Deep neural network-estimated age using optical coherence tomography predicts mortality.

The concept of biological age has emerged as a measurement that reflects physiological and functional decline with ageing. Here we aimed to develop a deep neural network (DNN) model that predicts biological age from optical coherence tomography (OCT). A total of 84,753 high-quality OCT images from 53,159 individuals in the UK Biobank were included, among which 12,631 3D-OCT images from 8,541 participants without any reported medical conditions at baseline were used to develop an age prediction model. For the remaining 44,618 participants, OCT age gap, the difference between the OCT-predicted age and chronological age, was calculated for each participant. Cox regression models assessed the association between OCT age gap and mortality. The DNN model predicted age with a mean absolute error of 3.27 years and showed a strong correlation of 0.85 with chronological age. After a median follow-up of 11.0 years (IQR 10.9-11.1 years), 2,429 deaths (5.44%) were recorded. For each 5-year increase in OCT age gap, there was an 8% increased mortality risk (hazard ratio [HR] = 1.08, CI:1.02-1.13, P = 0.004). Compared with an OCT age gap within ± 4 years, OCT age gap less than minus 4 years was associated with a 16% decreased mortality risk (HR = 0.84, CI: 0.75-0.94, P = 0.002) and OCT age gap more than 4 years showed an 18% increased risk of death incidence (HR = 1.18, CI: 1.02-1.37, P = 0.026). OCT imaging could serve as an ageing biomarker to predict biological age with high accuracy and the OCT age gap, defined as the difference between the OCT-predicted age and chronological age, can be used as a marker of the risk of mortality.

Retinal age gap as a predictive biomarker for future risk of clinically significant diabetic retinopathy.

AIMS: Retinal age derived from fundus images has been verified as a novel ageing biomarker. We aim to explore the association between retinal age gap (retinal age minus chronological age) and incident diabetic retinopathy (DR). METHODS: Retinal age prediction was performed by a deep learning model, trained and validated based on 19,200 fundus images of 11,052 disease-free participants. Retinal age gaps were determined for 2311 patients with diabetes who had no history of diabetic retinopathy at baseline. DR events were ascertained by data linkage to hospital admissions. Cox proportional hazards regression models were performed to evaluate the association between retinal age gaps and incident DR. RESULTS: During the median follow-up period of 11.0 (interquartile range: 10.8-11.1) years, 183 of 2311 participants with diabetes developed incident DR. Each additional year of the retinal age gap was associated with a 7% increase in the risk of incident DR (hazard ratio [HR] = 1.07, 95% confidence interval [CI] 1.02-1.12, P = 0.004), after adjusting for confounding factors. Participants with retinal age gaps in the fourth quartile had a significantly higher DR risk compared to participants with retinal age gaps in the lowest quartile (HR = 2.88, 95% CI 1.61-5.15, P < 0.001). CONCLUSIONS: We found that higher retinal age gap was associated with an increased risk of incident DR. As an easy and non-invasive biomarker, the retinal age gap may serve as an informative tool to facilitate the individualized risk assessment and personalized screening protocol for DR.

Glycemic status and its association with retinal age gap: Insights from the UK biobank study.

OBJECTIVE: To investigate associations between different glycemic status and biological age indexed by retinal age gap. METHODS: A total of 28,919 participants from the UK Biobank study with available glycemic status and qualified retinal imaging data were included in the present analysis. Glycemic status included type 2 diabetes mellitus (T2D) disease status and glycemic indicators of plasma glycated hemoglobin (HbA1c) and glucose. Retinal age gap was defined as the difference between the retina-predicted age and chronological age. Linear regression models estimated the association of different glycemic status with retinal age gap. RESULTS: Prediabetes and T2D was significantly associated with higher retinal age gaps compared to normoglycemia (regression coefficient [ß] = 0.25, 95% confidence interval [CI]: 0.11-0.40, P = 0.001; ß = 1.06, 95% CI: 0.83-1.29, P < 0.001; respectively). Multi-variable linear regressions further found an increase of HbA1c was independently associated with higher retinal age gaps among all subjects or subjects without T2D. Significant positive associations were noted across the increasing HbA1c and glucose groups with retinal age gaps compared to the normal level group. These findings remained significant after excluding diabetic retinopathy. CONCLUSIONS: Dysglycemia was significantly associated with accelerated ageing indexed by retinal age gaps, highlighting the importance of maintaining glycemic status.

