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Journal ArticleDOI

Combining Clinical and Polygenic Risk Improves Stroke Prediction Among Individuals With Atrial Fibrillation.

TL;DR: In this paper, atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke and a portion of this risk is heritable; however, current risk stratification tools (CHA2DS2-VASc)...
Abstract: Background: Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable; however, current risk stratification tools (CHA2DS2-VASc)...

Summary (3 min read)

Introduction

  • Atrial fibrillation (AF) is the most common cardiac arrhythmia and its prevalence is increasing (1).
  • Atrial fibrillation itself can cause substantial morbidity, including a 5-fold increased risk of ischemic stroke (2).
  • To help prevent the thromboembolic complications of AF, selected patients are offered prophylactic anticoagulation.
  • Additionally, CHA2DS2-VASc tool does not include family history or genetic risk of ischemic stroke, despite evidence suggesting the risk of ischemic stroke is heritable (~40% heritability) (9).
  • Previous research has shown that polygenic risk scores are comparable to clinical risk factors in the prediction of ischemic stroke in the general population (10), however this has not been extended into patients with AF, nor did it examine CHA2DS2-VASc .

Study design

  • The authors followed a similar study design to previously published PRS papers (10–14); in line with recommended methodological (15) and reporting guidance (16).
  • Eighty different PRS were constructed across the lassosum hyperparameters (λ and s).
  • The PRS with the greatest predictive accuracy (from step 4) was then validated in the UK Biobank incident cohort.
  • The authors UK Biobank prevalent cohort consisted of participants with a history of AF followed by a history of ischemic stroke before the beginning of the UK Biobank, and an identical number of randomly selected AF controls: participants with AF, but who did not suffer from an ischemic stroke before the beginning of the UK Biobank.
  • The MEGASTROKE GWAS was entirely independent from the UK Biobank incident and prevalent cohorts.

Study population

  • All participants included in their study were from the UK Biobank (UK Biobank).
  • Recruited participants were assessed at dedicated assessment centers across the UK.
  • As well as the clinical risk factors (CHA2DS2-VASc ) and the integrated clinical and genomic risk tool (CHA2DS2-VASc - G).the authors.

Genetic data

  • The UK Biobank genotyping and imputation techniques have been extensively described previously (23, 24).
  • In summary, initially custom Affymetrix arrays were used to genotype participants for the UK Biobank Lung Exome Variant Evaluation study, and subsequently the UK Biobank Axiom array was used (23, 24).
  • Genetic principal components analysis (PCA) was also performed by the UK Biobank.
  • On the UK Biobank data, the authors included SNVs that met the following quality control filters: minor allele frequency (MAF) > 0.001, P-value from Hardy-Weinberg Equilibrium Fisher’s exact or chi-squared test > 1e-6, SNVs that were present in >99% of included participants , an INFO score >99, nonduplicate, and non-mismatching.
  • Further, duplicate and mismatching SNVs were removed from the MEGASTROKE GWAS.

Clinical risk score

  • To compare their constructed PRS with the clinical risk factors used to predict ischemic stroke in those with AF, the authors identified if included participants had been diagnosed with (at or before their diagnosis of AF): heart failure, hypertension, vascular disease (coronary artery disease, peripheral vascular disease, and or atherosclerosis), thromboembolism and/or diabetes (type 1 or 2).
  • The authors also defined the participant’s age (at AF diagnosis), sex assigned at birth, and selfreported history of having been prescribed warfarin.
  • These clinical risk factors were selected to enable us to construct the currently recommended clinical tool to assess the risk of ischemic stroke in patients with AF: CHA2DS2-VASc (4).

Statistical Analysis

  • The authors completed the following broad steps: First, they investigated the predictive ability of PRS in logistic regression models.
  • Second, the authors assessed the correlation between PRS and CHA2DS2VASc scores, and third they investigated the ability of PRS to predict incident ischemic strokes via Cox-regression models (starting follow up from AF diagnosis).
  • The authors conducted further sensitivity analyses: logistic regression models including the aforementioned covariates as well as 1.
  • The authors calculated NRI using a risk threshold cut-off of 4% (to define high and low risk threshold, reflecting those eligible for anticoagulation (high) and those not eligible (low)).
  • The authors did this as the CHA2DS2-VASc score is currently recommended to be used in clinical practice by calculating each participant’s risk factors (method 2), however many other risk tools use a percentage risk threshold (method 1), such as the AHA/ACC PCE for Atherosclerotic Cardiovascular Disease .

