(2024) 22:359
Wang et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03578-7

BMC Medicine

RESEARCH ARTICLE

Open Access

The association of psychologicaland trauma‑related factors with biologicaland facial aging acceleration: evidence
from the UK Biobank
Junren Wang1,2, Xin Han1,2, Yao Yang1,2, Yu Zeng1,2, Yuanyuan Qu1,2, Huazhen Yang1,2, Jie Song1,2,3,
Changjian Qiu4* and Huan Song1,2,5*   

Abstract
Background Psychological and trauma-related factors are associated with many diseases and mortality. However,a comprehensive assessment of the association between psycho-trauma exposures and aging acceleration is currently lacking.
Methods Using data from 332,359 UK Biobank participants, we calculated biological aging acceleration, indexedby the presence of leukocyte telomere length (LTL) deviation (i.e., the difference between genetically determined
and observed LTL > 0). The acceleration of facial aging (i.e., looking older than the chronological age) was assessedusing a self-report question. Then, we estimated the associations of each psycho-trauma factor with biologicaland facial aging acceleration, using logistic regression models adjusted for multiple important covariates. Furthermore, restricted to 99,180 participants with complete psychological and trauma-related data, we identified clusters
of individuals with distinct psycho-trauma patterns using the latent class analysis method and assessed their associations with aging acceleration using similar models.
Results We observed most of the studied psycho-trauma factors were associated with biological and facial aging
acceleration. Compared to the “Absence of trauma and psychopathology” cluster, the “adverse childhood experiences (ACEs) with psychopathology” cluster showed strong associations with those aging measurements (odds ratio[OR] = 1.13 [1.05 − 1.23] for biological and 1.52 [1.18 − 1.95] for facial aging acceleration), while no such association
was observed for the “ACEs without psychopathology” cluster (1.04 [0.99 − 1.09] and 1.02 [0.84 − 1.24].
Conclusions Our study demonstrated significant associations of psycho-trauma factors with both biologicaland facial aging acceleration. The differential aging consequences observed among ACEs exposed individuals
with and without psychopathology prompt interventions aimed to improve individuals’ psychological resilienceto prevent aging acceleration.
Keywords Psychological factors, Trauma experience, Biological aging acceleration, Facial aging acceleration, Lengthof telomeres*Correspondence:
Changjian Qiu
qiuchangjian@wchscu.cn
Huan Song
songhuan@wchscu.cn
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To
view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Wang et al. BMC Medicine(2024) 22:359

Background
It has been estimated that approximately 970 million
people worldwide were living with psychiatric disorders
in 2019 [1]. Notably, individuals with psychiatric disorders (e.g., depression and substance use) were reported tohave a significant excess mortality risk [2] and a reductionof 10–20 years in life expectancy [3], compared to thegeneral population. Likewise, people suffering from current psychological symptoms (e.g., depressive and anxiety symptoms) were also associated with an elevated riskof major diseases, such as cancer [4] and cardiovasculardiseases [5]. Furthermore, given that one third (31.4%)of adults reported having experienced at least one lifetime traumatic experience [6], while close to half (47%)
of individuals experienced at least one adverse childhood
event (ACE) according to reports from England [7], theaccumulated negative effects of traumatic experience on
physiological, behavioral, and psychological functions
could be substantial at the population level [8, 9].
Telomeres are conserved complex DNA structuresat the ends of chromosomes that ensure their stability
and prevent damage [10]. In most human cells, the telomere shortens with cell division, making it a biomarker
of human aging [11]. Leukocyte telomere length (LTL) isa practicable measure of TL and has a close correlation
with TL across different tissues within individuals [12].
Therefore, LTL could serve as a biomarker for biologicalaging, and shorter LTL has been significantly associatedwith increased morbidity and mortality [13]. However,telomere attrition is highly associated with age, gender,and genetic background, rendering it difficult to measurechanges of LTL (i.e., aging acceleration) virtually induced
by environmental factors, such as those aforementioned
psychological and trauma-related factors (e.g., depression[14], anxiety [15], and ACEs [16]). This could be an explanation of inconsistent results observed for associations
between adulthood traumatic experiences and LTL [17].
In addition, besides the biological measures of aging
acceleration, facial aging acceleration (i.e., if the person
looks older than his/her chronological age) is also considered as a valid indicator of accelerated aging. In a cohortof 1826 twins, the twin individual who looked older
seemed to have a higher risk of mortality during a 7-year
follow-up, after adjustment for chronological age andsex, than his/her twin partner [18]. However, with limited
evidence for some specific psychological symptoms (e.g.,
depression [19]) or severe trauma (e.g., financial difficulty [20]), a comprehensive assessment on the effects ofpsychological and trauma-related factors on facial aging
acceleration is currently lacking.
Taking advantage of enriched data on phenotypes
and genotypes in the UK Biobank, we aimed to examine the association of a broad set of psychological andPage 2 of 13

trauma-related factors with aging acceleration indexed
by both biological and facial aging acceleration. Importantly, our study features the application of data-driven
approaches to identify psycho-trauma exposure patterns,
as illustrations of various existing stimulus–response
constellations among the study population. Also, theconceptualization of the aging acceleration index, which
was calculated based on an algorithm involving genetic
determinants of LTL, sex, and chronological age, might
facilitate the accurate measurement of possible changesof LTL due to an environmental exposure.

