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. Author manuscript; available in PMC: 2021 May 1.
Published in final edited form as: Psychoneuroendocrinology. 2020 Feb 20;115:104602. doi: 10.1016/j.psyneuen.2020.104602

Patterns of Change in Telomere Length over the First Three Years of Life in Healthy Children

Michelle Bosquet Enlow a,b, Finola Kane-Grade c, Immaculata De Vivo d,e,f, Carter R Petty g, Charles A Nelson c,h,i
PMCID: PMC7183438  NIHMSID: NIHMS1569800  PMID: 32120019

Abstract

There is growing interest in the use of telomere length as a biomarker of health and a predictor of later morbidity and mortality. However, little is known about developmentally expected telomere erosion over the first years of life. This gap hinders our ability to interpret the meaning of relative telomere length and rate of attrition in relation to risk factors and health outcomes. The overall goal of this study was to examine the rate of relative telomere length attrition in a large, normative sample of healthy children (N=630) followed from infancy to three years of age. A secondary goal was to explore associations between sociodemographic characteristics and telomere erosion over this time period. Relative telomere length was assessed from DNA in saliva samples collected in infancy (M =8.6 months), age 2 years (M =25.2 months), and age 3 years (M=38.3 months). In the sample as a whole, relative telomere length decreased from infancy to 2 years but remained stable from 2 years to 3 years. Notably, increases in relative telomere length were observed in 29% of children between infancy and 2 years of age and in 46% of children between 2 and 3 years of age; 62% of children showed both increases and decreases in relative telomere length across the study period. Females showed longer relative telomere length than males, regardless of timepoint. There was some evidence that parental age and family finances were associated with changes in child relative telomere length across time. Overall, the findings suggest that telomere length attrition is not uniform across the early years of life, with the most rapid attrition occurring during the first two years, and that increases as well as decreases in telomere length during this period are commonly observed.

Keywords: telomere, attrition, longitudinal, developmental, healthy, early life

1. Introduction

Telomeres are repeating nucleotide sequences of variable number that protect against chromosome deterioration and regulate cellular and tissue function (Blackburn and Gall, 1978). Shorter telomere length has been associated with chromosomal instability and is predictive of decreased immunocompetence, the development of chronic disease throughout life (e.g., cardiovascular disease, diabetes, obesity, inflammatory diseases, depression), abnormalities in brain structure and functioning, and earlier mortality (Baragetti et al., 2015; Factor-Litvak et al., 2016; Geronimus et al., 2015; Hochstrasser et al., 2012; Mundstock et al., 2015; Qi Nan et al., 2015; Rode et al., 2015). Consequently, there has been burgeoning interest in understanding the role of telomeres in health and in elucidating the factors that accelerate or inhibit telomere erosion.

To date, the main objectives of most studies examining changes in telomere length have been to explore the impact of risk factors on telomere erosion and to test whether telomere length is related to current or later mental and physical disease states (Bosquet Enlow et al., 2018; Monroy-Jaramillo et al., 2018; Notterman and Mitchell, 2015; Revesz et al., 2016; Shalev et al., 2014). Elucidating the effects of risk factors on telomere length in early development is of particular interest given evidence that this is a period of acute vulnerability to environmental exposures (e.g., adversity/stress, toxins, nutrition) and that such effects may contribute to poor health outcomes across the lifespan (Bosquet Enlow et al., 2018; Chatterjee and Walker, 2017). Importantly, these studies have rarely considered the role of normative age-related changes in telomere length. This omission is notable given that aging is one of the largest predictors of telomere shortening, as telomeres shorten with each cell replication (Blackburn et al., 2015; Lopez-Otin et al., 2013). Moreover, telomere attrition has been implicated in some aging related-processes as well as diseases of human aging, including diseases that may have their origins in very early development (Blackburn et al., 2015; Lopez-Otin et al., 2013). Understanding normative age-related changes in telomere length is critical to interpreting the growing literature on risk factors for accelerated telomere erosion and to evaluating the utility of telomere length as a biomarker of current and future health.

Telomere length at any given timepoint is currently assumed to be determined by newborn telomere length and subsequent attrition (Factor-Litvak et al., 2016; Martens et al., 2016). Existing data suggest that telomere attrition rate is not constant, with attrition occurring most rapidly in the first years of life, after which there is a more gradual reduction in length over time (Chen et al., 2011; Frenck et al., 1998; Holmes et al., 2009; Rufer et al., 1999; Sidorov et al., 2009; Zeichner et al., 1999). However, current knowledge on age-related changes in telomere length is largely based on cross-sectional studies and/or studies using very small sample sizes sampled over a short period of time (Muezzinler et al., 2013). The field is lacking longitudinal data that provide a developmental atlas for expected telomere attrition due to age. Thus, developmental studies that model telomere attrition rate longitudinally, particularly in the first years of life, are needed. Such studies should consider the possibility that normative developmental changes in telomere length are not confined to shortening, given evidence that telomeres may fail to shorten as expected or even lengthen under certain lifestyle (e.g., diet, exercise) and disease conditions (e.g., specific cancers) as well as following health promotion interventions (Bateson and Nettle, 2017; Epel, 2012; Freitas-Simoes et al., 2016; Haycock et al., 2017; Kuo et al., 2019; Puterman et al., 2010). Telomere lengthening as a normal developmental phenomenon has received little consideration.

