(2024) 22:368
Lin et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03553-2

BMC Medicine

RESEARCH ARTICLE

Open Access

Cardiovascular health and cancer mortality:
evidence from US NHANES and UK Biobank
cohort studies
Lijin Lin1,2†, Yulian Hu3,4†, Fang Lei2,5, Xuewei Huang2,6, Xingyuan Zhang2,7, Tao Sun1,2, Weifang Liu1,2, Ru Li1,2,
Xiao‑Jing Zhang2,7, Jingjing Cai2,6, Zhi‑Gang She1,2*, Guoping Wang4,8* and Hongliang Li1,2,4,5,9*   

Abstract
Background The American Heart Association recently introduced a novel cardiovascular health (CVH) metric, Life’s
Essential 8 (LE8), for health promotion. However, the relationship between LE8 and cancer mortality risk remains
uncertain.
Methods We investigated 17,076 participants from US National Health and Nutrition Examination Survey (USNHANES) and 272,727 participants from UK Biobank, all free of cancer at baseline. The CVH score, based on LE8
metrics, incorporates four health behaviors (diet, physical activity, smoking, and sleep) and four health factors (bodymass index, lipid, blood glucose, and blood pressure). Self-reported questionnaires assessed health behaviors. Primary
outcomes were mortality rates for total cancer and its subtypes. The association between CVH score (continuousand categorical variable) and outcomes was examined using Cox model with adjustments. Cancer subtypes-related
polygenic risk score (PRS) was constructed to evaluate its interactions with CVH on cancer death risk.
Results Over 141,526 person-years in US NHANES, 424 cancer-related deaths occurred, and in UK Biobank, 8,872
cancer deaths were documented during 3,690,893 person-years. High CVH was associated with reduced overallcancer mortality compared to low CVH (HR 0.58, 95% CI 0.37–0.91 in US NHANES; 0.51, 0.46–0.57 in UK Biobank).
Each one-standard deviation increase in CVH score was linked to a 19% decrease in cancer mortality (HR: 0.81; 95%
CI: 0.73–0.91) in US NHANES and a 19% decrease (HR: 0.81; 95% CI: 0.79–0.83) in UK Biobank. Adhering to ideal CVH
was linearly associated with decreased risks of death from lung, bladder, liver, kidney, esophageal, breast, colorectal,
pancreatic, and gastric cancers in UK Biobank. Furthermore, integrating genetic data revealed individuals with low PRS
and high CVH exhibited the lowest mortality from eight cancers (HRs ranged from 0.36 to 0.57) compared to thosewith high PRS and low CVH. No significant modification of the association between CVH and mortality risk for eightcancers by genetic predisposition was observed. Subgroup analyses showed a more pronounced protective association for overall cancer mortality among younger participants and those with lower socio-economic status.

†

Lijin Lin and Yulian Hu contributed equally to this work.

*Correspondence:
Zhi‑Gang She
zgshe@whu.edu.cn
Guoping Wangwangguoping@hgyy.org.cn
Hongliang Li
lihl@whu.edu.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/.

Lin et al. BMC Medicine(2024) 22:368

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Conclusions Maintaining optimal CVH is associated with a substantial reduction in the risk of overall cancer mortality. Adherence to ideal CVH correlates linearly with decreased mortality risk across multiple cancer subtypes. Individuals with both ideal CVH and high genetic predisposition demonstrated significant health benefits. These findings
support adopting ideal CVH as an intervention strategy to mitigate cancer mortality risk and promote healthy aging.
Keywords Life’s essential 8, Cardiovascular health, Cancer mortality, Cancer subtypes, Genetic predisposition

Background
Cancer is a prominent health issue globally and ranks
among the leading causes of premature death worldwide[1]. In recent years, there has been increasing attention
given to the impact of lifestyle factors on the occurrence
and progression of cancer. Adoption of healthy lifestylepractices has shown significant effects, with a remarkable
52% reduction in the risk of cancer mortality compared
to individuals with unhealthy lifestyles [2]. Researchemphasizes the crucial role of modifiable factors, suggesting that addressing these factors could potentiallyprevent approximately 40% of cancer cases [3]. Identifying multiple modifiable risk factors not only provides
opportunities for preventing or delaying the onset of cancer but also offers pathways for improving the prognosisof cancer patients facing challenging outcomes.
Cardiovascular diseases (CVDs) are also a major causeof death globally, similar to cancer [1, 4]. These conditions share common modifiable risk factors such as
smoking, obesity, lack of physical activity, and unhealthy
dietary habits, as well as underlying mechanisms likechronic inflammation and oxidative stress [5–9]. To
enhance cardiovascular health (CVH), the American
Heart Association (AHA) has evolved its assessment
approach from the traditional Life’s Simple 7 (LS7)score’s seven components (smoking, physical activity,
obesity, diet, total cholesterol, blood pressure, and blood
glucose) to the innovative Life’s Essential 8 (LE8) [10, 11].
This updated method integrates sleep health, measured
by optimal habitual sleep duration, as a novel component
supported by a mounting body of research consistently
associating sleep duration with CVH and overall healthoutcomes [12, 13]. Significantly, sleep duration is closely
associated with each of the original seven factors of CVH
and contributes independently to overall CVH [13]. The
inclusion of sleep health in the LE8 metrics holds great
potential for advancing CVH in the general population,although further substantiating data are still warranted.
Previous studies have reported the associations of CVH
defined by LE8 score with several important disease outcomes which include CVDs [14], nonalcoholic fatty liverdisease [15], dementia [16], chronic kidney disease [17],and all-cause mortality [18]. However, there are limited studies focused on cancer deaths. Although there
are several studies reported that adherence to higher

LS7-defined CVH is associated with lower cancer incidence [19–23]. However, most studies have been conducted in the United States and have primarily focusedon overall cancer incidence without delving into specific cancer subtypes [19–22]. One study carried out in
Europe that focused on the correlation between LS7CVH and overall cancer incidence, involving the French
population aged 30–50 rather than general population[23]. Additionally, a single-center study with a limited
sample size in the US examined the relationship between
LS7-CVH and cancer mortality, yielding non-significanceresults [24]. However, there is a lack of exploration of evidence supporting the association between LE8-defined
CVH and overall and cancer subtype mortality. Genomewide association study has successfully identified genetic
variants associated with specific common cancers [25].
Recent study highlights the dual contribution of genetic
factors and lifestyle factors to cancer risk [26]. Thus, further investigation into the combined effects of LE8 score,
genetic risk, and susceptibility to various cancer deaths iswarranted.
Consequently, we aim to assess the association
between LE8-indicated CVH and overall cancer and cancer subtype mortality among adult participants in the
US National Health and Nutrition Examination Survey(NHANES) and UK Biobank. Additionally, we seek todelve into the beneficial effects of CVH on cancer deathamong individuals with different levels of cancer genetic
susceptibility in the UK Biobank cohort.

Methods
Study populations

For both the US NHANES and UK Biobank cohorts,
we employed stringent inclusion and exclusion criteria
to ensure the reliability of our analysis. Detailed information regarding the study designs and data collection
can be found in prior publications (www.​cdc.​gov/​nchs/​nhanes/​about_​nhanes.​htm) [27, 28]. In brief, exclusion
criteria included individuals lacking complete CVH data,
those with prevalent cancer at baseline, participants
outside the specified age ranges, and those with missing
death data. Figure 1A and B provide an overview of theenrollment process for the US NHANES and UK Biobank
cohorts, respectively.

