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ORIGINAL RESEARCH
Sleep Pattern, Genetic Susceptibility,
and Abdominal Aortic Aneurysm in
UK Biobank Participants
Large-Scale Cohort Study
Dongliang Zhu, MD,
a,
*Xiaoguang Li, MD,
b,
*Qiuhong Man, MD,
c,
*Renjia Zhao, MD,
d
Shufan Zhang, MD,
e
Xiang Han, MD,
e
Yanfeng Jiang, PHD,
d,f
Kelin Xu, PHD,
f
Xingdong Chen, PHD,
d,f
Chen Suo, PHD,
a,g,f
Lize Xiong, MD
h
ABSTRACT
BACKGROUND Abdominal aortic aneurysm (AAA) is an important cause of cardiovascular mortality.
OBJECTIVES The authors aimed to explore the associations between sleep patterns and genetic susceptibility to AAA.
METHODS We included 344,855 UK Biobank study participants free of AAA at baseline. A sleep pattern was defined by
chronotype, sleep duration, insomnia, snoring, and daytime sleepiness, and an overall sleep score was constructed with a
range from 0 to 5, where a high score denotes a healthy sleep pattern. Polygenic risk score based on 22 single nucleotide
polymorphisms was categorized into tertiles and used to evaluate the genetic risk for AAA. Cox proportional hazards
regression models were used to assess the association between sleep, genetic factors, and the incidence of AAA.
RESULTS During a median of 12.59years of follow-up, 1,622 incidentAAA cases were identified. The HR per 1-pointincrease
in the sleep score was 0.91 (95% CI: 0.86-0.96)for AAA. Unhealthy sleeppatterns, defined as a sleep score rangingfrom 0 to
3, were found to be associated with a higher risk of AAA for the intermediate (HR: 1.18, 95% CI: 1.06-1.31) and poor sleep
patterns (HR: 1.40, 95% CI: 1.13-1.73), respectively, compared to the healthy pattern. Participants with poor sleep patterns
and high genetic risks had a 2.5-fold higher risk of AAA than those with healthy sleep patterns and low genetic risk.
CONCLUSIONS In this large prospective study, healthy sleep patterns were associated with a lower risk of AAA among
participants with low, intermediate, or high genetic risk. (JACC Adv 2024;3:100967) © 2024 The Authors. Published by
Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
ISSN 2772-963X https://doi.org/10.1016/j.jacadv.2024.100967
From the
a
Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health,
Fudan University, Shanghai, China;
b
Department of Thyroid, Breast and Vascular Surgery, Shanghai Fourth People’s Hospital,
School of Medicine, Tongji University, Shanghai, China;
c
Department of Clinical Laboratory, Shanghai Fourth People’s Hospital,
School of Medicine, Tongji University, Shanghai, China;
d
State Key Laboratory of Genetic Engineering, Human Phenome Institute,
School of Life Sciences, Fudan University, Shanghai, China;
e
Department of Neurology, Huashan Hospital, Fudan University,
Shanghai, China;
f
Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China;
g
Shanghai Institute of Infec-
tious Disease and Biosecurity, Shanghai, China; and the
h
Shanghai Key Laboratory of Anesthesiology and Brain Functional
Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of
Medicine, Tongji University, Shanghai, China. *Drs Zhu, Man, and Li have contributed equally to this work and share the first
authorship.
The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’
institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information,
visit the Author Center.
Manuscript received March 13, 2023; revised manuscript received December 20, 2023, accepted February 5, 2024.
JACC: ADVANCES VOL.3,NO.6,2024
ª2024 THE AUTHORS. PUBLISHED BY ELSEVIER ON BEHALF OF THE AMERICAN
COLLEGE OF CARDIOLOGY FOUNDATION. THIS IS AN OPEN ACCESS ARTICLE UNDER
THE CC BY-NC-ND LICENSE (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Abdominal aortic aneurysm (AAA) is a
pathological condition characterized
by the dilation of the abdominal
aorta. Despite being typically asymptomatic,
AAA is prone to rupture,
1
leading to devas-
tating consequences with a mortality rate of
nearly 80%.
2,3
The risk of AAA is associated
with hypertension, atherosclerosis, smoking,
and family history.
4-6
Given the stealthy na-
ture of AAA symptoms and the life-
threatening complications associated with
it, there is an urgent need for early preven-
tion strategies focusing on modifiable risk
factors.
Sleep, due to its vital role in health, has
garnered increasing attention.
7-9
Sleep dis-
turbances have emerged as a significant
public health issue, with a high prevalence
rate that exceeds 10%.
