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. 2020 Oct 30;15(10):e0241363. doi: 10.1371/journal.pone.0241363

Physical and sexual abuse in childhood and adolescence and leukocyte telomere length: A pooled analysis of the study on psychosocial stress, spirituality, and health

Erica T Warner 1,2,*, Ying Zhang 1,3, Yue Gu 1, Tâmara P Taporoski 4,5, Alexandre Pereira 5, Immaculata DeVivo 6, Nicholas D Spence 1,7, Yvette Cozier 8, Julie R Palmer 8, Alka M Kanaya 9, Namratha R Kandula 10, Shelley A Cole 11, Shelley Tworoger 12, Alexandra Shields 1
Editor: Gabriele Saretzki13
PMCID: PMC7598522  PMID: 33125425

Abstract

Introduction

We examined whether abuse in childhood and/or adolescence was associated with shorter telomere length in a pooled analysis of 3,232 participants from five diverse cohorts. We also assessed whether religion or spirituality (R/S) could buffer deleterious effects of abuse.

Methods

Physical and sexual abuse in childhood (age <12) and adolescence (age 12–18) was assessed using the Revised Conflict Tactics Scale and questions from a 1995 Gallup survey. We measured relative leukocyte telomere lengths (RTL) using quantitative real time polymerase chain reaction. We used generalized estimating equations to assess associations of physical and sexual abuse with log-transformed RTL z-scores. Analyses were conducted in each cohort, overall, and stratified by extent of religiosity or spirituality and religious coping in adulthood. We pooled study‐specific estimates using random‐effects models and assessed between-study heterogeneity.

Results

Compared to no abuse, severe sexual abuse was associated with lower RTL z-scores, in childhood: -15.6%, 95% CI: -25.9, -4.9; p-trend = 0.04; p-heterogeneity = 0.58 and in adolescence: -16.5%, 95% CI: -28.1, -3.0; p-trend = 0.08; p-heterogeneity = 0.68. Sexual abuse experienced in both childhood and adolescence was associated with 11.3% lower RTL z-scores after adjustment for childhood and demographic covariates (95% CI: -20.5%, -2.0%; p-trend = 0.03; p-heterogeneity = 0.62). There was no evidence of effect modification by R/S. Physical abuse was not associated with telomere length.

Conclusions

Sexual abuse in childhood or adolescence was associated with a marker of accelerated biological aging, decreased telomere length. The lack of moderation by R/S may be due to inability to capture the appropriate time period for those beliefs and practices.

Introduction

There is increasing evidence that adverse experiences in childhood and/or adolescence, including physical, sexual, and emotional abuse and neglect [1] are associated with higher risk of multiple conditions in adulthood [2] including cardiovascular disease [3], altered stress and inflammatory responses [4–7], obesity [8], cancers [9–11] and multimorbidity [12]. Previous studies have demonstrated that exposures during these important developmental periods can have long lasting effects on physical and mental health [13]. The specific mechanisms through which these exposures affect later disease risk have not been fully explicated, but telomeres, which form protective caps at the ends of chromosomes, are a likely pathway [14].

Telomeres are repetitive DNA sequences at the ends of chromosomes that prevent physical deterioration of the chromosome during cell division [15]. Telomeres shorten with each cellular replication, are an important regulator of cellular senescence and apoptosis [16], and are considered an important biological clock, measuring aging at a cellular level [17, 18]. Factors such as older age, smoking, diet, and inflammation have been associated with shorter mean leukocyte telomere length [19–23]. Chronic stress is associated with telomere attrition [24], and telomeres are a hypothesized mechanism for how stressful exposures ‘get under the skin’ leading to chronic disease [25, 26].

A recent meta-analysis identified 41 studies (12 case-control, 25 cross-sectional, 4 prospective) published through July 2016 that investigated the impact of early life adversity on telomere length [27]. Early-life adversity was associated with shorter telomere length (Cohen’s d effect size = -0.35; 95% confidence interval (CI), -0.46 to -0.24; P <0.0001) with stronger associations observed in case-control and cross-sectional analyses as compared to prospective studies [28]. Importantly, this analysis did not assess moderating effects of resources for resilience, such as religion and spirituality (R/S). However, they demonstrated that the association between early-life adversity and telomere length decreased with increasing time since exposure, suggesting that telomere shortening may be reversible, or that the rate of adversity-induced shortening may be modiable [28]. We propose, and others have demonstrated [29], that R/S is an important coping resource for many individuals and may reduce the deleterious effects of early-life adversity on health [30].

In the present study, we assess the association between severity of physical and sexual abuse in childhood and/or adolescence and adult telomere length, using data from five cohorts participating in the Study on Psychosocial Stress, Spirituality, and Health (SSSH) representing multiple racial/ethnic groups. We hypothesized that abuse in childhood and/or adolescence would be associated with shorter telomeres and that religious and spiritual (R/S) practices or beliefs in adulthood would modify the association such that telomere attrition associated with abuse would be lessened among participants with greater R/S.

Material and methods

Study population

SSSH, established in 2016, is designed to examine the mechanisms through which psychosocial stress contributes to disease and whether these associations are moderated or mediated by religiosity and/or spirituality. It includes a sample of participants from five US-based (Black Women’s Health Study (BWHS) [31], Hispanic Community Health Study/Study of Latinos (HCHS/SOL) [32], Mediators of Atherosclerosis in South Asians Living in America (MASALA) [33], Nurses’ Health Study II (NHSII) [34], Strong Heart Study (SHS) [35]) and two Brazilian (Baependi Heart Study (BHS) [36] and Advento [37]) prospective cohort studies. SSSH participants completed a supplementary questionnaire (R/S survey) and cohorts provided blood samples and historic questionnaire data that was centrally harmonized at Massachusetts General Hospital. Each cohort study obtained written informed consent from their participants as well as institutional review board approval for cohort maintenance and participation in the SSSH. SSSH data coordination and analyses were approved by the Partners Human Research Committee. The current analysis includes 3,243 participants from five SSSH participating cohorts: BHS (N = 386), BWHS (N = 957), MASALA (n = 505), NHSII (N = 1,097), and SHS (N = 279).

Cohort descriptions

The Baependi Heart Study (BHS) is a family-based cohort in which residents from Baependi, a town in Minas Gerais–Brazil, were randomly selected from 11 out of 12 census districts, followed by all proband’s extended family members who were older than 18 years old and willing to take part. Details on recruitment have been thoroughly described previously [36]. All participants provided informed written consent. The present analysis includes 386 BHS subjects aged 18 to 88 years old that provided a blood sample in 2005 or 2006 and completed the SSSH R/S survey between 2016–2019. We excluded 48 participants with missing information on sexual abuse and 31 missing data on R/S measures.

BWHS began in 1995 when 59,000 African American women aged 21–69 years were enrolled through self-administered questionnaires mailed to subscribers of Essence magazine, members of Black women’s professional organizations as well as the friends and relatives of early respondents [31]. Information on demographics, lifestyle factors, and medical history is collected at baseline and updated biennially. Between 2016–2017, a random sample of 1000 BWHS participants who provided a blood sample between 2013–2015 were selected and invited to complete the R/S survey. Of 997 participants that successfully completed the telomere assay, we excluded participants with missing data on physical (N = 183) or sexual abuse (N = 32) leaving a final analytic sample of 814 BWHS participants for analyses of physical abuse and 965 for analyses of sexual abuse.

MASALA is a prospective, community-based cohort of 906 immigrant South Asian men and women, aged 40–84 years old, recruited from two clinical field centers (University of California, San Francisco and Northwestern University, Chicago) between October 2010 and March 2013 [33]. At their in-person baseline examination, MASALA participants completed questionnaires on sociodemographic, lifestyle and behavioral factors, and provided a fasting blood sample. Of the 906 participants, 696 provided consent for DNA extraction. Between September 2015 and March 2018, participants were invited back for a follow-up examination (Exam 2) where the SSSH R/S survey was administered. Three participants failed the telomere assay, and we excluded participants with missing information on physical abuse (n = 166) or R/S measures (n = 22), leaving a final sample of 505. Information on sexual abuse was not available in MASALA.

NHSII began in 1989 with 116,430 female registered nurses between the ages of 25 and 42. Participants are invited to complete biennial questionnaires to assess sociodemographic, lifestyle, behavioral, and medical information. In 2016, participants were invited to complete a web-based version of the R/S survey. Eligibility criteria included: an email address, provision of a blood sample in at least two cohort blood collections, age 45–75 at the time of their most recent blood draw (in 2010–2013), and completion of four questionnaires (the 2001 violence questionnaire, the 2008 trauma questionnaire, and the 2013 and 2015 main questionnaires), and no active participation in an ongoing ancillary study. Of the 4,251participants that completed the R/S survey, 1129 had their most recent blood sample assayed for relative telomere length. We excluded participants with missing information on physical or sexual abuse (n = 2) or R/S measures (n = 30) for a final sample size of 1097.

SHS is a population-based longitudinal cohort of 4,549 American Indians from 13 communities in Arizona, North and South Dakota, and Oklahoma which began in 1989 [35]. The SHS has completed three clinical examinations of the original Cohort (Phase I: 1989–1991; Phase II: 1993–1995; Phase III: 1998–1999, respectively). In Phase IV, an additional 18 to 25 extended families (a total of about 900 members at least 15 years of age) were recruited from each of the field centers from 2001–2003. In 2006–2009, Phase V a second exam of the family cohort and continued surveillance of the original cohort was completed. Our analysis includes participants in Phase IV or Phase V, who completed the R/S survey (N = 709) and had available telomere assay data (N = 365) at the time of this analysis. We excluded participants with missing family ID (n = 16), data on physical (N = 61) or sexual abuse (N = 62), or R/S (N = 9) leaving a final analytic sample of 279 SHS participants for analyses of physical abuse and 278 for analyses of sexual abuse.

Physical abuse

Physical abuse was assessed in childhood (age <12) or adolescence (age 12–18) using questions from the Revised Conflict Tactics Scale (CTS2) [38] which asked participants to report how often an adult caregiver pushed, grabbed, or shoved; kicked, bit, or punched; hit with something that hurt; choked or burned; or physically attacked the participant. We assigned respondents 1 point for each report of a physical abuse item that occurred >4 times, except for “choked or burned” or “seriously harmed someone I loved” where we assigned 1 point if they occurred 1 to 3 times and 2 points if they occurred 4 times. The resulting scores were categorized using the following groupings: no abuse (0), mild abuse (1), moderate abuse (2), severe abuse (≥3) [39]. We evaluated the impact of physical abuse in childhood or adolescence separately, and cross-classified yes/no indicators of physical abuse in childhood and adolescence to generate a four-level variable that indicated no physical abuse, physical abuse in childhood only, adolescence only, or childhood and adolescence.

Sexual abuse

In NHSII, sexual abuse in childhood (age <12) or adolescence (age 12–18) was assessed using questions from a 1995 Gallup Organization national telephone survey [40]. Participants were asked about forced sexual touching and forced sexual activity. Response categories were: “No, this never happened”, “Yes, this happened once”, or “Yes, this happened more than once”. We categorized participants as no abuse (never touched and never forced sex), moderate abuse (never touched or touched once and forced sex once; OR touched once or more and never forced sex), or severe abuse (forced sex more than once; OR touched more than once and forced sex once). In BHS, BWHS, and SHS, using questions from the CTS2, participants reported whether someone was “sexual with me against my will” or “exposed their genitals against my will”. Response categories were never, 1 to 3 times, 4 or more times. We categorized participants as no abuse (never exposed genitals and never sexually abused), moderate abuse (exposed genitals or sexually abused 1–3 times), or severe abuse (exposed genitals or sexually abused ≥4 times). We evaluated the impact of sexual abuse in childhood or adolescence separately, and cross-classified yes/no indicators of sexual abuse in childhood and adolescence to generate a four-level variable that indicated no sexual abuse, sexual abuse in childhood only, adolescence only, or childhood and adolescence. We also cross classified physical and sexual abuse to create a five-level variable that ranged from no physical or sexual abuse to severe physical and severe sexual abuse. These abuse definitions have been used in previous publications [39, 41, 42].

Religion and Spirituality (R/S)

Using data from the SSSH R/S survey, completed between 2016 and 2019, we classified participants into two groups, those who reported being “very religious or spiritual” compared to “moderately”, “slightly” or “not at all”. Positive and negative religious coping was assessed using items from the Religious Coping Scale [43]. Participants were asked, “When dealing with recent stressful situations…” to what extent did they cope using any of seven positive attributes (e.g., “I saw my situation as part of God’s plan”, “I sought God’s love or care,”) or six negative attributes (e.g., “I wondered what I did for God to punish me,” “I questioned [God]’s love or care for me”). Items were scored from 1 (not at all) to 4 (a great deal), summed, and averaged to create a single continuous variable. Participants in each cohort were stratified by positive and negative religious coping scores using study-specific medians.

