Advancements in Biomarkers for Early Detection and Risk Stratification of Cardiovascular Diseases—A Literature Review

Cardiovascular diseases (CVDs) stand as a formidable global health challenge, exacting an enormous toll on societies in terms of both human suffering and economic burden. With their intricate pathophysiology and often asymptomatic early stages, CVDs continue to be a leading cause of morbidity, mortality, and healthcare expenditure worldwide [1]. According to the World Health Organization (WHO), an estimated 17.9 million deaths were attributed to CVDs in 2019 alone, representing 31% of all global deaths. This staggering statistic underscores the urgent need to employ strategies that facilitate early detection and precise risk stratification to curtail the CVD epidemic [1].

Early detection and precise risk stratification are crucial in mitigating the impact of cardiovascular diseases (CVDs). Identifying individuals at risk or in early stages of disease allows for timely interventions, personalized treatments, and preventive measures [2]. These proactive approaches not only enhance patient care and quality of life but also have the potential to reduce healthcare system burdens and long‐term costs. By preemptively identifying high‐risk individuals, healthcare providers can implement interventions that slow or halt disease progression, thereby preventing serious events such as heart attacks, strokes, and heart failure. The heterogeneous nature of CVDs, encompassing conditions like coronary artery disease, heart failure, arrhythmias, and valvular disorders, poses challenges in developing strategies that enable early detection and differentiate between varied manifestations. Biomarkers, including proteins, lipids, nucleic acids, and metabolites, have emerged as pivotal tools in cardiovascular medicine. They offer insights into physiological processes and disease mechanisms, aiding in the early detection of deviations from normal function and guiding targeted therapeutic approaches. Thus, biomarkers represent a transformative approach in cardiovascular healthcare by enabling more precise diagnosis and intervention strategies [3, 4, 5].

This review underscores the multifaceted role of biomarkers in revolutionizing the management of cardiovascular diseases (CVD). Beyond enhancing diagnostic accuracy, biomarkers enrich traditional risk prediction models by integrating dynamic biological data, thereby refining prognostic assessments. They also facilitate personalized treatment strategies by enabling targeted therapies tailored to an individual's molecular profile. Biomarkers serve crucial roles in monitoring treatment efficacy, enabling clinicians to make timely adjustments. The article explores a diverse array of biomolecules—from established markers like high‐sensitivity cardiac troponins and brain natriuretic peptides to emerging candidates in genomics, proteomics, and metabolomics. This comprehensive analysis not only highlights their diagnostic capabilities but also their potential to forecast disease progression and therapeutic outcomes. Ultimately, biomarkers are poised to transform cardiovascular medicine by advancing precision healthcare approaches (Figure 1).

Traditional biomarkers have played a foundational role in cardiovascular disease (CVD) detection and risk assessment, forming the basis of diagnostic and prognostic strategies for decades. These biomarkers encompass a range of physiological and biochemical indicators, including cholesterol levels, C‐reactive protein (CRP), and others. While they have provided valuable insights into CVD pathophysiology, their limitations underscore the need for novel biomarkers to enhance accuracy and predictive value. Cholesterol, a lipid molecule, has been a central focus of cardiovascular risk assessment due to its role in atherosclerosis, a condition characterized by the buildup of fatty deposits in arterial walls. Low‐density lipoprotein cholesterol (LDL‐C), often referred to as “bad” cholesterol, has been a primary target for risk assessment and intervention. Elevated LDL‐C levels are associated with an increased risk of atherosclerosis and subsequent cardiovascular events. However, the specificity of LDL‐C as a standalone biomarker is limited. Individuals with normal LDL‐C levels can still develop CVD, as other factors such as genetics, inflammation, and endothelial dysfunction contribute to disease progression [3, 5, 6]. C‐reactive protein (CRP) is an acute‐phase reactant produced by the liver in response to inflammation. High‐sensitivity CRP (hsCRP) has gained prominence as a marker of cardiovascular risk, particularly due to the role of chronic inflammation in atherosclerosis. Elevated hsCRP levels have been associated with increased cardiovascular risk. However, CRP's limitations are evident in its lack of specificity. Infections, injuries, and other inflammatory conditions can also elevate CRP levels, potentially leading to false‐positive results. As a result, while hsCRP offers insights into inflammation's role in CVD, it falls short in accurately predicting individual risk [5, 6].

