Prediction of Major Adverse Cardiovascular Events From Retinal, Clinical, and Genomic Data in Individuals With Type 2 Diabetes: A Population Cohort Study

Incremental Prognostic Value of a Coronary Heart Disease Polygenic Risk Score in Type 2 Diabetes

OBJECTIVE

The recent availability of cardiovascular risk-reducing type 2 diabetes (T2D) therapies makes it imperative to optimally identify individuals who could derive benefit. Current clinical risk prediction may misclassify individuals as low risk and could be improved. Our aim was to determine the incremental prognostic value of a coronary heart disease (CHD) genome-wide polygenic risk score (PRS) to a clinical risk score in prediction of major adverse cardiovascular events (MACE) in patients with T2D.

RESEARCH DESIGN AND METHODS

We evaluated 10,556 individuals with T2D aged 40-79 years without a prior cardiovascular hospitalization. We calculated 10-year clinical cardiovascular risk at the date of recruitment using the Pooled Cohort Equation (PCE Risk) and constructed a CHD PRS. The primary outcome was time to first MACE incidence, and we assessed the additional incremental predictive value of the CHD PRS to the PCE risk.

RESULTS

At 10 years, there were 1,477 MACE. After adjustment for clinical risk, the CHD PRS was significantly associated with MACE (hazard ratio [HR] 1.69 per SD increase, 95% CI 1.60-1.79). Individuals with PCE Risk <7.5% but in the top quintile of CHD PRS had a significantly increased likelihood of MACE (HR 10.69, 95% CI 5.07-22.55) compared with those in the lowest. The addition of the PRS to the clinical risk score led to significant improvements in risk prediction, particularly in those at low clinical risk.

CONCLUSIONS

The addition of a CHD PRS to clinical assessment improved MACE prediction in T2D individuals without prior cardiovascular disease, particularly in those at low clinical risk.

Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review

BACKGROUND

Systemic diseases, such as cardiovascular and cerebrovascular conditions, pose significant global health challenges due to their high mortality rates. Early identification and intervention in systemic diseases can substantially enhance their prognosis. However, diagnosing systemic diseases often necessitates complex, expensive, and invasive tests, posing challenges in their timely detection. Therefore, simple, cost-effective, and non-invasive methods for the management (such as screening, diagnosis, and monitoring) of systemic diseases are needed to reduce associated comorbidities and mortality rates.

AIM OF THE REVIEW

This systematic review examines the application of artificial intelligence (AI) algorithms in managing systemic diseases by analyzing ophthalmic features (oculomics) obtained from convenient, affordable, and non-invasive ophthalmic imaging.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Our analysis demonstrates the promising accuracy of AI in predicting systemic diseases. Subgroup analysis reveals promising capabilities of oculomics-based AI for disease staging, while caution is warranted due to the possible overestimation of AI capabilities in low-quality studies. These systems are cost-effective and safe, with high rates of acceptance among patients and clinicians. This review underscores the potential of oculomics-based AI approaches in revolutionizing the management of systemic diseases.

Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study

BACKGROUND

Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical practice, in contrast to fundus retinal imaging which is accessible and routinely performed. Whether artificial intelligence (AI) utilizing retinal images collected through diabetic eye screening can provide an efficient diagnostic method for CAN is unknown.

METHODS

This was a single center, observational study in a cohort of patients with DM as a part of the Cardiovascular Disease in Patients with Diabetes: The Silesia Diabetes-Heart Project (NCT05626413). To diagnose CAN, we used standard CV autonomic reflex tests. In this analysis we implemented AI-based deep learning techniques with non-mydriatic 5-field color fundus imaging to identify patients with CAN. Two experiments have been developed utilizing Multiple Instance Learning and primarily ResNet 18 as the backbone network. Models underwent training and validation prior to testing on an unseen image set.

RESULTS

In an analysis of 2275 retinal images from 229 patients, the ResNet 18 backbone model demonstrated robust diagnostic capabilities in the binary classification of CAN, correctly identifying 93% of CAN cases and 89% of non-CAN cases within the test set. The model achieved an area under the receiver operating characteristic curve (AUCROC) of 0.87 (95% CI 0.74-0.97). For distinguishing between definite or severe stages of CAN (dsCAN), the ResNet 18 model accurately classified 78% of dsCAN cases and 93% of cases without dsCAN, with an AUCROC of 0.94 (95% CI 0.86-1.00). An alternate backbone model, ResWide 50, showed enhanced sensitivity at 89% for dsCAN, but with a marginally lower AUCROC of 0.91 (95% CI 0.73-1.00).

