities practiced globally by generally 
46 
healthy individuals and would thus be relevant.  Moreover, since this was a cross-sectional study 
47 
based on self-reported exercise habits, we attempted to increase our capacity to infer intervention 
48 
effects, as well as tease out potential confounders, by performing 2S-MR in large independent 
49 
cohorts. 
50 
Materials and methods 
51 
Dataset 
52 
We conducted an observational analysis of data from InsideTracker users.  InsideTracker is a 
53 
direct-to-consumer (DTC) company established in 2009 that markets and sells InsideTracker 
54 
(insidetracker.com), a personalized lifestyle recommendation platform. The platform provides 
55 
serum biomarker and genomics testing, and performs integrative analysis of these datasets, 
56 
combined with activity/sleep tracker data toward biomarker and healthspan optimization (of note, 
57 
at the time of this analysis, we did not have sufficient users with activity/sleep tracker data to 
58 
include this data stream in the current study).   New users were continuously added to the 
59 
InsideTracker database from January 2011 to March 2022. 
60 
 
61 
Recruitment of participants 
62 
Recruitment of participants aged between 18 and 65 and residing in North America was conducted 
63 
through company marketing and outreach. Participants were subscribing members to the 
64 
 
 
InsideTracker platform and provided informed consent to have their blood test data and self-
65 
reported information used in an anonymized fashion for research purposes. Research was 
66 
conducted according to guidelines for observational research in tissue samples from human 
67 
subjects. Eligible participants completed a questionnaire that included age, ethnicity, sex, dietary 
68 
preferences, physical activity, and other variables. This study employed data from 23,237 
69 
participants that met our analysis inclusion requirements, namely absence of any chronic disease 
70 
as determined by questionnaire and metabolic blood biomarkers within normal clinical reference 
71 
ranges. The platform is not a medical service and does not diagnose or treat medical conditions, 
72 
so medical history and medication use were not collected.  The Institutional Review Board (IRB) 
73 
determine this work was not subject to a review based on category 4 exemption (“secondary 
74 
research” with de-identified subjects). 
75 
Biomarker collection and analysis 
76 
Blood samples were collected and analyzed by Clinical Laboratory Improvement Amendments 
77 
(CLIA)–approved, third-party clinical labs (primarily Quest Diagnostics and LabCorp). 
78 
Participants were instructed to fast for 12 hours prior to the phlebotomy, with the exception of 
79 
water consumption. Results from the blood analysis were then uploaded to the platform via 
80 
electronic integration with the CLIA-approved lab. Participants chose a specific blood panel from 
81 
7 possible offerings, each comprising some subset of the biomarkers available. Due to the variation 
82 
in blood panels offered, the participant sample size per biomarker is not uniform. 
83 
Biomarker dataset preparation 
84 
 
 
 
5 
In our raw dataset, occasional outlier values were observed that were deemed implausible (e.g. 
85 
fasting glucose < 65 mg/dL). To remove anomalous outliers in a systematic way, we used the 
86 
Interquartile Range (IQR) method of identifying outliers, removing data points which fell below 
87 
Q1 – 1.5 IQR or above Q3 + 1.5 IQR.  The cohort was divided into five groups: professional 
88 
endurance runners (PRO),  high volume amateur (>10 h/week, HVAM ), medium volume amateur 
89 
(3-10 h/week, MVAM), low volume amateur (<3 h/week, LVAM), and sedentary (SED). 
90 
Calculation of polygenic scores 
91 
The variants (SNPs) comprising the polygenic risk scores were derived from publicly available 
92 
GWAS summary statistics (https://www.ebi.ac.uk/gwas/).  Scores were calculated across users by 
93 
summing the product of effect allele doses weighted by the beta coefficient for each SNP, as 
94 
reported in the GWAS summary statistics.  Variant p-value thresholds were generally chosen based 
95 
on optimization of respective PGS-blood biomarker correlation in the entire InsideTracker cohort 
96 
with both blood and genomics datasets (~1000-1500 depending on the blood biomarker at the time 
97 
of analysis).  Genotyping data was derived from a combination of a custom InsideTracker array 
98 
and third party arrays such as 23andMe and Ancestry.  Not all variants for any particular PGS were 
99 
genotyped on every array; proxies for missing SNPs were extracted via the “LDlinkR” package 
100 
using the Utah Residents (CEPH) with Northern and Western European ancestry (CEU) population 
101 
(R2 > 0.8 cut-off).  Only results PGSs for which there was sufficient biomarker-genotyping dataset 
102 
overlap were reported (note that none of the blood biomarker PGSs met this requirement). 
103 
Blood biomarker analysis with respect to running volume and 
104 
polygenic scores 
105 
 
 
To estimate significance of differences for blood biomarkers levels among exercise groups, we 
106 
performed 3-way analysis of variance (ANOVA) analysis adjusting for age, gender, and BMI 
107 
(type-II analysis-of-variance tables function ANOVA from ‘car’ R package, version 3.0-12). 
108 
When estimating the effort of reported training volume on biomarkers, we assigned numerical 
109 
values corresponding to 4 levels of running and performed ANOVA analysis with those levels 
110 
treating it as an independent variable.  P-values were adjusted using the Benjamini & Hochberg 
111 
method [15]. P-values for interaction plots were calculated with ANOVA including interaction 
112 
between exercise group and polygenic scores category.  When comparing runners (PRO and 
113 
HVAM combined) versus sedentary individuals, we used propensity score matching method to 
114 
account for existing covariates (age and gender): we identified 745 sedentary individuals with 
115 
similar to runners’ age distributions among both males and females. We used ‘MatchIt’ R package 
116 
(version 4.3.3)  implementing nearest neighbor method for matching [16]. 
117 
Mendelian randomization  
118 
We attempted to add insight around the causality of exercise vs. BMI differences with respect to 
119 
serum marker improvement by performing MR analyses on a subset of biomarker observations 
120 
where BMI featured as a strong covariate and was thus used as the IV in the 2S-MR.   Thus, our 
121 
hypothesis here was that BMI differences were the primary (causal) driver behind the improvement 
122 
behind some biomarkers.  MR uses genetic variants as modifiable exposure (risk factor) proxies 
123 
to evaluate causal relationships in observational data while reducing the effects of confounders 
124 
and reverse causation (S1 Fig).  These SNPs are used as instrumental variables and must meet 3 
125 
basic assumptions: (1) they must be robustly associated with the exposure; (2) they must exert 
126 
their effect on outcome via the exposure, and (3) there must be no unmeasured confounders of the 
127 
 
 
 
7 
associations between the genetic variants and outcome (e.g. horizontal pleiotropy) [17].  
128 
Importantly, SNPs are proper randomization instruments because they are determined at birth and 
129 
thus serve as proxies of long-term exposures and cannot, in general, be modified by the 
130 
environment.  If the 3 above mentioned assumptions hold, MR-estimate effects of exposure on 
131 
outcomes are not likely to be significantly affected by reverse causation or confounding.  In the 
132 
2S-MR performed here, where GWAS summary statistics are used for both exposure and outcome 
133 
from independent cohorts, reverse causation and horizontal pleiotropy can readily be assessed, and 
134 
weak instrument bias and the likelihood of false positive findings are minimized as a result of the 
135 
much larger samples sizes [17].  Indeed, the bias in the 2S-MR using non-overlapping datasets as 
136 
performed here is towards the null [17].  Furthermore, to maintain the SNP-exposure associations 
137 
and linkage disequilibrium (LD) patterns in the non-overlapping populations we used GWAS 
138 
datasets from the MR-Base platform that were derived from ancestrally similar populations 
139 
(“ukb”:  analysis of UK Biobank phenotypes, and “ieu”: GWAS summary datasets generated by 
140 
many different European consortia).  To perform the analysis we used the R package 
141 
“TwoSampleMR” that combines the effects sizes of instruments on exposures with those on 
142 
outcomes via a meta-analysis.  We used “TwoSampleMR” package functions for allele 
143 
harmonization between exposure and outcome datasets, proxy variant substitution when SNPs 
144 
from exposure were not genotyped in the outcome data (Rsq>0.8 using the 1000G EUR reference 
145 
data integrated into MR-Base), and clumping to prune instrument SNPs for LD (the R script used 
146 
for MR analyses is available upon request).  We used 5 different MR methods that were included 
147 
as part of the “TwoSampleMR” package to control for bias inherent to any one technique [18].  
148 
For example, the multiplicative random effects inverse variance-weighted (IVW) method is a 
149 
weighted regression of instrument-outcome effects on instrument-exposure effects with the 
150 
 
 
intercept is set to zero.  This method generates a causal estimate of the exposure trait on outcome 
151 
traits by regressing the, for example, SNP-BMI trait association on the SNP-biomarker measure 
152 
association, weighted by the inverse of the SNP-biomarker measure association, and constraining 
153 
the intercept of this regression to zero.  This constraint can result in unbalanced horizontal 
154 
pleiotropy whereby the instruments influence the outcome through causal pathways distinct from 
155 
that through the exposure (thus violating the second above-mentioned assumption).  Such 
156 
unbalanced horizontal pleiotropy distorts the association between the exposure and the outcome, 
157 
and the effect estimate from the IVW method can be exaggerated or attenuated. However, 
158 
unbalanced horizontal pleiotropy can be readily assessed by the MR Egger method (via the MR 
159 
Egger intercept), which provides a valid MR causal estimate that is adjusted for the presence of 
160 
such directional pleiotropy, albeit at the cost of statistical efficiency.  Finally, to ascertain the 
161 
directionality of the various causal relationships examined, we also performed each MR analysis 
162 
in reverse where possible. 
163 
Results  
164 
Study population characteristics 
165 
 
Table 1 shows the demographic characteristics of the study population.  We observed a 
166 
significant trend toward younger individuals reporting higher running volume, with more than 
167 
75% of the professional (PRO) group falling between the ages of 18 and 35 (S1 Table).  Significant 
168 
differences were also observed in the distribution of males and females within study groups (Table 
169 
1).  Moreover, higher running volume associated with significantly lower body mass index (BMI).  
170 
 
 
 
9 
Thus, moving forward, combined comparisons of blood biomarkers as they relate to running 
171 
volume were adjusted for age, gender, and BMI. 
172 
 
173 
Table 1.  Study population demographics 
174 
Group 
N 
Female, % 
Age, yrs 
Body mass index, kg/m2 
PRO 
82 
53.7% 
33.68 
20.15 ± 6.02 
HVAM 
1103 
52.9% 
39.48 
22.57 ± 9.97 
MVAM 
6747 
54.2% 
41.49 
23.35 ± 9.76 
LVAM 
10877 
34.2% 
41.16 
24.72 ± 9.70 
SED 
4428 
48.9% 
44.25 
27.83 ± 10.70 
PRO = Professional, HVAM  = high volume amateur (>10 h/week ), MVAM = medium volume 
175 
amateur (3-10 h/week), LVAM = low volume amateur (<3 h/week), SED = sedentary 
176 
 
177 
Endurance exercise exhibits a modest association with clusters of 
178 
blood biomarker features  
179 
In order to begin to understand the most important variables that may associate with endurance 
180 
exercise in the form of running, we performed a principal component analysis (PCA), sub-dividing 
181 
the male and female cohorts into two most divergent groups in terms of exercise volume:  
182 
PRO/high volume amateur (HVAM) and sedentary (SED) groups.  Using propensity matching, 
183 
PRO and amateur athletes who reported running >10h per week were combined into the PRO-
184 
HVAM group to balance out the sample size between the exercising and non-exercising groups.  
185 
 
 
This approach yielded a modest degree of separation, with hematological, inflammation, and lipid 
186 
features, as well as BMI explaining some of the variance (Fig 1 A through D).  We hypothesized 
187 
that there may more subtle relationships between running volume and the blood biomarker features 
188 
that contributed to distinguishing the endurance exercise and sedentary groups, thus we next 
189 
performed ANOVA analyses stratified by running volume as categorized in Table 1. 
190 
Fig 1.  Principal component analysis and variables plots of PRO-HVAM runners and sedentary 
191 
user blood biomarkers. Females, (A) and (B); males (C) and (D). PRO-HVAM = combined 
192 
professional and high-volume amateur.  Alb = albumin, ALT = alanine transaminase, AST = 
193 
aspartate aminotransferase, B12 = vitamin B12, Ca = calcium, Chol = total cholesterol, CK = 
194 
creatine kinase, Cor = cortisol, FE = iron, EOS_PCT = eosinophil percentage, Fer = ferritin, Fol = 
195 
folate, FT = free testosterone, GGT = gamma-glutamyl transferase, Glu = glucose, Hb = 
196 
hemoglobin, HCT = hematocrit, HDL = high density lipoprotein, HbA1c = glycated hemoglobin, 
197 
hsCRP = high-sensitivity C-reactive protein, LDL = low density lipoprotein, LYMPS_PCT = 
198 
lymphocyte percentage, MCH = mean cell hemoglobin, Mg = magnesium, MONOS_PCT = 
199 
monocytes percentage, MPV = mean platelet volume, Na = sodium, RBC = red blood cells, 
200 
RBC_Mg = red blood cell magnesium, RDW = red blood cell distribution width, SHBG = sex 
201 
hormone binding globulin, Tg = triglycerides, TIBC = total iron binding capacity, WBC = white 
202 
blood cells 
203 
Significant trends in glycemic, hematological, blood lipid, and 
204 
inflammatory serum traits with increasing running volumes 
 
