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Image-based biological heart age estimation reveals differential aging patterns across cardiac chambers

Image-based biological heart age estimation reveals differential aging patterns across cardiac chambers
Image-based biological heart age estimation reveals differential aging patterns across cardiac chambers
Background: Biological heart age estimation can provide insights into cardiac aging. However, existing studies do not consider differential aging across cardiac regions.

Purpose: To estimate biological age of the left ventricle (LV), right ventricle (RV), myocardium, left atrium, and right atrium using magnetic resonance imaging radiomics phenotypes and to investigate determinants of aging by cardiac region.

Study type: Cross-sectional.

Population: A total of 18,117 healthy UK Biobank participants including 8338 men (mean age = 64.2 ± 7.5) and 9779 women (mean age = 63.0 ± 7.4).

Field Strength/Sequence: A 1.5 T/balanced steady-state free precession.

Assessment: An automated algorithm was used to segment the five cardiac regions, from which radiomic features were extracted. Bayesian ridge regression was used to estimate biological age of each cardiac region with radiomics features as predictors and chronological age as the output. The “age gap” was the difference between biological and chronological age. Linear regression was used to calculate associations of age gap from each cardiac region with socioeconomic, lifestyle, body composition, blood pressure and arterial stiffness, blood biomarkers, mental well-being, multiorgan health, and sex hormone exposures (n = 49).

Statistical Test: Multiple testing correction with false discovery method (threshold = 5%).

Results: The largest model error was with RV and the smallest with LV age (mean absolute error in men: 5.26 vs. 4.96 years). There were 172 statistically significant age gap associations. Greater visceral adiposity was the strongest correlate of larger age gaps, for example, myocardial age gap in women (Beta = 0.85, P = 1.69 × 10−26). Poor mental health associated with large age gaps, for example, “disinterested” episodes and myocardial age gap in men (Beta = 0.25, P = 0.001), as did a history of dental problems (eg LV in men Beta = 0.19, P = 0.02). Higher bone mineral density was the strongest associate of smaller age gaps, for example, myocardial age gap in men (Beta = −1.52, P = 7.44 × 10−6).

Data Conclusion: This work demonstrates image-based heart age estimation as a novel method for understanding cardiac aging.
1797-1812
Salih, Ahmed M
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Pujadas, Esmeralda Ruiz
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Campello, Victor M.
70b294e4-d5f3-4f65-9d26-d0d6c6c8227d
McCracken, Celeste
5d772e9e-3aaa-41da-a5ef-3943b1631fd9
Harvey, Nicholas C.
ce487fb4-d360-4aac-9d17-9466d6cba145
Neubauer, Stefan
c8a34156-a4ed-4dfe-97cb-4f47627d927d
Lekadir, Karim
b8de558a-869c-4574-b0d3-005dc52c3106
Nichols, Thomas E.
133c3799-aa55-4dcc-be24-88470d3425d9
Petersen, Steffen E.
04f2ce88-790d-48dc-baac-cbe0946dd928
Raisi-Estabragh, Zahra
43c85c5e-4574-476b-80d6-8fb1cdb3df0a
Salih, Ahmed M
c9783bc9-e223-4d3a-b0ec-34120337061c
Pujadas, Esmeralda Ruiz
e716562c-2efd-4392-881a-e79fedb85724
Campello, Victor M.
70b294e4-d5f3-4f65-9d26-d0d6c6c8227d
McCracken, Celeste
5d772e9e-3aaa-41da-a5ef-3943b1631fd9
Harvey, Nicholas C.
ce487fb4-d360-4aac-9d17-9466d6cba145
Neubauer, Stefan
c8a34156-a4ed-4dfe-97cb-4f47627d927d
Lekadir, Karim
b8de558a-869c-4574-b0d3-005dc52c3106
Nichols, Thomas E.
133c3799-aa55-4dcc-be24-88470d3425d9
Petersen, Steffen E.
04f2ce88-790d-48dc-baac-cbe0946dd928
Raisi-Estabragh, Zahra
43c85c5e-4574-476b-80d6-8fb1cdb3df0a

Salih, Ahmed M, Pujadas, Esmeralda Ruiz, Campello, Victor M., McCracken, Celeste, Harvey, Nicholas C., Neubauer, Stefan, Lekadir, Karim, Nichols, Thomas E., Petersen, Steffen E. and Raisi-Estabragh, Zahra (2023) Image-based biological heart age estimation reveals differential aging patterns across cardiac chambers. Journal of Magnetic Resonance Imaging, 58 (6), 1797-1812. (doi:10.1002/jmri.28675).