CONCLUSIONS

AI-based algorithms utilising retinal images can differentiate with high accuracy patients with CAN. AI analysis of fundus images to detect CAN may be implemented in routine clinical practice to identify patients at the highest CV risk.

TRIAL REGISTRATION

This is a part of the Silesia Diabetes-Heart Project (Clinical-Trials.gov Identifier: NCT05626413).

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.

AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.

Comprehensive Factors for Predicting the Complications of DiabetesMellitus: A Systematic Review

BACKGROUND

This article focuses on extracting a standard feature set for predicting the complications of diabetes mellitus by systematically reviewing the literature. It is conducted and reported by following the guidelines of PRISMA, a well-known systematic review and meta-analysis method. The research articles included in this study are extracted using the search engine "Web of Science" over eight years. The most common complications of diabetes, diabetic neuropathy, retinopathy, nephropathy, and cardiovascular diseases are considered in the study.

METHOD

The features used to predict the complications are identified and categorised by scrutinising the standards of electronic health records.

RESULT

Overall, 102 research articles have been reviewed, resulting in 59 frequent features being identified. Nineteen attributes are recognised as a standard in all four considered complications, which are age, gender, ethnicity, weight, height, BMI, smoking history, HbA1c, SBP, eGFR, DBP, HDL, LDL, total cholesterol, triglyceride, use of insulin, duration of diabetes, family history of CVD, and diabetes. The existence of a well-accepted and updated feature set for health analytics models to predict the complications of diabetes mellitus is a vital and contemporary requirement. A widely accepted feature set is beneficial for benchmarking the risk factors of complications of diabetes.

CONCLUSION

This study is a thorough literature review to provide a clear state of the art for academicians, clinicians, and other stakeholders regarding the risk factors and their importance.

Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus

PURPOSE OF REVIEW

The application of artificial intelligence (AI) technologies in screening and diagnosing retinal diseases may play an important role in telemedicine and has potential to shape modern healthcare ecosystems, including within ophthalmology.

RECENT FINDINGS

In this article, we examine the latest publications relevant to AI in retinal disease and discuss the currently available algorithms. We summarize four key requirements underlining the successful application of AI algorithms in real-world practice: processing massive data; practicability of an AI model in ophthalmology; policy compliance and the regulatory environment; and balancing profit and cost when developing and maintaining AI models.

SUMMARY

The Vision Academy recognizes the advantages and disadvantages of AI-based technologies and gives insightful recommendations for future directions.

Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes

There is increasing evidence that the complexity of the retinal vasculature measured as fractal dimension, Df, might offer earlier insights into the progression of coronary artery disease (CAD) before traditional biomarkers can be detected. This association could be partly explained by a common genetic basis; however, the genetic component of Df is poorly understood. We present a genome-wide association study (GWAS) of 38,000 individuals with white British ancestry from the UK Biobank aimed to comprehensively study the genetic component of Df and analyse its relationship with CAD. We replicated 5 Df loci and found 4 additional loci with suggestive significance (P < 1e-05) to contribute to Df variation, which previously were reported in retinal tortuosity and complexity, hypertension, and CAD studies. Significant negative genetic correlation estimates support the inverse relationship between Df and CAD, and between Df and myocardial infarction (MI), one of CAD's fatal outcomes. Fine-mapping of Df loci revealed Notch signalling regulatory variants supporting a shared mechanism with MI outcomes. We developed a predictive model for MI incident cases, recorded over a 10-year period following clinical and ophthalmic evaluation, combining clinical information, Df, and a CAD polygenic risk score. Internal cross-validation demonstrated a considerable improvement in the area under the curve (AUC) of our predictive model (AUC = 0.770 ± 0.001) when comparing with an established risk model, SCORE, (AUC = 0.741 ± 0.002) and extensions thereof leveraging the PRS (AUC = 0.728 ± 0.001). This evidences that Df provides risk information beyond demographic, lifestyle, and genetic risk factors. Our findings shed new light on the genetic basis of Df, unveiling a common control with MI, and highlighting the benefits of its application in individualised MI risk prediction.

Association Between Retinal Features From Multimodal Imaging and Schizophrenia

Importance

The potential association of schizophrenia with distinct retinal changes is of clinical interest but has been challenging to investigate because of a lack of sufficiently large and detailed cohorts.

Objective

To investigate the association between retinal biomarkers from multimodal imaging (oculomics) and schizophrenia in a large real-world population.

