BMC Molecular and Cell
Biology

Li et al. BMC Molecular and Cell Biology(2023) 24:35
https://doi.org/10.1186/s12860-023-00496-z

Open Access

RESEARCH

Association between plasma L-carnitine levels
and mitochondrial DNA copy number
Mingyue Li1 , Keming Yang2,3, Immaculata De Vivo4,5,6,7, A. Heather Eliassen4,5,6,8, Abrar A. Qureshi9, Hongmei Nan1,10
and Jiali Han1,10,11*
Abstract
Mitochondria are key cytoplasmic organelles in eukaryotic cells that generate adenosine triphosphate (ATP)through the electron transport chain and oxidative phosphorylation. Mitochondrial DNA (mtDNA) copy number(mtDNAcn) is considered a biomarker for both mitochondrial quantity and function as well as cellular oxidative
stress level. Previous epidemiologic findings revealed that weight gain, higher body mass index (BMI), smoking,and high insulinemic potential of lifestyle were associated with lower leukocyte mtDNAcn. Carnitines are agroup of compounds that play a critical role in energy production. We quantified the associations of plasma
L-carnitine levels with leukocyte mtDNAcn. We then examined the association between mtDNAcn and L-carnitine(HMDB0000062) in 538 U.S. men without cancers, diabetes, or cardiovascular disease at blood collection fromthe Health Professionals Follow-Up Study (HPFS). We found a significant inverse association between L-carnitine
and mtDNAcn (ρ = −0.1, P = 0.02). This implies that the carnitine metabolic pathway may be associated withmitochondrial function and oxidative stress.
Keywords Metabolites, L-carnitine, Mitochondria, Mitochondrial DNA copy number, Cross-sectional study, Body massindex

*Correspondence:
Jiali Han
jialhan@iu.edu
1
Department of Epidemiology, Richard M. Fairbanks School of Public
Health, Indiana University, 1050 Wishard Boulevard, RG 6124, Indianapolis,
IN 46202-2872, USA
2
Department of Medicine, University of Pittsburgh Medical Center,
Pittsburgh, PA, USA
3
Clinical and Translational Epidemiology Unit, Department of Medicine,
Massachusetts General Hospital, Harvard Medical School, Boston, MA,USA
4
Department of Medicine, Harvard Medical School, Boston, MA, USA

5

Channing Division of Network Medicine, Brigham and Women’s Hospital,
Boston, MA, USA
6
Department of Epidemiology, Harvard School of Public Health, Boston,
MA, USA
7
Program in Genetic Epidemiology and Statistical Genetics, Harvard
School of Public Health, Boston, MA, USA
8
Department of Nutrition, Harvard School of Public Health, Boston, MA,USA
9Warren Alpert Medical School, Brown University, Providence, RI, USA
10
Department of Global Health, Richard M. Fairbanks School of Public
Health, Indiana University, Indianapolis, IN, USA
11
Indiana University Melvin and Bren Simon Comprehensive Cancer
Center, Indianapolis, IN, USA© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available
in this article, unless otherwise stated in a credit line to the data.

