Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors

Copeptin as a surrogate marker for arginine vasopressin: analytical insights, current utility, and emerging applications

Copeptin is a 39-amino-acid long glycosylated peptide with a leucine-rich core segment in the C-terminal part of pre-pro-vasopressin. It exhibits a rapid response comparable to arginine vasopressin (AVP) in response to osmotic, hemodynamic, and nonspecific stress-related stimuli. This similarity can be attributed to equimolar production of copeptin alongside AVP. However, there are markedly different decay kinetics for both peptides, with an estimated initial half-life of copeptin being approximately two times longer than that of AVP. Like AVP, copeptin correlates strongly over a wide osmolality range in healthy individuals, making it a useful alternative to AVP measurement. While copeptin does not appear to be significantly affected by food intake, small amounts of oral fluid intake may result in a significant decrease in copeptin levels. Compared to AVP, copeptin is considerably more stable in vitro. An automated immunofluorescent assay is now available and has been used in recent landmark trials. However, separate validation studies are required before copeptin thresholds from these studies are applied to other assays. The biological variation of copeptin in presumably healthy subjects has been recently reported, which could assist in defining analytical performance specifications for this measurand. An established diagnostic utility of copeptin is in the investigation of polyuria-polydipsia syndrome and copeptin-based testing protocols have been explored in recent years. A single baseline plasma copeptin >21.4 pmol/L differentiates AVP resistance (formerly known as nephrogenic diabetes insipidus) from other causes with 100% sensitivity and specificity, rendering water deprivation testing unnecessary in such cases. In a recent study among adult patients with polyuria-polydipsia syndrome, AVP deficiency (formerly known as central diabetes insipidus) was more accurately diagnosed with hypertonic saline-stimulated copeptin than with arginine-stimulated copeptin. Glucagon-stimulated copeptin has been proposed as a potentially safe and precise test in the investigation of polyuria-polydipsia syndrome. Furthermore, copeptin could reliably identify those with AVP deficiency among patients with severe hypernatremia, though its diagnostic utility is reportedly limited in the differential diagnosis of profound hyponatremia. Copeptin measurement may be a useful tool for early goal-directed management of post-operative AVP deficiency. Additionally, the potential prognostic utility of copeptin has been explored in other diseases. There is an interest in examining the role of the AVP system (with copeptin as a marker) in the pathogenesis of insulin resistance and diabetes mellitus. Copeptin has been found to be independently associated with an increased risk of incident stroke and cardiovascular disease mortality in men with diabetes mellitus. Increased levels of copeptin have been reported to be independently predictive of a decline in estimated glomerular filtration rate and a greater risk of new-onset chronic kidney disease. Furthermore, copeptin is associated with disease severity in patients with autosomal dominant polycystic kidney disease. Copeptin predicts the development of coronary artery disease and cardiovascular mortality in the older population. Moreover, the predictive value of copeptin was found to be comparable with that of N-terminal pro-brain natriuretic peptide for all-cause mortality in patients with heart failure. Whether the measurement of copeptin in these conditions alters clinical management remains to be demonstrated in future studies.

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging

BACKGROUND

Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care.

MAIN TEXT

This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care.

CONCLUSION

AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.

Artificial intelligence in retinal imaging: current status and future prospects

INTRODUCTION

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution.

AREAS COVERED

This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature.

EXPERT OPINION

The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists.