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. 2022 Jul 18;17(7):e0270951. doi: 10.1371/journal.pone.0270951

Mitochondrial DNA copy number, metabolic syndrome, and insulin sensitivity: Insights from the Sugar, Hypertension, and Physical Exercise studies

Stephanie Y Yang 1, Caleb S Mirabal 1, Charles E Newcomb 1, Kerry J Stewart 2, Dan E Arking 1,*
Editor: Hans-Peter Kubis3
PMCID: PMC9292076  PMID: 35849594

Abstract

Mitochondrial DNA copy number (mtDNA-CN) measured in blood has been associated with many aging-related diseases, with higher mtDNA-CN typically associated with lower disease risk. Exercise training is an excellent preventative tool against aging-related disorders and has been shown to increase mitochondrial function in muscle. Using the Sugar, Hypertension, and Physical Exercise cohorts (N = 105), we evaluated the effect of 6-months of exercise intervention on mtDNA-CN measured in blood. Although there was no significant relationship between exercise intervention and mtDNA-CN change (P = 0.29), there was a nominally significant association between mtDNA-CN and metabolic syndrome (P = 0.04), which has been seen in previous literature. We also identified a nominally significant association between higher mtDNA-CN and higher insulin sensitivity (P = 0.02).

Introduction

Mitochondria are well-known for their essential roles in ATP production, though they perform additional functions such as calcium homeostasis, apoptosis signaling, and lipid metabolism [1–3]. ATP synthesis and supply is crucial for skeletal muscle contraction during exercise, and thus, functional mitochondria are necessary for aerobic exercise. Mitochondria contain their own genomes (mtDNA), which can range from tens to thousands of copies per cell. This variation in quantity is referred to as mitochondrial DNA copy-number (mtDNA-CN), and widely differs across cell types and individuals. Higher mtDNA-CN levels are positively associated with mitochondrial membrane potential, respiratory enzyme function, and energy reserves [4, 5], suggesting that mtDNA-CN may be a marker of mitochondrial health. Lower mtDNA-CN in buffy coat, the fraction of blood that contains leukocytes and platelets, has been associated with frailty, often characterized by decreased muscle tone [6]. Exercise has also been shown to be an excellent preventive tool for many of the aging-related disorders associated with lower mtDNA-CN [7, 8]. These findings suggest a relationship between exercise training, which can increase muscle density [9] and mtDNA-CN measured in blood. Indeed, persistent exercise training has been shown to increase mitochondrial function and mitochondrial volume in skeletal muscle [10, 11]. Additionally, Lanza et. al has shown that mtDNA-CN in the muscle of endurance-trained individuals is higher than that of sedentary subjects [8]. However, mtDNA-CN in skeletal muscle is difficult to obtain, as muscle biopsy is required. We hypothesize that exercise intervention can increase mtDNA-CN in blood. To answer this question, we used two randomized controlled exercise intervention cohorts. Participants were aged 30–65 years, and performed aerobic and resistance exercise training 3 times a week for a duration of 6 months.

Results

Exercise increases VO2max and decreases BMI in the SHAPE cohorts

The Sugar, Hypertension, and Physical Exercise (SHAPE) cohorts are a set of randomized controlled studies that aimed to evaluate the effects of exercise and diet interventions on blood pressure and other secondary outcomes [12]. Briefly, participants were randomized into two intervention groups, which varied based on the specific SHAPE study (Table 1). Interventions were for 6 months, and blood samples were drawn at both baseline (pre-intervention) and final (post-intervention) visits.

Table 1. SHAPE cohort data.

Study N Comorbidities Group1 Group2 Completed Protocol
SHAPE3 77 Overweight/obese + prediabetes/diabetes Diet Diet + Exercise 55
SHAPE5 77 Obese, otherwise healthy Exercise + Low CHO Exercise + Low Fat 60
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Cohort data for the SHAPE3 and SHAPE5 cohorts. Low CHO = low carbohydrate weight loss diet, Low Fat = low fat weight loss diet.

Maximal oxygen uptake (VO2max), is a measurement of an individual’s aerobic capacity and increases after exercise training [13, 14]. To confirm the efficacy of the exercise intervention, we examined associations between the 6-month change in VO2max and the number of exercise sessions that an individual attended. There was a positive association (R = 0.38, P = 7.44 x 10−5) between more exercise sessions and a 6-month increase VO2max (S1 Fig). Additionally, the number of exercise sessions was significantly associated with a 6-month decrease in BMI (R = -0.29, P = 0.002, S2 Fig). Taken together, these correlations indicate that exercise intervention was effective.