205 
Weighted ANOVA analyses adjusted for age, gender, and BMI showed significant differences 
206 
among groups for multiple blood biomarkers (Table 2 and S2 , Figs 2 and 3).  We observed a trend 
207 
 
 
 
11 
toward lower HbA1c, hsCRP, RDW, WBC, ferritin, gamma-glutamyl transferase (GGT), and 
208 
LDL.  HDL, hemoglobin (Hb), transferrin saturation (TS), alanine aminotransferase (ALT), 
209 
aspartate aminotransferase (AST), vitamin B12, folate, 25-hydroxy vitamin D, and creatine kinase 
210 
(CK) tended to be higher with increasing reported training volume, particularly in PRO runners 
211 
(Tables 2 and S2 , Figs 2 and S2 , Fig 3).   Hct and Hb were higher only in PRO males, whereas 
212 
increased running volume associated with upward trend in these biomarkers in females (Fig 3 A 
213 
and B).  Increased running volume was associated with markedly lower Fer in males, whereas 
214 
female runners did not exhibit varying levels, and SED females showed increased levels (Fig 3 C).  
215 
The low ferritin observed in male and female runners was not clinically significant.  ALT 
216 
positively associated with running volume in females only (S2 Fig).  Serum and RBC magnesium 
217 
(Mg) were both significantly lower in PRO runners relative to all other groups (Table 2 and Fig 3 
218 
D and E).   Increasing levels of endurance exercise also appeared to be associated with higher sex-
219 
hormone binding globulin (SHBG), particularly in PRO male runners (Fig 3 F).  
220 
 
221 
Table 2.  Blood biomarkers significantly different among sedentary individuals and those 
222 
who partake in running for exercise to various degrees 
223 
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 
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 
 
 
 
13 
WBC 
<1e-16 
<1e-16 
PRO 
SED 
 
224 
Fig 2.  Blood biomarkers associated with running: Inflammation proxies, (A) hsCRP = high-
sensitivity C-reactive protein and (B) WBC = white blood cells; blood lipids, (C) HDL = high 
density lipoprotein (D) LDL = low density lipoprotein, and (E) Tg = triglycerides; glycemia 
proxies, (F) Glu = glucose and (G) HgbA1c = glycated hemoglobin, and (H) Cor =cortisol 
Fig 3.  Blood biomarkers associated with running: (A and B) Hb (hemoglobin) and Hct 
(hematocrit) increase with increasing running volume, (C) Fer (ferritin) is reduced with 
increasing running volume, (D and E) Serum and RBC Mg (red blood cell magnesium) are 
reduced in professional runners, and (F) SHBG (sex hormone binding globulin) levels increase 
with increasing running volume in males 
 
225 
Endurance exercise correlates with lower BMI across categories of 
226 
genetic risk  
227 
Using publicly available GWAS summary statistics, we constructed blood biomarker polygenic 
228 
risk scores (PGSs) to explore potential genetic risk-mitigating effects of endurance exercise.  Since 
229 
only a subset of the individuals in our cohort were genotyped, we aggregated the groups into 2 
230 
categories—PRO-HVAM and sedentary—to increase statistical power.  This across-group sample 
231 
size increase generally did not sufficiently power the ANOVA analysis to detect statistically 
232 
 
 
significant trends, though the BMI polygenic risk was suggestively mitigated for both males and 
233 
female PRO-HVAM runners across categories of genetic risk (Fig 4 B).  
234 
Fig 4.  BMI significantly varied among running groups (A) with some suggestive effects on BMI 
PGS modification (total number for observations (N) for T1, T2, and T3 were 87, 84, and 100, 
respectively) (B) T1, T2, and T3 = 1st, and 2nd and 3rd tertials of the polygenic score distribution 
 
235 
Increased running volume is associated with lower BMI which may 
236 
drive biomarker changes  
237 
We observed a significant downward trend in the BMI with increased running volume for both 
238 
males and females, and, although some of the biomarker differences between sedentary and 
239 
exercising individuals remained significant after adjustment for BMI, their significance was 
240 
attenuated (Fig 4 A). Thus, we hypothesized that BMI may be driving a significant portion of the 
241 
observed variance in some of the biomarkers across the groups.  Thus, to explore causal 
242 
relationships between weight and biomarker changes, we performed 2S-MR with BMI-associated 
243 
single-nucleotide polymorphisms (SNPs) as the instrumental variables (IVs) for a subset of the 
244 
healthspan-related biomarkers where BMI explained a relatively large portion of the variance in 
245 
our analysis.   In general, these blood biomarkers associated with inflammation (hsCRP and 
246 
RDW), lipid metabolism (Tg and HDL), glycemic control (HbA1c and Glu), as well as Alb and 
247 
SHBG. We used GWAS summary statistics and found that most of these BMI-blood biomarker 
248 
relationships examined directionally aligned with our study (except for LDL), and some were 
249 
indicative of causal relationships in the BMI-biomarker direction even after considering directional 
250 
 
 
 
15 
pleiotropy (S3 Table).   We entertained the possibility of reverse causality and thus repeated the 
251 
2S-MR using each of the biomarker levels as the exposure and BMI as the outcome, and the results 
252 
were generally not significant (except for WBC – see S4 Table).  Of note, to estimate the direct 
253 
causal effects of running on blood parameters, we attempted to find an instrumental variable for 
254 
to approximate running as the exposure from publicly available GWAS summary statistics.  
255 
Toward this end, we found that increasing levels of vigorous physical activity did associate with 
256 
lower hsCRP, HbA1C, higher HDL, and possibly higher SHBG (although the explained variance 
257 
(R2) in this exposure was just 0.001009, the F statistic was 37.7, thus meeting the criteria of F > 
258 
10 for minimizing weak instrument bias) (Figs 5 and S3; S5 Table). 
259 
Fig 5.  Two-sample Mendelian randomization shows that increasing levels of vigorous physical 
activity such as running is associated with improvement of (A) hsCRP = high-sensitivity C-
reactive protein, (B) HDL = high density lipoprotein, and (C) HbA1c = glycated hemoglobin 
levels 
 
260 
Vigorous physical activity associates with healthier behaviors 
261 
We hypothesized that those who exercise regularly may also partake in other healthful lifestyle 
262 
habits that may be contributing to more optimal blood biomarker signatures of wellness.  However, 
263 
our dataset did not allow for systematic accounting of other lifestyle habits across all running 
264 
groups.  Thus, we again leveraged the potential of the 2S-MR approach to inform potential 
265 
confounding  associations between modifiable exposures and found that vigorous physical activity 
266 
such as running is at least suggestively associated with several behaviors associated with improved 
267 
 
 
health (S4 Fig).  Our analysis showed that those who participate in increasing levels of vigorous 
268 
physical activity may be less likely to eat processed meat (IVW p = 0.0000013), sweets (IVW p = 
269 
0.32), and nap during the day (IVW p = 0.13), while increasing their intake of oily fish (IVW p = 
270 
0.029), salad/raw vegetable intake (IVW p = 0.00016), and fresh fruit (IVW p = 0.0027) (S6 
271 
Table).  Furthermore, following our assessment of reverse causality, we found evidence for the 
272 
bidirectionality in the causal relationship between vigorous activity and napping during the day 
273 
and salad/raw vegetable intake, perhaps suggesting some degree of confounding due to population 
274 
stratification (S7 Table). The suggestive positive effect of fresh fruit and processed meat intake on 
275 
vigorous physical activity appeared to violate MR assumption (3) (S1 Fig) (horizontal pleiotropy 
276 
p-values 0.051 and 0.17, respectively – S5 Fig). 
277 
Discussion 
278 
In this report, we describe the variance in wellness-related blood biomarkers among self-reported 
279 
recreational runners, PRO runners, and individuals who do not report any exercise.  Overall, we 
280 
find that 1) recreational running as an exercise appears to be an effective intervention toward 
281 
modifying several biomarkers indicative of improved metabolic health, 2) an apparent dose-
282 
response relationship between running volume and BMI may itself be responsible for a proportion 
283 
of the apparent metabolic benefits, and 3) both PRO-level status and gender appear to associate 
284 
with heterogeneous physiological responses, particularly in iron and magnesium metabolism, as 
285 
well as some hormonal traits. 
286 
Self-reported running improves glycemia and lipidemia  
287 
 
 
 
17 
We did not observe distinct clusters corresponding to self-reported high-volume/PRO runners and 
288 
the sedentary upon dimension reduction.  This is, perhaps, not unexpected due, in part, to the self-
289 
selected healthspan-oriented nature of our cohort, where even the sedentary subset of individuals 
290 
tends to exhibit blood biomarker levels in the normal clinical reference ranges.  Furthermore, the 
291 
measurement of running volume via self-report may be vulnerable to overestimation, which may 
292 
have contributed to the blending of sedentary and exercise groups with respect to the serum 
293 
markers measured, resulting in only marginal separation between the groups [19, 20].  However, 
294 
we did observe significant individual blood biomarker variance with respect to reported running 
295 
volumes when the dataset was subjected to ANOVA, even after adjustment for age, sex, and BMI.  
296 
From among glycemic control blood biomarkers, we were able to detect a relatively small exercise 
297 
effect in both fasting glucose and HbA1c in this generally healthy cohort, where the average 
298 
measures of glycemia were below the prediabetic thresholds in even the sedentary subset of the 
299 
cohort. Larger exercise intervention effects on metabolic biomarkers may be expected in cohorts 
300 
that include individuals with more clinically significant baseline values [21].   
301 
Similarly, blood lipids improved with higher self-reported running volume, and this result has been 
302 
reported before in multiple controlled endurance exercise trials [22].  The literature indicates that 
303 
HDL and Tg are two exercise-modifiable blood lipid biomarkers, with HDL being the most widely 
304 
reported to be modified by aerobic exercise [23, 24].  Although the mechanism behind this is not 
305 
entirely clear, it likely involves the modification of lecithin acyltransferase and lipoprotein lipase 
306 
activities following exercise training [25].  We observed a similar trend in our blood biomarker 
307 
analysis, with HDL exhibiting an upward trend with increasing reported running volume.  While 
308 
we also found Tg and LDL to decrease with increasing exercise volume, these trends were less 
309 
pronounced.  Reports generally suggest that, in order to reduce LDL more consistently, the 
310 
 
 
intensity of aerobic exercise must be high enough [23].  In the case of Tg, baseline levels may have 
311 
a significant impact on the exercise intervention effect, with individuals exhibiting higher baselines 
312 
showing greater improvements [13]. 
313 
Importantly, these results suggests that exercise has a significant effect on glycemic control and 
314 
blood lipids even in the self-selected, already healthy individuals who are proactive about 
315 
preventing cardiometabolic disease.  
316 
Self-reported running and serum proxies of systemic inflammation  
317 
Chronic low-grade inflammation is one of the major risk factors for compromised cardiovascular 
318 
health and metabolic syndrome (MetS).  While there is no shortage of inflammation-reducing 
319 
intervention studies on CVD patients with clinically high levels of metabolic inflammation, there 
320 
is less emphasis on modifiable lifestyle factors that can help stave off CVD and extend healthspan 
321 
in the generally healthy individual.  Indeed, considering the pathological cardiovascular processes  
322 
begin shortly after birth, prevention in asymptomatic individuals may be a more appropriate 
323 
strategy toward decreasing the burden of CVD on the healthcare system [26].   
324 
Toward this end, increasing self-reported running volume appeared to associate with improved 
325 
markers of inflammation, as shown by the lower levels of hsCRP, WBC, as well as ferritin.  Of 
326 
note, while the acute-phase protein, ferritin, is often used in the differential diagnosis of iron 
327 
deficiency anemia, the biomarker’s specificity appears to depend on the inflammatory state of the 
328 
individual, as it associates with hsCRP and inflammation more than iron stores, particularly in 
329 
those with higher BMI [27].   Although serum ferritin and iron is reported to be lower in male and 
330 
female elite athletes [28], the observed overall negative association of ferritin with increased 
331 
 
