Cardiac Condition Evaluation

Many myths and misconceptions persist regarding the use of the biomarker of inflammation C-reactive protein measured with a highly sensitive assay (hsCRP.) Serial measurements are as stable as cholesterol or blood pressure. The independent association with future events, independent of traditional risk factors, has been replicated multiple times. This report of an analysis of almost half a million participants in the UK Biobank should help lay to rest some the concerns commonly voiced about this potent predictor of cardiovascular risk. Although not causal in cardiovascular events, this excellent analyte can select individuals who can benefit from therapies including statins (JUPITER) and anti-inflammatory interventions (CANTOS.) #inflammation #cardiovasculardisease #myocardialinfarction #heartattack #precisionmedicine #personalizedmedicine #Creactiveprotein #residualrisk #atherosclerosis #cardiology

𝗔𝗜 𝗖𝗮𝗻 𝗡𝗼𝘄 𝗣𝗿𝗲𝗱𝗶𝗰𝘁 𝗛𝗲𝗮𝗿𝘁 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗙𝗶𝘃𝗲 𝗬𝗲𝗮𝗿𝘀 𝗕𝗲𝗳𝗼𝗿𝗲 𝗜𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝘀 A fascinating paper published this week in the The American Journal of Cardiology. The important question it asked was the following. Can we predict heart failure before the heart has already begun to fail? The answer, it now appears, is yes. A team led by Prof Charalambos Antoniades MD PhD FRCP FMedSci at the University of Oxford has developed an AI tool that analyses the fat surrounding the heart from routine cardiac CT scans, predicting a patient's risk of developing heart failure up to five years before any clinical signs appear. Epicardial adipose tissue (EAT) is a metabolically active visceral fat depot that is both a sensor and a modulator of myocardial biology and changes its composition in response to paracrine signals from the myocardium. The team hypothesised that radiomic characterization of EAT from routine coronary computed tomographic angiography (CCTA) can noninvasively capture this adverse remodeling and enable early heart failure (HF) risk stratification. The study involved over 72,000 patients across nine NHS centres, followed for up to a decade. The fat around the heart, it turns out, acts as a potential biological sensor. Patients in the highest risk group were twenty times more likely to develop heart failure than those in the lowest. The tool predicted five-year risk with 86% accuracy, outperforming models built on traditional risk factors alone. What is striking is the conceptual shift this represents. We have spent decades in cardiovascular medicine treating disease that has already declared itself, responding to symptoms, managing complications, optimising a heart already under strain. We have been using risk stratification of cardiac disease using various methods like calcium scores. The team are now seeking NHS regulatory approval and adapting the tool for any CT scan of the chest, not just cardiac ones. Every scan, for any reason, could soon carry an embedded layer of cardiac risk intelligence. As the NHS shifts into prevention as part of the long term plan these tools become more important.

New research in JACC: Advances shows that the eye may offer a powerful, noninvasive window into coronary artery disease detection. In a multicenter study of 383 patients, deep learning models trained on retinal images were able to identify CAD with strong performance, outperforming traditional clinical risk scores, particularly in intermediate risk patients where clinical uncertainty is highest. When retinal imaging was combined with clinical indicators using a multimodal AI approach, diagnostic accuracy improved further, achieving an AUC of 0.91 with over 92 percent sensitivity. Because retinal and coronary vessels share similar vascular origins, microvascular changes captured by OCT and OCTA appear to reflect underlying coronary disease. AI enables these subtle patterns to be translated into scalable, radiation free screening and risk stratification tools. This work points toward a future where cardiovascular risk can be assessed earlier, more safely, and more equitably, especially in settings where invasive testing is limited. Multimodal AI may be key to shifting CAD detection upstream and personalizing prevention before clinical events occur. 🔗 https://lnkd.in/gWJUU447 Follow Zain Khalpey, MD, PhD, FACS for more on Ai & Healthcare. #AIinHealthcare #Cardiology #CoronaryArteryDisease #PreventiveCardiology #DigitalHealth #MedicalAI #MultimodalAI #DeepLearning #NonInvasiveDiagnostics #RetinalImaging #OCTA #OCT #CardiovascularHealth #RiskStratification #PrecisionMedicine #ClinicalInnovation #HealthEquity #CVImaging

