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4 February 2025

Associations of Prenatal Mercury Exposure and PUFA with Telomere Length and mtDNA Copy Number in 7-Year-Old Children in the Seychelles Child Development Nutrition Cohort 2

Publication: Environmental Health Perspectives
Volume 133, Issue 2
CID: 027002

Abstract

Background:

Telomere length (TL) and mitochondrial DNA copy number (mtDNAcn) variations are linked to age-related diseases and are associated with environmental exposure and nutritional status. Limited data, however, exist on the associations with mercury exposure, particularly early in life.

Objective:

We examined the association between prenatal mercury (Hg) exposure and TL and mtDNAcn in 1,145 Seychelles children, characterized by a fish-rich diet.

Methods:

Total mercury (THg) was determined in maternal hair at delivery and cord blood. TL and mtDNAcn were determined relative to a single-copy hemoglobin beta gene in the saliva of 7-y-old children. Linear regression models assessed associations between THg and relative TL (rTL) and relative mtDNAcn (rmtDNAcn) while controlling for maternal and cord serum polyunsaturated fatty acid (PUFA) status and sociodemographic factors. Interactions between THg and child sex, PUFA, and telomerase genotypes were evaluated for rTL and rmtDNAcn.

Results:

Higher THg concentrations in maternal hair and cord blood were associated with longer rTL [β=0.009; 95% confidence interval (CI): 0.002, 0.016 and β=0.002; 95% CI: 0.001, 0.003, respectively], irrespective of sex, PUFA, or telomerase genotypes. Maternal serum n-6 PUFA and n-6/n-3 ratio were associated with shorter [β=0.24; 95% CI: 0.33, 0.15 and β=0.032; 95% CI: 0.048, 0.016, respectively] and n3 PUFA with longer (β=0.34; 95% CI: 0.032, 0.65) rTL. Cord blood n-6 PUFA was associated with longer (β=0.15; 95% CI: 0.050, 0.26) rTL. Further analyses revealed linoleic acid in maternal blood and arachidonic acid in cord blood as the main drivers of the n-6 PUFA associations. No associations were observed for THg and PUFA with rmtDNAcn.

Discussion:

Our results indicate that prenatal THg exposure and PUFA status are associated with rTL later in childhood, although not consistently aligned with our initial hypothesis. Subsequent research is needed to confirm this finding, further evaluate the potential confounding of fish intake, and investigate the underlying molecular mechanisms to verify the use of rTL as a true biomarker of THg exposure. https://doi.org/10.1289/EHP14776

Introduction

Both telomeres and mitochondria play significant roles in the fitness of the human body. Telomeres maintain the integrity of the chromosomes, and mitochondria produce energy in the form of adenosine triphosphate (ATP). The telomere length (TL) and the copy number of mitochondrial DNA (mtDNAcn) are often used as a proxy of the (dys)function of telomeres1 and mitochondria,2 respectively. According to in vitro studies, the high guanine content in the telomere sequence and the absence of introns, protective histones, and DNA repair mechanisms in the mitochondrion result in high sensitivity of both telomeres and mtDNA to oxidative stress and inflammation.2,3 Oxidative stress and inflammation are the main pathways that can be triggered following exposure to environmental pollutants. Accordingly, exposure to various pollutants [e.g., metal(loid)s, phthalates, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), pesticides] has been associated with alterations in TL47 and mtDNAcn4,69 in humans. In addition, individuals’ TL is influenced by their underlying genetics, specifically in genes encoding the RNA component (TERC) and the reverse transcriptase (TERT) of the telomerase enzyme.10
Variations in TL and mtDNAcn have been considered as risk factors for various, mostly age-related diseases (e.g., cardiovascular disease, cancers, diabetes, lung diseases, and neurodegenerative disorders).1115 Thus, TL and mtDNAcn might serve as biomarkers of effect in relation to environmental pollutants and as potential mechanisms linking exposures to adverse health outcomes.16
Most studies investigating the relationship among environmental exposures, TL, mtDNAcn, and health outcomes have been conducted in adults. However, there is growing evidence that the in utero period might be a critical time window of susceptibility to exposure to external factors.17,18 TL has been observed to correlate through different stages in life. Thus, changes induced by prenatal exposures might persist and contribute to one’s susceptibility to diseases throughout life.18,19 The dynamics of mtDNAcn throughout different life stages is, however, poorly studied. Recently Kupsco et al. reported on low to moderate correlations between mtDNAcn at birth, mid-childhood, and adolescence.20 As with TL, it has been proposed that mtDNAcn might reflect influences of prenatal exposure and serve as a marker for disease risk later in life.9,21
Mercury (Hg) in the form of methylmercury (MeHg) bioaccumulates in aquatic systems, resulting in elevated exposure in populations with a fish-rich diet. During pregnancy, MeHg can cross the placental barrier and accumulate in fetal tissue.22 At high doses, it is a known neurotoxicant,23 whereas for exposure through fish consumption with naturally occurring background concentrations, there is uncertainty about its neurotoxicity in humans.22,24,25 According to animal and in vitro studies, MeHg accumulates in mitochondria where it can react with and inhibit antioxidant enzymes and electron transport chain proteins and influence calcium homeostasis,26,27 which could result in increased oxidative stress and indirectly result in mtDNA damage and variations of mtDNA copy number.2830 The mechanism of possible MeHg toxicity on TL is not well understood, but MeHg induces oxidative stress and inflammation, which were both reported to be associated with shorter TL in humans.31,32 Nevertheless, the influence of Hg on TL or mtDNAcn in humans has rarely been studied, and findings have been inconsistent. Some studies reported positive associations between Hg and TL7,33 or mtDNAcn,34 whereas others reported nonsignificant associations.33,3537 Accordingly, more epidemiological studies are needed to clarify possible associations of Hg with TL and mtDNAcn, especially in children who are believed to be the most vulnerable.
In addition to environmental pollutants, nutrition and dietary habits have also been reported to be associated with both TL and mtDNAcn dynamics. The knowledge, however, on such associations early in life, especially for mtDNAcn, is currently limited.9,38 The Mediterranean diet, which is characterized by anti-inflammatory and antioxidative properties and by a high fish and seafood intake, was in several human studies associated with longer TL38 and with a positive influence on mitochondrial function.39 Moreover, a fish-rich diet also has, besides MeHg, a high content of n-3 polyunsaturated fatty acids (PUFAs). PUFA have in some human studies been shown to modify MeHg associations40 and may also affect TL and mtDNAcn. Longer TL and higher mtDNAcn were associated with n-3 PUFA,34,4143 whereas the opposite association was observed for n-6 PUFA34,4143 in humans. This difference may reflect the properties of n-3 to reduce and n-6 to increase inflammation and oxidative stress.34,4143
In our study, we set out to investigate the possible associations of prenatal Hg exposure on TL and mtDNAcn in 7-y-old children from the Seychelles Child Development Study Nutrition Cohort 2 (NC2), characterized by a fish-rich diet. We tested associations between total Hg (THg) in maternal hair and cord blood with TL and mtDNAcn, calculated as relative to a single-copy hemoglobin beta gene (i.e., rTL and rmtDNAcn), in the saliva of children while controlling for prenatal PUFA status and other covariates. In addition, TERT and TERC genotypes10 were tested as possible modifying factors for the associations between prenatal THg exposure and rTL. We hypothesized that higher prenatal THg exposure would be associated with shorter rTL and decreased rmtDNAcn. In addition, we expected that higher n-3 PUFA would be associated with longer rTL and higher rmtDNAcn, whereas for n-6 PUFA we expected the opposite association.

Material and Methods

Study Population

The NC2 is part of the Seychelles Child Development Study (SCDS), which was designed to investigate the associations between prenatal THg exposure and neurodevelopmental outcomes in children while accounting for nutrients, genetics, and other factors (SCDS homepage: https://www.urmc.rochester.edu/labs/seychelles.aspx, which includes data catalog and data sharing plan; accessed 15 June 2024). Power calculations were based on the magnitudes of associations observed in the earlier Nutrition Cohort 1 (NC1), which indicated that a cohort of 1,200 mother–child pairs would provide sufficient power to detect a statistically significant interaction between PUFAs and THg with neurodevelopmental outcomes using a 2-sided significance level of α=0.05.40 A total of 1,535 mother–child pairs enrolled in the study between 2008 and 2011 during their first antenatal visit (14gestation weeks) at eight health centers across Mahé, the main island of the Republic of Seychelles. The study design, inclusion criteria, and sampling were described previously.40 In brief, inclusion criteria at enrollment were being a native Seychellois, age 16 y or older, singleton birth, and having no apparent health concerns. A nonfasting blood sample was collected at 28 wk of gestation, and cord blood and maternal hair samples were obtained at the delivery. Saliva samples from the children were collected at a 7-y follow-up44 and were used to extract DNA samples. For this study, the following exclusions were made for mother–child pairs: 68 due to missing saliva samples; 156 due to failed DNA extraction; 95 due to gestational complications, siblings, or child’s head trauma, disability or death; 35 due to missing data on both rTL and rmtDNAcn; and finally 36 due to missing data on THg concentrations in both hair and cord blood samples. After exclusions, data from a total of 1,145 mother–child pairs were available to assess the associations between prenatal THg exposure and rTL and rmtDNAcn (Figure 1).
Figure 1. Flow diagram of the participants from Seychelles Child Development Study Nutrition Cohort 2 (2008–2011).
All participants signed the informed consent documentation. The study was reviewed and approved by the Seychelles Ethics Board, the Research Subjects Review Board at the University of Rochester, and the Swedish Ethical Review Authority (Reference: 2013/233 and 2023-04,153-02).

Measurement of Prenatal Mercury Exposure Biomarkers

Total Hg (i.e., THg) measurements in maternal hair and cord blood were used to assess prenatal Hg exposure, reflecting exposure during the whole pregnancy and within the last trimester,45 respectively. Because more than 80% of total Hg in hair and blood is present as MeHg4648 and the present population is characterized by a fish-rich diet, measures of THg in the present study are likely an adequate surrogate for the total MeHg in the fish consumed during the pregnancy.
The hair sampling procedure is described by Cernichiari et al.46 No exclusion criteria were applied for hair sample collection or analysis.
THg in maternal hair was determined using the longest available hair segment corresponding to the pregnancy period, assuming a growth rate of 1.1cm/month.46 The measurements of THg in hair (reported as parts per million; ppm) and cord blood (reported as μg/L) were performed using cold vapor atomic absorption spectroscopy (CVAA) and a Laboratory Data Control Mercury Monitor Model No. 1235 at the University of Rochester, Rochester, New York, as described previously.46 The limit of quantification (LOQ) in hair was 0.61 ppm,49 and the limit of detection (LOD) in cord blood was 1.75 ngHg/mL.50 Although all cord blood samples had THg above LOD, THg in maternal hair was below LOQ in 73 (6.7%) of 1,089 samples. Internal quality control was assured by using certified mercury standards (Fisher SM114-100 and Ricca Chemical Company AHG1KN-100) and reference materials (Blood: Seronorm, Sero; Hair: IAEA-085/-086, International Atomic Energy Agency). For external quality control purposes, the laboratory took part in the Interlaboratory Comparison Program for blood, organized by the Center of Toxicology of Quebec (INSPQ), Canada.

Genetic Analyses

Due to the unavailability of child blood samples, DNA was extracted from child saliva using the Omega Bio-Tek E.Z.N.A. kit (Omega Bio-Tek) following the manufacturer’s protocol. Extracted DNA was stored at 20°C until further analyses.

TL and mtDNAcn measurement.

Quantitative polymerase chain reaction (qPCR; 7900HT, Applied Biosystems) and SYBR Green technology were used to determine TL and mtDNAcn relative to a single-copy hemoglobin beta gene (HBB) using two independent PCR, as previously described.34 Accordingly, results for TL and mtDNAcn in the present study are referred to as rTL and rmtDNAcn. Each PCR run included a standard curve, a reference DNA, and negative controls. A standard curve was prepared by a 2-fold serial dilution of a DNA sample (DNA mixed from three individuals), producing seven concentrations between 0.2516 ng/μL. All samples, standard curves, and negative and reference controls were run in duplicates. Each reaction (10μL) consisted of 2.5μL of DNA (2.5 ng/μL) and 7.5μL of master mix. The master mixes consisted of a) 1×PCR buffer, 0.8 mM dNTPs, 1.75 mM MgCl2, 0.3μM SybrGreen I (Invitrogen), 0.5 U Taq Platina (Invitrogen), 1×ROX (Invitrogen), and 450 nM primers for TL; b) 1x PerfeCTa SYBR Green FastMix, ROX, and 200 nM primers for mtDNAcn; and c) 2x Fast SYBR Green Master Mix, and 200 nM primers for HBB. Primers and PCR conditions are listed in Table S1.
The rTL and rmtDNAcn of each individual were calculated using the 2ΔCtmethod, by ΔCt=CtTLCtHBB and ΔCt=CtmtDNAcnCtHBB, respectively. The ratios were then adjusted by the ratios calculated for the reference DNA, to adjust for variations between several PCR plates. The reference samples demonstrated a coefficient of variance of 3.7% for TL, 2.0% for mtDNAcn, and 0.6% for HBB, based on eight PCR runs for each. R2 for each standard curve was >0.99. Cycle threshold (Ct) values were accepted if the standard deviation of the duplicates was <0.25; otherwise, the sample was excluded.

Genotyping of telomerase genes.

Single nucleotide polymorphisms (SNPs) rs2736100 (C>A), in the intronic region of the telomerase, reverse transcriptase gene (TERT), and rs10936599 (C>T), located upstream of the telomerase RNA template gene (TERC), were selected based on their association with TL.10
Genotypes of rs2736100 and rs10936599 in children were obtained by QuantStudio 5 Real-Time PCR instrument using TaqMan SNP genotyping assays (IDs: C___1844009_10 and C__11798256_10, respectively; Thermo Scientific) and TaqMan Universal Master Mix (ThermoFisher Scientific) with PCR conditions as recommended by the manufacturer. For quality control of genotyping data, at least 5% of samples underwent reanalysis for both SNPs and showed a 100% agreement between duplicates. Both SNPs agreed with the Hardy-Weinberg Equilibrium as assessed by the chi-square test.

Measurement of PUFAs

Preparation of the maternal and cord blood samples and the subsequent PUFA measurements in serum are described elsewhere.40,51,52 In brief, maternal blood and cord blood samples were prepared at the Public Health Laboratory of the Ministry of Health in Mahé. Serum aliquots were shipped at 80°C to the laboratory at the Nutrition Innovation Center for Food and Health (NICHE), Ulster University, Coleraine, UK, for PUFA measurements. Total lipids from serum were extracted according to the adaptation of the Folch method53 and methylated using boron trifluoride methanol (BF3; Sigma-Aldrich). Heptadecanoic acid (C17:0) as an internal standard (Sigma-Aldrich) was added to the sample before the extraction. Fatty acid methyl esters including arachidonic acid (AA, 20:4n-6), linoleic acid (LA, 18:2n-6), α-linoleic acid (ALA, 18:3n-3), eicosapentaenoic acid (EPA, 20:5n-3), and docosahexaenoic acid (DHA, 22:6n-3), were analyzed and quantified by gas chromatography–mass spectrometry (GC-MS; 7890A-5975C, Agilent). The LODs in mg/mL were: 0.001 for LA and AA, 0.006 for ALA, 0.005 for EPA, and 0.002 for DHA. None of the maternal samples had levels of PUFA below the LOD. In cord blood, ALA and EPA were below the LOD in 761 (87%) and 516 (59%) samples, respectively, and were therefore not used individually in the statistical analyses.
Prenatal PUFA status was defined as total n-3 PUFA (mg/mL; sum of ALA, EPA, and DHA) and total n-6 PUFA (mg/mL; sum of LA and AA), determined in either maternal blood or cord blood, and the n-6/n-3 ratio was calculated.

Statistical Analyses

Correlations between rTL, rmtDNAcn, THg exposure biomarkers, PUFA status biomarkers and child’s age were assessed using nonparametric Spearman’s rank correlation tests reporting correlation coefficient (i.e., rho). Multivariable linear regression analysis was used to examine the association between prenatal THg exposure and rTL and rmtDNAcn, using four separate primary models that differed based on the a) selected biomarker of prenatal THg exposure using maternal hair (hairTHg) or cord blood (cordTHg), and b) adjustment for prenatal PUFA status, as a possible effect modifier, measured in maternal blood (mb) or cord blood (cb): n-3 and n-6 PUFAs as individual covariates in one model and n-6/n-3 PUFA ratio as a covariate in another. The primary separate models were as follows:
Model 1: rTL or rmtDNAcn = hairTHg + mb n-3 PUFA + mb n-6 PUFA + confounders
Model 2: rTL or rmtDNAcn = hairTHg + mb n-6/n-3 PUFA +confounders
Model 3: rTL or rmtDNAcn = cordTHg + cb n-3 PUFA + cb n-6 PUFA + confounders
Model 4: rTL or rmtDNAcn = cordTHg + cb n-6/n-3 PUFA +confounders
Each model was adjusted for possible confounders, which were selected based on the literature and their known or suspected associations with TL and/or mtDNAcn as well as with THg exposure and PUFA status.3,6,7,5461 As such, included were maternal age at delivery (continuous; years), maternal BMI at child’s age of 20 months (continuous; kg/m2), Hollingshead 4-factor socioeconomic status (SES; continuous), and child sex (categorical; male vs. female) (Table S2 for rTL and Table S3 for rmtDNAcn models).
Data on those confounders were obtained through an interviewer-administered questionnaire. Given the strong correlation (ρ=0.93) between maternal postnatal BMI and early-pregnancy BMI observed in the previous Seychellois NC1,62 postnatal maternal BMI was used as a surrogate for prenatal BMI in this cohort. Maternal weight (kg) and height (m) were measured at the time of evaluation and used to calculate BMI as weight (kilograms) divided by height (square meter).2 SES score was estimated at the maternal enrolment into the study by using the modified Hollingshead 4-factor Social Status Index assessing the data on the primary caregiver′s education and occupation.63 Maternal smoking and alcohol use during pregnancy were in the present population rarely reported (n=10 and n=46, respectively) and were therefore not included as potential confounders.
Furthermore, we fit additional multivariable linear regression models as secondary analysis:
1.
to examine the association of interaction between prenatal THg and PUFA status with rTL and rmtDNAcn by repeating the above-listed Models 1–4 while including the interaction between prenatal THg and tertiles of prenatal n-6/n-3 PUFA; as described in Strain et al.25 (Table S4);
2.
to examine the associations between PUFA and rTL by using specific n-6 (LA and AA) and n-3 (ALA and EPA+DHA ) PUFAs as covariates (Table S5);
3.
to examine potential sex-specific associations of prenatal THg exposures with TL or mtDNAcn in children, which was previously observed for other chemical exposures5,6; the above-listed Models 1–4 were repeated while including an interaction between prenatal THg exposure biomarker and child sex (Table S6 for rTL and Table S7 for rmtDNAcn);
4.
to examine the possible influence of TERT (rs2736100, A>C) and TERC (rs10936599, C>A) SNPs on THg-rTL associations. Both SNPs were previously associated with TL, with C allele carriers of both SNPs having longer TL10; we tested possible differences in rTL between SNP genotypes using the Mann-Whitney U test (Table S8) and repeat above-listed Models 1–4 while including an interaction between prenatal THg biomarkers and SNPs (Table S9);
5.
to examine the possible influence of estimated fish intake during pregnancy on the associations of prenatal THg exposure with rTL and mtDNAcn; above-listed Models 1–4 were repeated with the additional adjustment for estimated fish intake during pregnancy (Table S10 for rTL and Table S11 for rmtDNAcn); data on the estimated fish intake variable during the pregnancy were obtained through the collection of the Fish Use Questionnaire, where the mother stated the consumption of fish dishes (by fish type) eaten in the week prior to the examination and the total was adjusted to reflect estimated fish consumption during pregnancy.
Model assumptions were assessed by graphically examining assumptions of linearity and constant variance of the residuals. We did not observe any substantial deviations from linearity or constant variance in the tested models (Figure S1). All models were conducted on participants with complete data for all variables included in the respective model. For measurements below LOD or LOQ, the actual measured values were used, as despite their uncertainty, those values provide more information than the usually employed approach of substitution by LOD/2.64
We have also tested the possible correlation between a child salivary rmtDNAcn determined at 7 y of age and rmtDNAcn determined at birth in cord blood using the nonparametric Spearman’s rank correlation test.
In addition, locally weighted scatterplot smoothing was used to test for possible nonlinear trends between prenatal THg exposure and the rTL or rmtDNAcn (Figure S2).
All statistical analyses were performed using the R software (version 4.2.1; R Development Core Team) and STATA (version 13; StataCorp.) using 2-tailed tests with α=0.05 as a criterion for statistical significance.