The primary goal of the current study was to create a normative template for changes in telomere length across the first three years of life in a large, healthy, low-risk community sample of children assessed longitudinally from infancy through 3 years of age. Analyses explored patterns of change in telomere length across this developmental period. A secondary goal was to explore associations of sociodemographic factors (child sex and race/ethnicity, parental age, family socioeconomic status) on telomere length at each timepoint (infancy, 2 years of age, 3 years of age) and on telomere rate of attrition across these timepoints. Prior work suggests that these characteristics may be associated with different telomere length at birth and differential rates of telomere attrition, although the magnitude and direction of effects attributed to such characteristics have differed widely across studies (Bosquet Enlow et al., 2018; Broer et al., 2013; Drury et al., 2015; Geronimus et al., 2015; Hunt et al., 2008; Martens et al., 2016; Wojcicki et al., 2016).

2. Methods

2.1. Participants

Participants were recruited from a registry of local births comprising families who had indicated willingness to participate in developmental research. Families for the current analyses participated in a prospective study (N=807) to examine the early development of emotion processing. Exclusion criteria included known prenatal or perinatal complications, maternal use of medications during pregnancy that may have significant impact on fetal brain development (i.e., anticonvulsants, antipsychotics, opioids), premature or post-term birth (±3 weeks from due date), developmental delay, uncorrected vision difficulties, and neurological disorder or trauma. After enrollment, families were no longer followed and their data were excluded from analyses if the child was diagnosed with an autism spectrum disorder or a genetic or other condition known to influence neurodevelopment.

Families were enrolled when the children were 5, 7, or 12 months old (T1), and a subsample was followed when the children were 2 years (T2) and 3 years (T3) of age. Telomere length data were provided for at least one timepoint by N=630 participants. There were no differences between participants who did and did not provide any telomere length data on maternal or paternal age at enrollment or on paternal educational attainment. Compared to participants who provided telomere length data, participants who did not provide telomere length data were more likely to be male; to be of Hispanic or Black race/ethnicity; to have a lower family income; and for maternal educational attainment to be at the lowest (Associate’s degree or less) or highest (graduate degree) ends of the distribution.

2.2. Procedures

Questionnaires and in-person study visits were administered at both the T1 and T3 timepoints. Subsequent to study initiation, a home-based assessment was incorporated at T2. Staff collected saliva samples during laboratory visits at T1 and T3. At T2, there were two options for participation: parents collected and returned the saliva sample via a mailer, or a research assistant collected the saliva sample during a home visit. Parents were instructed as to how to collect the sample during the T1 laboratory visit and were provided links to a video of instructions posted online prior to the T2 collection. Relevant sociodemographic data were obtained at each timepoint via online questionnaires completed by the child’s parent. Study procedures were approved by the Institutional Review Board of Boston Children’s Hospital, and parents provided written informed consent.

2.3. Measures

2.3.1. Sociodemographics.

At T1, the child’s parent (primarily the child’s mother) completed online questionnaires that inquired about the child’s age, sex, and race/ethnicity, parental age and educational attainment, and two indicators of family finances. Child and parental age were considered as continuous variables. Child race/ethnicity was categorized as Non-Hispanic White, Hispanic White, Asian, multi-racial/multi-ethnic, or other (Black, American Indian, Asian Indian). Parental educational attainment was categorized as Associate’s degree or less, Bachelor’s degree, Master’s degree, or graduate degree (M.D., Ph.D., J.D., or equivalent). Family finances were operationalized as annual family income and financial reserves. Family income over the prior year was scored into one of five categories, ranging from less than $35,000 per year to $100,000 per year or greater. Financial reserves was assessed as the total cash value available if all stocks, bonds, and checking and savings accounts were liquidated; this variable was scored on an 8-point scale, ranging from less than $5,000 to $500,000 or greater. At T2 and T3, child age at time of data collection was assessed.

2.3.2. Telomere length.

Telomere length was assessed from DNA extracted from saliva collected at T1, T2, and T3. Saliva samples were collected using the Oragene (DNA Genotek) kit and then stored at room temperature until DNA extraction. The number of days that transpired between time of saliva collection and time of DNA extraction was as follows: T1 M=121.19, SD=81.89; T2 M=108.63, SD=81.30; T3 M=93.92, SD=62.68. DNA was extracted from samples at the Psychiatric and Neurodevelopmental Genetics Unit (Massachusetts General Hospital) using the Oragene DNA extraction protocol. Relative telomere length was determined using a modified, high throughput (384-well format) version of the quantitative real-time polymerase chain reaction (PCR) (Cawthon, 2002; Wang et al., 2008). A recent meta-analysis determined that the PCR method is a valid technique for quantifying telomere length (Ridout et al., 2017). Laboratory personnel were blinded to participants’ characteristics.