Methods
Data source

The UK Biobank prospectively recruited 502,507 participants aged 40 to 69 across the UK between 2006 and2010 [21]. All participants filled in questionnaires providing information on socio-demographic, economic, andlifestyle factors as well as self-reported information about
their mental and physical health at baseline. Physicalexaminations and biological sample collection were also
performed at the initial health center visit. UK Biobank
data were periodically linked with several national registries to track health-related outcomes with the consent of the participants [22]. Inpatient hospital data wereobtained through linked data from Hospital Episode Statistics in England, the Scottish Morbidity Record, and
the Patient Episode Database in Wales that has covered
all UK Biobank participants since 1997 [23]. Primarycare data, covering approximately 45% of the entire UK
Biobank cohort [24], were derived from multiple data
providers, including the Phoenix Partnership and Egton
Medical Information Systems.
In the UK Biobank, genotyping data of 488,377 participants were released, based on DNA extracted fromblood samples collected at baseline and measured using
two similar genotyping arrays with 95% of marker content [21]. Based on the extracted DNA from 474,074 UK
Biobank participants, LTL was determined as the ratioof telomere repeat copy number (T) relative to that of asingle copy gene (S) using an established qPCR multiplex
method [25].
Study design

As this study focused on both biological and facial aging
acceleration, indexed by the presence of non-geneticallydetermined portion of shortened LTL (i.e., LTL deviation > 0) and self-reported facial aging acceleration (seeascertainment of those indexes in the “Indexes of aging
acceleration” section), respectively, we restricted ouranalyses to European ancestry participants with eligible genotyping data and the relevant questionnaire data(n = 334,765, Fig. 1A). To assess the biological aging

Wang et al. BMC Medicine(2024) 22:359

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Fig. 1 The flowchart of the study. LTL, length of telomeres

acceleration (i.e., the primary outcome of the study),
participants with non-European ancestry were excluded
for calculating polygenic risk score (PRS) for LTL, which
served as a proxy of genetically determined LTL andwas calculated based on publicly available genome-wide
association study (GWAS) data derived from European
ancestry samples. Furthermore, we excluded 2406 individuals who had outliers of LTL deviation (i.e., values surpassed three standard deviations from the mean), leaving
332,359 participants in the analytic dataset for assessing
the association of each psychological or trauma-related
factor (i.e., factors related to history of psychiatric disorders, current psychological symptoms, and traumatic
experiences) with biological and facial aging acceleration.
Then, for 99,180 participants with complete psychological and trauma-related data (i.e., no missing items for all
exposure variables), we identified clusters of participants
with distinct psycho-trauma exposure patterns using
the latent class analysis (LCA) method (see the “Identification of clusters of participants with distinct psychological and trauma exposure patterns” section for details)
and compared those indexes of biological and facial
aging acceleration between the identified psycho-trauma
clusters.
Psychological and trauma‑related factors

We studied psychological and trauma-related factors
related to two perspectives: psychological conditions

and traumatic experiences at recruitment (see Additional
file 1: Table S1).
Firstly, we assessed psychological conditions throughhistory of psychiatric disorders and current psychological symptoms. To ascertain the history of psychiatricdisorders, we retrieved information regarding the diagnoses of psychiatric disorders from the UK Biobank
self-report, inpatient hospital, or primary care data at
recruitment. We identified four major subtypes of psychiatric disorders, including depression [14], anxiety [15],stress-related disorders [26], and substance misuse [27],according to their corresponding International Classification of Diseases [ICD] 9th or 10th edition (ICD-9/10)
codes (see Additional file 1: Table S2). Besides the dichotomous variable (yes or no), to reflect the severity level, we
generated ordinal variables, which were in order labeled
as “No,” “Self-report or primary care only,” and “Inpatientcare” based on the data sources of identified diagnosis
codes. Current psychological symptoms at recruitment
were measured using four psychometric screening questions (i.e., the frequency of feeling depressed, feeling little interest in doing things, feeling tense, and feeling tired
over the preceding 2 weeks) asking for core symptoms
of depression [28] and generalized anxiety disorder [29].
Each response used a 4-point Likert scale, ranging from
“Not at all” to “Nearly every day.”
Then, the studied traumatic experiences included ACEs
and recent stressful life events (SLEs) within 2 years

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before the baseline. ACEs were assessed by the Childhood Trauma Screener (CTS) [30] during the online
follow-up, which consisted of five questions related tophysical abuse, physical neglect, emotional abuse, emotional neglect, and sexual abuse, respectively. Each item
was measured on a 5-point Likert scale (“Never true,”“Rarely true,” “Sometimes true,” “Often,” and “Very often
true”), and we applied previously validated thresholds[30] to determine the presence or absence of each ACE(see Additional file 1: Table S3). In addition, at recruitment, participants were asked about their experienceswith six recent SLEs, including serious illness, injury orassault to themselves, serious illness, injury or assault toclose relative, death of close relative, death of spouse orpartner, marital separation, and financial difficulty, during the last 2 years.

years, see Additional file 1: Table S4), and lifestyle (e.g.,
alcohol drinking status, smoking status, and use of sun
protection) factors were collected through questionnaires at baseline. In addition, body mass index (BMI)
was calculated from height and weight measured at theinitial assessment center visit. To measure area-based
deprivation, the Townsend deprivation index (TDI) [33]was generated based on the postcode of the participants’family address, with higher scores indicating greater deprivation. In addition, we used the Charlson comorbidity
index (CCI) [34] to assess baseline somatic comorbidities, based on self-reported, hospital inpatient, and primary care data.