Results

Characteristics of the Study Population

The distributions of rTL, mtDNAcn, biomarkers of prenatal THg exposure, prenatal PUFA status, and other covariates for 1,145 mother–child pairs are summarized in Table 1. Mothers were on average 27 y of age at delivery, and 49% gave birth to boys. Mothers reported a high fish consumption, with an average of 8.5 (range 0–37) fish dishes/wk during pregnancy. Median concentrations of THg in hair and cord blood were 2.84 ppm and 29.8 ng/mL, respectively. In comparison with the levels of n-3 PUFA, the concentration of n-6 PUFA was approximately four times higher in maternal blood, of which the majority was LA, and five times higher in cord blood, of which the highest levels were observed for AA. Children were on average age 7.38 y of age, and a negative correlation was observed for rTL and rmtDNAcn with the child’s age; ρ=0.38 and 0.20 (p<0.001), respectively.
Table 1 Characteristics of 1,145 mother–child pairs in the Seychelles Child Development Study Nutrition Cohort 2 (2008–2011).
 n (%)n missingmeanSD25%Med75%
Outcome
 Child rTL at 7 ya1,107381.950.881.421.802.32
 Child rmtDNAcn at 7 ya1,075701.840.931.201.672.26
Prenatal THg exposure
 Maternal hair THg (ppm)1,089563.843.461.402.845.08
 Cord blood THg (ng/mL)82831734.020.619.729.842.3
Prenatal PUFA status
Maternal blood (mb)
 mb n-6 (mg/mL)1,110351.100.300.891.091.30
 mb n-3 (mg/mL)1,110350.280.0880.210.270.34
 mb n-6:n-31,110354.291.603.293.924.73
 mb LA (mg/mL)1,110350.900.260.710.881.05
 mb AA (mg/mL)1,110350.210.0820.150.210.26
 mb ALA (mg/mL)1,110350.0370.0050.0350.0350.038
 mb EPA (mg/mL)1,110350.0520.0080.0490.0490.051
 mb DHA (mg/mL)1,110350.190.0830.120.180.25
 mb EPA+DHA  (mg/mL)1,110350.240.0860.170.240.30
Cord blood (cb)       
 cb n-6 (mg/mL)8752700.440.280.280.370.50
 cb n-3 (mg/mL)8752700.110.0520.0760.100.13
 cb n-6:n-38752705.425.762.623.334.95
 cb LA (mg/mL)8752700.170.160.120.150.19
 cb AA (mg/mL)8752700.280.220.150.190.26
 cb ALA (mg/mL)875270<LOD<LOD<LOD
 cb EPA (mg/mL)875270<LOD<LOD0.010
 cb DHA (mg/mL)8752700.100.0490.0720.0960.13
 cb DHA+EPA (mg/mL)8752700.110.0520.0750.100.13
Sociodemographic factors
 Mother’s age at delivery (y)1,14527.06.2822.026.031.4
 Mother’s BMI at child age 20 months (kg/m2)1,0816427.16.5421.926.331.0
 SES score1,1301531.710.423.030.539.5
 Fish intake during pregnancy (fish dishes/wk)1,0831078.54.55.08.011.0
Child sex
 Male556 (49)
 Female589 (51)
Child age (y)1,1457.380.217.177.347.67
TERT and TERC SNP genotypes
TERT rs2736100 (A>C)2
 AA295 (26)
 AC580 (51)
 CC268 (23)
 MAF(49)
TERC rs10936599 (C>T)1
 CC892 (78)
 CT237 (21)
 TT15 (1)
 MAF(26)
Note: —, no data; A, adenine; AA, arachidonic acid; ALA, α-linoleic acid; BMI, body mass index; C, cytosine; cb, cord blood; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; Hg, mercury; LA, linoleic acid; MAF, minor allele frequency; mb, maternal blood at 28th gestation week; mb, maternal blood; Med, median; rmtDNAcn, relative mitochondrial DNA copy number; rTL, relative telomere length; SD, standard deviation; SES, Hollingshead socioeconomic status; SNP, single nucleotide polymorphisms; T, thymine; TERC, telomerase RNA Component gene; TERT, telomerase reverse transcriptase gene; THg, total Hg.
a
Unitless.

Correlations between THg, PUFA, rTL, and rmtDNAcn

Maternal hair THg was positively correlated with THg in cord blood (ρ=0.311; Figure 2). THg in maternal hair and cord blood were positively correlated with rTL (ρ=0.131 and 0.124, respectively). Negative correlations were observed between rTL and both n-6 and n-6/n-3 PUFA in maternal blood (ρ=0.162 and 0.148, respectively), whereas a weaker and opposite correlation was observed for rTL and n-6 in cord blood (ρ=0.089; Figure 2). A weak positive correlation was observed between rTL and rmtDNAcn (ρ=0.091; Figure 2). The salivary rmtDNAcn determined at 7 y of age did not correlate with rmtDNAcn determined at birth in cord blood (ρ=0.009; p>0.05).
Figure 2. Spearman correlations between rTL, rmtDNAcn, prenatal THg exposure, and prenatal PUFA status in the Seychelles Child Development Study Nutrition Cohort 2 (2008–2011; n=661) Note: cb, cord blood; cordTHg, total Hg in cord blood; hairTHg, total Hg in maternal hair; mb, maternal blood; n6−PUFA/n3−PUFA, ratio between n-6 and n-3 PUFA; PUFA, polyunsaturated fatty acids; rmtDNAcn, relative mitochondrial DNA copy number; rTL, relative telomere length.

Associations between Prenatal THg, rTL and rmtDNAcn

Prenatal THg exposure was statistically significantly (p<0.05) associated with rTL in 7-y-old children (Table 2). Higher concentrations of THg in both maternal hair and cord blood were associated with longer rTL [β=0.009; 95% CI: 0.002, 0.016 (0.9% change per unit increased THg) for maternal hair THg and β=0.002; 95% CI: 0.001, 0.003 (0.2% change per unit increased THg) for cord blood THg]. Additional analyses evaluating potential interactions showed that these associations did not vary when considering interaction with prenatal PUFA status (Table S4), child sex (Table S6), or TERT and TERC genotypes (Table S9), or additional adjustment for estimated fish intake during pregnancy (Table S10). Prenatal THg exposure was not associated with rmtDNAcn in 7-y-old children (Table 2), and this interpretation did not change when potential interactions with prenatal PUFA status (Table S4) or child sex (Table S7) were considered, or after the additional adjustment for fish intake during the pregnancy (Table S11). Moreover, we did not observe any nonlinear association between THg and either rTL or rmtDNAcn (Figure S2).
Table 2 Summary of associations between child rTL or rmtDNAcn and prenatal THg exposure biomarkers and prenatal PUFA status in the Seychelles Child Development Study Nutrition Cohort 2 (2008–2011).a
Modelsln(rTL)ln(rmtDNAcn)
nβb (95% CI)p-Valuecnβb (95% CI)p-Valuec
Model 1967941
Maternal hair THg (ppm)0.009 (0.002, 0.016)0.0100.001 (0.010, 0.009)0.876
Maternal blood n-3 PUFA (mg/mL)0.34 (0.032, 0.65)0.0310.16 (0.58, 0.25)0.438
Maternal blood n-6 PUFA (mg/mL)0.24 (0.33, 0.15)<0.0010.093 (0.215, 0.029)0.135
Model 2967941
Maternal hair THg (ppm)0.009 (0.002, 0.016)0.0170.001 (0.011, 0.008)0.806
Maternal blood n-6/n-3 PUFA (mg/mL)0.032 (0.048, 0.016)<0.0010.004 (0.016, 0.025)0.670
Model 3726704
Cord blood THg (ng/mL)0.002 (0.001, 0.003)0.0030.001 (0.002, 0.001)0.600
Cord blood n-3 PUFA (mg/mL)0.49 (1.04, 0.054)0.0780.49 (1.21, 0.22)0.178
Cord blood n-6 PUFA (mg/mL)0.15 (0.050, 0.26)0.0040.031 (0.103, 0.166)0.648
Model 4726704
Cord blood THg (ng/mL)0.002 (0.001, 0.003)0.0070.001 (0.002, 0.001)0.538
Cord blood n-6/n-3 PUFA (mg/mL)0.004 (0.001, 0.009)0.0970.002 (0.004, 0.008)0.491
Note: Models 1–4 are four separate models. —, no data; BMI, body mass index; CI, confidence interval; Hg, mercury; n-6/n-3 PUFA, ratio between n-6 and n-3 PUFA; ppm, parts per million; PUFA, polyunsaturated fatty acid; rmtDNAcn, relative mitochondrial DNA copy number; rTL, relative telomere length; THg, total Hg; SES, socioeconomic status.
a
Based on multivariable linear regression models with the adjustment for maternal age, maternal BMI, SES, and child sex.
b
Presented are β coefficients with a 95% confidence interval for each unit increased THg or PUFA.
c
p-Value derived from multivariable linear regression with a significance threshold of 0.05.

