Mitochondrial DNA variants and their impact on epigenetic and biological aging in young adulthood

Abstract

The pace of biological aging varies between people independently of chronological age and mitochondria dysfunction is a key hallmark of biological aging. We hypothesized that higher functional impact (FI) score of mitochondrial DNA (mtDNA) variants might contribute to premature aging and tested the relationships between a novel FI score of mtDNA variants and epigenetic and biological aging in young adulthood. A total of 81 participants from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) prenatal birth cohort had good quality genetic data as well as blood-based markers to estimate biological aging in the late 20. A subset of these participants (n = 69) also had epigenetic data to estimate epigenetic aging in the early 20s using Horvath’s epigenetic clock. The novel FI score was calculated based on 7 potentially pathogenic mtDNA variants. Greater FI score of mtDNA variants was associated with older epigenetic age in the early 20s and older biological age in the late 20s. These medium to large effects were independent of sex, current BMI, cigarette smoking, cannabis, and alcohol use. These findings suggest that elevated FI score of mtDNA variants might contribute to premature aging in young adulthood.

Introduction

Recent demographic trends [1] coupled with increased life expectancy [2] have resulted in an unprecedented increase in the percentage of elderly individuals across most Western societies. According to the World Health Organization, the proportion of the world’s population over 60 years will nearly double from 12% in 2015 to 22% in 2050 [3]. Given this sharp increase in the proportion of older adults within society, a better understanding of how we age starts to be critical.

Markers of biological aging in young adulthood may be particularly useful in informing preventive efforts. It has been demonstrated that the pace of biological aging varies between people, independently of chronological age [4–8]. To measure the aging process, US National Health and Nutrition Survey (NHANES) studied participants aged 30–75 years and developed a 10-biomarker-based measure of “Biological Age”, which predicted mortality in a 20-year follow-up better than chronological age [9]. Belsky et al. [4] used the NHANES algorithm to calculate the Biological Age of Dunedin Study members and found large variations in biological aging in young individuals of the same chronological age. While all participants were 38 years old, their biological age varied from 28 to 61 years [4]. Premature biological aging (biological age > chronological age) also predicted poorer physical fitness, appearance, and cognitive decline [4].

Further research from our group [5] used an independent sample of young adults to study the predictors of such premature biological aging and demonstrated that premature biological aging in young adulthood was predicted by higher BMI in the early 20 s (Adj R² = 0.05) as well as the late 20 s (Adj R² = 0.22). Moreover, the older biological age was predicted by BMI increase over the 5 years between the two measurements in young adulthood (Adj R² = 0.09). A single hierarchical model revealed that shorter birth length, early puberty onset, and greater levels of visceral fat were the main predictors of premature biological aging, together explaining 21% of the variance [5].

A distinct measure of the aging process, which can capture individual differences therein but is rarely measured together with the biological age, is the epigenetic clock. DNA methylation patterns change predictably over the lifespan and thus the DNA methylation patterns can be used to estimate one’s chronological age [8, 10]. The most commonly used DNA methylation-based predictor of age is the multi-tissue Horvath’s epigenetic clock [10]. But premature epigenetic aging, defined as the residual variation in epigenetic age independent of chronological age, was associated also with decreased physical capability and cognitive functioning [5], male sex, and greater risk for cardiovascular disease and diabetes [11]. Based on twin studies, the heritability of epigenetic age acceleration is relatively high (h²~40%; [12]). Still, Horvath [10] suggested that the heritability might decrease with age as the environmental contribution to epigenetic aging increases.