Results

  • The characteristics of the UK Biobank incident cohort are reported in Table 1; there were 15,929 participants with AF , of which 684 suffered an ischemic stroke, and 15,245 did not, over follow up.
  • Compared with the currently recommended CHA2DS2-VASc only model, the integrated PRS and CHA2DS2-VASc risk model showed a significantly improved statistical fit (χ2 P =0.002), modestly improved discrimination and improved overall Net Reclassification Index (NRI): 2.3% (95%CI: 1.3% to 3.0%) (e Table 5).

Discussion

  • The authors constructed a polygenic risk score (PRS) for predicting ischemic stroke in patients with an established diagnosis of AF.
  • Additionally, the authors built an integrated genomic and clinical risk tool, integrating their PRS with the current gold standard risk tool (CHA2DS2-VASc).
  • These improvements, when applied to the large number of people with AF, translate to improved risk classification in thousands of people in the US.
  • The inclusion of all participants may explain the slightly higher HR observed by Abraham et al.

Implications for patients and clinicians

  • The authors results have implications for patients, researchers and policy makers (33, 34).
  • More specifically, of the 5.1 million people with AF in the US, ~30% are eligible for prophylactic anticoagulation (from their results: table 2) (>1.5 million people).
  • Any consideration of integrating the improved CHA2DS2-VASc -G should be cautious.
  • It is likely that these pros and cons will be of varying value to different people, however with presentation of all the available data the patient’s values can lead, with the aid of a healthcare professional, to an informed decision.
  • Risk tools are continually updated, mainly when new covariates are identified that improve model fit and prediction.

Implications for researchers

  • Third, their study design may be of interest to other researchers.
  • As stated in the methods and results, the authors used both a percentage risk threshold (method 1) as well as re-calculated each participant’s CHA2DS2-VASc score with the addition of their PRS (method 2).
  • This is reassuring as it suggests that their up-classification captures those at a similar risk to those at shared high risk (and vice versa for down-classified).
  • Lastly, the authors plan to make their PRS available upon publication at http://www.pgscatalog.org/.

Study limitations

  • The authors study should be interpreted with an understanding of its limitations.
  • The authors study was limited by the demographics of the UK Biobank.
  • Anticoagulant prescription could affect their study results by lowering the risk of ischemic stroke, however the authors believe their results remain robust for two reasons 1.
  • Unfortunately, the UK Biobank does not stratify ischemic stroke outcomes into subtypes.
  • A more broad weakness is the limitations of CHA2DS2-VASc; its discriminative ability to poorer than other cardiovascular risk tools (e.g. AHA/ACC’s pooled cohort equation) and hence improving on it is somewhat expected.

Conclusion

  • The authors PRS of over half a million SNVs is individually predictive of ischemic stroke in patients with an established diagnosis of AF, and this predicted risk appears independent of established clinical risk factors.
  • The combined PRS and clinical risk tool (our proposed, CHA2DS2-VASc-G) shows significantly improved risk prediction over the current gold standard risk tool (CHA2DS2VASc), however the prediction of ischemic stroke remains challenging.
  • The integration of clinical risk factors and polygenic risk score collectively had the greatest predictive accuracy to predict ischemic strokes in patients with Atrial Fibrillation.

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Figures (6)