The average relative telomere length was calculated as the ratio of telomere repeat copy number to a single gene (36B4) copy number. Telomere length is reported as the exponentiated ratio of telomere repeat copy number to a single gene copy number corrected for a reference sample. Ratios of telomere repeat copy number to a single gene copy number highly correlate with absolute telomere lengths determined by Southern blot (Cawthon, 2002). The T/S ratio value for all samples at all timepoints was compared to that of a reference DNA quality control standard sample to normalize for experimental variations and allow comparison among sample sets. The T/S ratio has been shown to be linearly proportional to average telomere length. When an unknown sample is identical to the reference DNA in its T/S ratio, the T/S ratio value is 1. The T/S ratio of one individual relative to the T/S ratio of another should thus correspond to the relative telomere lengths of their DNA.

All of the samples were run at the same time within one batch across 15 plates. Five nanograms of genomic DNA were dried down in each 384-well plate and resuspended in 10μL of either the telomere or 36B4 PCR reaction mixture and then stored at 4°C for up to 6 hours. The telomere reaction mixture consisted of 1x Thermo Fisher PowerUP SYBR Master Mix, 2.0mM of DTT, 270nM of Tel-1b primer, and 900nM of Tel-2b primer. The reaction proceeded for one cycle hold at 50°C for 2 minutes and at 95°C for 2 minutes, followed by 35 cycles at 95°C for 15 seconds and 54°C for 2 minutes. The 36B4 reaction consisted of 1x Thermo Fisher PowerUP SYBR Master Mix, 300nM of 36B4U primer, and 500nM of 36B4D primer. The 36B4 reaction proceeded for one cycle hold at 50°C for 2 minutes and at 95°C for 2 minutes, followed by 40 cycles at 95°C for 15 seconds and 58°C for 70 seconds. All samples for both the telomere and single-copy gene (36B4) reactions were performed in triplicate on different plates. Each 384-well plate also contained a 6-point standard curve from 0.625ng to 20ng using pooled saliva derived genomic DNA.

The standard curve assessed and compensated for inter-plate variations in PCR efficiency. The PCR efficiency was overall ~ 90%, and the linear correlation coefficient (R2) values for both reactions were overall > 0.99. The T/S ratio (-dCt) for each sample was calculated by subtracting the average 36B4 Ct value from the average telomere Ct value. The relative T/S ratio (-ddCt) was determined by subtracting the T/S ratio value of the 5ng standard curve point from the T/S ratio of each unknown sample. Quality control samples were interspersed throughout the test samples in order to assess inter-plate and intra-plate variability of threshold cycle (Ct) values. A combined inter- and intra-assay coefficient of variation (CV) calculated from the relative T/S ratio (-ddCt) of quality control samples was 10.43%.

Telomere PCR Primers:

  • Tel 1 GGTTTTTGAGGGTGAGGGTGAGGGTGAGGGTGAGGGT

  • Tel 2 TCCCGACTATCCCTATCCCTATCCCTATCCCTATCCCTA

36B4 (Single copy gene) PCR Primers:

  • 36B4u CAGCAAGTGGGAAGGTGTAATCC

  • 36B4d CCCATTCTATCATCAACGGGTACAA

2.4. Data Analysis Plan

Descriptive analyses were performed to describe overall patterns of relative telomere length change across ages, including group-level rate of relative telomere length change from infancy to 2 years to 3 years and relative frequencies of decreasing versus increasing relative telomere length between age periods. Changes in relative telomere length over time were tested in Stata (StataCorp., 2017) with linear mixed models with a random intercept at the participant level and adjusting for child sex.

Telomere length at time j for participant i was modeled with linear time in days within each of the three timepoints: Telomereij= β0 + β1time2 + β2time3 + β3agej + β4time2*agej + β5time3*agej + β6sex + ui + εij where time2 and time3 are indicators for the 2-year and 3-year follow-ups, respectively; age is a continuous measure of age in days, centered at the mean age in days (T1=259 days, T2=756 days, T3=1148 days), at each assessment timepoint; and ui represents the random intercept at the participant level.

Continuous age within time points was omitted for testing of different trajectories based on a sociodemographic factor (SDF): Telomereij= β0 + β1time2 + β2time3 + β3SDF + β4time2*SDF + β5time3*SDF + β6sex + ui + εij.

Alpha was set at .05; all tests were two-tailed.

3. Results

3.1. Sample characteristics.

Table 1 displays the sample sociodemographic characteristics. Children were predominantly non-Hispanic White (72.1%) and of high socioeconomic status as reflected in parental education, family annual income, and familial financial reserves. The correlation coefficients among the sociodemographic characteristics were moderate to weak (Table 2); the sociodemographic variables were not collinear. No observed characteristics were significantly associated with missing data at any of the three timepoints.

Table 1.