Indexes of aging acceleration

Based on the “poLCA” package in R [35], LCA was used to
identify the potential clusters of participants with distinct
psycho-trauma exposure patterns, taking into accountthe correlations and interactions of those trauma andpsychological factors. LCA is a model-based approachthat utilizes maximum likelihood estimation to evaluate
the probability of a participant belonging to each cluster [36]. We compared a series of LCA models in which
the number of clusters varied from 2 to 10. The modelwith 5 clusters was considered optimal as it obtained low
values of consistent Akaike information criterion andsample-size adjusted Bayesian information criterion (seeAdditional file 1: Table S5) but higher average latent classposterior probability and entropy [37], implying superior
statistical and interpretable significance. We then applied
radar charts to visualize the distinct psycho-trauma patterns of those clusters, which exhibited deviations of eachfactor in relation to the total population level (see details
about the visualization process of the identified patterns
in the Additional file 2: Supplementary method).

In the current study, we assessed aging accelerationin two ways. The primary index was biological aging
acceleration, which was defined as the presence of nongenetically determined portion of shortened LTL (i.e.,
LTL deviation > 0). The quantification of this index wasachieved through two steps. First, we calculated the polygenic risk score (PRS) for LTL, which serves as a proxy
for the genetically determined proportion of LTL. Specifically, the PRS for LTL was computed using least absolute shrinkage and selection operator (LASSO) [31, 32]
regression based on publicly available summary statistics of GWAS from independent samples [31], containing 5,067,039 single-nucleotide polymorphisms (SNPs)
for the 332,359 eligible UK Biobank participants. Second,
we constructed a linear regression model incorporating
the effects of age, sex, and PRS for LTL (i.e., observed
LTL ~ genetically determined LTL + age + sex). Then, we
defined LTL deviation (median [range], 1.23% [− 45.61%
to 45.52%]) as the non-genetically determined portionof shortened LTL which was 100 times of LTL residual
divided by predicted LTL (i.e., (predicted LTL − observed
LTL)/predicted LTL × 100%). LTL deviation > 0 indicatedthe presence of biological aging acceleration (i.e., the participants were biologically older than expected).
Facial aging acceleration was measured using a selfreported question at baseline, asking whether participants looked older or younger than their chronologicalage. The response options were recoded into a binary
variable (i.e., “No” = “Younger than you are” or “About
your age” and “Yes” = “Older than you are”), to indicate
the presence of facial aging acceleration.
Covariates

Data on socio-demographics (e.g., age and sex), socioeconomic (e.g., annual household income and educational

Statistical analysis
Identification of clusters of participants with distinct
psycho‑trauma patterns

Associations analyses for psychological and trauma‑related
factors and patterns with aging acceleration

Logistic regression models were used to examine theassociations of 19 individual psychological and trauma
factors (i.e., factor-based analyses) and the identified
exposure patterns (i.e., cluster-based analyses) with thestudied aging acceleration indexes, including the presence of LTL deviation > 0 (yes or no), and facial aging
acceleration (yes or no). The models were adjusted forage (as a continuous variable), sex (male or female), BMI(< 25 kg/m2, 25 to 30 kg/m2, ≥ 30 kg/m2, or unknown),
CCI (as a continuous variable), smoking and alcohol
drinking status (never, previous, current, or unknown),
annual household income (less than £18,000, £18,000 to
30,999, £31,000 to 51,999, £52,000 to 100,000, greater

Wang et al. BMC Medicine(2024) 22:359

than £100,000, or unknown), TDI (as a continuous variable), educational years (7, 10, 13, 15, 19, 20, or unknown),
and use of sun protection (most of the time, always,
sometimes, never/rarely, do not go out in the sunshine,or unknown). For the factor-based analyses, we used theBenjamini-Hochberg (BH) method to adjust p-values
for multiple testing. We considered a false discovery
rate (FDR) of < 0.05 as statistically significant. To test therobustness of those results across different outcome definitions, all above-mentioned analyses were repeated for
LTL deviation (as a continuous variable) and an alternative definition of the presence of biological aging acceleration (i.e., LTL deviation more than 1SD), based on thelinear and logistic regression models, respectively. Furthermore, to detect if the involvement of possible mediators or community-level variable (i.e., TDI that coded
according to postal address) in the logistic and linear
models could heavily bias our estimates, we conducted
additional sensitivity analyses for the factor-based andthe cluster-based analyses, including (1) partial (i.e., age,sex, education, annual household income, TDI, and BMI)
and full (i.e., additionally adjusted for possible mediators)
adjustment for covariates in the models and (2) construction of multi-level logistic and linear regression models
to consider TDI as a cluster variable (see Additional file 2:
Supplementary method for detailed methodology). For
LTL deviation > 0 and facial aging acceleration, we separately calculated odds ratios (ORs) for males and females
by dividing the study population into two different sex
subgroups. For cluster-based analyses, we then assessed
the differences of sub-grouped ORs by introducing aninteraction term to the regression models. We conducted
repeated cluster-based analyses after performing 10 timesof multiple imputation among 107,607 participants, whofully completed the online questionnaire, to assess theinfluence of missingness (0.0–2.58%, see Additional file 1:
Table S6) of psychological and trauma-related factors on
the estimation. The overview of the analysis steps wasshown in Fig. 1B. All data analysis and visualization were
performed based on R 4.0.2.

