Highlights
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Higher American Heart Association (AHA) cardiovascular health (CVH) scores are associated with lower overall cancer incidence and mortality in meta-analysis.
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Moderate and high CVH scores reduce overall cancer risk by 15% and 28%, respectively.
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High CVH scores are linked to significantly lower risk of breast, colorectal, and lung cancer.
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Better CVH is associated with markedly lower cancer mortality: 36% lower in the moderate group and 53% lower in the high group.
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Meta-regression confirms a dose-response pattern for both cancer incidence and cancer mortality.
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Our findings support CVH metrics as pragmatic tools for integrated cardiovascular and cancer prevention strategies.
ABSTRACT
Background
Cardiovascular disease and cancer have numerous shared risk factors. Our objective is to determine whether American Heart Association (AHA) cardiovascular health (CVH) metrics are associated with cancer outcomes.
Methods
We searched PubMed and Scopus (inception to July 15, 2025). We included cohort studies examining the association of AHA CVH metrics with cancer risk and cancer mortality. We conducted meta-analyses to generate summary risk ratios (RRs), hazard ratios (HRs), and standard errors for outcomes with at least 3 contributing studies reporting associations with low (worse), moderate, and high (best) CVH score groups. We utilized meta regression to examine dose-response relationships of CVH score groups.
Results
Our systematic review included 26 studies. Moderate (RR, 0.85; 95% CI, 0.79-0.91, P <.001) and high (RR, 0.72; 95% CI, 0.64-0.81, P <.001) CVH scores were associated with decreased overall cancer risk with evidence of a dose-response effect for more favorable groups (coefficient, −0.09; standard error [SE], 0.02; P <.001). High CVH scores were associated with statistically significantly decreased risk of breast (RR, 0.72; 95% CI, 0.58-0.90), colorectal (RR, 0.65, 95% CI, 0.57-0.74), and lung cancer (RR, 0.34; 95% CI, 0.16-0.70). We found evidence for lower cancer mortality in the moderate (HR, 0.64; 95% CI, 0.61-0.68, P <.001) and high (HR, 0.47; 95% CI, 0.41-0.53, P <.001) CVH score groups. Meta regression demonstrated a dose-response effect on all cancer mortality (coefficient, −0.33; SE, 0.05; P <.001).
Conclusions
We provide evidence for a dose response association of better CVH scores with decreased cancer incidence and cancer mortality. Optimizing CVH may improve cancer outcomes.
Background
Cardiovascular disease (CVD) and cancer are the 2 leading causes of death globally and commonly occur in the same individuals. This relationship has historically been explained by the existence of shared risk factors, such as smoking, obesity, and physical activity. Further, cancer therapies commonly have cardiotoxic effects that could increase CVD in cancer survivors. Emerging evidence suggests that, beyond shared risk factors, a causal relationship may exist between CVD and cancer. Recent population based studies have shown that patients diagnosed with CVD are at an increased risk of cancer ,,,, and preclinical models suggest that CVD may contribute to circulating factors, immune changes, and inflammation that directly drive both the initial development and subsequent spread of cancer. ,, Therefore, CVD, and cardiovascular health (CVH) more broadly, may contribute to cancer risk through both shared risk factors and potentially through the direct effects of CVD itself on cancer development and behavior.
In this context, improved CVH has the potential to prevent the formation of new cancers, and improve outcomes in patients who develop cancer, through multiple potential pathways. Seminal population-based studies have shown a link between CVH metrics and cancer risk more broadly, such as the Atherosclerosis Risk In Communities (ARIC) or Multi‐Ethnic Study of Atherosclerosis (MESA) studies. , The American Heart Association (AHA) previously developed their concept of CVH metrics, primarily as Life’s Simple 7 (LS7) and subsequently as Life’s Essential 8 (LE8). These CVH frameworks consist of health behaviors (diet, physical activity, nicotine exposure) and health factors (body mass index and blood lipids, blood glucose, and blood pressure) and were designed to operationalize ideal CVH and guide improved CVD prevention and outcomes. While numerous individual population-based studies examining CVH metrics and cancer outcomes exist, meta-analyses examining diverse cancer outcomes and individual cancer subtypes are lacking. The goal of this systematic review and meta-analysis is to synthesize the evidence for an association between AHA CVH metrics and cancer incidence and cancer mortality, including by individual cancer subtype. We sought to examine cohort studies of adults unselected for cancer in which CVH metrics were assessed at baseline and cancer outcomes were ascertained during follow-up. Our findings may have important public health implications that could inform approaches to prevent cancer development and cancer-related death.
