Analysis of Clinical Traits Associated With Cardiovascular Health, Genomic Profiles, and Neuroimaging Markers of Brain Health in Adults Without Stroke or Dementia | Cerebrovascular Disease | JAMA Network Open | JAMA Network
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Table 1.  Baseline Characteristics of the Study Population
Baseline Characteristics of the Study Population
Table 2.  Multivariable Linear Regression Results Showing Associations Between Observed and Genomic LS7 Scores and Neuroimaging Markers of Brain Health
Multivariable Linear Regression Results Showing Associations Between Observed and Genomic LS7 Scores and Neuroimaging Markers of Brain Health
Table 3.  Multivariable Linear Regression Results Showing Associations Between Lifestyle and Biological Components of Observed and Genomic LS7 and Neuroimaging Markers of Brain Health
Multivariable Linear Regression Results Showing Associations Between Lifestyle and Biological Components of Observed and Genomic LS7 and Neuroimaging Markers of Brain Health
1.
Lloyd-Jones  DM, Hong  Y, Labarthe  D,  et al; American Heart Association Strategic Planning Task Force and Statistics Committee.  Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic impact goal through 2020 and beyond.   Circulation. 2010;121(4):586-613. doi:10.1161/CIRCULATIONAHA.109.192703PubMedGoogle ScholarCrossref
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Dong  C, Rundek  T, Wright  CB, Anwar  Z, Elkind  MSV, Sacco  RL.  Ideal cardiovascular health predicts lower risks of myocardial infarction, stroke, and vascular death across Whites, Blacks, and Hispanics: the Northern Manhattan study.   Circulation. 2012;125(24):2975-2984. doi:10.1161/CIRCULATIONAHA.111.081083PubMedGoogle ScholarCrossref
3.
Malik  R, Georgakis  MK, Neitzel  J,  et al.  Midlife vascular risk factors and risk of incident dementia: Longitudinal cohort and Mendelian randomization analyses in the UK Biobank.   Alzheimers Dement. 2021;17(9):1422-1431. doi:10.1002/alz.12320PubMedGoogle ScholarCrossref
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Gardener  H, Caunca  M, Dong  C,  et al.  Ideal cardiovascular health and biomarkers of subclinical brain aging: the Northern Manhattan Study.   J Am Heart Assoc. 2018;7(16):e009544. doi:10.1161/JAHA.118.009544PubMedGoogle ScholarCrossref
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Bycroft  C, Freeman  C, Petkova  D,  et al.  The UK Biobank resource with deep phenotyping and genomic data.   Nature. 2018;562(7726):203-209. doi:10.1038/s41586-018-0579-zPubMedGoogle ScholarCrossref
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Woodfield  R, Grant  I, Sudlow  CL; UK Biobank Stroke Outcomes Group; UK Biobank Follow-Up and Outcomes Working Group.  Accuracy of electronic health record data for identifying stroke cases in large-scale epidemiological studies: a systematic review from the UK Biobank stroke outcomes group.   PLoS One. 2015;10(10):e0140533. doi:10.1371/journal.pone.0140533PubMedGoogle ScholarCrossref
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Wilkinson  T, Schnier  C, Bush  K,  et al; Dementias Platform UK and UK Biobank.  Identifying dementia outcomes in UK Biobank: a validation study of primary care, hospital admissions and mortality data.   Eur J Epidemiol. 2019;34(6):557-565. doi:10.1007/s10654-019-00499-1PubMedGoogle ScholarCrossref
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Said  MA, Verweij  N, van der Harst  P.  Associations of combined genetic and lifestyle risks with incident cardiovascular disease and diabetes in the UK Biobank study.   JAMA Cardiol. 2018;3(8):693-702. doi:10.1001/jamacardio.2018.1717PubMedGoogle ScholarCrossref
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Mozaffarian  D.  Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review.   Circulation. 2016;133(2):187-225. doi:10.1161/CIRCULATIONAHA.115.018585PubMedGoogle ScholarCrossref
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Choi  SW, Mak  TSH, O’Reilly  PF.  Tutorial: a guide to performing polygenic risk score analyses.   Nat Protoc. 2020;15(9):2759-2772. doi:10.1038/s41596-020-0353-1PubMedGoogle ScholarCrossref
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Littlejohns  TJ, Holliday  J, Gibson  LM,  et al.  The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions.   Nat Commun. 2020;11(1):2624. doi:10.1038/s41467-020-15948-9PubMedGoogle ScholarCrossref
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Battaglini  M, Jenkinson  M, De Stefano  N; Alzheimer’s Disease Neuroimaging Initiative.  SIENA-XL for improving the assessment of gray and white matter volume changes on brain MRI.   Hum Brain Mapp. 2018;39(3):1063-1077. doi:10.1002/hbm.23828PubMedGoogle ScholarCrossref
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Sacco  RL.  Achieving ideal cardiovascular and brain health: opportunity amid crisis: presidential address at the American Heart Association 2010 Scientific Sessions.   Circulation. 2011;123(22):2653-2657. doi:10.1161/CIR.0b013e318220dec1PubMedGoogle ScholarCrossref
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Debette  S, Markus  HS.  The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis.   BMJ. 2010;341:c3666. doi:10.1136/bmj.c3666PubMedGoogle ScholarCrossref
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Prins  ND, Scheltens  P.  White matter hyperintensities, cognitive impairment and dementia: an update.   Nat Rev Neurol. 2015;11(3):157-165. doi:10.1038/nrneurol.2015.10PubMedGoogle ScholarCrossref
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Prabhakaran  S.  Blood pressure, brain volume and white matter hyperintensities, and dementia risk.   JAMA. 2019;322(6):512-513. doi:10.1001/jama.2019.10849PubMedGoogle ScholarCrossref
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Armstrong  NM, An  Y, Shin  JJ,  et al.  Associations between cognitive and brain volume changes in cognitively normal older adults.   Neuroimage. 2020;223:117289. doi:10.1016/j.neuroimage.2020.117289PubMedGoogle ScholarCrossref
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Mars  N, Koskela  JT, Ripatti  P,  et al; FinnGen.  Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers.   Nat Med. 2020;26(4):549-557. doi:10.1038/s41591-020-0800-0PubMedGoogle ScholarCrossref
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Widén  E, Junna  N, Ruotsalainen  S,  et al.  How communicating polygenic and clinical risk for atherosclerotic cardiovascular disease impacts health behavior: an observational follow-up study.   Circ Genom Precis Med. 2022;15(2):e003459. doi:10.1161/CIRCGEN.121.003459Google ScholarCrossref
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Muse  ED, Chen  SF, Liu  S,  et al.  Impact of polygenic risk communication: an observational mobile application-based coronary artery disease study.   NPJ Digit Med. 2022;5(1):30. doi:10.1038/s41746-022-00578-wPubMedGoogle ScholarCrossref
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    Original Investigation
    Neurology
    May 27, 2022

    Analysis of Clinical Traits Associated With Cardiovascular Health, Genomic Profiles, and Neuroimaging Markers of Brain Health in Adults Without Stroke or Dementia

    Author Affiliations
    • 1Department of Neurology, Yale School of Medicine, New Haven, Connecticut
    • 2Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
    • 3Department of Radiology, Yale School of Medicine, New Haven, Connecticut
    JAMA Netw Open. 2022;5(5):e2215328. doi:10.1001/jamanetworkopen.2022.15328
    Key Points

    Question  Are observed and genomic American Heart Association Life’s Simple 7 scores associated with brain imaging markers of brain health in persons without stroke or dementia?

    Findings  This genetic association study among 35 914 participants from the UK Biobank study found that better observed Life’s Simple 7 profiles were associated with lower white matter hyperintensity volume and higher brain volume and better genomic Life’s Simple 7 profiles were associated with lower white matter hyperintensity volumes.

    Meaning  These findings suggest that cardiovascular health optimization, as measured by Life’s Simply 7 score, was associated with improved brain health in asymptomatic persons.

    Abstract

    Importance  The American Heart Association (AHA) Life’s Simple 7 (LS7) score captures 7 biological and lifestyle factors associated with promoting cardiovascular health.

    Objectives  To test whether healthier LS7 profiles are associated with significant brain health benefits in persons without stroke or dementia, and to evaluate whether genomic information can recapitulate the observed LS7.

    Design, Setting, and Participants  This genetic association study was a nested neuroimaging study within the UK Biobank, a large population-based cohort study in the United Kingdom. Between March 2006 and October 2010, the UK Biobank enrolled 502 480 community-dwelling persons aged 40 to 69 years at recruitment. This study focused on a subset of 35 914 participants without stroke or dementia who completed research brain magnetic resonance imaging (MRI) and had available genome-wide data. All analyses were conducted between March 2021 and March 2022.

    Exposures  The LS7 (blood pressure, low-density lipoprotein cholesterol, hemoglobin A1c, smoking, exercise, diet, and body mass index) profiles were ascertained clinically and genomically. Independent genetic variants known to influence each of the traits included in the LS7 were assessed. The total LS7 score ranges from 0 (worst) to 14 (best) and was categorized as poor (≤4), average (>4 to 9) and optimal (>9).

    Main Outcomes and Measures  The outcomes of interest were 2 neuroimaging markers of brain health: white matter hyperintensity (WMH) volume and brain volume (BV).

    Results  The final analytical sample included 35 914 participants (mean [SD] age 64.1 [7.6] years; 18 830 [52.4%] women). For WMH, compared with persons with poor observed LS7 profiles, those with average profiles had 16% (β = −0.18; SE, 0.03; P < .001) lower mean volume and those with optimal profiles had 39% (β = −0.39; SE, 0.03; P < .001) lower mean volume. Similar results were obtained using the genomic LS7 for WMH (average LS7 profile: β = −0.06; SE, 0.014; P < .001; optimal LS7 profile: β = −0.08; SE, 0.018; P < .001). For BV, compared with persons with poor observed LS7 profiles, those with average LS7 profiles had 0.55% (β = 0.09; SE, 0.02; P < .001) higher volume, and those with optimal LS7 profiles had 1.9% (β = 0.14; SE, 0.02; P < .001) higher volume. The genomic LS7 profiles were not associated with BV.

    Conclusions and Relevance  These findings suggest that healthier LS7 profiles were associated with better profiles of 2 neuroimaging markers of brain health in persons without stroke or dementia, indicating that cardiovascular health optimization was associated with improved brain health in asymptomatic persons. Genomic information appropriately recapitulated 1 of these associations, confirming the feasibility of modeling the LS7 genomically and pointing to an important role of genetic predisposition in the observed association among cardiometabolic and lifestyle factors and brain health.

    Introduction

    The American Heart Association Life’s Simple 7 (LS7) score captures 7 biological and lifestyle traits associated with a person’s overall cardiovascular health status.1 Higher LS7 scores, indicating better cardiovascular health, are associated with lower risk of acute cardiovascular events2 and dementia.3 Beyond these clinical end points, a few recent studies have evaluated whether better cardiovascular health is associated with better brain health, as measured by neuroimaging markers.4 In this study, we hypothesized that better LS7 profiles are associated with significant brain health benefits, as evaluated using 2 neuroimaging markers, in persons without stroke or dementia. Because the biological and lifestyle factors contained in the LS7 are highly heritable, we also hypothesize that the genetic predisposition to these traits can be expressed as the genomic LS7.

    Methods

    This genetic association study was approved by the North-West Multi-center Research Ethics Committee. All participants provided electronic informed consent. This study is reported following the Strengthening the Reporting of Genetic Association Studies (STREGA) reporting guidelines.

    Study Design

    The UK Biobank is a large population-based cohort study that enrolled 502 480 community-dwelling persons in the United Kingdom aged 40 to 69 years at recruitment between March 2006 and October 2010.5 We conducted a nested study within the UK Biobank focusing on participants who underwent dedicated research brain magnetic resonance imaging (MRI).5 We included participants with available genetic and neuroimaging data who did not have a history of stroke or dementia at the time of the neuroimaging assessment. Stroke and dementia were ascertained via self-reported data obtained during the baseline visit and previously validated International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes6,7 abstracted from electronic health records of hospital admissions and primary care visits that took place between enrollment and the neuroimaging assessment. All analyses were conducted between March 2021 and March 2022.