Relative leukocyte telomere length (RTL)

Blood collection and processing methods for each cohort have been described previously [33, 44–47]. Genomic DNA was extracted from peripheral blood leukocytes using the QIAmp (Qiagen) 96-spin blood protocol (BWHS and NHSII), phenol-chloroform standard protocol (SHS and BHS), or sodium dodecylsulfate cell lysis followed by a salt precipitation (MASALA). RTL assays for BWHS, MASALA, NHSII, and SHS samples were performed in the laboratory of Dr. Immaculata De Vivo (Boston, MA, USA), while BHS samples were assayed at the Laboratory of Genetics and Molecular Cardiology, Heart Institute (Incor) (São Paulo, Brazil). Both laboratories assayed each sample in triplicate using quantitative real-time polymerase chain reaction (qPCR) according to Cawthon et al.’s protocol [48]. RTL was calculated as the exponentiated ratio of telomere repeat copy number to single-copy gene (36B4) copy number (T/S) corrected for a reference sample [48]. RTL is reported as the exponentiated T/S ratio. RTL correlates with absolute telomere lengths determined by Southern blot (r = 0.68; p<0.001).[48] The total intra assay coefficients of variation (CV) ranged from 0.27% (NHSII) to 0.35% (MASALA) and the inter assay CVs ranged from 0.33% (MASALA) to 0.77% (SHS). CVs for the exponentiated T:S ratio ranged from 4.7% (SHS) to 10.3% (NHS). We excluded extreme outliers (>3 interquartiles above the 75th percentile value and < 3 interquartiles below the 25th percentile). To minimize the impact of potential batch effects on RTL measurements across different studies, we calculated z scores of log-transformed RTL by standardizing the value in comparison with the mean within each individual study-batch [49].

Statistical analysis

We used linear regression with generalized estimating equations (GEE) and an independence working correlation, to generate robust standard errors which accounts for relatedness among family members (in BHS and SHS). We modeled the association between severity of physical, sexual, or joint physical and sexual abuse experienced in childhood and/or adolescence and RTL z-scores (hereafter called RTL). Since RTL z scores are challenging to interpret, we converted beta coefficients (β) for point estimates and 95% confidence intervals into unitless relative differences (% differences) using the formula (eβ−1)X 100 [50].

We present three models, first adjusted for age at blood draw only, next adding demographic and childhood factors (race/ethnicity, gender, parental educational attainment at participant’s birth or during childhood, parental home ownership at participant’s birth or during childhood, loss of parent before age 18, childhood financial hardship, and receipt of public assistance in childhood), and lastly, to examine to what extent any observed associations are driven by lifestyle and behavioral factors, we adjusted for potential adult mediators including body mass index (BMI), household income, smoking status, physical activity, alternative healthy eating index (AHEI), and depressive symptoms. Childhood financial hardship and receipt of public assistance were not available in BHS or NHSII and AHEI was not available in BHS.

We conducted cohort-specific analyses and pooled estimates using the random effects model by DerSimonian and Laird which takes into account within and between study variation (heterogeneity) [51]. Between-study heterogeneity was evaluated with Q statistics [52]. To investigate potential sources of heterogeneity, we conducted subgroup analyses restricted to females [53] and restricted NHSII to white participants due to known differences in telomere length between populations of European and African ancestry [54].

We assessed potential effect modification by R/S variables using a cross-product term of ordinal sexual abuse and dichotomous R/S variables. We present stratified results along with the Wald p-value for the interaction term. To estimate p for linear trend we modeled the ordinal abuse exposure variable as continuous. All P values are two sided, and an α level of 0.05 was used. We used SAS version 9.4 (SAS Institute, Cary, NC) and R version 3.6.0 (R Foundation for Statistical Computing, Vienna, Austria) for all statistical analyses.

Results

Participant characteristics by cohort and severity of physical and sexual abuse in childhood are displayed in Tables 1 and 2 respectively. Compared to no abuse, severe physical abuse in childhood was associated with childhood financial hardship, greater physical activity (except in SHS). Reported sexual abuse did not differ by most childhood characteristics. The prevalence of reported physical abuse in childhood and adolescence was 31.6% overall (range: 15.3% in BHS to 64.7% in BWHS) and was 35.0% for sexual abuse (range: 3.6% in BHS to 50.4% in BWHS).

Table 1. Participant characteristics according to experience of physical abuse in childhood (Age 0–11) and cohort.

  BHS (N = 386) BWHS (N = 814) MASALA (N = 505) NHSII (N = 1097) SHS (N = 279)
Variable None Mild Moderate Severe None Mild Moderate Severe None Mild Moderate Severe None Mild Moderate Severe None Mild Moderate Severe
n(%)1 329(85.2) 27(7.0) 12(3.1) 18(4.7) 331(40.7) 153(18.8) 83(10.2) 247(30.3) 437(86.5) 44(8.7) 18(3.6) 6(1.2) 923(84.1) 111(10.1) 41(3.7) 22(2.0) 176(63.1) 34(12.2) 13(4.7) 56(20.1)
Log-RTL (mean (SD))2 -0.04(1.0) 0.07(1.1) -0.48(0.9) -0.12(1.0) 0.0(1.0) -0.1(1.0) -0.1(1.1) 0.0(0.9) 0.0(1.0) 0.1(0.9) -0.1(1.0) -0.2(0.6) 0.0(1.0) 0.1(0.9) -0.5(1.1) 0.5(0.7) 0.1(1.0) 0.5(0.8) 0.1(0.4) 0.2(0.8)
Age at blood draw (yrs) (mean (SD)) 46.8(15.8) 47.1(16.0) 47.5(17.1) 48.9(15.7) 56.9(7.5) 55.7(7.4) 55.9(7.7) 55.9(7.6) 55.4(9.3) 53.5(8.4) 51.4(9.4) 53.3(11.8) 57.3(4.3) 57.2(4.4) 57.7(4.1) 58.4(4.8) 40.6(13.6) 41.1(12.7) 38.9(8.4) 38.7(11.2)
Positive coping, (mean (SD)) 3.9(0.3) 3.9(0.3) 3.8(0.4) 3.9(0.2) 3.2(0.7) 3.2(0.7) 3.1(0.8) 3.1(0.8) 2.6(0.9) 2.7(0.9) 2.2(0.9) 3.2(0.8) 2.8(0.9) 2.8(1.0) 2.7(1.0) 2.8(0.7) 2.8(0.8) 2.8(0.6) 2.4(0.5) 2.5(0.8)
Negative coping, (mean (SD)) 1.9(0.7) 2.1(0.8) 2(0.7) 2.1(0.8) 1.5(0.6) 1.4(0.6) 1.4(0.4) 1.4(0.5) 1.4(0.6) 1.4(0.6) 1.5(0.4) 1.4(0.3) 1.2(0.4) 1.3(0.4) 1.2(0.3) 1.3(0.4) 1.6(0.6) 1.8(0.5) 1.6(0.3) 1.6(0.6)
AHEI, (mean (SD))3 NA NA NA NA 41.8(10.7) 43.1(10.6) 42.3(11.4) 41.8(10.4) 70.3(6.5) 68.7(6.3) 69.7(5.3) 73.0(3.6) 66.3(12.8) 64.8(12.6) 66.3(12.1) 68.3(10.7) 44.5(8.0) 42.8(6.5) 46.4(4.6) 43.7(6.7)
Physical activity, (mean (SD))4 NA NA NA NA 9.3(6.7) 11.1(11.2) 10.3(7.7) 12.2(18.7) 22.9(21.2) 18.8(17.9) 25.9(17.0) 39.5(26.5) 25.8(26.8) 28.7(41.0) 31.7(25.2) 39.9(26.1) 5786(3509) 5938(4328) 6767(1960) 6728(2855)
Very religious or spiritual, n(%) NA NA NA NA 144(43) 71(47) 37(49) 116(46) 74(17) 11(30) 2(6) 2(29) 385(42) 49(45) 17(37) 6(24) 51(30) 10(25) 2(19) 16(27)
Female, n(%) 204(62) 22(81.5) 9(75) 11(61.1) 331(100) 153(100) 83(100) 247(100) 187(43) 11(26) 4(24) 2(34) 923(100) 111(100) 41(100) 22(100) 118(68) 25(73) 8(63) 24(36)
Race, n(%)                    
White 218(66.3) 13(48.1) 6(50) 11(61.1) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 875(99) 102(96) 39(98) 22(100) 4(2) 0(0) 0(0) 1(0)
Black 14(4.3) 3(11.1) 0(0) 2(11.1) 331(100) 153(100) 83(100) 247(100) 0(0) 0(0) 0(0) 0(0) 1(0) 3(3) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0)
Mulatto/Brown 84(25.6) 11(40.7) 6(50) 5(27.8) NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
American Indian 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 5(1) 1(1) 0(0) 0(0) 170(97) 34(100) 13(100) 55(100)
South Asian 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 437(100) 44(100) 18(100) 6(100) 3(0) 1(1) 1(2) 0(0) 0(0) 0(0) 0(0) 0(0)
Other 4(1.2) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0)
Parental home ownership, n(%) NA NA NA NA 182(58) 80(54) 51(66) 128(52) 272(63) 24(58) 89(38) 5(76) 469(54) 53(50) 17(47) 9(47) 106(60) 16(44) 8(55) 24(44)
Childhood financial hardship, n(%) NA NA NA NA 63(21) 41(26) 18(22) 97(42) 116(28) 9(32) 3(8) 1(28) NA NA NA NA 65(48) 13(44) 8(71) 21(45)
Received public assistance, (%) NA NA NA NA 67(21) 34(21) 26(30) 86(38) 7(2) 3(10) 1(4) 0(0) NA NA NA NA 38(38) 9(66) 2(72) 9(57)
Mother's education level, n(%)     NA NA NA NA            
<12 grade 303(92.1) 25(92.6) 11(91.7) 18(100) 97(30) 36(25) 18(27) 64(27) 173(39) 19(53) 7(39) 2(24) 70(8) 12(11) 7(14) 1(3) 47(27) 10(27) 4(39) 14(30)
high school degree or GED 20(6.1) 1(3.7) 1(8.3) 0(0) 111(33) 50(33) 29(31) 69(29) 127(30) 13(27) 1(4) 2(38) 485(56) 62(58) 20(57) 14(70) 68(43) 8(26) 5(30) 20(34)
some college or vocational school 5(1.5) 1(3.7) 0(0) 0(0) 65(21) 42(29) 23(32) 64(27) 41(10) 2(3) 1(8) 0(0) 207(24) 21(22) 10(24) 5(25) 24(14) 8(31) 2(14) 8(15)
college graduate or higher 1(0.3) 0(0) 0(0) 0(0) 55(18) 22(14) 12(10) 44(17) 91(22) 10(17) 9(50) 2(38) 104(12) 8(9) 3(5) 1(2) 23(15) 4(16) 2(17) 10(21)
Father's education level, n(%)                    
<12 grade 313(95.1) 27(100) 12(100) 17(94.4) 109(35) 54(40) 27(36) 77(34) 64(15) 6(13) 1(10) 1(4) 99(12) 14(13) 8(22) 2(5) 58(41) 9(22) 2(28) 17(28)
high school degree or GED 12(3.6) 0(0) 0(0) 0(0) 92(30) 44(31) 19(23) 69(29) 98(22) 9(18) 2(9) 1(14) 416(48) 50(48) 15(45) 11(53) 57(40) 14(56) 7(61) 19(45)
some college or vocational school 4(1.2) 0(0) 0(0) 1(5.6) 55(19) 24(16) 21(26) 45(20) 46(11) 6(12) 3(19) 1(24) 140(16) 19(19) 9(19) 1(3) 20(14) 2(5) 1(11) 8(14)
college graduate or higher 0(0) 0(0) 0(0) 0(0) 45(16) 17(13) 13(15) 41(16) 225(52) 23(56) 12(62) 3(58) 202(24) 21(20) 8(14) 6(38) 7(5) 5(16) 0(0) 4(13)
Loss of mother before age 18, n(%) 17(2%) 3(3%) 2(4%) 2(5%) 10(3) 5(3) 5(5) 14(5) 14(3) 2(8) 1(6) 1(20) 17(2) 3(3) 2(4) 2(5) 10(6) 2(3) 1(11) 5(7)
Loss of father before age 18, n(%) 65(7%) 8(9%) 4(10%) 4(11%) 48(15) 31(21) 11(13) 51(21) 31(7) 7(23) 0(0) 1(14) 65(7) 8(9) 4(10) 4(11) 31(18) 4(10) 4(36) 13(20)
Adult BMI, n, %                    
<25 137(41.6) 8(29.6) 5(41.7) 5(27.8) 93(28) 36(22) 21(31) 74(32) 197(45) 19(42) 7(39) 2(44) 456(53) 63(61) 17(45) 12(56) 40(22) 7(21) 5(26) 9(18)
25–29.9 101(30.7) 11(40.7) 4(33.3) 7(38.9) 99(30) 55(36) 21(29) 70(30) 183(42) 21(51) 10(52) 4(56) 247(28) 26(23) 8(21) 3(22) 53(29) 10(25) 3(27) 12(23)
≥ 30 69(21.0) 7(25.9) 1(8.3) 6(33.3) 137(41) 62(42) 41(39) 101(39) 54(12) 4(7) 1(9) 0(0) 165(19) 19(16) 13(34) 4(23) 82(48) 17(53) 5(47) 35(59)
Adult Smoking Status, n(%)                
Never smoker 218(66.3) 17(63.0) 7(58.3) 10(55.6) 246(76) 11(76) 61(74) 172(70) 364(83) 34(78) 14(81) 4(62) 647(70) 70(63) 22(55) 9(51) 65(35) 10(36) 4(34) 16(31)
Former smoker 79(24.0) 6(22.2) 2(16.7) 7(38.9) 61(17) 24(17) 20(23) 52(21) 61(14) 7(13) 3(14) 2(38) 224(25) 37(34) 17(35) 12(47) 40(24) 9(18) 1(3) 11(16)
Current smoker 25(7.6) 4(14.8) 3(25.0) 1(5.6) 24(7) 10(7) 2(2) 23(9) 12(3) 3(8) 1(4) 0(0) 52(6) 4(3) 2(11) 1(2) 71(41) 15(46) 8(64) 29(54)
Adult Depressive Symptoms, n(%) 3(0.9) 0(0) 0(0) 0(0) 60(18) 32(21) 16(20) 66(26) 40(9) 11(30) 2(11) 0(0) 49(6) 6(5) 2(4) 2(5) 27(30) 14(36) 1(35) 12(27)
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1 Percentage presented are of the total sample in each cohort. All other percentages in table are column percentages.