Methods
This systematic review and meta-analysis was performed and reported in accordance with PRISMA Reporting Guidelines. This study was determined to be institutional review board exempt and was provided a waiver of informed consent by the University of Texas MD Anderson Cancer Center institutional review board. Our protocol was not a priori registered.
Search strategy
We systematically searched for articles indexed in PubMed and Scopus (inception to July 15, 2025). We sought to identify all articles examining the association of AHA CVH measures and cancer outcomes. Our search strings are presented in Supplementary Table 1. The reference lists of recent reviews and included studies were screened for additional eligible studies.
Study selection
Our study selection methodology was designed to identify cohort studies examining the association of AHA CVH metrics (eg, LS7, LE8) with cancer risk and cancer mortality. We included original research articles available in English. We included studies that examined cohorts of adults unselected for cancer in which CVH metrics were assessed at baseline and cancer outcomes were ascertained during follow-up. We included all published studies in the systematic review meeting these criteria. Screening was performed in Covidence. Three authors (JT, SN, KTN) jointly participated in each step of screening and full-text review. For each article, any 2 of these authors conducted screening and full-text review. Disagreements were resolved by the third author.
Data extraction
Data on the characteristics of each included study were extracted independently by 1 author and verified by a second author using an adapted Cochrane data extraction template ( http://cccrg.cochrane.org/author-resources ). We extracted study characteristics (year, location, cohort, data collection year range, inclusion/exclusion criteria sample size, mean age), exposure (AHA CVH metric), outcome (cancer incidence and mortality), and summary statistical measures for selected exposure-outcome associations (risk ratio or hazards ratio).
Quality evaluation
The Newcastle–Ottawa Scale was used to evaluate quality of evidence and risk of bias (JT). The Newcastle–Ottawa Scale is a scored instrument with a maximum of 9 points based on criteria related to selection, comparability, and outcomes.
Statistical analysis
We utilized specific criteria to select published studies and analyses for inclusion in the meta-analyses. We included analyses presenting low (worst), moderate, and high (best) CVH scores, with the low score group as the reference category, or where this information could be calculated from presented data, we used established methods for rereferencing relative risks based on the point estimates and confidence interval bounds of the new reference group. Where multiple publications analyzed overlapping cohorts, we included the estimate with the smallest standard error for the relevant high-versus-low score comparison (by outcome), as this provided the most statistically precise estimate and accounted for large variations in sample size relative to number of events across studies. Where a study presented data utilizing multiple models for the same outcome we selected data from the most adjusted model. We conducted cancer specific incidence meta-analyses where at least 3 unique studies reported on the same specific cancer.
We calculated the log transformed risk ratio and standard error of the risk ratio. We utilized the Stata (MP 17.0) meta function to calculate summary effect estimates and 95% confidence intervals. We a priori used the conservative random-effects model for all analyses regardless of calculated heterogeneity. The proportion of heterogeneity due to study variation was quantified using the I 2 statistic per the heterogi function. We performed meta-regression using the meta regress function. The presence of small study and missing results effects were a priori planned to be evaluated if a meta-analysis included at least 10 studies. We prespecified subgroup analysis by exposure type (eg, LE8). Tests were considered statistically significant if the 2-sided P -value was <.05. KTN had full access to all the data in the study and takes responsibility for its integrity and the data analysis.
Results
Search results
We screened 607 studies after removal of duplicates. There were 75 studies that underwent full-text review, of which 49 studies were excluded, as outlined in Figure 1 . Details of the 26 studies included in our systematic review and their characteristics are summarized in Table 1 . Sample size ranged from 785 to 342,226 individuals. Included studies analyzed 10 unique datasets examining the exposure of LS7 or LE8 scores. Outcomes examined included overall incident cancer, individual cancer risk, and overall cancer mortality. Studies included data collection from 1979-2020 and were primarily conducted in datasets from the US and Europe. Mean patient age ranged from 45-72 years across publications. The designations of low, moderate, and high CVH scores of studies included in the meta-analysis are detailed in Supplementary Table 2. Individual cancer types examined by study are detailed in Supplementary Table 3.
PRISMA flow diagram of study selection.