    Observed and Genomic Life’s Simple 7

    Details about the protocol followed to obtain self-reported information, electronic health records, blood pressure measurements, and laboratory data are available elsewhere.5 The observed LS7 scores were ascertained using values for blood pressure, low-density lipoprotein cholesterol, glycemic status (hemoglobin A1c), smoking, exercise, diet, and body mass index at the time of enrollment (eTable 1 in the Supplement).8,9 Each component is codified as either 0 (poor), 1 (average), or 2 (optimal).3 Clinical, blood pressure, and biometric data were obtained during the baseline interview. Laboratory data were obtained from blood obtained during the baseline visit. A detailed description of DNA collection, genotyping, quality control, and imputation procedures is available elsewhere.5 The genomic LS7 scores were calculated using 7 polygenic risk scores, 1 for each trait captured by the LS7. These polygenic risk scores were calculated using a previously curated and validated list of independent (r2 < 0.001) genetic risk variants known to influence the 7 components of the LS7 (eTable 2 in the Supplement).3 Each polygenic risk score was calculated as the sum of the products of the allele count at each locus multiplied by the effect of that allele on the corresponding trait.10 To emulate the categorization into 3 levels, we divided each polygenic risk score into tertiles and assigned participants a value of 0 (higher polygenic risk), 1 (average polygenic risk), and 2 (lower polygenic risk) (eFigure 1 in the Supplement). The total observed and genomic LS7 scores are calculated by summing up all the components, with a final score that ranges from 0 (worst) to 14 (best). This final score was further categorized as poor (≤4), average (>4 to 9), and optimal (>9).

    Outcome of Interest

    Details about the MRI neuroimaging research protocol used by the UK Biobank are available elsewhere.11 Briefly, a subset of the UK Biobank participants consented to the neuroimaging study. These participants underwent a dedicated research brain MRI imaging using a Siemens Skyra 3T. Brain volume (BV) and white matter hyperintensity (WMH) volume were calculated by the UK Biobank research team using previously validated tools.11,12 Our 2 main outcomes of interest were the WMH volume and BV normalized by head size. WMH volume was natural log–transformed to approximate normality. Both natural log–transformed WMH volume and BV were standardized by subtracting the mean and dividing by the SD.

    Statistical Analysis

    Because the genetic variants used in the study were discovered in studies including persons of European ancestry and that population stratification can lead to false positive associations, we restricted our main analysis to participants from genetically ascertained European ancestry. We tested for associations between the observed and genomic LS7 profiles and WMH volume and BV using multivariable linear regression adjusting for age and sex. For genomic LS7, we also adjusted for the first 4 genetic principal components. We conducted the 9 analyses in addition to our primary analysis. First, we evaluated participants of all ancestral groups. Second, we developed and tested 1 subscore for lifestyle components (ie, smoking status, body mass index, physical activity, and diet) and a second for the biological components (ie, blood pressure, cholesterol levels, and glycemic status). Third, we evaluated the association between each of the 7 traits and our outcomes of interest. Fourth, we tested the association between the continuous LS7 scores and our outcomes of interest. Fifth, we developed a polygenic score, including genetic variants for all these traits, and tested its association as a continuous variable with our outcomes of interest. Sixth, we tested the association between the LS7 profiles and 7 cognitive measurements provided by the UK Biobank. Seventh, we conducted stratified analyses and interaction analysis by sex. Eighth, we evaluated the correlation between the LS7 and both lifestyle and biological components and the overlap in single-nucleotide variations (SNVs; formerly single-nucleotide polymorphisms) shared by traits. Ninth, we tested the proportion of variance explained by models, including the observed and genomic LS7 scores and components.