Li et al. BMC Molecular and Cell Biology(2023) 24:35

Introduction
Mitochondria are multifunctional organelles found in thecytoplasm of eukaryotic cells that play a variety of roles
in cellular functions such as energy metabolism, intracellular calcium homeostasis, cell proliferation, and apoptosis [1–3]. The mitochondria produce the vast majority ofthe energy-rich molecule adenosine triphosphate (ATP)in eukaryotes that do not rely on photosynthesis [2].
Mitochondria, not unexpectedly, are crucial to humanhealth [3–5], and damage to mitochondria is the root
cause of many fatal conditions that are inherited matrilineally [5, 6]. Furthermore, mitochondria are profoundly
involved in apoptosis and aging [7]. Central to mitochondrial function is mitochondrial DNA (mtDNA), a doublestranded circular DNA molecule, inherited maternally.
Comprising approximately 16,569 bp, it encodes a mere
37 genes but is indispensable for mitochondrial operationand ensures nuclear genome stability [8–12].
One molecule in crucial to mitochondrial function is
carnitine [13]. It plays a pivotal role in energy metabolism, particularly in tissues such as the heart and skeletal
muscles that rely on fatty acid oxidation [14, 15]. Furthermore, carnitine aids in enhancing glucose consumption and maintains its energy-producing capacity despitechanges in osmolytic pressure [14, 16–18]. L-carnitine
biosynthesis begins with the synthesis of trimethyllysine,then trimethyllysine undergoes enzymatic reactions [17–19]. Notably, L-carnitine has also been shown to haveanti-inflammatory and antioxidant characteristics, as wellas the capability to improve insulin sensitivity, support
protein metabolism, maintain membrane integrity, andaddress dyslipidemia [17, 18, 20, 21]. Its metabolic role
becomes evident when acylcarnitines accumulate due tofatty acid oxidation (FAO), outpacing the tricarboxylic
acid cycle (TCA) and affecting insulin sensitivity [18, 20].
By moving acyl CoA derivatives and/or their metabolites
out of the mitochondria, L-carnitine can limit their accumulation [17–19]. Consequently, carnitine may serve as
an adjunct in the treatment or prevention of insulin resistance and type 2 diabetes [17, 18, 20].
The regulation of mitochondrial DNA copy number(mtDNAcn) is altered in several human mtDNA mutation disorders and is also crucial to a variety of normal
physiological processes [22, 23]. Low disease severity
and/or inadequate disease penetrance are both associatedwith high mtDNA copy number in mitochondrial disorders [23]. However, there has been very little research
into the link between carnitine metabolites and mtDNAcn. In this study we aimed to examine the association
between mtDNAcn and L-carnitine (HMDB0000062) in
538 U.S. men without cancers, diabetes, or cardiovasculardisease at blood collection from the Health Professionals
Follow-Up Study (HPFS). We found a significant inverse

Page 2 of 8

association between L-carnitine (HMDB0000062) levels
and mtDNAcn (ρ = −0.1, P = 0.02).

Methods
Study populations

We used data from the Health Professionals FollowUp Study (HPFS), a long-time prospective cohort study
in the U.S. Details of the HPFS have been previously
described. Briefly, the study started in 1986 with 50,529
male health professionals aged 40–75 years [24]. Questionnaires have been mailed to the participants every
two years to collect data on lifestyle behaviors, including
smoking, physical activity, diet, disease status, and medical history. Blood samples were provided by 18,225 HPFS
participants during 1993–1995 [25].
Dietary information and mtDNAcn were measured
through blood collection [26]. In the current analysis,
we used data from our previous colorectal cancer (CRC)
case-control studies nested within the HPFS [27]. A totalof 538 male patients from the HPFS with available mtDNAcn were included in the final analysis, which included
both CRC cases and controls. The study protocol wasapproved by the institutional review boards of Brigham
and Women’s Hospital and the Harvard T.H. Chan
School of Public Health.
mtDNAcn assessment

The detailed process of mtDNA copy number ascertainment and validation has been published by Meng [26,28]. To measure the mtDNAcn, total DNA was isolated
from buffy-coat fractions using the QIAmp (Qiagen,Chatsworth, CA) 96-spin blood procedure, and picogreen quantification was used to measure the DNA
concentrations using a Molecular Devices 96-well spectrophotometer [28]. The DNA concentration was set
at 5 ng/μL [28], then the relative mtDNA copy number
was determined in a high-throughput 384-well configuration by Applied Biosystems 7900HT Real-Time PCR[28]. Each qPCR experiment utilized 10ng of genomic
DNA. We used a multiplex reaction with primers forboth the nuclear element AluYb8 and the mitochondrial gene ND2. Specifically, the ND2-forward primer(5’-tgttggttatacccttcccgtacta-3’), ND2-reverse primer(5’-cctgcaaagatggtagagtagatga-3’),
AluYb8-forward
primer (5’-cttgcagtgagccgagatt − 3’), and AluYb8-reverse
primer (5’- gagacggagtctcgctctgtc − 3’) were utilized [28].
The ratio of mitochondrial ND2 gene copy number togenomic single-copy gene copy (N/S) is related to theaverage mtDNAcn [28]. To assess inter-assay variability,
the 10 ng DNA standard curve point in each 384-wellplate was employed as calibrator DNA [28]. The relative N/S ratio was computed by subtracting the calibrator DNA’s N/S ratio from each sample’s N/S, which wasLi et al. BMC Molecular and Cell Biology(2023) 24:35

Page 3 of 8

computed by subtracting the average AluYb8 Ct from theaverage ND2 Ct value [28].
Carnitine profiling