10,11
Inarecentstudy,Luetal
defined a healthy sleep pattern by combining 5 sleep
characteristics: sleep duration, chronotype, snoring,
insomnia, and excessive daytime sleepiness, and
revealed that adherence to a healthy sleep pattern
could decrease the risk of coronary heart disease and
stroke.
12
Other studies
13-15
have indicated that
obstructive sleep apnea (OSA) may influence the
development of aortic aneurysms, with severe OSA
being associated with rapid AAA progression. In these
studies, OSA is typically diagnosed through diag-
nostic tests such as multichannel polysomnography,
with symptoms including excessive daytime sleepi-
ness and loud snoring. However, research on the as-
sociation between sleep behaviors, such as snoring,
insomnia, and daytime sleepiness, and AAA devel-
opment is limited. The determination of sleep pat-
terns allows for easier identification of individuals at
risk for AAA than the clinical diagnosis of OSA. Pro-
moting healthy sleep patterns may help decrease the
risk of AAA in high-risk populations, such as in-
dividuals with a high genetic risk of AAA.
Over the past few decades, numerous single nucle-
otide polymorphisms (SNPs) have been identified as
being associated with AAA development through
genome-wide association studies (GWASs).
16-18
These
SNPs provide quantitative measures of genetic sus-
ceptibilities and contribute to population stratifica-
tion. Derek et al
16
found that participants with a 1-SD
increase in polygenic risk score (PRS) had a 26%
increased risk of AAA.
Therefore, in this study, we aimed to assess the
associations of sleep patterns and individual sleep
characteristics with AAA risk using data from a large-
scale prospective cohort of the UK population.
Furthermore, we probed the combined effect of
genetic risk and sleep pattern and whether adherence
to a healthy sleep pattern could compensate for the
nonmodifiable genetic risk of AAA.
METHODS
STUDY POPULATION. Details of the design and
methods of the UK Biobank study have been previ-
ously described.
19
Briefly, the UK Biobank is a large-
scale biomedical database and research resource
that has recruited over 0.5 million adults aged 40 to
69 between 2006 and 2010. The selection of the study
participants is shown in Figure 1. In the current study,
we included all participants with a Caucasian ethnic
background and complete data on genetics and
covariatesandexcludedanyonewithahistoryof
AAA, self-reported AAA, or AAA diagnosed by
aphysician.
DEFINITION OF AAA. The AAA status and related
conditions were identified based on the inpatient
diagnosis and death registry data provided by the UK
Biobank at baseline or during follow-up. Briefly, we
categorized individuals who had a main/secondary
inpatient diagnosis or a postrecruitment diagnosis of
an underlying cause of death by application of the
International Classification of Diseases, 10th Revision
guidelines (ICD-10: I71.3, I71.4) as having incident
AAA. Participants with a previous aortic aneurysm
(ICD-10: I71) or atherosclerosis (ICD-10: I70) were
excluded to ensure that all participants included in
this study were free of AAA at baseline, whether self-
reported or diagnosed as an inpatient.
ASSESSMEN T OF SLEEP BEH AVIORS AND DE FINITION
OF SLEEP PATTERNS. All sleep behaviors were ob-
tained from self-reported information based on a
baseline questionnaire that included sleep duration,
chronotype, snoring, insomnia, and excessive day-
time sleepiness. Details of the sleep questionnaire
and categories of sleep patterns are presented in
Supplemental Table 1.Briefly, we defined low-risk
sleep factors as described previously
12
: a sleep dura-
tion of 7 to <9 hours/day; morning person of chro-
notype (answer “morning”or “morning than
evening”); no self-reported snoring (answer “no”);
self-reported with never/rarely sleeplessness symp-
toms (answer “never/rarely”); and no regular daytime
dozing (answer “never/rarely”or “sometimes”). Par-
ticipants received a scoreof0iftheywereclassified
ashigh-riskforanyofthe5sleepfactorsand1ifthey
were at low-risk for that factor. A sleep score was
generated by adding the 5 component scores, with the
sum value ranging from 0 to 5, where higher scores
indicate a healthier sleep pattern. We then classified
an individual’s sleep pattern as “poor”(sleep
ABBREVIATIONS
AND ACRONYMS
AAA =abdominal aortic
aneurysm
BMI =body mass index
GWAS =genome-wide
association study
ICD-10 =International
Classification of Diseases-
10th Revision
OSA =obstructive sleep apnea
PAF =population attributable
fraction
PRS =polygenic risk score
SNP =single nucleotide
polymorphism
TDI =Townsend Deprivation
Index
Zhu et al JACC: ADVANCES, VOL. 3, NO. 6, 2024
Sleep Pattern, Genetic Susceptibility, and Incident AAA JUNE 2024:100967
2
score #1), “intermediate”(2 #sleep score #3), or
“healthy”(sleep score $4) based on the sleep score.