Among the most common causes of mortality and morbidity in developed as well as developing countries coronary artery disease and acute myocardial infarction (AMI) are still gone undiagnosed in their early stages [10]. In US death rate has been decreasing recently that may be attributed to early diagnosis and efficient revascularization techniques. It may be decreased further if new biomarkers are developed that can detect coronary artery disease as early as possible [11]. New biomarkers are being discovered and lots of clinical trials are being conducted on them to determine their effectiveness in early diagnosis, risk stratification and efficient management of the patients. In young age, the process of atherosclerosis is initiated and timed screening may ensue in outcomes that are in our best interests [12]. Three characteristics of biomarkers that must be present in it beside good sensitivity and specificity are [10]: 1Having a predictable nature2Having a good accessible site3Having a reliable method of detection.

Still their clinical uses and precision must be justified before inculcating them in clinical practice. Biomarkers associated with myocardial injury, instability and rupturing of plaques, inflammation, activation of platelets, myocardial stress, microRNAs and myocardial stress are being under investigation for further implementation. Cardiac troponins, high sensitive cardiac troponins (hs‐cTn), Heart‐type fatty acid binding protein, High‐sensitivity C‐reactive protein (hsCRP), Pregnancy‐associated plasma protein‐A (PAPP‐A), Matrix metalloproteinases (MMPs), Lipoprotein‐associated phospholipase A2 (Lp‐PLA2), Natriuretic peptides, and ST2 are some biomarkers that are still under investigation for their potential clinical benefits [13] (Table 1).

Now‐a‐days cardiac troponins are included in clinical practice for early detection of acute myocardial infarction. The two troponins that are measured to quantify the amount of damage of cardiomyocytes are cTnT and cTnI [14]. Their concentrations in blood will be directly to the amount of injury to cardiomyocytes. There is no evidence that which type of cardiac troponin is better for the diagnosis of myocardial infarction [15]. They have the greatest sensitivity and specificity in diagnosis AMI when clinical symptomology is evident than myoglobin and creatine kinase (CK) along with its isoenzyme MB (CK‐MB). In clinical symptomatic patients above 99th percentile of normal individuals indicates AMI [16]. Old methods of analysis have a lower sensitivity at an early stage of AMI and require continuous sampling over 6 to 9 h to reach diagnosis [17]. Hs‐cTn have solved this problem and have increased the accuracy of diagnosis at presentation and subsequently decreased the time interval for next measurement. It can even detect the troponins in clinical stable patients and healthy individuals. Cardiac troponins usually rise in first hour following AMI if using high‐sensitive assays and this lead to greater diagnostic accuracy in diagnosing early AMI and reducing healthcare cost [13, 14]. Finding troponins in healthy individuals and developing a more reliable definition are the two distinct features of high sensitive assays as compared to conventional assays [18]. So, finding a baseline value of hs‐cTn has lead to risk stratification in patients with CAD and diabetes for all cause death, stroke, AMI, and heart failure [19].

MicroRNAs (miRNAs), small noncoding RNA molecules about 17–25 nucleotides long, are emerging as potential biomarkers for coronary artery disease (CAD). They regulate gene expression post‐transcriptionally by inhibiting translation or causing mRNA degradation. Over 100 miRNAs have been identified circulating in plasma and serum, categorized into vesicle‐associated and non‐vesicle forms. In the heart, miRNAs like miR‐26a, miR‐1, miR‐133, miR‐126‐3p, let‐7, and miR‐30c are prevalent. Studies indicate that levels of specific miRNAs, such as miR‐208a, miR‐133a, miR‐92a, miR‐126, miR‐17, and miR‐155, vary in CAD patients, distinguishing between stable and unstable angina and acute myocardial infarction (AMI) [10, 11, 12, 13, 14, 15, 16]. Elevated miRNA levels like miR‐17‐5p correlate with CAD severity, while decreased levels of miR‐16 have been noted. Different miRNAs are implicated in various stages of atherosclerosis and are linked to conditions like type 2 diabetes mellitus, influencing cardiovascular disease development. MiRNAs such as miR‐9, miR‐30, miR‐126, miR‐1, miR‐15a, miR‐133a, miR‐342, miR‐210, miR‐92a, and miR‐155 show promise as biomarkers for risk stratification and noninvasive CAD diagnosis, particularly in patients with type 2 diabetes. Despite their potential, challenges such as reproducibility, cost‐effectiveness, standardization of detection methods, and sample size limitations need addressing before miRNAs can be widely adopted in clinical practice. Nevertheless, miRNAs have demonstrated sensitivity and specificity as markers for CAD in various studies [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]. Imaging techniques such as carotid intima‐media thickness (CIMT) measured by ultrasound and coronary artery calcium score (CACS) assessed by CT scan are under study for personalized risk stratification in cardiovascular disease. CIMT is sensitive for detecting subclinical atherosclerosis and is considered a potential predictor of adverse cardiovascular events. In contrast, CACS, which detects calcification—an advanced stage of atherosclerosis—has lower predictability compared to CIMT. Many individuals with subclinical atherosclerosis detected by CIMT have zero CACS, highlighting the difference in their sensitivity to early disease stages. Both CIMT and CACS are recommended for CAD risk stratification, with elevated CACS indicating higher risk and zero CACS suggesting lower or intermediate risk. Screening with these techniques not only identifies high‐risk patients but also reveals subclinical disease, particularly in those with low Framingham risk scores and zero CACS, informing strategies for primary prevention of CAD [28].