2 Log-transformed relative telomere length.

3 Alternative Healthy Eating Index.

4 Value is MET-hours per week in NHSII, BWHS, and MASALA, and steps per week in SHS.

NA—Not avaialble or Not applicable.

Table 2. Participant characteristics according to experience of sexual abuse in childhood (Age 0–11) and cohort.

  BHS (N = 352) BWHS(N = 965) NHSII (N = 1097) SHS(N = 278)
Variable None Moderate Severe None Moderate Severe None Moderate Severe None Moderate Severe
n(%)1 342(97.2) 9(2.6) 1(0.28) 626(64.9) 221(22.9) 118(12.2) 842(76.8) 128(11.7) 127(11.6) 242(87.1) 23(8.3) 13(4.7)
Log-RTL (mean (SD))2 -0.02(1.0) 0.28(0.7) -1.66(NA) 0.0(1.0) 0.1(1.0) -0.1(1.1) 0.0(1.0) 0.1(0.9) -0.2(1.0) 0.1(1.0) 0.3(0.6) 0.0(0.5)
Age at blood draw (yrs) (mean (SD)) 47.0(15.8) 46.1(10.7) 57(NA) 56.2(7.5) 56.5(7.5) 56.6(7.5) 57.4(4.4) 57.4(4.3) 57.1(4.0) 40.3(13.0) 38.0(11.6) 40.4(9.7)
Positive coping, (mean (SD)) 3.9(0.3) 3.9(0.1) 4 (NA) 3.1(0.8) 3.1(0.8) 3.2(0.7) 2.8(0.9) 2.7(1.0) 2.8(0.9) 2.7(0.8) 3.0(0.7) 2.5(0.6)
Negative coping, (mean (SD)) 1.9(0.7) 1.7(0.7) 1.7(NA) 1.5(0.5) 1.4(0.4) 1.4(0.4) 1.2(0.4) 1.2(0.3) 1.2(0.3) 1.7(0.6) 2.0(0.6) 1.6(0.3)
AHEI, (mean (SD))3 NA NA NA 42.1(10.4) 42.5(10.2) 43.5(10.8) 66.2(12.8) 66.6(12.6) 63.7(13.2) 44.2(8.1) 43.7(5.0) 48.3(6.1)
Physical activity, (mean (SD))4 NA NA NA 10.4(8.9) 11.5(10.8) 11.7(24.2) 26.9(30.7) 23.8(26.6) 27.3(24.6) 6012(3492) 6905(4208) 4846(1486)
Very religious or spiritual, n(%) NA NA NA 275(44) 98(44) 67(59) 366(43) 44(34) 47(38) 176(27) 15(43) 9(28)
Female, n(%) 214(62.6) 8(88.9) 1(100) 626(100) 221(100) 118(100) 842(100) 128(100) 127(100) 146(61) 18(81) 10(67)
Race, n(%)            
White 226(66.1) 6(66.7) 0(0) 0(0) 0(0) 0(0) 802(99) 117(97) 119(96) 5(2) 0(0) 0(0)
Black 17(5.0) 0(0) 0(0) 626(100) 221(100) 118(100) 0(0) 1(1) 3(2) 0(0) 0(0) 0(0)
Mulatto/Brown 94(27.5) 3(33.3) 1(100) NA NA NA NA NA NA NA NA NA
American Indian 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 4(1) 1(1) 1(2) 235(97) 23(100) 13(100)
South Asian 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 4(0) 1(1) 0(0) 0(0) 0(0) 0(0)
Other 4(1.2) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0)
Parental home ownership, n(%) NA NA NA 339(55) 106(50) 55(45) 431(54) 62(55) 55(45) 138(57) 10(42) 5(48)
Childhood financial hardship, n(%) NA NA NA 145(25) 80(37) 39(38) NA NA NA 85(46) 12(51) 9(80)
Received public assistance, (%) NA NA NA 141(24) 75(35) 40(33) NA NA NA 79(43) 14(65) 9(80)
Mother's education level, n(%)                
<12 grade 314(91.2) 8(88.9) 2(0) 158(26) 67(30) 28(22) 69(9) 9(8) 12(10) 64(28) 5(17) 5(46)
high school degree or GED 21(6.1) 1(11.1) 1(100) 207(33) 63(30) 37(29) 435(55) 70(60) 76(64) 90(41) 7(33) 4(31)
some college or vocational school 6(1.8) 0(0) 0(0) 143(23) 51(24) 30(30) 193(24) 26(22) 24(22) 38(17) 4(20) 0(0)
college graduate or higher 1(0.3) 0(0) 0(0) 108(17) 36(16) 20(18) 98(12) 13(10) 5(4) 30(13) 6(30) 3(23)
Father's education level, n(%)                
<12 grade 325(95.0) 9(100) 1(100) 207(36) 69(34) 37(34) 93(12) 14(12) 16(14) 76(38) 5(23) 4(34)
high school degree or GED 12(3.5) 0(0) 0(0) 170(29) 63(31) 33(29) 364(46) 64(55) 64(57) 81(41) 10(55) 6(58)
some college or vocational school 5(1.5) 0(0) 0(0) 114(20) 41(21) 21(21) 136(17) 16(14) 17(14) 28(14) 2(16) 1(8)
college graduate or higher 0(0) 0(0) 0(0) 90(19) 30(15) 15(15) 196(25) 23(18) 18(16) 14(7) 2(6) 0(0)
Loss of mother before age 18, n(%) NA NA NA 23(4) 14(6) 5(4) 15(2) 3(3) 6(4) 16(7) 1(2) 1(7)
Loss of father before age 18, n(%) NA NA NA 100(16) 47(22) 23(17) 61(7) 8(6) 12(11) 49(21) 2(5) 1(7)
Adult BMI, n, %                
<25 144(42.1) 1(11.1) 0(0) 177(28) 58(26) 23(21) 434(55) 63(50) 51(43) 55(23) 5(20) 1(8)
25–29.9 104(30.4) 3(33.3) 1(100) 184(30) 69(30) 34(32) 211(27) 34(28) 39(34) 70(29) 7(33) 1(7)
≥ 30 74(21.6) 5(55.5) 0(0) 262(42) 93(44) 60(47) 142(18) 29(22) 30(23) 116(48) 11(47) 11(86)
Adult Smoking Status, n(%)                
Never smoker 223(65.2) 5(55.6) 0(0) 472(75) 148(66) 83(72) 592(70) 79(59) 77(59) 88(36) 5(24) 2(15)
Former smoker 89(26.0) 2(22.2) 0(0) 108(17) 54(25) 27(21) 206(24) 42(36) 42(35) 52(22) 6(30) 2(11)
Current smoker 28(8.2) 2(22.2) 1(100) 46(7) 19(8) 8(6) 44(5) 7(5) 8(6) 102(42) 12(46) 9(74)
Adult Depressive Symptoms, n(%) 2(0.6) 0(0) 0(0) 112(18) 56(25) 36(28) 46(6) 7(6) 6(7) 55(27) 12(49) 6(53)
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1 Percentage presented are of the total sample in each cohort. All other percentages in table are column percentages.

2 Log-transformed relative telomere length.

3 Alternative Healthy Eating Index.

4 Value is MET-hours per week in NHSII, BWHS, and MASALA, and steps per week in SHS.

NA—Not avaialble or Not applicable.

In our pooled analysis, severe physical abuse was not associated with RTL, although we did observe an association for moderate physical abuse in childhood (Table 3). In models adjusted for childhood and demographic factors, we observed no association between severe physical abuse and telomere length in childhood (percent difference (PD): 2.0%, 95% CI: -9.5, 15.0; p-trend = 0.66) or adolescence (p-trend = 0.05). Compared to no abuse, moderate physical abuse in childhood was associated with 18.9% lower RTL (95% CI: -31.6%, -3.9%; p-heterogeneity = 0.89) after adjustment for childhood and demographic covariates (model 2). Results were unchanged by adjustment for adult factors.

Table 3. Severity of physical abuse in childhood or adolescence and adult telomere length.