Content maybe subject to copyright    Report

1
Combining clinical and polygenic risk improves stroke
prediction among individuals with atrial fibrillation.
The integration of genomic and clinical risk
Jack W. O’Sullivan, MBBS, DPhil,
a
Anna Shcherbina, MS,
a,b
Johanne M Justesen, PhD,
b
Mintu Turakhia,
MD,
a,c,d
Marco Perez, MD,
a
Hannah Wand, MS,
a
Catherine Tcheandjieu, PhD,
a
Shoa L. Clarke, MD,
PhD,
a
Robert A. Harrington, MD,
a
Manuel A. Rivas, DPhil,
b
Euan A Ashley, MB, ChB, DPhil.
a,e
a. Division of Cardiology, Department of Medicine, Stanford University School of Medicine, Stanford,
California, USA.
b. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
c. Center for Digital Health, Stanford University School of Medicine, Stanford, California, USA
d. Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA
e. Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
Address for correspondence: Dr Jack O’Sullivan or Professor Euan Ashley
Division of Cardiology
Department of Medicine
Stanford University, California, USA, 94304
jackos@stanford.edu
or euan@stanford.edu
(650) 736-7878
@DrJackOSullivan or @euanashley
Funding: The lead author (JOS) was supported by an NIH T32 grant, otherwise, there is no specific
funding.
Disclosures: EA (founder, advisor Personalis; founder, advisor Deepcell; advisor SequenceBio; advisor
Foresite Labs; advisor Apple)
Word count: 4974
Number of references: 30
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 20, 2020. ; https://doi.org/10.1101/2020.06.17.20134163doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

2
Abstract
Background
Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion
of this risk is heritable, however current risk stratification tools (CHA2DS2-VASc) don’t include
family history or genetic risk. We hypothesized that we could improve ischemic stroke
prediction in patients with AF by incorporating polygenic risk scores (PRS).
Objectives
To construct and test a PRS to predict ischemic stroke in patients with AF, both independently
and integrated with clinical risk factors.
Methods
Using data from the largest available GWAS in Europeans, we combined over half a million
genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally
validated this PRS in independent data from the UK Biobank (UK Biobank), both independently
and integrated with clinical risk factors.
Results
The integrated PRS and clinical risk factors risk tool had the greatest predictive ability.
Compared with the currently recommended risk tool (CHA2DS2-VASc ), the integrated tool
significantly improved net reclassification (NRI: 2.3% (95%CI: 1.3% to 3.0%)), and fit (
χ
2 P
=0.002). Using this improved tool, >115,000 people with AF would have improved risk
classification in the US. Independently, PRS was a significant predictor of ischemic stroke in
patients with AF prospectively (Hazard Ratio: 1.13 per 1 SD (95%CI: 1.06 to 1.23))). Lastly,
polygenic risk scores were uncorrelated with clinical risk factors (Pearson’s correlation
coefficient: -0.018).
Conclusions
In patients with AF, there appears to be a significant association between PRS and risk of
ischemic stroke. The greatest predictive ability was found with the integration of PRS and
clinical risk factors, however the prediction of stroke remains challenging.
Key words: Atrial fibrillation, stroke, genetics, cardiology, prediction, clinical risk tool.
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 20, 2020. ; https://doi.org/10.1101/2020.06.17.20134163doi: medRxiv preprint

3
Abbreviations
1. CHA2DS2-VASc : Acronym of the currently recommended tool for the risk stratification
of ischemic stroke in patients with AF. C = Congestive Heart Failure, H = Hypertension,
A
2
=Age (over 65 or over 75), D = Diabetes Mellitus, S = Stroke, V = Vascular Disease,
S = Sex
2. CHA2DS2-VASc -G: A proposed term for the integrated genetic and clinical risk
stratification tool, where G = Polygenic risk score.
3. GWAS: Genome-wide association study
4. AF: Atrial Fibrillation
5. SNV: Single nucleotide variant (polymorphism)
6. PRS: Polygenic risk score
7. SD: Standard deviation
8. NRI: Net reclassification index
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 20, 2020. ; https://doi.org/10.1101/2020.06.17.20134163doi: medRxiv preprint