Lin et al. BMC Medicine(2024) 22:368

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Fig. 1 Flowchart of participants from US NHANES (A) and UK Biobank (B) in this study. CVH, cardiovascular health; US, United States; UK,
United Kingdom; NHANES, national health and nutrition examination surveys; CVH, cardiovascular health; BMI, body mass index; non-HDL-C,
nonhigh-density lipoprotein cholesterol

For the US NHANES cohort, we enrolled a comprehensive cohort of 31,908 participants aged 20 years andolder, covering the continuous survey years from 2005 to

2018. After applying the exclusion criteria, our final analytical cohort comprised 17,076 participants.
Similarly, for the UK Biobank cohort, we recruited a
total of 502,412 participants aged 37–73 from 2006 toLin et al. BMC Medicine(2024) 22:368

2010. Following the same exclusion criteria, our final
analytical cohort comprised 272,727 participants.
The NHANES protocol was approved by the National
Center for Health Statistics’ (NCHS) Research Ethics Review Board, and all participants provided written
informed consent. Moreover, the UK Biobank received
ethical approval from the North West Multi-centre
Research Ethics Committee. The UK Biobank research
was carried out utilizing the UK Biobank Resource underapplication number 77195.
Assessment of updated CVH metrics in LE8

According to the AHA’s definition of the LE8 score, anoriginal version for assessing CVH was developed within
the US NHANES cohort, while a modified version was
implemented within the UK Biobank cohort [10]. In bothcohorts, CVH is delineated by eight metrics, comprising four health behaviors (diet, physical activity, nicotine
exposure, and sleep) and four health factors (body massindex [BMI], non-high-density lipoprotein cholesterol[HDL-C], blood glucose, and blood pressure).
The original Dietary Approaches to Stop Hypertension(DASH) diet score, applied in the US NHANES cohort,and its modified counterpart in the UK Biobank, wereboth based on the DASH-style eating pattern proposedby the AHA [10, 29] (Additional file 1: Table S1). While
the UK Biobank maintained the original seven-factor
classifications, all eight metrics in the US NHANES
cohort were derived from the original version. Detailed
information and scoring algorithms for each CVH metric in these cohorts can be found in Additional file 1:
Table S2. The total CVH score was obtained by summingthese eight components and dividing them by 8, witheach component ranging from 0 to 100 points. Higherscores indicate better CVH. In addition to considering
CVH continuous variables, our study also incorporated
CVH categorical variables. Following AHA recommendations [10], overall CVH was categorized into low (< 50points), moderate (50–79 points), and high (≥ 80 points)
based on the LE8 score.
In both the US NHANES cohort and the UK Biobank
cohort, various essential health metrics were assessedusing standardized methodologies.
For the US NHANES cohort, the original DASH eating pattern served as the basis for evaluating diet metrics[10] (Additional file 1: Table S1). This involved gathering
dietary intake data from two 24-h recall interviews to
calculate DASH scores. Additionally, self-report questionnaires were utilized to collect data on physical activity, smoking status, sleep patterns, diabetes history, andmedication usage. Physical examinations included measurements of blood pressure, height, and weight, fromPage 4 of 16

which BMI was derived. Blood samples were collected
and analyzed for blood lipids, plasma glucose, and hemoglobin A1c (HbA1c) levels using established laboratory
procedures.
Similarly, in the UK Biobank cohort, participants
reported their daily intake of a DASH-style eating pattern, which was adjusted using available dietary variables
within the dataset [10, 29] (Additional file 1: Table S1).
Baseline assessments included touch screen questionnaires to ascertain the frequency and duration of moderate and vigorous physical activity, nicotine exposure,
sleep duration, and medication usage. Height and weightmeasurements were taken during the initial assessment
center visit, and BMI was calculated accordingly. Laboratory analyses involved determining total cholesterol levels through CHO-POD analysis and measuring HDL-C
levels using enzyme immunoinhibition methods on a
Beckman Coulter AU5800 instrument. HbA1c levels
were assessed using high-performance liquid chromatography on a Bio-Rad VARIANT II Turbo system. Bloodpressure measurements were obtained using an Omron
device, with averaged values utilized for subsequent
analyses.
Outcome ascertainment

Outcomes were classified using ICD-10 (internationalclassification of diseases, 10th revisions) codes. The
primary outcomes were total cancer mortality (ICD10 codes, C00–C97, in US NHANES and UK Biobank
cohort), and the secondary outcome encompassed cancer subtype mortality (in UK Biobank cohort only). These
subtypes included lip, oral cavity and pharynx, esophagus, stomach, colorectum, liver, pancreas, lung, soft tissue, melanoma, breast, uterus, ovary, prostate, kidney,
bladder, brain, lymphoma, and leukemia cancers; subtypes with small case numbers (< 100) were excluded
from the analysis to avoid results of limited statisticalpower. The detailed ICD-10 revision codes are provided
in Additional file 1: Table S3.
In the US NHANES cohort, baseline data fromNHANES 2005–2018 were linked with causes of deathdata extracted from the National Death Index death certificate records until December 31, 2019. This process
employed an enhanced linkage methodology to accurately ascertain participants’ mortality status. Specifically,
the enhanced linkage methodology, rooted in the FellegiSunter paradigm, was adopted to account for changes inthe survey data collection process. The enhanced linkage
algorithm was designed to enhance match quality while
minimizing both type I (false positive) and type II (false
negative) linkage errors. Further details of the matching
method are available from the NCHS (https://​www.​cdc.​
gov/​nchs/​data-​linka​ge/​morta​lity-​public.​htm).

Lin et al. BMC Medicine(2024) 22:368

In the UK Biobank cohort, mortality data were systematically compiled from records maintained by the
National Health Service (NHS) England (England &Wales) and the NHS Central Register, National Recordsof Scotland (Scotland) up to December 19, 2022. Additional details about the linkage procedure are available
online at UK Biobank (https://​www.​ukbio​bank.​ac.​uk/).
Assessment of covariates

We evaluated potential confounders by using a comprehensive array of health-related data, including questionnaires, physical examinations, biochemical index
examinations, and medical history in both the US
NHANES cohort and the UK Biobank cohort.
Variables with the potential for confounding included
the following: age (continuous), sex (female/male), race/
ethnicity (White/non-White), marital status (married/
other status, US NHANES only), education level (college or above/high school or equivalent/less than highschool), assessment center (22 assessment centers, UK
Biobank only), Townsend deprivation index (continuous,
derived from the postcode of residence using aggregateddata on unemployment, car and homeownership, andhousehold overcrowding; a higher score indicates worsesocioeconomic status (SES) [30], UK Biobank only), family income level (continuous, operationalized using thefamily poverty to income ratio [31], US NHANES only),
first 10 principal components of ancestry at baseline(continuous, UK Biobank only), comorbidities (Charlson
comorbidity index [CCI] score used to evaluate overall
comorbidity status of participants in the UK Biobank;Baseline hypertension, diabetes, and CVD used to evaluate the overall comorbidity status of participants in the
US NHANES).
The CCI was calculated by summarizing the presenceof 15 medical conditions and their weight scores basedon severity [32]; SES in the NHANES cohort was measured by family income to poverty ratios and divided into
three categories: high (≥ 3.5), medium (> 1.3 to < 3.5), andlow (≤ 1.3) [33]. SES in the UK Biobank cohort was measured by the Townsend deprivation index categorized into
three categories: high (Q1), medium (Q2), and low (Q3).
The variables and codes used for disease diagnosis andCharlson comorbidities are detailed in Additional file 1:
Table S4 and Additional file 1: Table S5.
Calculation of the polygenic genetic risk score (PRS)

Genotyping was conducted by the UK Biobank teamusing two similar arrays: the UK BiLEVE Axiom Array
and the UK Biobank Axiom Array. Detailed information
is available at http://​www.​ukbio​bank.​ac.​uk/​scien​tists-3/​
genet​ic-​data/.

Page 5 of 16

To construct the PRS for eight specific cancers, we
obtained standard PRS for five specific cancers—colorectal cancer (Field ID: 26,218), breast cancer (Field
ID: 26,220), ovarian cancer (Field ID: 26,232), melanoma (Field ID: 26,252), and prostate cancer (Field ID:
26,267)—from genomics data in the UK Biobank.
Weighted PRSs for lung, esophageal, and pancreaticcancers were calculated using single nucleotide polymorphisms (SNPs) identified from genome-wide association
studies. Specifically, 9 SNPs were used for lung cancer, 14
SNPs for esophageal cancer, and 54 SNPs for pancreaticcancer. The coding for each SNP is 0, 1, or 2 based on thenumber of risk alleles. The weight for lung cancer PRS
was determined using the formula: PRS = (β1 × SNP1 +β2 × SNP2 + … + β9 × SNP9). Similarly, PRS for esophageal or pancreatic cancer was created using their corresponding SNPs identified from the PRS for each cancer.
A higher PRS indicates a greater genetic predisposition
to specific cancers. Participants were classified into low(Q1), moderate (Q2), and high (Q3) categories based on
the tertiles of each cancer PRS. Details on the sources
and components of the PRS scores for the eight cancers
are provided in Additional file 1: Table S6.
Statistical analysis

In both the US NHANES and UK Biobank cohorts, baseline characteristics of participants were presented asmean (standard deviation [SD]) or median (interquartile
range [IQR]) for continuous variables and percentages
for categorical variables. Cox proportional hazard models were used to estimate hazard ratios (HRs) and theircorresponding 95% confidence interval (CI) for the association between LE8 score and the risk of overall cancer
and subtype cancer. The proportional hazards assumptions of the Cox model were tested using the Schoenfeld
residual method and found to be satisfied [34]. Furthermore, a correlation matrix analysis was performed toevaluate collinearity between all included covariates, andno multicollinearity was detected. To correct for multiple
testing, the Benjamin-Hochberg method was applied toadjust the P values using the false discovery rate (FDR)[35].
In both the US NHANES and UK Biobank cohorts,
Kaplan–Meier survival curves were generated for the
calculation of cumulative total cancer mortality using
three categories of CVH (low, moderate, high), andcomparisons were using the log-rank test. Model 1
was a crude model. Model 2 was adjusted for sex, age,and race/ethnicity. Model 3 was further controlled forassessment center, education levels, SES, comorbidities,
and first 10 principal components of ancestry. The analyses for behavior and biological subscale scores wererepeated with mutual adjustment for each other in theLin et al. BMC Medicine(2024) 22:368

model. Restricted cubic spline models with three knots(10th, 50th, and 90th percentiles) were conducted toestimate the dose–response association of total CVH
score with the mortality of subtype cancers, with 50
points of CVH score as the reference.
In the UK Biobank cohort, we examined the associations of PRS categories, CVH categories, and their
combined impact on the mortality of subtype cancers.
To explore whether the genetic predisposition could be
mitigated by CVH, we tested the interaction between
CVH categories and the PRS categories of subtype cancers in the model. Additionally, we examined the relationship between CVH and outcomes by introducing
interaction terms between CVH and age, sex, or SES
into the model. This allowed us to evaluate whether the
association between CVH and outcomes was influenced
by sex differences, age variations, or differences in SES.
Several subgroup analyses were conducted using Cox
regression model to test the difference between subgroups by socio-demographic features, including age(< 65 vs. ≥ 65 years), sex (male vs. female), and SES status (low SES vs. high SES, defined by Townsend deprivation index, where low SES indicates high deprivationand high SES indicates less deprivation).
In the UK Biobank cohort, we conducted several sensitivity analyses to ensure the reliability and robustnessof our results. Firstly, the 24-h dietary recall questionnaire was distributed to participants who provided email
addresses during the initial visit to the UK biobank. In
our included participants, we utilized diet data fromthe 24-h dietary recall questionnaire instead of fromthe touch screen questionnaires in this sensitivity analysis, which were used to calculate the original DASH dietscore proposed by AHA. Subsequently, we computed
the original LE8 score and analyzed the association of
CVH with cancer outcomes (N = 122,303). Secondly, we
excluded events that occurred within two years at baseline to address any concerns regarding reverse causality(N = 266,261). Thirdly, we performed the main analyses
exclusively among participants with complete covariate
data to minimize potential biases resulting from missing
information (N = 232,035). Fourthly, we conducted themain analyses among participants without CCI scores
greater than one score to assess the potential impact ofthese comorbidities on the association between CVH
and cancer mortality (N = 252,114). Finally, based on themain model, we further corrected for more confounders,
including hormone replacement therapy and C-reactive
protein (N = 272,727).
To account for any missing covariate values in the analysis, we employed the "missForest" R package for multiple

Page 6 of 16

imputations, ensuring statistical power and minimizing
inferential bias [36]. Detailed missing rates for the covariates used in this study are provided in Additional file 1:
Table S7.
All statistical analyses were conducted using R software version 4.2. All statistical tests were two-sided, anda significant level of P < 0.05 was considered statisticallysignificant.

Results
Baseline characteristics of study participants in US
NHANES and UK Biobank cohorts

Descriptive characteristics of participants, stratified
by CVH categories, are presented in Additional file 1:
Table S8 for both the US NHANES and UK Biobank
cohorts.
In the US NHANES cohort, consisting of 17,076 participants with a mean age of 53.3 (14.6) years (50.8% female,
45.9% non-Hispanic White American background), 2,959(17.3%) had low CVH, 11,611 (68.0%) had moderate
CVH, and 2,506 (14.7%) had high CVH. Participants withhigher CVH tended to be younger, female, highly educated, married, of higher SES, and had a lower prevalenceof comorbidities. Excluded participants, due to missing
information, were aged < 30 or age > 80, or had prevalentcancer, were younger and more likely to be female, nonWhite, of lower SES, highly educated, unmarried, andhad better health status compared to the included participants (Additional file 1: Table S9).
In the UK Biobank cohort, comprising of 272,727
participants with a mean age of 56.5 (8.2) years (51.6%
female, 95.5% White European background), 15,364(5.6%) had low CVH, 220,571 (80.9%) had moderate
CVH, and 36,792 (13.5%) had high CVH. Participants
with higher CVH were generally younger, female, highly
educated, of higher SES, and had a lower prevalenceof comorbidities. Excluded participants, due to missing information or prevalent cancer, compared to theincluded participants, were older and more likely to befemale, of lower SES, highly educated, and had worse
health status compared to the included participants (seeAdditional file 1: Table S9).
The UK Biobank cohort demonstrates an older age, ahigher proportion of females, individuals of White ethnicity, and a higher CVH score compared to the US
NHANES cohort. However, the baseline characteristicsof the CVH groups between the two cohorts are similar.
Additional file 1: Table S10 presents the baseline characteristics of the included, categorized based on whether
they experienced cancer-related mortality in the twocohorts.