ASSESSMENT OF COVARIATES. Our analysis incor-
porated various covariates associated with AAA to
adjust for potential confounding,
12,20,21
including
demographic characteristics, clinical conditions, and
lifestyle. All covariates were documented using self-
reported information gathered through a
touchscreen questionnaire during recruitment. De-
mographic characteristics included age, sex (male/
female), and the Townsend Deprivation Index, which
served as a measure of socioeconomic status.
22,23
Clinical conditions encompassed an individual’sper-
sonal history of hypertension (yes/no) and a family
history of heart diseases (yes/no). Lifestyle factors
incorporated body mass index (kg/m
2
), smoking sta-
tus (ever/never), alcohol intake (high/low), and
physical activity, as recommended by the American
Heart Association Guidelines.
24
Following the guide-
lines of the UK Food Standards Agency, the daily
intake of pure alcohol, measured in grams, was
determined by multiplying the average quantity of
alcoholic beverages consumed by the average amount
of alcohol contained in each type of drink
(Supplemental Table 2). We defined <16 g/day as low
alcohol intake in this study. Physical activity was
divided into “low,”“moderate,”and “high”groups
depending on weekly exposure level. Details of the
categories for these variables are presented in
Supplemental Table 3.
GENOTYPE DATA AND POLYGENIC RISK SCORE
GENERATION. Thegenotypedataofover0.48
million participants in the UK Biobank were derived
from GWAS chip-based (Affymetrix UK BiLEVE and
UK Biobank Axiom arrays) testing of their blood
samples. These genotyping data were further imputed
using reference panels of the Haplotype Reference
Consortium, or UK10K, and 1,000 Genomes Project
phase 3.
25
Individuals were excluded due to sex
discrepancy (n ¼356), sample relatedness (n ¼33,299)
FIGURE 1 The Flowchart for the Selection of Study Participants from the UK Biobank Cohort
AAA ¼abdominal aortic aneurysm.
JACC: ADVANCES, VOL. 3, NO. 6, 2024 Zhu et al
JUNE 2024:100967 Sleep Pattern, Genetic Susceptibility, and Incident AAA
3
determined as kinship >0.088, and outliers of het-
erozygosity (n ¼902). Based on 32 previously reported
SNPs associated with AAA identified in GWAS of Eu-
ropean populations,
16,17
we selected 22 independent
SNP signals (Supplemental Table 4) after quality
control, including: 1) minor allele frequency $0.05
(one SNP excluded); 2) missing rate <5%; 3) Hardy-
Weinberg Equilibrium $1.0 10
5
;4)INFOscore(in-
formation metric) of imputation >0.5; 5) direction of
odds ratio (OR) consistent with previous studies (6
SNPs excluded); and 6) linkage disequilibrium r
2
<0.5
(3 SNPs excluded), to construct the final PRS repre-
senting genetic risk. For each participant, the PRS was
calculated as a weighted sum of these risk alleles,
where weights were derived from the OR value of each
allele in previous studies. The PRS was then catego-
rized as “low,”“intermediate,”or “high risk”based on
thetriplequantileofthedistributionamong
all participants.
STATISTICAL ANALYSIS. The baseline characteris-
tics of participants were described as mean SD or
frequency (percentages) in each category of sleep
scores. The follow-up years were calculated as the
time from the recruitment date to the date of
outcome diagnosis, death, or end of the follow-up
(September 17, 2021), whichever occurred first.
We used multivariate Cox proportional hazards
regression models to estimate the HR (95% CI) for
genetic and sleep factors associated with AAA risk.
The proportional assumption was evaluated the
Schoenfeld residuals test, and no violations of the
proportional hazard assumptions were found. We
evaluated the association of sleep score with AAA
using participants with a score #1 as the reference
group. All 5 sleep factorsweresimultaneously
included in the model to analyze individual sleep
factors. In our analysis, 3 different models were
developed to avoid the potential confounding effects
of known risk factors. Age and sex were adjusted in
model 1. Townsend Deprivation Index, history of
hypertension, and family history of heart diseases
were additionally added to adjust for model 2.
Finally, lifestyle factors (body mass index, smoking
status, alcohol intake, and physical activity) were
further added to model 3 (full model). Individuals
with missing values for any covariate were excluded
from the analysis.
TABLE 1 Baseline Characteristics of Study Participants From the UK Biobank
Sleep Scores
0-1
(n ¼14,787, 4.29%)
2
(n ¼67,831, 19.7%)
3
(n ¼132,572, 38.4%)
4
(n ¼105,057, 30.5%)
5
(n ¼24,608, 7.13%)
Follow up (y) 12.18 2.02 12.26 1.86 12.31 1.78 12.36 1.67 12.39 1.61
Age, y 56.64 7.74 56.84 7.79 56.94 7.88 56.60 8.09 55.20 8.48
Male 7,965 (53.9) 33,964 (50.1) 59,751 (45.1) 44,058 (41.9) 11,349 (46.1)
TDI 0.99 3.21 1.35 3.05 1.57 2.93 1.74 2.81 1.77 2.82
Hypertension 9,044 (61.2) 39,046 (57.6) 71,915 (54.2) 53,090 (50.5) 11,359 (46.2)
Family history of heart diseases 7,024 (47.5) 30,784 (45.4) 58,254 (43.9) 44,972 (42.8) 9,579 (38.9)
BMI, kg/m
2
29.59 5.49 28.35 4.98 27.37 4.64 26.60 4.36 26.11 4.17
Ever smoke 10,233 (69.2) 44,804 (66.1) 81,669 (61.6) 60,189 (57.3) 13,145 (53.4)
High alcohol intake 6,217 (42.0) 28,399 (41.9) 51,156 (38.6) 36,975 (35.2) 8,040 (32.7)
Physical activity
Low 5,881 (39.8) 23,595 (34.8) 41,171 (31.1) 29,436 (28.0) 6,099 (24.8)
Intermediate 7,062 (47.8) 34,965 (51.5) 70,856 (53.4) 57,738 (55.0) 13,867 (56.4)
High 1,844 (12.5) 9,271 (13.7) 20,545 (15.5) 17,883 (17.0) 4,642 (18.9)
Low-risk sleep factors
No frequent daytime sleepiness 11,548 (78.1) 64,428 (95.0) 130,700 (98.6) 104,784 (99.7) 24,608 (100.0)
No self-report snoring 705 (4.8) 21,430 (31.6) 80,077 (60.4) 89,739 (85.4) 24,608 (100.0)
No frequent insomnia 154 (1.0) 3,661 (5.4) 19,238 (14.5) 36,313 (34.6) 24,608 (100.0)
Sleep 7-8 h/d 642 (4.3) 25,262 (37.2) 88,672 (66.9) 98,637 (93.9) 24,608 (100.0)
Early chronotype 1,005 (6.8) 20,881 (30.8) 79,029 (59.6) 90,755 (86.4) 24,608 (100.0)
PRS
Low 4,918 (33.3) 22,475 (33.1) 44,268 (33.4) 34,985 (33.3) 8,306 (33.8)
Intermediate 4,976 (33.7) 22,505 (33.2) 44,229 (33.4) 35,039 (33.4) 8,202 (33.3)
High 4,893 (33.1) 22,851 (33.7) 44,075 (33.2) 35,033 (33.3) 8,100 (32.9)
Values are mean SD or n (%).
BMI ¼body mass index; PRS ¼polygenic risk score; TDI ¼Townsend Deprivation Index.
Zhu et al JACC: ADVANCES, VOL. 3, NO. 6, 2024
Sleep Pattern, Genetic Susceptibility, and Incident AAA JUNE 2024:100967
4
We then estimated the strength and direction of
the association between risk factors (sleep patterns
and PRS) and AAA events in the full model. In addi-
tion, ordered multicategorical variables were adapted
to perform linear trend tests. Array batches and the
significant principal components (P<0.05) were
additionally added to adjust the model, including PRS
for the impact of population stratification. We also
calculated the population attributable fraction (PAF),
an estimate of the proportion of events that theoret-
ically would not have occurred if all individuals had
been in the low-PRS and healthy sleep pattern
groups.
26
Finally, we calculated the 10-year event rates of
AAA according to different genetic risk groups and
sleep pattern categories using the Cox regression
model that were standardized to the mean of all
covariates. Considering the competing risk of non-
AAA mortality, we used a Fine and Gray model in
sensitivity analysis. All analyses were performed us-
ing R 4.2.0 and PRSice-2 2.3.5 software. All Pvalues
(2-sided) <0.05 were deemed significant.
RESULTS
BASIC CHARACTERISTICS. Atotalof334,855in-
dividuals were included in this prospective study. In
total, 1,622 AAA patients with a mean age of
63.49 4.84 years were confirmed during a median
follow-up of 12.59 years (IQR: 11.86-13.29 years),
86.9% of them were males. The comparison between
included and excluded participants was provided in
Supplemental Table 5.Table 1 delineates the baseline
characteristics of the participants, which have been
stratified according to their respective sleep scores.
Participants with higher sleep scores tend to exhibit a
healthy lifestyle.
A total of 14,787 (4.3%) and 200,403 (58.1%) par-
ticipants were categorized into poor and intermedi-
ate sleep pattern groups, respectively. Baseline
characteristics showed significant differences be-
tween individuals with and without AAA. Males and
older participants were more likely to develop AAA.