 
 
19 
running volume in our cohort may be an indication of lower levels of inflammation rather than 
332 
compromised iron stores, particularly since the average ferritin level across all groups was above 
333 
the clinical iron deficiency thresholds.  Moreover, increased levels of ferritin have been associated 
334 
with insulin resistance and lower levels of adiponectin in the general population, both indicators 
335 
of increased systemic inflammation [29].  Here, exercising groups with lower levels of ferritin also 
336 
exhibited glycemic and blood lipid traits indicative of improved metabolic states, further 
337 
supporting ferritin’s role as an inflammation proxy.  Finally, Hb, TS and iron tended to be higher 
338 
in those who run for exercise compared to the SED group (with the TIBC lower), again suggesting 
339 
that runners, including the PRO group, were iron-sufficient in this cohort.   
340 
PRO endurance runners exhibit distinct biomarker signatures  
341 
PRO athletes exhibited lower serum and RBC Mg, which may be indication of the often-reported 
342 
endurance athlete hypomagnesaemia [30].  While the serum Mg was still within normal clinical 
343 
reference range for both PRO female and male athletes, RBC Mg, a more sensitive biomarker of 
344 
Mg status [31], was borderline low in female PRO athletes and might suggest suboptimal dietary 
345 
intakes and/or much higher volume of running training compared to the other running groups (i.e. 
346 
>>10h /week).  Indeed, this group also had elevated baseline CK and AST, which suggests a much 
347 
higher training intensity and/or volume.  Moreover, PRO level athletes had adequate iron status 
348 
and serum B12 and folate in the upper quartile of the normal reference range, suggesting that these 
349 
athletes’ general nutrition status may have been adequate.  These observations suggest that elite 
350 
endurance runners may need to pay particular attention to their magnesium status. 
351 
Further, we observed higher levels of SHBG in PRO male runners, a biomarker whose levels 
352 
positively correlate with various indexes of insulin sensitivity [32].  However, since the average 
353 
 
 
SHBG levels in the SED group were not clinically low in both sexes, the observed increase in 
354 
SHBG levels induced by running in males may be a catabolic response, as cortisol levels in this 
355 
group were also higher.  Indeed,  Popovic et al have shown that endurance exercise may increase 
356 
SHBG, cortisol, and total testosterone levels at the expense of free testosterone levels [33].  This 
357 
could perhaps in part be explained by higher exercise-induced adiponectin levels, which have been 
358 
shown to increase SHBG via cAMP kinase (AMPK) activation [34].  However, since our data is 
359 
observational, we cannot rule out overall energy balance as a significant contributor to SHBG 
360 
levels.  For example, caloric restriction (CR) has been shown to result in higher SHBG and cortisol 
361 
levels [32]. 
362 
Finally, regarding the abovementioned PRO group elevated AST and CK biomarkers, evidence 
363 
suggests that normal reference ranges in both CK and AST in well-recovered athletes should be 
364 
adjusted up, as training and competition have a profound, non-pathological, impact on the activity 
365 
of these enzymes [35, 36].  Indeed, the recommendation appears to be not to use reference intervals 
366 
derived from the general population with hard-training (particularly competitive) athletes [36]. 
367 
Effect of BMI on blood biomarkers 
368 
Since the current study is a cross-sectional analysis of self-reported running, we could not rule out 
369 
the possibility that factors other than exercise were the driving force behind the observed 
370 
biomarker variance among the groups examined.  These factors, such as diet, sleep, and/or 
371 
medications were not readily ascertained in this free-living cohort at the time of this study, but 
372 
BMI was readily available to evaluate this biomarker’s potential relative contribution to the 
373 
observed mean biomarker differences among self-reported runner groups.  
374 
 
 
 
21 
Multiple studies have attempted to uncouple the effects of exercise and BMI reduction on blood 
375 
biomarker outcomes, with mixed results [37].  For example, it is relatively well-known that acute 
376 
bouts of exercise improve glucose metabolism, but long-term effects are less well described [38].  
377 
Indeed, whether exercise without significant weight-loss is effective toward preventing metabolic 
378 
disease (and the associated blood biomarker changes) is inconclusive [39-41].  From the literature, 
379 
it appears that, for endurance exercise to have significant effect on most blood biomarkers, the 
380 
volume of exercise needs to be very high, and this typically results in significant reduction in 
381 
weight.  Thus, in practice, it is difficult to demonstrably uncouple the effects of significant exercise 
382 
and the associated weight-loss, and the results may depend on the blood biomarker in question.  
383 
Indeed, there is evidence that exercise without weight-loss does improve markers of insulin 
384 
sensitivity but not chronic inflammation, with the latter apparently requiring a reduction in 
385 
adiposity in the general population [39-41].    
386 
In our study of apparently healthy individuals, we observed a downward trend in BMI with 
387 
increasing self-reported running volume, and, although this study was not longitudinal and we are 
388 
thus unable to claim weight-loss, our 2S-MR analysis using BMI as the exposure nonetheless 
389 
suggests this biomarker to be responsible for a significant proportion of the modification of some 
390 
blood biomarkers.   
391 
Serum markers of systemic inflammation 
392 
Through our 2S-MR analyses, we show that BMI is causally associated with markers of systemic 
393 
inflammation, including RDW, folate, and hsCRP [27, 42, 43].  Similar analyses have reported 
394 
that genetic variants that associate with higher BMI were associated with higher CRP levels, but 
395 
not the other way around [44].  The prevailing mechanism proposed to explain this relationship 
396 
 
 
appears to be the pathological nature of overweight/obesity-driven adipose tissue that results in 
397 
secretion of proinflammatory cytokines such as IL-6 and TNFa, which then stimulate an acute 
398 
hepatic response, resulting in increased hsCRP levels (among other effects) [45].  Thus, our 2S-
399 
MR analyses and those of others [44] would indicate that the primary factor behind the lower 
400 
systemic inflammation in our cohort may be the exercise-associated lower BMI and not running 
401 
exercise per se, though the lower hsCRP in runners remained significant after adjustment for BMI 
402 
in our analysis.   
403 
Indeed, although a major driver behind reduced systemic inflammation may be a reduction in BMI 
404 
in the general population, additive effects of other lifestyle factors such as exercise cannot be 
405 
excluded. For example, a large body of cross-sectional investigations does indicate that physically 
406 
active individuals exhibit CRP levels that are 19-35% lower than less active individuals, even 
407 
when adjusted for BMI as was the case in the current analysis [41].  Further, it’s been reported that 
408 
physical activity at a frequency of as little as 1 day per week is associated with lower CRP in 
409 
individuals who are otherwise sedentary, while more frequent exercise further reduces 
410 
inflammation [41].   
411 
Significantly, our entire cohort of self-selected apparently healthy individuals did not exhibit 
412 
clinically high hsCRP, with average BMI also below the overweight thresholds.  Because all 
413 
subjects were voluntarily participating in a personalized wellness platform intended to optimize 
414 
blood biomarkers that included hsCRP, it is possible that some individuals from across the study 
415 
groups (both running and sedentary) in our cohort partook in some form of inflammation-reducing 
416 
dietary and/or lifestyle-based intervention.  Thus, that we detected a significant difference in 
417 
hsCRP between exercising and non-exercising individuals in this self-selected already generally 
418 
 
 
 
23 
healthy cohort may be suggestive of the potential for additional preventative effect of scheduled 
419 
physical activity on low-grade systemic inflammation in the generally healthy individual.   
420 
Blood lipids 
421 
Controlled studies that tightly track exercise and the associated adiposity reduction have reported 
422 
that body fat reduction (and not improvement in fitness as measured via VO2max) is a predictor of 
423 
HDL, LDL, and Tg [46].  Similarly, though BMI is an imperfect measure of adiposity, our 2S-MR 
424 
analysis suggests that this biomarker is causally associated with improved levels of HDL and Tg, 
425 
though not LDL.  This latter finding replicates a report by Hu et al. who, using the Global Lipids 
426 
Genetics Consortium GWAS summary statistics, applied a network MR approach that revealed 
427 
causal associations between BMI and blood lipids, where Tg and HDL, but not LDL, were found 
428 
to trend toward unhealthy levels with increasing adiposity [47].  On the other hand, others 
429 
implemented a robust BMI genetic risk score and demonstrated a causal association of adiposity 
430 
with peripheral artery disease and a multiple linear regression showed a strong association with 
431 
HDL, TC, and LDL, among other metabolic parameters [48].  In our cohort, given the lack of 
432 
evidence for a causal BMI-LDL association and the overall healthy levels of BMI, the observed a 
433 
significant improvement in LDL may be a result of marked running intensity and/or volume, 
434 
possibly combined with the aforementioned additional wellness program intervention variables.  
435 
Hormonal traits 
436 
As described above, we observed a trend toward increased plasma cortisol and SHBG in runners, 
437 
particularly PRO level athletes.  The effects on cortisol are consistent with a report by Houmanrd 
438 
et al, who found male distance runners to exhibit higher levels of baseline cortisol [49].  With 
439 
 
 
respect to the effects of BMI on baseline cortisol levels, this observation is generally supported by 
440 
our 2S-MR analyses with evidence for a consistent effect of increased cortisol with decreasing 
441 
BMI.  However, this association was suggestive at best, indicating that the higher levels of cortisol 
442 
exhibited in the PRO runners with significant lower adiposity are not likely to be solely explained 
443 
by their lower BMI.  Indeed, the relationship between BMI and cortisol appears to be complex, 
444 
with some reports suggesting a U-shaped relationship, where the glucocorticoid’s levels associate 
445 
negatively up to about a BMI of 30 kg/m2, then exhibiting a positive correlation into obesity 
446 
phenotypes [50].  MR statistical models generally do not account for such non-linearity and would 
447 
require a more granular demographical treatment, which is not possible using only GWAS 
448 
summary statistics data in the context of 2S-MR [17, 51].  
449 
Behavioral traits associated with increase physical activity 
450 
The combination of the body of the literature that describes the effects of endurance training on 
451 
blood biomarkers, and our own analysis that included markers such as CK and AST, makes us 
452 
cautiously assured that most of the abovementioned blood biomarker signatures are indeed a result 
453 
of the interplay between self-reported running and the associated lower BMI.  However, as this is 
454 
a self-report-based analysis and we were unable to track other subject behaviors in this free-living 
455 
cohort, we acknowledge that multiple behaviors that associate with exercise may be influencing 
456 
our results.   
457 
Toward this end, our exploratory 2S-MR analyses revealed potentially causal relationships 
458 
between vigorous exercise and multiple dietary habits that have been shown to improve the 
459 
biomarkers we examined.  Indeed, diets that avoid processed meat and sweets while providing 
460 
ample amounts of fresh fruits, as well as oily fish have been shown to be anti-inflammatory, and 
461 
 
 
 