Associations between Prenatal PUFA, rTL, and rmtDNAcn

Independently of THg, statistically significant (p<0.05) associations with rTL were observed for prenatal PUFA status (Table 2). For maternal n-6 PUFA, higher concentrations of n-6 and the n-6/n-3 ratio in maternal blood were associated with shorter rTL [β =0.24; 95% CI: 0.33, 0.15 (21.3% change per unit increased PUFA) and β=0.032; 95% CI: 0.048, 0.016 (3.15% change per unit increased PUFA), respectively]. Higher concentrations of maternal n-3 were associated with longer rTL [β=0.34; 95% CI: 0.032, 0.65 (40.5% change per unit increased PUFA); Table 2]. The association with n-6 in cord blood was in the opposite direction to maternal blood; increasing n-6 concentrations were associated with longer rTL [β=0.15; 95% CI: 0.050, 0.26 (16.2% change per unit increased PUFA); Table 2]. Additional analyses showed that the observed opposite associations reflected the difference in the distribution of specific n-6 PUFAs in maternal and cord blood: LA is the dominating n-6 in maternal blood, and AA is the dominating n-6 in cord blood (Tables 1 and 3; Table S5). For n-3 PUFA, a significant association with rTL was observed only in maternal blood and was driven by ALA (Table 3; Table S5). No statistically significant associations were observed between prenatal PUFA status and rmtDNAcn (Table 2).
Table 3 Summary of associations between child rTL and specific n-3 or n-6 prenatal PUFA status in Seychelles Child Development Study Nutrition Cohort 2 (2008–2011).a
Modelsbln(rTL)p-Valued
nβc (95% CI)
1aMaternal blood n-6 PUFA (mg/mL)967
 mb LA0.25 (0.36, 0.15)<0.001
 mb AA0.12 (0.21, 0.44)0.484
1bMaternal blood n-3 PUFA (mg/mL)967
 mb ALA4.69 (0.61, 8.78)0.043
 mb DHA+EPA0.12 (0.40, 0.17)0.445
2aCord blood n-6 PUFA (mg/mL)726
 cb LA0.083 (0.25, 0.083)0.326
 cb AA0.24 (0.12, 0.37)<0.001
2bCord blood n-3 PUFA (mg/mL)726
 cb DHA+EPA0.24 (0.76, 0.28)0.371
Note: Models 1a, 1b, 2a, and 2b are four separate models. —, no data; AA, arachidonic acid; ALA, α-linoleic acid; BMI, body mass index; cb, cord blood; CI, confidence interval; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; LA, linoleic acid; mb, maternal blood at 28th week of pregnancy; PUFA, polyunsaturated fatty acid; rTL, relative telomere length; SES, socioeconomic status.
a
Based on multivariable linear regression models.
b
Models adjusted for: hairTHg (Model 1a and 1b) or cordTHg (Model 2a and 2b), and maternal age, maternal BMI, SES, and child sex.
c
Presented are β with a 95% confidence interval for each unit increased PUFA.
d
p-Value derived from multivariable linear regression with a significance threshold of 0.05.

Covariate Associations with rTL and rmtDNAcn

Higher SES was associated with longer rTL (Table S2, Models 1 to 4) and with higher rmtDNAcn (Table S3; Models 1–4), whereas males had higher rmtDNAcn than females (Table S3; Models 1–4). Maternal age and BMI were not significant predictors of either rTL or rmtDNAcn.
Children carrying the TERT rs2736100 variant C allele (n=848, Table 1) had slightly longer rTL in comparison with children not carrying this allele (median of 1.82 vs. 1.71, respectively; p=0.063; Table S8). The TERC rs10936599 variant T allele (n=252; Table 1) was not associated with rTL (Table S8).

Discussion

The main findings of this study were that prenatal Hg, measured as total Hg in maternal hair and cord blood, was associated with longer rTL in children 7 y of age. These associations are the opposite of what we hypothesized. We therefore carefully evaluated whether PUFAs could confound these associations. We found that THg associations could not be explained by PUFAs in our study. Independent of THg exposure, prenatal PUFA status was associated with rTL. As expected, maternal n-6 PUFA was associated with shorter rTL and n-3 PUFA with longer rTL. However, cord blood associations were the opposite and significant only for n-6 PUFA. Prenatal THg and PUFAs were not associated with rmtDNAcn in children 7 y of age.

Association between Prenatal THg, rTL, and rmtDNAcn

The association of prenatal THg exposure with a child’s rTL at birth and/or in early childhood was previously investigated by Yeates et al.,37 Smith et al.,36 and Lozano et al.33 Using data from the NC1, (n=229), Yeates et al. found no associations between prenatal THg in maternal hair and a child’s blood rTL at delivery or 5 y of age.37 Smith et al. reported no associations between THg in maternal red blood cells (first trimester of pregnancy) and cord blood rTL in a US birth cohort (n=408).36 However, in line with our study, Lozano et al. reported a positive association between maternal blood THg (mid-pregnancy) and blood rTL in European children 6–11 y of age (n=926) at lower exposure levels [geometric mean (GM); 3.05 ngTHg/mL] and lower fish intake (average 3.8 fish dishes/wk).33
Salivary TL has been reported as a reliable noninvasive source for TL measurement due to its significant correlation with TL in blood,65,66; however, limited data exist on the TL correlation between saliva and other human tissues. Blood TL has been shown to correlate with TL in several human tissues,67 and recently Carver et al.,68 in a study of individuals (n=32) undergoing neurosurgery, reported a significant correlation of TL in blood, saliva, and buccal cells with TL in the brain, which represents the primary target of Hg toxicity. This finding suggests that TL in saliva may reflect the variability of TL in other Hg tissues even though salivary cells are not among the primary targets of Hg toxicity. Therefore, the lack of significant associations in the first two studies might be, rather than different tissue selection, owing to the smaller sample size and consequently lower statistical power to detect subtle associations, as found in this larger study. It should be noted that in adults, higher urinary Hg concentrations (i.e., inorganic Hg form) in the Flemish population (n=175) were also associated with longer blood rTL.7
There is an increasing number of human studies linking environmental chemical exposures with TL, with some resulting in shortening (e.g., cadmium, lead, nickel, phthalates, PAH, pesticides)16,18,69 and others in lengthening (e.g., arsenic, short-term exposure to particulate matter, persistent organic pollutants).16,70 These findings could suggest that mechanisms, other than oxidative stress and inflammation, such as telomerase activation or other mechanisms maintaining telomere integrity, are acting on TL in response to chemical exposure.16 Observed associations of THg exposure with longer rather than shorter rTL in our study are puzzling, because Hg is known to induce systemic oxidative stress and inflammation in humans. Moreover, a significantly reduced mRNA expression of TERT after MeHg exposure was reported in animal studies71,72; nevertheless, extrapolation from animals to human TERT regulation should be cautiously interpreted. Furthermore, TERT rs2736100 was previously associated with higher TERT expression and consequently longer TL in humans.10 Association with longer rTL was also observed in the present study; however, the THg-rTL associations were independent of the TERT genotype.
It should be noted that the current NC2 cohort is characterized by high fish consumption, with an average of 8.5 fish dishes/wk during pregnancy, which is much higher in comparison with other populations, such as the United Kingdom73and the United States.74 The recruited cohort of pregnant women represented one-third of all pregnant women within the recruitment period; therefore, we believe that the NC2 fish consumption is representative of the general population in the Seychelles. Among NC2 mothers, Trevally was the most frequently consumed fish (73.6%).75 Although the present study accounted for the potential influence of PUFAs on THg-rTL associations, fish can also contain other factors (e.g., chemicals and nutrients) that could influence TL. In our study, sensitivity analyses indicated that estimated fish intake during pregnancy did not influence the THg–rTL associations. However, it is important to note that the absence of confounding might be due to measurement error in the estimation of fish intake (e.g., participants’ recall bias). Therefore, further studies focusing on a more reliable assessment of fish intake, as well as the underlying molecular mechanisms (e.g., potential induction of TERT gene expression), are needed to validate the associations between THg and rTL.
To the best of our knowledge, only a few studies have investigated the associations between prenatal THg exposure and mtDNAcn to date. Smith et al. reported in a study on the US birth cohort (n=419) that there was no association between THg concentrations in maternal red blood cells (first trimester of pregnancy) and rmtDNAcn in cord blood,36 and similarly Lozano et al. observed no significant association between maternal blood THg (mid-pregnancy) and rmtDNAcn in the blood of European children 6–11 y of age (n=926).33 Another study, conducted on the current SCDS NC2 child cohort, but at the time of birth, revealed that higher cord blood THg was significantly associated with lower fetal rmtDNAcn, whereas no association was observed for THg in maternal blood.34 In a recent review of the prenatal environment and its influences on mtDNAcn, the need for longitudinal studies was emphasized to determine whether the observed alternation of mtDNAcn at birth persists over the life course.9 The present study follows up on the SCDS NC2 children to evaluate whether the observed association by Xu et al.34 at birth persisted in children at 7 y of age. However, our results indicated that rmtDNAcn at age 7 y was not associated with prenatal THg concentrations determined in either cord blood or maternal hair. Furthermore, a recent study of mtDNAcn dynamics through childhood showed a moderate correlation of blood rmtDNAcn across life stages, i.e., at birth, early childhood (5–7 y), and adolescence (15–18 y).20 In the present study, no significant correlation was observed for rmtDNAcn at birth and 7 y of age (Spearman ρ=0.009; p>0.05). The discrepancy in THg-rmtDNAcn associations and lack of rmtDNAcn correlation in the NC2 cohort at birth and at 7-y follow-up might be related to the different tissues used for rmtDNAcn measurement; at birth, rmtDNAcn was determined in cord blood, whereas at 7 y, saliva samples were used owing to the unavailability of child blood samples. Unlike in the case of TL, different cell types and tissues exhibit significant variations in mtDNAcn, with higher copy numbers in metabolically active tissues (e.g., brain, heart, liver).8,76,77 Washmunt et al. examined different autopsy tissues (i.e., blood, cerebellum, cerebrum, cortex, kidney, large intestine, liver, myocardial muscle, ovary, small intestine, skin, and skeletal muscle) from 152 individuals and reported that an individual’s rmtDNAcn was mostly uncorrelated across different tissues.76 In epidemiological studies, mtDNAcn in blood is most commonly used,8 and to the best of our knowledge, no studies have yet examined the mtDNAcn correlation between blood and saliva. In addition, the correlation of mtDNAcn with other biomarkers of mitochondrial function (e.g., citrate synthase, housekeeping mitochondrial protein, measured mitochondrial respiratory function) was also found to depend on the selected tissues.77 Therefore, individuals do not consistently exhibit higher or lower mtDNAcn across all tissues, but rather its values are regulated in a cell- or tissue-specific manner.76,78 Accordingly, it is expected that environmental exposure to chemicals, including Hg, could result in tissue-specific changes in mtDNAcn.76 Unfortunately, to the best of our knowledge, no other study has investigated associations between Hg exposure and mtDNAcn in saliva.
Associations between THg and mtDNAcn also indicate inconsistent results in adults. In pregnant women from the SCDS NC2, cohort blood THg was positively associated with blood rmtDNAcn,34 whereas in a US birth cohort a nonlinear relationship was reported for THg in red blood cells, with higher rmtDNAcn at low THg concentrations and lower rmtDNAcn at higher THg concentrations.36 In a Peruvian population with high fish consumption who lived close to a gold-mining area, there was no association between leukocyte rmtDNAcn and THg in hair,35 nor was such an association found with THg in urine in a general adult Belgian population.7 Such inconsistencies might be attributed to the differences in study designs, including different study populations (e.g., adults, pregnant women) and types of Hg exposure biomarkers. The latter reflects exposure to different Hg forms (i.e., blood THg mostly reflects MeHg34,36) whereas urinary THg reflects inorganic Hg and possibly hair THg may to a larger extent also reflect inorganic Hg in populations living close to a gold mine.7,35