Sample characteristics (N = 630)

n % M SD
Child age, infant (T1) assessment (months) 8.59 3.07
Child age, 2-year (T2) assessment (months) 25.19 1.48
Child age, 3-year (T3) assessment (months) 38.28 2.09
Child sex (male) 329 52.2
Maternal age (years) 34.04 3.82
Paternal age (years) 35.74 4.79
Child race/ethnicity
 White, non-Hispanic 454 72.1
 Hispanic 42 6.7
 Asian 19 3.0
 Multi-racial 90 14.3
 Othera 10 1.6
Maternal education
 Associate’s degree or less 34 5.4
 College degree 184 29.2
 Master’s degree 284 45.1
 Graduate degree 122 19.4
Paternal education
 Associate’s degree or less 76 12.1
 College degree 189 30.0
 Master’s degree 195 31.0
 Graduate degree 161 25.6
Annual household income
 < $35,000 19 3.0
 $35,000-$49,999 15 2.4
 $50,000-$74,999 55 8.7
 $75,000-$99,999 85 13.5
 $100,000+ 402 63.8
Financial reserves
 < $5000 32 5.1
 $5,000-$9,999 28 4.4
 $10,000-$ 19,999 54 8.6
 $20,000-$49,999 67 10.6
 $50,000-$99,999 65 10.3
 $100,000-$ 199,999 88 14.0
 $200,000-$499,999 77 12.2
 $500,000+ 44 7.0
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a

Children categorized as “other” race/ethnicity were identified by parent as Black, American Indian, or Asian Indian.

Table 2.

Correlations among sociodemographic variables

Maternal education Paternal education Family income Family financial reserves Maternal age Paternal age
Maternal education --
Paternal education .448*** --
Family income .308*** .196*** --
Family financial reserves .290*** .314*** .404*** --
Maternal age .133** .029 .261*** 244*** --
Paternal age .034 −.053 .212*** .225*** .693*** --
Child race/ethnicitya −.098* −.113** −.195*** −.138** .007 .006
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a

Child race/ethnicity dichotomized into White = 0, Minority = 1.

*

p<.05.

**

p<.01.

***

p<.001.

Available health indicators suggest that the sample was healthy. Because of the study inclusion/exclusion criteria, pregnancy complications were rare (high blood pressure/preeclampsia/eclampsia=1.3%; gestational diabetes=3.8%), as were birth complications (M length of hospital stay=2.80 days, SD=1.37 days). The rate of Cesarean delivery was comparable to that of the general population (27.2%). Due to exclusionary criteria, all children were of normal gestational age (37 weeks to 43 weeks). Children were also of normal birthweight (M=3512g, SD=523g, 98%>2500g). As noted above, children were excluded from the study if there were any known developmental delays, neurological disorder or trauma, or maternal use of certain medications during pregnancy. If the child was ill at the time of a scheduled study visit, the visit was rescheduled for a time when the child was well. Although detailed health information was not collected in this study, mothers were asked the following question at infancy, “Has your child experienced any serious illnesses or difficulties in development since birth?” and the following question at the 2-year and 3-year follow-up assessments, “Has your child experienced any serious illnesses or difficulties since we saw you last?” This item was endorsed by 31 mothers (5% of respondents) at infancy, 41 at 2 years (6.5% of respondents), and 25 at 3 years (4.0% of respondents). None of these variables were associated with any of the telomere length measures at any timepoint or with change in telomere length between timepoints, all ps>.10.

3.2. Changes in relative telomere length across infancy to 3 years.

Telomere length data during at least one timepoint were available for 630 children, with 601 providing data at T1, 275 at T2, and 324 at T3; 195 provided data at one timepoint, 300 at two timepoints, and 135 at all three timepoints, with 452 providing data during T1 and at least one follow-up timepoint. Relative telomere length was normally distributed with no outliers. Relative telomere length was not associated with number of days saliva sample was in storage prior to DNA extraction at any of the three timepoints. Table 3 displays the mean, median, and interquartile range of relative telomere length at T1 by infant age at recruitment: 5 months, 7 months, or 12 months. (Note: These data are cross-sectional, as infants were assessed at T1 at either 5 months, 7 months, or 12 months.) ANOVA testing did not detect differences in relative telomere length by infant age at the T1 assessment, F(2,598)=0.69, p=.501. Additionally, there were no differences in the mean change in relative telomere length from T1 to T2 (p=.953) or from T1 to T3 (p=.613) by age at the T1 assessment (5, 7, or 12 months). Moreover, age in days at the T1 assessment was not correlated with T1 relative telomere length, r=−0.04, p=0.27. Therefore, for the remainder of the analyses, relative telomere length data collected at T1 were combined across infants assessed at 5 months, 7 months, or 12 months to create an “infant” timepoint (T1).

Table 3.

Relative telomere length by age

Mean (SD) Median (Interquartile Range) Range
5 months (n=174) 1.01 (.15) 1.02 [0.91, 1.11] 0.65–1.40
7 months (n =183) 1.00 (.17) 0.99 [0.90, 1.11] 0.20–1.53
12 months (n =244) 1.00 (.18) 0.98 [0.88, 1.10] 0.28–1.53
T1: Infancy (n =601) 1.00 (.17) 1.00 [0.89, 1.11] 0.20–1.53
T2: 2 years (n =275) .91 (.18) 0.89 [0.79, 1.01] 0.39–1.64
T3: 3 years (n =324) .89 (.17) 0.87 [0.77, 1.00] 0.53–1.57
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Table 3 also displays the mean, median, and interquartile range of relative telomere length at T1 (combined across infants assessed at 5 months, 7 months, or 12 months), T2, and T3. Analyses indicated that, overall, there was a significant decrease in mean relative telomere length from T1 to T2 (−0.10, 95% CI [−0.12,−0.07], p<.001), but no change from T2 to T3 (p=.39, Figure 1). We also modeled continuous time (i.e., exact age at saliva sample collection) within each timepoint (T1, T2, T3) by child sex; none of the negative slopes within timepoint were significant (all ps≥0.20, Figure 2).

