Association of Retinal Age Gap with Arterial Stiffness and Incident Cardiovascular Disease

Background: Retinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for ageing and mortality. Objective: To investigate associations of retinal age gap with arterial stiffness index (ASI) and incident cardiovascular disease (CVD). Methods: A deep learning (DL) model was trained based on 19,200 fundus images of 11,052 participants without any past medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35,917 participants. Regression models were used to assess the association between retinal age gap and ASI. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD. Results: We found each one-year increase in retinal age gap was associated with increased ASI (β=0.002, 95% confidence interval [CI]: 0.001-0.003, P<0.001). After a median follow-up of 5.83 years (interquartile range [IQR]: 5.73-5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each one-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio [HR]=1.03, 95% CI: 1.01-1.06, P=0.012). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (HR=1.05; 95% CI, 1.00-1.10; P-overall <0.0001; P-nonlinear=0.0681). Conclusion: We found that retinal age gap was significantly associated with ASI and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events.

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One-year Risks and Burdens of Incident Cardiovascular Disease in COVID-19: Cardiovascular Manifestations of Long COVID

10.21203/rs.3.rs-940278/v1 ◽

2021 ◽

Author(s):

Ziyad Al-Aly ◽

Benjamin Bowe ◽

Yan Xie ◽

Evan Xu

Keyword(s):

Cardiovascular Disease ◽

Cardiovascular Health ◽

Acute Infection ◽

Cerebrovascular Disorders ◽

Cardiovascular Complications ◽

Department Of Veterans Affairs ◽

Increased Risk ◽

Health And Disease ◽

One Year ◽

Incident Cardiovascular Disease

Abstract The cardiovascular complications of acute COVID-19 are well described; however, a comprehensive characterization of the post-acute cardiovascular manifestations of COVID-19 at one year has not been undertaken. Here we use the US Department of Veterans Affairs national healthcare databases to build a cohort of 151,195 people with COVID-19, 3,670,087 contemporary and 3,656,337 historical controls to estimate risks and 1-year burdens of a set of pre-specified incident cardiovascular outcomes. We show that beyond the first 30 days of infection, people with COVID-19 are at increased risk of incident cardiovascular disease spanning several categories including cerebrovascular disorders, dysrhythmias, ischemic and non-ischemic heart disease, pericarditis, myocarditis, heart failure, and thromboembolic disease. The risks and burdens were evident among those who were non-hospitalized during the acute phase of the infection and increased in a graded fashion according to care setting of the acute infection (non-hospitalized, hospitalized, and admitted to intensive care). Taken together, our results provide evidence that risk and 1-year burden of cardiovascular disease in survivors of acute COVID-19 are substantial. Care pathways of people who survived the acute episode of COVID-19 should include attention to cardiovascular health and disease.

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Abstract P349: Association Of Fibroblast Growth Factor 23 With Incident Cardiovascular Disease And All-Cause Mortality In The Framingham Heart Study

Circulation ◽

10.1161/circ.129.suppl_1.p349 ◽

2014 ◽

Vol 129 (suppl_1) ◽

Author(s):

Robin Haring ◽

Ramachandran S Vasan ◽

Henri Wallaschofski ◽

Lisa Sullivan ◽

Danielle Enserro

Keyword(s):