Associations between Prenatal PUFAs, rTL, and rmtDNAcn

Recently, systematic reviews highlighted that a diet rich in fish and n-3 PUFAs is beneficial for TL43 and mitochondrial function39 in humans, whereas at the same time, other review studies pointed out the need for further research, particularly on prenatal diet and possible longitudinal effects on TL79 and mitochondrial function.9
In the present study, we observed significant associations of maternal PUFA with salivary rTL in 7-y-old children. Total n-3 PUFA was associated with longer rTL, whereas n-6 PUFA or n-6/n-3 ratio resulted in shorter rTL; associations appeared to be mostly driven by ALA and LA PUFAs, respectively. Similarly, Liu et al. found in a Chinese birth cohort a positive association of total n-3 PUFA and DHA and a negative association of total n-6 PUFA, n-6/n-3 PUFA, and LA with cord blood rTL.80 An earlier study conducted on the smaller Seychelles NC1 (n=229) observed no significant association between maternal PUFA status and a child’s rTL in blood at birth or 5 y of age.37 Similarly, in an Australian randomized controlled trial, prenatal n-3 PUFA supplementation had no effect on a child’s blood rTL at birth or 12 y of age.81 It should be noted that in the current study, the association of rTL with n-6 PUFA in cord blood was in the opposite direction to n-6 in maternal blood, with AA appearing to be the main driver of this association. Because developing fetuses and newborns are not able to produce PUFAs, cord blood PUFA status reflects the selective placental transfer of maternal PUFA, with preferential transfer of AA over LA and DHA.51,82 In addition to DHA, AA is necessary for normal fetal growth and brain development and, as recently proposed, might have a dominant contribution.82 To the best of our knowledge, only one study investigated cord blood PUFAs and child’s rTL; AA or total n-6 were not associated with cord blood rTL.80 Such inconsistencies were also observed in adults, with higher AA associated with longer83 or shorter TL,84 or null associations.85 Contrary to n-3, the biological function of dietary dominant n-6 has not been investigated in detail.82,86 Recently pro-inflammatory properties of n-6 were questioned, and it was suggested that n-6 might have a beneficial effect on inflammatory factors and may reduce oxidative stress; the role of both n-3 and n-6 in inflammation is complex and not yet fully understood.82,86 It is also important to note that, due to the short half-life of PUFAs in the blood and their altered metabolism during pregnancy, the measurements of PUFAs in this study might not accurately reflect long-term PUFA status.
In comparison with TL, much fewer data exist on the relationship between PUFA and mtDNAcn. We previously assessed prenatal PUFA status and rmtDNAcn at birth in the Seychellois children (NC2).34 Like Xu et al.,34 we found no significant associations of PUFA status in maternal blood or cord blood with a child’s salivary rmtDNAcn at 7 y of age. Xu et al. did, however, observe a significant positive association of maternal n-3 PUFA and negative associations of maternal n-6 and n-6/n-3 PUFA with maternal rmtDNAcn.34 Similarly, in vitro and animal studies previously reported an increase in rmtDNAcn with n-3 PUFA exposure in human87 and mouse muscle88 cells,87,88 and increased oxidative damage of mtDNA following n-6 PUFA exposure in rats.89 However, to date no other in vivo human studies on PUFA-rmtDNAcn associations have been performed. It is clear, that associations of PUFAs with TL and mtDNAcn are complex and not fully understood, especially during pregnancy with resulting changed lipid metabolism.

Strengths and Limitations of the Study

A strength of this study is the large sample size of the NC2 mother–child cohort with a fish-rich diet, which gives more power to detect subtle associations, if any, with rmtDNA and rTL than smaller studies. Moreover, assessments of prenatal THg exposure and PUFA status in both mother and fetus along with the ability to account for some relevant covariates increased the overall robustness of the observed associations among THg, PUFA, rTL, and rmtDNAcn. A limitation of the study is the lack of information on rTL at birth, which could give additional insight into the influence of prenatal THg on TL attrition early in life. Although in populations with a fish-rich diet, THg is considered a predictor of MeHg, the lack of MeHg speciation prevented the exact measurement of prenatal MeHg exposure in the present study. In addition, salivary rmtDNAcn may not accurately reflect mitochondrial homeostasis in the relevant Hg target tissues, such as the brain, with higher energy demands.

Conclusions

Our study revealed statistically significant associations between both prenatal THg exposure and PUFA status with rTL in later childhood. However, those associations were not consistently aligned with our initial hypothesis. Therefore, further studies with more reliable fish intake assessment and research on possible underlying molecular mechanisms are necessary to better understand the relevance of rTL as an effect biomarker of THg exposure.

Acknowledgments

The authors would like to thank all the Seychellois citizens for their participation in the study and all the staff of the Child Development Centre in Seychelles for their help with sampling and data collection.
The study was supported by the US National Institutes of Health (grant numbers R01-ES010219, R24-ES029466, and P30-ES01247, T32 ES007271).

Article Notes

The authors declare they have no conflicts of interest related to this work to disclose.

Supplementary Material

File (ehp14776.smcontents.508.pdf)
File (ehp14776.s001.acco.pdf)