Figure 1.

Figure 1.

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Rate of change of relative telomere length from infancy to age 3 years. Relative telomere length at the infancy timepoint was assessed at infant age 5 months (n=174), 7 months (n=183), or 12 months (n=244). There were no differences in relative telomere length among infants assessed at 5 months versus 7 months versus 12 months at the infancy (T1) timepoint. There was a significant decrease in the average relative telomere length from infancy (T1) to age 2 years (T2; n=275) but not from 2 years (T2) to age 3 years (T3; n=324). Vertical lines represent 95% confidence intervals.

Figure 2.

Figure 2.

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Relative telomere length by child age and sex. Predicted relative telomere length from linear mixed model with sex, timepoint (infancy [T1], 2 years [T2], 3 years [T3]), continuous age of assessment centered at the mean age within each timepoint, interaction terms of the timepoint and continuous age variables, and random intercept at the child level. Predictions are for fixed effects only (i.e., the predictions for an “average” child with random effect=0). None of the negative slopes within timepoint were significant. Females had longer relative telomere lengths than males at each timepoint.

Approximately 29% of children showed an increase in relative telomere length from T1 to T2, 46% from T2 to T3, and 29% from T1 to T3. Among children who showed an increase in relative telomere length from T1 to T2, 76.2% showed a decrease from T2 to T3; among those who showed a decrease in relative telomere length from T1 to T2, 55.9% showed an increase from T2 to T3. Among participants with data at all three timepoints, 30.4% showed a decrease in relative telomere length across both time periods, 7.4% showed an increase across both time periods, and 62.2% showed an increase across one time period and a decrease across the other time period. In a t-test analysis, there was no difference in the magnitude of change (i.e., absolute difference) in relative telomere length from T1 to T2 among those who showed an increase in relative telomere length (M=0.19, SD=0.18, range=0.01 to 0.84) versus those who showed a decrease in relative telomere length (M=0.21, SD=0.14, range=0.01 to 0.63), t(251)=0.89, p=.372. There was also no difference in the magnitude of change in relative telomere length from T2 to T3 among children who showed an increase (M=.19, SD=.14, range=0.01 to 0.60) versus those who showed a decrease (M=0.18, SD=0.14, range=0.01 to 0.73), t(145)=−.697, p=.487. Children who showed a decrease in relative telomere length from T1 to T3 tended to experience a larger change in relative telomere length (M=0.21, SD=0.14, range=0.01 to 0.65) compared to children who showed an increase in relative telomere length from T1 to T3 (M=0.17, SD=0.15, range=0.01 to 0.80), t(303)=1.93, p=.055.

Correlational analyses demonstrated low correlations of relative telomere length across time (Table 4), with only 13% of the variation in relative telomere length explained at the subject level (intraclass correlation from mixed model).

Table 4.

Correlation coefficients for relative telomere length across ages

T1 to T2 .142* (n=253)
T2 to T3 .121 (n=147)
T1 to T3 .144* (n=305)
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T1=Infancy assessment. T2=2-year assessment. T3=3-year assessment.

*

p<.05.

***

p<.001.

3.3. Associations of sociodemographic characteristics with relative telomere length in early childhood.

Analyses first examined whether sociodemographic characteristics were associated with relative telomere length at each timepoint. Longer relative telomere length was found among females (0.04, 95% CI [0.02, 0.06], p<.001) compared to males irrespective of timepoint (Figure 2). Relative telomere length was not associated with child race/ethnicity, maternal or paternal age or education level, or the family financial variables.

Analyses then examined whether sociodemographic characteristics were associated with differences in the rate of change in relative telomere length across early childhood. There was a significant interaction of age of child and age of father on relative telomere length such that, at T3, older paternal age was associated with longer child relative telomere length. Specifically, at T1 and T2, paternal age (M=35.74 years, SD=4.79; range=19.74–51.88 at T1) was not associated with telomere length (p=.74 and p=.99, respectively); however, at T3, every 10 years of paternal age was associated with a 0.05 (95% CI [0.02,0.09], p=.004) increase in relative telomere length (interaction vs T1 p=.03). This interaction became non-significant (p=.47) when controlling for financial variables. Rate of change in relative telomere length across T1 to T3 was not associated with child sex or race/ethnicity, child sex by child race/ethnicity (i.e., sex by race/ethnicity interaction (Drury et al., 2015)), maternal age, maternal or paternal educational attainment, or family financial variables.

Analyses then explored whether experiencing an increase versus a decrease in relative telomere length across time was associated with any of the sociodemographic variables. Females were found to be more likely than males to show an increase in relative telomere length from T1 to T2 (OR=1.78, 95% CI [1.03,3.08], p=.04). Additionally, children whose families had fewer financial reserves were more likely to show an increase in relative telomere length from T1 to T3 than children whose families had greater financial reserves (OR=1.16, 95% CI [1.01,1.34], p=.04); however, this association dropped to a trend level (p=.06) when controlling for paternal age.