Measurement and validation of mtDNA-CN in the SHAPE cohorts

mtDNA-CN was measured from whole blood samples obtained at baseline and final visits. Briefly, a monochrome qPCR assay with a nuclear target (albumin) and a mitochondrial target (D-loop) was used to measure the proportion of mitochondrial DNA relative to nuclear DNA [15]. To avoid batch effects, samples derived from the same individual were run on the same plate, and the final mtDNA-CN metric was adjusted for plate as a random effect. The final data was then centered and scaled.

mtDNA-CN is typically higher in females and decreases with age [16, 17]. To validate our mtDNA-CN metric, we evaluated associations with these two known covariates, using only baseline (pre-intervention) samples (N = 145). Despite the small sample size, age and sex were both significantly associated in the expected directions [18] (Fig 1). As it has been shown that the relationship between mtDNA-CN and age is nonlinear [19], We modeled the effect of age on mtDNA-CN using a natural spline, yielding a knot at 52.6 years. However, using a log likelihood test, the spline age term did not perform significantly better than a linear term (P = 0.42), potentially due to the small sample size. Adding sex as a covariate significantly improved the model (P = 0.008).

Fig 1. Associations between baseline mtDNA-CN and known covariates.

Fig 1

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Baseline mtDNA-CN is associated with age and sex in the expected directions. Females have higher baseline mtDNA-CN, and baseline mtDNA-CN decreases with age.

These effect estimates are consistent across the two different SHAPE studies, confirming that the associations are not driven by any one study (S3 Fig).

During quality control checks, we discovered that higher baseline mtDNA-CN was significantly associated with study dropout (R = 0.21, P = 0.01, S4 Fig). This association persisted even when stratifying the analysis by SHAPE study (S5 Fig). To understand what could be driving the relationship between baseline mtDNA-CN and study completion, we examined associations between study completion and several other variables. However, none of these potential explanatory variables was significantly associated with study completion. We note that increased age was nominally significantly associated with dropout, however, since increased age is associated with lower mtDNA-CN, this would not explain the observed relationship between higher mtDNA-CN and increased rates of study dropout (S1 Table). After these analyses, we were unable to account for the variation in dropout explained by baseline mtDNA-CN, and currently do not have a biological explanation for this finding.

mtDNA-CN is correlated between visits

Because baseline and final measurements are only separated by six months, we expected baseline and final mtDNA-CN to be correlated. After correcting for plate effects, the Pearson correlation was 0.578 (N = 105) and is consistent between the two studies (Fig 2).

Fig 2. Strong correlations between baseline and final mtDNA-CN.

Fig 2

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Baseline (pre-intervention) and final (post-intervention) mtDNA-CN measurements taken six months apart are well-correlated, with a Pearson correlation of 0.578.

No significant change in mtDNA-CN after 6 months of study intervention

To calculate the change in mtDNA-CN, we subtracted baseline mtDNA-CN from final mtDNA-CN. As such, positive values indicate an increase in mtDNA-CN over the 6-month period. We found that more extreme baseline mtDNA-CN measurements were likely to have larger 6-month changes, suggesting a reversion to the mean (S6 Fig). To account for this, all analyses evaluating associations with 6-month change in mtDNA-CN are adjusted for baseline mtDNA-CN as a covariate.

When comparing the change in mtDNA-CN between exercisers and non-exercisers, there was no significant difference in the mtDNA-CN change (P = 0.29, Fig 3). We also analyzed associations between the number of exercise sessions attended and change in mtDNA-CN in the group of individuals who exercised and found no significant relationship (P = 0.45). However, both analyses were consistent with a positive correlation between mtDNA-CN change and exercise. With our current sample size (N = 105), we had 80% power to detect a 0.234 difference in means between exercisers and non-exercisers.

Fig 3. 6-month change in mtDNA-CN was not associated with exercise.

Fig 3

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There was no significant association between exercise intervention and 6-month change in mtDNA-CN.

We also examined associations between mtDNA-CN and VO2max, a measure of cardiorespiratory fitness. A linear mixed model, adjusting for individual as a random effect and age, sex, visit, and study as fixed effects, found no significant associations between mtDNA-CN and VO2max (P = 0.44). There was also no association between 6-month change in mtDNA-CN and 6-month change in VO2max (S7 Fig).

Evaluating associations between secondary outcomes

In addition to exercise and diet, we were interested in associations between mtDNA-CN and secondary outcomes such as muscle mass, insulin sensitivity, and resting metabolic rate (Table 2). To leverage data from both baseline and final visits, we utilized a linear mixed model, adjusting for age, sex, visit, and individual.

Table 2. Associations between secondary outcomes and mtDNA-CN.

Secondary outcome Effect size estimate Standard error P-value FDR-adjusted P-value
Muscle mass -0.25 0.38 0.51 0.63
Insulin sensitivity 0.005 0.002 0.02 0.10
Resting metabolic rate 0.33 7.95 0.97 0.97
Baseline glycemia -1.14 1.80 0.53 0.63
Metabolic syndrome -0.81 0.38 0.04 0.11
HbA1c 0.09 0.06 0.13 0.26
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Effect size estimates, standard errors, and p-values from linear mixed models evaluating the relationship between mtDNA-CN and secondary outcomes of interest.

Of these secondary outcomes, insulin sensitivity and metabolic syndrome were both associated with mtDNA-CN prior to multiple-testing correction. Associations between mtDNA-CN and metabolic syndrome have been previously reported, supporting this finding [19]. As individuals with prevalent diabetes are known to have lower mtDNA-CN [20] and type 2 diabetes is a disease primarily characterized by decreased insulin sensitivity [21], we re-examined this association after adjusting insulin sensitivity for diabetes status. The association between mtDNA-CN and insulin sensitivity remained, even after accounting for diabetes status (P = 0.007).