    For our primary analysis, we used a Bonferroni corrected 2-tailed P value of .05 / 4 = .0125 to determine statistical significance. Polygenic risk scoring was conducted using PRSice-2 (PRSice). Analyses were conducted using R statistical software version 3.6.3 (R Project for Statistical Computing).

    Results

    The final analytical sample included 35 914 participants (mean [SD] age, 64.1 [7.6] years, 18 830 [52.4%] women) (Table 1). The mean (SD) time from enrollment to the neuroimaging assessment was 8.93 (1.8) years. The flowchart of included participants is available in eFigure 2 in the Supplement.

    WMH Volume

    Compared with persons with poor observed LS7 profiles, those with average profiles had 16% (β = −0.18; SE, 0.03; P < .001) lower WMH volume, and those with optimal profiles had 39% (β = −0.39; SE, 0.03; P < .001) lower WMH volume (Table 2). Similar results were obtained when using the genomic LS7 profiles (average LS7 profile: β = −0.06; SE, 0.014; P < .001; optimal LS7 profile: β = −0.08; SE, 0.018; P < .001) (Table 2). Sensitivity analyses, including 39 976 participants from all ancestral backgrounds, including those from genetically ascertained African or Asian ancestry, with available neuroimaging and genomic data yielded similar results. In the secondary analyses, the lifestyle and biological components of the LS7 score were each associated with WMH volume for both the observed and genomic LS7 profiles (Table 3). Of note, for the genomic LS7, the association was stronger for the biological vs lifestyle component (Table 3; eTable 3 in the Supplement).

    Brain Volume

    Compared with persons with poor observed LS7 profiles, those with average LS7 profiles had 0.55% (β = 0.09; SE, 0.02; P < .001) higher BV, and those with optimal LS7 profiles had 1.9% (β = 0.14; SE, 0.02; P < .001) higher BV. The genomic LS7 profiles were not associated with BV (Table 2). Sensitivity analyses, including 41 303 participants from all ancestral backgrounds with available neuroimaging and genomic data, yielded similar results. In secondary analyses, only the lifestyle component of the observed LS7 profile was significantly associated with BV (Table 3).

    Additional Analyses

    When evaluating the association between both the observed and genomic LS7 scores using the continuous variable instead of the categorized profile variable, we found consistent results. Additionally, when evaluating a continuous polygenic score using genetic variants from all 7 traits, results remained consistent with our primary analysis: each SD of this polygenic score was associated with a decrease of 0.3 cc in WMH volume (β = −0.04; SE, 0.005; P < .001), while no significant results were seen for brain volume (β = 0.005; SE, 0.004; P = .22). When testing the association between the LS7 profiles and 7 cognitive measurements, we found that better profiles of both the observed and genomic LS7 were associated with higher measures of fluid intelligence and better performance in the symbol digit substitution test (eTable 4 in the Supplement). When analyzing association modification by sex, we found that the association between the observed LS7 profiles and brain volume was only present in men (eTable 5 in the Supplement). All other interaction analyses were not significant. In additional analyses, we found only up to 3 SNVs were shared among traits (eFigure 3 in the Supplement), and that the correlation between the genomic LS7 including all traits and the LS7 including only biological components was moderate (R = 0.65; 95% CI, 0.64-0.65; P < .001). However, models including all components vs only biological components did not improve significantly (eTable 6 in the Supplement). The addition of the genomic LS7 score to a model containing the observed LS7 score only marginally increased proportion of variance explained (eTable 6 in the Supplement).

    Discussion

    In this genetic association study analyzing data from nearly 36 000 persons without stroke or dementia, we found that a better (healthier) observed LS7 profile was associated with lower WMH volume and larger BV. We also found that a better (healthier) genomic LS7 profile was associated with lower WMH volume but not BV. Mounting evidence points to an important link between cardiovascular and brain health.13 A previous report from the US-based Northern Manhattan Study4 showed a similar association between observed LS7 score and subclinical imaging markers of brain health. Our study supports these findings in a British study population and suggests that the different components of the LS7 score have different associations with brain health: while the lifestyle component was associated with both WMH volume and BV, the biological component was only associated with WMH volume. Additionally, we found that the association between cardiovascular health and brain volume may differ by sex, with our results only being significant among men in stratified analyses.