Each case-control pair’s samples were sent together,handled the same way, and analyzed in the same run by
the same technicians in random order [29–31]. Liquid
chromatography tandem mass spectrometry (LC-MS)
techniques were used to obtain profiles of plasma L-carnitine [29–31]. Chromatographic retention periods, MS
multiple reaction-monitoring transitions, declustering
potentials, and collision energies were calculated usingreference standards of each metabolite for polar metabolite profiling [30]. Dr. Clary Clish supervised the MIT lab
where this scan was performed [32]. He has been studying species identification for over ten years [32].
Covariate assessment

We included all covariates in the regression model in ourstudy to adjust covariates, including age, race, body massindex, physical activity, smoking status, alternate Healthy
Eating Index, alcohol consumption, history of diabetes,
history of cardiovascular diseases, and history of hypercholesterolemia, as shown in Table 1. We collected height
and weight at baseline, updated weight in a biennial
Table 1 Age-standardized characteristics of participants atblood collection according to quartiles of mtDNAcn in overall
samples from HPFS
Characteristics
Number of participants
mtDNAcn, z score
Age, years*
Race, white, %Body mass index, kg/m2
Physical activity, MET-hours/week
Smoking status
Never, %Past, %
Current, %Alternative Healthy Eating Index
Alcohol consumption, g/dHistory of diabetes
History of cardiovascular diseases
History of hypercholesterolemia

Overall (N = 538)
Q1
Q2
Q3
134
135
135
-1.2
-0.3
0.3
(0.4) (0.1) (0.1)
67.1
66.2
64.3
(7.6) (7.7) (8.2)
97.1
93.8
88.4
26.1
25.8
26.4
(1.9) (2)
(2.3)
28.5
30.7
29.8
(16.4) (18.8) (20.2)

Q4
134
1.1
(0.3)
64.1
(8.6)
96.6
25.6
(2.1)
33.1
(20.4)

35.7
52.5
11.8
47.3
(6.6)
15.1
(11.2)
6.2
8.3
38.7

43
53.2
3.8
49.6
(7.5)
14
(11)
2.4
8.6
39.7

44.3
49.4
6.2
49.3
(8)
12.7
(11.1)
4.9
7.4
38.1

61.8
34.5
3.6
51.4
(8.9)
10.8
(10.1)
1.2
7.4
41.8

Note: Values are means (SD) for continuous variables, and percentages for
categorical variables are standardized to the age distribution of the study
population. Overall samples include both CRC cases and controls* Value is not age-adjusted

questionnaire, and then calculated BMI using height atbaseline and weight at blood sampling. We calculated
total activity by summing each activity’s metabolic equivalent hours per week [33].
Statistical analysis

We used the server of the Channing Division of Network
Medicine at Brigham and Women’s Hospital and Harvard
Medical School to calculate age-standard characteristics
of participants at blood collection according to quartiles
of mtDNAcn in overall samples [34]. We calculated meanand standard deviation for continuous variables and percentages for categorical variables.
We performed age-adjusted Spearman correlation
analyses to examine the correlation between carnitine
HMDB0000062 and mtDNAcn in overall and controlonly samples separately.
We used multiple linear regression models to calculate the mean difference and 95% CI of mtDNAcn acrossquartiles of carnitine HMDB0000062 in overall andcontrol samples separately. One model is age-adjusted,while the other is multivariate adjusted (MV-adjusted)
for age at blood collection, fasting status, case-control
status, body mass index (continuous), physical activity (continuous), smoking status (never, former, or current smokers), alcohol consumption (continuous), andAlternative Healthy Eating Index. The significance of theinteraction terms was subsequently evaluated using Wald
testing. We also drew the figures of the mean difference
and 95% CI of mtDNAcn across quartiles of carnitine
HMDB0000062 in overall and control samples separately.
We also examined the associations between L-carnitine(HMDB0000062) and BMI, and between BMI and mtDNAcn, also using the age-adjusted model and the MVadjusted model, described above. All statistical analyses
were performed utilizing SAS 9.4 (SAS Institute, Cary,
NC). P values less than 0.05 on both sides were set toindicate statistical significance.