Results
The mean age of the 332,359 participants that includedin the factor-based association analyses (i.e., the association of 19 individual psychological and trauma-related
factors with biological and facial aging) was 56.82 years,
and 46.40% (n = 154,224) were male (Fig. 1), which
was comparable with the 99,180 participants includedin the cluster-based analysis (mean age = 56.11 years,
male% = 44.86%). Also, other characteristics of those twoanalytic populations seemed to be similar, except for ahigher socioeconomic status (i.e., longer education years
and higher annual family incomes) and lower proportions

Page 5 of 13

of aging acceleration observed in the population of thecluster-based analyses (Table 1).
The identified clusters of participants with distinct
psycho‑trauma patterns

We labeled the 5 identified clusters with distinct psychotrauma patterns according to their most featured profile(Fig. 2 and Additional file 1: Table S7). Specifically, cluster 1 was labeled as “Absence of trauma and psychopathology” (n = 67,410), which was characterized by lower
possibilities of suffering all studied psychopathology andtrauma than the total population level. Likewise, cluster
2 to 3 were named as “recent SLEs and mild psychopathology” (n = 18,861) and “ACEs, recent SLEs, and severe
psychopathology” (n = 2256). Finally, we observed two
clusters of participants who both suffered ACEs but had
diverse psychological conditions, named as “ACEs without psychopathology” (cluster 4, n = 7880) and “ACEswith psychopathology” (cluster 5, n = 2773), respectively.
The associations of psychological and trauma‑related
factors and clusters with aging acceleration

Of the 19 psychological and trauma-related factors, 10(mainly factors related to the history of psychiatric disorders and ACEs) showed significant associations with biological aging acceleration (i.e., 9 for LTL deviation > 0, 8
for LTL deviation, and 4 for LTL deviation > 1 SD) (see theinner circle of Fig. 3 and Additional file 1: Tables S8 andS9), with the highest estimates observed for a previous
inpatient diagnosis of stress-related disorders (OR = 1.63
[95% CI 1.25 − 2.13]) and substance misuse (OR = 1.21
[95% CI 1.12 − 1.32]). Notably, most ACEs were associated with biological aging acceleration (ORs ranged from1.06 to 1.11). For facial aging acceleration, we identified
10 factors (mainly factors related to current psychological symptoms, see the outer circle in Fig. 3). Specifically,a previous diagnosis of depression (OR = 1.18 for selfreport or primary care and OR = 1.37 for inpatient) andthe presence of all current psychological symptoms (ORsranged from 1.40 to 2.45) were associated with facial
aging acceleration. Also, we observed significant associations of facial aging acceleration with emotion-related
ACEs (i.e., emotional abuse and emotional neglect) andsome subtypes of recent SLEs (e.g., financial difficulty
and serious illness, injury or assault to themselves).
For the cluster-based associations, using the “Absenceof trauma and psychopathology” cluster as a reference, the cluster featuring “ACEs, recent SLEs, andsevere psychopathology” demonstrated the most pronounced risk excess in biological aging acceleration(OR = 1.13 [95% CI 1.03 − 1.23], Table 2). Intriguingly,
while the “ACEs with psychopathology” cluster showeda strong association with biological aging acceleration

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Table 1 The characteristics of the included participantsParticipants involved in the factor-based
analysesa(n=332,359)

Participants involvedin the cluster-based
analysesb(n=99,180)

Age (mean (SD))

56.82 (7.97)

56.11 (7.65)

Sex = Male (%)

154,224 (46.40)

44,488 (44.86)

BMI (%)<25

109,081 (32.82)

38,193 (38.51)

25-30

142,458 (42.86)

41,545 (41.89)>=30

79,781 (24.00)

19,248 (19.41)

Unknown

1,039 (0.31)

194 (0.20)

CCI (mean (SD))

0.25 (0.52)

0.21 (0.46)

TDI (mean (SD))-1.56 (2.93)-1.89 (2.72)

Smoking status (%)
Never

181,129 (54.50)

57,418 (57.89)

Previous

116,938 (35.18)

34,840 (35.13)

Current

33,233 (10.00)

6,783 (6.84)

Unknown

1,059 (0.32)

139 (0.14)

Alcohol drinking status (%)
Never

9,933 (2.99)

2,247 (2.27)

Previous

11,138 (3.35)

2,540 (2.56)

Current

311,043 (93.59)

94,372 (95.15)

Unknown

245 (0.07)

21 (0.02)

Education years (%)
7 years

57,070 (17.17)

6,671 (6.73)

10 years

57,741 (17.37)

14,278 (14.40)

13 years

17,706 (5.33)

6,207 (6.26)

15 years

40,873 (12.30)

13,123 (13.23)

19 years

53,185 (16.00)

14,269 (14.39)

20 years

103,178 (31.04)

44,408 (44.78)

Unknown

2,606 (0.78)

224 (0.23)

Annual household income (%)
Less than 18,000

62,986 (18.95)

11,741 (11.84)