Individuals without AAA tend to exhibit healthy
sleep patterns and lifestyles. Three of the five sleep
factors were significantly different between incident
AAA and non-AAA cases, suggesting that no frequent
daytime sleepiness, no snoring, and sleeping for 7 to
8 hours per day were associated with a lower risk of
AAA.
ASSOCIATION OF SLEEP FACTORS WITH INCIDENT
AAA CASES. Utilizing multivariate Cox proportional
hazards models, the associations between the 5 sleep
factors, sleep scores, and AAA risk were estimated,
with adjustments considered. Three sleep compo-
nents, insomnia, sleep duration, and chronotype,
were independently associated with the risk of AAA
TABLE 2 Associations of Sleep Factors and Sleep Scores With Incident AAA in the UK Biobank
n(%)
Model 1
a
Model 2
b
Model 3
c
HR (95% CI) PValue HR (95% CI) PValue HR (95% CI) PValue
Low-risk sleep factors
No frequent daytime sleepiness 1,559 (4.64) 0.89 (0.69-1.14) 0.358 0.93 (0.72-1.19) 0.548 0.97 (0.75-1.25) 0.825
No self-report snoring 868 (4.01) 0.91 (0.83-1.01) 0.062 0.92 (0.83-1.01) 0.079 0.97 (0.88-1.07) 0.57
No frequent insomnia 373 (4.44) 0.83 (0.74-0.94) 0.002 0.85 (0.75-0.95) 0.006 0.88 (0.79-0.99) 0.037
Sleep 7-8 h/d 1,038 (4.36) 0.82 (0.74-0.91) <0.001 0.86 (0.77-0.95) 0.003 0.88 (0.80-0.98) 0.019
Early chronotype 999 (4.62) 0.83 (0.75-0.92) <0.001 0.84 (0.76-0.93) 0.001 0.89 (0.80-0.98) 0.018
Sleep scores
0-1 105 (7.10) Ref Ref Ref
2 375 (5.53) 0.78 (0.63-0.97) 0.026 0.81 (0.65-1.01) 0.057 0.85 (0.68-1.05) 0.138
3 654 (4.93) 0.72 (0.59-0.88) 0.002 0.76 (0.62-0.93) 0.009 0.84 (0.68-1.03) 0.09
4 412 (3.92) 0.59 (0.48-0.73) <0.001 0.64 (0.52-0.79) <0.001 0.73 (0.59-0.91) 0.005
5 76 (3.09) 0.47 (0.35-0.64) <0.001 0.51 (0.38-0.69) <0.001 0.62 (0.46-0.83) 0.002
Per 1 point (Pfor trend)
d
0.85 (0.81-0.89) <0.001 0.87 (0.83-0.91) <0.001 0.91 (0.86-0.96) <0.001
Sleep patterns
Healthy 488 (3.76) Ref Ref Ref
Intermediate 1,029 (5.13) 1.30 (1.17-1.45) <0.001 1.26 (1.13-1.41) <0.001 1.18 (1.06-1.31) 0.003
Poor 105 (7.10) 1.75 (1.42-2.16) <0.001 1.62 (1.31-2.01) <0.001 1.40 (1.13-1.73) 0.002
Pfor trend
d
<0.001 <0.001 <0.001
Bolding indicates statistical significance.
a
Adjusted for age and sex.
b
Adjusted for variables in model 1, TDI, history of hypertension, and family history of heart diseases.
c
Adjusted for variables
in model 2, BMI, smoking status, drinking status, and physical activity.
d
Test for linear trend across categories was performed by modeling the levels of sleep score/sleep pattern as a
continuous variable in a separate model.
AAA ¼abdominal aortic aneurysm; BMI ¼body mass index; TDI ¼Townsend Deprivation Index.
JACC: ADVANCES, VOL. 3, NO. 6, 2024 Zhu et al
JUNE 2024:100967 Sleep Pattern, Genetic Susceptibility, and Incident AAA
5
across all models (Table 2), exhibiting a lower risk of
17%, 18%, and 17%, respectively. Given that sleep is a
composite indicator, all 5 sleep factors were combined
into sleep scores. The sleep score was inversely asso-
ciated with the incidence of AAA, with higher scores
correlating with a lower relative risk for the outcome
(all Pforlineartrend<0.001). When compared to the
reference group (score 0-1), the HR for AAA was 0.47
(95% CI: 0.35-0.64), 0.51 (95% CI: 0.38-0.69), and 0.62
(95% CI: 0.46-0.83) for participants with a sleep score
of 5 across the 3 models, respectively. After adjusting
for demographic characteristics, clinical conditions,
and lifestyle factors (Model 3), each unit increase in
the sleep score was associated with a 9% decrease in
the risk of AAA (HR: 0.91, 95% CI: 0.86-0.96).