4. Discussion

The overall goal of the current study was to describe developmental changes in relative telomere length in a healthy sample of full-term children followed from infancy to three years of age. Findings suggest that, even within this relatively small developmental window, telomere attrition rate is not constant. At a group level, relative telomere length shortened significantly from infancy to age 2 years but showed minimal change from 2 years to 3 years. These findings are consistent with cross-sectional and short-term longitudinal data that have suggested that telomere attrition is most rapid in early life compared to later childhood and adulthood. This study builds upon prior work by demonstrating, within a longitudinal sample, that telomere attrition may be especially rapid within the first two years. This study also addresses recent calls to assess telomere length annually across multiple years and test correlations of telomere length over time to build accurate models of telomere dynamics (Bateson and Nettle, 2017).

Although the sample as a whole demonstrated decreases in relative telomere length over time, exploration of patterns of change suggests that both telomere shortening and lengthening are common in early development. Specifically, a sizeable minority showed increases in relative telomere length from infancy to age 2 years (28.9%) and from 2 years to 3 years (46.3%). The majority of children who experienced an increase in relative telomere length from infancy to 2 years experienced a decrease from 2 years to 3 years; similarly, the majority of children who experienced a decrease in relative telomere length from infancy to 2 years experienced an increase from 2 years to 3 years. Thus, the most common pattern (62%) was for children to exhibit both increases and decreases in relative telomere length across the first three years, with a minority (30%) showing decreases across both timepoints, and a small percentage (7%) showing increases across both timepoints. The fact that telomere lengthening was commonly observed is consistent with longitudinal studies of adults that have documented leukocyte telomere lengthening in up to 50% of participants (Aviv et al., 2009; Bateson and Nettle, 2017; Chen et al., 2011).

There is debate as to the meaning of observed telomere lengthening. Some attribute it to measurement error, particularly when observed over relatively short time periods (Chen et al., 2011; Steenstrup, Hjelmborg, Kark, et al., 2013; Steenstrup, Hjelmborg, Mortensen, et al., 2013). Others contend that telomere lengthening is biologically plausible, although the specific mechanisms responsible are as yet undetermined (Aviv et al., 2009; Bateson and Nettle, 2017). Yet others argue that biological mechanisms are unlikely to contribute to telomere lengthening given that telomerase activity in most human somatic cells is down-regulated and ALT (alternative lengthening of telomeres) may not be particularly active in normal cells (Gomez et al., 2012; Saretzki, 2018). Another potential explanation is that the cellular composition of saliva (as well as peripheral blood mononuclear cells, another common source for obtaining DNA for telomere length assaying) fluctuates (Proctor, 2016). Given that different cell lineages display different replicative histories, a participant’s aggregate telomere length may thus appear to increase (or decrease) across time due to differences in cellular composition across samples. Future longitudinal research should examine patterns of telomere lengthening across the lifespan and determine potential causes, including measurement error, regression to the mean, differences in sample cell composition across measurements, and biological processes (Bateson and Nettle, 2017). The current findings suggest that measures of telomere length at a single point in time may be relatively unreliable and thus have limited utility as a biomarker of health. Additional research that helps to establish expected telomere length values at different ages and expected rates of telomere attrition across the lifespan may improve the validity of telomere length as a predictor of future health.

The secondary goal of the study was to examine telomere length at each timepoint and telomere length change across timepoints in relation to sociodemographic characteristics. The current analyses found that telomere length was consistently longer in females compared to males. Notably, a number of studies have documented that telomere length does not differ by sex at birth but is consistently longer in females in later life (Chen et al., 2011). The results of the current findings suggest that this sex difference may appear very early in development. More research is needed to determine the mechanisms of sex effects. Interestingly, emerging research suggests that telomeres may be differentially susceptible to the effects of maternal pregnancy and pre-conception exposures in male versus female newborns (Bosquet Enlow et al., 2018; Bosquet Enlow et al., 2019). Thus, telomere biology may exhibit sex differences that begin in the fetal period and persist through the life course. Consequently, sex appears to be an important variable to consider when interpreting telomere data.

There was modest evidence for associations of paternal age, but not maternal age, and family financial well-being on child telomere length. Specifically, at age 3 years, but not earlier ages, increasing paternal age was associated with longer telomere length. Interestingly, studies have shown longer leukocyte telomere length in the offspring of older fathers, starting at birth (Broer et al., 2013; Eisenberg and Kuzawa, 2018; Factor-Litvak et al., 2016; Kimura et al., 2008). Moreover, evidence suggests that paternal age may have a more robust effect on offspring telomere length than maternal age (Eisenberg and Kuzawa, 2018; Factor-Litvak et al., 2016; Gerber et al., 2013). Some have suggested this finding may be attributable to age-dependent telomere dynamics in sperm, as mean telomere length in sperm has been found to increase with age (Kimura et al., 2008). Notably, in the current study, the association between paternal age and child telomere length was reduced to non-significance when family financial variables were considered, suggesting that the association between paternal age and child telomere length may be spurious, reflecting an association between older paternal age and higher socioeconomic status, the latter of which may promote increased offspring telomere length (Eisenberg and Kuzawa, 2018). However, data from other studies suggest that the paternal age effect on offspring telomere length is robust and not attributable to socioeconomic status (Eisenberg and Kuzawa, 2018; Prescott et al., 2012).