Record type: Article

Abstract

Background: Biological heart age estimation can provide insights into cardiac aging. However, existing studies do not consider differential aging across cardiac regions.

Purpose: To estimate biological age of the left ventricle (LV), right ventricle (RV), myocardium, left atrium, and right atrium using magnetic resonance imaging radiomics phenotypes and to investigate determinants of aging by cardiac region.

Study type: Cross-sectional.

Population: A total of 18,117 healthy UK Biobank participants including 8338 men (mean age = 64.2 ± 7.5) and 9779 women (mean age = 63.0 ± 7.4).

Field Strength/Sequence: A 1.5 T/balanced steady-state free precession.

Assessment: An automated algorithm was used to segment the five cardiac regions, from which radiomic features were extracted. Bayesian ridge regression was used to estimate biological age of each cardiac region with radiomics features as predictors and chronological age as the output. The “age gap” was the difference between biological and chronological age. Linear regression was used to calculate associations of age gap from each cardiac region with socioeconomic, lifestyle, body composition, blood pressure and arterial stiffness, blood biomarkers, mental well-being, multiorgan health, and sex hormone exposures (n = 49).

Statistical Test: Multiple testing correction with false discovery method (threshold = 5%).

Results: The largest model error was with RV and the smallest with LV age (mean absolute error in men: 5.26 vs. 4.96 years). There were 172 statistically significant age gap associations. Greater visceral adiposity was the strongest correlate of larger age gaps, for example, myocardial age gap in women (Beta = 0.85, P = 1.69 × 10−26). Poor mental health associated with large age gaps, for example, “disinterested” episodes and myocardial age gap in men (Beta = 0.25, P = 0.001), as did a history of dental problems (eg LV in men Beta = 0.19, P = 0.02). Higher bone mineral density was the strongest associate of smaller age gaps, for example, myocardial age gap in men (Beta = −1.52, P = 7.44 × 10−6).

Data Conclusion: This work demonstrates image-based heart age estimation as a novel method for understanding cardiac aging.

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More information

Accepted/In Press date: 28 February 2023
e-pub ahead of print date: 16 March 2023
Published date: 16 March 2023
Additional Information: Research Funding British Heart Foundation. Grant Numbers: PG/21/10619, PG/14/89/31194 National Institute for Health Research (NIHR) British Heart Foundation Clinical Research Training Fellowship. Grant Number: FS/17/81/33318 European Union's Horizon 2020. Grant Number: 825903 “SmartHeart” EPSRC program. Grant Number: EP/P001009/1 Oxford NIHR Biomedical Research Centre. Grant Number: IS-BRC-1215-20008 Medical Research Council (MRC). Grant Numbers: MC_PC_21001, MC_PC_21003, MR/L016311/1 NIHR Southampton Biomedical Research Centre Health Data Research UK.

Identifiers

Local EPrints ID: 476427
URI: http://eprints.soton.ac.uk/id/eprint/476427
PURE UUID: 1ab921ae-1f47-4e95-88fa-c8dc325acab4
ORCID for Nicholas C. Harvey: ORCID iD orcid.org/0000-0002-8194-2512

Catalogue record

Date deposited: 21 Apr 2023 12:00
Last modified: 17 Mar 2024 02:59

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Contributors

Author: Ahmed M Salih
Author: Esmeralda Ruiz Pujadas
Author: Victor M. Campello
Author: Celeste McCracken
Author: Stefan Neubauer
Author: Karim Lekadir
Author: Thomas E. Nichols
Author: Steffen E. Petersen
Author: Zahra Raisi-Estabragh

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