Table 1
Characteristics of studies included in systematic review
| Publication | Location | Study | Data collection time | Publication specific exclusion criteria | Total sample size | Mean age, y | CVH metric | Outcome |
|---|---|---|---|---|---|---|---|---|
| Abramov et al | USA | NHANES | 2009-2018 | History of CHD, angina, HA, stroke, cancer, CVD | 20,215 | 45 | LE8 | Cancer mortality |
| Chen et al | USA | NHANES | 2005-2018 | < 20 y, pregnant, not having baseline CKD | 3,814 | 47 | LE8 | Cancer mortality |
| Dinh et al | USA | NHANES | 2011-2018 | < 20 y, pregnant/breastfeeding | 21,183 | 48 | LE8, LC9 | Cancer mortality |
| Greenlee et al | USA | CHS | 1989-1993 | History of cancer or CVD | 3,491 | 72 | LS7 | Cancer mortality, incident cancer |
| Han et al | USA | NHANES | 1988-2016 | < 20 y, pregnant, BMI < 18.5 kg/m 2, history of cancer, heart attack, congestive heart failure, stroke | 12,299 | 47 | LS7 | Cancer mortality |
| Hou et al | UK | UK Biobank | 2006-2010 | Baseline CVD or cancer | 218,587 | 56 | LE8 | Incident cancer |
| Huether et al | USA | REGARDS | 2003-2007 | Races not Black/White, prevalent cancer, stroke, or CHD | 11,385 | 65 | LE8 | Cancer mortality |
| Jiang et al | China | Kailuan study | 2006-2020 | Deaths, cancer at baseline | 77,551 | 51 | LE8 | Incident cancer |
| Lau et al | USA | “FHS and PREVEND” | 1979-2005 | Prevalent end-stage renal disease, cancer, atrial fibrillation or major CVD | 20,305 | 50 | LS7 | Incident cancer |
| Li et al | USA | NHANES | 2005-2018 | < 20 y, pregnant, viral hepatitis, autoimmune hepatitis, excessive alcohol consumption, Fatty liver Index < 60 | 10,050 | 50 | LE8 | Cancer mortality |
| Lin et al | USA; UK | NHANES; UK Biobank | 2005-2018; 2006-2010 | ≤20 y, baseline cancer | 17,076; 272,727 | 53; 56 | LE8 | Cancer mortality, Incident cancer |
| Ning et al | USA | NHANES | 2007-2018 | ≥ 30 y, pregnant | 21,062 | 52 | LE8 | Cancer mortality |
| Ogunmoroti et al | USA | MESA | 2000-2015 | History of CVD | 6,506 | 62 | LS7 | Incident cancer |
| Peng et al | UK | UK Biobank | 2006-2010 | History of CVD or cancer | 277,002 | 58 | LE8 | Incident cancer |
| Peng et al | UK | UK Biobank | 2006-2010 | Baseline cancer, CVD or T2D | 277,997 | 56 | LE8 | Incident cancer |
| Rasmussen-Torvik et al | USA | ARIC | 1987-2016 | Race other than white or African American, baseline history of cancer | 13,253 | 54 | LS7 | Incident cancer |
| Sun C. et al | USA | NHANES | 2007-2012 | < 20 y, not having baseline COPD | 785 | 59 | LE8 | Cancer mortality |
| Sun M. et al | USA | NHANES | 2005-2018 | < 20 y, fatty liver index ≤60, pregnant women, alcoholism excessive, chronic viral hepatitis | 9,094 | 52 | LE8 | Cancer mortality |
| Van Sloten et al | France | GAZEL | 1989-2015 | Prevalent cancer, CVD, BMI < 18.5 kg/m 2 | 13,933 | 45 | LS7 | Incident cancer |
| Wang et al | UK | UK Biobank | 2006-2010 | Female | 25,656 | NR | LC9 | Incident cancer |
| Wu et al | UK | UK Biobank | 2006-2010 | Baseline tumor diagnoses | 234,102 | 57 | LE8 | Incident cancer |
| Yang et al | UK | UK Biobank | 2006-2010 | Baseline cancer | 332,417 | 58 | LE8 | Cancer mortality, incident cancer |
| Yu et al | UK | UK Biobank | 2006-2010 | History of noncommunicable chronic diseases | 170,726 | 56 | LE8 | Incident cancer |
| Zhang et al | UK | UK Biobank | 2006-2010 | < 50 y, prevalent cancer | 342,226 | 60 | LS7 | Incident cancer |
| Zhao et al | UK | UK Biobank | 2006-2010 | Baseline cancer, BMI < 18.5 kg/m 2, HRT users, male | 277,002 | 56 | LE8 | Incident cancer |
| Zhou et al | USA | NHANES | 2005-2018 | Not having baseline metabolic syndrome | 7,839 | 54 | LE8 | Cancer mortality |
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