A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images.

Purpose: The purpose of this study was to perform a systematic review and meta-analysis to synthesize evidence from studies using deep learning (DL) to predict cardiovascular disease (CVD) risk from retinal images. Methods: A systematic literature search was performed in MEDLINE, Scopus, and Web of Science up to June 2022. We extracted data pertaining to predicted outcomes, model development, and validation and model performance metrics. Included studies were graded using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. Model performance was pooled across eligible studies using a random-effects meta-analysis model. Results: A total of 26 studies were included in the analysis. There were 42 CVD risk-related outcomes predicted from retinal images were identified, including 33 CVD risk factors, 4 cardiac imaging biomarkers, 2 CVD risk scores, the presence of CVD, and incident CVD. Three studies that aimed to predict the development of future CVD events reported an area under the receiver operating curve (AUROC) between 0.68 and 0.81. Models that used retinal images as input data had a pooled mean absolute error of 3.19 years (95% confidence interval [CI] = 2.95-3.43) for age prediction; a pooled AUROC of 0.96 (95% CI = 0.95-0.97) for gender classification; a pooled AUROC of 0.80 (95% CI = 0.73-0.86) for diabetes detection; and a pooled AUROC of 0.86 (95% CI = 0.81-0.92) for the detection of chronic kidney disease. We observed a high level of heterogeneity and variation in study designs. Conclusions: Although DL models appear to have reasonably good performance when it comes to predicting CVD risk, further work is necessary to evaluate the real-world applicability and predictive accuracy. Translational Relevance: DL-based CVD risk assessment from retinal images holds great promise to be translated to clinical practice as a novel approach for CVD risk assessment, given its simple, quick, and noninvasive nature.

Central obesity and its association with retinal age gap: insights from the UK Biobank study.

BACKGROUND: Conflicting evidence exists on the association between ageing and obesity. Retinal age derived from fundus images has been validated as a novel biomarker of ageing. In this study, we aim to investigate the association between different anthropometric phenotypes based on body mass index (BMI) and waist circumference (WC) and the retinal age gap (retinal age minus chronological age). METHODS: A total of 35,550 participants with BMI, WC and qualified retinal imaging data available were included to investigate the association between anthropometric groups and retinal ageing. Participants were stratified into 7 different body composition groups based on BMI and WC (Normal-weight/Normal WC, Overweight/Normal WC, Mild obesity/Normal WC, Normal-weight/High WC, Overweight/High WC, Mild obesity/High WC, and Severe obesity/High WC). Linear regression and logistic regression models were fitted to investigate the association between the seven anthropometric groups and retinal age gap as continuous and categorical outcomes, respectively. RESULTS: A total of 35,550 participants (55.6% females) with a mean age 56.8 ± 8.04 years were included in the study. Individuals in the Overweight/High WC, Mild obesity/High WC and Severe obesity/High WC groups were associated with an increase in the retinal age gap, compared with those in the Normal Weight/Normal WC group (ß = 0.264, 95% CI: 0.105-0.424, P =0.001; ß = 0.226, 95% CI: 0.082-0.371, P = 0.002; ß = 0.273, 95% CI: 0.081-0.465, P = 0.005; respectively) in fully adjusted models. Similar findings were noted in the association between the anthropometric groups and retinal ageing process as a categorical outcome. CONCLUSION: A significant positive association exists between central obesity and accelerated ageing indexed by retinal age gaps, highlighting the significance of maintaining a healthy body shape.

The Ethnopharmacological, Phytochemical, and Pharmacological Review of Euryale ferox Salisb.: A Chinese Medicine Food Homology.

Euryale ferox Salisb. (prickly water lily) is the only extent of the genus Euryale that has been widely distributed in China, India, Korea, and Japan. The seeds of E. ferox (EFS) have been categorized as superior food for 2000 years in China, based on their abundant nutrients including polysaccharides, polyphenols, sesquineolignans, tocopherols, cyclic dipeptides, glucosylsterols, cerebrosides, and triterpenoids. These constituents exert multiple pharmacological effects, such as antioxidant, hypoglycemic, cardioprotective, antibacterial, anticancer, antidepression, and hepatoprotective properties. There are very few summarized reports on E. ferox, albeit with its high nutritional value and beneficial activities. Therefore, we collected the reported literature (since 1980), medical classics, database, and pharmacopeia of E. ferox, and summarized the botanical classification, traditional uses, phytochemicals, and pharmacological effects of E. ferox, which will provide new insights for further research and development of EFS-derived functional products.