Cardiovascular Disease ◽

Growth Factor ◽

Fibroblast Growth Factor ◽

Regression Models ◽

Cox Regression ◽

Fibroblast Growth Factor 23 ◽

Positive Association ◽

Fibroblast Growth ◽

All Cause Mortality ◽

Incident Cardiovascular Disease

Objective: To investigate the association of fibroblast growth factor 23 (FGF23) with incident cardiovascular disease (CVD) and mortality risk in the general population. Methods: We evaluated 3,236 Framingham Offspring and Omni Study participants to examine the associations of serum FGF23 (measured by immunoassay) with 10-year incident CVD (N = 2,823) and all-cause mortality (N = 3,223) using multivariable Cox regression models. Results: During a median follow-up time of 10.8 years (Q1, 10.0; Q3, 11.4), 347 participants developed new-onset CVD and 412 died. Age- and sex-adjusted Cox regression models revealed a positive association of FGF23 with incident CVD (hazard ratio (HR) per unit increase in logFGF23: 1.43, 95% confidence interval (CI) 1.11-1.84) and all-cause mortality (HR 2.26, 95% CI, 1.86-2.75). After multivariable adjustment, the association of FGF23 with incident CVD was rendered non-significant (HR 1.12, 95% CI 0.86-1.46), whereas the positive association of FGF23 with all-cause mortality was maintained (HR: 1.87, 95% CI: 1.52 - 2.29). Analyses modeling FGF23 quartiles yielded similar findings (multivariable-adjusted HR Q4 vs. Q1 for incident CVD: 1.17, 95% CI: 0.87 - 1.59; for death: 1.87, 95% CI: 1.38 - 2.53). Conclusion: In our large community-based sample, serum FGF23 shows an independent positive association with all-cause mortality, but not with incident CVD risk.

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Retinal Age as a Predictive Biomarker for Mortality Risk

10.1101/2020.12.24.20248817 ◽

2020 ◽

Author(s):

Zhuoting Zhu ◽

Danli Shi ◽

Guankai Peng ◽

Zachary Tan ◽

Xianwen Shang ◽

...

Keyword(s):

Mortality Risk ◽

Cox Regression ◽

Chronological Age ◽

Fundus Images ◽

Tailored Interventions ◽

Age Gap ◽

Non Linear ◽

One Year ◽

The Uk ◽

Accurate Quantification

SummaryBackgroundAgeing varies substantially, thus an accurate quantification of ageing is important. We developed a deep learning (DL) model that predicted age from fundus images (retinal age). We investigated the association between retinal age gap (retinal age-chronological age) and mortality risk in a population-based sample of middle-aged and elderly adults.MethodsThe DL model was trained, validated and tested on 46,834, 15,612 and 8,212 fundus images respectively from participants of the UK Biobank study alive on 28th February 2018. Retinal age gap was calculated for participants in the test (n=8,212) and death (n=1,117) datasets. Cox regression models were used to assess association between retinal age gap and mortality risk. A restricted cubic spline analyses was conducted to investigate possible non-linear association between retinal age gap and mortality risk.FindingsThe DL model achieved a strong correlation of 0·83 (P<0·001) between retinal age and chronological age, and an overall mean absolute error of 3·50 years. Cox regression models showed that each one-year increase in the retinal age gap was associated with a 2% increase in mortality risk (hazard ratio=1·02, 95% confidence interval:1·00-1·04, P=0·021). Restricted cubic spline analyses showed a non-linear relationship between retinal age gap and mortality (Pnon-linear=0·001). Higher retinal age gaps were associated with substantially increased risks of mortality, but only if the gap exceeded 3·71 years.InterpretationOur findings indicate that retinal age gap is a robust biomarker of ageing that is closely related to risk of mortality.FundingNational Health and Medical Research Council Investigator Grant, Science and Technology Program of Guangzhou.Research in contextEvidence before this studyAgeing at an individual level is heterogeneous. An accurate quantification of the biological ageing process is significant for risk stratification and delivery of tailored interventions. To date, cell-, molecular-, and imaging-based biomarkers have been developed, such as epigenetic clock, brain age and facial age. While the invasiveness of cellular and molecular ageing biomarkers, high cost and time-consuming nature of neuroimaging and facial ages, as well as ethical and privacy concerns of facial imaging, have limited their utilities. The retina is considered a window to the whole body, implying that the retina could provide clues for ageing.Added value of this studyWe developed a deep learning (DL) model that can detect footprints of aging in fundus images and predict age with high accuracy for the UK population between 40 and 69 years old. Further, we have been the first to demonstrate that each one-year increase in retinal age gap (retinal age-chronological age) was significantly associated with a 2% increase in mortality risk. Evidence of a non-linear association between retinal age gap and mortality risk was observed. Higher retinal age gaps were associated with substantially increased risks of mortality, but only if the retinal age gap exceeded 3·71 years.Implications of all the available evidenceThis is the first study to link the retinal age gap and mortality risk, implying that retinal age is a clinically significant biomarker of ageing. Our findings show the potential of retinal images as a screening tool for risk stratification and delivery of tailored interventions. Further, the capability to use fundus imaging in predicting ageing may improve the potential health benefits of eye disease screening, beyond the detection of sight-threatening eye diseases.