Both biological age and epigenetic age likely reflect complex biological processes [13], to which mitochondria dysfunction is a promising but relatively unexplored contributor [13, 14]. The mitochondrial genome (mtDNA) is distinct from the nuclear genome and comprises a 16 kb circular double-stranded DNA. It encodes 37 genes, including two ribosomal RNAs (rRNAs), 22 transfer RNAs (tRNAs), and 13 oxidative phosphorylation subunits [15]. These 13 proteins are essential for the effective functioning of electron transport chain (ETC) complexes I through V [16]. MtDNA is more susceptible to damage than nuclear DNA and has a higher mutation rate [17]. The accumulation of mtDNA mutations is one of the main processes underlying the decline in mitochondrial function during aging [13, 18], and research in mice demonstrated that the accumulation of mtDNA mutations translates into impairments of glucose metabolism and cognition [19] as well as life-shortening [20].

The majority of pathogenic mtDNA mutations result in impairments in the mitochondrial oxidative phosphorylation (OXPHOS) process, culminating in reduced energy production and increased generation of reactive oxygen species (ROS) [21]. According to the mitochondrial free radical theory of aging, the accumulation of ROS produced by dysfunctional mitochondria is considered a key factor driving the aging process [14, 22]. This theory is supported to some extent in the observed inverse relationship between mitochondrial ROS production and lifespan in mammals [23–25]. Mitochondria might also have a significant influence on epigenetic regulation by providing various co-substrates generated during the tricarboxylic acid cycle (TCA cycle), essential for epigenetic and transcriptional mechanisms, including histone modifications and chromatin restructuring [26]. Salminen and co-authors [26] suggested that mitochondria, when subjected to stress conditions, respond by modifying the epigenetic structure of chromatin to either enhance survival or induce a senescent state [26]. Mitochondrial dysfunction due to mutations of mtDNA was associated with epigenetic alterations [27], but it is not clear whether mitochondria dysfunction might partially explain commonly observed individual differences in the speed of biological and epigenetic aging in humans.

Based on the literature reviewed above, we hypothesized that pathogenic variants of mtDNA might contribute to premature biological and epigenetic aging in humans and that these relationships might be detectable already in young adulthood. We used genetic data from the members of the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) prenatal birth cohort to develop a novel functional impact (FI) score of mtDNA variants and subsequently tested its relationship with biological and epigenetic aging in young adulthood. In particular, we hypothesized that higher FI score of mtDNA variants will contribute to premature aging.

Methods

Participants

Participants were members of the European Longitudinal Study of Pregnancy and Childhood (ELSPAC; [28]), a prenatal birth cohort born between 1991 and 1992 in South Moravia, Czech Republic, who also participated in its two follow-ups in young adulthood: (1) Biomarkers and Underlying Mechanisms of Vulnerability to Depression (VULDE; age 23–24) and (2) Health Brain Age (HBA; age 28–30) at the Central European Institute of Technology, Masaryk University. A total of 81 young adults (age 28–30, 51% men) had good quality genetic data as well as blood-based markers collected in late 20 s, a total of 69 young adults (age 23–24, 49% men) had good quality genetic data as well as epigenetic data from early 20 s. Demographic information on both samples is provided in Table 1.

Table 1.

Demographic characteristics.

	Sample in the early 20s (n = 69)	Sample in the late 20s (n = 81)
Age (years), mean ± SD	23.84 ± 0.41	29.31 ± 0.60
Ethnicity (% European ancestry)	100%	100%
Sex (% men)	49%	51%
BMI, mean ± SD	23.22 ± 3.61	23.76 ± 3.88
Cigarette smokinga, mean ± SD	0.86 ± 1.57	0.79 ± 1.49
Cannabis useb, mean ± SD	3.20 ± 2.64	3.10 ± 2.66
Alcohol usec, mean ± SD	5.51 ± 3.89	5.46 ± 3.71

BMI body mass index.

^aCigarette smoking was assessed at each timepoint using a single question (How many times in the past 30 days did you smoke cigarettes?) and participants chose the best fitting answer (from 0, 1, 2, 3–5, 6–9, 10–19, 20–39, 40 and more), which were coded from 0 to 7, where 7 reflected the highest cigarette use.