25 
improve glycemic control and dyslipidemia [52, 53].  That physically active individuals are also 
462 
more likely to make healthier dietary choices adds insight to the potential confounders in ours and 
463 
others’ observational analyses, and this similar associations have previously been reported [54-
464 
56]. For example, using a calculated healthy eating motivation score, Naughton et al. showed that 
465 
those who partake in more than 2 hours of vigorous physical activity are almost twice as likely to 
466 
be motivated to eat healthy [56].  Indeed, upon closer examination, the genetic instruments used 
467 
to approximate vigorous physical activity as the exposure in this work included variants in the 
468 
genes DPY19L1, CADM2, CTBP2, EXOC4, and FOXO3 [57].  Of these, CADM2 encodes proteins 
469 
that are involved in neurotransmission in brain regions well known for their involvement in 
470 
executive function, including motivation, impulse regulation and self-control [58].  Moreover, 
471 
variants within this locus have been associated with obesity-related traits [59].  Thus, it is likely 
472 
that the improved metabolic outcomes seen here with our self-reported runners are a composite 
473 
result of both these individuals exercise and dietary habits.  Importantly, the above suggests that a 
474 
holistic wellness lifestyle approach is in practice the most likely to be most effective toward 
475 
preventing cardiometabolic disease.  Nonetheless, the focus of this work – exercise in the form of 
476 
running – is known to significantly improve cardiorespiratory fitness (CRF),  which has been 
477 
shown to be an independent predictor of CVD risk and total mortality, outcomes that indeed 
478 
correlate with dysregulated levels in many of the blood biomarkers examined in this work [7]. 
479 
Study limitations 
480 
This study is based on self-reported running and thus has several limitations.  First, it is generally 
481 
known that subjects tend to overestimate their commitment to exercise when self-reporting, 
482 
although in our cohort is a self-selected health-oriented population that is possibly less likely to 
483 
 
 
over-report their running volume. Furthermore, although the robust increasing trend in baselines 
484 
for muscle damage biomarkers (CK, AST) that have been shown to be associated with participation 
485 
in sports and exercise provides indirect evidence that the running groups were indeed participating 
486 
in increasing volumes of strenuous physical activity, we cannot confirm whether the reported 
487 
running was performed overground or on a treadmill, which may result in some heterogeneity in 
488 
physiological responses , nor can we ascertain the actual training volume of PRO-level runners.   
489 
We also cannot exclude the possibility that the running groups also participated in other forms of 
490 
exercise (such as strength training) or partook in other wellness program interventions that may 
491 
have influenced their blood biomarkers and/or BMI via lean muscle accretion.  Toward this end, 
492 
we have attempted to shed light on potential behavioral covariates related to vigorous physical 
493 
activity via 2S-MR.   Finally, while this cohort is generally healthy, we cannot exclude the potential 
494 
for unmeasured confounders such as medications, nutritional supplements, and unreported health 
495 
conditions.   
496 
2S- MR enables the assessment of causal relationships between modifiable traits and is less prone 
497 
to the so-called “winner’s curse” that more readily affects one-sample MR analyses [17, 51].  
498 
Because 2S-MR uses GWAS summary statistics for both exposure and outcome, it is possible to 
499 
increase statistical power because of the increased sample sizes.  However, horizontal pleiotropy 
500 
is still a concern that can skew the results.  Currently, there is no gold standard MR analysis 
501 
method, thus we used different techniques (IVW, MR-Egger, and median-based estimations – all 
502 
of which are based on different assumptions and thus biases) to evaluate the consistency among 
503 
these estimators and only reported associations as ‘causal’ if there was cross-model consistency.  
504 
Nonetheless, an exposure such as BMI is a complex trait that is composed of multiple sub-
505 
phenotypes (such as years of education) that could be driving the causal associations.  
506 
 
 
 
27 
Conclusions 
507 
Running is one of the most common forms of vigorous exercise practiced globally, thus making it 
508 
a compelling target of research studies toward understanding its applicability in chronic disease 
509 
prevention.  Our cross-sectional study offers insight into the biomarker signatures of self-reported 
510 
running in generally healthy individuals that suggest improved insulin sensitivity, blood lipid 
511 
metabolism, and systemic inflammation.  Furthermore, using 2S-MR in independent datasets we 
512 
provide additional evidence that some biomarkers are readily modified BMI alone, while others 
513 
appear 
to 
respond 
to 
the 
combination 
of 
varying 
exercise 
and 
BM 
514 
I.  Our additional bi-directional 2S-MR analyses toward understanding the causal relationships 
515 
between partaking in vigorous physical activity and other healthy behaviors highlight the inherent 
516 
challenge in disambiguating exercise intervention effects in cross sectional studies of free-living 
517 
populations, where healthy behaviors such as exercising and healthy dietary habits co-occur.   
518 
Overall, our analysis shows that the differences between those who run and the sedentary in our 
519 
cohort are likely a combination of the specific physiological effects of exercise, the associated 
520 
changes in BMI, and lifestyle habits associated with those who exercise, such as food choices and 
521 
baseline activity level.   Looking ahead, the InsideTracker database is continuously augmented 
522 
with blood chemistry, genotyping, and activity tracker data, facilitating further investigation of the 
523 
effects of various exercise modalities on phenotypes related to healthspan, including longitudinal 
524 
analyses and more granular dose-response dynamics. 
525 
Acknowledgments 
526 
 
 
We thank Michelle Cawley and Renee Deehan for their assistance with background subject matter 
527 
research and insightful conversations. 
528 
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2019;9(1):7339. doi: 10.1038/s41598-019-43861-9. 
742 
 
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Supporting information 
744 
 
 
 
33 
S1 Table.  Number of people in each category by age group. Significant trend toward 
745 
younger individuals reporting higher running volume, with more than 75% of the elite 
746 
group falling between the ages of 18 and 35.  
747 
S2 Table.  Full running volume vs. blood biomarker results 
748 
S3 Table.  2S-MR results with BMI as the exposure and select biomarkers as outcomes. 
749 
S4 Table.  2S-MR results with BMI with biomarkers as exposures and BMI as outcome to 
750 
assess reverse causality 
751 
S5 Table.  2S-MR results with vigorous physical activity as exposure and blood biomarkers 
752 
as outcomes 
753 
S6 Table.  2S-MR results with vigorous physical activity as exposure and lifestyle habits as 
754 
outcomes 
755 
S7 Table.  2S-MR with healthy/unhealthy dietary habits as exposures and vigorous physical 
756 
activity as outcome to assess reverse causality 
757 
S1 Fig.  Assumptions of Mendelian randomization 
758 
S2 Fig.  Blood biomarker levels with respect to self-reported running volume and 
759 
professional athletes 
760 
S3 Fig.  2S-MR scatter plot showing effects of vigorous physical activity as the exposure on 
761 
blood biomarkers. 
762 
S4 Fig.  2S-MR scatter plot showing effects of vigorous physical activity as the exposure 
763 
dietary habits. 
764 
 
 
S5 Fig.  2S-MR scatter plot showing effects of dietary behaviors as the exposures on vigorous 
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physical activity 
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Supporting Information
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Supporting Information
Supplementary_Materials_PONE_rev.pdf
  
Minimum dataset
Click here to access/download
Supporting Information
Dataset.txt
 
 
 
1 
Dose response of running on blood biomarkers of wellness in the 
generally healthy individuals 
 
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, 
United States of AmericaUSA.  
 
3Department of Integrative Structural and Computational Biology, The Scripps Research 
Institute, La Jolla, CA, United States of AmericaUSA. 
 
* Corresponding author 
E-mail:  gblander@insidetracker.com (GB) 
 
ﾅ¶Equal Contribution:  These authors contributed equally to this work 
* Correspondence and reprint requests:   Gil Blander, gblander@insdietracker.com 
Bartek Nogal 
 
 
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1 
bnogal@insidetracker.com 
1 
Svetlana Vinogradova 
2 
svinogradova@insidetracker.com 
3 
Ali Torkamani 
4 
atorkama@scripps.edu 
5 
Paul Fabian 
6 
pfabian@insdietracker.com 
7 
Running title:  Biomarker signature of runners 
8 
Abstract 
9 
Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence 
10 
supporting its effectiveness.  However, less is known about the specific healthspan-promoting 
11 
effects of exercise on blood biomarkers in the disease-free population.  In this work, we examine 
12 
23,237 generally healthy individuals who self-report varying weekly running volumes and 
13 
compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional 
14 
endurance athletesrunners.  We estimate the significance of differences among blood biomarkers 
15 
for groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, 
16 
gender, and BMI.  We attempt and add insight to our observational dataset analysis via two-sample 
17 
Mendelian randomization (2S-MR) using large independent datasets.   We find that self-reported 
18 
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running volume associates with biomarker signatures of improved wellness, with some serum 
19 
markers apparently being principally modified by BMI, whereas others show a dose-effect with 
20 
respect to running volume. We further detect hints of sexually dimorphic serum responses in 
21 
oxygen transport and hormonal traits, and we also observe a tendency toward pronounced 
22 
modifications in magnesium status in professional endurance athletes.   Thus, our results further 
23 
characterize blood biomarkers of exercise and metabolic health, particularly regarding dose-effect 
24 
relationships, and better inform personalized advice for training and performance. 
25 
 
26 
 
27 
 
28 
 
29 
Keywords:  physical activity, exercise, blood biomarkers, running, generally healthy, mendelian 
30 
randomization 
31 
 
32 
 
33 
 
34 
 
35 
 
36 
 
37 
 
 
 
3 
 
38 
 
39 
 
40 
 
41 
 
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43 
 
44 
 
45 
 
46 
1 
Introduction 
47 
Physical inactivity is one of the leading modifiable behavioral causes of death in the US [1].  
48 
Worldwide, physical inactivity is estimated to account for about 8.3% of premature mortality, an 
49 
effect size that is on the same order as smoking and obesity [2].  At the same time, the potent health 
50 
benefits of exercise have been proven time and time again, with results so consistent across a wide 
51 
variety of chronic diseases that some posit it can be considered a medical intervention [3-5].  
52 
However, since most investigators report the effects of exercise in either diseased populations or 
53 
athletes [6, 7], there exists a significant gap in knowledge as to the measurable effects of exercise 
54 
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in the generally healthy population who exercise for the purpose of improving their healthspan, 
55 
which can be projected via established measures such as blood biomarkers [8-11].   
56 
It is well established that routine laboratory biomarkers are validated proxies of the state of an 
57 
individual’s overall metabolic health and other healthpan-related parameters [12].  A large body 
58 
of evidence supports the effectiveness of exercise in modifying blood biomarkers toward disease 
59 
mitigation in clinical cohorts as well as athletes, where the effect sizes may be larger [6, 13].  
60 
Indeed, it’s been shown that more favorable changes in response to exercise training occur usually 
61 
in those with more pronounced dyslipidemia  [13].  In professional athletes, the sheer volume 
62 
and/or intensity of physical activity may drive large effects in various hematological, lipid, 
63 
immune, and endocrine variables [6]. Our aim is to help fill the gap in understanding of the effects 
64 
of exercise on blood biomarkers in the generally healthy, free-living population.  Toward this end, 
65 
we endeavored to explore the effects of vigorous exercise such as running in apparently healthy, 
66 
mostly non-athletic cohort to better understand the landscape of blood biomarker modifications 
67 
expected in the individual who partakes in recreational physical activity for the purpose of 
68 
maintaining good health. 
69 
For this purpose, we leveraged the InsideTracker dataset that includes information on self-reported 
70 
exercise habits combined with blood biomarker and genomics data.  We have previously reported 
71 
on the results of a longitudinal analysis on blood biomarker data from 1032 generally healthy 
72 
individuals who used our automated, web-based personalized nutrition and lifestyle platform [14].  
73 
For the purpose of this investigation, we focused on running as the exercise of choice as it is one 
74 
of the most common (purposeful) physical activity modalities practiced globally by generally 
75 
healthy individuals and would thus be relevant.  Moreover, since this was a cross-sectional study 
76 
based on self-reported exercise habits, we attempted to increase our capacity to infer intervention 
77 
 
 
 
5 
effects, as well as tease out potential confounders, by performing 2S-MR in large independent 
78 
cohorts. 
79 
2 
MethodsMaterials and methods 
80 
2.1 Dataset 
81 
 
82 
We conducted an observational analysis of data from InsideTracker users.  InsideTracker is a 
83 
direct-to-consumer (DTC) company established in 2009 that markets and sells InsideTracker 
84 
(insidetracker.com), a personalized lifestyle recommendation platform. The platform provides 
85 
serum biomarker and genomics testing, and performs integrative analysis of these datasets, 
86 
combined with activity/sleep tracker data toward biomarker and healthspan optimization (of note, 
87 
at the time of this analysis, we did not have sufficient users with activity/sleep tracker data to 
88 
include this data stream in the current study).   New users were continuously added to the 
89 
InsideTracker database from January 2011 to March 2022. 
90 
 
91 
2.2 Recruitment of participants 
92 
Recruitment of participants aged between 18 and 65 and residing in North America was conducted 
93 
through company marketing and outreach. Participants were subscribing members to the 
94 
InsideTracker platform and provided informed consent to have their blood test data and self-
95 
reported information used in an anonymized fashion for research purposes. Research was 
96 
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conducted according to guidelines for observational research in tissue samples from human 
97 
subjects. Eligible participants completed a questionnaire that included age, ethnicity, sex, dietary 
98 
preferences, physical activity, and exposure to sunlightother variables. This study employed data 
99 
from 23,237 participants that met our analysis inclusion requirements, namely absence of any 
100 
chronic disease as determined by questionnaire and metabolic blood biomarkers within normal 
101 
clinical reference ranges. The platform is not a medical service and does not diagnose or treat 
102 
medical conditions, so medical history and medication use were not collected.  The Institutional 
103 
Review Board (IRB) determine this work was not subject to a review based on category 4 
104 
exemption (“secondary research” with de-identified subjects). 
105 
2.3 Biomarker collection and analysis 
106 
Blood samples were collected and analyzed by Clinical Laboratory Improvement Amendments 
107 
(CLIA)–approved, third-party clinical labs (primarily Quest Diagnostics and LabCorp). 
108 
Participants were instructed to fast for 12 hours prior to the phlebotomy, with the exception of 
109 
water consumption. Results from the blood analysis were then uploaded to the platform via 
110 
electronic integration with the CLIA-approved lab. Participants chose a specific blood panel from 
111 
7 possible offerings, each comprising some subset of the biomarkers available. Due to the variation 
112 
in blood panels offered, the participant sample size per biomarker is not uniform. 
113 
2.3 Biomarker dataset preparation 
114 
 