Beside these biomarkers and imaging techniques bone biomarkers such as alkaline phosphatase, osteoprotegerin, and osteopontin have been found to aid in diagnosis and most importantly in prognosis of CAD [29]. In addition, Triglyceride‐glucose index is found to be a valuable index for early intervention and risk stratification in patients of premature coronary artery disease [30]. There is also evidence that in nondiabetic patients plasma biomarkers such as high‐sensitivity C‐reactive protein (hsCRP) and the ratio of total cholesterol (TC) to high‐density lipoprotein cholesterol (HDL‐C) remained high in patients with adverse cardiovascular events and thus anticipate the forthcoming cardiovascular events and so they may be used in clinical risk stratification [31].

No doubt that usage of biological biomarkers has increased the accuracy of diagnosis and risk stratification in CAD. But to our dismay the sensitivity and specificity all of them except in case of troponins and CK‐MB remain poor [32]. Studies conducted by Zampetaki, et al. and Bye, et al. have shown that the prediction power of the tradition risk factor model Framingham score system improved when it combined with miRNA profile but not a single miRNA level suggests an increased risk of AMI when used alone [33, 34]. Nowadays, multimarker analysis has been suggested as it increases the specificity and sensitivity of the prognostic tools. So, when multiple biomarkers are combined together, they may be useful in predicting cardiovascular events as compared to when they are used alone. Zagidullin found that combined multimarker analysis in two biomarker combinations (NT‐proBNP+Ptx‐3) have been found to be most accurate in predicting 2‐year cardiovascular mortality after STEMI. He also suggested that the most accurate combo was a three‐biomarker model (NT‐proBNP+Ptx‐3 + ST2) in predicting 2‐year cardiovascular mortality [35].

Recent genetic studies have identified specific genetic loci and genes that independently increase the likelihood of cardiovascular disease (CVD) and its risk factors from birth, with interactions with the environment potentially contributing to disease development. These genetic factors are now being considered in the development of personalized medications. Single‐nucleotide polymorphisms (SNPs), which involve a single nucleotide base change in DNA, have been associated with coronary artery disease (CAD). SNPs can alter gene expression, affecting the development of CAD and its severity, although outcomes can vary widely among individuals with the same polymorphisms. Genetic biomarkers, particularly SNPs, are more specific compared to nongenetic markers, guiding targeted therapies even in asymptomatic individuals. They play crucial roles in CAD diagnosis, prognostic evaluation, risk stratification, pharmacokinetics, pharmacodynamics, and personalized medicine. Genetic risk scores (GRS), calculated from genotype data, incorporate multiple SNPs to enhance prognostic accuracy. The inclusion of additional SNPs in GRS improves its predictive power independently of family history, particularly beneficial in younger patients. Despite the potential of GRS, its effectiveness varies across studies due to differences in SNP selection and ethnic diversity. Further functional studies are necessary to refine GRS for precise risk stratification and prognostic evaluation in modern cardiology, aiming to reduce CAD risk and enhance lifespan and quality of life [36, 37, 38] (Table 2).

A biomarker indicates a special state of a body that can be physiological, pathological or pharmalogical. Novel biomarkers are very much important because they aid in diagnosis, prognosis, and precision medicine as well as response to a particular therapy. Nowadays, advancement in fields of proteomics, genomics, and metabolomics have been made for the discovery of new biomarkers. This involves the use of techniques such as mass spectrometry, liquid chromatography–mass spectrometry (LC‐MS), 2‐gel electrophoresis (2DE) united to matrix‐assisted laser desorption time‐of‐flight mass spectrometry (MALDI‐TOF) tandem mass spectrometry, two‐dimension liquid chromatography, aptamer‐based assays, Proximity Extension Assays (Olink), multiplex immunoassays microarray and sequencing [39, 40].