Results
We present the age-standardized basic characteristics
of our 538 participants at blood collection according toquartiles of mtDNAcn in Table 1. Briefly, the mean ± SD
age was 65.4 ± 8.0 y; higher mtDNAcn were associatedwith younger age. As for race, a higher percentage ofwhite participants was associated with lower quartiles
of mtDNAcn (Q1-Q3), and the percentage of white participants was higher in Q4 than in Q3 or Q2. Lower mtDNAcn was associated with higher current smoking rates.
Higher physical activity levels and higher Alternative
Healthy Eating Index were associated with higher mtDNAcn quartiles, whereas greater alcohol consumption,
history of diabetes, and history of cardiovascular disease
were associated with lower mtDNAcn quartiles.

Li et al. BMC Molecular and Cell Biology(2023) 24:35

Table 2 Age-adjusted Spearman correlation between carnitine
HMDB0000062 and mtDNAcn in study patients
CohortOverall
Control

No. ofparticipants
538
280

Correlation
coefficient
-0.1
-0.15

P-correlation
0.02
0.01

Note: overall samples include both CRC cases and controls; and controls include
only samples without CRC. P-correlation is p-value for correlation

Age-adjusted Spearman correlation analyses were performed to examine the relationship between L-carnitine(HMDB0000062) and mtDNAcn, shown in Table 2. A
significant inverse relationship between L-carnitine(HMDB0000062) and mtDNAcn was found in both overall samples (ρ = −0.1, P = 0.02) and control samples (ρ =−0.15, P = 0.01). Data from CRC patients was not included

Page 4 of 8

due to concerns about the potential confounding effectsof colorectal cancer on the associations we investigated.
The association between L-carnitine (HMDB0000062)
and mtDNAcn was further examined using multiple
linear regression models (age-adjusted model, andMV-adjusted model, see see Table 3; Figs. 1 and 2).
A significant inverse association was found between
HMDB0000062 and mtDNAcn in both groups (overall and control samples). In the multivariable-adjusted
model, absolute least squares means ± SDs of mtDNAcn
across HMDB0000062 quartiles in overall samples wereas follows: Q1: reference; Q2: − 0.21 (-0.44, 0.01); Q3:− 0.20 (-0.43, 0.03); Q4: − 0.23 (-0.45, − 0.00); highest Q4compared with lowest Q1, P-trend = 0.06. Means ± SDs
in control samples were as follows: Q1: reference; Q2:

Table 3 Mean difference (95% CI) of mtDNAcn across quartiles of carnitine (HMDB0000062) in study patients

OverallAge-adj modelMV-adj model
Control
Age-adj modelMV-adj model

N

Q1

Q2

Q3

Q4

NominalP for trend

538
538

ref
ref

− 0.20 (-0.42, 0.03)− 0.21 (-0.44, 0.01)− 0.25 (-0.47, − 0.02)− 0.20 (-0.43, 0.03)− 0.26 (-0.48, − 0.03)− 0.23 (-0.45, − 0.00)

0.02
0.06

280
280

ref
ref

− 0.31 (-0.63, 0.00)− 0.33 (-0.64, − 0.02)− 0.28 (-0.60, 0.04)− 0.21 (-0.54, 0.12)− 0.38 (-0.70, − 0.06)− 0.40 (-0.72, − 0.08)

0.02
0.03

Notes: covariates adjusted in MV model include age at blood collection, fasting status, case-control status, body mass index (continuous), physical activity(continuous), smoking status (never, former, or current smokers), alcohol consumption (continuous), Alternate Health Eating Index (continuous), and histories ofdiabetes, cardiovascular diseases, and hypercholesterolemia. P is for p-value

Fig. 1 Mean difference (95% CI) of mtDNAcn across quartiles of carnitine (HMDB0000062) in overall study patientsLi et al. BMC Molecular and Cell Biology(2023) 24:35

Page 5 of 8

Fig. 2 Mean difference (95% CI) of mtDNAcn across quartiles of carnitine (HMDB0000062) in control study patients

Table 4 Mean difference (95% CI) of mtDNAcn across quartiles of carnitine (HMDB0000062) in study patients, stratified by age

OverallMV-adj model
Age = < median (67 yrs)
Age > median (67 yrs)
ControlMV-adj model
Age = < median (67 yrs)
Age > median (67 yrs)

N

Q1

Q2

Q3

Q4

P1

538
269

ref
ref

− 0.21(-0.44, 0.01)− 0.26(-0.59, 0.08)− 0.20(-0.43, 0.03)− 0.39(-0.72, − 0.06)

269

ref

− 0.13(-0.44, 0.17)