^bLifetime cannabis use was assessed at each timepoint using a single question (How many times in your life did you try cannabis) and participants chose the best fitting answer, coded from 0 to 7, where 7 reflected the highest cannabis use.

^cAlcohol use at each timepoint was assessed using following question: How many times in the past 30 days did you drink (a) beer, (b) wine, (c) shots. Participants chose the best fitting answers (from 0, 1, 2, 3–5, 6–9, 10–19, 20–39, 40 and more), which were coded from 0 to 7, where 7 reflected the highest alcohol use. Finally, the alcohol use variable was calculated as the sum of these three answers.

Ethical approval and consent to participate

All participants provided written informed consents to participate in the HBA and VULDE studies, including the agreement to merge data from HBA, VULDE, and their historical data from ELSPAC. Ethical approval for both the HBA and VULDE studies was obtained from the ELSPAC ethics committee (ELSPAC/EK/2/2020). All methods were performed in accordance with the relevant guidelines and regulations.

Procedures

In the early 20s, buccal swabs were collected from members of the ELSPAC prenatal birth cohort, DNA was isolated, and genetic and epigenetic analyses were performed as detailed below. A total of 99 participants had genetic data of good quality to calculate the novel functional impact (FI) score of mtDNA variants. A subset of these individuals (n = 69) also had epigenetic data to test the impact of the FI score of mtDNA variants on epigenetic aging in the early 20s.

In the late 20s, blood samples were taken in the morning before the first meal from the subset (n = 81) of the 99 individuals with good-quality genetic data. Cholesterol, C-reactive protein (CRP), glucose, albumin, creatinine, urea nitrogen serum levels (mg/dL) as well as alkaline phosphatase activity in serum (U/L) were measured on ROCHE analyzer (Cobas Integra 400, Roche diagnostics). The percentage of glycated hemoglobin was calculated based on glucose levels according to published equations and recommendations of the international consensus statement [29–31]. In addition, systolic and diastolic blood pressure were assessed according to standard protocols and forced expiratory volume in one second (FEV1) was calculated using MIR Smart One Spirometer. These data allowed the calculation of the biological aging as detailed bellow and test the impact of the FI score of mtDNA variants on biological aging in the late 20s.

At both time points, participants also filled in a self-reported questionnaire regarding their substance use. This questionnaire asked the young adult (1) How many times in the past 30 days did you smoke cigarettes, (2) How many times in the past 30 days did you drink (a) beer, (b) wine, (c) shots and (3) How many times in your life did you try cannabis.

Analysis of genetic data and calculation of the novel FI score of mtDNA variants

Mitochondrial DNA genotyping, variant calling and filtering

A set of 201 mitochondrial SNPs were genotyped in one batch using Illumina OmniExpressExome BeadArray 8 version 1.4. Samples that passed autosomal quality control (QC) procedures were selected (N = 102; see Supplementary Fig. 1 for details regarding the sample size available at each step of the analysis). Next, mtDNA was checked to ensure it was mapped to the revised Cambridge Reference Sequence (rCRS). Variant and individual QC filters were applied, including removing variants with genotyping rate of 5% (3 variants removed) and excluding individuals exhibiting missing data rates exceeding 5% for mitochondrial markers (2 individuals removed). HaploCheck (v1.0.5) was performed to estimate mtDNA contamination [32], resulting in the identification and removal of one individual from the analysis. Total genotyping rate in remaining samples was Higher than 99%, 198 variants and 99 people passed filters and QC. Genotype data were available for 20 coding region SNPs with a minor allele frequency (MAF) greater than 5% and 178 coding region SNPs with MAF ≤ 5%. Details regarding the genotypes for common SNPs are provided in Supplementary Table 1.