Furthermore, we performed a sensitivity analysis by
excluding the 2 insignificant sleep components in
calculating sleep scores and found that the association
between sleep scores and incident AAA remained
robust (Supplemental Table 6). In competing risk an-
alyses, the association between sleep scores and inci-
dent AAA remained robust after adjusting for non-AAA
mortality (Supplemental Table 7).
Subsequently, the participants were categorized
into different sleep pattern groups based on their
sleep scores, and the effects of these sleep patterns
were estimated. When compared to the group with a
healthy sleep pattern (sleep score $4), individuals
with intermediate (2 #sleep score #3) and poor (sleep
score #1) sleep patterns were found to have an
increased risk of developing AAA by 18% and 40%,
respectively (HR: 1.18, 95% CI: 1.06-1.31 and
HR ¼1.40, 95% CI: 1.13-1.73, respectively) in the fully
adjusted model (Model 3).
FIGURE 2 Association of PRS and Sleep pattern with Standardized rates of AAA events in the UK Biobank Cohort
(A) Distribution and category of PRS for AAA; (B) Standardized rate of AAA events in low, intermediate, and high genetic risk group; HRs and CIs were estimated in full
model; (C) Standardized rate of AAA events in healthy, intermediate, and poor sleep pattern groups; (D) Cumulative effects of genetic risk and sleep pattern on risk of
AAA. AAA ¼abdominal aortic aneurysm; PRS ¼polygenic risk score.
Zhu et al JACC: ADVANCES, VOL. 3, NO. 6, 2024
Sleep Pattern, Genetic Susceptibility, and Incident AAA JUNE 2024:100967
6
ASSOCIATION OF GENETIC RISK WITH INCIDENT
AAA CASES. For the PRS analysis, array batches and
10 significant principal components (P<0.05) were
added to adjust for the impact of population stratifi-
cation. The 22 GWAS-identified SNPs (Supplemental
Table 4)wereusedtoconstructthePRSandtoesti-
mate the potential association of genetic factors with
AAA risk according to genetic variants. In our cohort,
the PRS values approximated a normal distribution
(Figure 2A)andsignificantly predicted AAA risk in the
UK Biobank cohort (Figure 2B). Specifically, there was
a gradient of risk across each grade of PRS, and thus
participants with high genetic risk (the highest tertile
of PRS) had a significantly higher risk of AAA events
than those with low genetic risk (the lowest tertile of
PRS), with a multivariate adjusted HR of 2.15 (95% CI:
1.89-2.44).
We further analyzed the association of sleep
patterns and genetic factors with AAA incidence and
assessed the extent to which adherence to a healthy
sleep pattern can counteract genetic susceptibility.
Figures 2B and 2C show the adjusted cumulative
incidences of AAA according to sleep patterns and
genetic risk, respectively. A joint cumulative effect
of sleep patterns and genetic risk was observed for
incident AAA events, according to the results of the
UK Biobank cohort analysis (Figure 2D). Figure 3
shows that a gradual increase in AAA risk was
observed for sleep patterns across all genetic risk
groups. Participants with high genetic risk and a
poor sleep pattern had a more than 2.5-fold relative
risk of AAA in contrast to those with low genetic
risk and a healthy sleep pattern (HR: 2.56, 95% CI:
1.78-3.69). Stratification and interaction analyses
were performed to assess the association of sleep
patterns among genetic risk groups, and no signifi-
cant interaction between genetic risk and sleep
patterns was observed (Supplemental Table 8)
FIGURE 3 The Combined Association of Genetic Risk and Sleep Patter ns With the Risk of Incident AAA Among 344,855 Participants
AAA ¼abdominal aortic aneurysm; PT ¼total person-years; rciRef ¼reference group.
JACC: ADVANCES, VOL. 3, NO. 6, 2024 Zhu et al
JUNE 2024:100967 Sleep Pattern, Genetic Susceptibility, and Incident AAA
7
(P¼0.64 for interaction). We then calculated the
PAF for sleep patterns and genetic risks, assuming
that every participant was in the healthy sleep
pattern or low-PRS group, respectively. Sleep
pattern was estimated to explain 11.2% (95% CI:
4.62%-17.8%) of the population risk of developing
AAA, suggesting that more than 10% of AAA events
would have been prevented if all individuals
adhered to a healthy sleep pattern. Genetic risk
contributed more to AAA risk than sleep pattern,
with the PAF value estimated to be 31.5% (95% CI:
25.5%-37.6%).