As many of these secondary outcomes are known to be linked with metabolic syndrome, we repeated the analysis, adjusting for metabolic syndrome as a covariate. Results did not significantly change, suggesting that metabolic syndrome does not mediate relationships between mtDNA-CN and these secondary outcomes (S2 Table).

Discussion

In the SHAPE cohorts, metabolic syndrome and insulin sensitivity were nominally significantly associated with mtDNA-CN, with lower mtDNA-CN associated with metabolic syndrome and lower insulin sensitivity. mtDNA-CN from baseline and final visits were well-correlated, indicating that while mtDNA-CN may change over time, measures taken six months apart are relatively consistent.

We were powered to detect a difference of 0.234 standard deviations for 6-month change in mtDNA-CN between exercising and non-exercising groups and did not observe a significant difference in our dataset. Previous literature has described a significant increase in blood mtDNA-CN after exercise, as well as a significant association between mtDNA-CN and VO2max [22]. However, these methods do not normalize mtDNA content to a nuclear DNA target, normalizing instead to a spike-in standard DNA target. As such, the metrics used in the aforementioned study do not adjust for the number of cells and cell type differentials present in each sample.

After evaluating associations between mtDNA-CN and several secondary outcomes, insulin sensitivity as estimated from the QUICKI score was significantly associated with mtDNA-CN, with higher mtDNA-CN associated with increased insulin sensitivity. Loss of mitochondrial function in elderly subjects has been shown to lead to lipid accumulation and ultimate insulin resistance, corroborating our finding [23].

Since we do not have cell-type composition data, it is difficult to determine whether observed changes in mtDNA-CN are due to changes in mitochondrial content or changes in cell type composition, as aerobic exercise is known to cause decreases in neutrophils [24] and monocyte-platelet aggregates [21], but also causes increases in overall platelet count [25].

mtDNA-CN is known to be confounded by cell-type composition, with increased platelet count leading to higher mtDNA-CN and increased neutrophil count leading to lower mtDNA-CN [26]. As such, these findings must be interpreted with this limitation in mind.

Also, the type of exercise training may affect our ability to detect changes in mitochondrial DNA quantity. Both of the SHAPE cohorts were subjected to both aerobic and resistance exercise training. However, previous studies have shown that ribosomal and mitochondrial biogenesis may be competitive processes, with resistance training favoring ribosomal biogenesis and with aerobic exercise prioritizing mitochondrial biogenesis [27].

An additional constraint to this study is the varying comorbidities between SHAPE3 and SHAPE5. Although both SHAPE cohorts are comprised of obese and overweight individuals, SHAPE3 recruited subjects with type 2 diabetes or having prediabetes, while SHAPE5 individuals were otherwise healthy except for having abdominal obesity. As diabetes is known to cause abnormalities in mitochondrial function [20, 28, 29], this may affect the relationship between exercise and mtDNA-CN in individuals with diabetes. However, with the limited sample size in this study, there did not appear to be an association between exercise and mtDNA-CN, and addition of diabetes status as a covariate did not change results.

In summary, we do not detect a significant change in mtDNA-CN after exercise intervention in these study cohorts, despite marked improvements in fitness and substantial weight loss. After examining secondary outcomes, we uncovered a significant association between mtDNA-CN and insulin sensitivity, likely driven by biological pathways that connect mitochondria, lipid accumulation, and insulin resistance.

Methods

Participant recruitment

This study was approved by the Johns Hopkins Medicine IRB under retrospective application IRB0007178. Written informed consent was obtained for all participants, and all DNA samples and associated phenotype data were de-identified prior to analysis. All studies are listed under ClinicalTrials.gov (SHAPE3: NCT00928005, SHAPE5: NCT00990457).

SHAPE3

Subjects were overweight or obese (BMI between 26 and 42 kg/m2), sedentary men and women (n = 77), 30–65 years, with prediabetes or diabetes, according to American Diabetes Association criteria (fasting glucose > 126 mg/dl, casual plasma glucose > 200 mg/dl, or 2-hour plasma glucose > 200 mg/dl after a 75-gram oral glucose load). Individuals with uncontrolled diabetes, defined as fasting blood glucose over 300 mg/dl or A1C > 11% were excluded.

SHAPE5

Subjects were overweight or obese, sedentary men and women (n = 77), BMI 25–42 kg/m2, 30–65 years, who were otherwise healthy.

Exercise and diet intervention

SHAPE3

Diet intervention for SHAPE3 was a nutritionally balanced, moderately hypocaloric diet with reduced saturated fat consistent with American Diabetes Association guidelines. The diet was adjusted to produce a 600 kcal deficit/day for each individual, using resting metabolic rate calculated from the Mifflin-St Jeor equation [30].

Exercise intervention for SHAPE3 was designed based on guidelines from the American College of Sports Medicine and the American Diabetes Association, consisting of warm-up, 45 minutes of aerobic exercise, several resistance training exercises, and cool-down. Exercise sessions were supervised by exercise physiologists to ensure safety and that the exercises were carried out properly. Individuals assigned to exercise intervention were asked to exercise 3 times a week over a 26-week period.

SHAPE5

The low-carbohydrate (CHO) group adhered to the New Atkins for Life diet, consisting initially of 15% CHO, 30% protein, and 55% fat, followed by a gradual shift to 40% CHO, 20% protein, and 40% fat.

The low-fat group followed American Heart Association (AHA) and National Cholesterol Education Program (NCEP) guidelines, following a diet of 30% fat, 50–55% CHO, and 15–20% protein.

All subjects in SHAPE5 participated in 3 times per week supervised exercise training following ACSM guidelines for moderate intensity aerobic and resistance training, consisting of 45 minutes of aerobic exercise and 2 sets of 7 resistance exercises.

Measurement of study variables

Maximal oxygen uptake (VO2 max ml/kg/min). A Cardinal Health Metabolic/EKG system was used to measure VO2 max. The exercise began at 3 mph, 0% grade, and increased 2.5% grade every 3 minutes. There was continuous EKG and cardiorespiratory monitoring. The 12 lead ECG was recorded at every stage. BP was measured during the last 30 seconds and the Rating of perceived exertion (RPE), using the Borg 6 to 20 scale, was obtained during each stage. An RPE of 18–20 and a respiratory exchange ratio > 1.1 were considered as indicators of maximal effort. The highest observed value of VO2 was recorded as VO2 max.

Muscle mass was measured using Dual Energy X-Ray Absorptiometry (DEXA) with a GE Lunar Prodigy. DEXA lean mass measurements were utilized as a representation for muscle mass. Insulin sensitivity was calculated from fasting glucose and fasting insulin measurements using the quantitative insulin sensitivity check index (QUICKI) formula [31]. Insulin, glucose, and Hb1ac levels were measured from a fasting blood draw. Anthropometry was performed to obtain height and weight measurements with a balance scale and stadiometer. BMI was then calculated using these measurements. Waist and hip measurements were taken using a tape measure. Resting metabolic rate was estimated using the Mifflin-St Jeor equation [30].

Subjects were categorized as having metabolic syndrome if they had ≥ 3 of the 5 factors: central obesity with a waist circumference of > 40 inches (M) or > 35 inches (F); hyperglycemia, fast glucose ≥ 100 mg/dl or taking medications; dyslipidemia, triglycerides ≥ 150 mg/dl or taking medications; dyslipidemia 2nd, separate criteria, HDL cholesterol ≤ 40 mg/dl (M) or ≤ 50 mg/dl (F) or taking medication for both sexes; hypertension, ≥130 mm Hg systolic or ≥85 mm Hg diastolic, or taking medications.

mtDNA-CN measurement

mtDNA-CN was measured using a monochrome qPCR method [15]. Previous work comparing this assay with mtDNA-CN derived from whole genome sequencing has shown that individuals with polymorphisms in the D-loop primer region have unreliable mtDNA-CN monochrome assay measurements [32]. As such, samples that had deltaCT (difference between nuclear and mitochondrial probe CTs) less than 7 were filtered out due to assumed polymorphisms in the D-loop primer (7 total samples). One outlier individual was removed due to a baseline and a final mtDNA-CN value that was greater than 3 SD from the mean.

Genetic fingerprinting

Genetic fingerprinting using the Agena iPLEX Pro SampleID Panel was used to identify sample swaps and confirm that baseline and final samples originated from the same individual. Seven samples (two total individuals) were removed due to duplicated sample IDs with non-matching genetic information, as there was no way to match which sample corresponded to the correct individual. Four samples were removed due to poor sample quality, with greater than 50% missingness on the array. Finally, two samples (one individual) were removed due to fewer than 90% matching calls between baseline and final samples.

Statistical analyses

All statistical analyses were performed with R version 4.1.1 [33]. Linear mixed models were performed using the lme4 package and plots were created with ggplot2 [34, 35].

Supporting information

S1 Fig. Exercise is associated with an increase in VO2max.

The number of exercise sessions attended is significantly associated with a 6-month increase in VO2max.

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S2 Fig. Exercise is associated with a decrease in BMI.

There was a significant association between a greater number of exercise sessions attended and decreased BMI over the 6-month intervention period.

(TIF)

Click here for additional data file. (59.5KB, tif)
S3 Fig. mtDNA-CN is associated with known covariates in both cohorts.

Associations between mtDNA-CN and age and sex are in the expected directions when stratifying by SHAPE study. SHAPE3 on the left, SHAPE5 on the right.

(TIF)

Click here for additional data file. (135.3KB, tif)
S4 Fig. mtDNA-CN is correlated with study retention.

Individuals who dropped out of the study had significantly higher mtDNA-CN.

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S5 Fig. Correlation with study dropout appears in both cohorts.

Individuals who dropped out of the study have significantly higher mtDNA-CN than those who were retained (On left, SHAPE3, on right, SHAPE5).

(TIF)

Click here for additional data file. (29.2KB, tif)
S6 Fig. Baseline mtDNA-CN is associated with a change in mtDNA-CN in the direction of the mean.

mtDNA-CN measured at baseline is associated with a change in mtDNA-CN in the direction of the mean. Purple points denote individuals with final mtDNA-CN measurements closer to the mean than their baseline measurements, while yellow is vice versa. Significantly more individuals move towards the mean (chi-squared p = 0.004). For the purple samples, absolute magnitude of baseline mtDNA-CN is positively correlated with the absolute value of 6-month change, however, this association is not significant (P = 0.51).

(TIF)

Click here for additional data file. (64.3KB, tif)
S7 Fig. VO2 max and change in mtDNA-CN are not associated.

There was no significant association between 6-month change in VO2 max and 6-month change in mtDNA-CN.

(TIF)

Click here for additional data file. (55.3KB, tif)
S1 Table. Odds ratios and p-values for associations between explanatory variables and study completion.

There was no significant relationship between study completion and variables of interest, save for age. However, the directionality of the association between age and study completion does not explain the relationship between higher mtDNA-CN and higher study dropout.

(XLSX)

Click here for additional data file. (8.9KB, xlsx)
S2 Table. Adjusting for metabolic syndrome does not change results.

Effect size estimates for secondary outcomes after including metabolic syndrome status as a covariate. Results generally stay the same, indicating that metabolic syndrome is not mediating the effects of mtDNA-CN on these outcomes.

(XLSX)

Click here for additional data file. (9.1KB, xlsx)

Acknowledgments

The authors gratefully acknowledge use of the facilities at the Joint High Performance Computing Exchange (JHPCE) in the Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health that have contributed to the results reported within this paper.

Data Availability

The minimal dataset necessary to replicate our analyses is available on Figshare: https://figshare.com/articles/dataset/shape_for_release_txt/20152202.

Funding Statement

This work was supported by NHLBI grants R01HL13573 (SYY, CSM, CEN, DEA) and R01HL144569 (SYY, CSM, CEN, DEA). SHAPE projects were supported by NIDDK and NICRR grants R01DK062368 (KJS) and UL1RR025005 (KJS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Victoria J Vieira-Potter

12 May 2022

PONE-D-21-37204Mitochondrial DNA copy number and insulin sensitivity: Insights from the Sugar, Hypertension, and Physical Exercise StudiesPLOS ONE

Dear Dr. Arking,

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Additional Editor Comments:

Due to this unfortunate circumstance of of only one peer review, I am making the decision "major revision". Please refer to the reviewer's comments. The decision was reject, but I do not feel comfortable rejecting the paper outright based on only one review. Hopefully these comments will be helpful as you revise your work either to resubmit to PLOS one or elsewhere.

Best Regards,

VVP

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

**********

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Reviewer #1: Yes

**********

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Reviewer #1: Yes

**********

5. Review Comments to the Author

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Reviewer #1: This study capitalized on a rich human dataset, including V02 max data and pre and post exercise-intervention data including several metabolic variables on obese individuals. This study correlates these data with circulating levels of mtDNA-CN, an emerging indicator of metabolic health. Although interesting, the new data provided (above what has already been published in the SHAPE interventions) do not seem to warrant publication on their own. Most of the data are not significant, yet some are confirmatory, which is somewhat beneficial to the field. It is particularly interesting that there are age and sex differences. The data do not seem to support strongly a relationship between exercise training and mtDNA-CN, but perhaps the lack of relationship was due to the population mostly being obese and not highly fit. The vast majority of data are presented as supplementary figures, indicating that most of the content is not deemed of sufficient value to most readers (by the authors). There are some interesting findings here. It would be of value if the authors did have skeletal muscle biopsy data to relate to the mtDNA-CN data. This would highlight the potential clinical value that mtDNA-CN may have, leading future studies to use blood mtDNA-CN as an indicator of skeletal muscle quality/mitochondrial activity. The finding that baseline values predicted dropout is peculiar and interesting on its own. If the authors could dig deeper into the data to find a potential explanation for this finding, that could be a central novel element of the paper. Unfortunately, as it currently reads, the paper lacks a major novel finding. It appears that the major finding is that exercise training does not affect mtDNA-CN in obese individuals. Maybe this is an important enough finding to warrant publication, but I just was not convinced of this, especially given the study limitations. I offer my suggestions below and hope this comments are helpful to the authors.

Introduction:

Line 50, Do you mean “positively” correlated? Are there ever instances where mtDNA-CN are elevated in conditions where mitochondrial are dysfunction, such as with dysfunction in mitophagy pathways leading to greater numbers of dysfunctional mitochondria?

Line 52, Please describe “buffy coat” for readers unfamiliar with blood analysis

Line 58, has shown should be have shown

Line 59, for should be of?

More detailed description of “exercise training” is required. Type of exercise? Subject population? Duration/chronicity/intensity?

Results:

The sex differences are interesting and important. How does age affect the sex difference? Have any studies addressed how estrogen or ot her sex hormones may affect mtDNA-CN?

The “effect estimates” that are described – are there p values associated with these graphs (Supp Fig 3)?

The dropout/baseline mtDNA-CN values: a more detailed explanation of what potential covariates were ruled out to explain this relationship would be helpful. (eg, age? BMI? Some sociological variable?)