0.04(-0.28, 0.37)− 0.23(-0.45, − 0.00) 0.06
− 0.33(-0.66,
0.02− 0.003)− 0.16(-0.49, 0.17)
0.73

280
138
142

ref
ref
ref

− 0.33(-0.64, − 0.02)− 0.39(-0.87, 0.09)− 0.21(-0.62, 0.20)− 0.21(-0.54, 0.12)− 0.55(-1.0, − 0.08)
0.30(-0.17, 0.76)− 0.40(-0.72, − 0.08) 0.03
− 0.48(-0.99, 0.03)
0.03− 0.34(-0.75, 0.07)
0.32

Pinteraction

0.08

0.12

Notes: covariates adjusted in MV model include age at blood collection, fasting status, case-control status, body mass index (continuous), physical activity(continuous), smoking status (never, former, or current smokers), alcohol consumption (continuous), Alternate Health Eating Index (continuous), and histories ofdiabetes, cardiovascular diseases, and hypercholesterolemia. P1 is for normal p-value for trend. P interaction is p-value for age interaction− 0.33 (-0.64, − 0.02); Q3: − 0.21 (-0.54, 0.12); Q4: − 0.40(-0.72, − 0.08); highest Q4 compared with lowest Q1,
P-trend = 0.03.
There was no significant interaction between L-carnitine (HMDB0000062) and age in either group in the age
interaction analysis (Table 4). Some positive relationships
were found between L-carnitine (HMDB0000062) and
mtDNAcn in some subgroups, including age greater than
67 years in Q3 in both groups (overall: ρ = 0.04, control:ρ = 0.3).

In overall samples, a non-significant positive association between L-carnitine (HMDB0000062) and BMI wasfound in both models; ρ = 0.01, P = 0.35 (ρ = 0.04, P = 0.66
in BMI ≥ 25) in the age-adjusted model, ρ = 0.01, P = 0.42
(ρ = 0.02, P = 0.86 in BMI ≥ 25) in the MV-adjusted model.
In control samples, a non-significant positive association
between L-carnitine (HMDB0000062) and BMI was also
found in both models; ρ = 0.01, P = 0.66 (ρ = 0.01, P = 0.99
in BMI ≥ 25) in the age-adjusted model, ρ = 0.01, P = 0.69
(ρ = 0.02, P = 0.42 in BMI ≥ 25) in the MV-adjusted model.

Li et al. BMC Molecular and Cell Biology(2023) 24:35

In overall samples, a non-significant inverse association
between mtDNAcn and BMI was found in both models;ρ = -0.01, P = 0.39 (ρ = -0.12, P = 0.17 in BM ≥ 25) in the
age-adjusted model, ρ = -0.01, P = 0.80 (ρ = -0.06, P = 0.49
in BMI ≥ 25) in the MV-adjusted model. In control
samples, a non-significant inverse association between
mtDNAcn and BMI was also found in both models; ρ =-0.02, P = 0.38 (ρ = -0.13, P = 0.28 in BMI ≥ 25) in the ageadjusted model, ρ = -0.01, P = 0.61 (ρ = -0.08, P = 0.53 in
BMI ≥ 25) in the MV-adjusted model.

Discussion
In this cross-sectional study, we found that higher levelsof carnitine HMDB0000062 are associated with lower
mtDNAcn (Table 2). To the best of our knowledge, this is
the first epidemiologic study of the association between
L-carnitine (HMDB0000062) and mtDNAcn [18]. L-carnitine (HMDB0000062) was positively correlated withBMI (ρ = 0.01, P = 0.42 in the MV-adjusted model). Thisis consistent with previous HPFS studies showing thatsome carnitine metabolites were positively correlatedwith BMI [29]. L-carnitines play an important role inmitochondrial long chain fatty acid transport and areessential for maintaining normal mitochondrial activity [17, 20]. Fatty acid oxidation deficiencies may lead toacylcarnitine buildup in people who are overweight andinsulin resistant [35, 36]. L-carnitine has also been shownto have anti-inflammatory and antioxidant properties, aswell as the capability to improve insulin sensitivity, protein nutrition, dyslipidemia, and membrane integrity [16,18]. The rate of fatty acid oxidation (FAO) exceeds thatof the tricarboxylic acid cycle (TCA), resulting in thebuildup of intermediary metabolites including acylcarnitines, which may alter insulin sensitivity [18]. By moving acyl CoA derivatives and/or their metabolites out of
the mitochondria, carnitine can limit their accumulation[17, 20]. Consequently, carnitine may serve as an adjunctin the treatment or prevention of insulin resistance andtype 2 diabetes [18]. In our study, there was a non-significant inverse association between mtDNAcn and BMI(ρ = -0.01, P = 0.80 in the MV-adjusted model in overall
samples), which was consistent with earlier findings thatlower mtDNAcn was associated with higher BMI [37].
The regulation of mtDNA is altered in several human
mtDNA-mutation disorders and is also crucial in a variety of normal physiological processes [1, 22]. Low disease
severity and/or inadequate disease penetrance are both
associated with high mtDNAcn in mitochondrial disorders [6], and lower metabolic plasticity corresponds withthe observed decrease in mtDNAcn [38]. Obesity-related
low-grade inflammation decreases mitochondrial energy
generation and consumption, which has been proven to
exacerbate obesity and create a positive feedback loopthat promotes fat storage and weight gain [39].