Lin et al. BMC Medicine(2024) 22:368

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Association of LE8 score with overall cancer mortality in US
NHANES and UK Biobank cohorts

In the US NHANES cohort, a total of 424 cancer deaths
were recorded during a mean follow-up of 8.3 (3.4) years.
The total cancer death rates per 1,000 person-years
among participants with CVH at moderate and high levels were significantly lower than those with a low level(as shown in Table 1). The cumulative mortality of totalcancer exhibited a graded relationship based on the levelsof CVH categories during follow-up in the US NHANES
cohort (P < 0.001 for the log-rank test, as illustrated inFig. 2A).
After adjusting for potential covariates, including age,sex, race, education level, marital status, SES, and history of comorbidities, a significant inverse association
between CVH and cancer mortality was observed in the
US NHANES cohort (Ptrend = 0.012). Compared to participants with low CVH, the HRs (95%CI) of cancer mortality were 0.81 (0.64–1.02, P = 0.069) and 0.58 (0.37–0.91,
P = 0.019) for the moderate CVH and high CVH groups
in the US NHANES cohort, respectively. Additionally,
per SD increment in LE8 score was associated with a 19%

decrease in risk (HR, 0.81, 95% CI 0.73–0.91, P < 0.001)
for cancer mortality in the US NHANES cohort.
Furthermore, the subscale of the CVH scores in the
US NHANES study, specifically the behavior subscale,showed a significant association with reduced risks ofcancer mortality. This finding supports the LE8 score,suggesting that behaviors have a vital role in lowering
the risk of cancer-related deaths. Notably, the behavior scale exhibited enhanced magnitudes of association,
particularly in nicotine exposure score. Although there
were non-significant associations for diet, physical activity, and sleep health scores, a similar trend was observedwith higher scores being associated with a reduced risk ofcancer mortality, while the biological scale, as well as theother health factors of the LE8, did not reach statisticalsignificance in this regard (as shown in Table 1 and Additional file 1: Table S11).
In the UK Biobank cohort, a total of 8872 cancer deaths
were recorded during a mean follow-up of 13.5 (1.8)
years. The total cancer death rates per 1,000 person-years
among participants with CVH at the moderate and highlevels were significantly lower than those with low level(as shown in Table 1). The cumulative mortality of total

Table 1 Associations of CVH score with overall cancer mortality in US NHANES and UK Biobank cohort
N

Event/person-years

Model 1 HR (95%CI) a;
P-value

Model 2 HR (95%CI) b;
P-value

Model 3 HR (95%CI) c;
P-value

Low CVH

2,959

108/23,916

Ref

Ref

Ref

Moderate CVH

11,611

290/96,783

0.66 (0.53–0.82); < 0.001

0.71 (0.57–0.89); 0.003

0.81 (0.64–1.02); 0.069

High CVH

2,506

26/20,827

0.28 (0.18–0.42); < 0.001

0.46 (0.30–0.71); < 0.001

0.58 (0.37–0.91); 0.019< 0.001< 0.001

0.012

424/141,526

0.70 (0.64–0.77); < 0.001

0.77 (0.70–0.86); < 0.001

0.81 (0.73–0.91); < 0.001

All cancer mortality
US NHANESP for trend
Per SD increase in CVH

17,076Per SD increase in CVH subscale d
Behavior scale

17,076424/141,526

0.71 (0.65–0.78); < 0.001

0.70 (0.64–0.77); < 0.001

0.75 (0.68–0.82); < 0.001

Biological scale

17,076424/141,526

0.80 (0.73–0.88); < 0.001

1.02 (0.91–1.13); 0.790

1.03 (0.91–1.17); 0.602

Low CVH

15,364851/203,525

Ref

Ref

Ref

Moderate CVH

220,571

7,378/2,982,590

0.59 (0.55–0.63); < 0.001

0.60 (0.56–0.65); < 0.001

0.65 (0.60–0.70); < 0.001

High CVH

36,792643/504,778

0.30 (0.27–0.33); < 0.001

0.45 (0.41–0.50); < 0.001

0.51 (0.46–0.57); < 0.001< 0.001< 0.001< 0.001

8,872/3,690,893

0.72 (0.70–0.73); < 0.001

0.78 (0.77–0.80); < 0.001

0.81 (0.79–0.83); < 0.001

UK biobank

P for trend
Per SD increase in CVH

272,727

Per SD increase in CVH subscale d
Behavior scale

272,727

8,872/3,690,893

0.79 (0.77–0.80); < 0.001

0.77 (0.75–0.78); < 0.001

0.79 (0.77–0.80); < 0.001

Biological scale

272,727

8,872/3,690,893

0.78 (0.76–0.79); < 0.001

0.91 (0.89–0.94); < 0.001

0.89 (0.85–0.92); < 0.001

US United States, UK United Kingdom, NHANES national health and nutrition examination surveys, HR Hazard ratio, CI Confidence interval, CVH Cardiovascular health,
SD Standard deviationaMode1 1: crude modelb

Model 2: adjusting sex, age and race/ethnicitycModel 3: model 2 + additional adjusting education level, Townsend deprivation index (ratio of family income to poverty in NAHANES), Charlson comorbidity index(diabetes, hypertension and cardiovascular disease history in NAHANES), and the first 10 principal components of ancestry (UK Biobank only)dBehavior factor including diet, physical activity, tobacco/nicotine exposure, and sleep health. Biological factor including body mass index, blood lipids, blood
glucose, and blood pressure. Behavior scale and biological scale were further mutually adjusted for subscale analyses

Lin et al. BMC Medicine(2024) 22:368

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Fig. 2 Cumulative Survival probability of overall cancer according to CVH categories defined by LE8 in US NHANES (A) and UK Biobank (B) cohort.
US, United States; UK, United Kingdom; NHANES, national health and nutrition examination surveys; CVH, cardiovascular health; Life’s Essential 8, LE8