Mitochondrial DNA imputation and haplogroups assignment

Mitochondrial DNA imputation was conducted following the methodology outlined in a previous publication by Gonçalves et al. [33], resulting in the acquisition of data for an additional 11 common SNPs giving us 31 in total (considering post-imputation filters of “info” score >0.3 and MAF > 5%). A reference panel comprising 7141 public European mitochondrial sequences obtained from the Human Mitochondrial Database [34] was utilized (SNP N = 300 after filtering by MAF > 5%). IMPUTE2 v.2 software [35] was employed for the imputation process, with instructions tailored for chromosome X, and all individuals in the dataset were recoded as males for analysis purposes. The list of variants containing genotyped/imputed SNPs can be found in Supplementary Table 2. Additionally, the following steps were undertaken: i) identification of haplogroups present in the dataset using HaploGrep 2 [36]; ii) extraction of the complete profile for each haplogroup from Phylotree17 [37]; iii) selection of only those SNPs present on the Illumina OmniExpressExome BeadArray 8 version 1.4 to generate pseudo-samples from these profiles; and iv) imputation of any missing data for these pseudo-samples. Subsequently, a comparison was made between haplogroup assignments based on Phylotree and those derived from imputed (genotyped/imputed SNPs) and genotyped-only SNPs.

Phylogenetically related haplogroups were combined into macro-haplogroups (H-HV [H and HV], J-T [J and T], U-K [U and K] and others) due to relatively small sample size. Individuals assigned as non-European based on their haplogroups were filtered out because they were less than 10% of the total number of individuals available for analysis. Principal component analysis (PCA) was conducted, and data was plotted to visualize clustering of haplogroups. The visualization of haplogroup clustering was used as a validation of HaploGrep 2 results to determine whether samples clustered well together, based on their macro-haplogroup (Fig. 1).

Fig. 1. mtDNA genetic grouping.

Colors correspond to the traditional mtDNA haplogroups according to HaploGrep 2. The four macro-haplogroups H-HV, J-T, U-K and others (I and W), defined by first and second dimensions are highlighted.

Functional annotation of mtDNA variant and impact score

The impact of amino acid changes caused by mutations on protein function was assessed through a comprehensive analysis using a combination of tools that utilize sequence homology, evolutionary conservation, and protein structural information [38]. These tools include: MutPred [39], mtDNA Selection [40] and MitoTool [41]. We assessed the pathogenicity levels of all non-synonymous variants that exhibited MutPred, mtDNA Selection, and MitoTool scores. Higher functional impact scores correspond to a greater likelihood that the amino acid variation is pathogenic. The functional impact (FI) score for each individual’s mtDNA variants was determined by summing the predicted MutPred, mtDNA Selection, and MitoTool scores of each variant (Table 2). Other information, including allele frequency in several known datasets and reported associations with diseases, and whether the mutations were novel or known were obtained from databases specialized for mtDNA variants, such as Mitomap (RRID:SCR_002996) [42] and ClinVar [43]. After identification of mtDNA variants, we annotated to include the mutation category, region, and whether they were synonymous or not.

Table 2.

Functional annotation and potential functional effects of seven non-synonymous common variants.

Position	Var allele	Ref allele	Substitution	rCRS	AAC	MP	MS	MT	FI	OXPHOS complex	Haplogroup marker
4917	G	A	transition	MT-ND2	N150D	0.63	0.78	0.92	2.33	I	T
5460	A	G	transition	MT-ND2	A331T	0.51	0.48	0.15	1.14	I	Q and W
9477	A	G	transition	MT-CO3	V91I	0.25	0.18	0.67	1.10	IV	–
10,398	G	A	transition	MT-ND3	T114A	0.17	0.13	0.37	0.67	I	Several haplogroups
13,708	A	G	transition	MT-ND5	A458T	0.41	0.33	0.37	1.11	I	J
14,798	C	T	transition	MT-CYB	F18L	0.61	0.72	0.54	1.87	III	J and K
15,452	A	C	transversion	MT-CYB	L236I	0.10	0.10	0.71	0.91	III	J and T

Var allele variant allele, ref allele reference allele, rCRS the revised Cambridge Reference Sequence, AAC amino acid change for variants in coding region, MP MutPred score, MS mtDNA selection score, MT mito tool score, FI functional impact score of mtDNA variant.