Page 6 of 8

Therefore, our study suggests that BMI may help
explain the fact that mtDNAcn decreased acrossthe increasing quartiles of carnitine HMDB0000062(Table 3). There could be several reasons for the inverse
association between L-carnitine and mtDNAcn. One
explanation is that the increased L-carnitine levels in
those with greater BMI might be a compensatory mechanism to counteract the impaired mitochondrial function
seen in this population. Considering such mitochondrial impairment, increased amounts of L-carnitine may
be required to aid in the transport of fatty acids into themitochondria for energy generation. While those withobesity may have higher L-carnitine levels, their mtDNAcn levels are lower, suggesting that they may still
be suffering from mitochondrial dysfunction, which
can contribute to a host of metabolic disorders linked
to obesity, including insulin resistance and type 2 diabetes. The inverse relationship between L-carnitine
and mtDNAcn may also be mediated by other factors
related to BMI. For example, chronic inflammation andoxidative stress, which are commonly observed in individuals with obesity, can impair mitochondrial function
and reduce mtDNAcn. In this scenario, higher levelsof L-carnitine may be a marker of increased oxidative
stress and inflammation, which could contribute to thelower levels of mtDNAcn in individuals with higher BMI.
Moreover, we found only a modest association between
L-carnitine HMDB0000062 and mtDNAcn (Table 2),
possibly because of multiple risk factors. Prior epidemiological research demonstrated that weight, the insulinemic potential of lifestyle, BMI, and smoking wereinversely associated with mtDNAcn, but whole fruit
intake was positively associated with mtDNAcn [26]. The
current study contributes to the field by highlighting thathigh plasma L-carnitine (HMDB0000062) is a possible
risk factor for mitochondrial dysfunction.
Interestingly, here the inverse association between
L-carnitine HMDB0000062 and mtDNAcn among those
aged ≤ 67 y was not detected among some subgroups
aged > 67 y (Table 4). Research has shown that aging isinversely associated with mtDNAcn [40]. Accordingly,
we would expect to see a more significantly inverse association between carnitine and mtDNAcn. However, theinteraction between age and carnitine was not significant
in our stratified analysis (Table 4), so this finding in our
stratified study may have occurred by chance.
Our study has several strengths. First, the sample size
is relatively large, which means it can provide more precise estimates of the association between L-carnitine
and mtDNAcn than a smaller study and supports theexternal validity of our findings. According to our best
understanding, both our prior and present studies are
the only epidemiologic investigations of the factors that
influence mtDNAcn in individuals who are healthy andLi et al. BMC Molecular and Cell Biology(2023) 24:35