    Our study provides important new findings on this topic by evaluating the association between cardiovascular and brain health from a genetic perspective. We show that it is possible to calculate a genomic version of the LS7 score using the numerous genetic variants that are known to influence each of its 7 traits. The validity of this approach is demonstrated by the significant association between the genomic LS7 profiles and WMH volume. These findings suggest that an individual’s genetic predisposition constitutes an important determinant of neuroimaging biomarkers of brain health. These biomarkers have been shown to be associated, in turn, with clinical end points, such as cognitive performance, stroke, and dementia.14-17 Along these lines, we found that better LS7 profiles might be associated with improved cognitive function. Finally, because genetic information from SNVs is present since birth and remains mostly unchanged throughout life, our findings lay the foundation for future research focused on evaluating whether the use of the genomic LS7 could lead to the ultra-early identification of individuals with high risk who could benefit from tailored diagnostic or therapeutic interventions.18 Along these lines, a few studies have shown that communicating polygenic risk is associated with positive changes in health behavior.19,20

    Limitations

    Our study has several limitations. First, some lifestyle traits, such as diet and physical activity, do not have well-established genetic instruments, limiting our results for these traits. Furthermore, the categorization of the LS7 scores into profiles may remove information, adding noise to our analyses. However, when testing these associations using continuous variables, results remained consistent. Additionally, the variables included in the observed LS7 score were only evaluated at a single point in time (at recruitment), which further limits our analyses. In addition, we did not find an association between the genomic LS7 profiles and BV. These null results could be due to the inevitable decrease in statistical power produced by working with genetic proxies of cardiometabolic traits instead of the observed traits. Alternatively, these null results could indicate that the association between the observed the LS7 profiles and BV identified by this and prior observational studies may not be causal.

    Conclusions

    This genetic association study found that better cardiovascular health profiles, as expressed by the LS7, were associated with better neuroimaging-defined brain health in persons without stroke or dementia. These findings suggest that it may be possible to use genetic information from variants known to influence the 7 cardiometabolic and lifestyle traits contained in the LS7 to calculate the genomic LS7 score.

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    Article Information

    Accepted for Publication: April 17, 2022.

    Published: May 27, 2022. doi:10.1001/jamanetworkopen.2022.15328

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Acosta JN et al. JAMA Network Open.

    Corresponding Author: Guido J. Falcone, MD, ScD, MPH, Department of Neurology, Yale School of Medicine, 100 York St, Office 111, New Haven, CT 06511 (guido.falcone@yale.edu).

    Author Contributions: Drs Acosta and Falcone had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Sheth and Falcone jointly supervised this work.

    Concept and design: Acosta, Szejko, Payabvash, Sheth, Falcone.

    Acquisition, analysis, or interpretation of data: Acosta, Both, Rivier, Leasure, Gill, Payabvash, Falcone.

    Drafting of the manuscript: Acosta, Szejko, Payabvash.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Acosta, Both, Rivier, Payabvash, Falcone.

    Obtained funding: Falcone.

    Administrative, technical, or material support: Both, Leasure.

    Supervision: Szejko, Leasure, Gill, Payabvash, Sheth, Falcone.

    Conflict of Interest Disclosures: Dr Sheth reported grants from the National Institutes of Health (NIH), American Heart Association (AHA), Hyperfine, and Biogen, serving on a data safety monitoring board for Zoll, and owning equity in Alva outside the submitted work. Dr Sheth reported receiving grants from Hyperfine, Biogen, and Astrocyte outside the submitted work. No other disclosures were reported.

    Funding/Support: Dr Acosta is supported by the AHA Bugher Research Fellowship. Ms Leasure is supported by the AHA Medical Student Research Fellowship. Dr Gill is supported by the National Institute on Aging (grant No. P30AG021342). Dr Szejko is supported by the NIH (grant No. R03NS112859, R01NS110721, R01NS075209, U01NS113445, U01NS106513, R01NR01833, U24NS107215, and U24NS107136) and the AHA (grant No. 17CSA33550004 and 817874). Dr Falcone is supported by the NIH (grant No. K76AG059992, R03NS112859, and P30AG021342), the AHA (grant No. 18IDDG34280056 and 817874), the Yale Pepper Scholar Award (award No. P30AG021342), and the Neurocritical Care Society Research Fellowship.

