Evidence map›Paper›PMID 40525300›Full record

ArticleCirculation. Genomic and precision medicine2025

Machine Learning-Based Plasma Protein Risk Score Improves Atrial Fibrillation Prediction Over Clinical and Genomic Models.

Min Seo Kim, Shaan Khurshid, Shinwan Kany, Lu-Chen Weng, Sarah Urbut, Carolina Roselli, Leonoor F J M Wijdeveld, Sean J Jurgens, Joel T Rämö, Patrick T Ellinor and 1 more

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Article in Circulation. Genomic and precision medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Min Seo Kim *Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).ORCID 0000-0003-2115-7835
Shaan Khurshid *Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).ORCID 0000-0002-2840-4539
Shinwan KanyCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).ORCID 0000-0001-8113-733X
Lu-Chen WengCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).ORCID 0000-0003-1475-4930
Sarah UrbutCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).ORCID 0000-0002-1135-9647
Carolina RoselliCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).ORCID 0000-0001-5267-6756
Leonoor F J M WijdeveldCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).ORCID 0000-0003-2601-5801
Sean J JurgensCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).ORCID 0000-0002-1605-9782
Joel T RämöCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).ORCID 0000-0002-6429-5149
Patrick T EllinorCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).ORCID 0000-0002-2067-0533
Akl C FahedCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (M.S.K., S. Khurshid, S. Kany, L.-C.W., S.U., C.R., L.W., S.J.J., J.T.R., P.T.E., A.C.F.).ORCID 0000-0002-4849-6389

Funding

IDENTIFICATION OF COMMON GENETIC VARIANTS FOR ATRIAL FIBRILLATION AND PR INTERVALR01HL092577 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI BENJAMIN, EMELIA J., ELLINOR, PATRICK THOMAS · 2009 to 2025
$20.6M
Coronary plaque changes with statin and colchicine among people with high polygenic risk- a mechanistic pilot studyR01HL164629 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Akl C Fahed, Michael TseYin Lu · 2023 to 2026
$3.3M
Using Electrocardiogram Genetics to Inform Arrhythmia RiskR01HL157635 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI ELLINOR, PATRICK THOMAS, MIRSHAHI, TOORAJ · 2022 to 2025
$2.9M
Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimationK23HL169839 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Shaan Khurshid · 2023 to 2026
$860k
Integrating genomic and nongenomic risk for coronary artery diseaseK08HL161448 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Akl C Fahed · 2022 to 2026
$751k
NHLBI NIH HHS K08 HL161448NHLBI NIH HHS K23 HL169839NHLBI NIH HHS R01 HL092577NHLBI NIH HHS R01 HL157635NHLBI NIH HHS R01 HL164629
6 · The paper itself

Abstract

backgroundClinical factors discriminate incident atrial fibrillation (AF) risk with moderate accuracy, with only modest improvement after incorporation of polygenic risk scores. Whether emerging large-scale proteomic profiling can augment AF risk estimation is unknown.

methodsIn the UK Biobank cohort, we derived and validated a machine learning model to predict incident AF risk using serum proteins (Pro-AF). We compared Pro-AF to a validated clinical risk score (Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation, CHARGE-AF) and an AF polygenic risk score. Models were evaluated in a multiply resampled test set from nested cross-validation (internal test set), and a sample of UK Biobank participants separate from model development (hold-out test set). Metrics included discrimination of 5-year incident AF using time-dependent area under the receiver operating characteristic curve and net reclassification.

resultsTrained in 32 631 UK Biobank participants, Pro-AF predicts incident AF using 121 protein levels (out of 2911 protein analytes). When assessed in the internal test set comprising 30 632 individuals (mean age 57±8 years, 54% women, 2045 AF events) and hold-out test set comprising 13 998 individuals (mean age 57±8 years, 54% women, 870 AF events), discrimination of 5-year incident AF was highest using Pro-AF (area under the receiver operating characteristic curve internal: 0.761 [95% CI, 0.745-0.780], hold-out: 0.763 [0.734-0.784]), followed by CHARGE-AF (0.719 [0.700-0.737]; 0.702 [0.668-0.730]) and the polygenic risk score (0.686 [0.668-0.702]; 0.682 [0.660-0.710]). AF risk estimates were well-calibrated, and the addition of Pro-AF led to substantial continuous net reclassification improvement over CHARGE-AF (eg, internal test set 0.410 [0.330-0.492]). A simplified Pro-AF including only the 5 most influential proteins (NT-proBNP [N-terminal pro-brain natriuretic peptide], EDA2R [ectodysplasin A2 receptor], NPPB [B-type natriuretic peptide], BCAN [brevican core protein], and GDF15 [growth/differentiation factor 15]), retained favorable discriminative value (area under the receiver operating characteristic curve internal: 0.750 [0.733-0.768]; hold-out: 0.759 [0.732-0.790]).

conclusionsA machine learning-based protein score discriminates 5-year incident AF risk favorably compared with clinical and genetic risk factors. Large-scale proteomic analysis may assist in the prioritization of individuals at risk for AF for screening and related preventive interventions.

Indexed as

Atrial FibrillationBlood ProteinsMachine LearningAgedFemaleGenomicsHumansMaleMiddle AgedRisk AssessmentRisk FactorsROC CurveUnited KingdomBlood Proteinsatrial fibrillationblood proteinshumansproteomicsROC curve

Identifiers

PMID40525300
PMCPMC12257488

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