Dugani, Sagar B.

Abstract Background Glycosylation, a key post-translational modification, plays a crucial role in biological pathways related to atherosclerosis. Purpose To investigate whether specific immunoglobulin G (IgG) N-glycosylation patterns are associated with premature myocardial infarction (MI) and to evaluate whether a glycan-based score improves case identification beyond traditional cardiovascular risk factors. Methods We measured 27 IgG N-glycans using capillary gel electrophoresis in 377 cases of premature first MI (men ≤55 years; women ≤65 years) and 377 controls matched for age (±5 years), sex, and nationality from the Gulf PREVENT study, a case-control study investigating risk factors for premature MI in the United Arab Emirates. We performed conditional logistic regression to evaluate associations between IgG N-glycans and premature MI, adjusting for age, body mass index, LDL-C, HDL-C, hs-CRP, hypertension, diabetes, and smoking. Using LASSO regression, we derived a novel IgG glycan score as a linear combination of selected glycans associated with premature MI. We evaluated model performance using likelihood ratio tests and the area under the curve (AUC) adapted for the case-control design, using 10-fold cross-validation. Results After adjusting for cardiovascular risk factors, glycan peaks (P) 4, 13, 23, and 27 were positively associated with premature MI (false discovery rate [FDR]<0.05). Bisecting N-acetylglucosamine (GlcNAc) was the consistent glycosylation pattern among these four glycans. In contrast, P 22, a core-fucosylated and monogalactosylated glycan, had inverse association (FDR=0.02) with premature MI. An IgG glycan score, composed of Ps 2, 4, 8, 17, 20, and 24, was associated with twofold increase in premature MI risk per standard deviation increment (aOR,1.98 [95%CI,1.56–2.52]; P<0.001) after adjusting for cardiovascular risk factors. Further, the IgG glycan score significantly improved model fit and performance, as indicated by a significant improvement in the likelihood ratio test and AUC (P<0.001). Conclusion We identified novel IgG N-glycosylation patterns that were associated with premature MI independent of cardiovascular risk factors. Glycans enriched with bisecting GlcNAc had increased risk, while core fucosylation and monogalactosylation had decreased risk. A novel IgG glycan score had 2-fold increased risk of premature MI independent of other risk factors and improved model performance. These findings highlight the potential of IgG N-glycans as biomarkers and therapeutic targets for premature MI.

We highlight the complex management considerations in a patient with cerebral amyloid angiopathy who developed venous thromboembolism, explicitly navigating the balance between risks of thromboembolism and bleeding with anticoagulation therapy. This case highlights a man in his 70s who presented to the emergency department with an acute right frontal intracerebral haemorrhage secondary to cerebral amyloid angiopathy with an acute segmental pulmonary embolism managed with an inferior vena cava (IVC) filter. During hospitalisation, the patient developed a large expansile thrombus extending inferior from the IVC filter to the right popliteal vein. This necessitated the initiation of anticoagulation therapy, beginning with unfractionated heparin, transitioning to enoxaparin and ultimately to apixaban. He was treated with 3 months of anticoagulation with no haemorrhagic complications. The patient’s successful outcome underscored the potential for safe and effective anticoagulation management in this complex clinical scenario.

Poor diet is the leading preventable risk factor for death worldwide, associated with over 10 million premature deaths and USD 8 trillion related costs every year. Artificial intelligence or AI is rapidly emerging as the most historically disruptive, innovatively dynamic, rapidly scaled, cost-efficient, and economically productive technology (which is increasingly providing transformative countermeasures to these negative health trends, especially in low- and middle-income countries (LMICs) and underserved communities which bear the greatest burden from them). Yet widespread confusion persists among healthcare systems and policymakers on how to best identify, integrate, and evolve the safe, trusted, effective, affordable, and equitable AI solutions that are right for their communities, especially in public health nutrition. We therefore provide here the first known global, comprehensive, and actionable narrative review of the state of the art of AI-accelerated nutrition assessment and healthy eating for healthcare systems, generated by the first automated end-to-end empirical index for responsible health AI readiness and maturity: the Responsible Health AI readiness and Maturity Index (RHAMI). The index is built and the analysis and review conducted by a multi-national team spanning the Global North and South, consisting of front-line clinicians, ethicists, engineers, executives, administrators, public health practitioners, and policymakers. RHAMI analysis identified the top-performing healthcare systems and their nutrition AI, along with leading use cases including multimodal edge AI nutrition assessments as ambient intelligence, the strategic scaling of practical embedded precision nutrition platforms, and sovereign swarm agentic AI social networks for sustainable healthy diets. This index-based review is meant to facilitate standardized, continuous, automated, and real-time multi-disciplinary and multi-dimensional strategic planning, implementation, and optimization of AI capabilities and functionalities worldwide, aligned with healthcare systems’ strategic objectives, practical constraints, and local cultural values. The ultimate strategic objectives of the RHAMI’s application for AI-accelerated public health nutrition are to improve population health, financial efficiency, and societal equity through the global cooperation of the public and private sectors stretching across the Global North and South.