Proteomics is the study of proteins, encompassing their composition, interactions, structures, functions, and cellular activities. It involves a meticulously regulated multi‐step process to prevent nonorganic factors that could alter protein expression and interactions. Sample acquisition is crucial, particularly in solubilizing proteins and mitigating interference from molecules like lipids. Plasma, containing thousands of proteins, is a key source for biomarker investigation, though many protein‐disease relationships remain undiscovered, particularly in diseases like coronary artery disease (CAD). Plasma is convenient but lacks specificity, as it may contain proteins from various tissues. Tissue‐based analysis is considered the gold standard for diagnosis due to its reliability. Recent studies have identified specific proteins in plasma associated with conditions such as myocardial infarction and left ventricular dysfunction, highlighting their potential as biomarkers. Combining protein panels with established clinical risk scores enhances the accuracy of risk stratification compared to using scores alone, underscoring proteomics' role in advancing diagnostic and prognostic capabilities in cardiovascular medicine [41, 42, 43].

Genomics involves the study of DNA sequencing, chimeric DNA, and bioinformatics to analyze and understand the structure and function of an organism's entire set of DNA, known as its genome. In clinical medicine, genomics focuses on the functional genome, exploring individual genes from their expression to their roles in biological functions. Genome Wide Association Studies (GWAS) are crucial in identifying genetic variants like SNPs (single nucleotide polymorphisms) associated with conditions such as coronary artery disease (CAD). Techniques employed include genome annotation, mutagenesis, RNA interference (RNAi), SNP analysis, and genetic interaction mapping. The National Human Genome Research Institute (NHGRI) plays a key role in storing and exploring the human genome and its implications for various diseases. Family and twin studies indicate that a substantial portion (40%–50%) of CAD risk is heritable. While GWAS have identified around 31 loci linked to CAD, these explain less than 10% of its heritability. Recent research has identified approximately 45 loci specifically associated with CAD in South Asian and European populations, highlighting ongoing efforts to understand the genetic underpinnings of cardiovascular diseases [44, 45, 46].

Metabolomics is the comprehensive study of all metabolites present in biological samples or organisms, encompassing over 19,000 small molecules derived from both endogenous enzymatic processes and exogenous sources like food, drugs, microbiota, and the environment. This field differs from proteomics and genomics in its broad scope and focus on understanding how metabolite levels change in response to external factors and disease. By integrating metabolomics with other omics disciplines, new biomarkers associated with cardiovascular disease can be identified and combined with existing genetic and clinical data to enhance risk stratification, diagnosis, and prognosis. Metabolomics plays a crucial role in early disease detection by identifying molecular changes that precede clinical symptoms, offering insights into disease mechanisms such as insulin resistance and obesity leading to type II diabetes mellitus. Specific metabolites like branched chain amino acids (BCAA) and markers such as trimethylamine N‐oxide (TMAO) have been linked to conditions like coronary artery disease and myocardial infarction, detecting abnormalities shortly after the onset of symptoms. Metabolomics also reveals associations between metabolite levels and cardiovascular events, providing predictive markers for disease progression and patient outcomes [47, 48, 49, 50, 51, 52].

The concept of understanding human physiology extends beyond studying individual molecules like DNA, RNA, proteins, and metabolites, to encompassing environmental and other factors. This holistic approach is known as “multi‐omics,” which aims to elucidate the relationships between these molecules and their roles in disease pathology, diagnosis, and prognosis. For instance, genetic causes of diseases such as familial hypercholesterolemia involve complex interactions that go beyond lipid‐lowering drugs alone. External factors like diet can influence genetic expression, impacting disease risk. Multiple biomarkers have been identified for coronary artery disease (CAD), enhancing risk stratification. Integrating vast amounts of data requires digital tools such as algorithms and risk calculators that incorporate nuclear, cellular, and enzymatic components. Multi‐omics is particularly valuable for noncoding genes and understanding the downstream effects of genetic variants. Studies have linked genes like PCSK9 and loci such as 9p21 to CAD risk through their influence on lipid levels and vascular health. Epigenetic modifications such as DNA methylation contribute to CAD risk by affecting processes like atherosclerosis. This integrative approach, spanning genomics, transcriptomics, proteomics, and other omics fields, uses software and artificial intelligence to predict and manage CAD risk effectively [47, 53, 54].

