BACKGROUND: The microbially produced amino acid–derived metabolite imidazole propionate (ImP) contributes to the pathogenesis of type 2 diabetes. However, the effects of ImP on endothelial cell (EC) physiology and its role in atherosclerotic coronary artery disease are unknown. Using both human and animal model studies, we investigated the potential contributory role of ImP in the development of atherosclerosis. METHODS: Plasma levels of ImP were measured in patients undergoing elective cardiac angiography (n=831) by ultra-high performance liquid chromatography coupled to tandem mass spectrometry. Odds ratios and corresponding 95% confidence intervals for coronary artery disease were calculated based on the ImP quartiles using both univariable and multivariable logistic regression models. The effects of ImP on functional properties of ECs were assessed using HAECs (human aortic endothelial cells). In a mouse model of carotid artery injury, the impact of ImP on vascular regeneration was examined. Additionally, atheroprone Apoe ^−/− mice fed a high-fat diet were treated with and without ImP (800 µg), and aortic atherosclerotic lesion area was evaluated after 12 weeks. Next-generation sequencing, Western blot analysis, small interfering RNA–based gene knockdown, and tamoxifen-inducible Cre-loxP experiments were performed to investigate ImP-mediated molecular mechanisms. RESULTS: Plasma ImP levels in subjects undergoing cardiac evaluation were associated with increased risk of prevalent coronary artery disease. We found that ImP dose dependently impaired migratory and angiogenic properties of human ECs and promoted an increased inflammatory response. Long-term exposure to ImP compromised the repair potential of the endothelium after an arterial insult. In atheroprone Apoe ^−/− (apolipoprotein E ^−/− ) mice, ImP increased atherosclerotic lesion size. Mechanistically, ImP attenuated insulin receptor signaling by suppressing the PI3K (phosphoinositide 3-kinase)/AKT pathway leading to sustained activation of the FOXO1 (forkhead box protein O1) transcription factor. Genetic inactivation of endothelial FOXO1 signaling in ImP-treated mice enhanced the angiogenic activity and preserved the vascular repair capacity of ECs after carotid injury. CONCLUSIONS: Our findings reveal a hitherto unknown role of the microbially produced histidine-derived metabolite ImP in endothelial dysfunction and atherosclerosis, suggesting that ImP metabolism is a potential therapeutic target in atherosclerotic cardiovascular disease.

Induction of cardiomyocyte proliferation is a promising option to regenerate the heart. Thus, it is important to elucidate mechanisms that contribute to the cell cycle arrest of mammalian cardiomyocytes. Here, we assessed the contribution of the pericentrin (Pcnt) S isoform to cell cycle arrest in postnatal cardiomyocytes. Immunofluorescence staining of Pcnt isoforms combined with SiRNA-mediated depletion indicates that Pcnt S preferentially localizes to the nuclear envelope, while the Pcnt B isoform is enriched at centrosomes. This is further supported by the localization of ectopically expressed FLAG-tagged Pcnt S and Pcnt B in postnatal cardiomyocytes. Analysis of centriole configuration upon Pcnt depletion revealed that Pcnt B but not Pcnt S is required for centriole cohesion. Importantly, ectopic expression of Pcnt S induced centriole splitting in a heterologous system, ARPE-19 cells, and was sufficient to impair DNA synthesis in C2C12 myoblasts. Moreover, Pcnt S depletion enhanced serum-induced cell cycle re-entry in postnatal cardiomyocytes. Analysis of mitosis, binucleation rate, and cell number suggests that Pcnt S depletion enhances serum-induced progression of postnatal cardiomyocytes through the cell cycle resulting in cell division. Collectively, our data indicate that alternative splicing of Pcnt contributes to the establishment of cardiomyocyte cell cycle arrest shortly after birth.

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

Induction of cardiomyocyte proliferation is a promising option to regenerate the heart. Thus, it is important to elucidate mechanisms that contribute to the cell cycle arrest of mammalian cardiomyocytes. Here, we assessed the contribution of the pericentrin (Pcnt) S isoform to the cell cycle arrest in postnatal cardiomyocytes. Immunofluorescence staining of Pcnt isoforms combined with siRNA-mediated depletion indicates that Pcnt S preferentially localizes to the nuclear envelope, while the Pcnt B isoform is enriched at centrosomes. This is further supported by the localization of ectopically expressed FLAG-tagged Pcnt S and Pcnt B in postnatal cardiomyocytes. Analysis of centriole configuration upon Pcnt depletion revealed that Pcnt B but not Pcnt S is required for centriole cohesion. Importantly, ectopic expression of Pcnt S induced centriole splitting in a heterologous system, ARPE-19 cells, and was sufficient to impair DNA synthesis in C2C12 myoblasts. Moreover, Pcnt S depletion enhanced serum-induced cell cycle re-entry in postnatal cardiomyocytes. Analysis of mitosis, binucleation rate, and cell number suggests that Pcnt S depletion promotes progression of postnatal cardiomyocytes through the cell cycle resulting in cell division. Collectively, our data indicate that alternative splicing of Pcnt contributes to the establishment of cardiomyocyte cell cycle arrest shortly after birth.