Design, Setting, and Participants

This cross-sectional analysis used data from a retrospective cohort of 154 830 patients 40 years and older from the AlzEye study, which linked ophthalmic data with hospital admission data across England. Patients attended Moorfields Eye Hospital, a secondary care ophthalmic hospital with a principal central site, 4 district hubs, and 5 satellite clinics in and around London, United Kingdom, and had retinal imaging during the study period (January 2008 and April 2018). Data were analyzed from January 2022 to July 2022.

Main Outcomes and Measures

Retinovascular and optic nerve indices were computed from color fundus photography. Macular retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (mGC-IPL) thicknesses were extracted from optical coherence tomography. Linear mixed-effects models were used to examine the association between schizophrenia and retinal biomarkers.

Results

A total of 485 individuals (747 eyes) with schizophrenia (mean [SD] age, 64.9 years [12.2]; 258 [53.2%] female) and 100 931 individuals (165 400 eyes) without schizophrenia (mean age, 65.9 years [13.7]; 53 253 [52.8%] female) were included after images underwent quality control and potentially confounding conditions were excluded. Individuals with schizophrenia were more likely to have hypertension (407 [83.9%] vs 49 971 [48.0%]) and diabetes (364 [75.1%] vs 28 762 [27.6%]). The schizophrenia group had thinner mGC-IPL (-4.05 μm, 95% CI, -5.40 to -2.69; P = 5.4 × 10-9), which persisted when investigating only patients without diabetes (-3.99 μm; 95% CI, -6.67 to -1.30; P = .004) or just those 55 years and younger (-2.90 μm; 95% CI, -5.55 to -0.24; P = .03). On adjusted analysis, retinal fractal dimension among vascular variables was reduced in individuals with schizophrenia (-0.14 units; 95% CI, -0.22 to -0.05; P = .001), although this was not present when excluding patients with diabetes.

Conclusions and Relevance

In this study, patients with schizophrenia had measurable differences in neural and vascular integrity of the retina. Differences in retinal vasculature were mostly secondary to the higher prevalence of diabetes and hypertension in patients with schizophrenia. The role of retinal features as adjunct outcomes in patients with schizophrenia warrants further investigation.

Diabetic vascular diseases: molecular mechanisms and therapeutic strategies

Vascular complications of diabetes pose a severe threat to human health. Prevention and treatment protocols based on a single vascular complication are no longer suitable for the long-term management of patients with diabetes. Diabetic panvascular disease (DPD) is a clinical syndrome in which vessels of various sizes, including macrovessels and microvessels in the cardiac, cerebral, renal, ophthalmic, and peripheral systems of patients with diabetes, develop atherosclerosis as a common pathology. Pathological manifestations of DPDs usually manifest macrovascular atherosclerosis, as well as microvascular endothelial function impairment, basement membrane thickening, and microthrombosis. Cardiac, cerebral, and peripheral microangiopathy coexist with microangiopathy, while renal and retinal are predominantly microangiopathic. The following associations exist between DPDs: numerous similar molecular mechanisms, and risk-predictive relationships between diseases. Aggressive glycemic control combined with early comprehensive vascular intervention is the key to prevention and treatment. In addition to the widely recommended metformin, glucagon-like peptide-1 agonist, and sodium-glucose cotransporter-2 inhibitors, for the latest molecular mechanisms, aldose reductase inhibitors, peroxisome proliferator-activated receptor-γ agonizts, glucokinases agonizts, mitochondrial energy modulators, etc. are under active development. DPDs are proposed for patients to obtain more systematic clinical care requires a comprehensive diabetes care center focusing on panvascular diseases. This would leverage the advantages of a cross-disciplinary approach to achieve better integration of the pathogenesis and therapeutic evidence. Such a strategy would confer more clinical benefits to patients and promote the comprehensive development of DPD as a discipline.

Integrating oculomics with genomics reveals imaging biomarkers for preventive and personalized prediction of arterial aneurysms

Objective

Arterial aneurysms are life-threatening but usually asymptomatic before requiring hospitalization. Oculomics of retinal vascular features (RVFs) extracted from retinal fundus images can reflect systemic vascular properties and therefore were hypothesized to provide valuable information on detecting the risk of aneurysms. By integrating oculomics with genomics, this study aimed to (i) identify predictive RVFs as imaging biomarkers for aneurysms and (ii) evaluate the value of these RVFs in supporting early detection of aneurysms in the context of predictive, preventive and personalized medicine (PPPM).