DNA methylation and epigenetic aging in the early 20s

DNA methylation was assessed using the Ilumina EPIC Platform and „Methylation age“ was estimated using the Horvath’s epigenetic clock [10] as described in Mareckova et al. [8]. Briefly, raw Illumina microarray data were processed using R package ChAMP [44]. The raw data were trimmed of (1) probes with <3 beads in at least 5% of samples per probe, (2) SNP-related probes, (3) multi-hit probes, (4) probes located in chromosomes X and Y. Beta mixture quantile normalization (BMIQ; [45]) method was used to adjust the beta-values of type II design probes into a statistical distribution characteristic of type I probes. Next, DNA methylation age was calculated using an epigenetic clock developed by Horvath [10], which uses 353 CpG sites to estimate DNA methylation age. Finally, we residualized the DNA methylation age estimates at each timepoint for batch, chronological age, and the proportion of epithelial cells (the average proportion was 80% of epithelial and 20% of immune cells; SD = 13% in each group) in each participant and saved the residuals from the analysis as the epigenetic age gap (EpiAGE). Thus, positive values of EpiAGE reflect premature aging/faster maturation and negative values reflect slower aging/slower maturation.

Biological aging in the late 20s

Biological age was calculated using Klemera-Doubal Method (KDM), available through the R package “Bio-Age” [9] that applies a 9-biomarker algorithm including forced expiratory volume in one second (FEV1), blood pressure (systolic), glycated hemoglobin, total cholesterol, C-reactive protein, creatinine, urea nitrogen, albumin, and alkaline phosphatase. The difference between biological age and chronological age (BioAGE) thus reflects premature (positive values) or slower (negative values) aging.

Statistical analyses

All statistical analyses were performed in JMP version 10.0.0 (SAS Institute Inc., Cary, NC. First, we assessed the distribution of data, and variables that did not follow a normal distribution (e.g. FI score of mtDNA variants) were transformed using logarithmic transformation. Next, we used linear regression to assess (1) the impact of the FI score of mtDNA variants on epigenetic aging in the early 20s and (2) the impact of the FI score of mtDNA variants on biological aging in the late 20s. Covariates included sex, current BMI, cigarette smoking, cannabis, and alcohol use. Finally, exploratory analyses using multiple regression evaluated the impact of the 7 different variants on (a) epigenetic aging in the early 20s and (b) biological aging in the late 20s.

Results

Mitochondrial DNA variants characterization and functional impact score

A comprehensive analysis of mtDNA variants was performed across the total of 102 participants with genetics data and resulted in the identification of a total of 201 variants. After the quality control process, a final set of 198 variants from 99 participants, representing 98.51% of the raw data, were deemed reliable and retained for subsequent analysis. Furthermore, our analysis revealed that 20 of these variants were classified as common variants (MAF > 0.05), indicating their relatively higher frequency in the population (see Supplementary Table 1). The common variants were distributed as follows: 7 were in the coding region (35%), 3 control region (CR) (15%), 6 rRNAs (30%), and 4 tRNAs (20%). Among the common variants, we found the following 7 non-synonymous ones: m.4917A > G and m.5460G > A in the gene MT-ND2, m.9477G > A in the gene MT-CO3, m.10398A > G in the gene MT-ND3, m.13708G > A in the gene MT-ND5, m.14798T > C and m.15452C > A in the gene MT-CYB. The FI score of mtDNA variants was determined by the presence of seven specific pathogenic variants among the participants (Table 2). The mean FI score for mtDNA variants was 0.82 (standard deviation [SD] = 1.28). Notably, half of the sample did not exhibit any of these seven pathogenic variants, while the remaining participants manifested one or more pathogenic variants, originating from the same or different OXPHOS complexes (see Table 3).

