Big data and AI for precision panvascular aging management and healthy longevity

Atherosclerosis is a systemic disease that affects all vascular beds, including coronary, cerebral, and peripheral arteries. With an estimated 236.6 million people living with peripheral artery disease (PAD), its prevalence ranks only behind coronary artery disease (CAD) and ischemic stroke in atherosclerotic diseases (Song et al., Lancet Glob Health 7(8):e1020–e1030, 2019). PAD is a common, underrecognized, and debilitating atherosclerotic disease of the lower extremities. Recognition and treatment of PAD remain relatively poor, in part due to the absence or subtlety of symptoms of early disease. Advanced PAD can manifest as a severely morbid condition resulting in progressive loss of function, as well as rest pain from claudication, gangrene, and limb amputation. Ischemic heart disease and stroke remain the leading causes of death globally and were responsible for 27% of all deaths in 2019 (The top 10 causes of death. World Health Organization, Geneva, 2020). Remarkably, however, the risk of an atherosclerotic cardiovascular disease (ASCVD) event is only modestly lower in patients with PAD compared to patients with CAD or prior stroke, and patients with PAD have an even higher risk of all-cause mortality (Colantonio et al., J Am Coll Cardiol 76(3):251–264, 2020).Our understanding of atherosclerosis has significantly advanced from earlier theories. In 1858, Virchow proposed that atherosclerosis was due to arterial injury propagating a maladaptive inflammatory and cellular process to generate atherosclerotic plaques (Basatemur et al., Nat Rev Cardiol 16(12):727–744, 2019). Work in the early 1900s by Ignatowski and Anichkov demonstrated that cholesterol was a critical mediator of atherosclerosis and recognized that lipid-rich diets increase the burden of atherosclerosis (Konstantinov and Jankovic, Tex Heart Inst J 40(3):247–249, 2013; Konstantinov et al., Tex Heart Inst J 33(4):417–423, 2006). Ross spearheaded the “response-to-injury” hypothesis in the 1970s, which further developed Virchow’s vascular injury theory to include platelet-derived factors, smooth muscle proliferation, and extracellular matrix deposition (Ross et al., Am J Pathol 86(3):675–684, 1977). Two decades later, the “response-to-retention” hypothesis proposed that atherogenic lipids are retained in arterial walls and induce an inflammatory cascade (Williams and Tabas, Arterioscler Thromb Vasc Biol 15(5):551–562, 1995; Ross, N Engl J Med 340(2):115–126, 1999). Since then, remarkable advances in scientific capabilities have greatly expanded our understanding of atherogenesis.The pathogenesis of PAD likely shares many common features of atherosclerosis in the coronary and cerebral arteries. Yet, the phenotype of PAD is unique, notable for intimal thickening with a paucity of inflammatory cells, a high prevalence of vascular calcification, and evidence that severe PAD that manifests as critical limb ischemia is at least in part a thromboembolic disease. This chapter will describe PAD, including its risk factors and epidemiology, normal and abnormal vascular function, pathogenesis of atherosclerosis, and the unique phenotype of PAD.KeywordsAtherosclerosisCholesterolClaudicationEndothelial dysfunctionEfferocytosisInflammationMacrophageOxidationThrombosis

Purpose This paper aims to integrate Big Data in e-government in Oman, also known as “e-Oman”, wherein Big Data might be better harnessed to tackle real-time challenges. Design/methodology/approach Besides a description of the concepts of e-government and Big Data in general, the paper underscores the dimensions of “e-Oman”. Following a qualitative approach, the paper asserts how integration of Big Data in “e-Oman” may be useful by invoking examples from four short case studies across different sectors. Findings The paper supports the integration of “e-Oman” and Big Data wherein besides providing smooth public services, the government is encouraged to forge inter- and intra-ministerial collaboration and public-private partnership. The paper probes through the challenges and opportunities in effecting this integration. Practical implications The paper provides a platform for the policymakers to conceive of a synchronized programme for integrating “e-Oman” and the Big Data generated by it. This integration would go a long way in building upon the economy of Oman, besides providing better public services to the individuals and businesses on a real-time basis. Social implications The paper does throw light on the issues of privacy and confidentiality of data available with the government. There are challenges of cybercrime as well. Therefore, the paper posits that a robust fool-proof infrastructure should be instituted by the government for effecting integration of e-government and Big Data. Originality/value This paper seeks to fill the gap in extant literature which remains scant on the integration of e-government with Big Data. This is especially true in the case of Oman where not a single study has been presented to probe this issue. Given that “e-Oman” is expanding its scope over the years, this paper foresees the concomitant opportunities and challenges in the integration of Big Data in “e-Oman”.