4
Genomic and clinical risk score for the prediction of
ischemic stroke in atrial fibrillation.
Jack W O’Sullivan, MBBS, DPhil,
a
Anna Shcherbina, MS,
a,b
Johanne M Justesen, PhD,
b
Mintu
Turakhia, MD,
a,c,d
Marco Perez, MD,
a
Hannah Wand, MS,
a
Shoa Clarke, MD, PhD,
a
Robert A.
Harrington, MD,
a
Manuel A. Rivas, DPhil,
b
Euan A Ashley, MB, ChB, DPhil.
a,e
Introduction
Atrial fibrillation (AF) is the most common cardiac arrhythmia and its prevalence is increasing
(1). Atrial fibrillation itself can cause substantial morbidity, including a 5-fold increased risk of
ischemic stroke (2).
To help prevent the thromboembolic complications of AF, selected patients are offered
prophylactic anticoagulation. This prophylaxis is highly effective in the right patient (3–5), but
the selection of these patients remains difficult (6, 7). The current gold standard risk stratification
tool is an amalgamation of clinical risk factors (CHA2DS2-VASc ) (4). However, there are
limitations in the development, validation and performance of CHA2DS2-VASc . Most notably
the small number of AF patients in the development (n=1,084) (6), and short follow up, small
numbers, and conflicting performance in validation studies (8). Additionally, CHA2DS2-VASc
tool does not include family history or genetic risk of ischemic stroke, despite evidence
suggesting the risk of ischemic stroke is heritable (~40% heritability) (9). Previous research has
shown that polygenic risk scores are comparable to clinical risk factors in the prediction of
ischemic stroke in the general population (10), however this has not been extended into patients
with AF, nor did it examine CHA2DS2-VASc .
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 20, 2020. ; https://doi.org/10.1101/2020.06.17.20134163doi: medRxiv preprint

5
Given the known heritability of ischemic stroke, and the apparent need to improve the existing
gold standard risk tool (CHA2DS2-VASc ), we set out to construct a polygenic risk score (PRS),
and then an integrated genetic and clinical risk tool (CHA2DS2-VASc + PRS) to help predict
which patients with AF will go on to develop ischemic stroke.
Methods
Study design
We followed a similar study design to previously published PRS papers (10–14); in line with
recommended methodological (15) and reporting guidance (16). We will briefly describe the five
broad steps we completed in this paragraph (Figure1), and then we elaborate on each of these
steps individually in the below paragraphs. The five steps were: 1. Curation of previously
published GWAS summary statistics, 2. Accounting for linkage disequilibrium (LD) in GWAS
summary statistics, using the R package lassosum (17) 3. Construction of PRS (see eMethods) in
our UK Biobank prevalent cohort. Eighty different PRS were constructed across the lassosum
hyperparameters (
λ
and s). 4. Determining the most accurate PRS in the UK Biobank prevalent
Cohort. 5. The PRS with the greatest predictive accuracy (from step 4) was then validated in the
UK Biobank incident cohort.
We attained GWAS summary statistics from the MEGASTROKE consortium
(http://www.megastroke.org/
). This GWAS was performed on 446,696 participants (40,585 cases
(stroke); 406,111 noncases (no stroke)) and stratified results by ancestry and stroke sub-type
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 20, 2020. ; https://doi.org/10.1101/2020.06.17.20134163doi: medRxiv preprint

Citations
More filters
Journal ArticleDOI
TL;DR: A review of the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases is presented in this article , where five cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy are selected.
Abstract: Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation‚ which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care–associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

35 citations

Journal ArticleDOI
TL;DR: In patients with cardiovascular conditions, AF PRS is a strong independent predictor of incident AF that provides complementary predictive value when added to a validated clinical risk score and NT-proBNP.
Abstract: AIMS Interest in targeted screening programmes for atrial fibrillation (AF) has increased, yet the role of genetics in identifying patients at highest risk of developing AF is unclear. METHODS AND RESULTS A total of 36,662 subjects without prior AF were analyzed from four TIMI trials. Subjects were divided into quintiles using a validated polygenic risk score (PRS) for AF. Clinical risk for AF was calculated using the CHARGE-AF model. Kaplan-Meier event rates, adjusted hazard ratios (HRs), C-indices, and net reclassification improvement were used to determine if the addition of the PRS improved prediction compared with clinical risk and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Over 2.3 years, 1018 new AF cases developed. AF PRS predicted a significant risk gradient for AF with a 40% increased risk per 1-SD increase in PRS [HR: 1.40 (1.32-1.49); P < 0.001]. Those with high AF PRS (top 20%) were more than two-fold more likely to develop AF [HR 2.45 (1.99-3.03), P < 0.001] compared with low PRS (bottom 20%). Furthermore, PRS provided an additional gradient of risk stratification on top of the CHARGE-AF clinical risk score, ranging from a 3-year incidence of 1.3% in patients with low clinical and genetic risk to 8.7% in patients with high clinical and genetic risk. The subgroup of patients with high clinical risk, high PRS, and elevated NT-proBNP had an AF risk of 16.7% over 3 years. The C-index with the CHARGE-AF clinical risk score alone was 0.65, which improved to 0.67 (P < 0.001) with the addition of NT-proBNP, and increased further to 0.70 (P < 0.001) with the addition of the PRS. CONCLUSION In patients with cardiovascular conditions, AF PRS is a strong independent predictor of incident AF that provides complementary predictive value when added to a validated clinical risk score and NT-proBNP.