Table 3.

Prevalence of the seven potentially pathogenic variants within the participants.

SNPs	OXPHOS complex	MAF	Participants with variant	Participants without variant
m.4917A > G	I	0.08	8	89
m.5460G > A	I	0.10	9	89
m.9477G > A	IV	0.10	10	87
m.10398A > G	I	0.09	9	88
m.13708G > A	I	0.08	8	89
m.14798T > C	III	0.07	7	90
m.15452C > A	III	0.13	13	84

SNPs single nucleotide polymorphisms, MAF minor allele frequency in the whole cohort.

Relationship between the haplogroup and the FI score of mtDNA variants

All individuals included in the study were exclusively of European ancestry and the proportion of each macro-haplogroup represented in our dataset was H-HV (n = 60), J-T (n = 13), U-K (n = 21), or Other European (n = 5) (Fig. 1). There was a significant effect of haplogroup on the FI score of mtDNA variants (F_(3,98) = 82.87, p < 0.0001) and post-hoc analyses revealed that the J-T group had significantly higher FI score than any of the other groups (p < 0.0001), indicating a higher burden of potentially pathogenic variants, while the H-HV group had significantly lower FI score than any of the other groups (p < 0.001). Consistently, there was also a significant effect of the haplogroup on the presence of each of the 7 pathogenic variants (p < 0.0006). Detailed distribution of SNPs across haplogroups can be found in Supplementary Table 3.

Relationships between the FI score of mtDNA variants and epigenetic or biological aging

While there was no relationship between epigenetic aging in the early 20s and biological aging in the late 20s (r = 0.003, p = 0.982), greater FI score of mtDNA variants was associated with both higher epigenetic age in the early 20s (_AdjR² = 0.17, beta = 0.27, p = 0.02; Fig. 2A) as well as higher biological age in the late 20s (_AdjR² = 0.22, beta = 0.24, p = 0.019; Fig. 2B). These effects were independent of sex, current BMI, cigarette smoking, cannabis, and alcohol use.

Fig. 2. Functional impact (FI) score of mtDNA variants and its relationship with epignetic and biological aging in young adulthood.

Greater FI score of mtDNA variants was associated with premature epigenetic aging in the early 20s (A), and with premature biological aging in the late 20s (B). The models were corrected for sex, BMI, cigarette smoking, cannabis, and alcohol use.

Further exploratory analyses using multiple regression and evaluating the impact of the 7 different variants on epigenetic aging in the early 20 s revealed that the effect of the FI score was driven by the m.9477G > A and m.15452C > A variants (see Table 4). Similar multiple regression evaluating the impact of the 7 different variants on biological aging in the late 20s revealed that the effect was additive—each of the 7 variants showed a significant effect (see Table 4).

Table 4.

Results of the multiple regression evaluating the impact of the seven different variants on epigenetic aging in the early 20s and biological aging the late 20s.

	Epigenetic aging in the early 20s		Biological aging in the late 20s
SNPs	Std beta	p-value	Std beta	p-value
m.4917A > G	0.42	0.1721	−0.82	<0.0001
m.5460G > A	0.01	0.8956	−0.39	<0.0001
m.9477G > A	−0.37	0.0009	−0.40	<0.0001
m.10398A > G	−0.05	0.8428	−0.32	<0.0001
m.13708G > A	−0.02	0.8631	−0.37	<0.0001
m.14798T > C	0.04	0.8722	−0.28	<0.0001
m.15452C > A	−0.63	0.0504	0.14	0.0047
Sex	−0.40	0.0003	−0.01	0.0102

SNPs single nucleotide polymorphisms.

Significant relationships are in bold.