Further analyses stratified by genetic risk category
confirmed that adherence to a healthy sleep pattern
was associated with a lower risk of AAA across genetic
groups (Central Illustration). Among participants with
high genetic risk, 5.7% (95% CI: 3.9%-7.5%) of those
with poor sleep patterns and 3.9% (95% CI: 3.3%-
4.4%) of those with healthy sleep patterns developed
AAA in the 10 years following up. A similar pattern
could be observed in populations with low and in-
termediate genetic risk, where the impact of genetic
risk was partially mitigated by adherence to healthy
sleep patterns.
CENTRAL ILLUSTRATION Ten-Year AAA Event Rates for Different Sleep Pattern Categories
According to 3 Genetic Risk Populations
Zhu D, et al. JACC Adv. 2024;3(6):100967.
Standardized 10-year cumulative AAA event rates in UK Biobank according to sleep pattern and genetic risk. Error bars are 95% CIs.
AAA ¼abdominal aortic aneurysm.
Zhu et al JACC: ADVANCES, VOL. 3, NO. 6, 2024
Sleep Pattern, Genetic Susceptibility, and Incident AAA JUNE 2024:100967
8
DISCUSSION
In this large-scale prospective study, we probed the
combined association of genetic risk and 5 sleep be-
haviors (sleepiness, snoring, insomnia, sleep dura-
tion, and chronotype) for AAA risk. We found that
healthy sleep factors were inversely associated with
the incidence of AAA and that genetic risk and sleep
patterns were independently associated with the risk
of AAA. Participants with a poor sleep pattern (sleep
score 0-1) and high genetic risk had a more than 2.5-
fold higher risk of incident AAA than those with low
genetic risk and a healthy sleep pattern (sleep score
4-5). Adherence to a healthy sleep pattern was asso-
ciated with a lower risk of AAA in all genetic risk
categories.
These findings lead to several conclusions. First,
the analysis results indicated that healthy sleep be-
haviors, including no frequent insomnia, sleeping 7 to
8 hours/day, and an early chronotype, were associ-
ated with a lower AAA risk. This conclusion is similar
to the one reported recently by a Mendelian
randomization study of insomnia and AAA,
27
though
the association was not significant (OR: 1.14, 95% CI:
0.98-1.33), probably due to the small number of cases.
Studies on OSA and AAA also suggest an association
between sleep behaviors and AAA progression,
13,14
but other features of OSA, such as apnea, may also
be associated with AAA. We also compared the dis-
tribution of sleep patterns between participants with
and without OSA and found that participants with
OSA were more likely to have poor sleep patterns
(Supplemental Table 9). Considering the correlation
of sleep behaviors, we combined all 5 sleep behaviors
to generate a sleep pattern as described previ-
ously,
12,22
even though some of them were not
significantly associated with AAA in our analysis.
Wefoundthatparticipantswithahealthysleep
pattern had a 40% lower risk of developing AAA. Our
results suggest that more than 11% of AAA cases may
be prevented if all participants adhered to a healthy
sleep pattern (PAF ¼11.2%). The precise mechanisms
underlying the combined effects of sleep factors on
the risk of developing AAA remain unclear. This
mechanism may involve the sympathetic nervous
system.
13
A previous study showed that shortened
sleep may increase the risk of endocrine and meta-
bolic disruption and elevated sympathetic nervous
activity.
28,29
From a public health perspective, our
findings support the implications of promoting
healthy sleep patterns in public health and clinical
practice.However,thiswasanobservationalstudy,
and further research on sleep should be conducted to
investigate the pathophysiology underlying this
association.
Similar to previous studies, our study found that
male and older participants were more likely to
develop AAA, and individuals with a healthy lifestyle,
such as no smoking,
30
more physical activity,
31
and a
healthy weight,
32
had a lower risk of developing AAA.
The known mechanisms by which smoking leads to
AAA development include disruption of collagen
synthesis, altered expression of metalloproteinases,
and response to oxidative stress.
33
Second, genetic risk and sleep patterns were
independently associated with the risk of incident
AAA. Numerous studies have highlighted the signifi-
cance of genetic susceptibility in primary preven-
tion.
16,34-36
In line with previous studies, the current
findings suggest that a higher genetic risk increases
the risk of AAA. However, while previous studies
were more often designed based on case-control
studies and a small population, the present study
validated the findings of previous GWASs in a large-
scale cohort of the European population. In this
study, the HR value for the high genetic risk popula-
tion (top tertile of the PRS) was 2.15, reinforcing the
importance of genetic susceptibility in AAA develop-
ment. With the availability of genome sequencing
technology, the application of genetic risk in pre-
dicting the occurrence of disease in individuals at the
early stages of life has become possible.
37-39
These
findings could complement the traditional classifica-
tion of populations with a high risk of AAA.