115 
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7 
In our raw dataset, occasional outlier values were observed that were deemed implausible (e.g. 
116 
fasting glucose < 65 mg/dL). To remove anomalous outliers in a systematic way, we used the 
117 
Interquartile Range (IQR) method of identifying outliers, removing data points which fell below 
118 
Q1 – 1.5 IQR or above Q3 + 1.5 IQR.  The cohort was divided into five groups: professional 
119 
endurance runners (PRO),  high volume amateur (>10 h/week, HVAM ), medium volume amateur 
120 
(3-10 h/week, MVAM), low volume amateur (<3 h/week, LVAM), and sedentary (SED). 
121 
2.4 Calculation of polygenic scores 
122 
The variants (SNPs) comprising the polygenic risk scores were derived from publicly available 
123 
GWAS summary statistics (https://www.ebi.ac.uk/gwas/).  Scores were calculated across users by 
124 
summing the product of effect allele doses weighted by the beta coefficient for each SNP, as 
125 
reported in the GWAS summary statistics.  Variant p-value thresholds were generally chosen based 
126 
on optimization of respective PGS-blood biomarker correlation in the entire InsideTracker cohort 
127 
with both blood and genomics datasets (~1000-1500 depending on the blood biomarker at the time 
128 
of analysis).  Genotyping data was derived from a combination of a custom InsideTracker array 
129 
and third party arrays such as 23andMe and Ancestry.  Not all variants for any particular PGS were 
130 
genotyped on every array; proxies for missing SNPs were extracted via the “LDlinkR” package 
131 
using the Utah Residents (CEPH) with Northern and Western European ancestry (CEU) population 
132 
(R2 > 0.8 cut-off).  Only results PGSs for which there was sufficient biomarker-genotyping dataset 
133 
overlap were reported (note that none of the blood biomarker PGSs met this requirement). 
134 
2.5 Blood biomarker analysis with respect to running volume and 
135 
polygenic scores 
136 
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To estimate significance of differences for blood biomarkers levels among exercise groups, we 
137 
performed 3-way analysis of variance (ANOVA) analysis adjusting for age, gender, and BMI 
138 
(type-II analysis-of-variance tables function ANOVA from ‘car’ R package, version 3.0-12). 
139 
When estimating the effort of reported training volume on biomarkers, we assigned numerical 
140 
values corresponding to 4 levels of running and performed ANOVA analysis with those levels 
141 
treating it as an independent variable.  P-values were adjusted using the Benjamini & Hochberg 
142 
method [15]. P-values for interaction plots were calculated with ANOVA including interaction 
143 
between exercise group and polygenic scores category.  When comparing runners (PRO and 
144 
HVAM combined) versus sedentary individuals, we used propensity score matching method to 
145 
account for existing covariates (age and gender): we identified 745 sedentary individuals with 
146 
similar to runners’ age distributions among both males and females. We used ‘MatchIt’ R package 
147 
(version 4.3.3)  implementing nearest neighbor method for matching [16]. 
148 
2.6 Mendelian randomization  
149 
We attempted to add insight around the causality of exercise vs. BMI differences with respect to 
150 
serum marker improvement by performing MR analyses on a subset of biomarker observations 
151 
where BMI featured as a strong covariate and was thus used as the IV in the 2S-MR.   Thus, our 
152 
hypothesis here was that BMI differences were the primary (causal) driver behind the improvement 
153 
behind some biomarkers.  MR uses genetic variants as modifiable exposure (risk factor) proxies 
154 
to evaluate causal relationships in observational data while reducing the effects of confounders 
155 
and reverse causation (Figure 1SS1 Fig).  These SNPs are used as instrumental variables and must 
156 
meet 3 basic assumptions: (1) they must be robustly associated with the exposure; (2) they must 
157 
exert their effect on outcome via the exposure, and (3) there must be no unmeasured confounders 
158 
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9 
of the associations between the genetic variants and outcome (e.g. horizontal pleiotropy) [17].  
159 
Importantly, SNPs are proper randomization instruments because they are determined at birth and 
160 
thus serve as proxies of long-term exposures and cannot, in general, be modified by the 
161 
environment.  If the 3 above mentioned assumptions hold, MR-estimate effects of exposure on 
162 
outcomes are not likely to be significantly affected by reverse causation or confounding.  In the 
163 
2S-MR performed here, where GWAS summary statistics are used for both exposure and outcome 
164 
from independent cohorts, reverse causation and horizontal pleiotropy can readily be assessed, and 
165 
weak instrument bias and the likelihood of false positive findings are minimized as a result of the 
166 
much larger samples sizes [17].  Indeed, the bias in the 2S-MR using non-overlapping datasets as 
167 
performed here is towards the null [17].  Furthermore, to maintain the SNP-exposure associations 
168 
and linkage disequilibrium (LD) patterns in the non-overlapping populations we used GWAS 
169 
datasets from the MR-Base platform that were derived from ancestrally similar populations 
170 
(“ukb”:  analysis of UK Biobank phenotypes, and “ieu”: GWAS summary datasets generated by 
171 
many different European consortia).  To perform the analysis we used the R package 
172 
“TwoSampleMR” that combines the effects sizes of instruments on exposures with those on 
173 
outcomes via a meta-analysis.  We used “TwoSampleMR” package functions for allele 
174 
harmonization between exposure and outcome datasets, proxy variant substitution when SNPs 
175 
from exposure were not genotyped in the outcome data (Rsq>0.8 using the 1000G EUR reference 
176 
data integrated into MR-Base), and clumping to prune instrument SNPs for LD (the R script used 
177 
for MR analyses is available upon request).  We used 5 different MR methods that were included 
178 
as part of the “TwoSampleMR” package to control for bias inherent to any one technique [18].  
179 
For example, the multiplicative random effects inverse variance-weighted (IVW) method is a 
180 
weighted regression of instrument-outcome effects on instrument-exposure effects with the 
181 
 
 
intercept is set to zero.  This method generates a causal estimate of the exposure trait on outcome 
182 
traits by regressing the, for example, SNP-BMI trait association on the SNP-biomarker measure 
183 
association, weighted by the inverse of the SNP-biomarker measure association, and constraining 
184 
the intercept of this regression to zero.  This constraint can result in unbalanced horizontal 
185 
pleiotropy whereby the instruments influence the outcome through causal pathways distinct from 
186 
that through the exposure (thus violating the second above-mentioned assumption).  Such 
187 
unbalanced horizontal pleiotropy distorts the association between the exposure and the outcome, 
188 
and the effect estimate from the IVW method can be exaggerated or attenuated. However, 
189 
unbalanced horizontal pleiotropy can be readily assessed by the MR Egger method (via the MR 
190 
Egger intercept), which provides a valid MR causal estimate that is adjusted for the presence of 
191 
such directional pleiotropy, albeit at the cost of statistical efficiency.  Finally, to ascertain the 
192 
directionality of the various causal relationships examined, we also performed each MR analysis 
193 
in reverse where possible. 
194 
3 
Results  
195 
Study population characteristics 
196 
 
Table 1 shows the demographic characteristics of the study population.  We observed a 
197 
significant trend toward younger individuals reporting higher running volume, with more than 
198 
75% of the professional (PRO) group falling between the ages of 18 and 35 (Table 1SS1 Table).  
199 
Significant differences were also observed in the distribution of males and females within study 
200 
groups (Table 1).  Moreover, higher running volume associated with significantly lower body mass 
201 
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11 
index (BMI).  Thus, moving forward, combined comparisons of blood biomarkers as they relate 
202 
to running volume were adjusted for age, gender, and BMI. 
203 
 
204 
Table 1.  Study population demographics 
205 
Group 
N 
Female, % 
Age, yrs 
Body mass index, kg/m2 
PRO 
82 
53.7% 
33.68 
20.15 ± 6.02 
HVAM 
1103 
52.9% 
39.48 
22.57 ± 9.97 
MVAM 
6747 
54.2% 
41.49 
23.35 ± 9.76 
LVAM 
10877 
34.2% 
41.16 
24.72 ± 9.70 
SED 
4428 
48.9% 
44.25 
27.83 ± 10.70 
PRO = Professional, HVAM  = high volume amateur (>10 h/week ), MVAM = medium volume 
206 
amateur (3-10 h/week), LVAM = low volume amateur (<3 h/week), SED = sedentary 
207 
 
208 
Endurance exercise exhibits a modest association with clusters of 
209 
blood biomarker features  
210 
In order to begin to understand the most important variables that may associate with endurance 
211 
exercise in the form of running, we performed a principal component analysis (PCA), sub-dividing 
212 
the male and female cohorts into two most divergent groups in terms of exercise volume:  
213 
PRO/high volume amateur (HVAM) and sedentary (SED) groups.  Using propensity matching, 
214 
PRO and amateur athletes who reported running >10h per week were combined into the PRO-
215 
HVAM group to balance out the sample size between the exercising and non-exercising groups.  
216 
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Using this approach, we did not observe a significant separation between these groups (data not 
217 
shown).  However, dividing this dataset further into males and femalesThis approach yielded a 
218 
modest degree of separation, with hematological, inflammation, and lipid features, as well as BMI 
219 
explaining some of the variance (FigureFig 1 A through D).  We hypothesized that there may more 
220 
subtle relationships between running volume and the blood biomarker features that contributed to 
221 
distinguishing the endurance exercise and sedentary groups, thus we next performed ANOVA 
222 
analyses stratified by running volume as categorized in Table 1. 
223 
Fig 1.  Principal component analysis and variables plots of PRO-HVAM runners and sedentary 
224 
user blood biomarkers. Females, (A) and (B); males (C) and (D). PRO-HVAM = combined 
225 
professional and high-volume amateur.  Alb = albumin, ALT = alanine transaminase, AST = 
226 
aspartate aminotransferase, B12 = vitamin B12, Ca = calcium, Chol = total cholesterol, CK = 
227 
creatine kinase, Cor = cortisol, FE = iron, EOS_PCT = eosinophil percentage, Fer = ferritin, Fol = 
228 
folate, FT = free testosterone, GGT = gamma-glutamyl transferase, Glu = glucose, Hb = 
229 
hemoglobin, HCT = hematocrit, HDL = high density lipoprotein, HbA1c = glycated hemoglobin, 
230 
hsCRP = high-sensitivity C-reactive protein, LDL = low density lipoprotein, LYMPS_PCT = 
231 
lymphocyte percentage, MCH = mean cell hemoglobin, Mg = magnesium, MONOS_PCT = 
232 
monocytes percentage, MPV = mean platelet volume, Na = sodium, RBC = red blood cells, 
233 
RBC_Mg = red blood cell magnesium, RDW = red blood cell distribution width, SHBG = sex 
234 
hormone binding globulin, Tg = triglycerides, TIBC = total iron binding capacity, WBC = white 
235 
blood cells 
236 
Significant trends in glycemic, hematological, blood lipid, and 
237 
inflammatory serum traits with increasing running volumes 
 
238 
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13 
Weighted ANOVA analyses adjusted for age, gender, and BMI showed significant differences 
239 
among groups for multiple blood biomarkers (Table 2 and 2SS2 , FigureFigs 2 and 3).  We 
240 
observed a trend toward lower HbA1c, hsCRP, RDW, WBC, ferritin, gamma-glutamyl transferase 
241 
(GGT), and LDL.  HDL, hemoglobin (Hb), transferrin saturation (TS), alanine aminotransferase 
242 
(ALT), aspartate aminotransferase (AST), vitamin B12, folate, 25-hydroxy vitamin D, and creatine 
243 
kinase (CK) tended to be higher with increasing reported training volume, particularly in PRO 
244 
runners (Tables 2 and 2SS2 , FigureFigs 2 and 2SS2 , FigureFig 3).   Hct and Hb were higher only 
245 
in PRO males, whereas increased running volume associated with upward trend in these 
246 
biomarkers in females (FigureFig 3 A and B).  Increased running volume was associated with 
247 
markedly lower Fer in males, whereas female runners did not exhibit varying levels, and SED 
248 
females showed increased levels (FigureFig 3 C).  The low ferritin observed in male and female 
249 
runners was not clinically significant.  ALT positively associated with running volume in females 
250 
only (Figure 2SS2 Fig).  Serum and RBC magnesium (Mg) were both significantly lower in PRO 
251 
runners relative to all other groups (Table 2 and FigureFig 3 D and E).   Increasing levels of 
252 
endurance exercise also appeared to be associated with higher sex-hormone binding globulin 
253 
(SHBG), particularly in PRO male runners (FigureFig 3  F).  
254 
 