18,000 to 30,999

74,030 (22.27)

21,177 (21.35)

31,000 to 51,999

76,560 (23.04)

26,415 (26.63)

52,000 to 100,000

59,611 (17.94)

24,332 (24.53)

Greater than 100,000

15,523 (4.67)

7,349 (7.41)

Unknown

43,649 (13.13)

8,166 (8.23)

Use of sun protection (%)
Most of the time

122,335 (36.81)

39,341 (39.67)

Always

71,995 (21.66)

20,279 (20.45)

Sometimes

109,895 (33.07)

32,783 (33.05)

Never/rarely

26,425 (7.95)

6,389 (6.44)

Do not go out in the sunshine

1,593 (0.48)

387 (0.39)

Unknown

116 (0.03)<5 (0.00)

Biological aging acceleration = Yes (%)

177,797 (53.50%)

51,311 (51.74%)

Facial aging acceleration = Yes (%)

6,641 (2.00%)

1,702 (1.72%)

TDI Townsend deprivation index, CCI Charlson comorbidity index, BMI body mass indexabrefers to overall participants for the analysis to examine the impacts of 19 psychological and trauma-related factors on biological and facial aging accelerationrefers to participants used to identify the psycho-trauma clusters and examine the impacts of identified clusters on biological and facial aging acceleration after
excluding participants with missingness of psychological and trauma-related factors

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Fig. 2 The identified psycho-trauma clusters. The y-axis values (ranging from 0 to 10) for each psychological and trauma-related exposure
represent the relative level within identified cluster compared to the total population (for detailed methodology, please refer to Additional
file 2: Supplementary method). The colors represent three distinct patterns of combined psychological and trauma-related factors. Greenrefers to the cluster “Absence of trauma and psychopathology” (n = 67,410), characterized by lower possibilities of suffering all studied trauma
and psychopathology than the total population level. Red represents the cluster “Recent SLEs and mild psychopathology” (n = 18,861) and “ACEs,
recent SLEs, and severe psychopathology” (n = 2256), which have a similar pattern with combining traumatic experiences along with a matchinglevel of psychopathology (i.e., different levels of traumatic experiences [recent SLEs and recent SLEs plus ACEs] and psychopathology [mildand severe]). Yellow represents the cluster as “ACEs without psychopathology” (n = 7880) and “ACEs with psychopathology” (n = 2773), whoboth suffered ACEs but had diverse psychological conditions. ACEs, adverse childhood events. SLEs, stressful life events(OR = 1.13 [95% CI 1.05 − 1.23]), no such association
was observed for the “ACEs without psychopathology”
cluster (OR = 1.04 [95% CI 0.99 − 1.09]). We observed
largely similar risk patterns when using LTL deviationand the presence of LTL deviation more than 1SD asalternative measurements of biological aging acceleration (Additional file 1: Table S10). For facial aging acceleration, we found significant associations for all clusters
with the presence of current psychological symptoms(irrespective of symptom severity, Table 2), particularly
for the cluster “ACEs, recent SLEs, and severe psychopathology” cluster (OR = 2.20 [95% CI 1.75 − 2.76]).
Also, largely similar results were obtained when performing a repeated cluster-based analyses using the 10
datasets after multiple interpolation (Additional file 1:
Table S11).
After conducting the two sensitivity analyses mentioned above (i.e., partial and full adjustment forcovariates, and considering TDI as a cluster variablein the logistic and linear regression models), our estimation for the factor-based and the cluster-based
analyses remained largely unchanged, demonstrating

the robustness of the results (Additional file 1: Tables
S12-S15).
The sex‑specific association of psychologicaland trauma‑related factors and patterns with aging
acceleration

Compared with those of male participants, we observeda stronger magnitude of these associations for bothbiological and facial aging acceleration among female
participants (see Additional file 1: Fig. S1). We thenobserved sex-specific associations between psychological and trauma-related factors and facial aging acceleration, with some recent SLEs over the past 2 years(e.g., financial difficulty) only being associated with anincreased risk of aging acceleration in men. There wereno significant interactions between sex group and theidentified psychological and trauma-related clusters
for risk of biological aging acceleration (P for interaction = 0 0.166, Additional file 1: Table S16) and facial
aging acceleration (P for interaction = 0 0.640, Additional file 1: Table S17).

Wang et al. BMC Medicine(2024) 22:359

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Fig. 3 The association of the psychological and trauma-related factors with biological aging acceleration (LTL deviation > 0) and facial aging
acceleration. The numbers in the circle and degree of colors of the circle in Fig. 3 indicates the magnitude of odds ratios (ORs), derived from the fully
adjusted logistic regression models. ORs with BH adjusted p-value < 0.05 were considered as statistically significant and shown in bold. Estimateswith 95% confidence intervals are detailed in Additional file 1: Table S8. LTL, the length of telomeres. TDI, Townsend deprivation index. CCI, Charlson
comorbidity index. BMI, body mass index. “a” symbol indicates the following: the associations of the psychological and trauma-related factors
with biological aging acceleration were adjusted for age, sex, education, annual household income, TDI, BMI, smoking status, alcohol drinking
status, and CCI. “b” indicates the following: the associations of the psychological and trauma factors with facial aging acceleration were adjusted
for age, sex, education, annual household income, TDI, BMI, smoking status, alcohol drinking status, CCI, and the use of sun protection