cancer demonstrated a graded relationship based on thelevels of CVH categories during the follow-up in the UK
Biobank cohort (P < 0.001 for the log-rank test, as illustrated in Fig. 2B).
After adjusting for potential covariates, including age,sex, race, education level, SES, assessment centers, history of comorbidities, and first 10 principal components
of ancestry, similarly, a significant negative association
between CVH and cancer mortality was observed in the
UK Biobank cohort (Ptrend < 0.001). The HRs (95%CI)
were 0.65 (0.60–0.70, P < 0.001) and 0.51 (0.46–0.57,
P < 0.001) in the UK Biobank study, respectively. Additionally, per SD increment in LE8 score was associatedwith a 19% decrease in risk (HR, 0.81, 95% CI 0.79–0.83,
P < 0.001) for cancer mortality.
Furthermore, for the subscales of the CVH in the UK
Biobank study, both the behavior and biological subscale scores remained significantly associated with lower
risks of cancer mortality, similar to the overall LE8 score.
However, there was a greater magnitude of association
observed in the behavior scale, while the magnitudes inthe biological scale was attenuated (as shown in Table 1).
When we analyzed the individual component of LE8 andsimultaneously included all the metrics in the model, we
found that higher scores of diets, sleep health, nicotine
exposure, body mass index, blood glucose, and bloodpressure were significantly associated with a reduced riskof overall cancer mortality (all Ptrend < 0.01). Conversely,a higher score of blood lipid was significantly associated with an increased risk of cancer mortality. Although
there were non-significant associations for physical activity, a similar trend was observed, with higher scores being
associated with a reduced risk of outcomes (as shown in
Additional file 1: Table S11).
In summary, our study demonstrated a protective
association of high CVH with total cancer deaths in
two cohorts, namely the US NHANES and UK Biobank
cohorts. Notably, the association was found to be moresignificant in the UK Biobank cohort. Building uponthese findings, we further examined the relationshipbetween CVH and cancer-specific deaths, with a particular focus on the combinations and interaction of genetic
predisposition and CVH in relation to subtype-specificcancer deaths.
Association of LE8 score with subtype‑specific cancer
mortality in the UK Biobank cohort

In the UK Biobank cohort, we examined the impact of
the LE8 score on subtype-specific cancer mortality, as
detailed in Table 2. The analysis revealed a compellingassociation between higher CVH scores and reduced
mortality risks across various cancer subtypes.
For lung, bladder, liver, kidney, esophagus, breast,colorectum, and pancreas cancers, participants withhigher CVH scores exhibited significantly lower mortality risks (HRs ranged from 0.20 to 0.58, all adjusted P

Lin et al. BMC Medicine(2024) 22:368

Page 9 of 16

Table 2 Associations of CVH score with cancer subtype mortality in the UK Biobank cohort
Cancer subtype
mortality

Low CVH

Moderate CVH

N all participants

15,364

220,571

P-value a High CVH

P-value a Per SD increment in
CVH score

36,792

P-value a272,727

Lip, oral cavity and pharynx
Event/person-years

14/203,525

85/2,982,590

HR (95% CI) b; P-value

Ref

0.55 (0.31–0.98); 0.044

8/504,778
0.099

0.47 (0.19–1.14);0.096

107/3,690,893
0.173

0.80 (0.65–0.98); 0.027

0.049

Esophagus
Event/person-years

56/203,525

378/2,982,590

26/504,778

460/3,690,893

HR (95% CI) b; P-value

Ref

0.55 (0.41–0.73); < 0.001 < 0.001

0.40 (0.25–0.63); < 0.001 0.001

0.70 (0.64–0.78); < 0.001< 0.001

StomachEvent/person-years

25/203,525

219/2,982,590

HR (95% CI) b; P-value

Ref

0.68 (0.45–1.04); 0.073

18/504,778
0.146

0.55 (0.30–1.02); 0.056

262/3,690,893
0.111

0.82 (0.72–0.94); 0.003

0.007

Colorectum
Event/person-years

83/203,525

789/2,982,590

71/504,778

943/3,690,893

HR (95% CI) b; P-value

Ref

0.67 (0.53–0.84); < 0.001 0.002

0.50 (0.36–0.69); < 0.001 < 0.001

0.85 (0.79–0.91); < 0.001< 0.001

Liver
Event/person-years

37/203,525

282/2,982,590

HR (95% CI) b; P-value

Ref

0.60 (0.42–0.85); 0.004

0.013

17/504,778

336/3,690,893

0.33 (0.18–0.59); < 0.001 0.001

0.71 (0.63–0.80); < 0.001< 0.001

Pancreas
Event/person-years

77/203,525

659/2,982,590

68/504,778

HR (95% CI) b; P-value

Ref

0.62 (0.49–0.79); < 0.001 < 0.001

0.58 (0.42–0.81); 0.001

804/3,690,893
0.005

0.83 (0.77–0.89); < 0.001< 0.001Lung
Event/person-years

246/203,525 1,275/2,982,590

57/504,778

1,578/3,690,893

HR (95% CI) b; P-value

Ref

0.20 (0.15–0.27); < 0.001 < 0.001

0.60 (0.57–0.63); < 0.001

0.45 (0.39–0.51); < 0.001 < 0.001< 0.001

Soft tissue
Event/person-years

16/203,525

283/2,982,590

HR (95% CI) b; P-value

Ref

1.33 (0.80–2.20); 0.274

21/504,778
0.448

1.03 (0.53–1.98); 0.937

0.593

0.91 (0.39–2.11); 0.822

0.085

0.36 (0.18–0.71); 0.004

0.487

0.29 (0.11–0.72); 0.008

0.706

1.11 (0.71–1.76); 0.644

0.593

0.84 (0.49–1.43); 0.516

0.719

1.01 (0.53–1.91); 0.979

320/3,690,893
0.979

1.06 (0.94–1.20); 0.355

0.979

1.09 (0.90–1.31); 0.373

0.010

0.75 (0.65–0.86); < 0.001

0.018

0.74 (0.64–0.86); < 0.001

0.828

1.00 (0.90–1.10); 0.962

0.714

0.92 (0.82–1.02); 0.121

0.979

0.99 (0.87–1.12); 0.847

0.419

Melanoma
Event/person-years

9/203,525

105/2,982,590

HR (95% CI) b; P-value

Ref0.80 (0.40–1.59); 0.527

15/504,778

129/3,690,893
0.419

KidneyEvent/person-years

25/203,525

205/2,982,590

HR (95% CI) b; P-value

Ref

0.63 (0.41–0.95); 0.028

12/504,778

242/3,690,893< 0.001

Bladder
Event/person-years

18/203,525

183/2,982,590

HR (95% CI) b; P-value

Ref

0.78 (0.48–1.28); 0.325

6/504,778

207/3,690,893< 0.001

Brain
Event/person-years

29/203,525

376/2,982,590

HR (95% CI) b; P-value

Ref

0.92 (0.63–1.35); 0.667

57/504,778

462/3,690,893
0.962

Lymphoma
Event/person-years

25/203,525

301/2,982,590

HR (95% CI) b; P-value

Ref

0.87 (0.58–1.32); 0.51230/504,778

356/3,690,893
0.168

Leukemia
Event/person-years

17/203,525

247/2,982,590

HR (95% CI) b; P-value

Ref

1.10 (0.66–1.83); 0.719

5954

109,132

N (Females)