Personalized medicine is a new field in which a specific treatment in addition to all tailored clinical decisions is given to a particular person. It combines the information from various omics to identify the culprit step and give the treatment according to that step. Biomarkers are very much important as they can indicate that specific disturbed steps even at earlier stages. In this era, genomics and other biomarkers improves the diagnosis and therapeutic effects of disease through personalized medicine. It is often used in genetic testing, lifestyle changes, and risk prevention. It is a multidisciplinary approach in which all aspects of an individual are explored before any interpretation and intervention. For example, long QT syndromes (LQTS) can be used for risk probability as 12 different alleles are found at its loci that necessities the genetic testing of the individuals. Beta‐blockers can be used in case of LQTS 1 but not in LQTS2 or LQTS 3. So, personalized medicine is focused on analyzing all the particular factors for the CAD and identifying those particular factors that should be addressed in that particular individuals. For example, when we are dealing with familial hypercholesterolemia, we have to led a specific pathway in dealing with rather than following a traditional way in case of others because diagnosis, prognosis and therapeutics are all different in this case. Finally, this all depends on particular biomarkers that can indicate a specific state at which a specific treatment can be given to a specific person [55, 56].

Biomarkers must undergo rigorous validation to ensure their accuracy and reliability. Validation involves assessing their performance across diverse populations and different clinical contexts. This process involves evaluating sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves. Reproducibility, the ability to obtain consistent results across different laboratories and platforms, is critical to ensure that the biomarker's performance is consistent regardless of where it is tested [57]. Measurements of biomarkers can vary depending on the assay method, equipment, and laboratory practices. Lack of standardization can lead to discrepancies in results between different laboratories. Establishing standardized protocols and reference materials is essential to minimize variability and ensure that biomarker results are comparable across different settings. Biomarkers should provide meaningful information that influences clinical decision‐making and patient outcomes. Simply demonstrating a correlation between a biomarker and a disease is not sufficient. Clinical utility must be demonstrated through prospective studies that show the biomarker's ability to improve patient outcomes, guide treatment decisions, or predict disease progression beyond traditional risk factors [58]. Some biomarker tests can be expensive due to the need for specialized equipment, reagents, and expertise. Ensuring that biomarker testing is affordable and accessible to a wide range of patients is crucial for equitable healthcare delivery. High costs can limit the adoption of biomarkers, especially in resource‐constrained settings [40, 50, 65]. For biomarker implementation to be successful, the testing process should seamlessly integrate into clinical workflows. If biomarker testing is time‐consuming, requires additional appointments, or disrupts the flow of patient care, healthcare providers may be less inclined to use them routinely. Interpreting biomarker results can be complex, especially in the context of risk stratification. Determining appropriate cutoff values for identifying different risk categories requires careful consideration and validation. Overly simplistic interpretation can lead to misclassification of patients' risk levels. Some biomarkers may exhibit variability over time due to factors like lifestyle changes, medications, or disease progression. Longitudinal tracking is necessary to capture trends and assess changes in risk over time. However, this requires repeated testing, which can be cumbersome for both patients and healthcare providers.

Advancements in biomarkers for cardiovascular diseases hold promise for transforming risk assessment, diagnosis, treatment, and patient outcomes. Integrating state‐of‐the‐art technologies such as AI and personalized medicine approaches will enhance accuracy in risk assessment and early detection. Genomics, proteomics, and metabolomics offer opportunities to develop tailored risk scores by considering genetic predispositions, molecular profiles, and environmental influences. Combining data from these omics disciplines can unveil new biomarkers and disease signatures for precise risk prediction. AI and machine learning algorithms analyze complex biomarker profiles, improving predictive capabilities and aiding clinical decision‐making. Wearable devices and mobile health apps provide real‐time physiological data, complementing traditional biomarkers for a comprehensive view of cardiovascular health. Personalized therapeutic interventions guided by biomarkers ensure optimal treatment outcomes while minimizing risks. Exploring microbiome biomarkers and advancing liquid biopsy techniques offer noninvasive insights into disease mechanisms and potential monitoring solutions. Continuous biomarker monitoring over time informs disease progression and treatment efficacy, necessitating robust validation through large‐scale clinical trials for widespread clinical adoption.

Abubakar Nazir: conceptualization, writing – original draft, writing – review and editing, visualization, project administration. Awais Nazir: writing – original draft, writing – review and editing. Usama Afzaal: writing – original draft, writing – review and editing. Shafaq Aman: writing – original draft, writing – review and editing. Safi ur Rehman Sadiq: writing – original draft, writing – review and editing. Ozoemena Z. Akah: writing – original draft, writing – review and editing. Muhammad Shah Wali Jamal: writing – original draft, writing – review and editing. Syed Zawahir Hassan: writing – original draft, writing – review and editing.

The authors have nothing to report.

The authors declare no conflicts of interest.

The lead author Abubakar Nazir affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.