A heart attack rarely comes out of nowhere. 𝐓𝐡𝐞 𝐛𝐨𝐝𝐲 𝐮𝐬𝐮𝐚𝐥𝐥𝐲 𝐬𝐞𝐧𝐝𝐬 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐛𝐮𝐭 𝐰𝐞 𝐣𝐮𝐬𝐭 𝐝𝐞𝐭𝐞𝐜𝐭 𝐭𝐡𝐞𝐦 𝐭𝐨𝐨 𝐥𝐚𝐭𝐞. Now, Indian researchers have built something that could change that. They’ve developed 𝐁𝐢𝐨𝐅𝐄𝐓, a biological sensing chip designed to 𝐝𝐞𝐭𝐞𝐜𝐭 𝐞𝐚𝐫𝐥𝐲 𝐰𝐚𝐫𝐧𝐢𝐧𝐠 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐨𝐟 𝐚 𝐡𝐞𝐚𝐫𝐭 𝐚𝐭𝐭𝐚𝐜𝐤 by tracking molecular changes in blood serum. Instead of waiting for symptoms to become severe, this chip monitors key cardiac biomarkers such as: 𝟏. Troponin 𝟐. C-reactive protein 𝟑. Myoglobin These markers often rise 𝐛𝐞𝐟𝐨𝐫𝐞 a major cardiac event occurs.  Detecting them early allows doctors to intervene sooner when treatment is most effective. And that’s why the shift matters more than we realise. Because in India, many heart attacks occur people in their 30s and 40s,  often with little warning,  often when families are financially and emotionally unprepared. A technology like this could mean: A father reaches the hospital before collapse. A working professional gets treatment before permanent damage. A family avoids an overnight medical emergency that changes everything. Healthcare’s real win isn’t dramatic surgeries or miracle recoveries. It’s when nothing dramatic needs to happen at all, because the danger was caught early enough to stop it. #HeartHealth #PreventiveHealthcare #MedicalInnovation #HealthTech #DigitalHealth

🧪 Urine ACR: The Unsung Hero of Cardiovascular, Kidney & Metabolic Risk Prediction 💥 A landmark state-of-the-art review in Circulation confirms what many of us in primary and specialist care already suspect: urinary albumin-to-creatinine ratio (UACR) is one of the most powerful and underutilised biomarkers in modern medicine. Whether in the context of diabetes, CKD, hypertension, heart failure, or metabolic syndrome, albuminuria is a strong, independent predictor of cardiovascular and renal outcomes—even below the traditional threshold of 3.4 mg/mmol (30 mg/g). 📌 Key Takeaways: 🔹 Albuminuria reflects widespread vascular dysfunction, not just kidney damage 🔸 Even low-grade albuminuria (UACR ~0.8–1.1 mg/mmol) is associated with increased risk of CVD, CKD, and all-cause mortality 🔹 UACR is non-invasive, inexpensive, widely available, and provides prognostic insight well before eGFR declines 🔸 Cardio–renal–metabolic therapies (SGLT2is, GLP-1 RAs, MRAs) are most impactful when guided by early UACR measurement 🚨 Still Underused Despite strong recommendations from NICE, ESC, KDIGO, and ADA, UACR screening remains infrequently performed, particularly in primary care. 💬 The authors call for: ✅ Wider and earlier ACR testing, not just in diabetes and CKD, but also in hypertension, obesity, liver disease, and CVD ✅ Routine UACR use in cardiology and metabolic clinics ✅ Recognition of albuminuria as a core CKM biomarker, not just a “renal test” 👉 Bottom line: If you’re not checking urine ACR, you’re missing a critical early signal. Let’s stop waiting for eGFR to fall—UACR reveals risk earlier and enables earlier, more effective action. 💭 Are you using ACR routinely in your practice? What are the barriers? 🔗 https://lnkd.in/eBgPfdB8 Kevin FernandoKevin LeeSarah DaviesSarah Jarvis, MBEBeverley Bostock RGN MSc MA Queen's NurseAhmet FuatProf Derek ConnollyBethany Kelly RN, QN, MSc, PgDipNadia Malik MRPharm BSc (hons)Hannah BebaPhilip Newland-Jones