255 
Table 2.  Blood biomarkers significantly different among sedentary individuals and those 
256 
who partake in running for exercise to various degrees 
257 
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 
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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 
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 
 
 
 
15 
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 
 
258 
Fig 2.  Blood biomarkers associated with running: Inflammation proxies, (A) hsCRP = high-
sensitivity C-reactive protein and (B) WBC = white blood cells; blood lipids, (C) HDL = high 
density lipoprotein (D) LDL = low density lipoprotein, and (E) Tg = triglycerides; glycemia 
proxies, (F) Glu = glucose and (G) HgbA1c = glycated hemoglobin, and (H) Cor =cortisol 
Fig 3.  Blood biomarkers associated with running: (A and B) Hb (hemoglobin) and Hct 
(hematocrit) increase with increasing running volume, (C) Fer (ferritin) is reduced with 
increasing running volume, (D and E) Serum and RBC Mg (red blood cell magnesium) are 
reduced in professional runners, and (F) SHBG (sex hormone binding globulin) levels increase 
with increasing running volume in males 
 
259 
Endurance exercise correlates with lower BMI across categories of 
260 
genetic risk  
261 
Using publicly available GWAS summary statistics, we constructed blood biomarker polygenic 
262 
risk scores (PGSs) to explore potential genetic risk-mitigating effects of endurance exercise.  Since 
263 
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only a subset of the individuals in our cohort were genotyped, we aggregated the groups into 2 
264 
categories—PRO-HVAM and sedentary—to increase statistical power.  This across-group sample 
265 
size increase generally did not sufficiently power the ANOVA analysis to detect statistically 
266 
significant trends (data not shown), though the BMI polygenic risk was suggestively mitigated for 
267 
both males and female PRO-HVAM runners across categories of genetic risk (FigureFig 4 B).  
268 
Fig 4.  BMI significantly varied among running groups (A) with some suggestive effects on BMI 
PGS modification (total number for observations (N) for T1, T2, and T3 were 87, 84, and 100, 
respectively) (B) T1, T2, and T3 = 1st, and 2nd and 3rd tertials of the polygenic score distribution 
 
269 
Increased running volume is associated with lower BMI which may 
270 
drive biomarker changes  
271 
We observed a significant downward trend in the BMI with increased running volume for both 
272 
males and females, and, although some of the biomarker differences between sedentary and 
273 
exercising individuals remained significant after adjustment for BMI, their significance was 
274 
attenuated (FigureFig 4 A, p-value attenuation data not shown). Thus, we hypothesized that BMI 
275 
may be driving a significant portion of the observed variance in some of the biomarkers across the 
276 
groups.  Thus, to explore causal relationships between weight and biomarker changes, we 
277 
performed 2S-MR with BMI-associated single-nucleotide polymorphisms (SNPs) as the 
278 
instrumental variables (IVs) for a subset of the healthspan-related biomarkers where BMI 
279 
explained a relatively large portion of the variance in our analysis.   In general, these blood 
280 
biomarkers associated with inflammation (hsCRP and RDW), lipid metabolism (Tg and HDL), 
281 
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17 
glycemic control (HbA1c and Glu), as well as Alb and SHBG. We used GWAS summary statistics 
282 
and found that most of these BMI-blood biomarker relationships examined directionally aligned 
283 
with our study (except for LDL), and some were indicative of causal relationships in the BMI-
284 
biomarker direction even after considering directional pleiotropy (Table 3SS3 Table).   We 
285 
entertained the possibility of reverse causality and thus repeated the 2S-MR using each of the 
286 
biomarker levels as the exposure and BMI as the outcome, and the results were generally not 
287 
significant (except for WBC – see Table 4SS4 Table).  Of note, to estimate the direct causal effects 
288 
of running on blood parameters, we attempted to find an instrumental variable for to approximate 
289 
running as the exposure from publicly available GWAS summary statistics.  Toward this end, we 
290 
found that increasing levels of vigorous physical activity did associate with lower hsCRP, HbA1C, 
291 
higher HDL, and possibly higher SHBG (although the explained variance (R2) in this exposure 
292 
was just 0.001009, the F statistic was 37.7, thus meeting the criteria of F > 10 for minimizing weak 
293 
instrument bias) (FigureFigs 5 and 3SS3; Table 5SS5 Table). 
294 
Fig 5.  Two-sample Mendelian randomization shows that increasing levels of vigorous physical 
activity such as running is associated with improvement of (A) hsCRP = high-sensitivity C-
reactive protein, (B) HDL = high density lipoprotein, and (C) HbA1c = glycated hemoglobin 
levels 
 
295 
Vigorous physical activity associates with healthier behaviors 
296 
We hypothesized that those who exercise regularly may also partake in other healthful lifestyle 
297 
habits that may be contributing to more optimal blood biomarker signatures of wellness.  However, 
298 
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our dataset did not allow for systematic accounting of other lifestyle habits across all running 
299 
groups.  Thus, we again leveraged the potential of the 2S-MR approach to inform potential 
300 
confounding  associations between modifiable exposures and found that vigorous physical activity 
301 
such as running is at least suggestively associated with several behaviors associated with improved 
302 
health (Figure 4SS4 Fig).  Our analysis showed that those who participate in increasing levels of 
303 
vigorous physical activity may be less likely to eat processed meat (IVW p = 0.0000013), sweets 
304 
(IVW p = 0.32), and nap during the day (IVW p = 0.13), while increasing their intake of oily fish 
305 
(IVW p = 0.029), salad/raw vegetable intake (IVW p = 0.00016), and fresh fruit (IVW p = 0.0027) 
306 
(Table 6SS6 Table).  Furthermore, following our assessment of reverse causality, we found 
307 
evidence for the bidirectionality in the causal relationship between vigorous activity and napping 
308 
during the day and salad/raw vegetable intake, perhaps suggesting some degree of confounding 
309 
due to population stratification (Table 7SS7 Table). The suggestive positive effect of fresh fruit 
310 
and processed meat intake on vigorous physical activity appeared to violate MR assumption (3) 
311 
(Figure 1SS1 Fig) (horizontal pleiotropy p-values 0.051 and 0.17, respectively – Figure 5SS5 Fig). 
312 
4 
Discussion 
313 
In this report, we describe the variance in wellness-related blood biomarkers among self-reported 
314 
recreational runners, PRO runners, and individuals who do not report any exercise.  Overall, we 
315 
find that 1) recreational running as an exercise appears to be an effective intervention toward 
316 
modifying several biomarkers indicative of improved metabolic health, 2) an apparent dose-
317 
response relationship between running volume and BMI may itself be responsible for a proportion 
318 
of the apparent metabolic benefits, and 3) both PRO-level status and gender appear to associate 
319 
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19 
with heterogeneous physiological responses, particularly in iron and magnesium metabolism, as 
320 
well as some hormonal traits. 
321 
4.1 Self-reported running improves glycemia and lipidemia  
322 
We did not observe distinct clusters corresponding to self-reported high-volume/PRO runners and 
323 
the sedentary upon dimension reduction.  This is, perhaps, not unexpected due, in part, to the self-
324 
selected healthspan-oriented nature of our cohort, where even the sedentary subset of individuals 
325 
tends to exhibit blood biomarker levels in the normal clinical reference ranges.  Furthermore, the 
326 
measurement of running volume via self-report may be vulnerable to overestimation, which may 
327 
have contributed to the blending of sedentary and exercise groups with respect to the serum 
328 
markers measured, resulting in only marginal separation between the groups [19, 20].  However, 
329 
we did observe significant individual blood biomarker variance with respect to reported running 
330 
volumes when the dataset was subjected to ANOVA, even after adjustment for age, sex, and BMI.  
331 
From among glycemic control blood biomarkers, we were able to detect a relatively small exercise 
332 
effect in both fasting glucose and HbA1c in this generally healthy cohort, where the average 
333 
measures of glycemia were below the prediabetic thresholds in even the sedentary subset of the 
334 
cohort. Larger exercise intervention effects on metabolic biomarkers may be expected in cohorts 
335 
that include individuals with more clinically significant baseline values [21].   
336 
Similarly, blood lipids improved with higher self-reported running volume, and this result has been 
337 
reported before in multiple controlled endurance exercise trials [22].  The literature indicates that 
338 
HDL and Tg are two exercise-modifiable blood lipid biomarkers, with HDL being the most widely 
339 
reported to be modified by aerobic exercise [23, 24].  Although the mechanism behind this is not 
340 
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entirely clear, it likely involves the modification of lecithin acyltransferase and lipoprotein lipase 
341 
activities following exercise training [25].  We observed a similar trend in our blood biomarker 
342 
analysis, with HDL exhibiting an upward trend with increasing reported running volume.  While 
343 
we also found Tg and LDL to decrease with increasing exercise volume, these trends were less 
344 
pronounced.  Reports generally suggest that, in order to reduce LDL more consistently, the 
345 
intensity of aerobic exercise must be high enough [23].  In the case of Tg, baseline levels may have 
346 
a significant impact on the exercise intervention effect, with individuals exhibiting higher baselines 
347 
showing greater improvements [13]. 
348 
Importantly, these results suggests that exercise has a significant effect on glycemic control and 
349 
blood lipids even in the self-selected, already healthy individuals who are proactive about 
350 
preventing cardiometabolic disease.  
351 
4.2 Self-reported running and serum proxies of systemic 
352 
inflammation  
353 
Chronic low-grade inflammation is one of the major risk factors for compromised cardiovascular 
354 
health and metabolic syndrome (MetS).  While there is no shortage of inflammation-reducing 
355 
intervention studies on CVD patients with clinically high levels of metabolic inflammation, there 
356 
is less emphasis on modifiable lifestyle factors that can help stave off CVD and extend healthspan 
357 
in the generally healthy individual.  Indeed, considering the pathological cardiovascular processes  
358 
begin shortly after birth, prevention in asymptomatic individuals may be a more appropriate 
359 
strategy toward decreasing the burden of CVD on the healthcare system [26].   
360 
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21 
Toward this end, increasing self-reported running volume appeared to associate with improved 
361 
markers of inflammation, as shown by the lower levels of hsCRP, WBC, as well as ferritin.  Of 
362 
note, while the acute-phase protein, ferritin, is often used in the differential diagnosis of iron 
363 
deficiency anemia, the biomarker’s specificity appears to depend on the inflammatory state of the 
364 
individual, as it associates with hsCRP and inflammation more than iron stores, particularly in 
365 
those with higher BMI [27].   Although serum ferritin and iron is reported to be lower in male and 
366 
female elite athletes [28], the observed overall negative association of ferritin with increased 
367 
running volume in our cohort may be an indication of lower levels of inflammation rather than 
368 
compromised iron stores, particularly since the average ferritin level across all groups was above 
369 
the clinical iron deficiency thresholds.  Moreover, increased levels of ferritin have been associated 
370 
with insulin resistance and lower levels of adiponectin in the general population, both indicators 
371 
of increased systemic inflammation [29].  Here, exercising groups with lower levels of ferritin also 
372 
exhibited glycemic and blood lipid traits indicative of improved metabolic states, further 
373 
supporting ferritin’s role as an inflammation proxy.  Finally, Hb, TS and iron tended to be higher 
374 
in those who run for exercise compared to the SED group (with the TIBC lower), again suggesting 
375 
that runners, including the PRO group, were iron-sufficient in this cohort.   
376 
4.3 PRO athletes endurance runners exhibit distinct biomarker 
377 
signatures  
378 
PRO athletes exhibited lower serum and RBC Mg, which may be indication of the often-reported 
379 
endurance athlete hypomagnesaemia [30].  While the serum Mg was still within normal clinical 
380 
reference range for both PRO female and male athletes, RBC Mg, a more sensitive biomarker of 
381 
Mg status [31], was borderline low in female PRO athletes and might suggest suboptimal dietary 
382 
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intakes and/or much higher volume of running training compared to the other running groups (i.e. 
383 
>>10h /week).  Indeed, this group also had elevated baseline CK and AST, which suggests a much 
384 
higher training intensity and/or volume.  Moreover, PRO level athletes had adequate iron status 
385 
and serum B12 and folate in the upper quartile of the normal reference range, suggesting that these 
386 
athletes’ general nutrition status may have been adequate.  These observations suggest that elite 
387 
endurance runners may need to pay particular attention to their magnesium status. 
388 
Further, we observed higher levels of SHBG in PRO male runners, a biomarker whose levels 
389 
positively correlate with various indexes of insulin sensitivity [32].  However, since the average 
390 
SHBG levels in the SED group were not clinically low in both sexes, the observed increase in 
391 
SHBG levels induced by running in males may be a catabolic response, as cortisol levels in this 
392 
group were also higher.  Indeed,  Popovic et al have shown that endurance exercise may increase 
393 
SHBG, cortisol, and total testosterone levels at the expense of free testosterone levels [33].  This 
394 
could perhaps in part be explained by higher exercise-induced adiponectin levels, which have been 
395 
shown to increase SHBG via cAMP kinase (AMPK) activation [34].  However, since our data is 
396 
observational, we cannot rule out overall energy balance as a significant contributor to SHBG 
397 
levels.  For example, caloric restriction (CR) has been shown to result in higher SHBG and cortisol 
398 
levels [32]. 
399 
Finally, regarding the abovementioned PRO group elevated AST and CK biomarkers, evidence 
400 
suggests that normal reference ranges in both CK and AST in well-recovered athletes should be 
401 
adjusted up, as training and competition have a profound, non-pathological, impact on the activity 
402 
of these enzymes [35, 36].  Indeed, the recommendation appears to be not to use reference intervals 
403 
derived from the general population with hard-training (particularly competitive) athletes [36]. 
404 
 