To investigate the prevalence of coronary artery disease (CAD), ischemic stroke, and peripheral arterial disease (PAD), alone and in combination, in older persons. A retrospective analysis of charts from all older persons seen from April 1, 1998, through December 31, 1998, at an academic hospital-based geriatrics practice. An academic hospital-based geriatrics practice staffed by fellows in a geriatrics training program and full-time faculty geriatricians. A total of 474 men and 1328 women, mean age 80 +/- 9 years (range 60 to 102 years) were included in the study. Of 1802 persons studied, 612 (34%) had CAD, 351 (19%) had ischemic stroke, 236 (13%) had PAD, and 816 (45%) had either CAD, stroke, or PAD. Three hundred twenty-eight (18%) of the 1802 persons had CAD alone, 128 (7%) had stroke alone, 50 (3%) had PAD alone, 123 (7%) had CAD + stroke and no PAD, 86 (5%) had CAD + PAD and no stroke, 25 (1%) had PAD + stroke and no CAD, 75 (4%) had CAD + stroke + PAD, and 986 (55%) had no CAD, PAD, or stroke. If CAD was present, coexistent PAD was present in 26% and coexistent stroke in 32% of persons studied. If stroke was present, coexistent CAD was present in 56% and coexistent PAD in 28%. If PAD was present, coexistent CAD was present in 68% and coexistent stroke in 42% of persons studied. These data showed that if CAD was present, ischemic stroke was also present in 32% and PAD in 26% of the population. If ischemic stroke was present, CAD was also present in 56% and PAD in 28% of the population. If PAD was present, CAD was also present in 68% and ischemic stroke in 42% of the population.

Peripheral artery disease is common and associated with high mortality. There are limited data detailing causes of death among patients with peripheral artery disease. EUCLID (Examining Use of Ticagrelor in Peripheral Artery Disease) was a randomized clinical trial that assigned patients with peripheral artery disease to clopidogrel or ticagrelor. We describe the causes of death in EUCLID using mortality end points adjudicated through a clinical events classification process. The association between baseline factors and cardiovascular death was evaluated by Cox proportional hazards modeling. The competing risk of noncardiovascular death was assessed by the cumulative incidence function for cardiovascular death and the Fine and Gray method to ascertain the association between baseline characteristics and cardiovascular mortality. A total of 1263 out of 13 885 (9.1%) patients died (median follow-up: 30 months). There were 706 patients (55.9%) with a cardiovascular cause of death and 522 (41.3%) with a noncardiovascular cause of death. The most common cause of cardiovascular death was sudden cardiac death (20.1%); while myocardial infarction (5.2%) and ischemic stroke (3.2%) were uncommon. The most common causes of noncardiovascular death were malignancies (17.9%) and infections (11.9%). The factor most associated with a higher risk of cardiovascular death was age per 5 year increase (HR, 1.26 [95% CI, 1.20-1.32]). Female sex was associated with a lower risk of cardiovascular death (HR, 0.68 [95% CI, 0.56-0.82]). To evaluate the effect of noncardiovascular death as a competing risk, we superimposed the cumulative incidence function curve with the Kaplan-Meier curve. These curves closely approximated each other. After accounting for the competing risk of noncardiovascular death, the magnitude and direction of the factors associated with cardiovascular death were minimally changed. Among patients with symptomatic peripheral artery disease, noncardiovascular causes of death reflected a high proportion (40%) of deaths. Accounting for noncardiovascular deaths as a competing risk, there was not a significant change in the risk estimation for cardiovascular death. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01732822.

Percutaneous coronary intervention with peripheral artery disease in the contemporary era: Still life or limb?

Peripheral arterial disease versus other localizations of vascular disease: The ATTEST study

Objective: Through the practice discussion and exchange of the integration of big data and pharmaceutical standards, to provide experience reference for the practice of information technology in the management of rational drug use. Methods: The application of data information in the practice of hospital pharmacy management is demonstrated through the introduction of practical cases and data achievements at different levels. Results: The integration of medical big data and pharmaceutical standards effectively improved the hospital's quantitative quality management indicators and the informatization of many pharmaceutical management methods in the hospital is realized. Conclusions: The integration of medical big data and pharmaceutical standards can effectively help improve the management level of rational drug use in hospitals, and there are still many possibilities for future exploration.