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Personality, Cardiovascular Disease, and Cancer:

Zeitschrift für Gesundheitspsychologie ◽

10.1026/0943-8149.12.3.102 ◽

2004 ◽

Vol 12 (3) ◽

pp. 102-115 ◽

Cited By ~ 9

Author(s):

Manfred Amelang ◽

Petra Hasselbach ◽

Til Stürmer

Keyword(s):

Cardiovascular Disease ◽

Behavioral Control ◽

Smoking Behavior ◽

Type A Behavior ◽

Incremental Validity ◽

Personality Factors ◽

Emotional Lability ◽

Prevalent Disease ◽

Incident Cardiovascular Disease

Abstract. Ten years ago a sample of N = 5.133 male and female subjects (age 28-74) responded to questionnaires including scales for personality, life style, work stress as well as questions on prevalent disease. We now report on the follow-up regarding self-reported incidence of cardiovascular disease and cancer. During a mean follow-up of 10 years, 257 participants had died. Of those alive, N = 4.010 (82%) participated in the follow-up. Of these, 120 and 180 persons reported incident cardiovascular disease and cancer, respectively. The incidence of cardiovascular disease could be significantly predicted by the personality factors “Emotional Lability”, “Behavioral Control” and “Type-A-Behavior” as well as by the “Rationality/Antemotionality”-scale according to Grossarth-Maticek. After controlling for age, gender and smoking behavior only the significant effect of “Emotional Lability” remained and the predictors according to Grossarth-Maticek had no incremental validity. Cancer could not be predicted by any personality factors.

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1655-P: Genetic Risk Factors for Incident Cardiovascular Disease in Type 2 Diabetes Patients

Diabetes ◽

10.2337/db20-1655-p ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 1655-P

Author(s):

SOO HEON KWAK ◽

JOSEP M. MERCADER ◽

AARON LEONG ◽

BIANCA PORNEALA ◽

PEITAO WU ◽

...

Keyword(s):

Risk Factors ◽

Type 2 Diabetes ◽

Cardiovascular Disease ◽

Genetic Risk ◽

Genetic Risk Factors ◽

Incident Cardiovascular Disease ◽

Diabetes Patients

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Age at natural menopause and risk of incident cardiovascular disease: a pooled analysis of individual patient data

The Lancet Public Health ◽

10.1016/s2468-2667(19)30155-0 ◽

2019 ◽

Vol 4 (11) ◽

pp. e553-e564 ◽

Cited By ~ 21

Author(s):

Dongshan Zhu ◽

Hsin-Fang Chung ◽

Annette J Dobson ◽

Nirmala Pandeya ◽

Graham G Giles ◽

...

Keyword(s):

Cardiovascular Disease ◽

Individual Patient Data ◽

Pooled Analysis ◽

Patient Data ◽

Natural Menopause ◽

Incident Cardiovascular Disease

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Automatic Classification of Exudates in Color Fundus Images Using an Augmented Deep Learning Procedure

Proceedings of the Third International Symposium on Image Computing and Digital Medicine - ISICDM 2019 ◽

10.1145/3364836.3364843 ◽

2019 ◽

Author(s):

Lei Wang ◽

Ying Huang ◽

Bing Lin ◽

Wencan Wu ◽

Hao Chen ◽

...