Association between cardiovascular health metrics and retinal ageing.

The study aims to investigate associations between cardiovascular health (CVH) metrics and retinal ageing indexed by retinal age gap. A total of 26,354 participants from the UK Biobank study with available CVH metrics and qualified retinal imaging were included in the present analysis. CVH included 7 metrics (smoking, physical activity, diet, body mass index [BMI], total cholesterol, blood pressure [BP], blood glucose). These were summarized to classify the overall CVH as poor (0-7), intermediate (8-10) or ideal (11-14). Retinal age gap was defined as the difference between biological age predicted by fundus images and chronological age. Accelerated and non-accelerated retinal ageing was defined if retinal age gap was in the upper or lower 50% quantiles of the study population, respectively. Linear and logistic regression models estimated the association of overall CVH and each metric of CVH with retinal age gap respectively. Our results showed that in the fully adjusted model, each one-unit score increase in overall CVH was negatively associated with retinal age gap (odds ratio [OR] = 0.89, 95% confidence interval [CI]: 0.87-0.92, P < 0.001). Compared with poor overall CVH, people with intermediate and ideal overall CVH had significantly lower retinal age gap (OR = 0.76, 95%CI: 0.67-0.85, P < 0.001; OR = 0.58, 95%CI: 0.50-0.67, P < 0.001). Similar associations were found between overall CVH and accelerated retinal ageing. CVH metrics including smoking, BMI, BP, and blood glucose were also significantly associated with higher retinal age gap. Taken together, we found a significant and inverse dose-response association between CVH metrics and retinal age gap, indicating that maintaining healthy metrics especially smoking, BMI, BP, and blood glucose may be crucial to slow down biological ageing.

The Association of Retinal age gap with metabolic syndrome and inflammation.

BACKGROUND: Metabolic syndrome (MetS) is a clustering of cardiometabolic components, posing tremendous burdens in the aging society. Retinal age gap has been proposed as a robust biomarker associated with mortality and Parkinson's disease. Although MetS and chronic inflammation could accelerate the aging process and increase the risk of mortality, the association of the retinal age gap with MetS and inflammation has not been examined yet. METHODS: Retinal age gap (retina-predicted age minus chronological age) was calculated using a deep learning model. MetS was defined as the presence of three or more of the following: central obesity, hypertension, dyslipidemia, hypertriglyceridemia, and hyperglycemia. Inflammation index was defined as a high-sensitivity C-reactive protein level above 3.0 mg/L. Logistic regression models were used to examine the associations of retinal age gaps with MetS and inflammation. RESULTS: We found that retinal age gap was significantly associated with MetS and inflammation. Specifically, compared to participants with retinal age gaps in the lowest quartile, the risk of MetS was significantly increased by 10% and 14% for participants with retinal age gaps in the third and fourth quartile (odds ratio [OR]:1.10; 95% confidence interval [CI], 1.01,1.21;, p = .030; OR: 1.14, 95% CI, 1.03,1.26; p = .012, respectively). Similar trends were identified for the risk of inflammation and combined MetS and inflammation. CONCLUSION: We found that retinal age gaps were significantly associated with MetS as well as inflammation. Given the noninvasive and cost-effective nature and the efficacy of the retinal age gap, it has great potential to be used as a screening tool for MetS in large populations.

Association of Retinal Age Gap and Risk of Kidney Failure: A UK Biobank Study.