Recurrent cardiovascular and limb events in 294,428 patients with coronary or peripheral artery disease or ischemic stroke on antiplatelet monotherapy: The RESRISK cohort study

The integration of big data and artificial intelligence (AI) is transforming population health research by enabling more precise disease surveillance, risk prediction, and targeted interventions. By combining large-scale, heterogeneous datasets, including electronic health records, genomic profiles, wearable device outputs, environmental measures, and social determinants of health, AI systems can identify complex relationships, reveal hidden risk factors, and forecast health outcomes with unprecedented accuracy. These capabilities enhance early detection of epidemics, support individualized care planning, and inform policies aimed at reducing health disparities. Machine learning and deep learning techniques allow healthcare systems to manage resources more efficiently, predict service demand, and optimize allocation during both routine operations and public health emergencies. Applications extend to chronic disease prevention and management, precision public health initiatives, and policy simulations that model the potential impact of interventions such as vaccination strategies, environmental regulations, or taxation policies. Despite their promise, the integration of AI and big data into population health research presents significant challenges. These include safeguarding data privacy, ensuring cybersecurity, mitigating algorithmic bias, overcoming interoperability barriers, and addressing ethical concerns related to transparency and accountability. Effective use of these technologies requires interdisciplinary collaboration among data scientists, healthcare professionals, policymakers, and ethicists. This paper critically examines the roles, benefits, and limitations of AI and big data in advancing population health research. It highlights case studies demonstrating improved health outcomes and operational efficiencies, while also outlining frameworks for ethical governance and equitable implementation. By addressing current challenges, AI and big data hold the potential to revolutionize healthcare delivery, promote health equity, and enhance population-level well-being on a global scale.

The world has seen exponential data growth due to social media, mobility, E-commerce, and other factors. The issues related to avalanche of data being produced are immense and cover variety of challenges that need a careful consideration. The use of HPDA (High Performance Data Analytics) is increasing at brisk speed in many industries and has resulted in expansion of HPC market in many new territories. HPC (High Performance Computing) and big data are different systems, not only at the technical level, but also have different ecosystems. HPC systems are mainly developed for computationally intensive applications but recently data intensive applications are also among the major workload in HPC environment. Big data analytics have grown in different perspectives and have separate developer communities. As we head towards the exascale and smart infrastructure era, the necessary integration of big data and HPC is currently a hot topic of research but still at very infant stages. Both systems have different architecture and their integration brings many challenges. The aim of this work is to identify the driving forces, challenges, current and future trends associated with the integration of HPC and big data. This paper is an extension of our earlier work. We have reviewed programming models and frameworks of big data and HPC. The big data and HPC challenges in the exascale-computing era are discussed. Additional elaborations are provided on HPC and big data convergence research efforts and future directions are provided. The HPC-big data convergence architecture proposed in our earlier paper has been enhanced.

Exercise Training for Peripheral Arterial Disease

Since the initial scientific statement on Secondary Prevention of Coronary Heart Disease (CHD) in the Elderly was published in 2002,1 several trends have continued that make an update highly appropriate. First, the graying of the US population and those of other industrialized countries has progressed unabated because more adults are surviving into their senior years. The number of Americans aged ≥75 years was estimated at 18.6 million in 2010, representing ≈6% of the population,2 and it is expected to double by 2050. The population aged ≥85 years is growing the most rapidly, with numbers expected to reach 19.5 million by 2040. In 2008, 67% of the 811 940 cardiovascular deaths in the United States occurred in people aged ≥75 years.3 In parallel to this increase in the older adult demographic, the number of Americans with CHD has increased to an estimated 16.3 million, more than half of whom are >65 years of age.3 Similarly, 7 million have had a stroke, the incidence of which approximately doubles with successive age decades after 45 to 54 years.3 Peripheral artery disease (PAD) affects 8 to 10 million Americans, the majority of whom are >65 years of age. Between 2015 and 2030, annual US costs related to atherosclerotic cardiovascular disease (ASCVD) are projected to increase from $84.8 billion to $202 billion.3 Moreover, given that ASCVD often undermines functional capacity and independence and increases reliance on long-term care, indirect expenses related to ASCVD are also expected to increase. Thus, the need for effective secondary prevention measures in the older adult population with known ASCVD has never been greater. Notably, the 2011 American Heart Association (AHA)/American College of Cardiology Foundation (ACCF) updated guidelines for secondary prevention of CHD broadened …