 
 
23 
4.4 Effect of BMI on blood biomarkers 
405 
Since the current study is a cross-sectional analysis of self-reported running, we could not rule out 
406 
the possibility that factors other than exercise were the driving force behind the observed 
407 
biomarker variance among the groups examined.  While These factors, such as diet, sleep, and/or 
408 
medication medications use were not readily ascertained in this free-living cohort at the time of 
409 
this study, but BMI was readily available to evaluate this biomarker’s potential relative 
410 
contribution to the observed mean biomarker differences among self-reported runner groups.  
411 
Multiple studies have attempted to uncouple the effects of exercise and BMI reduction on blood 
412 
biomarker outcomes, with mixed results [37].  For example, it is relatively well-known that acute 
413 
bouts of exercise improve glucose metabolism, but long-term effects are less well described [38].  
414 
Indeed, whether exercise without significant weight-loss is effective toward preventing metabolic 
415 
disease (and the associated blood biomarker changes) is inconclusive [39-41].  From the literature, 
416 
it appears that, for endurance exercise to have significant effect on most blood biomarkers, the 
417 
volume of exercise needs to be very high, and this typically results in significant reduction in 
418 
weight.  Thus, in practice, it is difficult to demonstrably uncouple the effects of significant exercise 
419 
and the associated weight-loss, and the results may depend on the blood biomarker in question.  
420 
Indeed, there is evidence that exercise without weight-loss does improve markers of insulin 
421 
sensitivity but not chronic inflammation, with the latter apparently requiring a reduction in 
422 
adiposity in the general population [39-41].    
423 
In our study of apparently healthy individuals, we observed a downward trend in BMI with 
424 
increasing self-reported running volume, and, although this study was not longitudinal and we are 
425 
thus unable to claim weight-loss, our 2S-MR analysis using BMI as the exposure nonetheless 
426 
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suggests this biomarker to be responsible for a significant proportion of the modification of some 
427 
blood biomarkers.   
428 
4.5.1 Serum markers of systemic inflammation 
429 
Through our 2S-MR analyses, we show that BMI is causally associated with markers of systemic 
430 
inflammation, including RDW, folate, and hsCRP [27, 42, 43].  Similar analyses have reported 
431 
that genetic variants that associate with higher BMI were associated with higher CRP levels, but 
432 
not the other way around [44].  The prevailing mechanism proposed to explain this relationship 
433 
appears to be the pathological nature of overweight/obesity-driven adipose tissue that results in 
434 
secretion of proinflammatory cytokines such as IL-6 and TNFa, which then stimulate an acute 
435 
hepatic response, resulting in increased hsCRP levels (among other effects) [45].  Thus, our 2S-
436 
MR analyses and those of others [44] would indicate that the primary factor behind the lower 
437 
systemic inflammation in our cohort may be the exercise-associated lower BMI and not running 
438 
exercise per se, though the lower hsCRP in runners remained significant after adjustment for BMI 
439 
in our analysis.   
440 
Indeed, although a major driver behind reduced systemic inflammation may be a reduction in BMI 
441 
in the general population, additive effects of other lifestyle factors such as exercise cannot be 
442 
excluded. For example, a large body of cross-sectional investigations does indicate that physically 
443 
active individuals exhibit CRP levels that are 19-35% lower than less active individuals, even 
444 
when adjusted for BMI as was the case in the current analysis [41].  Further, it’s been reported that 
445 
physical activity at a frequency of as little as 1 day per week is associated with lower CRP in 
446 
individuals who are otherwise sedentary, while more frequent exercise further reduces 
447 
inflammation [41].   
448 
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25 
Significantly, our entire cohort of self-selected apparently healthy individuals did not exhibit 
449 
clinically high hsCRP, with average BMI also below the overweight thresholds.  Because all 
450 
subjects were voluntarily participating in a personalized wellness platform intended to optimize 
451 
blood biomarkers that included hsCRP, it is possible that some individuals from across the study 
452 
groups (both running and sedentary) in our cohort partook in some form of inflammation-reducing 
453 
dietary and/or lifestyle-based intervention.  Thus, that we detected a significant difference in 
454 
hsCRP between exercising and non-exercising individuals in this self-selected already generally 
455 
healthy cohort may be suggestive of the potential for additional preventative effect of scheduled 
456 
physical activity on low-grade systemic inflammation in the generally healthy individual.   
457 
4.5.2 Blood lipids 
458 
Controlled studies that tightly track exercise and the associated adiposity reduction have reported 
459 
that body fat reduction (and not improvement in fitness as measured via VO2max) is a predictor of 
460 
HDL, LDL, and Tg [46].  Similarly, though BMI is an imperfect measure of adiposity, our 2S-MR 
461 
analysis suggests that this biomarker is causally associated with improved levels of HDL and Tg, 
462 
though not LDL.  This latter finding replicates a report by Hu et al. who, using the Global Lipids 
463 
Genetics Consortium GWAS summary statistics, applied a network MR approach that revealed 
464 
causal associations between BMI and blood lipids, where Tg and HDL, but not LDL, were found 
465 
to trend toward unhealthy levels with increasing adiposity [47].  On the other hand, others 
466 
implemented a robust BMI genetic risk score and demonstrated a causal association of adiposity 
467 
with peripheral artery disease and a multiple linear regression showed a strong association with 
468 
HDL, TC, and LDL, among other metabolic parameters [48].  In our cohort, given the lack of 
469 
evidence for a causal BMI-LDL association and the overall healthy levels of BMI, the observed a 
470 
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significant improvement in LDL may be a result of marked running intensity and/or volume, 
471 
possibly combined with the aforementioned additional wellness program intervention variables.  
472 
4.5.3 Hormonal traits 
473 
As described above, we observed a trend toward increased plasma cortisol and SHBG in runners, 
474 
particularly PRO level athletes.  The effects on cortisol are consistent with a report by Houmanrd 
475 
et al, who found male distance runners to exhibit higher levels of baseline cortisol [49].  With 
476 
respect to the effects of BMI on baseline cortisol levels, this observation is generally supported by 
477 
our 2S-MR analyses with evidence for a consistent effect of increased cortisol with decreasing 
478 
BMI.  However, this association was suggestive at best, indicating that the higher levels of cortisol 
479 
exhibited in the PRO runners with significant lower adiposity are not likely to be solely explained 
480 
by their lower BMI.  Indeed, the relationship between BMI and cortisol appears to be complex, 
481 
with some reports suggesting a U-shaped relationship, where the glucocorticoid’s levels associate 
482 
negatively up to about a BMI of 30 kg/m2, then exhibiting a positive correlation into obesity 
483 
phenotypes [50].  MR statistical models generally do not account for such non-linearity and would 
484 
require a more granular demographical treatment, which is not possible using only GWAS 
485 
summary statistics data in the context of 2S-MR [17, 51].  
486 
4.6 Behavioral traits associated with increase physical activity 
487 
The combination of the body of the literature that describes the effects of endurance training on 
488 
blood biomarkers, and our own analysis that included markers such as CK and AST, makes us 
489 
cautiously assured that most of the abovementioned blood biomarker signatures are indeed a result 
490 
of the interplay between self-reported running and the associated lower BMI.  However, as this is 
491 
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27 
a self-report-based analysis and we were unable to track other subject behaviors in this free-living 
492 
cohort, we acknowledge that multiple behaviors that associate with exercise may be influencing 
493 
our results.   
494 
Toward this end, our exploratory 2S-MR analyses revealed potentially causal relationships 
495 
between vigorous exercise and multiple dietary habits that have been shown to improve the 
496 
biomarkers we examined.  Indeed, diets that avoid processed meat and sweets while providing 
497 
ample amounts of fresh fruits, as well as oily fish have been shown to be anti-inflammatory, and 
498 
improve glycemic control and dyslipidemia [52, 53].  That physically active individuals are also 
499 
more likely to make healthier dietary choices adds insight to the potential confounders in ours and 
500 
others’ observational analyses, and this similar associations have previously been reported [54-
501 
56]. For example, using a calculated healthy eating motivation score, Naughton et al. showed that 
502 
those who partake in more than 2 hours of vigorous physical activity are almost twice as likely to 
503 
be motivated to eat healthy [56].  Indeed, upon closer examination, the genetic instruments used 
504 
to approximate vigorous physical activity as the exposure in this work included variants in the 
505 
genes DPY19L1, CADM2, CTBP2, EXOC4, and FOXO3 [57].  Of these, CADM2 encodes proteins 
506 
that are involved in neurotransmission in brain regions well known for their involvement in 
507 
executive function, including motivation, impulse regulation and self-control [58].  Moreover, 
508 
variants within this locus have been associated with obesity-related traits [59].  Thus, it is likely 
509 
that the improved metabolic outcomes seen here with our self-reported runners are a composite 
510 
result of both these individuals exercise and dietary habits.  Importantly, the above suggests that a 
511 
holistic wellness lifestyle approach is in practice the most likely to be most effective toward 
512 
preventing cardiometabolic disease.  Nonetheless, the focus of this work – exercise in the form of 
513 
running – is known to significantly improve cardiorespiratory fitness (CRF),  which has been 
514 
 
 
shown to be an independent predictor of CVD risk and total mortality, outcomes that indeed 
515 
correlate with dysregulated levels in many of the blood biomarkers examined in this work [7]. 
516 
4.7 Study limitations 
517 
This study is based on self-reported running and thus has several limitations.  First, it is generally 
518 
known that subjects tend to overestimate their commitment to exercise when self-reporting, 
519 
although in our cohort is a self-selected health-oriented population that is possibly less likely to 
520 
over-report their running volume.  First, it is generally known that subjects tend to overestimate 
521 
their commitment to exercise when self-reporting, although in our cohort is a self-selected health-
522 
oriented population that is possibly less likely to over-report their running volume. Furthermore, 
523 
although the robust increasing trend in baselines for muscle damage biomarkers (CK, AST) that 
524 
have been shown to be associated with participation in sports and exercise provides indirect 
525 
evidence that the running groups were indeed participating in increasing volumes of strenuous 
526 
physical activity, we cannot confirm whether the reported running was performed overground or 
527 
on a treadmill, which may result in some heterogeneity in physiological responses , nor can we 
528 
ascertain the actual training volume of PRO-level runners.   We also cannot exclude the possibility 
529 
that the running groups also participated in other forms of exercise (such as strength training) or 
530 
partook in other wellness program interventions that may have influenced their blood biomarkers 
531 
and/or BMI via lean muscle accretion.  Toward this end, we have attempted to shed light on 
532 
potential behavioral covariates related to vigorous physical activity via 2S-MR.   Finally, while 
533 
this cohort is generally healthy, we cannot exclude the potential for unmeasured confounders such 
534 
as medications, nutritional supplements, and unreported health conditions.   
535 
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29 
2S- MR enables the assessment of causal relationships between modifiable traits and is less prone 
536 
to the so-called “winner’s curse” that more readily affects one-sample MR analyses [17, 51].  
537 
Because 2S-MR uses GWAS summary statistics for both exposure and outcome, it is possible to 
538 
increase statistical power because of the increased sample sizes.  However, horizontal pleiotropy 
539 
is still a concern that can skew the results.  Currently, there is no gold standard MR analysis 
540 
method, thus we used different techniques (IVW, MR-Egger, and median-based estimations – all 
541 
of which are based on different assumptions and thus biases) to evaluate the consistency among 
542 
these estimators and only reported associations as ‘causal’ if there was cross-model consistency.  
543 
Nonetheless, an exposure such as BMI is a complex trait that is composed of multiple sub-
544 
phenotypes (such as years of education) that could be driving the causal associations.  
545 
 