11 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined a publicly available GWAS database and identified two independent GWAS on the same phenotype (an earlier, discovery GWAS and a later, "replication" GWAS done in the UK biobank).
Abstract: With the establishment of large biobanks, discovery of single nucleotide variants (SNVs, also known as single nucleotide polymorphisms (SNVs)) associated with various phenotypes has accelerated. An open question is whether genome-wide significant SNVs identified in earlier genome-wide association studies (GWAS) are replicated in later GWAS conducted in biobanks. To address this, we examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, "discovery" GWAS and a later, "replication" GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNVs (of which 6289 reached P < 5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0%; although lower for binary than quantitative phenotypes (58.1% versus 94.8% respectively). There was a 18.0% decrease in SNV effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNV effect size, phenotype trait (binary or quantitative), and discovery P value, we built and validated a model that predicted SNV replication with area under the Receiver Operator Curve = 0.90. While non-replication may reflect lack of power rather than genuine false-positives, these results provide insights about which discovered associations are likely to be replicated across subsequent GWAS.

5 citations

Journal ArticleDOI
TL;DR: In this article, the association between eight single-nucleotide variants (SNVs) and the risk of atrial fibrillation development and recurrence after sinus rhythm restoration with direct current cardioversion (DCC) is examined.
Abstract: Background and Objectives: Recurrence of atrial fibrillation (AF) within six months after sinus rhythm restoration with direct current cardioversion (DCC) is a significant treatment challenge. Currently, the factors influencing outcome are mostly unknown. Studies have found a link between genetics and the risk of AF and efficacy of rhythm control. The aim of this study was to examine the association between eight single-nucleotide variants (SNVs) and the risk of AF development and recurrence after DCC. Materials and Methods: Regarding the occurrence of AF, 259 AF cases and 108 controls were studied. Genotypes for the eight SNVs located in the genes CAV1, MYH7, SOX5, KCNN3, ZFHX3, KCNJ5 and PITX2 were determined using high-resolution melting analysis and confirmed with Sanger sequencing. Six months after DCC, a telephone interview was conducted to determine whether AF had recurred. A polygenic risk score (PRS) was calculated as the unweighted sum of risk alleles. Multivariate regression analyses were performed to assess SNV and PRS association with AF occurrence and recurrence after DCC. Results: The risk allele of rs2200733 (PITX2) was significantly associated with the development of AF (p = 0.012, OR = 2.31, 95% CI = 1.206-4.423). AF recurred in 60% of patients and the allele generally associated with a decreased risk of AF of rs11047543 (SOX5) was associated with a greater risk of AF recurrence (p = 0.014, OR = 0.223, 95% CI = 0.067-0.738). A PRS of greater than 7 was significantly associated (p = 0.008) with a higher likelihood of developing AF after DCC (OR = 4.174, 95% CI = 1.454-11.980). Conclusions: A higher PRS is associated with increased odds of AF recurrence after treatment with DCC. PITX2 (rs2200733) is significantly associated with an increased risk of AF. The protective allele of rs11047543 (SOX5) is associated with a greater risk of AF recurrence. Further studies are needed to predict the success of rhythm control and guide patient selection towards the most efficacious treatment.