BackgroundAtherosclerotic cardiovascular disease (ASCVD) remains the leading cause of death worldwide. This study aimed to investigate the contributions of acquired risk factors to ASCVD across different genetic risk groups.MethodsThis study included 430,191 participants aged 40 − 69 from the UK Biobank cohort. Population-attributable fractions (PAFs) of 25 acquired risk factors (including four socioeconomic determinants, three psychosocial factors, five lifestyles, nine cardiometabolic factors, and four clinical comorbidities) for coronary artery disease (CAD), ischemic stroke (IS), and peripheral artery disease (PAD) were assessed across three genetic risk groups (low, moderate, and high). The contributions of 25 risk factors to ASCVD were ranked by PAFs within each genetic risk group.ResultsA total of 32,908 CAD events, 6,819 IS events, and 5,560 PAD events occurred during a median follow-up of 13.8 years. The associations between acquired risk factors and ASCVD varied across the three genetic risk groups, with the most significant diversity observed in the associations between cardiometabolic factors and CAD (PInteraction < 0.001). In addition, the associations of psychosocial factors with CAD and IS decreased with increasing genetic risk (both PInteraction =0.018). Cardiometabolic factors were the leading contributors to ASCVD incidence across all genetic risk groups (PAF for CAD: 48.5%−56.5%; IS: 35.5%−45.5%; PAD: 48.1%−52.0%), with hypertension being the predominant factor in all groups. Still, the contributions of certain cardiometabolic factors (e.g., overweight/obesity, high low-density lipoprotein cholesterol, and elevated C-reactive protein) to ASCVD varied by genetic risk. Socioeconomic determinants ranked second in contribution to ASCVD across different genetic risk levels (CAD: 6.0%−11.9%; IS: 4.8%−12.8%; PAD: 23.6%−33.4%), which was primarily driven by less education. Socioeconomic determinants (CAD: 9.0% vs. 6.0%; IS: 12.8% vs. 4.8%; PAD: 33.4% vs. 23.6%) and psychosocial factors (CAD: 3.5% vs. 2.5%; IS: 7.6% vs. 0.0%; PAD: 4.9% vs. 4.8%) contributed more to ASCVD in the low genetic risk groups than in the high genetic risk groups. The PAFs of lifestyles were relatively consistent across different genetic risk groups. Although the associations between clinical comorbidities and ASCVD were the strongest, their contributions to ASCVD were relatively small (CAD: 2.7%%−4.9%; IS: 1.7%−4.9%; PAD: 9.3%−10.4%).ConclusionsThis study provides comprehensive information regarding the genetic risk-specific contributions of acquired risk factors to ASCVD. Prioritizing risk factors based on genetic predisposition may help to promote precise and efficient prevention of ASCVD.Graphical abstractSupplementary InformationThe online version contains supplementary material available at 10.1186/s12872-025-05312-4.

Big data and geographic information systems (GIS) are two technologies that have increasingly influenced many areas in the last 10 years and will continue to improve and help solve serious global problems, such as consequences of climate change or global pandemics. A wide spectrum of GIS applications interacts with the continuous growth of geospatial big data sources to drive precise and informed decisions. Geospatial big data integration is designed to accomplish the compatibility of distinct geospatial datasets regardless of their spatial coverage. The large number of geospatial big data sources demand effective data integration for storing and handling such datasets, which will be used for geospatial data analysis and visualization. For instance, risk management datasets related to healthcare and the environment are heterogeneous and disparate. Obtaining a unified view of such geospatial big datasets is complicated and challenging, especially if we consider problems related to healthcare pandemics and environmental disasters. Hence, before we can attempt to predict and mitigate processes occurring in these domains, we must realize that geospatial big data integration is crucial in consolidating datasets. We explore and discuss issues involved in integrating geospatial big datasets in this study. We then classify big data integration processes into three categories, namely, data warehousing, data transformation and integration methods. Furthermore, several research challenges focused on geospatial big data, big earth data, data warehousing, data transformation and linked data are presented. Lastly, open research issues and emerging trends that require in-depth investigations in the near future are highlighted in this study.

The data growth over the last couple of decades increases on a massive scale. As the volume of the data increases so are the challenges associated with big data. The issues related to avalanche of data being produced are immense and cover variety of challenges that needs a careful consideration. The use of (High Performance Data Analytics) HPDA is increasing at brisk speed in many industries resulted in expansion of HPC market in these new territories. HPC and Big data are different systems, not only at the technical level, but also have different ecosystems. The world of workload is diverse enough and performance sensitivity is high enough that, we cannot have globally optimal and locally high sub-optimal solutions to all the issues related to convergence of big data and HPC. As we are heading towards exascale systems, the necessary integration of big data and HPC is a current hot topic of research but still at very infant stages. Both systems have different architecture and their integration brings many challenges. The main aim of this paper is to identify the driving forces, challenges, current and future trends associated with the integration of HPC and big data. We also propose architecture of big data and HPC convergence using design patterns.