Keyword(s):

Deep Learning ◽

Automatic Classification ◽

Fundus Images ◽

Learning Procedure ◽

Color Fundus Images

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Longitudinal association between cardiovascular health and arterial stiffness in the Chinese adult population

Journal of International Medical Research ◽

10.1177/0300060521998889 ◽

2021 ◽

Vol 49 (3) ◽

pp. 030006052199888

Author(s):

Yu Sang ◽

Kaimin Mao ◽

Ming Cao ◽

Xiaofen Wu ◽

Lei Ruan ◽

...

Keyword(s):

Cardiovascular Disease ◽

Arterial Stiffness ◽

Cardiovascular Health ◽

Adult Population ◽

Annual Change ◽

Chinese Adult ◽

Lower Baseline ◽

Confounding Variables ◽

Longitudinal Association

Objective Arterial stiffness may be an intermediary biological pathway involved in the association between cardiovascular health (CVH) and cardiovascular disease. We aimed to evaluate the effect of CVH on progression of brachial–ankle pulse wave velocity (baPWV) over approximately 4 years. Methods We included 1315 cardiovascular disease-free adults (49±12 years) who had two checkups from 2010 to 2019. CVH metrics (current smoking, body mass index, total cholesterol, blood pressure, and fasting plasma glucose) were assessed at baseline, and the number of ideal CVH metrics and CVH score were calculated. Additionally, baPWV was examined at baseline and follow-up. Results Median baPWV increased from 1340 cm/s to 1400 cm/s, with an average annual change in baPWV of 15 cm/s. More ideal CVH metrics and a higher CVH score were associated with lower baseline and follow-up baPWV, and the annual change in baPWV, even after adjustment for confounding variables. Associations between CVH parameters and baseline and follow-up baPWV remained robust in different sex and age subgroups, but they were only able to predict the annual change in baPWV in men and individuals older than 50 years. Conclusions Our findings highlight the benefit of a better baseline CVH profile for progression of arterial stiffness.

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Assessment of patient specific information in the wild on fundus photography and optical coherence tomography

Scientific Reports ◽

10.1038/s41598-021-86577-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Marion R. Munk ◽

Thomas Kurmann ◽

Pablo Márquez-Neila ◽

Martin S. Zinkernagel ◽

Sebastian Wolf ◽

...

Keyword(s):

Deep Learning ◽

Cross Sections ◽

Underlying Disease ◽

Geographic Atrophy ◽

Clinical Population ◽

Fundus Photography ◽

Peak Performance ◽

Patient Specific ◽

Specific Information ◽

Fundus Images

AbstractIn this paper we analyse the performance of machine learning methods in predicting patient information such as age or sex solely from retinal imaging modalities in a heterogeneous clinical population. Our dataset consists of N = 135,667 fundus images and N = 85,536 volumetric OCT scans. Deep learning models were trained to predict the patient’s age and sex from fundus images, OCT cross sections and OCT volumes. For sex prediction, a ROC AUC of 0.80 was achieved for fundus images, 0.84 for OCT cross sections and 0.90 for OCT volumes. Age prediction mean absolute errors of 6.328 years for fundus, 5.625 years for OCT cross sections and 4.541 for OCT volumes were observed. We assess the performance of OCT scans containing different biomarkers and note a peak performance of AUC = 0.88 for OCT cross sections and 0.95 for volumes when there is no pathology on scans. Performance drops in case of drusen, fibrovascular pigment epitheliuum detachment and geographic atrophy present. We conclude that deep learning based methods are capable of classifying the patient’s sex and age from color fundus photography and OCT for a broad spectrum of patients irrespective of underlying disease or image quality. Non-random sex prediction using fundus images seems only possible if the eye fovea and optic disc are visible.

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