24/504,778

288/3,690,893

25,7190.896

140,805

BreastEvent/person-years

24/79,853

292/1,486,725

HR (95% CI) b; P-value

Ref

0.65 (0.43–0.99); 0.044

9/79,853

132/1,486,725

47/353,770
0.099

0.48 (0.29–0.79); 0.004

363/1,920,348
0.010

0.85 (0.77–0.95); 0.003

UterusEvent/person-years

17/353,770

158/1,920,348

0.007

Lin et al. BMC Medicine(2024) 22:368

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Table 2 (continued)
Low CVH

Moderate CVH

P-value a High CVH

P-value a Per SD increment in
CVH score

P-value a

Ref

0.79 (0.40–1.57); 0.504

0.593

0.401

0.116

Event/person-years

12/79,853

263/1,486,725

HR (95% CI) b; P-valueRef

1.22(0.68–2.18); 0.506

9410

111,439

Cancer subtype
mortality
HR (95% CI) b; P-value

0.63 (0.28–1.43); 0.267

0.86 (0.73–1.02); 0.077

Ovary

N (Male)36/353,770
0.593

0.99 (0.51–1.92); 0.979

311/1,920,348
0.979

11,0730.94 (0.83–1.06); 0.301

0.388

131,922

Prostate
Event/person-years

40/123,672

417/1,495,865

HR (95% CI) b; P-value

Ref

0.82 (0.59–1.14); 0.238

28/151,008
0.428

0.68 (0.41–1.11); 0.119

485/1,770,545
0.194

0.91 (0.82–1.00); 0.052

0.085

UK, United Kingdom; HR, hazard ratio; CI, confidence interval; CVH, cardiovascular health; SD, standard deviationaAdjusted P values were corrected via the false discovery rate by using the Benjamin-Hochberg methodbModel was controlled for age, sex, and ethnicity, assessment center, education levels, Townsend deprivation index, Charlson comorbidity index, and first 10 principal
components of ancestry

values < 0.05). Moreover, per SD increment in CVH demonstrated an inverse association with mortality acrossthe same eight subtype-specific cancers (HRs ranged
from 0.60 to 0.85, all adjusted P values < 0.05). Beyond
the initially highlighted cancer types, per SD incrementin CVH was linked to decreased mortality risks for lip,oral cavity, and pharynx cancer (HR 0.80, 95% CI 0.65–
0.98, Padjusted < 0.05) and stomach cancer (HR 0.82, 95%
CI 0.72–0.94, Padjusted < 0.01). While there were no statistically significant associations between CVH scores andmortality risks for soft tissue cancer, melanoma, uterus
cancer, ovary cancer, prostate cancer, brain cancer, lymphoma, and leukemia, a consistent protective trend wasobserved.
Notably, the graphical representation in Additional
file 1: Fig. S1 highlights a steep linear association for
mortality across nine cancer subtypes with CVH, allexhibiting significant linear trends (all Pnon-linear > 0.05).
No evidence of linear associations was found for mortality caused by the other nine cancer subtypes in relation
to CVH.
Combined association and interaction of genetic risk
with LE8 score on multiple subtype‑specific cancer
mortality in the UK Biobank cohort

In the UK Biobank cohort, we successfully constructed
the PRS for eight subtype cancers, as outlined in Additional file 1: Table S12. The joint impact of the LE8 score
and corresponding PRS on multiple subtype-specificcancer mortality is visually presented in Fig. 3. Notably,
participants with high CVH and low PRS exhibited themost favorable outcomes for each subtype-specific cancer mortality. Compared to those who had low CVH
and high PRS, participants with high CVH and low PRS
showcased the lowest mortality risks across a spectrumof cancers, including ovary, prostate, colorectum, lung,pancreas, esophagus, breast, and melanoma (HRs ranged
from 0.36 to 0.57, with all P values < 0.001).
Detailed subgroup analyses, stratified by PRS, are presented in Additional file 1: Table S13. Notably, the genetic
predisposition to the eight cancer subtypes did not significantly alter the association between the LE8 Score
and mortality risk. However, within each genetic risk category, a consistent and compelling observation emerged.
Higher CVH was consistently associated with a significantly reduced risk of death across multiple cancer sites
in the UK population. This emphasizes the potential ofelevated CVH as a protective factor mitigating mortality
risks associated with various cancer subtypes.
Subgroup and sensitivity analysis

After conducting subgroup analyses on CVH and totalcancer mortality, a significant protective effect was identified, the protective effect was significantly enhanced inindividuals younger than 65 years and those with lower
SES (Pinteraction < 0.05). While no significant differences in
the associations of the CVH score with multiple cancer
subtype-specific mortalities were observed across subgroups, notable distinctions emerged in some cancer subtypes. A statistically significant difference was noted in
the association between the CVH score and prostate cancer mortality within age subgroups. The protective association demonstrated a notably heightened magnitudein individuals younger than 65 years (Pinteraction = 0.011).
Similarly, a statistically significant difference in the associations of the CVH score with esophagus cancer mortality was observed within sex subgroups. The protective
association demonstrated a notably heightened magnitude in the male population (Pinteraction = 0.001) (Additional file 1: Fig. S2–Fig. S4).
To ensure the robustness of our findings, we conducted several sensitivity analyses, all of which

Lin et al. BMC Medicine(2024) 22:368

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Fig. 3 Associations of joint categories of CVH score and each cancer genetic risk with cancer subtypes mortality in the UK Biobank cohort. The
model was adjusted for age, sex, ethnicity, assessment center, Townsend deprivation index, CCI, and education levels, hormone replacement
therapy, C-reactive protein, and the first 10 principal components of ancestry. CVH, cardiovascular health; HR, hazard ratio; CI, confidence interval;
PRS, polygenic genetic risk; CCI, Charlson comorbidity index

consistently supported the primary results (Additional
file 1: Table S14). In the first sensitivity analysis, we utilized the more accurate 24-h recall questionnaire data

to calculate the original DASH diet score. Encouragingly, the findings aligned closely with the main results,
confirming the stability of our conclusions. Second, weLin et al. BMC Medicine(2024) 22:368

explored the temporal aspect by restricting the analysis
to participants with an overall cancer death occurring
more than two years from baseline. Notably, the results
remained in harmony with the primary analysis, suggesting the durability of the observed associations over time.
Moreover, ensuring the completeness of covariate data is
crucial. When restricting the analysis to participants withcomplete covariate information, the results exhibitedno substantial changes, further reinforcing the robustness of our findings. In addition, to assess the impact ofbaseline comorbidities, participants with a baseline CCI
score greater than one score were excluded. Gratifyingly,
the inverse association of CVH with cancer deaths persisted, mirroring the results of the main analysis. In our
final sensitivity analysis, we introduced adjustments forhormone replacement therapy and C-reactive protein.
Impressively, the results showed no significant deviations
from the main analysis, providing additional confidence
in the stability of our findings.

Discussion
Overall, the findings from two prospective cohort studies
conducted in the UK Biobank and US NHANES underscore a compelling association between a higher LE8
score and a reduced risk of overall cancer mortality. Thisconsistency across diverse populations adds robustness
to the observed relationship, emphasizing the potential universality of the impact of a healthful lifestyle on
cancer outcomes. Expanding our inquiry within the UK
Biobank, our analysis delves into the site-specific nuancesof cancer mortality. The results reveal a linear association
between a higher LE8 score and reduced mortality acrossnine specific cancer types. This granularity provides valuable insights into the diverse ways in which a comprehensive healthy lifestyle may influence cancer outcomes.
Importantly, our exploration of the interplay between
genetic risk and CVH unveils a significant finding—irrespective of high or low genetic risk, a high CVH score islinked to reduced mortality across eight distinct cancers.
This suggests that fostering CVH may notably reducecancer mortality, regardless of genetic predisposition.
Further stratification demonstrates a more pronounced
protective association among younger participants andthose with low SES. This age and SES add depth to ourunderstanding, suggesting that the benefits of a healthy
lifestyle, as measured by the LE8 score, may be especially
impactful in these demographic subgroups.
To the best of our knowledge, this study stands as
the inaugural exploration of correlation between the
new CVH metrics, as characterized by the LE8 score,
and the risk of cancer outcomes in two prospective
cohort studies. The observed association between better CVH at baseline and a diminished risk of cancer