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Information: UK Biobank data was accessed using project application No. 58743.

    References
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    Lloyd-Jones  DM, Hong  Y, Labarthe  D,  et al; American Heart Association Strategic Planning Task Force and Statistics Committee.  Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic impact goal through 2020 and beyond.   Circulation. 2010;121(4):586-613. doi:10.1161/CIRCULATIONAHA.109.192703PubMedGoogle ScholarCrossref
    2.
    Dong  C, Rundek  T, Wright  CB, Anwar  Z, Elkind  MSV, Sacco  RL.  Ideal cardiovascular health predicts lower risks of myocardial infarction, stroke, and vascular death across Whites, Blacks, and Hispanics: the Northern Manhattan study.   Circulation. 2012;125(24):2975-2984. doi:10.1161/CIRCULATIONAHA.111.081083PubMedGoogle ScholarCrossref
    3.
    Malik  R, Georgakis  MK, Neitzel  J,  et al.  Midlife vascular risk factors and risk of incident dementia: Longitudinal cohort and Mendelian randomization analyses in the UK Biobank.   Alzheimers Dement. 2021;17(9):1422-1431. doi:10.1002/alz.12320PubMedGoogle ScholarCrossref
    4.
    Gardener  H, Caunca  M, Dong  C,  et al.  Ideal cardiovascular health and biomarkers of subclinical brain aging: the Northern Manhattan Study.   J Am Heart Assoc. 2018;7(16):e009544. doi:10.1161/JAHA.118.009544PubMedGoogle ScholarCrossref
    5.
    Bycroft  C, Freeman  C, Petkova  D,  et al.  The UK Biobank resource with deep phenotyping and genomic data.   Nature. 2018;562(7726):203-209. doi:10.1038/s41586-018-0579-zPubMedGoogle ScholarCrossref
    6.
    Woodfield  R, Grant  I, Sudlow  CL; UK Biobank Stroke Outcomes Group; UK Biobank Follow-Up and Outcomes Working Group.  Accuracy of electronic health record data for identifying stroke cases in large-scale epidemiological studies: a systematic review from the UK Biobank stroke outcomes group.   PLoS One. 2015;10(10):e0140533. doi:10.1371/journal.pone.0140533PubMedGoogle ScholarCrossref
    7.
    Wilkinson  T, Schnier  C, Bush  K,  et al; Dementias Platform UK and UK Biobank.  Identifying dementia outcomes in UK Biobank: a validation study of primary care, hospital admissions and mortality data.   Eur J Epidemiol. 2019;34(6):557-565. doi:10.1007/s10654-019-00499-1PubMedGoogle ScholarCrossref
    8.
    Said  MA, Verweij  N, van der Harst  P.  Associations of combined genetic and lifestyle risks with incident cardiovascular disease and diabetes in the UK Biobank study.   JAMA Cardiol. 2018;3(8):693-702. doi:10.1001/jamacardio.2018.1717PubMedGoogle ScholarCrossref
    9.
    Mozaffarian  D.  Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review.   Circulation. 2016;133(2):187-225. doi:10.1161/CIRCULATIONAHA.115.018585PubMedGoogle ScholarCrossref
    10.
    Choi  SW, Mak  TSH, O’Reilly  PF.  Tutorial: a guide to performing polygenic risk score analyses.   Nat Protoc. 2020;15(9):2759-2772. doi:10.1038/s41596-020-0353-1PubMedGoogle ScholarCrossref
    11.
    Littlejohns  TJ, Holliday  J, Gibson  LM,  et al.  The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions.   Nat Commun. 2020;11(1):2624. doi:10.1038/s41467-020-15948-9PubMedGoogle ScholarCrossref
    12.
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