Early detection of high-risk individuals is crucial for healthcare systems to cope with changing demographics and an ever-increasing patient population. Images of the retinal fundus are a non-invasive, low-cost examination routinely collected and potentially scalable beyond ophthalmology. Prior work demonstrated the potential of retinal images for risk assessment for common cardiometabolic diseases, but it remains unclear whether this potential extends to a broader range of human diseases. Here, we extended a retinal foundation model (RETFound) to systematically explore the predictive potential of retinal images as a low-cost screening strategy for disease onset across >750 incident diseases in >60,000 individuals. For more than a third (n=308) of the diseases, we demonstrated improved discriminative performance compared to readily available patient characteristics. This included 281 diseases outside of ophthalmology, such as type 2 diabetes (Delta C-Index: UK Biobank +0.073 (0.068, 0.079)) or chronic obstructive pulmonary disease (Delta C-Index: UK Biobank +0.047 (0.039, 0.054)), showcasing the potential of retinal images to complement screening strategies more widely. Moreover, we externally validated these findings in 7,248 individuals from the EPIC-Norfolk Eye Study. Notably, retinal information did not improve the prediction for the onset of cardiovascular diseases compared to established primary prevention scores, demonstrating the need for rigorous benchmarking and disease-agnostic efforts to design cost-efficient screening strategies to improve population health. We demonstrated that predictive improvements were attributable to retinal vascularisation patterns and less obvious features, such as eye colour or lens morphology, by extracting image attributions from risk models and performing genome-wide association studies, respectively. Genetic findings further highlighted commonalities between eye-derived risk estimates and complex disorders, including novel loci, such as IMAP1 , for iron homeostasis. In conclusion, we present the first comprehensive evaluation of predictive information derived from retinal fundus photographs, illustrating the potential and limitations of easily accessible and low-cost retinal images for risk assessment across common and rare diseases. Research in context: Evidence before this study: Before undertaking this study, we reviewed the literature on the predictive utility of medical imaging for disease onset, focusing particularly on retinal fundus photographs. We conducted searches in databases including PubMed and Google Scholar, spanning from the inception of these databases to January 1, 2023. Our search terms included “retinal fundus photography”, “disease prediction”, “machine learning”, “deep learning”, and “healthcare AI”, without language restrictions. Prior research has shown the promise of retinal images in diagnosing and predicting a range of conditions, notably within ophthalmology and specific systemic diseases such as diabetes and cardiovascular diseases. However, a comprehensive evaluation of retinal images’ predictive potential across a broad spectrum of diseases, particularly those without known associations to retinal changes, was lacking. Studies identified varied in quality, with many focusing on single diseases or small datasets, indicating a potential risk of bias and overfitting. Added value of this study: Our study extends the application of retinal fundus photographs from ophthalmological and systemic diseases to more than 750 incident diseases, leveraging a foundation model combined with a deep multi-task neural network. This represents the first systematic exploration of the predictive potential of retinal images across the human phenome, significantly expanding the scope of diseases for which these images could serve as a low-cost screening strategy. Moreover, we rigorously compare the predictive value of retinal images against established primary prevention scores for cardiovascular diseases, showing both the strengths and limitations of this approach. This dual focus provides a nuanced understanding of where retinal imaging can complement existing screening strategies and where it may not offer additional predictive value. Implications of all the available evidence: The evidence from our study, combined with existing research, suggests that retinal fundus photographs hold promise for predicting disease onset across a wide range of conditions, far beyond their current use. However, our work also emphasizes the importance of contextualizing these findings within the broader landscape of available prediction tools and established primary prevention. The implications for practice include the potential integration of retinal imaging into broader screening programs, particularly for diseases where predictive gains over existing methods are demonstrated. For policy, our findings advocate for further investment in AI and machine learning research in healthcare, particularly in methods that improve upon or complement existing prediction models. Future research should focus on refining these predictive models, exploring the integration of retinal imaging with other biomarkers, and conducting prospective studies to validate the clinical utility of these approaches in diverse populations.