Methods

This study involved 51,597 UK Biobank participants who had retinal images available to extract oculomics of RVFs. Phenome-wide association analyses (PheWASs) were conducted to identify RVFs associated with the genetic risks of the main types of aneurysms, including abdominal aortic aneurysm (AAA), thoracic aneurysm (TAA), intracranial aneurysm (ICA) and Marfan syndrome (MFS). An aneurysm-RVF model was then developed to predict future aneurysms. The performance of the model was assessed in both derivation and validation cohorts and was compared with other models employing clinical risk factors. An RVF risk score was derived from our aneurysm-RVF model to identify patients with an increased risk of aneurysms.

Results

PheWAS identified a total of 32 RVFs that were significantly associated with the genetic risks of aneurysms. Of these, the number of vessels in the optic disc ('ntreeA') was associated with both AAA (β = -0.36, P = 6.75e-10) and ICA (β = -0.11, P = 5.51e-06). In addition, the mean angles between each artery branch ('curveangle_mean_a') were commonly associated with 4 MFS genes (FBN1: β = -0.10, P = 1.63e-12; COL16A1: β = -0.07, P = 3.14e-09; LOC105373592: β = -0.06, P = 1.89e-05; C8orf81/LOC441376: β = 0.07, P = 1.02e-05). The developed aneurysm-RVF model showed good discrimination ability in predicting the risks of aneurysms. In the derivation cohort, the C-index of the aneurysm-RVF model was 0.809 [95% CI: 0.780-0.838], which was similar to the clinical risk model (0.806 [0.778-0.834]) but higher than the baseline model (0.739 [0.733-0.746]). Similar performance was observed in the validation cohort, with a C-index of 0.798 (0.727-0.869) for the aneurysm-RVF model, 0.795 (0.718-0.871) for the clinical risk model and 0.719 (0.620-0.816) for the baseline model. An aneurysm risk score was derived from the aneurysm-RVF model for each study participant. The individuals in the upper tertile of the aneurysm risk score had a significantly higher risk of aneurysm compared to those in the lower tertile (hazard ratio = 17.8 [6.5-48.8], P = 1.02e-05).

Conclusion

We identified a significant association between certain RVFs and the risk of aneurysms and revealed the impressive capability of using RVFs to predict the future risk of aneurysms by a PPPM approach. Our finds have great potential to support not only the predictive diagnosis of aneurysms but also a preventive and more personalized screening plan which may benefit both patients and the healthcare system.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00315-7.

Review: Machine learning in precision pharmacotherapy of type 2 diabetes-A promising future or a glimpse of hope?

Precision pharmacotherapy of diabetes requires judicious selection of the optimal therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding discipline, holds substantial potential to transform current practices in diabetes diagnosis and management. This manuscript provides a comprehensive review of contemporary research investigating drug responses in patient subgroups, stratified via either supervised or unsupervised machine learning approaches. The prevalent algorithmic workflow for investigating drug responses using machine learning involves cohort selection, data processing, predictor selection, development and validation of machine learning methods, subgroup allocation, and subsequent analysis of drug response. Despite the promising feature, current research does not yet provide sufficient evidence to implement machine learning algorithms into routine clinical practice, due to a lack of simplicity, validation, or demonstrated efficacy. Nevertheless, we anticipate that the evolving evidence base will increasingly substantiate the role of machine learning in molding precision pharmacotherapy for diabetes.

Using Data to Improve the Management of Diabetes: The Tayside Experience

Tayside is a region in the East of Scotland and forms one of nine local government regions in the country. It is home to approximately 416,000 individuals who fall under the National Health Service (NHS) Tayside health board, which provides health care services to the population. In Tayside, Scotland, a comprehensive informatics network for diabetes care and research has been established for over 25 years. This has expanded more recently to a comprehensive Scotland-wide clinical care system, Scottish Care Information - Diabetes (SCI-Diabetes). This has enabled improved diabetes screening and integrated management of diabetic retinopathy, neuropathy, nephropathy, cardiovascular health, and other comorbidities. The regional health informatics network links all of these specialized services with comprehensive laboratory testing, prescribing records, general practitioner records, and hospitalization records. Not only do patients benefit from the seamless interconnectedness of these data, but also the Tayside bioresource has enabled considerable research opportunities and the creation of biobanks. In this article we describe how health informatics has been used to improve care of people with diabetes in Tayside and Scotland and, through anonymized data linkage, our understanding of the phenotypic and genotypic etiology of diabetes and associated complications and comorbidities.