  BHS N = 386 BWHS N = 814 MASALA N = 505 NHSII N = 1097 SHS N = 279 Pooled N = 3081 p-het
Childhood (Age <12) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI)
None Model 1ck1 329 ref 331 ref 437 ref 923 ref 176 ref 2196 ref  
Mild 27 11.6 (-24.5, 65.7)   153 -10.9 (-25.8, 7.0)   44 -4.7 (-29.7, 29.3)   111 6.7 (-10.7, 27.6)   34 35.8 (0.8, 82.9)   369 4.1 (-8.6, 18.5)   0.22
Moderate 12 -33.4 (-62.9, 19.4)   83 -13.0 (-32.9, 12.7)   18 -21.6 (-55.4, 38.0)   41 -26.8 (-47.2, 1.6)   13 -3.2 (-32.2, 38.2)   167 -17.3 (-29.5, -2.0)   0.71
Severe 18 -6.8 (-42.5, 51.3)   247 -0.8 (-15.2, 16.1)   6 -10.4 (-66.6, 140.1)   22 48.5 (-0.3, 121.2)   56 -7.8 (-25.7, 14.5)   349 -1.0 (-12.2, 15.0)   0.34
p-trend     0.53     0.86     0.45     0.73     0.57     0.58  
None Model 22 329 ref 331 ref 437 ref 923 ref 176 ref 2196 ref  
Mild 27 9.7 (-26.2, 63.2)   153 -9.6 (-24.6, 8.3)   44 -2.0 (-26.6, 30.7)   111 8.0 (-9.5, 28.9)   34 21.3 (-2.5, 50.8)   369 4.1 (-5.8, 16.2)   0.38
Moderate 12 -34.0 (-63.3, 18.5)   83 -14.8 (-34.3, 10.5)   18 -18.4 (-51.8, 37.9)   41 -25.6 (-46.7, 3.8)   13 -12.4 (-41.2, 30.5)   167 -18.9 (-31.6, -3.9)   0.89
Severe 18 -5.7 (-41.9, 53.0)   247 -1.4 (-15.9, 15.5)   6 -5.4 (-64.6, 152.9)   22 44.6 (-4.1, 118.0)   56 -1.7 (-22.5, 24.6)   349 2.0 (-9.5, 15.0)   0.54
p-trend     0.52     0.76     0.56     0.71     0.82     0.66  
None Model 33 329 ref 331 ref 437 ref   923 ref 176 ref 2196 ref  
Mild 27 7.9 (-27.3, 60.2)   153 -10.0 (-24.8, 7.8)   44 -0.9 (-26.4, 33.6)   111 7.6 (-9.6, 28.1)   34 21.1 (0.7, 45.5)   369 5.1 (-5.8, 18.5)   0.26
Moderate 12 -37.1 (-64.9, 12.9)   83 -13.7 (-33.5, 11.9)   18 -18.7 (-51.3, 35.6)   41 -25.5 (-45.8, 2.5)   13 -10.5 (-43.1, 40.7)   167 -18.9 (-31.6, -3.9)   0.82
Severe 18 3.4 (-36.7, 68.7)   247 -0.8 (-15.5, 16.4)   6 -0.5 (-65.0, 182.7)   22 39.6 (-6.7, 108.9)   56 -4.9 (-20.8, 14.2)   349 1.0 (-10.4, 12.7)   0.56
p-trend     0.66     0.82     0.62     0.80     0.61     0.63  
Adolescence (Age 12–18)   n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI)
None Model 11 347 ref 494 ref 466 ref 1037 ref 182 ref 2526 ref  
Mild 19 -10.7 (-43.5, 41.9)   147 0.3 (-15.5, 19.0)   30 -8.3 (-41.2, 43.1)   33 -23.0 (-46.9, 11.5)   41 3.5 (-13.7, 24.2)   270 -2.0 (-12.2, 9.4)   0.70
Moderate 11 -46.6 (-71.3, -2.0)   66 -10.9 (-31.2, 15.4)   3 25.8 (-16.0, 88.4)   13 -25.0 (-53.8, 21.6)   14 -10.9 (-40.9, 34.3)   107 -12.2 (-29.5, 9.4)   0.20
Severe 9 -12.5 (-55.1, 69.9)   107 -13.4 (-28.7, 5.1)   6 -35.8 (-75.9, 71.3)   14 29.5 (-20.1, 109.8)   42 -2.6 (-32.2, 40.1)   178 -8.6 (-21.3, 7.3)   0.58
p-trend     0.13     0.12     0.44     0.75     0.84     0.05  
None Model 22 347 ref 494 ref 466 ref 1037 ref 182 ref 2526 ref  
Mild 19 -10.0 (-42.9, 41.9)   147 0.0 (-15.7, 18.7)   30 3.4 (-33.8, 61.4)   33 -21.6 (-45.8, 13.4)   41 -0.6 (-17.5, 19.8)   270 -3.0 (-13.1, 8.3)   0.80
Moderate 11 -47.4 (-71.5, -2.8)   66 -10.0 (-30.7, 16.7)   3 27.6 (-18.6, 100.1)   13 -26.1 (-54.3, 19.5)   14 -21.4 (-52.6, 30.4)   107 -14.8 (-33.0, 8.3)   0.20
Severe 9 -10.7 (-54.1, 73.8)   107 -15.2 (-30.8, 3.8)   6 -27.6 (-72.0, 87.5)   14 23.8 (-24.7, 103.6)   42 0.1 (-22.8, 29.9)   178 -7.7 (-20.5, 7.3)   0.63
p-trend     0.14     0.10     0.76     0.66     0.79     0.05  
None Model 33 347 ref 494 ref 466 ref 1037 ref 182 ref 2526 ref  
Mild 19 -13.7 (-45.2, 35.9)   147 0.3 (-15.6, 19.2)   30 5.8 (-32.7, 66.4)   33 -22.6 (-45.8, 10.6)   41 -0.6 (-16.0, 17.7)   270 -2.0 (-12.2, 8.3)   0.68
Moderate 11 -48.9 (-72.4, -5.4)   66 -7.6 (-29.0, 20.4)   3 15.3 (-30.7, 91.9)   13 -31.6 (-58.2, 11.9)   14 -20.4 (-53.0, 34.7)   107 -17.3 (-34.3, 4.1)   0.26
Severe 9 -4.5 (-51.1, 86.6)   107 -15.1 (-30.9, 4.4)   6 -25.0 (-72.7, 105.8)   14 23.5 (-27.5, 110.2)   42 -2.0 (-23.6, 25.6)   178 -8.6 (-21.3, 6.2)   0.74
p-trend     0.15     0.14     0.81     0.56     0.67     0.12  
Childhood & Adolescence   n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI)
None Model 11 327     287 ref 427 ref   914 ref 151 ref 2106      
Childhood only 20 19.6 (-23.8, 87.8) 207 -6.8 (-22.1, 11.4)   39 -5.0 (-29.2, 27.5) 123 11.4 (-6.2, 32.2)   31 -11.5 (-30.3, 12.2)   420 0.0 (-9.5, 10.5)   0.48
Adolescence only 2 -69.3 (-92.0, 18.1) 44 -13.5 (-35.1, 15.2)   10 3.4 (-39.7, 77.2) 9 21.6 (-21.5, 88.3)   25 -27.3 (-42.5, -8.1)   90 -13.1 (-32.3, 10.5)   0.13
Both 37 -17.4 (-41.4, 16.4) 276 -8.9 (-22.5, 7.0)   29 -16.1 (-47.8, 34.9) 51 -17.4 (-38.7, 11.3)   72 6.6 (-17.4, 37.5)   465 -8.6 (-18.1, 2.0)   0.70
p-trend     0.25     0.25     0.48     0.56     0.93     0.13  
None Model 22 327     287 ref 427 ref   914 ref   151 ref   2106      
Childhood only 20 17.4 (-25.6, 85.5) 207 -7.4 (-22.2, 10.4)   39 -4.6 (-27.7, 26.0) 123 13.2 (-4.8, 34.6)   31 -15.4 (-37.0, 13.6)   420 0.0 (-9.5, 11.6)   0.40
Adolescence only 2 -67.9 (-91.7, 23.6) 44 -14.3 (-35.8, 14.4)   10 24.3 (-34.0, 134.1) 9 22.6 (-20.5, 89.1)   25 -31.0 (-45.1, -13.4)   90 -13.1 (-34.9, 16.2)   0.05
Both 37 -17.2 (-41.3, 16.6) 276 -9.5 (-23.1, 6.5)   29 -8.5 (-41.9, 44.1) 51 -17.8 (-38.9, 10.7)   72 5.0 (-17.6, 33.7)   465 -8.6 (-18.1, 2.0)   0.74
p-trend     0.25     0.23     0.81     0.58     0.89     0.14  
None Model 33 327     287 ref 427 ref   914 ref   151 ref   2106      
Childhood only 20 20.4 (-23.7, 90.2) 207 -6.6 (-21.7, 11.3)   39 -4.8 (-27.8, 25.7) 123 12.1 (-5.2, 32.6)   31 -17.5 (-36.1, 6.6)   420 -1.0 (-11.3, 11.6)   0.29
Adolescence only 2 -71.4 (-92.6, 9.3) 44 -11.4 (-33.9, 18.7)   10 20.7 (-35.6, 126.3) 9 11.3 (-31.0, 79.5)   25 -31.9 (-46.2, -13.9)   90 -14.8 (-35.6, 12.7)   0.09
Both 37 -18.0 (-42.0, 15.7) 276 -8.8 (-22.7, 7.6)   29 -5.8 (-40.7, 49.6) 51 -18.4 (-39.2, 9.5)   72 3.6 (-13.1, 23.5)   465 -6.8 (-15.6, 4.1)   0.60
p-trend     0.23     0.28     0.88     0.48     0.76     0.15  
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1. Model 1: Adjusted for age.

2. Model 2: Model 1 plus race/ethnicity, gender, mother's educational attainment, father's educational attainment, parental home ownership, loss of parent as a child, childhood financial hardship, childhood public assistance.

3. Model 3: Model 2 plus current BMI, household income, smoking status, physical activity, alternative healthy eating index, depressive symptoms.

Severe sexual abuse in childhood or adolescence was associated with decreased RTL in our pooled analysis, as was any sexual abuse experienced in childhood and adolescence (Table 4). Compared to no abuse, severe sexual abuse in childhood was associated with 13.9% lower RTL (95% CI: -23.7%, -2.0%; p-trend = 0.15; p-heterogeneity = 0.56) in age-adjusted models, and 15.6% lower after adjustment for childhood and demographic covariates (95% CI: -25.9%, -4.9%; p-trend = 0.04; p-heterogeneity = 0.58). Additional adjustment for adult covariates did not attenuate the association. Compared to no abuse, participants that reported severe sexual abuse in adolescence had 15.6% lower RTL (95% CI: -27.4%, -2.0%; p-trend = 0.18; p-heterogeneity = 0.80) in model 1, and 16.5% lower (95% CI: -28.1%, -3.0%; p-trend = 0.08; p-heterogeneity = 0.68) in model 2. Results were similar after accounting for adult factors in model 3. Sexual abuse experienced in both childhood and adolescence was associated with 11.3% lower RTL in model 2 (95% CI: -20.5%, -2.0%; p-trend = 0.03; p-heterogeneity = 0.62), and this association was maintained in model 3.

Table 4. Severity of sexual abuse in childhood or adolescence and adult telomere length.

    BHS N = 352 BWHS N = 965 NHSII N = 1097 SHS N = 278 Pooled N = 2678 p-het
Childhood (Age <12)   n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI)
None Model 11 342 Reference 626 Reference 842 Reference 242 Reference 2052 Reference  
Moderate 9 34.7 (-30.8, 162.2)   221 9.8 (-5.2, 27.1)   128 0.3 (-15.0, 18.2)   23 10.0 (-12.8, 38.8)   381 7.3 (-3.0, 18.5)   0.74
Severe 1 -75.8 (-96.6, 71.6)   118 -11.6 (-28.1, 8.8)   127 -16.6 (-30.4, -0.1)   13 -5.5 (-34.6, 36.6)   259 -13.9 (-23.7, -2.0)   0.56
p-trend     0.96     0.61     0.08     0.93     0.15  
None Model 22 342 Reference 626 Reference 842 Reference 242 Reference 2052 Reference  
Moderate 9 36.3 (-30.2, 166.4)   221 9.2 (-5.9, 26.7)   128 -0.1 (-15.4, 17.8)   23 -4.0 (-23.1, 19.9)   381 4.1 (-5.8, 15.0)   0.60
Severe 1 -74.8 (-96.4, 76.8)   118 -11.6 (-28.1, 8.7)   127 -17.4 (-31.1, -0.9)   13 -21.8 (-45.5, 12.2)   259 -15.6 (-25.9, -4.9)   0.58
p-trend     1.00     0.58     0.07     0.19     0.04  
None Model 33 342 Reference 626 Reference 842 Reference 242 Reference 2052 Reference  
Moderate 9 40.1 (-28.3, 173.7)   221 9.3 (-6.1, 27.1)   128 2.6 (-13.3, 21.4)   23 -6.6 (-24.5, 15.5)   381 4.1 (-5.8, 15.0)   0.49
Severe 1 -74.6 (-96.4, 80.2)   118 -9.7 (-26.4, 10.9)   127 -17.8 (-31.5, -1.4)   13 -22.6 (-44.5, 7.9)   259 -15.6 (-25.9, -4.9)   0.50
p-trend     0.95     0.71     0.07     0.13     0.04  
Adolescence (Age 12–18)   n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI)
None Model 11 341 Reference 633 Reference 855 Reference 241 Reference 2070 Reference  
Moderate 5 -68.4 (-86.9, -24.0)   275 -1.3 (-14.1, 13.4)   144 -2.2 (-17.1, 15.5)   31 0.3 (-20.7, 26.8)   455 -4.9 (-18.9, 11.6)   0.10
Severe 0     57 -22.0 (-39.6, 0.6)   98 -12.0 (-27.4, 6.6)   6 -16.6 (-60.7, 76.6)   161 -15.6 (-27.4, -2.0)   0.80
p-trend           0.17     0.23     0.75     0.18  
None Model 22 341 Reference 633 Reference 855 Reference 241 Reference 2070 Reference  
Moderate 5 -67.4 (-86.4, -21.7)   275 -1.8 (-14.9, 13.3)   144 -2.0 (-16.8, 15.5)   31 -12.8 (-32.3, 12.4)   455 -3.9 (-9.5, 3.0)   0.10
Severe 0 not estimable   57 -22.1 (-39.8, 0.8)   98 -12.5 (-27.9, 6.0)   6 -30.6 (-63.5, 32.1)   161 -16.5 (-28.1, -3.0)   0.68
p-trend           0.15     0.21     0.15     0.08  
None Model 33 341 Reference 633 Reference 855 Reference 241 Reference 2070 Reference  
Moderate 5 -69.1 (-87.1, -25.7)   275 -0.4 (-13.8, 15.2)   144 -0.3 (-15.4, 17.6)   31 -14.1 (-35.5, 14.6)   455 -3.9 (-10.4, 4.1)   0.06
Severe 0     57 -19.2 (-37.6, 4.5)   98 -14.1 (-29.6, 4.7)   6 -23.5 (-60.0, 46.4)   161 -16.5 (-28.1, -3.0)   0.90
p-trend           0.26     0.19     0.23     0.12  
Childhood & Adolescence   n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI)
None Model 11 326     479 Reference 709 Reference 227 Reference 1741      
Childhood only 7 42.2 (-32.6, 200.1) 154 7.2 (-10.9, 29.0)   146 0.4 (-16.1, 20.1)   14 -4.4 (-30.0, 30.6)   321 3.0 (-7.7, 16.2)   0.73
Adolescence only 3 -82.7 (-94.4, -46.4) 147 -3.2 (-19.5, 16.4)   133 4.5 (-13.4, 26.1)   15 -16.8 (-42.8, 21.0)   298 -13.1 (-34.3, 15.0)   0.02
Both 2 -22.9 (-80.6, 205.9) 185 -3.9 (-18.3, 13.1)   109 -17.9 (-30.2, -3.4)   22 7.8 (-19.2, 43.9)   318 -8.6 (-18.9, 3.0)   0.34
p-trend     0.12     0.59     0.17     0.97     0.15  
None Model 22 326     479 Reference 709 Reference   227 Reference   1741      
Childhood only 7 43.9 (-31.8, 203.7) 154 6.4 (-11.6, 28.0)   146 -0.3 (-16.7, 19.4)   14 -17.5 (-41.1, 15.6)   321 1.0 (-10.4, 13.9)   0.45
Adolescence only 3 -82.1 (-94.2, -45.0) 147 -4.1 (-20.6, 15.9)   133 4.4 (-13.4, 25.8)   15 -26.8 (-52.4, 12.4)   298 -15.6 (-37.5, 12.7)   0.01
Both 2 -19.2 (-79.5, 218.4) 185 -4.5 (-19.3, 12.9)   109 -18.2 (-30.4, -3.8)   22 -10.6 (-30.1, 14.5)   318 -11.3 (-20.5, -2.0)   0.62
p-trend     0.14     0.53     0.15     0.12     0.03  
None Model 33 326     479 Reference 709 Reference   227 Reference   1741      
Childhood only 7 53.0 (-28.1, 225.4) 154 6.8 (-11.2, 28.6)   146 2.2 (-14.5, 22.2)   14 -16.8 (-40.2, 15.8)   321 2.0 (-9.5, 15.0)   0.39
Adolescence only 3 -83.8 (-94.7, -50.0) 147 -2.1 (-19.1, 18.3)   133 6.0 (-12.0, 27.6)   15 -22.4 (-49.8, 19.9)   298 -14.8 (-37.5, 16.2)   0.01
Both 2 -19.4 (-79.6, 218.4) 185 -2.7 (-17.7, 15.2)   109 -18.4 (-30.9, -3.6)   22 -13.2 (-34.5, 14.9)   318 -11.3 (-20.5, -1.0)   0.53
p-trend     0.12     0.72     0.19     0.15     0.07  
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1. Model 1: Adjusted for age.