RATIONALE & OBJECTIVE: The incidence of kidney failure is known to increase with age. We have previously developed and validated the use of retinal age based on fundus images as a biomarker of aging. However, the association of retinal age with kidney failure is not clear. We investigated the association of retinal age gap (the difference between retinal age and chronological age) with future risk of kidney failure. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: 11,052 UK Biobank study participants without any reported disease for characterizing retinal age in a deep learning algorithm. 35,864 other participants with retinal images and no kidney failure were followed to assess the association between retinal age gap and the risk of kidney failure. EXPOSURE: Retinal age gap, defined as the difference between model-based retinal age and chronological age. OUTCOME: Incident kidney failure. ANALYTICAL APPROACH: A deep learning prediction model used to characterize retinal age based on retinal images and chronological age, and Cox proportional hazards regression models to investigate the association of retinal age gap with incident kidney failure. RESULTS: After a median follow-up period of 11 (IQR, 10.89-11.14) years, 115 (0.32%) participants were diagnosed with incident kidney failure. Each 1-year greater retinal age gap at baseline was independently associated with a 10% increase in the risk of incident kidney failure (HR, 1.10 [95% CI, 1.03-1.17]; P=0.003). Participants with retinal age gaps in the fourth (highest) quartile had a significantly higher risk of incident kidney failure compared with those in the first quartile (HR, 2.77 [95% CI, 1.29-5.93]; P=0.009). LIMITATIONS: Limited generalizability related to the composition of participants in the UK Biobank study. CONCLUSIONS: Retinal age gap was significantly associated with incident kidney failure and may be a promising noninvasive predictive biomarker for incident kidney failure.

Diet-Wide Association Study for the Incidence of Type 2 Diabetes Mellitus in Community-Dwelling Adults Using the UK Biobank Data.

This longitudinal study used diet-wide association studies (DWAS) to investigate the association between diverse dietary food and nutrient intakes and the onset of type 2 diabetes mellitus (T2DM). Out of 502,505 participants from the UK Biobank, 119,040 with dietary data free of T2DM at the baseline were included, and 3241 developed T2DM during a median follow-up of 11.7 years. The DWAS analysis, which is based on Cox regression models, was used to analyse the associations between dietary food or nutrient intake factors and T2DM risk. The study found that 10 out of 225 dietary factors were significantly associated with the T2DM risk. Total alcohol (HR = 0.86, 0.85-0.92, p = 1.26 × 10-32), red wine (HR = 0.89, 0.88-0.94, p = 7.95 × 10-19), and fresh tomatoes (HR = 0.92, 0.89-0.94, p = 2.3 × 10-11) showed a negative association with T2DM risk, whereas sliced buttered bread exhibited a positive association. Additionally, 5 out of 21 nutrient intake variables revealed significant associations with the T2DM risk, with iron having the highest protective effect and starch as a risk factor. In conclusion, DWAS is an effective method for discovering novel associations when exploring numerous dietary variables simultaneously and could provide valuable insight into future dietary guidance for T2DM.

Retinal age gap as a predictive biomarker of stroke risk.

BACKGROUND: The aim of this study is to investigate the association of retinal age gap with the risk of incident stroke and its predictive value for incident stroke. METHODS: A total of 80,169 fundus images from 46,969 participants in the UK Biobank cohort met the image quality standard. A deep learning model was constructed based on 19,200 fundus images of 11,052 disease-free participants at baseline for age prediction. Retinal age gap (retinal age predicted based on the fundus image minus chronological age) was generated for the remaining 35,917 participants. Stroke events were determined by data linkage to hospital records on admissions and diagnoses, and national death registers, whichever occurred earliest. Cox proportional hazards regression models were used to estimate the effect of retinal age gap on risk of stroke. Logistic regression models were used to estimate the predictive value of retinal age and well-established risk factors in 10-year stroke risk. RESULTS: A total of 35,304 participants without history of stroke at baseline were included. During a median follow-up of 5.83 years, 282 (0.80%) participants had stroke events. In the fully adjusted model, each one-year increase in the retinal age gap was associated with a 4% increase in the risk of stroke (hazard ratio [HR] = 1.04, 95% confidence interval [CI]: 1.00-1.08, P = 0.029). Compared to participants with retinal age gap in the first quintile, participants with retinal age gap in the fifth quintile had significantly higher risks of stroke events (HR = 2.37, 95% CI: 1.37-4.10, P = 0.002). The predictive capability of retinal age alone was comparable to the well-established risk factor-based model (AUC=0.676 vs AUC=0.661, p=0.511). CONCLUSIONS: We found that retinal age gap was significantly associated with incident stroke, implying the potential of retinal age gap as a predictive biomarker of stroke risk.