Page 12 of 16

mortality in both the US and UK cohorts complements
the findings of three prior US-based studies. Notably,
the Multi-Ethnic Study of Atherosclerosis (MESA) [21]and the Women’s Health Initiative study reported significant protective associations for cancer-related mortality [22]. In contrast, the Aerobics Center longitudinal
study yielded nonsignificant results [24]. However, it isworth noting that prior studies predominantly focusedon the old CVH score (defined by LS7), targeted specific populations, or produced inconsistent results. In
contrast, our study extends the scope by utilizing two
extensive cohort analyses encompassing US adults aged
30–80 and UK adults aged 37–73. This robust approach
not only confirms the consistent protective associationof newly defined high CVH scores and cancer mortality
but also ensures more accurate and stable results. This
broader perspective enhances the generalizability of
our findings, emphasizing the reliability of the observed
relationship between high CVH and reduced cancer
mortality risk.
Taking into account the heterogeneous etiology of various cancer subtypes [37] and recent evidence suggesting a significant association between CVDs and specificcancer subtypes [38], it becomes imperative to investigate the correlations between CVH and cancer subtypes. Our findings reveal that a higher CVH score waslinked to 42–80% lower risks of mortality in lung, bladder, kidney, esophagus, breast, colorectal, and pancreaticcancers. Notably, the Women’s Health Initiative findings
support our results, demonstrating that elevated CVH isassociated with reduced mortality in lung and colorectal
cancer, and while the association with breast cancer mortality trends is expected, it does not reach statistical significance [22]. In the GAZEL (GAZ et ELECTRICITE deFrance) study in France, an observable trend suggested apotential link between CVH and a decreased risk of prostate cancer, although the results did not attain statistical significance [23]. Contrastingly, our study found thateach SD increase in the CVH score approached statisticalsignificance in reducing the risk of prostate cancer mortality (P = 0.085). This underscores the need for further
prospective cohort studies to delve deeper into the relationship between CVH and the risk of prostate cancer.
Other studies have delved into the subscale of CVH
regarding the risk of cancer subtypes. For instance, adiminished adherence to lifestyle recommendations has
been correlated with an elevated risk of overall cancer
and cancer subtypes [26]. Adopting the healthiest lifestyle has been found to reduce the risk of bladder, breast,colon, endometrial, esophagus, kidney, liver, lung, rectum, and stomach cancers by 17 to 58% [2], which werecomparable to our results. Furthermore, we demonstrated a linear relationship between CVH and the risk

Lin et al. BMC Medicine(2024) 22:368

of mortality for eight cancer subtypes. This finding isparticularly encouraging for individuals with low CVH
scores, suggesting that, regardless of their current score,
it is never too late to enhance their CVH.
In the stratified analyses, a noteworthy finding deserving of mention is that the associations between CVH
and mortality from any cancer consistently strengthened
among relatively young participants (< 65 vs. ≥ 65 years).
Previous research has also suggested that the impact oftraditional cardiovascular risk factors tends to diminish
with age [39]. Hence, we hypothesize that maintaining
high CVH scores in younger individuals may yield greater
benefits compared to preserving scores in older individuals. Our stratified analyses further revealed that the protective associations of high CVH on mortality from any
cancer were significant in both men and women. Whilesex appears to play a crucial role in cancer-specific survival, with female patients outperforming male patientsin most cancers [40], our study indicates that for men,maintaining a healthier CVH score can effectively reduce
the risk of cancer death and appears to be more significant than in women. The sex disparities influencing theconnection between CVH and cancer death are reflectedin various aspects of life, such as hormonal levels, behavioral psychology, economic preferences, emotional traits,
and the immune system [41, 42]. Given the intricate relationship between sex and cancer, additional prospective studies are warranted to explore the gender-specificeffects of CVH on cancer mortality.
Additionally, acknowledging the substantial impact of
SES on optimizing and maintaining CVH [10], we conducted a stratified analysis of SES and CVH in relation
to cancer mortality. Our results revealed that each SD
increase of CVH score was associated with total cancer mortality in both high and low SES groups, with aparticularly notable effect in the low SES population.
Previous literature has indicated that cancer mortality in Europe is predominantly influenced by the levels
and trends of cancer mortality rates in lower-educationgroups [43]. Consequently, for individuals in low SES
populations, maintaining a high CVH score can significantly reduce cancer risk. This finding suggests that by
improving CVH, it may be possible to reduce the gap incancer mortality between low SES and high SES, thereby
contributing to an improvement in the prognosis ofcancer.
Cancer ranks among the leading causes of death, andour findings reveal significant associations between CVH
and reduced risks of cancer mortality. Several potential mechanisms may explain the impact of poor CVH
on increased cancer mortality risk. Unhealthy behaviors can induce cancer risk by influence immune function and inflammatory response, contributing to DNA

Page 13 of 16

damage [44], the generation of reactive oxygen species[45], and disruption of circadian rhythm [46, 47]. Moreover, unfavorable health factors may increase cancer risk
through metabolic disruption. For instance, dysregulation of cholesterol homeostasis can promote cancer cells’
resistance to iron-induced cell death, thereby enhancing
tumorigenicity and metastatic capabilities [48]. Hyperinsulinemia, chronic inflammation, and certain medication are potential mechanisms underlying the association
between diabetes, obesity, and cancer risk [49, 50]. While
hypertension’s specific mechanisms in relation to cancer risk remain unclear, existing research suggests a significant association between hypertension and malignant
tumors [51]. Additionally, certain targeted drugs maylead to adverse reactions, including hypertension, potentially exacerbating the risk of cancer-related mortality[52].
In summary, achieving an optimal LE8 score encompasses maintaining robust immune function, minimizing
inflammation, and promoting a healthy metabolic state,
all of which contribute to reducing the risk of cancerrelated mortality.
Our study is the first to investigate the correlation
between CVH, genetic predisposition, and the mortality
risk of eight cancer subtypes. The current analyses revealthat CVH demonstrates no interactions with PRS, andoptimal CVH is associated with reduced mortality foreight specific cancers compared to poor CVH within thesame PRS group. A previous study reported an additive
interaction between genetic and lifestyle factors on overall cancer risk, observed in women but not in men [53].
The inconsistent findings might stem from variations in
the definition and quantification of genetic predisposition and CVH. These results underscore the substantial
potential benefits of adhering to optimal CVH, irrespective of PRS. Consequently, preventive policies should
advocate for stricter adherence to optimal CVH.
Strengths and limitations

We believe our study possesses several strengths, which
include, but are not limited to, the following aspects:
firstly, we employed a large sample size and implemented
a long-term tracking design within two well-establishednationwide cohorts. This approach effectively mitigatedselection bias and recall bias, thereby enhancing thereliability and consistency of our research findings. Secondly, the ample sample size enabled us to conduct joint
and stratified analyses with sufficient statistical power.
This allowed for a more comprehensive examination of
the data. Furthermore, the incorporation of the new LE8score to assess CVH, along with the genetic risk score forcancer, facilitated further exploration of the interaction
and combined effects of CVH and genetic susceptibility