Increasing relative telomere length from infancy to 3 years of age was more likely among children of families with less cash available than among children of families with greater cash available; however, this association dropped to a trend level when controlling for paternal age. No other indicators of family socioeconomic status were associated with child telomere length. Some prior studies have found higher maternal socioeconomic status (e.g., educational attainment, household income) to be associated with longer newborn telomere length (Bosquet Enlow et al., 2018; Martens et al., 2016; Wojcicki et al., 2016), whereas others have failed to find such effects (Drury et al., 2015; Factor-Litvak et al., 2016).

There was no evidence of differences in relative telomere length or attrition rate by race/ethnicity. To date, race/ethnicity differences in telomere length have varied widely in nature and degree across studies of infants, children, and adults (Bosquet Enlow et al., 2018; Drury et al., 2015; Factor-Litvak et al., 2016; Martens et al., 2016; Needham et al., 2012; Okuda et al., 2002). More research in samples with sufficient representation of various racial/ethnic groups as well as socioeconomic backgrounds is needed to (a) determine whether telomere length attrition rate across the lifespan varies by racial/ethnic background, possibly in interaction with sex/gender (Drury et al., 2015); (b) disentangle any observed race/ethnicity effects from socioeconomic effects, as these variables are often confounded in the literature; and (c) explore whether any observed differences in telomere length among racial/ethnic groups are attributable to different distributions of health risk factors noted in the literature (e.g., exposure to severe stressors, discrimination) (Geronimus et al., 2015).

Strengths and limitations of the current study deserve consideration. The relatively large sample consisted of healthy children recruited in infancy and followed to three years of age. There were limited differences in the sample in race/ethnicity (72% non-Hispanic White), and the great majority of the sample was of high socioeconomic status by measures of parental education and family annual income. These sample characteristics are both a strength and a limitation. As noted above, some previous studies have found differences in telomere length by race and socioeconomic status. The reasons for such associations are not established but may be related to racial/ethnic differences in polygenic adaptation (Hansen et al., 2016) and to higher rates of stress exposures and other health risk factors among both minority racial groups and low socioeconomic status groups (Geronimus et al., 2015). Moreover, also as noted above, race/ethnicity and socioeconomic status are often confounded in research studies (Geronimus et al., 2015), further complicating efforts to understand the impact of specific sociodemographic characteristics on telomere parameters. Using a sample of relatively high socioeconomic status reduces the potential variability in telomere length due to exposure to stressors associated with economic adversity. This allows for greater confidence in attributing observed changes in telomere length over time to normative developmental processes. However, the limited representation of individuals of minority racial/ethnic backgrounds and varied socioeconomic status restricts the generalizability of the findings. Developmental changes in telomere length should be explored in samples representing a range of sociodemographic characteristics.

Although available indicators suggest that the sample was healthy, exclusion criteria did not include all factors that have been associated with shortened telomere length (e.g., stress exposures, diet, elevated body mass index). Excluding on the basis of all such factors was not possible due to the lack of validated cut-off scores that could be used for making appropriate exclusion rules. Moreover, applying excessive exclusionary rules would further diminish the generalizability of the findings. Nevertheless, some of the change in relative telomere length observed in the current study was likely due to factors other than normative age-related processes. Additionally, the current study did not consider the role of heritability, which prior research suggests is relatively high for telomere length (Broer et al., 2013).

Another strength of the study includes the use of saliva samples for assaying telomere length. Telomere studies have typically relied on sampling blood, particularly venous blood, for DNA, which presents a number of challenges, including compliance, invasiveness, labor intensity (e.g., need for trained phlebotomist), processing complexities (e.g., requires immediate processing, cold storage, specific transporting considerations), and cost (Goldman et al., 2018). These challenges are particularly acute in studies of young children. Saliva sampling is a less risky and more feasible and cross-culturally acceptable method for assessing telomere length in children, particularly in large-scale non-clinical research (Goldman et al., 2018). Moreover, compared to other sample types (e.g., venous blood, capillary blood, Oasis saliva), Oragene saliva has been shown to produce the greatest DNA yield (Goldman et al., 2018). Additionally, studies have shown that Oragene saliva samples can be stored at room temperature for long periods of time without compromising DNA quality (Goldman et al., 2018), although studies are needed to determine if storage temperature influences telomere length measures (Goldman et al., 2018). Studies are also needed to determine if developmental changes observed in telomere length differ by sampling method. This information is critical for allowing comparison of findings across studies employing different methodologies. Limited research in adults and children suggests that telomere length differs by sample type (buffy coat, Oragene saliva, Oasis saliva) but that telomere length derived from venous blood and from Oragene saliva are moderately to highly correlated (Goldman et al., 2018; Mitchell et al., 2014). Moreover, telomere length assessed from saliva has been shown to correlate negatively with chronological age (Goldman et al., 2018). Research is also needed to determine if the predictive validity of telomere length as a biomarker of current or future health varies by sampling method and if different sampling methods demonstrate varying validity depending on the specific health outcome(s) of interest. Finally, the method utilized for quantifying telomere length, real-time PCR, presents both strengths and challenges. Because it requires small amounts of DNA and is relatively inexpensive and less labor intensive than other methods, it is particularly amenable for use in large-scale studies (Montpetit et al., 2014). However, because this method produces relative telomere length rather than absolute kilobase length estimates, it limits comparability with findings from other studies (Montpetit et al., 2014).