Discussion

Our study investigated mtDNA variants and their potential functional impact on epigenetic and biological aging in young adulthood. We identified seven common variants with potential functional effects related to the OXPHOS complexes I, III, and IV and introduced a novel FI score of mtDNA variants. We observed that the J-T group displayed higher FI score of the mtDNA variants than other European macrohaplogroups, suggesting higher pathogenicity in the J-T group, whereas the H-HV group exhibited significantly lower FI score of the mtDNA variants than the other European macrohaplogroups, suggesting lower pathogenicity in the H-HV group. Moreover, we demonstrated that a higher FI score of mtDNA variants was associated with both premature epigenetic aging in the early 20s and premature biological aging in the late 20s. These effects were independent of sex, current BMI, cigarette smoking, cannabis, and alcohol use.

Mitochondria are essential for several vital biological functions including energy production, regulation of ROS, calcium balance, inflammation, and programmed cell death [46–49]. Endogenous or exogenous cellular stressors can impair mitochondrial function, resulting in elevated ROS levels and accumulation of mutations in the mtDNA [50]. Excessive intracellular ROS levels lead to increased oxidative stress (OS), resulting in oxidative damage to macromolecules such as proteins, lipids, and DNA [51]. Research suggests that increased OS and ROS levels contribute to the occurrence of somatic mutations in mtDNA [52–54]. Genetic mouse models have demonstrated that somatic mtDNA mutations and cell type-specific dysfunction in the respiratory chain can lead to various phenotypes associated with aging and age-related diseases [55, 56]. In our study, we identified mtDNA variants in the MT-ND2, MT-ND3, MT-ND5, MT-CO3 and MT-CYB genes. Mutations in these genes have been associated with impaired mitochondrial energy production in various aging-related diseases, including Alzheimer’s disease [57, 58], Parkinson’s disease [59], and type 2 diabetes mellitus [60, 61]. Therefore, we hypothesize that the presence of the identified variants may significantly diminish the activity of complexes I, III and IV, resulting in decreased energy production.

Specifically, the higher FI score associated with these variants appears to be more harmful, correlating with both epigenetic and biological aging. Our findings support and substantially extend research in mice, which reported that mtDNA mutations are associated with life-shortening [20]. Moreover, given the fact that one of the biomarkers used in the KDM formula is glycated hemoglobin, which is closely linked with one’s levels of glucose, our findings are also consistent with other research in mice, which reported that accumulation of mtDNA mutations translates into impairments of glucose metabolism [19]. Our findings regarding the relationship between the FI score of mtDNA and premature epigenetic aging then support and extend the literature on mitochondrial dysfunction due to mutations of mtDNA and epigenetic alterations [27].

Interestingly, there were no relationships between epigenetic aging in the early 20s and biological aging in the late 20s, suggesting these two estimates of aging reflect different aging-related processes. Still, the fact that the FI score of mtDNA variants was able to predict premature aging estimated based on the DNA methylation as well as blood-based markers at two different timepoints suggests that the impact of the FI score of mtDNA variants is robust and most likely influences two different pathways leading to premature aging. Our findings are consistent with previous research indicating that somatic mtDNA mutations occurring during mouse embryogenesis or early life stages could potentially influence the development of aging-related phenotypes in adult mice [23, 52].

Contradictory evidence suggests that the role of mitochondrial genome mutations in longevity remains uncertain. The haplogroup J, characterized by specific mutations, including m.489T > C, m.10398A > G, m.1262A > G, and m.13708G > A, as well as substitutions m.4216T > C, m.11251A > G, and m.15452C > A shared with haplogroup T, appears to be associated with a higher likelihood of achieving longevity in certain populations such as Northern Italians, Northern Irish, Finns, and Northern Spaniards [62–65]. However, this association is not consistently observed in Southern Italians and central Spaniards [66, 67], suggesting population-specific effects. Differences in study methodologies, including ethnic backgrounds and age ranges of subjects, may have contributed to these discrepancies. Ruiz-Pesini et al. [68] proposed that the prevalence of J mitochondrial haplogroups in colder climates may offer an evolutionary advantage by enhancing mitochondrial energy and heat production [68]. However, this advantage may come at the cost of increased oxidative stress and susceptibility to degenerative diseases in unfavorable cellular environments. Despite associations with longevity, J and related haplogroups have also been linked to degenerative diseases like Parkinson’s disease [69–71]. Our results showed that the J-T group displayed higher pathogenicity FI scores compared to all other European macrohaplogroups, whereas the H-HV group exhibited significantly lower pathogenicity FI scores than the others. These findings suggest that individuals within the J-T haplogroup may be predisposed to the premature aging process, potentially increasing their susceptibility to age-related diseases when compared to the other groups. These results underscore the importance of incorporating mitochondrial genetics, specifically haplogroup membership, into the study of epigenetic and biological aging.

