Association of lifestyle with deep learning predicted electrocardiographic age
© 2023 Zhang, Miao, Wang, Thomas, Ribeiro, Brant, Ribeiro and Lin..
Background: People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear.
Methods: This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Δage. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Δage, and the models were adjusted for sex and chronological age.
Results: This study included 44,094 individuals (mean age 64 ± 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Δage (absolute error of biological age and chronological age) was 9.8 ± 7.4 years. Δage was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 ± 0.11 for the healthy diet to 2.37 ± 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 ± 0.29 years of older predicted ECG-age.
Conclusion: In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
|---|---|
| Enthalten in: |
Frontiers in cardiovascular medicine - 10(2023) vom: 04., Seite 1160091 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Zhang, Cuili [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Biological age |
|---|
| Anmerkungen: |
Date Revised 01.07.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3389/fcvm.2023.1160091 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM356715418 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM356715418 | ||
| 003 | DE-627 | ||
| 005 | 20231226071044.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3389/fcvm.2023.1160091 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1189.xml |
| 035 | |a (DE-627)NLM356715418 | ||
| 035 | |a (NLM)37168659 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Zhang, Cuili |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Association of lifestyle with deep learning predicted electrocardiographic age |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 01.07.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2023 Zhang, Miao, Wang, Thomas, Ribeiro, Brant, Ribeiro and Lin. | ||
| 520 | |a Background: People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear | ||
| 520 | |a Methods: This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Δage. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Δage, and the models were adjusted for sex and chronological age | ||
| 520 | |a Results: This study included 44,094 individuals (mean age 64 ± 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Δage (absolute error of biological age and chronological age) was 9.8 ± 7.4 years. Δage was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 ± 0.11 for the healthy diet to 2.37 ± 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 ± 0.29 years of older predicted ECG-age | ||
| 520 | |a Conclusion: In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a biological age | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a electrocardiogram | |
| 650 | 4 | |a epidemiology—analytic (risk factors) | |
| 650 | 4 | |a lifestyle | |
| 700 | 1 | |a Miao, Xiao |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Biqi |e verfasserin |4 aut | |
| 700 | 1 | |a Thomas, Robert J |e verfasserin |4 aut | |
| 700 | 1 | |a Ribeiro, Antônio H |e verfasserin |4 aut | |
| 700 | 1 | |a Brant, Luisa C C |e verfasserin |4 aut | |
| 700 | 1 | |a Ribeiro, Antonio L P |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Honghuang |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Frontiers in cardiovascular medicine |d 2014 |g 10(2023) vom: 04., Seite 1160091 |w (DE-627)NLM255534736 |x 2297-055X |7 nnns |
| 773 | 1 | 8 | |g volume:10 |g year:2023 |g day:04 |g pages:1160091 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3389/fcvm.2023.1160091 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 10 |j 2023 |b 04 |h 1160091 | ||