References

1.
Blackburn EH. 1991. Structure and function of telomeres. Nature 350(6319):569–573. https://pubmed.ncbi.nlm.nih.gov/1708110/, https://doi.org/10.1038/350569a0.
2.
Lee HC, Wei YH. 2005. Mitochondrial biogenesis and mitochondrial DNA maintenance of mammalian cells under oxidative stress. Int J Biochem Cell Biol 37(4):822–834. https://pubmed.ncbi.nlm.nih.gov/15694841/, https://doi.org/10.1016/j.biocel.2004.09.010.
3.
Oikawa S, Kawanishi S. 1999. Site-specific DNA damage at GGG sequence by oxidative stress may accelerate telomere shortening. FEBS Lett 453(3):365–368. https://pubmed.ncbi.nlm.nih.gov/10405177/, https://doi.org/10.1016/s0014-5793(99)00748-6.
4.
Ameer SS, Xu YYi, Engström K, Li H, Tallving P, Nermell B, et al. 2016. Exposure to inorganic arsenic is associated with increased mitochondrial DNA copy number and longer telomere length in peripheral blood. Front Cell Dev Biol 4:87. https://pubmed.ncbi.nlm.nih.gov/27597942/, https://doi.org/10.3389/fcell.2016.00087.
5.
Herlin M, Broberg K, Igra AM, Li H, Harari F, Vahter M. 2019. Exploring telomere length in mother-newborn pairs in relation to exposure to multiple toxic metals and potential modifying effects by nutritional factors. BMC Med 17(1):77. https://pubmed.ncbi.nlm.nih.gov/30971237/, https://doi.org/10.1186/s12916-019-1309-6.
6.
Paz-Sabillón M, Torres-Sánchez L, Piña-Pozas M, Del Razo L, Quintanilla-Vega B. 2023. Prenatal exposure to potentially toxic metals and their effects on genetic material in offspring: a systematic review. Biol Trace Elem Res 201(5):2125–2150. https://pubmed.ncbi.nlm.nih.gov/35713810/, https://doi.org/10.1007/s12011-022-03323-2.
7.
Vriens A, Nawrot TS, Janssen BG, Baeyens W, Bruckers L, Covaci A, et al. 2019. Exposure to environmental pollutants and their association with biomarkers of aging: a multipollutant approach. Environ Sci Technol 53(10):5966–5976. https://pubmed.ncbi.nlm.nih.gov/31041867/, https://doi.org/10.1021/acs.est.8b07141.
8.
Avilés-Ramírez C, Moreno-Godínez ME, Bonner MR, Parra-Rojas I, Flores-Alfaro E, Ramírez M, et al. 2022. Effects of exposure to environmental pollutants on mitochondrial DNA copy number: a meta-analysis. Environ Sci Pollut Res Int 29(29):43588–43606. https://pubmed.ncbi.nlm.nih.gov/35399130/, https://doi.org/10.1007/s11356-022-19967-5.
9.
Smith AR, Hinojosa Briseño A, Picard M, Cardenas A. 2023. The prenatal environment and its influence on maternal and child mitochondrial DNA copy number and methylation: a review of the literature. Environ Res 227:115798. https://pubmed.ncbi.nlm.nih.gov/37001851/, https://doi.org/10.1016/j.envres.2023.115798.
10.
Codd V, Nelson CP, Albrecht E, Mangino M, Deelen J, Buxton JL, et al. 2013. Identification of seven loci affecting mean telomere length and their association with disease. Nat Genet 45(4):422–427. https://pubmed.ncbi.nlm.nih.gov/23535734/, https://doi.org/10.1038/ng.2528.
11.
Castellani CA, Longchamps RJ, Sun J, Guallar E, Arking DE. 2020. Thinking outside the nucleus: mitochondrial DNA copy number in health and disease. Mitochondrion 53:214–223. https://pubmed.ncbi.nlm.nih.gov/32544465/, https://doi.org/10.1016/j.mito.2020.06.004.
12.
Filograna R, Mennuni M, Alsina D, Larsson NG. 2021. Mitochondrial DNA copy number in human disease: the more the better? FEBS Lett 595(8):976–1002. https://pubmed.ncbi.nlm.nih.gov/33314045/, https://doi.org/10.1002/1873-3468.14021.
13.
Gao M, Zheng G, Li Y, Zhang Y, Hu P, Pan Y. 2023. Telomere length in multiple cancer: insight into recurrence risk from a meta‐analysis. J Gastroenterol Hepatol 38(6):844–853. https://pubmed.ncbi.nlm.nih.gov/36999210/, https://doi.org/10.1111/jgh.16186.
14.
Rossiello F, Jurk D, Passos JF, d’Adda di Fagagna F. 2022. Telomere dysfunction in ageing and age-related diseases. Nat Cell Biol 24(2):135–147. https://pubmed.ncbi.nlm.nih.gov/35165420/, https://doi.org/10.1038/s41556-022-00842-x.
15.
Azcona-Sanjulian MC. 2021. Telomere length and pediatric obesity: a review. Genes (Basel) 12(6):946. https://pubmed.ncbi.nlm.nih.gov/34205609/, https://doi.org/10.3390/genes12060946.
16.
Zhang X, Lin S, Funk WE, Hou L. 2013. Environmental and occupational exposure to chemicals and telomere length in human studies. Postgrad Med J 89(1058):722–728. https://pubmed.ncbi.nlm.nih.gov/24243983/, https://doi.org/10.1136/postgradmedj-2012-101350rep.
17.
García-Calzón S, Moleres A, Martínez-González MA, Martínez JA, Zalba G, Marti A, et al. GENOI members. 2015. Dietary total antioxidant capacity is associated with leukocyte telomere length in a children and adolescent population. Clin Nutr 34(4):694–699. https://pubmed.ncbi.nlm.nih.gov/25131600/, https://doi.org/10.1016/j.clnu.2014.07.015.
18.
Herrera-Moreno JF, Prada D, Baccarelli AA. 2023. Early environment and telomeres: a long-term toxic relationship. Curr Environ Health Rep 10(2):112–124. https://pubmed.ncbi.nlm.nih.gov/36944821/, https://doi.org/10.1007/s40572-023-00395-7.
19.
Martens DS, Van Der Stukken C, Derom C, Thiery E, Bijnens EM, Nawrot TS. 2021. Newborn telomere length predicts later life telomere length: tracking telomere length from birth to child- and adulthood. EBioMedicine 63:103164. https://pubmed.ncbi.nlm.nih.gov/33422989/, https://doi.org/10.1016/j.ebiom.2020.103164.
20.
Kupsco A, Bloomquist TR, Hu H, Reddam A, Tang D, Goldsmith J, et al. 2023. Mitochondrial DNA copy number dynamics and associations with the prenatal environment from birth through adolescence in a population of dominican and african American children. Mitochondrion 69:140–146. https://pubmed.ncbi.nlm.nih.gov/36804466/, https://doi.org/10.1016/j.mito.2023.02.008.
21.
Fukunaga H. 2021. Mitochondrial DNA copy number and developmental origins of health and disease (DoHaD). Int J Mol Sci 22(12):6634. https://pubmed.ncbi.nlm.nih.gov/34205712/, https://doi.org/10.3390/ijms22126634.
22.
Fowler BA, Zalups RK. 2022. Mercury. In: Handbook on the Toxicology of Metals: Volume II: Specific Metals. Fifth Ed. London, UK: Academic Press/Elsevier, 539–599.
23.
Clarkson TW. 2002. The three modern faces of mercury. Environ Health Perspect 110 Suppl 1(suppl 1):11–23. https://pubmed.ncbi.nlm.nih.gov/11834460/, https://doi.org/10.1289/ehp.02110s111.
24.
Julvez J, Méndez M, Fernandez-Barres S, Romaguera D, Vioque J, Llop S, et al. 2016. Maternal consumption of seafood in pregnancy and child neuropsychological development: a longitudinal study based on a population with high consumption levels. Am J Epidemiol 183(3):169–182. https://pubmed.ncbi.nlm.nih.gov/26740026/, https://doi.org/10.1093/aje/kwv195.
25.
Strain JJ, Love TM, Yeates AJ, Weller D, Mulhern MS, McSorley EM, et al. 2021. Associations of prenatal methylmercury exposure and maternal polyunsaturated fatty acid status with neurodevelopmental outcomes at 7 years of age: results from the Seychelles Child Development Study Nutrition Cohort 2. Am J Clin Nutr 113(2):304–313. https://pubmed.ncbi.nlm.nih.gov/33330939/, https://doi.org/10.1093/ajcn/nqaa338.
26.
Jia G, Aroor AR, Martinez-Lemus LA, Sowers JR. 2015. Mitochondrial functional impairment in response to environmental toxins in the cardiorenal metabolic syndrome. Arch Toxicol 89(2):147–153. https://pubmed.ncbi.nlm.nih.gov/25559775/, https://doi.org/10.1007/s00204-014-1431-3.
27.
Zalups RK. 2000. Molecular interactions with mercury in the kidney. Pharmacol Rev 52(1):113–143. https://pubmed.ncbi.nlm.nih.gov/10699157/, https://doi.org/10.1016/S0031-6997(24)01438-8.
28.
Polunas M, Halladay A, Tjalkens RB, Philbert MA, Lowndes H, Reuhl K. 2011. Role of oxidative stress and the mitochondrial permeability transition in methylmercury cytotoxicity. Neurotoxicology 32(5):526–534. https://pubmed.ncbi.nlm.nih.gov/21871920/, https://doi.org/10.1016/j.neuro.2011.07.006.
29.
Wang X, Yan M, Zhao L, Wu Q, Wu C, Chang X, et al. 2016. Low-dose methylmercury-induced genes regulate mitochondrial biogenesis via miR-25 in immortalized human embryonic neural progenitor cells. Int J Mol Sci 17(12):2058. https://pubmed.ncbi.nlm.nih.gov/27941687/, https://doi.org/10.3390/ijms17122058.
30.
Yoshino Y, Mozai T, Nakao KIKU. 1966. Distribution of mercury in the brain and its subcellular units in experimental organic mercury poisonings. J Neurochem 13(5):397–406, https://doi.org/10.1111/j.1471-4159.1966.tb06816.x.
31.
Wong JYY, De Vivo I, Lin X, Fang SC, Christiani DC. 2014. The relationship between inflammatory biomarkers and telomere length in an occupational prospective cohort study. PLoS One 9(1):e87348. https://pubmed.ncbi.nlm.nih.gov/24475279/, https://doi.org/10.1371/journal.pone.0087348.
32.
Guan X, Fu W, Wei W, Li G, Wu X, Bai Y, et al. 2020. Mediation of the association between polycyclic aromatic hydrocarbons exposure and telomere attrition by oxidative stress: a prospective cohort study. J Hazard Mater 399:123058. https://pubmed.ncbi.nlm.nih.gov/32512281/, https://doi.org/10.1016/j.jhazmat.2020.123058.
33.
Lozano M, McEachan RRC, Wright J, Yang TC, Dow C, Kadawathagedara M, et al. 2024. Early life exposure to mercury and relationships with telomere length and mitochondrial DNA content in european children. Sci Total Environ 932:173014. https://pubmed.ncbi.nlm.nih.gov/38729362/, https://doi.org/10.1016/j.scitotenv.2024.173014.
34.
Xu Y, Wahlberg K, Love TM, Watson GE, Yeates AJ, Mulhern MS, et al. 2019. Associations of blood mercury and fatty acid concentrations with blood mitochondrial DNA copy number in the Seychelles Child Development Nutrition Study. Environ Int 124:278–283. https://pubmed.ncbi.nlm.nih.gov/30660840/, https://doi.org/10.1016/j.envint.2019.01.019.
35.
Berky AJ, Ryde IT, Feingold B, Ortiz EJ, Wyatt LH, Weinhouse C, et al. 2019. Predictors of mitochondrial DNA copy number and damage in a mercury-exposed rural Peruvian population near artisanal and small-scale gold mining: an exploratory study. Environ Mol Mutagen 60(2):197–210. https://pubmed.ncbi.nlm.nih.gov/30289587/, https://doi.org/10.1002/em.22244.
36.
Smith AR, Lin P-ID, Rifas-Shiman SL, Rahman ML, Gold DR, Baccarelli AA, et al. 2021. Prospective associations of early pregnancy metal mixtures with mitochondria DNA copy number and telomere length in maternal and cord blood. Environ Health Perspect 129(11):117007. https://pubmed.ncbi.nlm.nih.gov/34797165/, https://doi.org/10.1289/EHP9294.
37.
Yeates AJ, Thurston SW, Li H, Mulhern MS, McSorley EM, Watson GE, et al. 2017. PUFA status and methylmercury exposure are not associated with leukocyte telomere length in mothers or their children in the Seychelles Child Development Study. J Nutr 147(11):2018–2024. https://pubmed.ncbi.nlm.nih.gov/28978678/, https://doi.org/10.3945/jn.117.253021.
38.
Galiè S, Canudas S, Muralidharan J, García-Gavilán J, Bulló M, Salas-Salvadó J. 2020. Impact of nutrition on telomere health: systematic review of observational cohort studies and randomized clinical trials. Adv Nutr 11(3):576–601. https://pubmed.ncbi.nlm.nih.gov/31688893/, https://doi.org/10.1093/advances/nmz107.
39.
Pollicino F, Veronese N, Dominguez LJ, Barbagallo M. 2023. Mediterranean diet and mitochondria: new findings. Exp Gerontol 176:112165. https://pubmed.ncbi.nlm.nih.gov/37019345/, https://doi.org/10.1016/j.exger.2023.112165.
40.
Strain JJ, Yeates AJ, van Wijngaarden E, Thurston SW, Mulhern MS, McSorley EM, et al. 2015. Prenatal exposure to methyl mercury from fish consumption and polyunsaturated fatty acids: associations with child development at 20 mo of age in an observational study in the republic of Seychelles. Am J Clin Nutr 101(3):530–537. https://pubmed.ncbi.nlm.nih.gov/25733638/, https://doi.org/10.3945/ajcn.114.100503.
41.
Cassidy A, De Vivo I, Liu Y, Han J, Prescott J, Hunter DJ, et al. 2010. Associations between diet, lifestyle factors, and telomere length in women. Am J Clin Nutr 91(5):1273–1280. https://pubmed.ncbi.nlm.nih.gov/20219960/, https://doi.org/10.3945/ajcn.2009.28947.
42.
Ogłuszka M, Lipiński P, Starzyński RR. 2022. Effect of omega-3 fatty acids on telomeres—are they the elixir of youth? Nutrients 14(18):3723. https://pubmed.ncbi.nlm.nih.gov/36145097/, https://doi.org/10.3390/nu14183723.
43.
Valera-Gran D, Prieto-Botella D, Hurtado-Pomares M, Baladia E, Petermann-Rocha F, Sánchez-Pérez A, et al. 2022. The impact of foods, nutrients, or dietary patterns on telomere length in childhood and adolescence: a systematic review. Nutrients 14(19):1–14. https://pubmed.ncbi.nlm.nih.gov/36235538/, https://doi.org/10.3390/nu14193885.
44.
Cediel Ulloa A, Gliga A, Love TM, Pineda D, Mruzek DW, Watson GE, et al. 2021. Prenatal methylmercury exposure and DNA methylation in seven-year-old children in the Seychelles Child Development Study. Environ Int 147:106321. https://pubmed.ncbi.nlm.nih.gov/33340986/, https://doi.org/10.1016/j.envint.2020.106321.
45.
WHO (World Health Organization). 2018. Assessment of prenatal exposure to mercury: standard operating procedures. https://www.who.int/publications/i/item/9789240002845 [accessed 20 January 2024].
46.