2. Model 2: Model 1 plus race/ethnicity, gender, mother's educational attainment, father's educational attainment, parental home ownership, loss of parent as a child, childhood financial hardship, childhood public assistance.

3. Model 3: Model 2 plus current BMI, household income, smoking status, physical activity, alternative healthy eating index, depressive symptoms.

Our cross-classified measure of physical and sexual abuse was not associated with telomere length in the pooled analysis (Table 5). Compared to no abuse, severe physical and sexual abuse in childhood (-1.0%, 95% CI: -28.1%, 36.3%; p-trend = 0.15; p-heterogeneity = 0.06) or adolescence (-4.9%, 95% CI: -47.8%, 73.3%; p-trend = 0.06; p-heterogeneity = 0.07) was not associated with RTL. We did not observe evidence that the impact of physical and sexual abuse on RTL differed by the extent of religiosity/spirituality or level of positive or negative religious coping in our pooled or cohort-specific analyses (Table 6). In the pooled analysis, all p-values for the interaction term were >0.20.

Table 5. Joint effects of physical and sexual abuse in childhood or adolescence on adult telomere length.

BHS N = 356 BWHS N = 787 NHSII N = 1097 SHS N = 278 Pooled N = 2198 p-het
Childhood (Age <12) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI)
None Model 11 295 Reference 291 Reference 733 Reference 167 Reference 1486 Reference
mild physical abuse 23 13.5 (-25.5, 74.2) 101 -20.1 (-35.4, -1.3) 72 0.3 (-19.2, 24.3) 26 56.1 (2.8, 137.0) 222 4.1 (-18.1, 33.6) 0.04
moderate physical or sexual abuse 21 -12.5 (-43.8, 37.2) 128 -5.8 (-23.8, 16.5) 152 -6.3 (-20.4, 10.4) 25 6.2 (-13.6, 30.6) 326 -3.0 (-13.1, 8.3) 0.72
severe physical or sexual abuse 17 -14.1 (-47.6, 40.9) 227 -3.6 (-18.5, 13.9) 131 -16.4 (-30.0, -0.2) 51 3.3 (-16.9, 28.4) 426 -6.8 (-16.5, 3.0) 0.47
severe physical and sexual abuse 0 40 -7.3 (-32.6, 27.7) 9 55.1 (-14.1, 180.2) 9 -20.7 (-49.6, 24.7) 58 -1.0 (-28.1, 36.3) 0.19
p-trend 0.53 0.76 0.14 0.96 0.25
None Model 22 295 Reference 291 Reference 733 Reference 167 Reference 1486 Reference
mild physical abuse 23 14.0 (-25.8, 75.1) 101 -18.8 (-34.1, 0.0) 72 0.7 (-18.8, 24.9) 26 42.9 (5.2, 94.2) 222 4.1 (-18.1, 33.6) 0.02
moderate physical or sexual abuse 21 -11.5 (-43.4, 38.5) 128 -7.2 (-25.0, 14.9) 152 -6.2 (-20.4, 10.5) 25 -8.6 (-27.7, 15.4) 326 -3.0 (-13.1, 8.3) 1.00
severe physical or sexual abuse 17 -12.5 (-46.7, 43.5) 227 -3.9 (-18.7, 13.6) 131 -17.3 (-30.7, -1.3) 51 10.9 (-10.9, 38.0) 426 -6.8 (-16.5, 3.0) 0.23
severe physical and sexual abuse 0 40 -8.4 (-34.4, 28.0) 9 51.6 (-19.0, 183.5) 9 -33.5 (-52.4, -7.0) 58 -1.0 (-28.1, 36.3) 0.06
p-trend 0.58 0.67 0.12 0.84 0.15
None Model 33 295 Reference 291 Reference 733 Reference 167 Reference 1486 Reference
mild physical abuse 23 14.1 (-25.8, 75.6) 101 -17.9 (-33.4, 1.3) 72 1.5 (-17.8, 25.4) 26 46.0 (11.8, 90.6) 222 7.3 (-16.5, 37.7) 0.01
moderate physical or sexual abuse 21 -11.7 (-43.8, 38.8) 128 -5.5 (-23.6, 17.0) 152 -4.1 (-18.5, 12.8) 25 -11.1 (-32.1, 16.6) 326 -5.8 (-15.6, 6.2) 0.98
severe physical or sexual abuse 17 -8.8 (-45.2, 51.7) 227 -3.4 (-18.5, 14.5) 131 -17.4 (-31.0, -1.1) 51 6.7 (-8.4, 24.2) 426 -4.9 (-15.6, 8.3) 0.20
severe physical and sexual abuse 0 40 -5.1 (-32.2, 32.8) 9 46.6 (-16.3, 156.7) 9 -32.0 (-51.8, -4.0) 58 -6.8 (-37.5, 37.7) 0.05
p-trend 0.67 0.78 0.14 0.65 0.27
Adolescence (Age 12–18) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI) n % Difference (95% CI)
None Model 11 306 Reference 424 Reference 823 Reference 173 Reference 1726 Reference
mild physical abuse 17 -12.3 (-45.7, 41.8) 84 -8.0 (-26.1, 14.6) 19 -0.8 (-40.1, 64.3) 32 6.8 (-14.8, 33.8) 152 -2.0 (-15.6, 12.7) 0.82
moderate physical or sexual abuse 15 -54.9 (-73.1, -24.3) 163 -6.2 (-21.3, 11.9) 147 -2.0 (-16.8, 15.4) 29 2.7 (-27.4, 45.4) 354 -11.3 (-28.8, 9.4) 0.04
severe physical or sexual abuse 9 -17.0 (-57.0, 60.3) 94 -6.5 (-23.5, 14.3) 104 -12.8 (-27.6, 5.1) 40 -2.4 (-30.4, 37.0) 247 -9.5 (-19.7, 3.0) 0.92
severe physical and sexual abuse 0 22 -31.2 (-56.0, 7.6) 4 83.3 (-2.5, 244.5) 4 -1.2 (-63.9, 170.4) 30 5.1 (-45.7, 105.4) 0.05
p-trend 0.02 0.14 0.35 0.97 0.09
None Model 22 306 Reference 424 Reference 823 Reference 173 Reference 1726 Reference
mild physical abuse 17 -10.8 (-44.7, 43.9) 84 -10.0 (-27.5, 11.7) 19 1.0 (-38.7, 66.7) 32 3.4 (-14.3, 24.7) 152 -3.0 (17.4, 10.5) 0.79
moderate physical or sexual abuse 15 -54.6 (-72.9, -23.9) 163 -6.6 (-22.0, 11.8) 147 -1.9 (-16.6, 15.4) 29 -5.9 (-35.5, 37.4) 354 15.0 (-29.5, 7.3) 0.05
severe physical or sexual abuse 9 -14.2 (-55.6, 65.7) 94 -7.4 (-24.8, 14.2) 104 -13.6 (-28.3, 4.3) 40 1.6 (-21.6, 31.5) 247 -8.6 (-18.9, 3.0) 0.80
severe physical and sexual abuse 0 22 -33.3 (-58.2, 6.4) 4 76.2 (-12.2, 253.9) 4 -20.2 (-64.4, 79.0) 30 -4.9 (-47.8, 73.3) 0.07
p-trend 0.03 0.12 0.32 0.84 0.06
None Model 33 306 Reference 424 Reference 823 Reference 173 Reference 1726 Reference
mild physical abuse 17 -12.3 (-45.6, 41.5) 84 -9.1 (-26.8, 12.9) 19 0.9 (-36.8, 61.1) 32 3.8 (-10.7, 20.6) 152 -1.0 (-11.3, 11.6) 0.73
moderate physical or sexual abuse 15 -56.1 (-73.9, -26.2) 163 -5.4 (-21.1, 13.4) 147 0.0 (-14.8, 17.5) 29 -7.4 (-35.1, 32.2) 354 -12.2 (-29.5, 9.4) 0.04
severe physical or sexual abuse 9 -15.1 (-56.6, 65.9) 94 -6.7 (-24.9, 15.8) 104 -13.8 (-29.1, 4.9) 40 -0.6 (-24.0, 30.0) 247 -8.6 (-19.7, 3.0) 0.88
severe physical and sexual abuse 0 22 -31.8 (-57.6, 9.7) 4 58.4 (-21.7, 220.5) 4 -11.8 (-55.7, 75.5) 30 -42.3 (-42.3, 55.3) 0.15
p-trend 0.02 0.17 0.35 0.77 0.08
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1. Model 1: Adjusted for age.

2. Model 2: Model 1 plus race/ethnicity, gender, mother's educational attainment, father's educational attainment, parental home ownership, loss of parent as a child, childhood financial hardship, childhood public assistance.

3. Model 3: Model 2 plus current BMI, household income, smoking status, physical activity, alternative healthy eating index, depressive symptoms.

Table 6. Associations of sexual Aabuse in childhood and adolescence and telomere length stratified by religion and spirituality factors.