The current findings would have been strengthened by data from the newborn period, as telomere attrition during the first year of life may show a different pattern than at later ages. Notably, we did not observe significant differences among children assessed at 5 months versus 7 months versus 12 months at their T1 assessment or in their rate of change between the T1 and T2 assessments; however, these specific analyses were cross-sectional and therefore cannot definitively address patterns of change in telomere length across the first year of life. Also, the mean length of time between the T1 and T2 assessments (16.6 months) was longer than that between than T2 and T3 assessments (13.1 months). However, this time difference is unlikely to account for the differences in attrition rate from T1 to T2 versus T2 to T3 given (a) the lack of difference in relative telomere length at T1 in those assessed at 5 months versus 7 months versus 12 months and (b) the lack of difference in change in relative telomere length from T1 to T2 or from T1 to T3 by age at the T1 assessment.

The current study focused on describing age-related changes in telomere length over the first three years of life in a healthy community sample, including potential differences in rates of change in relation to sociodemographic characteristics. Exploring the biological mechanisms responsible for telomere attrition, particularly those outside of normative cellular replication processes associated with aging, was outside the scope of this study. A growing body of research suggests a number of possible mechanisms that may contribute to individual differences in rate of telomere attrition outside of aging (Bosquet Enlow et al., 2019). For example, some have hypothesized that exposure to elevated levels of glucocorticoids may affect oxidative balance via genomic or non-genomic mechanisms and that exposure to increased oxidative stress is a primary cause of telomere shortening (Angelier et al., 2018; Nelson et al., 2018). Glucocorticoids may also influence telomere length by modulating telomerase activity (Angelier et al., 2018; Choi et al., 2008). Other physiological systems involved in oxidative stress and/or telomere biology have been implicated, including immune activation and metabolic processes that result in increased production of reactive oxygen species (e.g., glucose and lipid mobilization) (Angelier et al., 2018). Notably, these biological mechanisms are associated with environmental and health conditions linked to differences in telomere attrition rate (e.g., heightened stress exposures, elevated BMI associated with increased cortisol output, oxidative stress, inflammation as well as with shorter telomere length/more rapid telomere attrition; increased intake of antioxidants, anti-inflammatory agents associated with attenuation of telomere shortening) (Bosquet Enlow, et al., 2018, 2019; Freitas-Simoes et al., 2016; Prasad et al., 2017). Moreover, emerging research suggests that these processes may be operating in very early life (e.g., Bosquet Enlow, 2018, 2019). Merging developmental research on normative telomere attrition with studies of risk and protective factors and underlying biological mechanisms that influence telomere attrition rate is critical for advancing our understanding of the value of telomere length as a predictor of disease risk and as a potential intervention target for maximizing health outcomes.

4.1. Conclusions.

Overall, the findings suggest that changes in relative telomere length are not uniform across the early years of life. Attrition appears most rapid during the first two years. Moreover, observed increases as well as decreases in relative telomere length from infancy to age 3 years appear common, although the reasons for observed telomere increases are not currently understood. These findings should be confirmed and extended to later ages in samples of varying sociodemographic characteristics in future developmental studies. The rapid expansion of methods for DNA extraction and telomere assaying in recent years has led to challenges in comparing findings across telomere studies. Consideration of methodological details, including methods utilized for extracting DNA and assaying telomere length (e.g., PCR, Southern blots of terminal restriction fragments [TRFs]) as well as tissue samples used for deriving telomere data (e.g., cord blood, heel or finger needle sticks/dried blood spots, venous blood, saliva, buccal cells), is important to confirm that any findings are not attributable to methodological differences and are reproducible across methods (Montpetit et al., 2014). Such information will help move the field forward by providing context for interpreting telomere findings. Moreover, establishing a developmental atlas for expected changes in telomere length across the lifespan may identify developmental periods of heightened vulnerability to deleterious environmental exposures as well as increased sensitivity to health-promoting interventions (e.g., prior to or during periods of more rapid telomere length attrition) (Montpetit et al., 2014). This information may be used to develop targeted, well-timed interventions that maximize long-term health benefits.

Highlights.

  • Lack of knowledge about expected telomere changes over first years of life

  • Gap hinders ability to understand telomere biology in relation to health risk

  • This longitudinal study examined telomere length changes in healthy children

  • Attrition not uniform in early life, with most rapid attrition over first two years

  • Increases as well as decreases in telomere length common in early life

Acknowledgements

We are grateful for the study families, whose generous donation of time made this project possible.

Role of Funding Sources

This work was supported by grants from the National Institutes of Health (MH078829) and the Tommy Fuss Center for Neuropsychiatric Disease Research and the Program for Behavioral Science, Department of Psychiatry, Boston Children’s Hospital. The content is solely the responsibility of the authors and does not represent the official views of any granting agency. Study data were collected and managed using REDCap electronic data capture tools hosted at Boston Children’s Hospital.

Footnotes

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Declarations of Interest: None.

Data Statement

Data will be made available upon request.

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