Cernichiari E, Toribara TY, Liang L, Marsh DO, Berlin MW, Myers GJ, et al. 1995. The biological monitoring of mercury in the Seychelles study. Neurotoxicology 16(4):613–628. https://pubmed.ncbi.nlm.nih.gov/8714867/.
47.
National Research Council. 2000. Toxicological Effects of Methylmercury. Washington, DC: National Academies Press. https://doi.org/10.17226/9899.
48.
Wells EM, Kopylev L, Nachman R, Radke EG, Congleton J, Segal D. 2022. Total blood mercury predicts methylmercury exposure in fish and shellfish consumers. Biol Trace Elem Res 200(8):3867–3875. https://pubmed.ncbi.nlm.nih.gov/34686996/, https://doi.org/10.1007/s12011-021-02968-9.
49.
Llop S, Tran V, Ballester F, Barbone F, Sofianou-Katsoulis A, Sunyer J, et al. 2017. CYP3A genes and the association between prenatal methylmercury exposure and neurodevelopment. Environ Int 105:34–42. https://pubmed.ncbi.nlm.nih.gov/28500872/, https://doi.org/10.1016/j.envint.2017.04.013.
50.
de Paula HK, Love TM, Pineda D, Watson GE, Thurston SW, Yeates AJ, et al. 2023. KEAP1 polymorphisms and neurodevelopmental outcomes in children with exposure to prenatal MeHg from the Seychelles Child Development Study Nutrition Cohort 2. Neurotoxicology 99:177–183. https://pubmed.ncbi.nlm.nih.gov/37858899/, https://doi.org/10.1016/j.neuro.2023.10.008.
51.
Conway MC, McSorley EM, Mulhern MS, Spence T, Weslowska M, Strain JJ, et al. 2021. Maternal and child fatty acid desaturase genotype as determinants of cord blood long-chain PUFA (LCPUFA) concentrations in the Seychelles Child Development Study. Br J Nutr 126(11):1687–1697. https://pubmed.ncbi.nlm.nih.gov/33526157/, https://doi.org/10.1017/S0007114521000441.
52.
Strain JJ, Davidson PW, Bonham MP, Duffy EM, Stokes-Riner A, Thurston SW, et al. 2008. Associations of maternal long-chain polyunsaturated fatty acids, methyl mercury, and infant development in the Seychelles Child Development Nutrition Study. Neurotoxicology 29(5):776–782. https://pubmed.ncbi.nlm.nih.gov/18590765/, https://doi.org/10.1016/j.neuro.2008.06.002.
53.
Folch J, Lees M, Stanley GHS. 1957. A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem 226(1):497–509. https://pubmed.ncbi.nlm.nih.gov/13428781/, https://doi.org/10.1016/S0021-9258(18)64849-5.
54.
Gielen M, Hageman GJ, Antoniou EE, Nordfjall K, Mangino M, Balasubramanyam M, et al. 2018. Body mass index is negatively associated with telomere length: a collaborative cross-sectional meta-analysis of 87 observational studies. Am J Clin Nutr 108(3):453–475. https://pubmed.ncbi.nlm.nih.gov/30535086/, https://doi.org/10.1093/ajcn/nqy107.
55.
Cohen S, Janicki-Deverts D, Turner RB, Marsland AL, Casselbrant ML, Li-Korotky H-S, et al. 2013. Childhood socioeconomic status, telomere length, and susceptibility to upper respiratory infection. Brain Behav Immun 34:31–38. https://pubmed.ncbi.nlm.nih.gov/23845919/, https://doi.org/10.1016/j.bbi.2013.06.009.
56.
Gorenjak V, Petrelis AM, Stathopoulou MG, Visvikis-Siest S. 2020. Telomere length determinants in childhood. Clin Chem Lab Med 58(2):162–177. https://pubmed.ncbi.nlm.nih.gov/31465289/, https://doi.org/10.1515/cclm-2019-0235.
57.
Tyrrell J, Melzer D, Henley W, Galloway TS, Osborne NJ. 2013. Associations between socioeconomic status and environmental toxicant concentrations in adults in the USA: NHANES 2001–2010. Environ Int 59:328–335. https://pubmed.ncbi.nlm.nih.gov/23892225/, https://doi.org/10.1016/j.envint.2013.06.017.
58.
Vahter M, Broberg K, Harari F. 2020. Placental and cord blood telomere length in relation to maternal nutritional status. J Nutr 150(10):2646–2655. https://pubmed.ncbi.nlm.nih.gov/32678440/, https://doi.org/10.1093/jn/nxaa198.
59.
Mengel-From J, Svane AM, Pertoldi C, Nygaard Kristensen T, Loeschcke V, Skytthe A, et al. 2019. Advanced parental age at conception and sex affects mitochondrial DNA copy number in human and fruit flies. J Gerontol A Biol Sci Med Sci 74(12):1853–1860. https://pubmed.ncbi.nlm.nih.gov/30874797/, https://doi.org/10.1093/gerona/glz070.
60.
Zhao H, Shen J, Leung E, Zhang X, Chow W, Zhang K. 2020. Leukocyte mitochondrial DNA copy number and built environment in Mexican Americans: a cross-sectional study. Sci Rep 10(1):14988. https://pubmed.ncbi.nlm.nih.gov/32917938/, https://doi.org/10.1038/s41598-020-72083-7.
61.
Wilson NA, Mantzioris E, Middleton PF, Muhlhausler BS. 2020. Influence of sociodemographic, lifestyle and genetic characteristics on maternal DHA and other polyunsaturated fatty acid status in pregnancy: a systematic review. Prostaglandins Leukot Essent Fatty Acids 152:102037. https://pubmed.ncbi.nlm.nih.gov/31811955/, https://doi.org/10.1016/j.plefa.2019.102037.
62.
McSorley EM, Yeates AJ, Mulhern MS, van Wijngaarden E, Grzesik K, Thurston SW, et al. 2018. Associations of maternal immune response with MeHg exposure at 28 weeks’ gestation in the Seychelles Child Development Study. Am J Reprod Immunol 80(5):e13046. https://pubmed.ncbi.nlm.nih.gov/30295973/, https://doi.org/10.1111/aji.13046.
63.
Davidson PW, Myers GJ, Cox C, Axtell C, Shamlaye C, Sloane-Reeves J, et al. 1998. Effects of prenatal and postnatal methylmercury exposure from fish consumption on neurodevelopment. JAMA 280(8):701–707. https://pubmed.ncbi.nlm.nih.gov/9728641/, https://doi.org/10.1001/jama.280.8.701.
64.
Helsel DR. 1990. Less than obvious - statistical treatment of data below the detection limit. Environ Sci Technol 24(12):1766–1774, https://doi.org/10.1021/es00082a001.
65.
Charoenying T, Kruanamkam W, Yu-Iam S, U-Chuvadhana P, Rerksngarm T. 2020. Telomere length distribution in blood and saliva by RT-PCR in age-varying Thais: a pilot study. PTU J Sci Technol 1(1):35–48.
66.
Stout SA, Lin J, Hernandez N, Davis EP, Blackburn E, Carroll JE, et al. 2017. Validation of minimally-invasive sample collection methods for measurement of telomere length. Front Aging Neurosci 9:397. https://pubmed.ncbi.nlm.nih.gov/29270121/, https://doi.org/10.3389/fnagi.2017.00397.
67.
Demanelis K, Jasmine F, Chen LS, Chernoff M, Tong L, Delgado D, et al. 2020. Determinants of telomere length across human tissues. Science 369(6509):eaaz6876. https://pubmed.ncbi.nlm.nih.gov/32913074/, https://doi.org/10.1126/science.aaz6876.
68.
Carver AJ, Hing B, Elser BA, Lussier SJ, Yamanashi T, Howard MA, et al. 2024. Correlation of telomere length in brain tissue with peripheral tissues in living human subjects. Front Mol Neurosci 17:1303974. https://pubmed.ncbi.nlm.nih.gov/38516039/, https://doi.org/10.3389/fnmol.2024.1303974.
69.
Cowell W, Colicino E, Tanner E, Amarasiriwardena C, Andra SS, Bollati V, et al. 2020. Prenatal toxic metal mixture exposure and newborn telomere length: modification by maternal antioxidant intake. Environ Res 190:110009. https://pubmed.ncbi.nlm.nih.gov/32777275/, https://doi.org/10.1016/j.envres.2020.110009.
70.
De Loma J, Krais AM, Lindh CH, Mamani J, Tirado N, Gardon J, et al. 2022. Arsenic exposure and biomarkers for oxidative stress and telomere length in indigenous populations in Bolivia. Ecotoxicol Environ Saf 231:113194. https://pubmed.ncbi.nlm.nih.gov/35051766/, https://doi.org/10.1016/j.ecoenv.2022.113194.
71.
Crespo-López ME, Soares ES, Macchi B. D M, Santos-Sacramento L, Takeda PY, Lopes-Araújo A, et al. 2019. Towards therapeutic alternatives for mercury neurotoxicity in the Amazon: unraveling the pre-clinical effects of the superfruit açaí (Euterpe oleracea, Mart.) as juice for human consumption. Nutrients 11(11):2585. https://pubmed.ncbi.nlm.nih.gov/31717801/, https://doi.org/10.3390/nu11112585.
72.
Roque CR, Sampaio LR, Ito MN, Pinto DV, Caminha JSR, Nunes PIG, et al. 2021. Methylmercury chronic exposure affects the expression of DNA single-strand break repair genes, induces oxidative stress, and chromosomal abnormalities in young dyslipidemic APOE knockout mice. Toxicology 464:152992. https://pubmed.ncbi.nlm.nih.gov/34670124/, https://doi.org/10.1016/j.tox.2021.152992.
73.
Bates B, Lennox A, Prentice A, Bates C, Page P, Nicholson S, et al. 2016. National Diet and Nutrition Survey: Results from Years 1, 2, 3, and 4 (Combined) of the Rolling Programme. (2008/2009–2011/2012), Executive Summary. London, UK: Public Health England, 1–24.
74.
Jahns L, Raatz S, Johnson L, Kranz S, Silverstein J, Picklo M. 2014. Intake of seafood in the US varies by age, income, and education level but not by race-ethnicity. Nutrients 6(12):6060–6075. https://pubmed.ncbi.nlm.nih.gov/25533013/, https://doi.org/10.3390/nu6126060.
75.
Wesolowska M, Yeates AJ, McSorley EM, Watson GE, van Wijngaarden E, Bodin N, et al. 2024. Dietary selenium and mercury intakes from fish consumption during pregnancy: Seychelles Child Development Study Nutrition Cohort 2. Neurotoxicology 101:1–5. https://pubmed.ncbi.nlm.nih.gov/38135192/, https://doi.org/10.1016/j.neuro.2023.12.012.
76.
Wachsmuth M, Hübner A, Li M, Madea B, Stoneking M. 2016. Age-related and heteroplasmy-related variation in human mtDNA copy number. PLoS Genet 12(3):e1005939. https://pubmed.ncbi.nlm.nih.gov/26978189/, https://doi.org/10.1371/journal.pgen.1005939.
77.
Picard M. 2021. Blood mitochondrial DNA copy number: what are we counting? Mitochondrion 60:1–11. https://pubmed.ncbi.nlm.nih.gov/34157430/, https://doi.org/10.1016/j.mito.2021.06.010.
78.
Kelly RDW, Mahmud A, McKenzie M, Trounce IA, St John JC. 2012. Mitochondrial DNA copy number is regulated in a tissue specific manner by DNA methylation of the nuclear-encoded DNA polymerase gamma A. Nucleic Acids Res 40(20):10124–10138. https://pubmed.ncbi.nlm.nih.gov/22941637/, https://doi.org/10.1093/nar/gks770.
79.
Habibi N, Bianco-Miotto T, Phoi YY, Jankovic-Karasoulos T, Roberts CT, Grieger JA. 2021. Maternal diet and offspring telomere length: a systematic review. Nutr Rev 79(2):148–159. https://pubmed.ncbi.nlm.nih.gov/32968801/, https://doi.org/10.1093/nutrit/nuaa097.
80.
Liu X, Shi Q, Fan X, Chen H, Chen N, Zhao Y, et al. 2021. Associations of maternal polyunsaturated fatty acids with telomere length in the cord blood and placenta in chinese population. Front Nutr 8:779306. https://pubmed.ncbi.nlm.nih.gov/35155512/, https://doi.org/10.3389/fnut.2021.779306.
81.
See VHL, Mas E, Burrows S, O’Callaghan NJ, Fenech M, Prescott SL, et al. 2016. Prenatal omega-3 fatty acid supplementation does not affect offspring telomere length and F2-isoprostanes at 12 years: a double blind, randomized controlled trial. Prostaglandins Leukot Essent Fatty Acids 112:50–55. https://pubmed.ncbi.nlm.nih.gov/27637341/, https://doi.org/10.1016/j.plefa.2016.08.006.
82.
Crawford MA, Sinclair AJ, Hall B, Ogundipe E, Wang Y, Bitsanis D, et al. 2023. The imperative of arachidonic acid in early human development. Prog Lipid Res 91:101222. https://pubmed.ncbi.nlm.nih.gov/36746351/, https://doi.org/10.1016/j.plipres.2023.101222.
83.
Dhillon VS, Deo P, Chua A, Thomas P, Fenech M. 2021. Telomere length in healthy adults is positively associated with polyunsaturated fatty acids, including arachidonic acid, and negatively with saturated fatty acids. J Gerontol A Biol Sci Med Sci 76(1):3–6. https://pubmed.ncbi.nlm.nih.gov/32894749/, https://doi.org/10.1093/gerona/glaa213.
84.
Freitas-Simoes T-M, Cofán M, Blasco MA, Soberón N, Foronda M, Corella D, et al. 2019. The red blood cell proportion of arachidonic acid relates to shorter leukocyte telomeres in Mediterranean elders: a secondary analysis of a randomized controlled trial. Clin Nutr 38(2):958–961. https://pubmed.ncbi.nlm.nih.gov/29478886/, https://doi.org/10.1016/j.clnu.2018.02.011.
85.
Chang X, Dorajoo R, Sun Y, Wang L, Ong CN, Liu J, et al. 2020. Effect of plasma polyunsaturated fatty acid levels on leukocyte telomere lengths in the Singaporean Chinese population. Nutr J 19(1):119. https://pubmed.ncbi.nlm.nih.gov/33126880/, https://doi.org/10.1186/s12937-020-00626-9.
86.
Poli A, Agostoni C, Visioli F. 2023. Dietary fatty acids and inflammation: focus on the n-6 series. Int J Mol Sci 24(5):4567. https://pubmed.ncbi.nlm.nih.gov/36901998/, https://doi.org/10.3390/ijms24054567.
87.
Vaughan RA, Garcia-Smith R, Bisoffi M, Conn CA, Trujillo KA. 2012. Conjugated linoleic acid or omega 3 fatty acids increase mitochondrial biosynthesis and metabolism in skeletal muscle cells. Lipids Health Dis 11(1):142. https://pubmed.ncbi.nlm.nih.gov/23107305/, https://doi.org/10.1186/1476-511X-11-142.
88.
Lee MS, Shin Y, Moon S, Kim S, Kim Y. 2016. Effects of eicosapentaenoic acid and docosahexaenoic acid on mitochondrial DNA replication and PGC-1 α gene expression in C2 C12 muscle cells. Prev Nutr Food Sci 21(4):317–322. https://pubmed.ncbi.nlm.nih.gov/28078253/, https://doi.org/10.3746/pnf.2016.21.4.317.
89.
Ghosh S, Kewalramani G, Yuen G, Pulinilkunnil T, An D, Innis SM, et al. 2006. Induction of mitochondrial nitrative damage and cardiac dysfunction by chronic provision of dietary omega-6 polyunsaturated fatty acids. Free Radic Biol Med 41(9):1413–1424. https://pubmed.ncbi.nlm.nih.gov/17023268/, https://doi.org/10.1016/j.freeradbiomed.2006.07.021.