    Extent of Religion/Spirituality Positive Religious Coping Negative Religious Coping
    Not, Slightly or Moderately Religious or Spiritual Very Religious or Spiritual   At or below median Above Median   At or below median Above Median  
Childhood (Age <12)   n % Difference (95% CI) n % Difference (95% CI) p n % Difference (95% CI) n % Difference (95% CI) p n % Difference (95% CI) n % Difference (95% CI) p
BHS None         116 210   0.81 157 Reference 166 Reference 0.57
  Moderate         4 3.0 (182.9, -62.5) 5 66.5 (301.5, -31.6)   8 35.0 (163.8, -31.6) 1 -36.2 (-91.9, 400)  
  Severe         0 Not estimated 1 -81.0 (35.0, -97.3)   1 -82.3 (15.0, -97.3) 0 Not estimated  
BWHS None 351 Reference 275 Reference 0.05 354 Reference 263   0.23 293 Reference 324 Reference 0.75
  Moderate 123 -7.4 (-25.6, 15.2) 98 23.5 (0.8, 51.3)   126 18.3 (-3.1, 44.4) 87 -5.4 (-25.3, 19.7)   107 13.4 (-10.2, 43.2) 106 6.0 (-13.4, 29.7)  
  Severe 51 -23.8 (-42.6, 1.2) 67 0.9 (-26.0, 37.6)   67 -5.6 (-28.8, 25.3) 50 -21.0 (-41.7, 7.0)   52 -9.8 (-32.7, 20.9) 65 -12.9 (-34.8, 16.5)  
NHSII None 476   366 Reference 0.58 397 Reference 375   0.16 433 Reference 350 Reference 0.90
  Moderate 84 -6.2 (-29.4, 24.4) 44 3.8 (-15.2, 27.0)   66 -13.7 (-31.3, 8.5) 52 15.0 (-11.5, 49.6)   62 -1.5 (-22.9, 25.8) 52 4.9 (-18.6, 35.1)  
  Severe 80 -7.1 (-32.8, 28.5) 47 -22.0 (-36.9, -3.7)   68 -27.8 (-43.2, -8.2) 45 -4.6 (-31.8, 33.4)   65 -23.2 (-40.3, -1.3) 49 -20.1 (-40.1, 6.7)  
SHS None 336 Reference 147 Reference 0.14 220 Reference 230   0.95 267 Reference 185 Reference 0.31
  Moderate 38 28.0 (-2.5, 68.1) 16 -16.5 (-35.9, 8.8)   21 19.7 (-9.3, 57.9) 30 -9.3 (-33.1, 22.9)   22 0.8 (-26.8, 38.9) 27 7.3 (-25.0, 53.7)  
  Severe 14 22.8 (-28.1, 109.9) 8 -43.2 (-60.0, -19.2)   10 -43.7 (-61.2, -18.3) 11 -12.3 (-36.3, 20.8)   13 -13.8 (-41.1, 26.1) 8 -41.3 (-68.6, 9.8)  
Pooled None 1163 Reference 788   0.93 1087 Reference 1078   0.94 1150 Reference 1025 Reference 0.53
  Moderate 245 3.0 (25.9, -16.5) 158 4.1 (27.1, -15.6)   217 6.2 (28.4, -11.3) 174 1.0 (17.4, -13.1)   199 6.2 (23.4, -8.6) 186 5.1 (22.1, -8.6)  
  Severe 145 -11.3 (11.6, -29.5) 122 -22.9 (2.0, -41.7)   145 -25.9 (-3.0, -43.4) 107 -14.8 (3.0, -28.8)   131 -18.1 (-1.0, -31.6) 122 -18.9 (-2.0, -33.6)  
Adolescence (Age 12–18) n % Difference (95% CI) n % Difference (95% CI) p n % Difference (95% CI) n % Difference (95% CI) p n % Difference (95% CI) n % Difference (95% CI) p
BHS None         116 Reference 209 Reference 0.14 160 Reference 162 Reference 0.11
  Moderate         1 -90.8 (-36.2, -98.7) 4 -56.8 (15.0, -83.8)   2 -30.2 (161.2, -81.4) 3 -79.4 (-34.3, -93.5)  
  Severe         0 Not estimated 0 Not estimated   0 Not estimated 0 Not estimated  
BWHS None 338 Reference 295 Reference 0.80 367 Reference 256 Reference 0.81 295 Reference 328 Reference 0.54
  Moderate 157 -2.2 (-21.0, 21.1) 118 0.1 (-17.5, 21.5)   149 -4.5 (-21.7, 16.4) 120 -2.1 (-20.7, 20.9)   128 8.6 (-11.8, 33.8) 141 -10.6 (-27.4, 10.2)  
  Severe 30 -25.4 (-50.2, 12.0) 27 -17.4 (-40.1, 13.9)   31 -19.4 (-41.7, 11.3) 24 -28.7 (-54.5, 11.7)   29 -27.3 (-49.2, 4.0) 26 -19.6 (-45.1, 17.8)  
NHSII None 483 Reference 372 Reference 0.01 406 Reference 379 Reference 0.61 446 Reference 351 Reference 0.61
  Moderate 90 11.3 (-12.7, 41.9) 54 -12.3 (-29.3, 8.8)   70 -1.4 (-21.9, 24.4) 60 -7.7 (-28.2, 18.7)   66 -6.2 (-26.2, 19.2) 61 -7.5 (-28.7, 20.0)  
  Severe 67 14.3 (-16.2, 55.8) 31 -24.8 (-40.6, -4.8)   55 -18.2 (-36.9, 6.0) 33 0.1 (-29.2, 41.6)   48 -17.1 (-38.9, 12.5) 39 -3.2 (-26.4, 27.4)  
SHS None 341 Reference 147 Reference 0.20 216 Reference 239 Reference 0.75 267 Reference 188 Reference 0.90
  Moderate 39 16.8 (-3.4, 41.1) 19 -12.5 (-31.0, 10.8)   29 2.8 (-19.0, 30.6) 25 3.9 (-31.8, 58.3)   24 5.9 (-18.3, 37.2) 30 20.0 (-12.5, 64.4)  
  Severe 8 0.4 (-51.1, 106.4) 5 -42.8 (-65.7, -4.3)   6 -39.3 (-68.5, 16.9) 7 -27.8 (-53.1, 11.1)   11 -20.9 (-47.3, 18.9) 2 -64.4 (-90.1, 27.8)  
Pooled None 1162 Reference 814 Reference 0.12 1105 Reference 1083 Reference 0.91 1168 Reference 1029 Reference 0.98
  Moderate 286 8.3 (23.4, -3.9) 191 -7.7 (4.1, -18.1)   249 -3.9 (17.4, -21.3) 209 -4.9 (10.5, -18.1)   220 3.0 (17.4, -10.4) 235 -9.5 (-32.3, 20.9)  
  Severe 105 -3.9 (28.4, -27.4) 63 -25.2 (-10.4, -37.5)   92 -20.5 (-3.9, -34.9) 64 -16.5 (5.1, -33.6)   88 -21.3 (-3.9, -35.6) 67 -13.9 (-34.9, 13.9)  
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Models are adjusted for: Age, race/ethnicity, gender, mother educational attainment, father educational attainment, parental home ownership, loss of parent as a child, childhood financial hardship, childhood public assistance.

p: p-value for interaction between ordinal abuse and R/S indicator variables.

Discussion

In this study of 3,232 participants from five diverse prospective cohorts in the US and Brazil, we found that severe sexual abuse in childhood and/or adolescence was associated with shorter telomeres. 28.7% of participants reported any physical abuse and 23.8% reported any sexual abuse in childhood or adolescence though there was variation in prevalence across cohorts. This was higher than national estimates from the 2011–2014 Behavioral Risk Factor Surveillance Survey where 17.9% of respondents reported a history of physical abuse (17.5% of women; 18.4% of men) and 11.6% reported a history of sexual abuse (16.3% of women; 6.7% of men) [55]. We hypothesized that R/S may be a resource for resiliency, but we did not observe evidence that the deleterious impact of sexual abuse was modified by positive or negative religious coping or the extent of religiosity or spirituality in adulthood. Our results demonstrate the long-lasting impact of early-life sexual abuse on health across racial/ethnic groups.

Sexual abuse was more strongly and consistently associated with telomere length than physical abuse. Two recent meta-analyses and one narrative review also concluded that the type of adverse experiences have different impacts on telomere length, though they did not demonstrate differences between sexual and physical abuse [27, 56, 57]. Li et al (2017) found that experiences of childhood separation were associated with shorter telomeres, but physical and sexual abuse and loss of a parent were not [56], while in Ridout et al. (2018), experiences of abuse and neglect were associated with shorter telomeres [27]. Most of the studies included in these reviews did not report estimates for physical and sexual abuse separately. Among those that did, Vincent et al. (2017) found no association between physical or sexual abuse and telomere length [58], while a previous NHSII study found that moderate physical abuse was associated with shorter telomeres, but observed no association with sexual abuse [49]. Our results may reflect severity. Most studies capturing multiple domains of childhood adversity generally assessed the extent of adversity by counting the number of different types of adverse experiences, with each type of adversity measured as present or absent. The previous NHSII study did investigate sexual abuse severity, but our definitions differed, such that some individuals classified as moderate sexual abuse in our study, would have been classified as severe abuse in that publication [49]. Overall, our results suggest that sexual abuse may have long-lasting implications for health.

With respect to abuse timing, adversity earlier in development has shown greater negative effects on telomere length than those occurring later [27]. However, our sexual abuse finding was of similar magnitude in childhood and adolescence. Previous findings of greater impact of abuse earlier in life may reflect a true impact of abuse experienced in a key developmental period [59, 60], or longer duration of abuse exposure. Abuse duration is not captured well by current measures [57]. We attempted to address this by cross-classifying abuse in childhood and adolescence, which showed, that among individuals that experienced physical or sexual abuse in childhood, around half were also abused in adolescence.

We did not observe evidence of effect modification by the extent of religiosity and spirituality or extent of positive and negative religious coping. This could be due to timing of R/S measurement, choice of R/S measures, and low statistical power. Our assessment of R/S occurred in midlife, and may have been influenced by experiences of abuse [61]. Several resources in childhood and adolescence have been associated with resilience including household stability, school engagement, and caregiver or family support [62–64]. Few resources in adulthood have been identified [65]. Our multivariable models demonstrate that adult factors did not meaningfully attenuate associations between sexual abuse, while accounting for childhood socioeconomic status modestly strengthened associations. Lastly, while our total sample size of 3,232 is large in comparison to many studies, we had relatively small numbers of exposed individuals in stratified analyses, limiting our power to detect modest associations.

Our study has several important limitations. DNA extraction methods differed between studies, which can influence RTL measurement [66, 67]. However, extraction methods were the same for all participants within a cohort, and we pooled cohort-specific estimates using meta-analysis, limiting the potential impact of this factor. RTL was assessed using a single measure in mid-life, preventing the estimation of associations between abuse in childhood or adolescence and telomere attrition rate. Additionally, our study evaluated associations with telomere length as a marker of accelerated aging, but other markers including DNA methylation, are important and their examination will enhance our understanding of the biological impact of abuse experienced in early-life [68]. Our group has previously shown that childhood abuse victimization was associated with hypermethlation in NR3C1 [39]. Ongoing analyses within the Study on Psychosocial Stress, Spirituality, and Health are examining the impact of abuse and other stressful life experiences on DNA methylation within candidate genes in the stress pathway (HSD11B1, HSD11B2, NR3C1, FKBP5) and in epigenome-wide association studies. Although we controlled for multiple potential confounders, the possibility of residual confounding remains. Abuse assessment differed between cohorts. We attempted to minimize this heterogeneity through data harmonization. Physical and sexual abuse are sensitive topics and participants’ willingness to disclose may differ by how the data are collected (in-person vs. online or paper-based questionnaire), cohort composition (family-based vs. unrelated individuals), and cultural beliefs. However, at least one prospective study has shown that bias is minimized in recall of traumatic life events [69], and the meta-analysis by Hanssen et al. (2017) did not observe heterogeneity between studies with retrospective recall of abuse vs. objective measures [70]. Yet, despite cohort differences in participant demographic characteristics, exposure assessment and sample processing, we observed consistent associations between severe sexual abuse and shortened telomeres.

In conclusion, our study suggests that sexual abuse in childhood or adolescence is associated with accelerated biological aging as evidenced by decreased telomere length. This association was observed across populations representing multiple racial/ethnic group.

Data Availability

The data from the Study on Psychosocial Stress, Spirituality, and Health (SSSH) are only available upon request due to consent restrictions on publicly sharing data imposed by the Partners Human Research Committee (the institutional review board of Mass General Brigham) to protect participants’ privacy and confidentiality. We encourage enquiry about data access; more information can be found at https://cgvh.harvard.edu/national-consortium-psychosocial-stress-spirituality-and-health or by contacting sssh@partners.org.