Finally, the benefits of a healthy sleep pattern for
AAA were observed in all genetic risk categories,
indicating that the rise in AAA risk contributed by
genetics can be balanced by healthy sleep, at least to
some extent. To our knowledge, our study is the first
to examine the association between combined
healthy sleep behaviors and the risk of AAA and to
evaluate the benefits in different genetic risk groups.
TherelativeriskforAAAdecreasedbymorethan30%
with healthy sleep patterns compared to poor sleep
patterns in the high genetic risk group (3.9% vs 5.7%)
(Central Illustration), whereas the relative reduction
in risk was higher in the moderate and low genetic
risk groups, emphasizing the advantages of adhering
to a healthy sleep pattern across the population.
Similarly, another cohort study found that partici-
pants with a sleep score of 5 had a 35% lower risk of
cardiovascular disease than those with a score of 0 to
1.
12
These findings further illustrate the implications
of healthy sleep patterns in public health and clinical
JACC: ADVANCES, VOL. 3, NO. 6, 2024 Zhu et al
JUNE 2024:100967 Sleep Pattern, Genetic Susceptibility, and Incident AAA
9
practice, particularly among individuals with high
genetic susceptibility to AAA.
The main strengths of the present study compared
to previous studies are that we estimated the associ-
ations of sleep patterns and the 5 components of
sleep behaviors with AAA and combined sleep pat-
terns and genetic factors to evaluate their combined
association with AAA. The design of this large-scale
prospective study was based on a UK Biobank study
that included data on extensive AAA-related SNPs
and the adoption of a standardized information
collection protocol.
STUDY LIMITATIONS. Despite these advantages, our
study has a few limitations. First, changes in the sleep
behaviors of participants after enrollment may affect
our results, and our study was conducted under the
assumption that sleep habits would not change
significantly over time. Second, although the known
potential confounders were adjusted in the Cox
model, it is possible that unmeasured confounders
and biases remained. Third, AAA is a progressive
disease, and patients in the early stages often have no
obvious symptoms and will not attend the hospital
unless they have life-threatening conditions; there-
fore, its incidence may be underestimated. Fourth,
the diagnosis of AAA may overlook certain undiag-
nosed cases when relying on the ICD-10 guidelines.
Additionally, the assessment of covariates and sleep
factors was conducted using a questionnaire, which
may have introduced some bias. Finally, this study
was limited to White British participants aged 39 to 73
years at recruitment; therefore, more research is
required to investigate the extent to which these re-
sults can be generalized to the populations of other
geographic regions in the world.
CONCLUSIONS
In the present study, a healthy sleep pattern was
found to be associated with a lower risk of AAA
despite the genetic risk, indicating that adherence to
a healthy sleep pattern may offset the influence of
genetic susceptibility. Our results provide a rationale
for developing healthy sleep patterns to prevent AAA.
From a public health perspective, our findings can
encourage policymakers to focus on primary preven-
tion and lower the risk of AAA by promoting healthy
sleep patterns. Further research is needed to explore
the mechanisms of sleep factorsaswellasgeneticrisk
in the development of AAA in the future.
FUNDING SUPPORT AND AUTHOR DISCLOSURES
This work was supported by the Ministry of Science and Technology
China Brain Initiative Grant (grant number: 2021ZD0202804), the
National Key Research and Development Program of China (grant
number: 2019YFC1315804, 2017YFC0907500), the Innovation Grant
from the Science and Technology Commission of Shanghai Munici-
pality, China (grant number: 20ZR1405600), 3-Year Action Plan for
Strengthening the Public Health System in Shanghai (grant number:
GWV-10.2-YQ32), and the Shanghai Municipal Science and Technol-
ogy Major Project (grant number: 2017SHZDZX01). The authors have
reported that they have no relationships relevant to the contents of
this paper to disclose.
ADDRESS FOR CORRESPONDENCE: Dr Lize Xiong,
Translational Research Institute of Brain and Brain-
Like Intelligence, Shanghai Fourth People’sHospital,
School of Medicine, Tongji University, No.1279, San-
men Road, Hongkou District, Shanghai 200434,
China. E-mail: mzkxlz@126.com.ORDrChenSuo,
Department of Epidemiology, School of Public Health,
Fudan University, No.130 Dong’an Road, Xuhui Dis-
trict, Shanghai 200032, China. E-mail: suochen@
fudan.edu.cn.
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KEY WORDS abdominal aortic aneurysm,
genetic risk, sleep pattern, sleep score
APPENDIX For supplemental tables, please
see the online version of this paper.
JACC: ADVANCES, VOL. 3, NO. 6, 2024 Zhu et al
JUNE 2024:100967 Sleep Pattern, Genetic Susceptibility, and Incident AAA
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