Our findings should be viewed in light of some limitations. The sample of our study is small and thus our findings should be replicated by future research using a larger sample. Further, the inclusion of other ancestry populations is essential for comprehensive insights. Moreover, while we have identified potential pathogenic associations, further research is required to validate the functional impacts of these polymorphisms, particularly with respect to mitochondrial dysfunction. This will involve in vitro and in vivo aging models to explore the role of these variants in the acceleration of the aging process. In addition, follow-up studies using independent datasets will be crucial to confirm our current findings. Considering the complex interplay between genetic and environmental factors in aging, future studies should also explore how the variants identified here affect mtDNA-nDNA communication. Still, we believe that the identification of the novel FI score of mtDNA variants as well as its large effects on premature aging in young adults in the early as well as the late 20s bring important evidence regarding the potential origin of premature aging in young adulthood. We also speculate that future research might develop targeted interventions allowing the attenuation or correction of the mtDNA mutations and contribute to the extension of healthspan.

Overall, our study presents preliminary evidence suggesting the involvement of seven mtDNA variants—m.4917A > G and m.5460G > A in the gene MT-ND2, m.9477G > A in the gene MT-CO3, m.10398A > G in the gene MT-ND3, m.13708G > A in the gene MT-ND5, and m.14798T > C and m.15452C > A in the gene MT-CYB—in premature aging in young adulthood. These findings emphasize the need for further investigation into mitochondrial genetics in the aging process to unravel its underlying mechanisms.

Author contributions

KM: conceptualization, formal analysis, writing the original draft, funding acquisition; APMS: methodology, software, and formal analysis of the mitochondrial DNA data, writing the original draft; MJ: software and formal analysis of the biological aging score; AP: software and formal analysis of the epigenetic data; PP: methodology and analysis of the blood-based markers, resources; VFG: supervision of the mitochondrial DNA analyses; YSN: supervision, writing—review and editing.

Funding

This work has received funding from Czech Science Foundation, project no. 24-12183M, Czech Health Research Council (No. NU20J-04-00022), and the Czech Ministry of Education, Youth and Sports (MEYS CR) (CEITEC 2020, LQ1601). The project was also supported by project nr. LX22NPO5107 (MEYS): Funded by European Union – Next Generation EU. Authors also thank the RECETOX Research Infrastructure (No LM2023069) financed by the Ministry of Education, Youth and Sports for supportive background. This work was also supported from the European Union’s Horizon 2020 research and innovation program under grant agreement No 857560 (CETOCOEN Excellence). This publication reflects only the author's view, and the European Commission is not responsible for any use that may be made of the information it contains. APM-S acknowledges support from CIHR Fellowship Award and the CAMH Discovery Fund Fellowship. VFG is supported by Larry and Judy Tanenbaum Foundation and Discovery Fund Seed Grant. YSN is supported by Koerner New Scientist Award from the CAMH Foundation and a Discovery Grant from the National Sciences and Engineering Research Council of Canada (NSERC).

Data availability

Data are available from the corresponding author upon reasonable request.

Competing interests

The authors declare no competing interests.

Supplementary information

The online version contains supplementary material available at 10.1038/s41398-025-03235-4.