Funding Statement

This work was supported by The John Templeton Foundation https://www.templeton.org/ [59607 to A.E.S] and the National Institutes of Health https://www.nih.gov/ [K01CA188075 to E.T.W; U01CA164974, R01CA098663, and R01CA058420 to J.R.P (Black Women’s Health Study); 1R01HL093009, 2R01HL093009, R01HL120725, UL1RR024131, UL1TR001872, and P30DK098722 to A.M.K and N.R.K (MASALA); U01CA176726, R01CA163451, and R01CA67262 to Nurses’ Health Study II; 75N92019D00027, 75N92019D00028, 75N92019D00029, 75N92019D00030, R01HL109315, R01HL109301, R01HL109284, R01HL109282, R01HL109319, U01HL41642, U01HL41652, U01HL41654, U01HL65520, and U01HL65521 to Strong Heart Study. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Indian Health Service (IHS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Slopen N, Lewis TT, Gruenewald TL, Mujahid MS, Ryff CD, Albert MA, et al. Early Life Adversity and Inflammation in African Americans and Whites in the Midlife in the United States Survey. Psychosomatic medicine. 2010;72(7):694–701. 10.1097/PSY.0b013e3181e9c16f . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Wegman HL, Stetler C. A meta-analytic review of the effects of childhood abuse on medical outcomes in adulthood. Psychosom Med. 2009;71(8):805–12. Epub 2009/09/24. 10.1097/PSY.0b013e3181bb2b46 . [DOI] [PubMed] [Google Scholar]
  • 3.Batten SV, Aslan M, Maciejewski PK, Mazure CM. Childhood maltreatment as a risk factor for adult cardiovascular disease and depression. Journal of Clinical Psychiatry. 2004;65(2):249–54. 10.4088/jcp.v65n0217 . [DOI] [PubMed] [Google Scholar]
  • 4.Felitti MD, Vincent J, Anda MD, Robert F, Nordenberg MD, Williamson MS, et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: the Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine. 1998;14(4):245–58. 10.1016/s0749-3797(98)00017-8 . [DOI] [PubMed] [Google Scholar]
  • 5.Poulton R, Caspi A, Milne BJ, Thomson WM, Taylor A, Sears MR, et al. Association between children's experience of socioeconomic disadvantage and adult health: a life-course study. The Lancet. 2002;360(9346):1640–5. 10.1016/S0140-6736(02)11602-3 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Repetti RL, Taylor SE, Seeman TE. Risky families: family social environments and the mental and physical health of offspring. Psychological bulletin. 2002;128(2):330 . [PubMed] [Google Scholar]
  • 7.Danese A MTEHH, et al. Adverse childhood experiences and adult risk factors for age-related disease: Depression, inflammation, and clustering of metabolic risk markers. Archives of Pediatrics & Adolescent Medicine. 2009;163(12):1135–43. 10.1001/archpediatrics.2009.214 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Thomas C, Hyppönen E, Power C. Obesity and Type 2 Diabetes Risk in Midadult Life: The Role of Childhood Adversity. Pediatrics. 2008;121(5):e1240–e9. 10.1542/peds.2007-2403 . [DOI] [PubMed] [Google Scholar]
  • 9.Brown DW, Anda RF, Felitti VJ, Edwards VJ, Malarcher AM, Croft JB, et al. Adverse childhood experiences are associated with the risk of lung cancer: a prospective cohort study. BMC Public Health. 2010;10(1):20 10.1186/1471-2458-10-20 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Kelly-Irving M, Lepage B, Dedieu D, Lacey R, Cable N, Bartley M, et al. Childhood adversity as a risk for cancer: findings from the 1958 British birth cohort study. BMC public health. 2013;13(1):767–. 10.1186/1471-2458-13-767 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Brown MJ, Thacker LR, Cohen SA. Association between adverse childhood experiences and diagnosis of cancer. PloS one. 2013;8(6):e65524 10.1371/journal.pone.0065524 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Tomasdottir MO, Sigurdsson JA, Petursson H, Kirkengen AL, Krokstad S, McEwen B, et al. Self Reported Childhood Difficulties, Adult Multimorbidity and Allostatic Load. A Cross-Sectional Analysis of the Norwegian HUNT Study. PloS one. 2015;10(6):e0130591 10.1371/journal.pone.0130591 2062. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Heindel JJ, Balbus J, Birnbaum L, Brune-Drisse MN, Grandjean P, Gray K, et al. Developmental Origins of Health and Disease: Integrating Environmental Influences. Endocrinology. 2015;156(10):3416–21. Epub 2015/08/04. 10.1210/EN.2015-1394 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Frenck RW, Blackburn EH, Shannon KM. The rate of telomere sequence loss in human leukocytes varies with age. Proc Natl Acad Sci U S A. 1998;95(10):5607–10. 10.1073/pnas.95.10.5607 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Epel ES, Blackburn EH, Lin J, Dhabhar FS, Adler NE, Morrow JD, et al. Accelerated telomere shortening in response to life stress. Proc Natl Acad Sci U S A. 2004;101(49):17312–5. Epub 2004/12/01. 10.1073/pnas.0407162101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Blackburn EH, Epel ES, Lin J. Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection. Science. 2015;350(6265):1193–8. 10.1126/science.aab3389 . [DOI] [PubMed] [Google Scholar]
  • 17.Blasco MA. Telomere length, stem cells and aging. Nat Chem Biol. 2007;3(10):640–9. 10.1038/nchembio.2007.38 . [DOI] [PubMed] [Google Scholar]
  • 18.Shay JW. Role of Telomeres and Telomerase in Aging and Cancer. Cancer Discov. 2016;6(6):584–93. Epub 2016/03/30. 10.1158/2159-8290.CD-16-0062 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Valdes AM, Andrew T, Gardner JP, Kimura M, Oelsner E, Cherkas LF, et al. Obesity, cigarette smoking, and telomere length in women. Lancet. 2005;366(9486):662–4. 10.1016/S0140-6736(05)66630-5 . [DOI] [PubMed] [Google Scholar]
  • 20.Lin J, Epel E, Blackburn E. Telomeres and lifestyle factors: roles in cellular aging. Mutat Res. 2012;730(1–2):85–9. Epub 2011/08/22. 10.1016/j.mrfmmm.2011.08.003 . [DOI] [PubMed] [Google Scholar]
  • 21.Crous-Bou M, Fung TT, Prescott J, Julin B, Du M, Sun Q, et al. Mediterranean diet and telomere length in Nurses’ Health Study: population based cohort study. Bmj. 2014;349:g6674 10.1136/bmj.g6674 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Crous-Bou M, Molinuevo JL, Sala-Vila A. Plant-Rich Dietary Patterns, Plant Foods and Nutrients, and Telomere Length. Adv Nutr. 2019;10(Suppl_4):S296–S303. 10.1093/advances/nmz026 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Astuti Y, Wardhana A, Watkins J, Wulaningsih W, Network PR. Cigarette smoking and telomere length: A systematic review of 84 studies and meta-analysis. Environ Res. 2017;158:480–9. Epub 2017/07/10. 10.1016/j.envres.2017.06.038 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Rentscher KE, Carroll JE, Mitchell C. Psychosocial Stressors and Telomere Length: A Current Review of the Science. Annu Rev Public Health. 2020;41:223–45. Epub 2020/01/03. 10.1146/annurev-publhealth-040119-094239 . [DOI] [PubMed] [Google Scholar]
  • 25.Oliveira BS, Zunzunegui MV, Quinlan J, Fahmi H, Tu MT, Guerra RO. Systematic review of the association between chronic social stress and telomere length: A life course perspective. Ageing Res Rev. 2016;26:37–52. Epub 2015/12/28. 10.1016/j.arr.2015.12.006 . [DOI] [PubMed] [Google Scholar]
  • 26.Turner KJ, Vasu V, Griffin DK. Telomere Biology and Human Phenotype. Cells. 2019;8(1). Epub 2019/01/19. 10.3390/cells8010073 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Ridout KK, Levandowski M, Ridout SJ, Gantz L, Goonan K, Palermo D, et al. Early life adversity and telomere length: a meta-analysis. Mol Psychiatry. 2018;23(4):858–71. Epub 2017/03/21. 10.1038/mp.2017.26 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Epel E. How “reversible” is telomeric aging? Cancer Prevention Research. 2012;5(10):1163–8. 10.1158/1940-6207.CAPR-12-0370 [DOI] [PubMed] [Google Scholar]
  • 29.Ano GG, Vasconcelles EB. Religious coping and psychological adjustment to stress: A meta‐analysis. Journal of clinical psychology. 2005;61(4):461–80. 10.1002/jclp.20049 [DOI] [PubMed] [Google Scholar]
  • 30.Reinert KG, Campbell JC, Bandeen-Roche K, Lee JW, Szanton S. The Role of Religious Involvement in the Relationship Between Early Trauma and Health Outcomes Among Adult Survivors. J Child Adolesc Trauma. 2016;9:231–41. Epub 2015/11/23. 10.1007/s40653-015-0067-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Rosenberg L, Adams-Campbell L, Palmer JR. The Black Women's Health Study: a follow-up study for causes and preventions of illness. J Am Med Womens Assoc (1972). 1995;50(2):56–8. . [PubMed] [Google Scholar]
  • 32.Lavange LM, Kalsbeek WD, Sorlie PD, Aviles-Santa LM, Kaplan RC, Barnhart J, et al. Sample design and cohort selection in the Hispanic Community Health Study/Study of Latinos. Ann Epidemiol. 2010;20(8):642–9. Epub 2010/07/09. 10.1016/j.annepidem.2010.05.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Kanaya AM, Kandula N, Herrington D, Budoff MJ, Hulley S, Vittinghoff E, et al. Mediators of Atherosclerosis in South Asians Living in America (MASALA) study: objectives, methods, and cohort description. Clin Cardiol. 2013;36(12):713–20. Epub 2013/11/05. 10.1002/clc.22219 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Bao Y, Bertoia ML, Lenart EB, Stampfer MJ, Willett WC, Speizer FE, et al. Origin, Methods, and Evolution of the Three Nurses’ Health Studies. Am J Public Health. 1062016. p. 1573–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.North KE, Howard BV, Welty TK, Best LG, Lee ET, Yeh J, et al. Genetic and environmental contributions to cardiovascular disease risk in American Indians: the strong heart family study. American journal of epidemiology. 2003;157(4):303–14. 10.1093/aje/kwf208 [DOI] [PubMed] [Google Scholar]
  • 36.Egan KJ, von Schantz M, Negrão AB, Santos HC, Horimoto AR, Duarte NE, et al. Cohort profile: the Baependi Heart Study-a family-based, highly admixed cohort study in a rural Brazilian town. BMJ Open. 2016;6(10):e011598 Epub 2016/10/21. 10.1136/bmjopen-2016-011598 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Franco-de-Moraes AC, de Almeida-Pititto B, da Rocha Fernandes G, Gomes EP, da Costa Pereira A, Ferreira SRG. Worse inflammatory profile in omnivores than in vegetarians associates with the gut microbiota composition. Diabetol Metab Syndr. 2017;9:62 Epub 2017/08/15. 10.1186/s13098-017-0261-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Straus MA, Gelles RJ. Physical violence in American families: Risk factors and adaptations to violence in 8,145 families. Family Relations. 1990;39:349–53. [Google Scholar]
  • 39.Shields AE, Wise LA, Ruiz-Narvaez EA, Seddighzadeh B, Byun HM, Cozier YC, et al. Childhood abuse, promoter methylation of leukocyte NR3C1 and the potential modifying effect of emotional support. Epigenomics. 2016;8(11):1507–17. Epub 2016/09/13. 10.2217/epi-2016-0074 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Moore DW. Disciplining children in America: A Gallup poll report. Princeton, NJ: The Gallup Organization; 1995. [Google Scholar]
  • 41.Wise LA, Palmer JR, Rothman EF, Rosenberg L. Childhood abuse and early menarche: findings from the black women's health study. American Journal of Public Health. 2009;99(S2):S460–S6. 10.2105/AJPH.2008.149005 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Wise LA, Palmer JR, Rosenberg L. Lifetime abuse victimization and risk of uterine leiomyomata in black women. Am J Obstet Gynecol. 2013;208(4):272e1-.e13. Epub 2013/01/04. 10.1016/j.ajog.2012.12.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Pargament K, Feuille M, Burdzy D. The Brief RCOPE: Current psychometric status of a short measure of religious coping. Religions. 2011;2(1):51–76. [Google Scholar]
  • 44.Lu D, Palmer JR, Rosenberg L, Shields AE, Orr EH, DeVivo I, et al. Perceived racism in relation to telomere length among African American women in the Black Women's Health Study. Ann Epidemiol. 2019;36:33–9. Epub 2019/06/21. 10.1016/j.annepidem.2019.06.003 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Zhao J, Zhu Y, Lin J, Matsuguchi T, Blackburn E, Zhang Y, et al. Short leukocyte telomere length predicts risk of diabetes in american indians: the strong heart family study. Diabetes. 2014;63(1):354–62. Epub 2013/08/15. 10.2337/db13-0744 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Huang T, Trudel-Fitzgerald C, Poole EM, Sawyer S, Kubzansky LD, Hankinson SE, et al. The Mind-Body Study: study design and reproducibility and interrelationships of psychosocial factors in the Nurses' Health Study II. Cancer Causes Control. 2019;30(7):779–90. Epub 2019/05/02. 10.1007/s10552-019-01176-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.de Oliveira CM, Pereira AC, de Andrade M, Soler JM, Krieger JE. Heritability of cardiovascular risk factors in a Brazilian population: Baependi Heart Study. BMC Med Genet. 2008;9:32 Epub 2008/04/22. 10.1186/1471-2350-9-32 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Cawthon RM. Telomere measurement by quantitative PCR. Nucleic Acids Res. 2002;30(10):e47 10.1093/nar/30.10.e47 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Mason SM, Prescott J, Tworoger SS, De Vivo I, Rich-Edwards JW. Childhood Physical and Sexual Abuse History and Leukocyte Telomere Length among Women in Middle Adulthood. PLoS One. 2015;10(6):e0124493 Epub 2015/06/08. 10.1371/journal.pone.0124493 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Sun Q, Shi L, Prescott J, Chiuve SE, Hu FB, De Vivo I, et al. Healthy lifestyle and leukocyte telomere length in U.S. women. PLoS One. 2012;7(5):e38374 Epub 2012/05/31. 10.1371/journal.pone.0038374 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.DerSimonian R, Laird N. Meta-analysis in clinical trials. Controlled clinical trials. 1986;7(3):177–88. 10.1016/0197-2456(86)90046-2 [DOI] [PubMed] [Google Scholar]
  • 52.Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58. 10.1002/sim.1186 . [DOI] [PubMed] [Google Scholar]
  • 53.Gardner M, Bann D, Wiley L, Cooper R, Hardy R, Nitsch D, et al. Gender and telomere length: systematic review and meta-analysis. Exp Gerontol. 2014;51:15–27. Epub 2013/12/21. 10.1016/j.exger.2013.12.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Hansen ME, Hunt SC, Stone RC, Horvath K, Herbig U, Ranciaro A, et al. Shorter telomere length in Europeans than in Africans due to polygenetic adaptation. Hum Mol Genet. 2016;25(11):2324–30. Epub 2016/03/02. 10.1093/hmg/ddw070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Merrick MT, Ford DC, Ports KA, Guinn AS. Prevalence of Adverse Childhood Experiences From the 2011–2014 Behavioral Risk Factor Surveillance System in 23 States. JAMA Pediatr. 2018;172(11):1038–44. 10.1001/jamapediatrics.2018.2537 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Li Z, He Y, Wang D, Tang J, Chen X. Association between childhood trauma and accelerated telomere erosion in adulthood: A meta-analytic study. J Psychiatr Res. 2017;93:64–71. Epub 2017/06/03. 10.1016/j.jpsychires.2017.06.002 . [DOI] [PubMed] [Google Scholar]
  • 57.Bürgin D, O'Donovan A, d'Huart D, di Gallo A, Eckert A, Fegert J, et al. Adverse Childhood Experiences and Telomere Length a Look Into the Heterogeneity of Findings-A Narrative Review. Front Neurosci. 2019;13:490 Epub 2019/05/22. 10.3389/fnins.2019.00490 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Vincent J, Hovatta I, Frissa S, Goodwin L, Hotopf M, Hatch SL, et al. Assessing the contributions of childhood maltreatment subtypes and depression case-control status on telomere length reveals a specific role of physical neglect. J Affect Disord. 2017;213:16–22. Epub 2017/02/03. 10.1016/j.jad.2017.01.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Shonkoff JP, Garner AS, Health CoPAoCaF, Committee on Early Childhood Ao, and Dependent Care, Pediatrics SoDaB. The lifelong effects of early childhood adversity and toxic stress. Pediatrics. 2012;129(1):e232–46. Epub 2011/12/26. 10.1542/peds.2011-2663 . [DOI] [PubMed] [Google Scholar]
  • 60.Shonkoff JP. Capitalizing on Advances in Science to Reduce the Health Consequences of Early Childhood Adversity. JAMA Pediatr. 2016;170(10):1003–7. 10.1001/jamapediatrics.2016.1559 . [DOI] [PubMed] [Google Scholar]
  • 61.Walker DF, Reid HW, O'Neill T, Brown L. Changes in personal religion/spirituality during and after childhood abuse: A review and synthesis. Psychological Trauma: Theory, Research, Practice, and Policy. 2009;1(2):130. [Google Scholar]
  • 62.Afifi TO, Macmillan HL. Resilience following child maltreatment: a review of protective factors. Can J Psychiatry. 2011;56(5):266–72. 10.1177/070674371105600505 . [DOI] [PubMed] [Google Scholar]
  • 63.Meng X, Fleury MJ, Xiang YT, Li M, D'Arcy C. Resilience and protective factors among people with a history of child maltreatment: a systematic review. Soc Psychiatry Psychiatr Epidemiol. 2018;53(5):453–75. Epub 2018/01/18. 10.1007/s00127-018-1485-2 . [DOI] [PubMed] [Google Scholar]
  • 64.Topitzes J, Mersky JP, Dezen KA, Reynolds AJ. Adult Resilience among Maltreated Children: A Prospective Investigation of Main Effect and Mediating Models. Child Youth Serv Rev. 2013;35(6):937–49. 10.1016/j.childyouth.2013.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Pitzer LM, Fingerman KL. Psychosocial resources and associations between childhood physical abuse and adult well-being. J Gerontol B Psychol Sci Soc Sci. 2010;65(4):425–33. Epub 2010/04/28. 10.1093/geronb/gbq031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Raschenberger J, Lamina C, Haun M, Kollerits B, Coassin S, Boes E, et al. Influence of DNA extraction methods on relative telomere length measurements and its impact on epidemiological studies. Sci Rep. 2016;6:25398 Epub 2016/05/03. 10.1038/srep25398 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Tolios A, Teupser D, Holdt LM. Preanalytical Conditions and DNA Isolation Methods Affect Telomere Length Quantification in Whole Blood. PLoS One. 2015;10(12):e0143889 Epub 2015/12/04. 10.1371/journal.pone.0143889 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Lang J, McKie J, Smith H, McLaughlin A, Gillberg C, Shiels PG, et al. Adverse childhood experiences, epigenetics and telomere length variation in childhood and beyond: a systematic review of the literature. Eur Child Adolesc Psychiatry. 2020;29(10):1329–38. Epub 2019/04/09. 10.1007/s00787-019-01329-1 PubMed Central PMCID: PMC7501093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Lalande K, Bonanno G. Retrospective memory bias for the frequency of potentially traumatic events: A prospective study. Psychological Trauma: Theory, Research, Practice, and Policy. 2011;3(2):165–70. http://dx.doi.org.ezp-prod1.hul.harvard.edu/10.1037/a0020847. 2011-01997-001. [Google Scholar]
  • 70.Hanssen LM, Schutte NS, Malouff JM, Epel ES. The Relationship Between Childhood Psychosocial Stressor Level and Telomere Length: A Meta-Analysis. Health Psychol Res. 2017;5(1):6378 Epub 2017/05/16. 10.4081/hpr.2017.6378 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Gabriele Saretzki