Information & Authors

Information

Published In

Environmental Health Perspectives
Volume 133Issue 2February 2025
PubMed: 39903555

History

Received: 6 February 2024
Revision received: 9 January 2025
Accepted: 10 January 2025
Published online: 4 February 2025

Notes

Conclusions and opinions are those of the individual authors and do not necessarily reflect the policies or views of EHP Publishing or the National Institute of Environmental Health Sciences.

Authors

Affiliations

Anja Stajnko
Department of Laboratory Medicine, Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden
Daniela Pineda
Department of Laboratory Medicine, Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden
Jonathan K. Klus
School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, New York, USA
Tanzy M. Love
School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, New York, USA
Sally W. Thurston
School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, New York, USA
Maria S. Mulhern
Nutrition Innovation Centre for Food and Health (NICHE), Ulster University, Coleraine, UK
J. J. Strain
Nutrition Innovation Centre for Food and Health (NICHE), Ulster University, Coleraine, UK
Emeir M. McSorley
Nutrition Innovation Centre for Food and Health (NICHE), Ulster University, Coleraine, UK
Gary J. Myers
School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, New York, USA
Gene E. Watson
School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, New York, USA
Emelyn Shroff
The Ministry of Health, Mahé, Republic of Seychelles
Conrad F. Shamlaye
The Ministry of Health, Mahé, Republic of Seychelles
Alison J. Yeates
Nutrition Innovation Centre for Food and Health (NICHE), Ulster University, Coleraine, UK
Edwin van Wijngaarden
School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, New York, USA
Department of Laboratory Medicine, Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden

Notes

Address correspondence to Karin Broberg, Scheelevägen 2, 221 85, Lund, Sweden. Email: [email protected]

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