free of major chronic diseases including diabetes, cardiovascular disease, and cancer. Our findings will facilitate
further assessments of mtDNAcn as a possible biomarker
for diabetes and other chronic disorders. Moreover, wecollected extensive data on covariates and used a systematic method to account for possible batch effects. Furthermore, considering the public health perspective, it isimportant to understand the how mtDNAcn and L-carnitine contribute to the likelihood of developing diabetes.
Early identification, preventative tactics, and targeted
therapies can help lessen the impact of diabetes on the
population if mtDNAcn and L-carnitine are together
validated as a reliable biomarker. These advances canimprove quality of life and lead to more cost-effective
healthcare strategies, underlining the importance of ourresearch in shaping future public health research.
Nevertheless, our study also has some limitations. First,
because our study was cross-sectional, we cannot draw
any firm conclusions about causation; more prospective
studies are needed to determine whether higher carnitine HMDB0000062 is associated with lower mtDNAcn.
Second, our study participants were all from the HPFS,
which only included men, so we did not consider any
gender influence. Further studies of the gender effect on
the association between carnitine HMDB0000062 andmtDNAcn are needed.
In conclusion, our study indicates that higher plasma
L-carnitine is associated with lower mtDNAcn. To further verify our findings, more prospective and interventional investigations are necessary.
Acknowledgements
Participants in the HPFS have made significant contributions. All authors
had complete access to data management and statistical analysis (statisticalanalysis results), and they all accept full responsibility for both the accuracy ofthe data analysis and the integrity of the data. We also acknowledge Dr. Clary
Clish who oversees the MIT lab.
Author contributions
JH was responsible for the study concept and design. JH and ID were involved
in mtDNAcn measurement and the validation process. ML and KY analyzed
the data. ML drafted the manuscript. All authors (ML, KY, ID, HE, AQ, HN, JH)
made substantial contributions to the analysis of results and critical reviewof the manuscript. All authors had complete access to the study’s data andassume responsibility for the data’s integrity and the precision of analyses.
Funding
U01 CA167552 was used to fund this research project.
Data Availability
The data used in this study are accessible upon reasonable request. Additional
information, including procedures for obtaining and accessing the HealthProfessionals Follow-up Study data, is available at https://sites.sph.harvard.
edu/hpfs/for-collaborators/.

Declarations
Ethics approval and consent to participate
All procedures performed in this study involving human participants were inaccordance with the ethical standards of the Institutional Review Board for
Cohort Follow-Up and Database Management and with the 1964 Helsinki

Page 7 of 8

declaration and its later amendments or comparable ethical standards.
Ethical approval was obtained from the Institutional Review Board for Cohort
Follow-Up and Database Management (Approval Reference No. 10,162 - Core
C). Informed consent was obtained from all individual participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 9 September 2023 / Accepted: 6 December 2023

References
1. Al-Kafaji G, et al. Peripheral blood mitochondrial DNA copy number as anovel potential biomarker for diabetic nephropathy in type 2 diabetes
patients. Exp Ther Med. 2018;16(2):1483–92.
2. Kühlbrandt W. Structure and function of mitochondrial membrane protein
complexes. BMC Biol. 2015;13:89.
3. Ashrafi G, Schwarz TL. The pathways of mitophagy for quality control andclearance of mitochondria. Cell Death Differ. 2013;20(1):31–42.
4. Nunnari J, Suomalainen A. Mitochondria: in sickness and in health. Cell.
2012;148(6):1145–59.
5. Akbari M, Kirkwood TBL, Bohr VA. Mitochondria in the signaling pathways
that control longevity and health span. Ageing Res Rev. 2019;54:100940.
6. Khan NA, et al. Mitochondrial disorders: challenges in diagnosis & treatment.
Indian J Med Res. 2015;141(1):13–26.
7. Klein HU, et al. Characterization of mitochondrial DNA quantity and quality
in the human aged and Alzheimer’s disease brain. Mol Neurodegener.
2021;16(1):75.
8. Chinnery PF, Hudson G. Mitochondrial genetics. Br Med Bull.
2013;106(1):135–59.
9. Guo HW, et al. Genetic diversity of mtDNA D-loop sequences in four native
Chinese chicken breeds. Br Poult Sci. 2017;58(5):490–7.
10. Nicholls TJ, Minczuk M. In D-loop: 40 years of mitochondrial 7S DNA. Exp
Gerontol. 2014;56:175–81.
11. McCormick EM, Muraresku CC, Falk MJ. Mitochondrial genomics: a complex
field now coming of age. Curr Genet Med Rep. 2018;6(2):52–61.
12. Fransen M, Lismont C, Walton P. The peroxisome-mitochondria connection:
how and why? Int J Mol Sci, 2017;18(6).
13. Idle JR, Gonzalez FJ. Metabolomics Cell Metab. 2007;6(5):348–51.
14. Zhang A et al. Metabolomics for biomarker discovery: moving to the clinic.
Biomed Res Int. 2015:354671.
15. Borum PR. Carnitine Annu Rev Nutr. 1983;3:233–59.
16. Flanagan JL, et al. Role of carnitine in disease. Nutr Metab (Lond). 2010;7:30.
17. McCann MR et al. L-Carnitine and acylcarnitines: mitochondrial biomarkers
for precision medicine. Metabolites, 2021;11(1).
18. Bene J, Hadzsiev K, Melegh B. Role of carnitine and its derivatives inthe development and management of type 2 diabetes. Nutr Diabetes.
2018;8(1):8.
19. Pekala J, et al. L-carnitine–metabolic functions and meaning in humans life.
Curr Drug Metab. 2011;12(7):667–78.
20. Longo N, Frigeni M, Pasquali M. Carnitine transport and fatty acid oxidation.
Biochim Biophys Acta. 2016;1863(10):2422–35.
21. Rebouche CJ. Kinetics, pharmacokinetics, and regulation of L-carnitine andacetyl-L-carnitine metabolism. Ann N Y Acad Sci. 2004;1033:30–41.
22. Das SC, et al. Mitochondria DNA copy number, mitochondria DNA total
somatic deletions, complex I activity, synapse number, and synaptic mitochondria number are altered in schizophrenia and bipolar disorder. Transl
Psychiatry. 2022;12(1):353.
23. Filograna R, et al. Mitochondrial DNA copy number in human disease: themore the better? FEBS Lett. 2021;595(8):976–1002.
24. Rimm EB, et al. Prospective study of alcohol consumption and risk of coronary disease in men. Lancet. 1991;338(8765):464–8.
25. Hunter DJ, et al. A prospective study of NAT2 acetylation genotype, cigarette
Smoking, and risk of breast cancer. Carcinogenesis. 1997;18(11):2127–32.