22 Jun 2020

PONE-D-20-13222

Physical and sexual abuse in childhood and adolescence and leukocyte telomere length: A pooled analysis of the study on psychosocial stress, spirituality, and health

PLOS ONE

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Reviewer #1: Partly

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

Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: This is an important study and of much merit.

I do have some concerns about the data analyses that should be addressed.

1. Referencing is not always sufficiently contemporaneous for telomere biology/ageing. Citing reviews from 2007 for this topic is outdated. Please use more recent referencing.

2. Assays of <20% for biomarker CVs are simply not acceptable in the field for data to be considered to be robust. Typical inter and intra assay CVs are below 5% and in my experience typically below 3%. This needs addressed and discussed.

3. Discussion of whether sample collection was equivalent ( e.g. PBLs versus PBMC etc) shoudl be included.

4. Discussion of relative socio-economic position , living density , geo-physical environment and crucially diet and inflammatory statusat blood draw are crucial to providing proper context to assess just how robust data interpretations are.

5. Discussion of epigentic influences shoudl also be included. The authors are encouraged to see Laing et al Eur Child Adolesc Psychiatry. 2019 Apr 9. doi: 10.1007/s00787-019-01329-1.

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PLoS One. 2020 Oct 30;15(10):e0241363. doi: 10.1371/journal.pone.0241363.r002

Author response to Decision Letter 0

9 Oct 2020

PONE-D-20-13222: Physical and sexual abuse in childhood and adolescence and leukocyte telomere length: A pooled analysis of the study on psychosocial stress, spirituality, and health

We appreciate the insightful feedback on the manuscript and for the opportunity to revise our manuscript to address it.

Editorial

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

a. We updated author affiliations and removed key messages to meet format guidelines.

2. Please provide additional details regarding participant consent. In the Methods section, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

a. On page 4 we now state: “Each cohort study obtained written informed consent from their participants as well as institutional review board approval for cohort maintenance and participation in the SSSH.”

Reviewer 1

1. Referencing is not always sufficiently contemporaneous for telomere biology/ageing. Citing reviews from 2007 for this topic is outdated. Please use more recent referencing.

a. Thank you for your feedback. We previously chose to cite seminal papers that established key concepts related to telomere biology and aging. We have updated the literature review to include more recent references.

2. Assays of <20% for biomarker CVs are simply not acceptable in the field for data to be considered to be robust. Typical inter and intra assay CVs are below 5% and in my experience typically below 3%. This needs addressed and discussed.

a. The CV’s presented in the manuscript we were for the exponentiated T:S ratio. These CV’s tend to be higher than raw CVs because they represent the ratio of a ratio. To address this concern, the laboratory calculated the inter and intra assay CVs and we now present those along with the CV for the exponentiated T:S ratio in the text. The inter and intra assay CV’s are all below 1.0%. The revised text, presented on page 7, is shown below.

i. The total intra assay coefficients of variation (CV) ranged from 0.27% (NHSII) to 0.3% (MASALA) and the inter assay CVs ranged from 0.33% (MASALA) to 0.77% (SHS). CVs for the exponentiated T:S ratio ranged from 4.7% (SHS) to 10.3% (NHS).

3. Discussion of whether sample collection was equivalent (e.g. PBLs versus Peripheral blood mononuclear cells etc) should be included.

a. We describe the processing and DNA extraction procedures in the Relative Telomere Length section of the methods on page 8 and 9: “Genomic DNA was extracted from peripheral blood leukocytes using the QIAmp (Qiagen) 96-spin blood protocol (BWHS and NHSII), phenol-chloroform standard protocol (SHS and BHS), or sodium dodecylsulfate cell lysis followed by a salt precipitation (MASALA).”

b. According to Tolios et al. (doi.org/10.1371/journal.pone.0143889) DNA extraction methods, but not preanalytic storage conditions and other sample processing methods affect measured telomere length. We acknowledge this in the limitation section on page 13. We state: “DNA extraction methods differed between studies, which can influence RTL measurement. However, extraction methods were the same for all participants within a cohort, and we pooled cohort-specific estimates using meta-analysis, limiting the potential impact of this factor on our results.”

4. Discussion of relative socio-economic position, living density, geo-physical environment and crucially diet and inflammatory status at blood draw are crucial to providing proper context to assess just how robust data interpretations are.

a. Data on characteristics of the participants’ geo-physical environment or inflammatory status was not available in the Study on Psychosocial Stress, Spirituality, and Health. However, adult characteristics associated with socioeconomic position and inflammation including, body mass index (BMI), household income, smoking status, physical activity, alternative healthy eating index (AHEI), and depressive symptoms are included in Table 1a and 1b, and we adjusted for them in our model 3. From a causal inference perspective, these factors cannot confound the association between abuse in childhood and adolescence and telomere length in adulthood because they occur after the exposure. We position them as potential mediators of the association, which could potentially operate differently across cohorts. However, adjustment for these current characteristics had relatively little impact on the association between abuse in childhood and adolescence and telomere length. Additionally, while there is significant variation between cohorts in these adult characteristics, we observed little evidence of heterogeneity in observed associations across cohorts suggesting that these factors were not effect modifiers.

b. To provide more context for the studies, we moved text that describes the population of each study from supplemental table 1 to the methods section of the manuscript on pages 5-7. We have added text to the discussion on page 22 discuss the different populations data and the impact these differences might be expected to have on our results. We conclude that the consistency of association is evidence of the robustness of our findings. We state: “However, despite cohort differences in participant demographic characteristics, exposure assessment and sample processing, we observed consistent associations between severe sexual abuse and shortened telomeres.”

5. Discussion of epigenetic influences should also be included. The authors are encouraged to see Laing et al Eur Child Adolesc Psychiatry. 2019 Apr 9. doi: 10.1007/s00787-019-01329-1.

a. We have added to the study limitations on p.22 the following text: “Additionally, our study evaluated associations with telomere length as a marker of accelerated aging, but other markers including DNA methylation, are important and their examination will enhance our understanding of the biological impact of abuse experienced in early-life.68 Our group has previously shown that childhood abuse victimization was associated with hypermethlation in NR3C1.39 Ongoing analyses within the Study on Psychosocial Stress, Spirituality, and Health are examining the impact of abuse and other stressful life experiences on DNA methylation within candidate genes in the stress pathway (HSD11B1, HSD11B2, NR3C1, FKBP5) and in epigenome-wide association studies.”

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Decision Letter 1

Gabriele Saretzki

14 Oct 2020

Physical and sexual abuse in childhood and adolescence and leukocyte telomere length: A pooled analysis of the study on psychosocial stress, spirituality, and health

PONE-D-20-13222R1

Dear Dr. Warner,

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Acceptance letter

Gabriele Saretzki

20 Oct 2020

PONE-D-20-13222R1

Physical and sexual abuse in childhood and adolescence and leukocyte telomere length: A pooled analysis of the study on psychosocial stress, spirituality, and health

Dear Dr. Warner:

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    Data Availability Statement

    The data from the Study on Psychosocial Stress, Spirituality, and Health (SSSH) are only available upon request due to consent restrictions on publicly sharing data imposed by the Partners Human Research Committee (the institutional review board of Mass General Brigham) to protect participants’ privacy and confidentiality. We encourage enquiry about data access; more information can be found at https://cgvh.harvard.edu/national-consortium-psychosocial-stress-spirituality-and-health or by contacting sssh@partners.org.

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