Retinal BioAge Reveals Indicators of Cardiovascular-Kidney-Metabolic Syndrome in US and UK Populations DOI
Ehsan Vaghefi, Songyang An,

Shima Moghadam

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 19, 2024

Abstract Background There is a growing recognition of the divergence between biological and chronological age, as well interaction among cardiovascular, kidney, metabolic (CKM) diseases, known CKM syndrome, in shortening both lifespan healthspan. Detecting indicators syndrome can prompt lifestyle risk-factor management to prevent progression adverse clinical events. In this study, we tested novel deep-learning model, retinal BioAge, determine whether it could identify individuals with higher prevalence compared their peers similar age. Methods Retinal images health records were analyzed from UK Biobank population study US-based EyePACS 10K dataset persons living diabetes. 77,887 44,731 unique participants used train BioAge model. For validation, separate test sets 10,976 (5,476 individuals) 19,856 (9,786 analyzed. AgeGap (retinal – age) was calculated for each participant, those top bottom quartiles abnormal blood pressure, cholesterol, kidney function, hemoglobin A1c. Results Biobank, quartile had significantly hypertension (36.3% vs. 29.0%, p<0.001), while elevated non-HDL cholesterol (77.9% 78.4%, p=0.80), impaired function (4.8% 4.2%, p=0.60), diabetes (3.1% 2.2%, p=0.24). contrast, (49.9% 43.0%, (36.7% 23.1%, suboptimally controlled (76.5% 60.0%, diabetic retinopathy (52.9% 8.0%, but not (53.8% 55.4%, p=0.33). Conclusion A model identified who underlying peers, particularly diverse US Clinical Perspective What Is New? Accelerated aging predicted by analysis standard able detect multiple new cardiovascular-kidney-metabolic populations. Are Implications? Rapid, point-of-care routine eye exams broaden access detection awareness health. With broad range prevention interventions reduce earlier broader important improve public outcomes.

Language: Английский

Association between Biological Aging and Cardiovascular Health: Combined Evidence based on Cross-sectional and Prospective Study DOI
Xiaoyi Zhu, Xinyi Wang, Xiaocao Tian

et al.

Archives of Gerontology and Geriatrics, Journal Year: 2025, Volume and Issue: 132, P. 105785 - 105785

Published: Feb. 15, 2025

Language: Английский

Citations

1
Outcomes of Balloon-Expandable Transcatheter Aortic Valve Replacement in Younger Patients in the Low-Risk Era DOI Creative Commons
Megan Coylewright, Kendra J. Grubb, Suzanne V. Arnold

et al.

JAMA Cardiology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 30, 2024

Guidelines advise heart team assessment for all patients with aortic stenosis, surgical valve replacement recommended younger than 65 years or a life expectancy greater 20 years. If bioprosthetic valves are selected, repeat procedures may be needed given limited durability of tissue valves; however, stenosis have major comorbidities that can limit expectancy, impacting decision-making.

Language: Английский

Citations

8
Beyond Years: Is AI Ready to Predict Biological Age and Cardiovascular Risk Using Echocardiography? DOI
Bjørnar Grenne, Andreas Østvik

Journal of the American Society of Echocardiography, Journal Year: 2024, Volume and Issue: 37(8), P. 736 - 739

Published: May 24, 2024

Language: Английский

Citations

0
Retinal BioAge Reveals Indicators of Cardiovascular-Kidney-Metabolic Syndrome in US and UK Populations DOI
Ehsan Vaghefi, Songyang An,

Shima Moghadam

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 19, 2024

Abstract Background There is a growing recognition of the divergence between biological and chronological age, as well interaction among cardiovascular, kidney, metabolic (CKM) diseases, known CKM syndrome, in shortening both lifespan healthspan. Detecting indicators syndrome can prompt lifestyle risk-factor management to prevent progression adverse clinical events. In this study, we tested novel deep-learning model, retinal BioAge, determine whether it could identify individuals with higher prevalence compared their peers similar age. Methods Retinal images health records were analyzed from UK Biobank population study US-based EyePACS 10K dataset persons living diabetes. 77,887 44,731 unique participants used train BioAge model. For validation, separate test sets 10,976 (5,476 individuals) 19,856 (9,786 analyzed. AgeGap (retinal – age) was calculated for each participant, those top bottom quartiles abnormal blood pressure, cholesterol, kidney function, hemoglobin A1c. Results Biobank, quartile had significantly hypertension (36.3% vs. 29.0%, p<0.001), while elevated non-HDL cholesterol (77.9% 78.4%, p=0.80), impaired function (4.8% 4.2%, p=0.60), diabetes (3.1% 2.2%, p=0.24). contrast, (49.9% 43.0%, (36.7% 23.1%, suboptimally controlled (76.5% 60.0%, diabetic retinopathy (52.9% 8.0%, but not (53.8% 55.4%, p=0.33). Conclusion A model identified who underlying peers, particularly diverse US Clinical Perspective What Is New? Accelerated aging predicted by analysis standard able detect multiple new cardiovascular-kidney-metabolic populations. Are Implications? Rapid, point-of-care routine eye exams broaden access detection awareness health. With broad range prevention interventions reduce earlier broader important improve public outcomes.

Language: Английский

Citations

0