546 
5 
Conclusions 
547 
Running is one of the most common forms of vigorous exercise practiced globally, thus making it 
548 
a compelling target of research studies toward understanding its applicability in chronic disease 
549 
prevention.  Our cross-sectional study offers insight into the biomarker signatures of self-reported 
550 
running in generally healthy individuals that suggest improved insulin sensitivity, blood lipid 
551 
metabolism, and systemic inflammation.  Furthermore, using 2S-MR in independent datasets we 
552 
provide additional evidence that some biomarkers are readily modified BMI alone, while others 
553 
appear 
to 
respond 
to 
the 
combination 
of 
varying 
exercise 
and 
BM 
554 
I.  Our additional bi-directional 2S-MR analyses toward understanding the causal relationships 
555 
between partaking in vigorous physical activity and other healthy behaviors highlight the inherent 
556 
challenge in disambiguating exercise intervention effects in cross sectional studies of free-living 
557 
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populations, where healthy behaviors such as exercising and healthy dietary habits co-occur.   
558 
Overall, our analysis shows that the differences between those who run and the sedentary in our 
559 
cohort are likely a combination of the specific physiological effects of exercise, the associated 
560 
changes in BMI, and lifestyle habits associated with those who exercise, such as food choices and 
561 
baseline activity level.   Looking ahead, the InsideTracker database is continuously augmented 
562 
with blood chemistry, genotyping, and activity tracker data, facilitating further investigation of the 
563 
effects of various exercise modalities on phenotypes related to healthspan, including longitudinal 
564 
analyses and more granular dose-response dynamics. 
565 
Data Availability Statement 
566 
The full set of biomarker change correlations has been made available in the Supplementary 
567 
Information files. Specific components of the raw dataset are available upon reasonable request 
568 
from the corresponding author.  2S-MR analysis was performed using publicly available datasets 
569 
via the TwoSampleMR R package. 
570 
Ethics statement 
571 
This study was submitted to The Institutional Review Board (IRB), which determined this work 
572 
was not subject to a review based on category 4 exemption (“secondary research” with de-
573 
identified subjects). 
574 
Author contributions 
575 
BN performed the 2S-MR analyses, calculated PGSs, and wrote the manuscript; SV performed 
576 
blood biomarker and blood biomarker X PGS interaction analysis; PF calculated PGSs; MJ, AT, 
577 
 
 
 
31 
and GB provided guidance.  All authors have read and agreed to the published version of the 
578 
manuscript. 
579 
Funding 
580 
InsideTracker was the sole funding source. 
581 
Conflict of interest 
582 
InsideTracker is a direct-to-consumer blood biomarker and genomics company providing its 
583 
users with nutritional and exercise recommendations toward improving wellness.  B.N., S.V., 
584 
P.F., and G.B. are employees of InsideTracker. 
585 
 
586 
 
587 
 
588 
 
589 
 
590 
Acknowledgments 
591 
InsideTracker is the sole funding source.  We thank Michelle Cawley and Renee Deehan for their 
592 
assistance with background subject matter research and insightful conversations. 
593 
 
594 
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822 
Supporting information 
823 
S1 Table.  Number of people in each category by age group. Significant trend toward 
824 
younger individuals reporting higher running volume, with more than 75% of the elite 
825 
group falling between the ages of 18 and 35.  
826 
S2 Table.  Full running volume vs. blood biomarker results 
827 
S3 Table.  2S-MR results with BMI as the exposure and select biomarkers as outcomes. 
828 
S4 Table.  2S-MR results with BMI with biomarkers as exposures and BMI as outcome to 
829 
assess reverse causality 
830 
Formatted: Font: Times New Roman, 12 pt, Bold
Formatted: Font: Bold
Formatted: Font: Bold
 
 
S5 Table.  2S-MR results with vigorous physical activity as exposure and blood biomarkers 
831 
as outcomes 
832 
S6 Table.  2S-MR results with vigorous physical activity as exposure and lifestyle habits as 
833 
outcomes 
834 
S7 Table.  2S-MR with healthy/unhealthy dietary habits as exposures and vigorous physical 
835 
activity as outcome to assess reverse causality 
836 
S1 Fig.  Assumptions of Mendelian randomization 
837 
S2 Fig.  Blood biomarker levels with respect to self-reported running volume and 
838 
professional athletes 
839 
S3 Fig.  2S-MR scatter plot showing effects of vigorous physical activity as the exposure on 
840 
blood biomarkers. 
841 
S4 Fig.  2S-MR scatter plot showing effects of vigorous physical activity as the exposure 
842 
dietary habits. 
843 
S5 Fig.  2S-MR scatter plot showing effects of dietary behaviors as the exposures on vigorous 
844 
physical activity 
845 
 
846 
 
847 
Table 1 Study Population Demographics 
848 
 
849 
Formatted: Line spacing:  single, Don't suppress line
numbers
 
 
 
39 
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 
850 
volume amateur (3-10hr), LVAM = low volume amateur (<3 hr), SED = sedentary 
851 
 
852 
 
853 
 
854 
 
855 
 
856 
 
857 
 
858 
 
859 
 
860 
Formatted Table
 
 
Table 2 Blood Biomarkers Significantly Different Among Sedentary 
861 
Individuals and Those Who Partake in Running for Exercise to 
862 
Various Degrees 
863 
 
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 
Formatted Table
 
 
 
41 
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 
 
 
 
864 
 
865 
 
866 
Reviewer #1: The majority parts of the articles are technically sound. Moreover, the purpose of 
the study is very sound since it focused on healthy active population. Among the few drawbacks 
of the study the way the study subjects categorized into groups based on the duration of the 
activity (>10hours per week and less that 10hours per week is not appropriate. Moreover, the 
reliability/validity of the information sources in relation to the biomarker tests and lifestyle 
habits of the study subjects didn't consider the immediate effects of medical services and 
medication conditions of the respondents at the time of reporting the volume of exercise and 
biomarker test results. Medical services and lifestyle habits specially all the habits in addition to 
exercises/running are very important to reach informative decision in this research. So, the 
above two points need further explanation or modification. 
 
Response to Reviewer #1: We appreciate the reviewer's feedback and are pleased that they 
find the majority of our study technically sound and recognize the importance of our focus on a 
healthy, active population.  We also appreciate the reviewer pointing out an opportunity to 
improve the clarity around our experimental design as it pertains subject groupings. 
 
Regarding the categorization of study subjects, we want to clarify that we actually categorized 
them into five groups. These groups include professional endurance runners, high volume 
amateur runners (>10 hours per week), medium volume amateur runners (3-10 hours per 
week), low volume amateur runners (<3 hours per week), and the sedentary.  We now added a 
sentence starting on line 125 the explicitly states this categorization (“The cohort was divided 
into five groups:…”).  These groupings were determined based on the respondents' self-
reported data. 
 
We acknowledge the potential influence of medication use on our analysis, and we now address 
it starting on line 461 (“These factors, such as diet, sleep, and/or medications were not readily 
ascertained in this free-living cohort…”) and in the Study Limitations section (line 595). We 
noted that unmeasured confounders such as medications, nutritional supplements, and 
unreported health conditions may exist. However, given the nature of our cohort, which 
primarily consists of self-selected, generally healthy individuals, the impact of significant 
medication use is expected to be limited. We believe that the observed trends in healthier 
biomarker levels with increased reported running volume support this assertion. 
 
Furthermore, we recognize the importance of lifestyle habits beyond exercise in influencing our 
results. To address this, we employed statistical genomics, specifically two-sample Mendelian 
randomization with physical activity as the exposure. This analysis allowed us to explore other 
potential habits and behaviors contributing to improved biomarker signatures in physically 
active runners within our cohort. We kindly refer the reviewer to the "Vigorous physical activity 
associates with healthier behaviors" section in the results for a detailed examination of this 
aspect. Notably, our entire cohort is composed of health-conscious individuals within the same 
health advisory platform, with the primary differentiator being self-reported running activity. 
We also controlled for key variables such as age, sex, and BMI in our ANOVA analyses. 
 
Response to Reviewers
We hope these explanations clarify our approach and address the reviewer's concerns 
adequately. 
 
Reviewer #2:   How your data is reliable by using A cross-sectional study design? & 
How again the Data is reliable by using self-reported running. I understand that Biomarkers are 
objective measure, but do you think that Self-report is trustworthy?  Thank you 
 
Response to Reviewer #2: We appreciate the reviewer's questions and concerns regarding the 
reliability of our runners data, which is largely derived from self-reported exercise habits. Cross-
sectional studies inherently have limitations when it comes to establishing causality, and we 
acknowledge this challenge. To address potential confounding factors, we conducted additional 
causal analyses, specifically investigating the effects of BMI on the biomarkers under 
examination to begin to disentangle the relative contributions of known factors. Furthermore, 
we performed secondary Mendelian randomization (MR) analyses to identify and account for 
potential confounders in our findings. We kindly invite the reviewer to explore the "Vigorous 
physical activity associates with healthier behaviors" section in the results for a comprehensive 
exploration of these confounding aspects. 
 
Regarding the reliability of self-reported running activity, we recognize that self-reports can be 
subject to biases, and individuals may tend to overestimate their exercise commitment.  To 
address this drawback, we added language addressing these limitations in the “Study 
limitations” section (Line 579:  “First, it is generally known that subjects tend to overestimate 
their commitment to exercise …”).  We do note that our study cohort comprises self-selected 
individuals who are health-conscious and possibly less prone to over-report their running 
volume. Additionally, the robust increasing trend in baseline levels of muscle damage 
biomarkers (CK, AST), which are known to be associated with participation in sports and 
exercise, provides indirect evidence that the different running groups in our study were indeed 
engaging in increasing volumes of strenuous physical activity. 
 
While self-reporting has its limitations, it remains a valuable method for capturing individuals' 
exercise behaviors in large-scale observational studies. We took measures to mitigate potential 
biases, and our findings align with established trends in biomarker responses to physical 
activity. 
 
Reviewer #3: Upon a meticulous review of the article in question, I wish to commend the 
authors for crafting a piece that not only carries immense scientific weight but is also articulated 
with great clarity. Such insightful work surely merits publication in your distinguished journal. 
It's admirable how the authors have navigated through a myriad of physiological and 
biochemical variables (blood biomarkers) across five distinct participant categories and 
presented their results with lucidity. The experimental framework is robust, the statistical 
evaluations are apt, and the narrative progresses seamlessly. The references provided are both 
relevant and adequate. Nevertheless, I'd like to offer a few observations and suggestions: 
 
Response:  We appreciate the reviewer's positive feedback and kind words about our 
manuscript. We eagerly await their observations and suggestions should they see further 
opportunities to improve our work based on our responses to the current suggestions. 
 
Original Title: “Dose response of running on blood biomarkers of wellness in the generally 
healthy.” 
Proposed Title: “Dose-response relationship between running and blood biomarkers of wellness 
in generally healthy individuals.” 
 
Response:  Thank you – title has been changed. 
 
Page 2, Line 8: The mention of “exposure to sunlight” seems somewhat out of context. Could 
the authors clarify its relevance or indicate if it has been discussed elsewhere in the article? 
 
Response:  Thank you for the suggestion, we removed this as we agree it was not relevant in 
this manuscript. 
 
Page 17, Lines 17-18: The text reads: "These observations suggest that elite endurance 
runners………to their magnesium status." 
Comments: It would be helpful to clarify whether the professional athletes (PRO) participating 
in this study are specifically elite endurance runners. Kindly integrate this distinction into the 
main text if accurate. 
 
Response:  Thank you for the clarifying suggestion.  We included the pro/elite endurance 
runners clarification within the abstract as well as a section heading (lines 7 and 425) 
 
Page 19, Lines 1-2: The assertion: “Indeed whether exercise………..is inconclusive,” needs to be 
substantiated with a relevant citation. 
 
Response:  Thank you – citations have been added.   
 
Table 1: Please include standard deviation (SD) values. I also recommend expressing exercise 
duration in terms of "h/week" instead of "hr". 
 
Response:  Thank you for the catch – units changed to “h/week” and SDs added to Table 1.   
We are grateful for your valuable feedback, which has contributed to improving the clarity and 
accuracy of our manuscript. 

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