Discussion
Based on 332,359 participants from the UK Biobank, ourstudy provided robust evidence that most adverse psychological and trauma exposures were associated with
an elevated risk of biological aging acceleration (10 out
of 19 studied factors) measured by a newly developed
biological aging acceleration index (LTL deviation > 0or LTL deviation or LTL deviation > 1 SD) which takes

into account genetic determinants of LTL shortening,
and facial aging acceleration (10 out of 19), highlighting
the substantial impacts of psychological and traumatic
experiences on accelerating the aging process. Specifically, factors related to history of psychiatric disorders
and childhood adversities were primarily linked with biological aging acceleration, whereas current psychologicalsymptoms were noted for their pronounced associations

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Table 2 The association of the identified potential psychological and trauma clusters with biological and facial aging acceleration
The identified potential psychological andtrauma cluster

Biological aging acceleration (LTL deviation>0)

Facial aging acceleration

Number of casesn/N (%)

Number of casesn/N (%)

Adjusted OR(95%CI)a

Adjusted OR(95%CI)b

Absence of trauma and psychopathology

34,553/67,410 (51.26%)

REF

993/67,410 (1.47%)

REF

Recent SLEs and mild psychopathology

9,811/18,861 (52.02%)

1.01 (0.98-1.04)

425/18,861 (2.25%)

1.39 (1.24-1.57)

ACEs, recent SLEs, and severe psychopathology

1,254/2,256 (55.59%)

1.13 (1.03-1.23)

93/2,256 (4.12%)

2.20 (1.75-2.76)

ACEs without psychopathology

4,151/7,880 (52.68%)

1.04 (0.99-1.09)

118/7,880 (1.5%)

1.02 (0.84-1.24)

ACEs with psychopathology

1,542/2,773 (55.61%)

1.13 (1.05-1.23)

73/2,773 (2.63%)

1.52 (1.18-1.95)

OR Odds Ratio, ACEs adverse childhood events, SLEs stressful life events, TDI Townsend deprivation index, CCI Charlson comorbidity index, BMI body mass indexa

The associations of the psychological and trauma-related factors with biological aging acceleration were adjusted for age, sex, education, annual household income,
TDI, BMI, smoking status, alcohol drinking status, and CCIb

The associations of the psychological and trauma factors with facial aging acceleration were adjusted for age, sex, education, annual household income, TDI, BMI,
smoking status, alcohol drinking status, CCI, and the use of sun protection

with an excess risk of facial aging acceleration. More
importantly, we identified five major psycho-trauma clusters based on clustering analyses, and further found thatthe cluster characterized by “ACEs with psychopathology” was associated with both biological and facial aging
acceleration, in contrast to null results for the cluster featured by “ACEs without psychopathology.” Such findings
imply that interventions targeting on psychological resilience to ACEs might have the potential to mitigate thedetrimental effects of ACEs on the aging process.
In line with the results of previous meta-analyses
investigating the association of LTL with depression [14] (n = 34,347), anxiety [15] (n = 19,424), stressrelated disorders [26] (n = 3851), and substance misuse[27] (n = 7203) on LTL, our study also observed significant associations between a history of those psychiatric disorders and biological aging acceleration indexedby LTL deviation, based on a much larger sample size(n = 332,359). Likewise, significant negative associations
between ACEs and biological aging acceleration (e.g.,shorter LTL [17], older biological age [38], and DNA
methylation-based epigenetic age acceleration [39])have been reported, while existing data on adulthoodor recent traumatic events are scarce. For facial aging
acceleration, only one previous prospective cohort studyof 1826 twins found that higher depression scores (i.e.,emotion-related current psychological symptoms) wereassociated with an increased risk of self-reported facial
aging acceleration [19]. Therefore, our study substantiallyextended the current evidence on the importance of various psycho-trauma factors, particularly current psychological symptoms and emotion-related ACEs, on facial
aging acceleration. Recent major adverse experiences,such as serious illness, injury, or assault to a close relative or themselves, showed moderate impacts on facial
aging acceleration but a very mild effect on biologicalaging acceleration. This fits findings based on a study of3034 US residents aged 25 to 74, suggesting self-reported
health condition decline would produce increases in subjective age [40]. However, financial difficulty experienced
during the last 2 years seemed to be linked to facial aging
acceleration among males. Sex-specific associations
between certain SLEs (e.g., financial difficulty) and healthadversities (e.g., cardiovascular disease and all-cause
mortality [41]) were also found in prior investigations,
possibly reflecting the differentiated social roles related
to each sex.
The complexity of psycho-trauma adversities has
been revealed [42]. For instance, individuals exposed toadverse childhood events were also prone to adversities during adulthood [43], both of which contributed toundermined physical conditions among exposed individuals [44]. Therefore, it is crucial, but challenging, todetermine the patterns of psycho-trauma exposure forweighting the attributable risk to each individual factor
on a specific outcome of interest. In the present study,
with enriched data on psychological and trauma factors
and well-developed unsupervised machine learning algorithms, we, for the first time, identified five major exposure patterns among our study population. In brief, morethan two thirds (67.97%, 67,410/99,180) of the included
individuals were considered to have total absence of
trauma and psychopathology, while all the others sufferedcertain trauma and psychopathology. The most common exposure patterns are those combining traumatic
experiences (mainly recent SLEs) along with a matchingextent of psychopathology (e.g., patterns 2–3, including different levels of traumatic experience [recent SLEs
and recent SLEs plus ACEs] and psychopathology [mildand severe]). Intriguingly, we additionally identified two
clusters of individuals with similar ACEs exposure but
opposite psychological condition (i.e., with and without