Lin et al. BMC Medicine(2024) 22:368

on cancer mortality. Finally, we employed rigorous statistical analysis methods and supplemented them withvarious sensitivity analyses to ensure the robustness
and coherence of our results. However, it is importantto acknowledge the existence of limitations within ourstudy. First, these two cohort studies specifically represent adult populations in the US and the UK, respectively, which raises concerns about generalizing summary
statistics to a broader global population. Second, theseprospective cohort studies investigating the relationshipbetween CVH and cancer mortality face the challenge ofreverse causation, prompting the exclusion of individuals who died from cancer within the initial 2 years of thefollow-up period. Despite the implementation of a robust
correction scheme, the possibility of residual confounding remains, which could impact the accuracy of thestudy’s findings. Furthermore, the absence of detailed
cancer subtype and genetic data in NHANES limited ourability to assess the correlation between CVH scores anddeaths from specific cancer subtypes in the US population. Lastly, utilizing data from hospital admissions anddeath registrations in the UK introduces the possibilityof misclassification for cancer subtypes or less prevalent
forms of cancer. Despite its substantial observational
nature, the UK Biobank faces limitations in terms of theoccurrence frequency for certain malignancies, thereby
constraining the study’s ability to uncover relationships
with CVH scores.

Conclusions
Ideal CVH demonstrated an association with reduced
overall cancer mortality, particularly noteworthy inyounger individuals and those with low SES. Adhering to
CVH score exhibited a linear association with mortality
for various cancers, including lung, bladder, liver, kidney,esophagus, breast, colorectal, and pancreatic cancer. The
promotion of improving CVH based on LE8 guidelines
is encouraged, due to its benefit effects demonstrated
in individual with either high or low cancer genetic
predispositions.
Abbreviations
CVD	Cardiovascular disease
CVH	Cardiovascular health
AHA	American Heart Association
LS7	Life’s Simple 7
LE8	Life’s Essential 8
US NHANES	
United States National Health and Nutrition Examination
SurveyUK	United Kingdom
NCHS	National Center for Health Statistics
BMI	Body mass index
HDL-C	High-density lipoprotein cholesterol
DASH	Dietary approaches to stop hypertension
HbA1c	Hemoglobin A1c
ICD-10	International classification of disease, 10th revision
NHS	National Health Service

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CCI	Charlson comorbidity index
SES	Socioeconomic status
PRS	Polygenic genetic risk score
SNP	Single nucleotide polymorphism
SD	Standard deviation
IQR	Interquartile rangeHR	Hazard ratio
CI	Confidence interval
FDR	False discovery rate
MESA	Multi-Ethnic Study of Atherosclerosis

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03553-2.
Additional file 1: Table S1. Scoring criteria of diet in two cohorts according
to the DASH-style diet score. Table S2. Definition of CVH metrics andscoring algorithm in two cohorts. Table S3. Cancer classification according
to the ICD-10 in the UK Biobank. Table S4. Disease definitions used in twocohorts. Table S5. Diagnosis codes used for the Charlson comorbidities
identification in the UK Biobank. Table S6. Sources and components of thepolygenic risk score scores for the eight cancer types in the UK Biobank.
Table S7. The percentages of participants with missing covariates involvedin two cohorts. Table S8. Baseline characteristics of study population in
two cohorts. Table S9. Baseline Characteristics of all participants with
Life’s Essential 8 data and those with and without prevalent cancer in twocohorts. Table S10. Baseline Characteristics According to cancer deathin two cohorts. Table S11. Associations of individual CVH score with all
cancer mortality in two cohorts. Table S12. Death risk of 8 cancer subtypes
according to their respective PRS in the UK Biobank. Table S13. The joint
association of PRS and CVH score with cancer subtype mortally in the
UK Biobank. Table S14. Sensitivity analysis of the association of per SD
increase in CVH score with all cancer deaths and 18 cancer deaths in the
UK Biobank. Fig. S1. Association of CVH score with mortality from various
cancers in the UK Biobank. Fig. S2. Associations of CVH score with overalland subtype cancer mortality by sex in the UK Biobank. Fig. S3. Associations of CVH score with overall and subtype cancer mortality by age in the
UK Biobank. Fig. S4. Associations of CVH score with overall and subtype
cancer mortality by socioeconomic status in the UK Biobank
Acknowledgements
This research utilized data from the US NHANES and UK Biobank resources.
We extend our sincere appreciation to the participants of US NHANES and
UK Biobank for their valuable contributions to this study. Additionally, we
acknowledge the participants and investigators of finnGen study.
Authors’ contributions
LL and YH designed the study, collected and analyzed data, and wrote themanuscript. FL, XH, XZ, TS, TS, WL, and RL collected and reviewed data andcontributed to data analysis. X-JZ and JC revised the manuscript and provided
valuable suggestions for study design and data analysis. Z-GS, GW, and HL
contributed equally, designed the project, edited the manuscript, and supervised the study. All authors read and approved the final manuscript.
Funding
This work was supported by the National Science Foundation of China(82270390), the Hubei Provincial Engineering Research Center of Comprehensive Care for Heart-Brain Diseases, and the Hubei Provincial Industrial
Technology Research Institute of Comprehensive Care for Heart-Brain Diseases(2021YJY003).
Availability of data and materials
For the US NHANES study, the datasets generated and analyzed are publicly
available at NHANES website: https://​www.​cdc.​gov/​nchs/​nhanes/​index.​
htm. For the UK Biobank study, the data are available at UK Biobank website:
https://​www.​ukbio​bank.​ac.​uk/, but restrictions apply to the availability ofthese data, which were used under license for the current study, and so areLin et al. BMC Medicine(2024) 22:368

Page 15 of 16

not publicly available. Data are however available from the authors upon
reasonable request and with permission of UK Biobank.

Declarations
Ethics approval and consent to participate
The US NHANES study received ethical approval from the NCHS’ Research
Ethics Review Board, and all participants provided written informed consent.
Similarly, ethical approval for the UK Biobank research was granted by theNorth West Multicenter Research Ethical Committee, and written informed
consent was obtained from all the participants during the baseline recruitment to the UK Biobank. This current study was specifically approved by the
UK Biobank under application number 77195.
Consent for publication
Not applicable.

10.

11.

12.
13.

Competing interests
The authors declare no competing interests.
Author details
1
Department of Cardiology, Renmin Hospital of Wuhan University,
Wuhan 430060, China. 2 Institute of Model Animal, Wuhan University, Wuhan,
China. 3 Department of Neonatology, Huanggang Central Hospital of Yangtze University, Huanggang, China. 4 Huanggang Institute of Translational
Medicine, Huanggang, Hubei Province, China. 5 Medical Science ResearchCenter, Zhongnan Hospital of Wuhan University, Wuhan, China. 6 Departmentof Cardiology, The Third Xiangya Hospital, Central South University, Changsha,
China. 7 School of Basic Medical Science, Wuhan University, Wuhan, China.
8
Department of Integrated TCM & Western Medicine, Huanggang Central Hospital of Yangtze University, Huanggang 438000, China. 9 State Key Laboratoryof New Targets Discovery and Drug Development for Major Diseases, GannanInnovation and Translational Medicine Research Institute, Gannan Medical
University, Ganzhou, China.
Received: 9 January 2024 Accepted: 5 August 2024

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