👉 Lipoprotein(a) [Lp(a)] is not just about coronary disease. 👆 Robust genetic and population data confirm Lp(a) as a causal risk factor across multiple vascular beds. 1️⃣ Peripheral Arterial Disease (PAD): Rising Lp(a) levels show a clear, stepwise association with PAD risk—up to ~3-fold higher risk at extreme concentrations. 2️⃣ Major Adverse Limb Events (MALE): In patients with PAD, high Lp(a) markedly increases the risk of amputations and repeat revascularizations. 3️⃣ Abdominal Aortic Aneurysm (AAA): Elevated Lp(a) is consistently associated with higher AAA risk, supported by Mendelian randomization. 4️⃣ Inflammation matters—but doesn’t negate Lp(a) risk: Lp(a)-associated cardiovascular risk persists independently of hsCRP levels, in both primary and secondary prevention. 👉 Clinical takeaway: Lp(a) identifies residual vascular risk beyond LDL-C and traditional factors—supporting routine measurement and future targeted therapy. 📌 Phase 3 Lp(a)-lowering outcome trials may redefine prevention across coronary and non-coronary vascular disease. Børge Nordestgaard Peter Thomas Pia R. Kamstrup Signe Vedel Science Exploration Press Gert Kostner 🔗 Open Access https://lnkd.in/dbCHnQFs

🔴 Lp(a): A 30-Year Cardiovascular Risk Signal We May Be Underestimating A 30-year follow-up of ~27,000 initially healthy women (JAMA Cardiology, 2026) shows that elevated Lipoprotein(a) [Lp(a)] independently predicts long-term cardiovascular risk. 📌 What the Data Show • Lp(a) ≥30 mg/dL → ↑ major adverse cardiovascular events • Lp(a) ≥120 mg/dL → ↑ coronary heart disease, ischemic stroke & CV mortality • Clear dose–response gradient • Risk persists beyond traditional factors • Lp(a) is largely genetically determined 🆕 Why This Matters Clinically • Prevention remains centered on LDL-C • Most 10-year risk models do not integrate Lp(a) • “Normal cholesterol” ≠ absence of inherited risk • Short-term prediction may miss lifelong exposure 🎯 Perspective This study invites reconsideration of how we define and operationalize cardiovascular risk in the era of genomic medicine. 📖 Nordestgaard AT et al. JAMA Cardiology. 2026;11(2):175–185. doi:10.1001/jamacardio.2025.5043 #Cardiology #PreventiveMedicine #Lipidology #PrecisionMedicine #Lp(a)

Predicting a heart attack 15 years out — using proteins and metabolites in your blood today. A new study in Nature Communications introduces CardiOmicScore, a deep learning framework trained on UK Biobank data that learns separate risk scores from 2,920 proteins and 168 metabolites for six types of cardiovascular disease. On their own, the proteomic scores hit a C-index (CVD risk index) of up to 0.82. Combined with standard clinical data, they push prediction accuracy significantly higher — up to 15 years before disease onset. Critically, the model identifies which proteins and metabolites matter most, generating concrete leads for new biomarkers and drug targets. The code enables a shift from generic population risk tables to molecular-level, personalized cardiovascular prevention. #Proteomics #MultiOmics #CardiovascularDisease #DeepLearning #PrecisionMedicine https://lnkd.in/e5A6Nb9H