Wang et al. BMC Medicine(2024) 22:359

psychopathology). The finding that an excess risk of biological and facial aging acceleration was only noted forthe “ACEs with psychopathology” pattern but not “ACEswithout psychopathology” pattern imply a strong mediating role of psychological distress on the effect of ACEs
on the aging acceleration. Without any directly comparable data, a similar conclusion could be drawn from a
Danish study of 324 male adults, which also found that
16% of the effect of ACEs on LTL shortening (i.e., biological aging acceleration) was mediated by current depressive symptoms [45]. As aging acceleration, particularly
biological aging acceleration, is considered as a proxy ofoverall health status [46] and has been linked with many
disease outcomes in later life (e.g., cancer [47] and cardiovascular diseases [48]), our findings (i.e., psychologicaldistress might play a mediating role on the effect of ACEs
on the aging acceleration) provide strongest evidence sofar, prompting the use of psychological interventions forpreventing adverse health consequences among individuals exposed to ACEs.
The potential mechanisms linking trauma and psychopathology to accelerated aging might include
neuroendocrine responses through the hypothalamic–
pituitary–adrenal (HPA) axis, oxidative stress, andinflammation [49]. The activation of the HPA axis bystress from trauma causes a surge of glucocorticoids[50] such as cortisol, leading to the generation of reactive oxygen species (ROS) by increasing the metabolic
rate and mitochondrial activity [51]. Subsequently, ROS
might damage telomeres and inhibit telomerase activity,
both of which contribute to telomeres shortening [49].
Elevated glucocorticoids can increase the expressionof pro-inflammatory genes (e.g., iNOS, IL-1β, TNF-α)
and decrease the expression of anti-inflammatory genes
including IL-1ra, IL-10, and MKP-1, also leading to telomeres shortening [49]. Additionally, the production of
glucocorticoids, ROS, and inflammatory markers would
destroy collagen and elastin in the skin, accelerating
wrinkling and sagging and leading to facial aging [52].
Interestingly, it is reported that the alternations of HPA
axis induced by stress actions after traumatic events [53]or psychopathology (e.g., depression [54] and anxiety[55]) are possibly reversible, which explains our findingsof no accelerated aging among participants with childhood adversities but who maintained good psychologicalconditions.
The major merit of this study is the use of a large community-based cohort from the UK Biobank, providing
data of comprehensive assessments on psychological andtrauma-related factors (e.g., history of psychiatric disorders, current psychological symptoms, recent SLEs,
and ACEs), social demographic factors, somatic comorbidities, and lifestyles. In addition, benefiting from thePage 10 of 13

availability of individual-level genotyping data and measurement of telomeres, we proposed LTL deviation > 0
as an index of biological aging, which represented thepresence of non-genetically determined proportions
shortened LTL (i.e., non-genetically determined aging
acceleration), thereby contributing to a more accurate
measure of aging and better interpretation of results.
Last, to account for the complex interactions between
psychological and trauma-related factors, we used LCA
to capture clusters with distinct psycho-trauma patterns.
The cluster-based analyses provide clearer clues about
the substantial contributing factors to the studied outcomes, as well as identifying individuals at high risk of
aging acceleration who need targeted interventions.
Major limitations include its cross-sectional design.
Although the studied trauma-related factors (such as
ACEs and recent SLEs) ensure the chronological order(from exposure to outcomes of interest) by their definitions, the fact that data on most exposure factors andmeasurements of aging acceleration were collected at thesame time provides a very weak basis for any causal inference. The inclusion of several covariates (e.g., smoking
status and alcohol drinking status) that might mediate
the impact of psycho-trauma related factors and clusters
on aging acceleration resulted an underestimation of thestudied association. In addition, despite the self-related
facial aging acceleration have also been widely used inprevious studies [56, 57], as an index for subjective aging[58] with proven associations with risk of mortality [59],the validity of such measurement remains uncertain.
It is particularly notable as merely less than 2% of participants reported “looking older than the chronologicalage” in the present study. Furthermore, there are biases
in the assessment of facial aging acceleration, which canbe influenced by psychological and traumatic factors,
making this measure less valid compared to biologicalaging acceleration in our study. Therefore, the results for
facial aging acceleration analyses need to be interpretedwith cautious, and studies which applied more objective or preferable approach for facial aging acceleration
measurement (e.g., facial aging evaluated by nurses orother healthcare professionals [18]) are needed to verify
our findings. Finally, the generalization of our findings toboth the general UK population and other populations
needs to be done with caution, as the UK Biobank participants were only 5.5% of the invited individuals [23].