**********

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PLoS One. 2022 Jul 18;17(7):e0270951. doi: 10.1371/journal.pone.0270951.r002

Author response to Decision Letter 0

16 Jun 2022

We thank the reviewer for their comments, and have responded to them below.

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study capitalized on a rich human dataset, including V02 max data and pre and post exercise-intervention data including several metabolic variables on obese individuals. This study correlates these data with circulating levels of mtDNA-CN, an emerging indicator of metabolic health. Although interesting, the new data provided (above what has already been published in the SHAPE interventions) do not seem to warrant publication on their own. Most of the data are not significant, yet some are confirmatory, which is somewhat beneficial to the field. It is particularly interesting that there are age and sex differences. The data do not seem to support strongly a relationship between exercise training and mtDNA-CN, but perhaps the lack of relationship was due to the population mostly being obese and not highly fit. The vast majority of data are presented as supplementary figures, indicating that most of the content is not deemed of sufficient value to most readers (by the authors). There are some interesting findings here. It would be of value if the authors did have skeletal muscle biopsy data to relate to the mtDNA-CN data. This would highlight the potential clinical value that mtDNA-CN may have, leading future studies to use blood mtDNA-CN as an indicator of skeletal muscle quality/mitochondrial activity. The finding that baseline values predicted dropout is peculiar and interesting on its own. If the authors could dig deeper into the data to find a potential explanation for this finding, that could be a central novel element of the paper. Unfortunately, as it currently reads, the paper lacks a major novel finding. It appears that the major finding is that exercise training does not affect mtDNA-CN in obese individuals. Maybe this is an important enough finding to warrant publication, but I just was not convinced of this, especially given the study limitations. I offer my suggestions below and hope this comments are helpful to the authors.

We thank the reviewer for their insightful comments and suggestions. While we agree with the reviewer’s conclusions that the current data does not support a relationship between exercise training and mtDNA-CN, we would like to note the criteria for publication in PLoS One (taken directly from the website) are as follows:

1. The study presents the results of original research.

2. Results reported have not been published elsewhere.

3. Experiments, statistics, and other analyses are performed to a high technical standard and are described in sufficient detail.

4. Conclusions are presented in an appropriate fashion and are supported by the data.

5. The article is presented in an intelligible fashion and is written in standard English.

6. The research meets all applicable standards for the ethics of experimentation and research integrity.

We believe that the current study and all analyses were performed with scientific rigor, and that all conclusions in the paper are supported by the data. Furthermore, the manuscript is written in standard English, has not been published elsewhere, and describes the results of original research. Research was conducted in accordance with IRB protocols (IRB00071780), and all clinical studies are listed on ClinicalTrials.gov (SHAPE3: NCT00928005, SHAPE5: NCT00990457).

Given this, we believe that this manuscript meets all the criteria for publication in PLoS One. Though negative results are not as exciting or flashy, they are still important and should be disseminated to the rest of the research community.

Introduction:

Line 50, Do you mean “positively” correlated? Are there ever instances where mtDNA-CN are elevated in conditions where mitochondrial are dysfunction, such as with dysfunction in mitophagy pathways leading to greater numbers of dysfunctional mitochondria?

We thank the reviewer for this suggestion and have clarified and adjusted the text on line 50 to read as follows:

Higher mtDNA-CN levels are positively associated with mitochondrial membrane potential, respiratory enzyme function, and energy reserves [1,2]…

There have been instances where elevated mtDNA-CN is associated with cancer, however, we have not seen literature where elevated mtDNA-CN is associated with mitochondrial dysfunction.

Line 52, Please describe “buffy coat” for readers unfamiliar with blood analysis

We have adjusted the text on lines 52-53 to define buffy coat:

Lower mtDNA-CN in buffy coat, the fraction of blood that contains leukocytes and platelets, has been associated with frailty, often characterized by decreased muscle tone [3].

Line 58, has shown should be have shown

We disagree with this suggestion, as we believe version 1 of the sentence is more grammatically sound than version 2.

[1] Indeed, persistent exercise training has been shown to increase mitochondrial function and mitochondrial volume in skeletal muscle [4,5].

[2] Indeed, persistent exercise training have shown to increase mitochondrial function and mitochondrial volume in skeletal muscle [4,5].

Line 59, for should be of?

We have corrected this sentence.

More detailed description of “exercise training” is required. Type of exercise? Subject population? Duration/chronicity/intensity?

Later on in the Methods section of the paper, we rigorously describe the type of exercise (lines 271-291), the subject populations (lines 262-269), and the duration/chronicity/intensity (lines 271-291). We agree with the reviewer that increased detail in the introduction would be useful, and have included some more details in that section (lines 61-64):

To answer this question, we used two randomized controlled exercise intervention cohorts. Participants were aged 30-65 years, and performed aerobic and resistance exercise training 3 times a week for a duration of 6 months.

We have included the more detailed methods sections below for ease of reference:

SHAPE3

Subjects were overweight or obese (BMI between 26 and 42 kg/m2), sedentary men and women (n=77), 30-65 years, with prediabetes or diabetes, according to American Diabetes Association criteria (fasting glucose > 126 mg/dl, casual plasma glucose > 200 mg/dl, or 2-hour plasma glucose > 200 mg/dl after a 75-gram oral glucose load). Individuals with uncontrolled diabetes, defined as fasting blood glucose over 300 mg/dl or A1C > 11% were excluded.

SHAPE5

Subjects were overweight or obese, sedentary men and women (n=77), BMI 25-42 kg/m2, 30-65 years, who were otherwise healthy.

Exercise and Diet Intervention

SHAPE3

Diet intervention for SHAPE3 was a nutritionally balanced, moderately hypocaloric diet with reduced saturated fat consistent with American Diabetes Association guidelines. The diet was adjusted to produce a 600 kcal deficit/day for each individual, using resting metabolic rate calculated from the Mifflin-St Jeor equation [6].

Exercise intervention for SHAPE3 was designed based on guidelines from the American College of Sports Medicine and the American Diabetes Association, consisting of warm-up, 45 minutes of aerobic exercise, several resistance training exercises, and cool-down. Exercise sessions were supervised by exercise physiologists to ensure safety and that the exercises were carried out properly. Individuals assigned to exercise intervention were asked to exercise 3 times a week over a 26-week period.

SHAPE5

The low-carbohydrate (CHO) group adhered to the New Atkins for Life diet, consisting initially of 15% CHO, 30% protein, and 55% fat, followed by a gradual shift to 40% CHO, 20% protein, and 40% fat.

The low-fat group followed American Heart Association (AHA) and National Cholesterol Education Program (NCEP) guidelines, following a diet of 30% fat, 50-55% CHO, and 15-20% protein.

All subjects in SHAPE5 participated in 3 times per week supervised exercise training following ACSM guidelines for moderate intensity aerobic and resistance training, consisting of 45 minutes of aerobic exercise and 2 sets of 7 resistance exercises.

Results:

The sex differences are interesting and important. How does age affect the sex difference? Have any studies addressed how estrogen or other sex hormones may affect mtDNA-CN?

It has been well-established that mtDNA-CN varies between sexes[7,8]. While we agree with the reviewer that these questions are interesting and important, we believe that they fall beyond the scope of this manuscript, as the goal of this study was to examine the effect of exercise on mtDNA-CN.

The “effect estimates” that are described – are there p values associated with these graphs (Supp Fig 3)?

We thank the reviewer for this observation, and agree that this information should be included. We have revised the figure to include effect size estimates and p-values. As a spline does not significantly improve the model for mtDNA-CN and age, we have used a simple linear regression for the plots that are stratified by study.

The dropout/baseline mtDNA-CN values: a more detailed explanation of what potential covariates were ruled out to explain this relationship would be helpful. (eg, age? BMI? Some sociological variable?)

To clarify covariates that were examined, we have added details on the potential underlying covariates that we evaluated for correlation with dropout (lines 118-126), along with a table (Supplemental Table 1).

To understand what could be driving the relationship between baseline mtDNA-CN and study completion, we examined associations between study completion and several other variables. However, none of these potential explanatory variables was significantly associated with study completion. We note that increased age was nominally significantly associated with dropout, however, since increased age is associated with lower mtDNA-CN, this would not explain the observed relationship between higher mtDNA-CN and increased rates of study dropout (Supplemental Table 1). After these analyses, we were unable to account for the variation in dropout explained by baseline mtDNA-CN, and currently do not have a biological explanation for this finding.

Variable Name Odds Ratio p-value

Age 1.055 0.012

BMI 1.018 0.679

Insulin level 1.004 0.734

Sleep score 1.007 0.760

C-reactive protein 1.014 0.734

Supplemental Table 1. Odds ratios and p-values for associations between explanatory variables and study completion. There was no significant relationship between study completion and variables of interest, save for age. However, the directionality of the association between age and study completion does not explain the relationship between higher mtDNA-CN and higher study dropout.

References

1. Guha M, Avadhani NG. Mitochondrial retrograde signaling at the crossroads of tumor bioenergetics, genetics and epigenetics. Mitochondrion. 2013;13: 577–591. doi:10.1016/j.mito.2013.08.007

2. Jeng J-Y, Yeh T-S, Lee J-W, Lin S-H, Fong T-H, Hsieh R-H. Maintenance of mitochondrial DNA copy number and expression are essential for preservation of mitochondrial function and cell growth. J Cell Biochem. 2008;103: 347–357. doi:10.1002/jcb.21625

3. Ashar FN, Moes A, Moore AZ, Grove ML, Chaves PHM, Coresh J, et al. Association of Mitochondrial DNA levels with Frailty and All-Cause Mortality. J Mol Med Berl Ger. 2015;93: 177–186. doi:10.1007/s00109-014-1233-3

4. Jacobs RA, Lundby C. Mitochondria express enhanced quality as well as quantity in association with aerobic fitness across recreationally active individuals up to elite athletes. J Appl Physiol. 2013;114: 344–350. doi:10.1152/japplphysiol.01081.2012

5. Menshikova EV, Ritov VB, Fairfull L, Ferrell RE, Kelley DE, Goodpaster BH. Effects of Exercise on Mitochondrial Content and Function in Aging Human Skeletal Muscle. J Gerontol A Biol Sci Med Sci. 2006;61: 534–540.

6. Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990;51: 241–247. doi:10.1093/ajcn/51.2.241

7. Tin A, Grams ME, Ashar FN, Lane JA, Rosenberg AZ, Grove ML, et al. Association between Mitochondrial DNA Copy Number in Peripheral Blood and Incident CKD in the Atherosclerosis Risk in Communities Study. J Am Soc Nephrol JASN. 2016;27: 2467–2473. doi:10.1681/ASN.2015060661

8. Knez J, Winckelmans E, Plusquin M, Thijs L, Cauwenberghs N, Gu Y, et al. Correlates of Peripheral Blood Mitochondrial DNA Content in a General Population. Am J Epidemiol. 2016;183: 138–146. doi:10.1093/aje/kwv175

Attachment

Submitted filename: Response_To_Reviewer.docx

Click here for additional data file. (33.1KB, docx)

Decision Letter 1

Hans-Peter Kubis

22 Jun 2022

Mitochondrial DNA copy number, metabolic syndrome, and insulin sensitivity: Insights from the Sugar, Hypertension, and Physical Exercise Studies

PONE-D-21-37204R1

Dear Dr. Arking,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Hans-Peter Kubis, PD. Dr. rer. nat.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear Dr Arking,

Thank you for the resubmission of your manuscript. The manuscript was reviewed in the current form and is now acceptable for publication in PLOS ONE. We thank you for submitting your work to PLOS ONE and hope to see more manuscripts being sent to us in the future. Many thanks for considering PLOS ONE and good luck for your future research.

Acceptance letter

Hans-Peter Kubis

8 Jul 2022

PONE-D-21-37204R1

Mitochondrial DNA copy number, metabolic syndrome, and insulin sensitivity: Insights from the Sugar, Hypertension, and Physical Exercise Studies 

Dear Dr. Arking:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Hans-Peter Kubis

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Exercise is associated with an increase in VO2max.

    The number of exercise sessions attended is significantly associated with a 6-month increase in VO2max.

    (TIF)

    Click here for additional data file. (59.6KB, tif)
    S2 Fig. Exercise is associated with a decrease in BMI.

    There was a significant association between a greater number of exercise sessions attended and decreased BMI over the 6-month intervention period.

    (TIF)

    Click here for additional data file. (59.5KB, tif)
    S3 Fig. mtDNA-CN is associated with known covariates in both cohorts.

    Associations between mtDNA-CN and age and sex are in the expected directions when stratifying by SHAPE study. SHAPE3 on the left, SHAPE5 on the right.

    (TIF)

    Click here for additional data file. (135.3KB, tif)
    S4 Fig. mtDNA-CN is correlated with study retention.

    Individuals who dropped out of the study had significantly higher mtDNA-CN.

    (TIF)

    Click here for additional data file. (20.9KB, tif)
    S5 Fig. Correlation with study dropout appears in both cohorts.

    Individuals who dropped out of the study have significantly higher mtDNA-CN than those who were retained (On left, SHAPE3, on right, SHAPE5).

    (TIF)

    Click here for additional data file. (29.2KB, tif)
    S6 Fig. Baseline mtDNA-CN is associated with a change in mtDNA-CN in the direction of the mean.

    mtDNA-CN measured at baseline is associated with a change in mtDNA-CN in the direction of the mean. Purple points denote individuals with final mtDNA-CN measurements closer to the mean than their baseline measurements, while yellow is vice versa. Significantly more individuals move towards the mean (chi-squared p = 0.004). For the purple samples, absolute magnitude of baseline mtDNA-CN is positively correlated with the absolute value of 6-month change, however, this association is not significant (P = 0.51).

    (TIF)

    Click here for additional data file. (64.3KB, tif)
    S7 Fig. VO2 max and change in mtDNA-CN are not associated.

    There was no significant association between 6-month change in VO2 max and 6-month change in mtDNA-CN.

    (TIF)

    Click here for additional data file. (55.3KB, tif)
    S1 Table. Odds ratios and p-values for associations between explanatory variables and study completion.

    There was no significant relationship between study completion and variables of interest, save for age. However, the directionality of the association between age and study completion does not explain the relationship between higher mtDNA-CN and higher study dropout.

    (XLSX)

    Click here for additional data file. (8.9KB, xlsx)
    S2 Table. Adjusting for metabolic syndrome does not change results.

    Effect size estimates for secondary outcomes after including metabolic syndrome status as a covariate. Results generally stay the same, indicating that metabolic syndrome is not mediating the effects of mtDNA-CN on these outcomes.

    (XLSX)

    Click here for additional data file. (9.1KB, xlsx)
    Attachment

    Submitted filename: Response_To_Reviewer.docx

    Click here for additional data file. (33.1KB, docx)

    Data Availability Statement

    The minimal dataset necessary to replicate our analyses is available on Figshare: https://figshare.com/articles/dataset/shape_for_release_txt/20152202.

    Articles from PLoS ONE are provided here courtesy of PLOS

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