Li et al. BMC Molecular and Cell Biology(2023) 24:35

26. Yang K, et al. Insulinemic potential of lifestyle is inversely associated withleukocyte mitochondrial DNA copy number in US white adults. J Nutr.
2020;150(8):2156–63.
27. Yang K, et al. Association between pre-diagnostic leukocyte mitochondrial
DNA copy number and survival among colorectal cancer patients. Cancer
Epidemiol. 2020;68:101778.
28. Meng S, et al. Pre-diagnostic leukocyte mitochondrial DNA copy number andrisk of lung cancer. Oncotarget. 2016;7(19):27307–12.
29. Dickerman BA et al. A metabolomics analysis of adiposity and advanced
prostate cancer risk in the health professionals follow-up study. Metabolites,
2020;10(3).
30. Townsend MK, et al. Reproducibility of metabolomic profiles among men
and women in 2 large cohort studies. Clin Chem. 2013;59(11):1657–67.
31. Wang F, et al. Plasma metabolite profiles related to plant-based diets and therisk of type 2 diabetes. Diabetologia. 2022;65(7):1119–32.
32. Zeleznik OA, et al. Plasma metabolite profile for primary open-angle
glaucoma in three US cohorts and the UK Biobank. Nat Commun.
2023;14(1):2860.
33. Willett WC, et al. Validation of a semi-quantitative food frequency questionnaire: comparison with a 1-year diet record. J Am Diet Assoc. 1987;87(1):43–7.
34. Feskanich D, Hankinson SE, Schernhammer ES. Nightshift work and fracturerisk: the nurses’ health study. Osteoporos Int. 2009;20(4):537–42.

Page 8 of 8

35. Lopaschuk GD. Fatty acid oxidation and its relation with insulin resistanceand associated disorders. Ann Nutr Metab. 2016;68(Suppl 3):15–20.
36. Smith RL, et al. Metabolic flexibility as an adaptation to energy resources andrequirements in health and disease. Endocr Rev. 2018;39(4):489–517.
37. Bordoni L et al. Mitochondrial DNA and epigenetics: investigating interactions with the one-carbon metabolism in obesity. Oxid Med Cell Longev.
2022:9171684.
38. Castellani CA, et al. Mitochondrial DNA copy number can influence mortality
and cardiovascular disease via methylation of nuclear DNA CpGs. Genome
Med. 2020;12(1):84.
39. Manna P, Jain SK. Obesity, oxidative stress, adipose tissue dysfunction, andthe associated health risks: causes and therapeutic strategies. Metab Syndr
Relat Disord. 2015;13(10):423–44.
40. Dolcini J, et al. Age and mitochondrial DNA copy number influence theassociation between outdoor temperature and cognitive function: insightsfrom the VA normative aging study. Environ Epidemiol. 2020;4(4):e0108.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.