Conclusions
In conclusion, our study demonstrates significant associations of psycho-trauma related factors and patterns
with both biological and facial aging acceleration. The
findings of aging acceleration among ACEs exposed
individuals with psychopathology, but not those with

Wang et al. BMC Medicine(2024) 22:359

psychopathology, underscore the potential of interventions aiming to improve individuals’ psychological resilience on preventing aging acceleration.
Abbreviations
LTL	Leukocyte telomere length
ACEs	Adverse childhood experiences
PRS	Polygenic risk score
GWAS	Genome-wide association study
LCA	Latent class analysis
ICD	International Classification of Diseases
SLEs	Stressful life events
CTS	Childhood Trauma Screener
LASSO	Least absolute shrinkage and selection operator
SNPs	Single-nucleotide polymorphism
BMI	Body mass index
TDI	Townsend deprivation index
CCI	Charlson comorbidity index
BH	Benjamini-Hochberg
FDR	False discovery rateORs	Odds ratios
HPA	Hypothalamic-pituitary-adrenal
ROS	Reactive oxygen species

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03578-7.
Additional file 1: Figure S1. Sex-specific odds ratio of the psychologicaland trauma-related factors with biological aging accelerationand facial
ageing acceleration. Table S1. The coding book. Table S2. International
Classification of Diseasescodes for the history of psychiatric disorders.
Table S3. The cut-off values of the Childhood Trauma Screener–5 items.
Table S4. Educational attainment measures. Table S5. The Fitting of thelatent class analysis model with different numbers of clusters. Table S6. The
missing proportion of psychological and trauma related factors among
the included participants who fully completed the online questionnaire.
Table S7. The description of the psychological and trauma-related factors
among the identified potential mental health clusters. Table S8. The
association of the psychological and trauma-related factors with biologicalaging accelerationand facial ageing acceleration. Table S9. Sensitivity
analyses for the association of the psychological and trauma-related
clusters with biological aging acceleration by using the presence of LTL
deviation more than 1SD and LTL deviation as a continuous variable.
Table S10. Sensitivity analyses for the association of the psychological andtrauma-related clusters with biological aging acceleration using the presence of LTL deviation more than 1SD and LTL deviation as a continuous
variable. Table S11. Sensitivity analyses for the association of the identified
psychological and trauma-related clusters with aging acceleration by
conducting 10 times of multiple imputation. Table S12. The association of
the psycho-trauma related factors with biological and facial aging acceleration when adjusting for different covariates in the logistic and linear
regression model. Table S13. The association of the psycho-trauma related
clusters with biological and facial aging acceleration when adjusting fordifferent covariates in the logistic and linear regression model. Table S14.
The association of the psycho-trauma related factors with biological andfacial aging acceleration whether or not TDI was considered as a cluster
variable in the logistic and linear regression model. Table S15. The association of the psycho-trauma related clusters with biological and facial aging
acceleration whether or not TDI was considered as a cluster variable in thelogistic and linear regression model. Table S16. Sex-specific association of
the identified psychological and trauma-related clusters with biologicalaging acceleration. Table S17. Sex-specific association of the identified
psychological and trauma-related clusters with facial aging acceleration.
Additional file 2.

Page 11 of 13

Acknowledgements
This research has been conducted using the UK Biobank Resource under
Application 54803. This work uses data provided by patients and collected by
the NHS as part of their care and support. This research used data assets madeavailable by National Safe Haven as part of the Data and Connectivity NationalCore Study, led by Health Data Research UK in partnership with the Officefor National Statistics and funded by UK Research and Innovation. We thankthe team members of the West China Biomedical Big Data Center for Disease
Control and Prevention for their support.
Authors’ contributions
HS, CQ, and JW were responsible for the study’s concept and design. JW andHY were responsible for data and project management. JW, XH, YY, and YZ
performed the data cleaning and analysis. JW, XH, HS, and CQ interpreted thedata. JW, XH, YY, YZ, YQ, HY, JS, CQ, and HS drafted and revised the manuscript.
All authors read and approved the final manuscript.
Authors’ Twitter handles
Twitter handles: @JoyLab14169974 (Joy Lab).
Funding
This work was supported by 1.3.5 Project for Disciplines of Excellence, West
China Hospital, Sichuan University (grant no. ZYYC21005 to HS), 1.3.5 Projectfor Disciplines of Excellence-Clinical Research Incubation Project, West China
Hospital, Sichuan University (grant no. 2022HXFH029 to CQ), the Department of Science and Technology of Sichuan provincial government (grantno. 2022YFS0345 to CQ), Sichuan Science and Technology Program (grant no.
2024NSFSC1574 to JW), and the National Natural Science Foundation of China(No. 8230051978 to XH). The funders had no role in study design, data collection, preparation of the manuscript, and decision to publish.
Availability of data and materials
Data from the UK Biobank are available to all researchers upon submitted
application. All codes associated with the current submission are available andcan be requested by contacting the corresponding authors.

Declarations
Ethics approval and consent to participate
The UK Biobank has full ethical approval from the NHS National Research Ethics Service (16/NW/0274) and the informed consent was obtained from eachparticipant before data collection. The present study was also approved by
the biomedical research ethics committee of West China Hospital (reference
number: 2019–1171).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Mental Health Center and West China Biomedical Big Data Center, West
China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China. 2 Med‑X
Center for Informatics, Sichuan University, Chengdu, China. 3 Departmentof Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm,
Sweden. 4 Mental Health Center, West China Hospital, Sichuan University,
Chengdu, China. 5 Center of Public Health Sciences, Faculty of Medicine,
University of Iceland, Reykjavík, Iceland.
Received: 17 February 2024 Accepted: 22 August 2024

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