3 citations

Journal ArticleDOI
TL;DR: In this article , the authors examined whether models that include a clinical/environmental risk index (CERI) and polygenic scores (PGS) are able to identify individuals at increased risk of substance use disorders in young adulthood across four longitudinal cohorts for a combined sample of N = 15,134.
Abstract: Substance use disorders (SUDs) incur serious social and personal costs. The risk for SUDs is complex, with risk factors ranging from social conditions to individual genetic variation. We examined whether models that include a clinical/environmental risk index (CERI) and polygenic scores (PGS) are able to identify individuals at increased risk of SUD in young adulthood across four longitudinal cohorts for a combined sample of N = 15,134. Our analyses included participants of European (NEUR = 12,659) and African (NAFR = 2475) ancestries. SUD outcomes included: (1) alcohol dependence, (2) nicotine dependence; (3) drug dependence, and (4) any substance dependence. In the models containing the PGS and CERI, the CERI was associated with all three outcomes (ORs = 01.37-1.67). PGS for problematic alcohol use, externalizing, and smoking quantity were associated with alcohol dependence, drug dependence, and nicotine dependence, respectively (OR = 1.11-1.33). PGS for problematic alcohol use and externalizing were also associated with any substance dependence (ORs = 1.09-1.18). The full model explained 6-13% of the variance in SUDs. Those in the top 10% of CERI and PGS had relative risk ratios of 3.86-8.04 for each SUD relative to the bottom 90%. Overall, the combined measures of clinical, environmental, and genetic risk demonstrated modest ability to distinguish between affected and unaffected individuals in young adulthood. PGS were significant but added little in addition to the clinical/environmental risk index. Results from our analysis demonstrate there is still considerable work to be done before tools such as these are ready for clinical applications.

3 citations

References
More filters
Journal ArticleDOI
01 Aug 1991-Stroke
TL;DR: The data suggest that the elderly are particularly vulnerable to stroke when atrial fibrillation is present, and the effects of hypertension, coronary heart disease, and cardiac failure on the risk of stroke became progressively weaker with increasing age.
Abstract: The impact of nonrheumatic atrial fibrillation, hypertension, coronary heart disease, and cardiac failure on stroke incidence was examined in 5,070 participants in the Framingham Study after 34 years of follow-up. Compared with subjects free of these conditions, the age-adjusted incidence of stroke was more than doubled in the presence of coronary heart disease (p less than 0.001) and more than trebled in the presence of hypertension (p less than 0.001). There was a more than fourfold excess of stroke in subjects with cardiac failure (p less than 0.001) and a near fivefold excess when atrial fibrillation was present (p less than 0.001). In persons with coronary heart disease or cardiac failure, atrial fibrillation doubled the stroke risk in men and trebled the risk in women. With increasing age the effects of hypertension, coronary heart disease, and cardiac failure on the risk of stroke became progressively weaker (p less than 0.05). Advancing age, however, did not reduce the significant impact of atrial...

6,692 citations

Journal ArticleDOI
01 Feb 2010-Chest
TL;DR: In this article, a simple stroke risk stratifi cation schema, based on a risk factor approach, provides some improvement in predictive value for TE over the CHADS 2 schema, with low event rates in low-risk subjects and only a small proportion of subjects into the intermediate-risk category.

5,499 citations

Journal ArticleDOI
TL;DR: This year's edition of the Statistical Update includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association’s 2020 Impact Goals.
Abstract: Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovas...

5,078 citations

Journal ArticleDOI
13 Jun 2001-JAMA
TL;DR: The 2 existing classification schemes and especially a new stroke risk index, CHADS, can quantify risk of stroke for patients who have AF and may aid in selection of antithrombotic therapy.
Abstract: a c statistic of 0.82 (95% CI, 0.80-0.84), the CHADS2 index was the most accurate predictor of stroke. The stroke rate per 100 patient-years without antithrombotic therapy increased by a factor of 1.5 (95% CI, 1.3-1.7) for each 1-point increase in the CHADS2 score: 1.9 (95% CI, 1.2-3.0) for a score of 0; 2.8 (95% CI, 2.0-3.8) for 1; 4.0 (95% CI, 3.1-5.1) for 2; 5.9 (95% CI, 4.6-7.3) for 3; 8.5 (95% CI, 6.3-11.1) for 4; 12.5 (95% CI, 8.2-17.5) for 5; and 18.2 (95% CI, 10.5-27.4) for 6. Conclusion The 2 existing classification schemes and especially a new stroke risk index, CHADS2, can quantify risk of stroke for patients who have AF and may aid in selection of antithrombotic therapy.

4,498 citations

Journal ArticleDOI
TL;DR: Genome-wide polygenic risk scores derived from GWAS data for five common diseases can identify subgroups of the population with risk approaching or exceeding that of a monogenic mutation.
Abstract: A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation1. Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature2-5, it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk6. We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues.

1,962 citations